Bacillus velezensis ES2-4 Modulates Root Exudation and Microbiome Remodeling to Enhance Soybean Resistance Against Gray Mold

Bacillus velezensis ES2-4 Modulates Root Exudation and Microbiome Remodeling to Enhance Soybean Resistance Against Gray Mold | 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 Article Bacillus velezensis ES2-4 Modulates Root Exudation and Microbiome Remodeling to Enhance Soybean Resistance Against Gray Mold Rui Chen, Xinpeng Guo, Maohua Wu, Ting Zheng, Siwei Chen, Bing He This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7283311/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Gray mold, caused by Botrytis cinerea , represents a significant threat to soybean productivity, while conventional chemical control strategies raise concerns regarding long-term sustainability. Plant-associated beneficial microbes, such as Bacillus velezensis , have been proposed as environmentally sustainable alternatives; however, their specific roles in modulating root-microbe interactions remain insufficiently characterized. This study investigated the mechanisms by which B. velezensis ES2-4 enhances soybean resistance by modulating root exudate composition and restructuring rhizosphere microbial communities. Metabolomic and metagenomic analyses indicated that ES2-4 inoculation led to the upregulation of antifungal metabolites (e.g., oxalic acid, eicosane) in root exudates, which facilitated the recruitment of beneficial bacteria while inhibiting B. cinerea proliferation. Pathogen infection was associated with disruptions in rhizosphere microbial diversity; however, ES2-4 application restored bacterial richness, particularly within the Alphaproteobacteria and Streptomyces lineages, while reducing the relative abundance of fungal pathogens. Co-occurrence network analysis further demonstrated that ES2-4 inoculation promoted microbial interactions associated with stress-responsive pathways, including two-component signaling systems and fatty acid metabolism, while downregulating pathogen-associated metabolic functions. These findings elucidate a dual mechanism through which ES2-4 enhances plant immunity via metabolite-mediated microbiome modulation, highlighting its potential as a sustainable biocontrol agent against soybean gray mold. Biological sciences/Biotechnology Biological sciences/Microbiology Biological sciences/Plant sciences Soybean Bacillus velezensis Gray mold Root exudates Rhizosphere microbiome Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Soybean gray mold, caused by the necrotrophic fungal pathogen Botrytis cinerea , has emerged as a devastating disease threatening global soybean production, with yield losses reaching 30–50% 1,2 . This pathogen typically invades host tissues through wounds or natural openings, leading to characteristic symptoms including leaf blotches, stem wilting, and fruit rot 3 , 4 . Its broad host adaptability enables it to infect over 200 economically important crops, including soybean, resulting in substantial economic losses. Although chemical fungicides remain the primary control strategy, their overuse has not only driven the evolution of multidrug resistance in pathogens but also disrupted soil microbial communities 5 . Consequently, biocontrol strategies utilizing plant growth-promoting rhizobacteria (PGPR) have gained prominence as sustainable alternatives 6 , 7 , highlighting the urgency to develop sustainable alternatives such as PGPR-based biocontrol. Bacillus velezensis , first isolated from the Vélez River estuary in Málaga, Spain, by Ruiz-García et al. in 1999 and formally described in 2005 8 , has emerged as a model Gram-positive PGPR due to its broad-spectrum antimicrobial activity and plant growth-promoting traits 9 , 10 . Its biocontrol mechanisms involve the synthesis of functional metabolites (e.g., cyclic lipopeptides, polyketides, siderophores), coupled with nutrient competition, biofilm formation, and induction of systemic resistance (ISR) in host plants 10 – 12 . Notably, accumulating evidence demonstrates the efficacy of B. velezensis against B. cinerea : For instance, cell-free supernatants from strain YTQ3 suppress mycelial growth (> 60% inhibition) and spore germination of B. cinerea in a dose-dependent manner 13 , while strain BE1 significantly reduces postharvest gray mold incidence in tomato fruits 14 . Additionally, this bacterium exhibits protective effects against soybean root rot (caused by Fusarium oxysporum ), Phytophthora root rot ( Phytophthora sojae ), and pustule disease ( Xanthomonas axonopodis pv. glycines) 15 – 17 . However, its potential for controlling soybean gray mold remains unexplored. Recent studies highlight the role of B. velezensis in reshaping plant-microbe interactions through modulation of rhizosphere exudates. These root-secreted metabolites, including sugars, organic acids, and amino acids, serve as key signaling molecules in rhizosphere communication 18 , 19 . B. velezensis can induce the secretion of antimicrobial compounds such as cinnamic acid and malic acid, which directly inhibit pathogens (e.g., Ralstonia solanacearum ) while recruiting beneficial microbes (e.g., Pseudomonas spp., Streptomyces spp.) to establish pathogen-suppressive microbiomes 18 , 20 . Metabolomic analyses reveal that this bacterium reprograms plant metabolic pathways, particularly trehalose biosynthesis and phenylpropanoid metabolism, thereby altering rhizosphere metabolite profiles and optimizing microbial community structure 21 , 22 . Furthermore, lipopeptides (e.g., fengycin) secreted by B. velezensis synergize with root exudates to suppress pathogens, while enhanced biofilm formation ensures long-term rhizosphere colonization 23 , 24 . Nevertheless, the metabolic reprogramming mechanisms and microbiome-mediated immune regulation in soybean- B. cinerea interactions remain poorly understood. In this study, integrated metabolomic and metagenomic analyses were employed to elucidate how B. velezensis ES2-4 enhances soybean resistance by specifically inducing the secretion of antimicrobial metabolites (e.g., oxalic acid, eicosane) and reconstructing rhizosphere microbial networks. Our findings innovatively decipher the microbiome engineering-mediated plant immunity mechanisms, offering a theoretical foundation for developing rhizosphere-focused biocontrol strategies against soybean gray mold. Results Alterations in rhizosphere exudate profiles following co-inoculation of soybean with strain ES2-4 and B. cinerea To investigate the dynamic changes in soybean rhizosphere exudates under individual or co-inoculation with B. velezensis ES2-4 and B. cinerea , comprehensive metabolite profiling was conducted using GC-MS. Total ion chromatogram (TIC) analysis from 24 biological replicates (6 per group) showed high inter-sample peak overlap within each treatment group (Fig. 1 A-B, S1A-B), with consistent retention times and ion intensities, confirming methodological reproducibility and instrument stability. Orthogonal partial least squares-discriminant analysis (OPLS-DA) revealed clear separation between the BE (ES2-4 + B. cinerea ) and B ( B. cinerea alone) groups (Fig. 1 C), and distinct metabolic divergence between the E (ES2-4 alone) and CK (control) groups (Fig. S1 C). Screening with VIP ≥ 1 and |log2FC| ≥1 thresholds identified 33 differentially abundant metabolites in the BE vs B comparison (11 upregulated, 22 downregulated), including oxalic acid, eicosane, stearic acid, and 9-octadecenamide (Fig. 1 D). The E vs CK comparison revealed 14 differential metabolites (7 upregulated, 7 downregulated), with shared biomarkers (e.g., eicosane, oxalic acid), indicating conserved stress-responsive mechanisms (Fig. S1 D). Ward's hierarchical clustering based on Euclidean distances showed tight intra-group clustering and distinct inter-group expression patterns of differential metabolites (Fig. 1 E, S1E). KEGG pathway enrichment analysis indicated that E vs CK differential metabolites were primarily linked to starch/sucrose metabolism and unsaturated fatty acid biosynthesis (Fig. 1 F) 25 , 26 . In contrast, BE vs B comparisons implicated 11 pathways, including branched-chain amino acid metabolism and glyoxylate/dicarboxylate cycling, with unsaturated fatty acid biosynthesis being the most enriched (Fig. S1 F). The upregulation of four metabolites—9-octadecenamide, oxalic acid, eicosane, and stearic acid—was observed in the BE group prior to B. cinerea infection (Fig. 1 G). These findings demonstrate that B. velezensis ES2-4 significantly induces the upregulation of key rhizosphere exudates, including oxalic acid and stearic acid, during B. cinerea infection in soybean plants. Strain ES2-4 elicits soybean resistance against B. cinerea through rhizosphere exudate-mediated mechanisms To investigate the regulatory effects of soybean rhizosphere secretions on the chemotactic behavior of B. velezensis ES2-4, we analyzed the impact of key differential metabolites on bacterial growth and chemotaxis using a semi-solid plate assay. The results revealed that oxalic acid, eicosane, and 9-octadecenamide significantly increased the colony area of B. velezensis ES2-4 (p < 0.05) within a 0.05-1 mM concentration range, demonstrating a concentration-dependent pattern that suggests their role in inducing directional chemotaxis (Fig. 2 A-E). In contrast, stearic acid exhibited a unique concentration response: while low concentrations (0.05 mM) markedly enhanced chemotactic motility, higher concentrations progressively attenuated this effect, indicating potential metabolic inhibition or receptor saturation (Fig. 2 A, 2 E). Further analysis of antifungal activity revealed that all four metabolites significantly suppressed the growth of B. cinerea at 0.05 mM. Notably, eicosane, 9-octadecenamide, and stearic acid exhibited enhanced inhibitory effects at higher concentrations (0.1-1 mM), with stearic acid showing the strongest pathogen inhibition (Fig. 2 F-I). However, oxalic acid did not exhibit enhanced antifungal activity beyond 0.05 mM, suggesting distinct molecular targets or regulatory mechanisms compared to the other metabolites (Fig. 2 F, 2 I). These findings collectively suggest that B. velezensis ES2-4 may enhance plant systemic resistance by modulating root secretion of oxalic acid, eicosane, and 9-octadecenamide, which synergistically promote bacterial chemotaxis and suppress pathogen growth. Strain ES2-4 Modulates Rhizosphere Microbiome Diversity and Structure in Response to B. cinerea Infection This study evaluated soybean rhizosphere microbiome variations across treatments using α- and β-diversity indices. For bacterial communities, the Sobs, Chao, and Ace indices followed the order E > CK > BE > B (Table S1 , Fig. 3 A-D), indicating that B. velezensis ES2-4 enhanced bacterial richness in both healthy and B. cinerea -infected rhizospheres, while B. cinerea (Group B) significantly reduced bacterial richness. The BE group (ES2-4 pretreated before infection) partially mitigated this reduction. Shannon index analysis revealed lower diversity in Group B compared to Groups E and CK, while the BE group exhibited higher diversity than Group B, and Group E surpassed CK, suggesting that ES2-4 improved bacterial diversity in both healthy and diseased plants. In contrast, fungal communities displayed opposite trends (Table S2). Groups B and BE exhibited higher Sobs, Chao, and Ace indices than Groups CK and E, indicating that B. cinerea increased fungal richness, while ES2-4 (Groups E and BE) reduced fungal richness in both healthy and infected plants. Simpson index analysis confirmed higher fungal diversity in Group B compared to other treatments, with the BE group showing a significant decline relative to Group B, highlighting ES2-4's inhibitory effect on pathogen-enhanced fungal diversity. Hierarchical clustering supported these findings, with BE/B groups and E/CK groups forming distinct clusters (Fig. 3 E-F). PCoA based on Bray-Curtis distance revealed significant β-diversity differences in bacterial communities among treatments (PERMANOVA: R² = 0.669, p = 0.001), with PC1 and PC2 collectively explaining 24.15% of the variation (Fig. 3 G). B. cinerea -treated groups (B and BE) separated from E and CK groups along PC1, while ES2-4 effects differentiated BE from Group B along PC2. Fungal communities showed similar β-diversity differences (R² = 0.5676, p = 0.001), with PC1 and PC2 explaining 24.48% of the variation (Fig. 3 H), mirroring bacterial clustering patterns. In conclusion, B. velezensis ES2-4 alleviated B. cinerea -induced dysbiosis in the soybean rhizosphere by differentially regulating bacterial and fungal community diversity and structure. Strain ES2-4 alters taxonomic composition and enriches beneficial microbes in soybean rhizosphere under pathogen stress Taxonomic annotation based on the NR database revealed that bacteria dominated the soybean rhizosphere microbiota (mean relative abundance: 99.61%), followed by viruses (0.16%), eukaryotes (0.13%), and archaea (0.09%). Bacterial communities consisted of 162 phyla, 269 classes, 482 orders, 998 families, 3,906 genera, and 27,342 species, while fungal communities included 10 phyla, 39 classes, 100 orders, 258 families, 436 genera, and 739 species (Fig. 4 A). Genus-level Venn analysis identified 3,497 bacterial genera shared across all treatments (Fig. 4 B). The dominant bacterial genera included Rugosimonospora , Reticulibacter , Actinomadura , Pseudonocardia , Alphaproteobacteria , Bradyrhizobium , Streptomyces , and Nitrobacteraceae (Fig. 4 C). Treatment with strain ES2-4 significantly increased the abundance of Actinomadura and Ktedonobacter in healthy plants, while reducing the abundance of Edaphobacter . In infected plants, ES2-4 pretreatment decreased the abundance of Rugosimonospora , Reticulibacter , Actinomadura , and Pseudonocardia (Fig. 4 C). Differential analysis further revealed higher abundances of Alphaproteobacteria , Proteobacteria , Acidobacteria , Pseudolabrys , Gemmatimonadetes , Betaproteobacteria , and Opitutus in the BE group compared to the B group (Fig. 4 D-E). For fungal communities, 324 genera were shared across treatments (Fig. 4 F), with dominant genera including Tulasnella , Pseudogymnoascus , Aspergillus , Tulasnellaceae , Rhizopus , Mycoblastus , and Amanita (Fig. 4 G). Strain ES2-4 treatment significantly increased the abundance of Aspergillus and Rhizopus in infected plants, while the BE group exhibited higher abundances of Mycoblastus , Amanita , Suillus , and Ceratobasidium compared to other groups (Fig. 4 H). Differential analysis confirmed that the BE group had significantly higher abundances of Aspergillus , Daldinia , Mycoblastus , Anaeromyces , Rhizopus , Amanita , and Trichoderma compared to the B group (Fig. 4 H-I). In conclusion, strain ES2-4 reshapes microbial community composition by selectively enriching beneficial taxa (e.g., Alphaproteobacteria , Aspergillus ) and suppressing pathogen-associated genera under biotic stress. Strain ES2-4 coordinates metabolic rewiring and defense activation in the rhizosphere to suppress B. cinerea KEGG functional annotation revealed distinct metabolic potentials across the treatment groups. The metabolic category accounted for the highest proportion of functional annotations in all groups: 51.84% in the B group, 51.41% in the BE group, 50.71% in the CK group, and 50.72% in the E group (Fig. 5 A). Environmental information processing represented 16.90% (B), 16.79% (BE), 16.45% (CK), and 16.50% (E), while cellular processes made up 11.55% (B), 11.76% (BE), 11.75% (CK), and 11.69% (E). Genetic information processing accounted for 10.74% (B), 10.96% (BE), 11.51% (CK), and 11.59% (E), with organismal systems showing the lowest values: 3.66% (B), 3.71% (BE), 3.86% (CK), and 3.83% (E). Comparative analysis of KEGG pathways demonstrated significant treatment-specific effects. In ES2-4-treated healthy plants (E vs CK), starch/sucrose metabolism (p = 0.01219) and the PI3K-Akt signaling pathway (p = 0.02157) showed increased gene abundance (Fig. 5 B). For pathogen-infected plants pretreated with ES2-4 (BE vs B), several metabolic pathways were significantly downregulated, including metabolic pathways (p = 0.01219), microbial metabolism in diverse environments (p = 0.01219), cofactor biosynthesis (p = 0.01219), nucleotide sugar biosynthesis, fatty acid metabolism (p = 0.01219), and butanoate metabolism (p = 0.03671). In contrast, pathways such as two-component systems (p = 0.01219), purine metabolism (p = 0.01219), nucleotide metabolism, and glycan biosynthesis/metabolism (p = 0.01219) were upregulated in the BE group (Fig. 5 C). COG functional classification grouped microbial proteins into three major categories. Metabolic functions dominated across treatments: 44.29% (B), 44.29% (BE), 44.04% (CK), and 43.88% (E), followed by cellular processes/signaling: 23.93% (B), 24.30% (BE), 25.75% (CK), and 25.54% (E). Information storage/processing had the lowest proportions: 18.16% (B), 17.94% (BE), 17.43% (CK), and 17.69% (E) (Fig. 5 D). Differential analysis revealed significant enrichment in the BE group compared to the B group in pathways such as signal transduction mechanisms (p = 0.03671), translation/ribosome biogenesis (p = 0.01219), DNA replication/recombination/repair (p = 0.02157), defense mechanisms (p = 0.03671), cell cycle control/division (p = 0.03671), cellular motility (p = 0.03671), and extracellular structure formation (p = 0.03671) (Fig. 5 E). In conclusion, strain ES2-4 reprograms rhizosphere functional processes by suppressing pathogen-promoting metabolic pathways and enhancing stress-responsive and defense-associated functions, thereby establishing a microbially-driven defense network against B. cinerea . Strain ES2-4 modulates microbial interaction networks to enhance pathogen resistance in soybean rhizosphere Spearman correlation networks (|r| ≥ 0.7, p < 0.05) revealed distinct interaction patterns between bacterial and fungal communities at the genus level. For bacterial networks, the CK group exhibited the highest complexity (nodes = 49, edges = 388, average degree = 15.837, clustering coefficient = 0.753), with a balanced distribution of positive and negative edges (50.77% vs 49.23%). In contrast, strain ES2-4 treatment in healthy plants (E group) slightly reduced network complexity (nodes = 48, edges = 379, average degree = 15.792, clustering coefficient = 0.736), but increased the proportion of positive edges to 54.35% (Fig. 6 A-B). Pathogen infection (B group) drastically simplified bacterial networks (nodes = 47, edges = 139, average degree = 5.915), while ES2-4 pretreatment (BE group) partially restored network complexity (nodes = 50, edges = 154, average degree = 6.16) (Fig. 6 C-D). Keystone genus analysis (Table S3, S5) demonstrated that ES2-4 enhanced the synergism between beneficial bacteria (e.g., Bradyrhizobium-Streptomyces) while suppressing pathogen-associated genera (e.g., Ralstonia) in network centrality. Fungal networks exhibited contrasting dynamics. ES2-4 treatment in healthy plants (E group) reduced the number of edges from 180 (CK) to 108, with a lower average degree (4.408 vs 7.347), but a higher clustering coefficient (0.645) (Fig. 6 E-F). Pathogen challenge (B group) increased fungal connectivity (edges = 159, average degree = 6.49), whereas ES2-4 pretreatment (BE group) weakened the interaction intensity (edges = 104, average degree = 4.426) and elevated the proportion of positive edges to 68.27% (Fig. 6 G-H). Fungal keystone analysis (Table S4, S6) indicated that ES2-4 reduced the network centrality of pathogenic fungi (e.g., Fusarium) while strengthening antagonistic genera (e.g., Trichoderma), thereby optimizing community stability. In summary, strain ES2-4 enhances soybean's ecological defense against B. cinerea by differentially restructuring bacterial synergy networks and fungal antagonism networks, establishing a host-beneficial microbiome equilibrium. Discussion This study integrates metabolomic and metagenomic approaches to uncover the dual defense mechanisms of B. velezensis ES2-4 against soybean gray mold caused by B. cinerea . Metabolomic profiling revealed that ES2-4 reprogrammed soybean root exudates, inducing antifungal metabolites (e.g., oxalic acid and eicosane) that directly suppressed pathogen growth while promoting the chemotaxis of beneficial bacteria. Metagenomic analysis demonstrated that ES2-4-mediated microbiome remodeling was characterized by an enrichment of Alphaproteobacteria and Streptomyces , alongside the activation of stress-responsive pathways. Notably, ES2-4 synergistically suppressed pathogen-associated metabolic networks while fostering microbial cooperation through reconstructed co-occurrence networks. This work innovatively deciphers the cascade interactions between root exudates and microbiome engineering in plant immunity, bridging the knowledge gap in PGPR-mediated biocontrol. These findings advance sustainable agriculture by proposing rhizosphere microbiome manipulation as a targeted strategy against soil-borne diseases, offering both theoretical and practical insights into ecological plant protection. Plants undergo metabolic reprogramming to adapt to biotic and abiotic stresses, with PGPR enhancing this process to improve stress resistance 27 . This study systematically elucidates the regulatory mechanism of B. velezensis ES2-4 on soybean root metabolic reprogramming and its role in inducing systemic resistance (ISR) through metabolomics. Principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) revealed that ES2-4 treatment significantly reshaped the metabolic profiles of healthy and infected soybean roots. Differential metabolite analysis identified 12 metabolites (including oxalic acid, eicosane, and stearic acid) upregulated in healthy plants, and 22 metabolites accumulated in diseased plants, with eicosane, oxalic acid, stearic acid, and 9-octadecenamide being common key functional metabolites. Notably, eicosane, a crucial effector molecule in PGPR-plant interactions, has been validated for its antimicrobial activity in various plant-microbe systems 28 – 30 . This study first demonstrates that Bacillus can enhance disease resistance by inducing plant self-secretion of this metabolite, offering new insights into rhizosphere microbial regulation of plant immunity. KEGG pathway enrichment analysis revealed that ES2-4 primarily regulated starch/sucrose metabolism, galactose metabolism, and unsaturated fatty acid synthesis in healthy plants, while significantly affecting 11 metabolic pathways in diseased plants. The upregulation of unsaturated fatty acid biosynthesis was particularly critical, as its products participate in membrane construction and signal transduction 31 , with enhancement closely related to PGPR-mediated plant disease resistance 32 , 33 . Concurrently, the activation of valine/leucine/isoleucine pathways through jasmonic acid (JA) signaling, with isoleucine serving as a JA-Ile precursor, activated core JA signaling to enhance resistance against B. cinerea 34 , 35 . Additionally, ES2-4-induced reconstruction of carbohydrate metabolism (starch/sucrose and galactose metabolism) displayed dual functions: providing energy substrates for defense reactions and acting as signaling molecules to activate immune responses 36 , sharing mechanistic similarities with Trichoderma -plant interactions 37 . These findings demonstrate that ES2-4 establishes a multi-dimensional defense system through metabolic reprogramming, including direct induction of antimicrobial metabolites, JA/ET-mediated ISR activation, and reinforcement of metabolic networks. Further studies should employ gene silencing to validate the spatiotemporal dynamics and molecular mechanisms of key metabolites. In vitro experiments revealed that ES2-4 chemotaxis was regulated by specific root exudates. Chemotaxis assays demonstrated that 0.05-1 mM concentrations of oxalic acid, eicosane, 9-octadecenamide, and stearic acid significantly enhanced bacterial chemotaxis, with the efficacy in the following order: oxalic acid > 9-octadecenamide > stearic acid > eicosane. Notably, higher concentrations (1 mM) exhibited a stronger chemotactic attraction. Biofilm formation analysis showed that 0.05-1 mM oxalic acid and 0.5-1 mM stearic acid significantly promoted ES2-4 biofilm development, with 1 mM stearic acid doubling biofilm biomass. This suggests that stearic acid enhances rhizosphere colonization, likely through exopolysaccharide synthesis or quorum sensing regulation. This phenomenon is consistent with the findings of Ankati et al. 38 , who observed that Pseudomonas sp. RP2 utilized peanut-derived fatty acids to enrich the rhizosphere. Similar mechanisms were also noted in Bacillus amyloliquefaciens SQR9 (cucumber root tryptophan induction) 39 , Tsukamurella tyrosinosolvens P9 (peanut oxalate-activated siderogenesis) 40 , and banana root- B. amyloliquefaciens NJN-6 interactions 41 , confirming the universality of root exudates in regulating PGPR behavior. Strain ES2-4 likely establishes chemical gradients through the induced secretion of eicosane and 9-octadecenamide to drive directional migration and stable colonization. Importantly, strain ES2-4-induced stearic acid, oxalic acid, and eicosane exhibited significant antifungal effects against B. cinerea . The antifungal properties of eicosane have been documented in Streptomyces griseus VSG4 metabolites 41 , n-eicosane in Streptomyces KX852460 fermentation broth 28 , and oxalic acid from Bacillus cereus AR156 [51]. However, oxalic acid’s role in plant-microbe interactions is concentration-dependent, showing dual effects in some contexts 42 . Soil microbial diversity plays a crucial role in maintaining ecological functions and suppressing pathogens 43 . Metagenomic analysis revealed that ES2-4 significantly enhanced bacterial diversity and richness in the healthy soybean rhizosphere, while reversing the pathogen-induced decline in diversity, suggesting pathogen suppression via optimized microenvironments 44 . Notably, B. cinerea infection increased fungal diversity, whereas ES2-4 reduced fungal diversity in both healthy and diseased plants, indicating microbial antagonism against pathogenic fungi. At the genus level, ES2-4 pretreatment enriched beneficial taxa, including Alphaproteobacteria , Acidobacteriaceae , and Pseudolabrys . Alphaproteobacteria plays a role in carbon/nitrogen cycling and enhances antioxidant capacity 45 , while Acidobacteriaceae is associated with Fusarium head blight suppression 46 , aligning with the findings of You et al. 47 on Bacillus subtilis -mediated rhizosphere regulation. KEGG annotation revealed that ES2-4 upregulated starch/sucrose metabolism genes in healthy plants and activated two-component systems in diseased plants. As core prokaryotic environmental sensors, two-component systems enhance microbial stress adaptation through biofilm regulation 48 , with functional reinforcement consistent with Pham et al. 49 on histidine kinase-mediated disease resistance. COG analysis further revealed that strain ES2-4 significantly increased defense mechanisms and signal transduction proteins, indicating a coordinated establishment of microbial-host defense. Microbial network analysis demonstrated that strain ES2-4 enhanced bacterial mutualism by increasing positive interactions. Pathogen invasion reduced network complexity, whereas strain ES2-4 pretreatment (BE group) reconstructed highly connected networks through strengthened Acidobacteria - Proteobacteria interactions—a structure known to reduce pathogen colonization success 50 . For fungal communities, ES2-4 optimized functionality by reducing network complexity and suppressing pathogenic Tulasnella . Notably, Chloroflexi , as a potential pathogen-associated taxon, exhibited significant negative connectivity in BE networks, suggesting beneficial microbiome competition. This resembles Acidobacteria -mediated pathogen suppression in maize rotation systems 51 . In conclusion, Bacillus velezensis ES2-4 combats soybean gray mold through a dual defense mechanism: (1) reprogramming root exudates (e.g., oxalic acid, eicosane) to directly inhibit B. cinerea while enhancing the recruitment of beneficial microbes, and (2) restoring bacterial diversity (e.g., Alphaproteobacteria , Streptomyces ) while suppressing fungal pathogens. This study establishes a novel link between PGPR-induced metabolic shifts and microbiome remodeling, revealing microbial synergy in stress-response pathways, such as two-component signaling systems. Future research should focus on field validation, elucidating molecular signaling mechanisms (e.g., JA/ET pathways), and investigating the colonization dynamics of strain ES2-4. Optimizing strain-plant-microbiome interactions will contribute to the development of eco-friendly and sustainable biocontrol strategies. Materials and methods Bacterial strain, and fungal inoculum Bacillus velezensis ES2-4, originally isolated from soil and preserved at the Laboratory of Applied Botany, Sichuan Normal University, was maintained in Luria-Bertani (LB) broth supplemented with 25% glycerol and stored at − 80°C. Prior to experimental use, the strain was reactivated by culturing in LB broth at 37°C for 24 h. Botrytis cinerea was cultured on potato dextrose agar (PDA) plates at 28°C for 7 days. Spore suspensions were prepared by flooding the culture surface with sterile distilled water, followed by stirring and filtration through four layers of sterile degreased gauze. Spore concentrations were determined using a hemocytometer and adjusted to the required density with sterile water before use in experiments. Preparation of Soybean Root Exudates Soybean seeds were surface-sterilized with 0.1% (w/v) sodium hypochlorite for 5 min, rinsed 5–6 times with sterile distilled water, and placed on moist filter paper in Petri dishes. Germination was induced in darkness at 25°C using a germination chamber. Uniformly germinated seedlings were transferred to culture bottles containing one-quarter-strength Hoagland nutrient solution for hydroponic cultivation, with roots shielded from light. After two weeks of cultivation, root irrigation treatments were initiated. The experiment included four treatment groups: (1) B group ( B. cinerea -infected soybean plants); (2) E group ( B. velezensis ES2-4-treated soybean plants); (3) BE group (soybean plants pre-treated with B. velezensis ES2-4 followed by B. cinerea infection); and (4) CK group (control plants treated with sterile distilled water). In the BE and E groups, 20 mL of B. velezensis ES2-4 suspension (1×10⁷ CFU·mL⁻¹) was applied to the rhizosphere 20 days after transplanting. Five days later, the BE and B groups were inoculated with 20 mL of B. cinerea spore suspension (1×10⁷ CFU·mL⁻¹). Five days post-inoculation, plant roots were thoroughly rinsed with ultrapure water to remove residual metabolites of B. velezensis ES2-4 and B. cinerea , followed by hydroponic cultivation in ultrapure water for two days. The culture solution was then collected, filtered, and freeze-dried 52 . Each treatment included five biological replicates. Root exudates metabolome assay Sample pretreatment was performed following the method described by Chen et al. 53 , with minor modifications. Briefly, 3 mg of freeze-dried root exudate powder was dissolved in 1 mL of methanol, and 50 µL of L-2-chloro-phenylalanine (0.3 mg/mL) was added as an internal standard. The mixture was vortexed for 30 s and sonicated on ice for 15 min. The solution was then filtered through a 0.22 µm membrane and centrifuged at 16,000 r/min for 15 min at 4°C. The supernatant was completely dried under nitrogen gas, derivatized, and homogenized prior to instrumental analysis. Gas chromatography-mass spectrometry (GC-MS) parameters were set according to reference 54 . Mass spectrometric data were matched against the National Institute of Standards and Technology (NIST) Mass Spectral Library. Metabolites were identified by comparing their electron ionization (EI) mass spectra, including fragment patterns, with reference standards in the database. Final metabolite confirmation was achieved using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Human Metabolome Database (HMDB), with reliability further validated against published literature. Semi-quantitative metabolite analysis was performed using the internal standard method, followed by chromatographic peak area normalization, data transformation, and scaling. Normalized datasets were subjected to multivariate statistical analyses, including Principal Component Analysis (PCA), Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), volcano plot analysis, and hierarchical clustering. Functional annotation and enrichment analysis of metabolites were conducted using KEGG pathway mapping. All statistical analyses were performed using MetaboAnalyst 5.0. Effect of soybean rhizosphere exudates on the chemotactic response of B. velezensis ES2-4 and B. cinerea The qualitative assessment of microbial inhibition was performed following the method described by Soo-Young et al. 55 , with modifications. Briefly, sterile filter paper discs (6 mm in diameter) were placed at the center of semi-solid agar plates containing gradient concentrations of differential metabolites. A 10 µL aliquot of bacterial suspension was applied to each disc, with three biological replicates per treatment. For fungal inhibition assays, Potato Dextrose Agar (PDA) medium supplemented with varying concentrations of differential metabolites was prepared and poured into 90 mm sterile Petri dishes. Mycelial plugs (6 mm in diameter) were excised from the periphery of 7-day-old B. cinerea colonies and transferred to the center of each PDA plate. Plates were incubated at 25°C, and colony diameters were measured at 12-hour intervals using a digital caliper. Three biological replicates were included for each treatment. Biocontrol agent and pathogen treatments and experimental design Soybean seeds were initially germinated in seedling trays and then transferred into pots for further cultivation. After 20 days of growth under controlled conditions, the experimental treatments were initiated as follows: standard watering without inoculation (CK), inoculation with the biocontrol bacterium ES2-4 (E), application of ES2-4 followed by B. cinerea inoculation (BE), and inoculation with B. cinerea alone (B). In the E and BE groups, a 20 mL suspension of ES2-4 (1×10⁷ CFU·mL⁻¹) was applied to the rhizosphere of soybean seedlings 20 days after transplantation. Five days later, the BE and B groups received a 20 mL suspension of B. cinerea spores (1×10⁷ CFU·mL⁻¹). Rhizosphere soil samples were collected five days post-fungal inoculation for metagenomic sequencing 56 . Each experimental condition was replicated five times biologically. DNA extraction, PCR amplification, and high-throughput sequencing Genomic DNA was extracted from six soil samples using the TIANamp Soil DNA Kit (TIANGEN, Beijing, China) following the manufacturer’s protocol. The quality and concentration of the extracted DNA were evaluated via 1% agarose gel electrophoresis and NanoDrop® ND-2000 spectrophotometry (Thermo Scientific, Wilmington, NC, USA) before storage at -80°C. To amplify bacterial 16S rDNA and fungal ITS rDNA, the following specific primer pairs were used: 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3') for 16S rDNA, and ITS1-F (CTTGGTCATTTAGAGGAAGTAA) and ITS4-R (TCCTCCGCTTATTGATATGC) for the ITS rDNA gene. PCR amplification was performed using a T100 Thermal Cycler (Bio-Rad, California, USA) with 2× TransStart® Fast Pfu PCR SuperMix, according to the manufacturer's instructions 57 . The thermal cycling conditions were as follows: initial denaturation at 95°C for 3 min, followed by 27 cycles of 95°C for 30 s, 55°C for 30 s, and 72°C for 45 s, with a final extension at 72°C for 10 min and a hold at 4°C. Each sample was amplified in triplicate. PCR products were separated on a 2% agarose gel, purified using the TIANgel Purification Kit (TIANGEN, Beijing, China), and quantified using a Quantus™ Fluorometer (Promega, Beijing, China). The purified amplicons were sequenced on the Illumina NovaSeq PE250 platform by Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China) following standard protocols. Data Processing and Bioinformatics Analysis Raw FASTQ files were demultiplexed using a custom Perl script developed in-house. Quality filtering and merging of paired-end reads were performed using fastp v0.19.6 and FLASH v1.2.7, respectively, with the following criteria: (1) reads with an average quality score below 20 over a 50 bp sliding window were trimmed, and reads shorter than 50 bp or containing ambiguous bases were discarded; (2) overlapping sequences of at least 10 bp with a maximum mismatch ratio of 0.2 in the overlap region were merged; and (3) samples were demultiplexed based on barcode and primer sequences, allowing exact barcode matches and up to two nucleotide mismatches in the primers. Operational taxonomic units (OTUs) were identified from the processed sequences using UPARSE v7.1 58,59 , with a 97% sequence similarity threshold. The most abundant sequence within each OTU was selected as its representative. To standardize sequencing depth, 16S rRNA gene sequences were rarefied to 44,980 reads, while ITS rDNA sequences were rarefied to 84,995 reads, yielding an average Good’s coverage of 99.09%. Taxonomic classification of OTU representative sequences was conducted using the RDP Classifier v2.2, referencing the SILVA database (v138.1) for bacterial identification and the UNITE database (v138.1) for fungal classification, with a confidence threshold of 0.7. The functional potential of the metagenome was inferred using PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) 60 , utilizing its integrated pipeline, which includes HMMER for sequence alignment, EPA-NG and Gappa for phylogenetic placement, castor for 16S gene copy normalization, and MinPath for gene family and pathway predictions, all following the standard PICRUSt2 protocol. Bioinformatic analyses of the soil microbiota were performed on the Majorbio Cloud platform ( https://cloud.majorbio.com ). Based on OTU data, rarefaction curves and alpha diversity indices—including observed OTUs, Chao1 richness, Shannon index, and Good’s coverage—were calculated using Mothur v1.30.1 61 . Principal coordinate analysis (PCoA) based on Bray-Curtis dissimilarity was performed using the Vegan v2.5-3 package to assess microbial community similarities among samples. Significant differences between treatments were determined using the Welch t-test in STAMP, while variations in OTU relative abundance across treatments were analyzed using likelihood ratio tests in the “EdgeR” package. A Manhattan plot was generated for visualization using the “ggplot2” package. Co-Occurrence Network Analysis Co-occurrence networks were constructed to examine shifts in microbial community interactions for both bacterial and fungal populations. These networks were generated in R (v4.3.1) using Spearman correlation coefficients, incorporating only significant correlations (p < 0.05) with an absolute correlation coefficient (|R|) greater than 0.6 62 . The resulting networks were visualized using the Fruchterman-Reingold layout in Gephi. Statistical analysis All quantitative data are presented as the mean ± standard error (SE). Statistical analyses were performed using GraphPad Prism 9.5.0. Analysis of variance (ANOVA) was conducted to assess significant differences, followed by Duncan’s multiple range test for mean separation. A p-value of < 0.05 was considered statistically significant. Declarations Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors confirm that there are no conflicts of interest. Funding Project supported by the National Natural Science Foundation of China (NO. 32100240). Data availability Sequence data that support the findings of this study have been deposited in the NCBI with the primary accession code PRJNA1303063. Author contributions Siwei Chen made contribution to the conception and design; Xinpeng Guo and Maohua Wu analyzed and interpreted data; Rui Chen drafted the article; Bing He and Ting Zheng revisied it critically for important intellectual content; All authors approved the final version to be published. Acknowledgements Not applicable. References Savary, S. et al. The global burden of pathogens and pests on major food crops. Nature Ecology & Evolution 3 , 430-439 (2019). https://doi.org:10.1038/s41559-018-0793-y Zhao, Y. et al. Comparison of Nutritional Diversity in Five Fresh Legumes Using Flavonoids Metabolomics and Postharvest Botrytis cinerea Defense Analysis of Peas Mediated by Sakuranetin. 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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-7283311","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":500445092,"identity":"2dd48dad-d2ab-4978-8e45-2e3af9076919","order_by":0,"name":"Rui Chen","email":"","orcid":"","institution":"Sichuan Normal University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Chen","suffix":""},{"id":500445093,"identity":"c08b855a-e742-4464-844d-3cd2f478cc3c","order_by":1,"name":"Xinpeng Guo","email":"","orcid":"","institution":"Sichuan Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xinpeng","middleName":"","lastName":"Guo","suffix":""},{"id":500445094,"identity":"2f53fb45-50ae-4379-9887-8865aad93bdd","order_by":2,"name":"Maohua Wu","email":"","orcid":"","institution":"Sichuan Normal University","correspondingAuthor":false,"prefix":"","firstName":"Maohua","middleName":"","lastName":"Wu","suffix":""},{"id":500445095,"identity":"6125030e-87d0-414f-ba32-72bef111cc4c","order_by":3,"name":"Ting Zheng","email":"","orcid":"","institution":"Sichuan Normal University","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Zheng","suffix":""},{"id":500445096,"identity":"5400d1af-a10f-4d27-b9b7-64f63050b452","order_by":4,"name":"Siwei Chen","email":"","orcid":"","institution":"Sichuan Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Siwei","middleName":"","lastName":"Chen","suffix":""},{"id":500445097,"identity":"00484ace-3486-4594-a741-6a27ce5fd2e9","order_by":5,"name":"Bing He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYBACAwYehgMMDDYMbBA+M9Fa0kjUAgSHYXwitJiz9x48XPDrvD0f+9ljEgwV1okN7GcP4NVi2XMu4fDMvtuJbTx5aRIMZ9ITG3jyEvA77EaOwWHentsJbBI8ZhKMbYcTGyR4DPBruf8GpOWcPUTLP2K03OAxOMzz4wBjG1hLAzFazuQlHOZtSAb6JcfYIuFYujGQQUDL8bOHP/P8sbOXbz9jeONDjbVsP/sZ/FrAgLENykgAYjbC6kHgD3HKRsEoGAWjYIQCAEQ/QkN2/DZ2AAAAAElFTkSuQmCC","orcid":"","institution":"Sichuan Normal University","correspondingAuthor":true,"prefix":"","firstName":"Bing","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2025-08-03 12:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7283311/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7283311/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-21135-x","type":"published","date":"2025-10-23T16:16:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89576046,"identity":"274fb728-ea4d-472b-8d4d-e0b61f07c4e0","added_by":"auto","created_at":"2025-08-21 13:06:02","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1152911,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetabolomic analysis of soybean root exudates under \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBacillus velezensis\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e ES2-4 treatment.\u003c/strong\u003e (A, B) Total ion chromatograms (TIC) of metabolites between B and BE groups, showing distinct intensity profiles across retention times. (C) OPLS-DA score plot demonstrating clear metabolic separation between B and BE groups. (D) Volcano plot highlighting significantly upregulated (red) and downregulated (blue) metabolites (|log2FC| \u0026gt; 1.5, VIP \u0026gt; 1.0). Key antifungal metabolites, including oxalic acid), were markedly elevated. (E) Heatmap of the top 25 enriched metabolite clusters, with red indicating high abundance between B and BE groups. (F) Overview of enriched metabolic pathways mapping was conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. (G) Table summarizes VIP scores, log\u003csub\u003e2\u003c/sub\u003eFC values, and regulation trends of differential metabolites.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7283311/v1/9e73c82963cbb8d5c4fa6c52.jpeg"},{"id":89577090,"identity":"c1c39244-bf99-4654-a539-5b2d2f9f363f","added_by":"auto","created_at":"2025-08-21 13:14:02","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3165849,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInhibitory effects of key root exudate metabolites induced by \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBacillus velezensis\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e ES2-4 on \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBotrytis cinerea\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003ecolony growth.\u003c/strong\u003e (A) Representative images of strain ES2-4 treated with oxalic acid, eicosane, 9-octadecenamide, or stearic acid at concentrations ranging from 0 to 1 mM. (B) - (E) Quantitative analysis of colony diameter (cm) of strain ES2-4 treated with oxalic acid, eicosane, 9-octadecenamide, or stearic acid at concentrations ranging from 0 to 1 mM. (F) - (I) Quantitative analysis of colony diameter (cm) of \u003cem\u003eB. cinerea\u003c/em\u003e treated with oxalic acid, eicosane, 9-octadecenamide, or stearic acid at concentrations ranging from 0 to 1 mM. Note: the bars indicate the mean ± SE (standard error) of three replicates, and different letters indicate significant differences from the other treatments (p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7283311/v1/cc0b9c4e4ea94e78923d83f9.jpeg"},{"id":89576047,"identity":"672bb90c-3efd-4960-8a85-40d8020b90d1","added_by":"auto","created_at":"2025-08-21 13:06:02","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2638074,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobial diversity and community structure analysis under experimental treatments.\u003c/strong\u003e(A) Sobs index on species level. (B) Chao index on species level. (C) Ace index on species level. (D)Shannon index on species level. (E) Cluster analysis between samples based on the Bray–Curtis distance of species abundance. (F) Heatmap of Bray-Curtis distances revealing closer microbial similarity within treated groups. (G) PCoA dimensionality reduction analysis based on the Bray–Curtis distance of species abundance of bacteria. (H) PCoA dimensionality reduction analysis based on the Bray–Curtis distance of species abundance of fungi. (I) Beta diversity boxplots highlighting increased inter-group dissimilarity (Bray-Curtis distance: 0.15–0.35).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7283311/v1/6fd6b04a0b8e4be4201b45a5.jpeg"},{"id":89577512,"identity":"4caf980f-1e47-4713-808b-47fd8067ace9","added_by":"auto","created_at":"2025-08-21 13:22:02","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3386197,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobial community composition and functional diversity across experimental groups.\u003c/strong\u003e(A) Domain-level taxonomic distribution: Bacteria dominated in all groups, while Eukaryota and Archaea were minor components. (B) Venn diagrams identified 3,497 shared microbial taxa across groups. (C) Relative abundances of major genus present in the bacterial communities under different treatments. (D) Comparative analysis of rhizosphere bacteria in group E and CK at genus level (Top15). (E) Comparative analysis of rhizosphere bacteria in group B and BE at genus level (Top15). (F) Venn diagrams identified 324 shared microbial taxa across groups. (G) Relative abundances of major genus present in the fungal communities under different treatments. (H) Comparative analysis of rhizosphere fungi in group E and CK at genus level (Top15). (I) Comparative analysis of rhizosphere fungi in group B and BE at genus level (Top15).\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7283311/v1/d3ac638953d5be06972db255.jpeg"},{"id":89576051,"identity":"c714ab15-b9ce-4ede-9578-14a3d34d40d6","added_by":"auto","created_at":"2025-08-21 13:06:02","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2996914,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional and statistical analyses of metabolic pathways and microbial interactions.\u003c/strong\u003e (A) Circular plot of metabolic subsystem distribution.Color gradients represent functional categories. (B) Bar plot of Wilcoxon rank-sum test results for pathway mapping was conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Significant differences (p\u0026lt; 0.05) included upregulated starch/sucrose metabolism (p = 0.0121) and downregulated phosphatidylinositol signaling (p = 0.0121). Green/red markers denote 95% confidence intervals. (C) Bar plot of differential metabolic pathway mapping was conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Error bars indicate inter-group variance. (D) Circular plot of cellular process distribution. (E) COG functional comparison analysis of rhizosphere microorganisms in E and CK group. (F) COG functional comparison analysis of rhizosphere microorganisms in B and BE group.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7283311/v1/86cdb35a18c8491f33823641.jpeg"},{"id":89577094,"identity":"bac71ffb-db26-41d8-92e5-6e406b7193e7","added_by":"auto","created_at":"2025-08-21 13:14:03","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4212277,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation network analysis of soybean rhizosphere soil microbial community. \u003c/strong\u003e(A) Soil microbial community of soybean rhizosphere at the genus level in CK group. Soil microbial community of soybean rhizosphere bacteria at the genus level in groups: (A) CK, (B) E, (C) B. (D) BE. Soil microbial community of soybean rhizosphere fungi at the genus level in groups: (E) CK, (F) E, (G) B. (H) BE. The node size is proportional to the relative abundance of each OTU; Links betwee’s correlation\u0026gt;0.8 or ≤0.8); Different node colors indicate different genus to which the OTU belongs; Line color indicates direction, as shown in the legend.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7283311/v1/f9735d62c232bb4ad8f561fd.jpeg"},{"id":94489959,"identity":"6a92cc2c-c60f-40de-9f8b-e47d836cbaac","added_by":"auto","created_at":"2025-10-27 17:06:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7582864,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7283311/v1/0ec0bd8a-ebf9-43fe-b7b9-e703cb321f2d.pdf"},{"id":89577092,"identity":"57b284e4-18aa-4ea7-929a-8a3132783c94","added_by":"auto","created_at":"2025-08-21 13:14:02","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1843582,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7283311/v1/4583a70cee1f9c8b6429d7ae.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bacillus velezensis ES2-4 Modulates Root Exudation and Microbiome Remodeling to Enhance Soybean Resistance Against Gray Mold","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSoybean gray mold, caused by the necrotrophic fungal pathogen \u003cem\u003eBotrytis cinerea\u003c/em\u003e, has emerged as a devastating disease threatening global soybean production, with yield losses reaching 30\u0026ndash;50%\u003csup\u003e1,2\u003c/sup\u003e. This pathogen typically invades host tissues through wounds or natural openings, leading to characteristic symptoms including leaf blotches, stem wilting, and fruit rot\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Its broad host adaptability enables it to infect over 200 economically important crops, including soybean, resulting in substantial economic losses. Although chemical fungicides remain the primary control strategy, their overuse has not only driven the evolution of multidrug resistance in pathogens but also disrupted soil microbial communities\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Consequently, biocontrol strategies utilizing plant growth-promoting rhizobacteria (PGPR) have gained prominence as sustainable alternatives\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, highlighting the urgency to develop sustainable alternatives such as PGPR-based biocontrol.\u003c/p\u003e\u003cp\u003e\u003cem\u003eBacillus velezensis\u003c/em\u003e, first isolated from the V\u0026eacute;lez River estuary in M\u0026aacute;laga, Spain, by Ruiz-Garc\u0026iacute;a et al. in 1999 and formally described in 2005\u003csup\u003e8\u003c/sup\u003e, has emerged as a model Gram-positive PGPR due to its broad-spectrum antimicrobial activity and plant growth-promoting traits\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Its biocontrol mechanisms involve the synthesis of functional metabolites (e.g., cyclic lipopeptides, polyketides, siderophores), coupled with nutrient competition, biofilm formation, and induction of systemic resistance (ISR) in host plants\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNotably, accumulating evidence demonstrates the efficacy of \u003cem\u003eB. velezensis\u003c/em\u003e against \u003cem\u003eB. cinerea\u003c/em\u003e: For instance, cell-free supernatants from strain YTQ3 suppress mycelial growth (\u0026gt;\u0026thinsp;60% inhibition) and spore germination of \u003cem\u003eB. cinerea\u003c/em\u003e in a dose-dependent manner\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, while strain BE1 significantly reduces postharvest gray mold incidence in tomato fruits\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Additionally, this bacterium exhibits protective effects against soybean root rot (caused by \u003cem\u003eFusarium oxysporum\u003c/em\u003e), Phytophthora root rot (\u003cem\u003ePhytophthora sojae\u003c/em\u003e), and pustule disease (\u003cem\u003eXanthomonas axonopodis\u003c/em\u003e pv. glycines)\u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. However, its potential for controlling soybean gray mold remains unexplored.\u003c/p\u003e\u003cp\u003eRecent studies highlight the role of \u003cem\u003eB. velezensis\u003c/em\u003e in reshaping plant-microbe interactions through modulation of rhizosphere exudates. These root-secreted metabolites, including sugars, organic acids, and amino acids, serve as key signaling molecules in rhizosphere communication\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eB. velezensis\u003c/em\u003e can induce the secretion of antimicrobial compounds such as cinnamic acid and malic acid, which directly inhibit pathogens (e.g., \u003cem\u003eRalstonia solanacearum\u003c/em\u003e) while recruiting beneficial microbes (e.g., \u003cem\u003ePseudomonas\u003c/em\u003e spp., \u003cem\u003eStreptomyces\u003c/em\u003e spp.) to establish pathogen-suppressive microbiomes\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Metabolomic analyses reveal that this bacterium reprograms plant metabolic pathways, particularly trehalose biosynthesis and phenylpropanoid metabolism, thereby altering rhizosphere metabolite profiles and optimizing microbial community structure\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Furthermore, lipopeptides (e.g., fengycin) secreted by \u003cem\u003eB. velezensis\u003c/em\u003e synergize with root exudates to suppress pathogens, while enhanced biofilm formation ensures long-term rhizosphere colonization\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Nevertheless, the metabolic reprogramming mechanisms and microbiome-mediated immune regulation in soybean-\u003cem\u003eB. cinerea\u003c/em\u003e interactions remain poorly understood.\u003c/p\u003e\u003cp\u003eIn this study, integrated metabolomic and metagenomic analyses were employed to elucidate how \u003cem\u003eB. velezensis\u003c/em\u003e ES2-4 enhances soybean resistance by specifically inducing the secretion of antimicrobial metabolites (e.g., oxalic acid, eicosane) and reconstructing rhizosphere microbial networks. Our findings innovatively decipher the microbiome engineering-mediated plant immunity mechanisms, offering a theoretical foundation for developing rhizosphere-focused biocontrol strategies against soybean gray mold.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAlterations in rhizosphere exudate profiles following co-inoculation of soybean with strain ES2-4 and \u003cem\u003eB. cinerea\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTo investigate the dynamic changes in soybean rhizosphere exudates under individual or co-inoculation with \u003cem\u003eB. velezensis\u003c/em\u003e ES2-4 and \u003cem\u003eB. cinerea\u003c/em\u003e, comprehensive metabolite profiling was conducted using GC-MS. Total ion chromatogram (TIC) analysis from 24 biological replicates (6 per group) showed high inter-sample peak overlap within each treatment group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B, S1A-B), with consistent retention times and ion intensities, confirming methodological reproducibility and instrument stability. Orthogonal partial least squares-discriminant analysis (OPLS-DA) revealed clear separation between the BE (ES2-4\u0026thinsp;+\u0026thinsp;\u003cem\u003eB. cinerea\u003c/em\u003e) and B (\u003cem\u003eB. cinerea\u003c/em\u003e alone) groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), and distinct metabolic divergence between the E (ES2-4 alone) and CK (control) groups (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC). Screening with VIP\u0026thinsp;\u0026ge;\u0026thinsp;1 and |log2FC| \u0026ge;1 thresholds identified 33 differentially abundant metabolites in the BE vs B comparison (11 upregulated, 22 downregulated), including oxalic acid, eicosane, stearic acid, and 9-octadecenamide (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). The E vs CK comparison revealed 14 differential metabolites (7 upregulated, 7 downregulated), with shared biomarkers (e.g., eicosane, oxalic acid), indicating conserved stress-responsive mechanisms (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eD). Ward's hierarchical clustering based on Euclidean distances showed tight intra-group clustering and distinct inter-group expression patterns of differential metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, S1E). KEGG pathway enrichment analysis indicated that E vs CK differential metabolites were primarily linked to starch/sucrose metabolism and unsaturated fatty acid biosynthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF)\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In contrast, BE vs B comparisons implicated 11 pathways, including branched-chain amino acid metabolism and glyoxylate/dicarboxylate cycling, with unsaturated fatty acid biosynthesis being the most enriched (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eF). The upregulation of four metabolites\u0026mdash;9-octadecenamide, oxalic acid, eicosane, and stearic acid\u0026mdash;was observed in the BE group prior to \u003cem\u003eB. cinerea\u003c/em\u003e infection (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). These findings demonstrate that \u003cem\u003eB. velezensis\u003c/em\u003e ES2-4 significantly induces the upregulation of key rhizosphere exudates, including oxalic acid and stearic acid, during \u003cem\u003eB. cinerea\u003c/em\u003e infection in soybean plants.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eStrain ES2-4 elicits soybean resistance against \u003cem\u003eB. cinerea\u003c/em\u003e through rhizosphere exudate-mediated mechanisms\u003c/p\u003e\u003cp\u003eTo investigate the regulatory effects of soybean rhizosphere secretions on the chemotactic behavior of \u003cem\u003eB. velezensis\u003c/em\u003e ES2-4, we analyzed the impact of key differential metabolites on bacterial growth and chemotaxis using a semi-solid plate assay. The results revealed that oxalic acid, eicosane, and 9-octadecenamide significantly increased the colony area of \u003cem\u003eB. velezensis\u003c/em\u003e ES2-4 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) within a 0.05-1 mM concentration range, demonstrating a concentration-dependent pattern that suggests their role in inducing directional chemotaxis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-E). In contrast, stearic acid exhibited a unique concentration response: while low concentrations (0.05 mM) markedly enhanced chemotactic motility, higher concentrations progressively attenuated this effect, indicating potential metabolic inhibition or receptor saturation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Further analysis of antifungal activity revealed that all four metabolites significantly suppressed the growth of \u003cem\u003eB. cinerea\u003c/em\u003e at 0.05 mM. Notably, eicosane, 9-octadecenamide, and stearic acid exhibited enhanced inhibitory effects at higher concentrations (0.1-1 mM), with stearic acid showing the strongest pathogen inhibition (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF-I). However, oxalic acid did not exhibit enhanced antifungal activity beyond 0.05 mM, suggesting distinct molecular targets or regulatory mechanisms compared to the other metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI). These findings collectively suggest that \u003cem\u003eB. velezensis\u003c/em\u003e ES2-4 may enhance plant systemic resistance by modulating root secretion of oxalic acid, eicosane, and 9-octadecenamide, which synergistically promote bacterial chemotaxis and suppress pathogen growth.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eStrain ES2-4 Modulates Rhizosphere Microbiome Diversity and Structure in Response to \u003cem\u003eB. cinerea\u003c/em\u003e Infection\u003c/p\u003e\u003cp\u003eThis study evaluated soybean rhizosphere microbiome variations across treatments using α- and β-diversity indices. For bacterial communities, the Sobs, Chao, and Ace indices followed the order E\u0026thinsp;\u0026gt;\u0026thinsp;CK\u0026thinsp;\u0026gt;\u0026thinsp;BE\u0026thinsp;\u0026gt;\u0026thinsp;B (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-D), indicating that \u003cem\u003eB. velezensis\u003c/em\u003e ES2-4 enhanced bacterial richness in both healthy and \u003cem\u003eB. cinerea\u003c/em\u003e-infected rhizospheres, while \u003cem\u003eB. cinerea\u003c/em\u003e (Group B) significantly reduced bacterial richness. The BE group (ES2-4 pretreated before infection) partially mitigated this reduction. Shannon index analysis revealed lower diversity in Group B compared to Groups E and CK, while the BE group exhibited higher diversity than Group B, and Group E surpassed CK, suggesting that ES2-4 improved bacterial diversity in both healthy and diseased plants. In contrast, fungal communities displayed opposite trends (Table S2). Groups B and BE exhibited higher Sobs, Chao, and Ace indices than Groups CK and E, indicating that \u003cem\u003eB. cinerea\u003c/em\u003e increased fungal richness, while ES2-4 (Groups E and BE) reduced fungal richness in both healthy and infected plants. Simpson index analysis confirmed higher fungal diversity in Group B compared to other treatments, with the BE group showing a significant decline relative to Group B, highlighting ES2-4's inhibitory effect on pathogen-enhanced fungal diversity. Hierarchical clustering supported these findings, with BE/B groups and E/CK groups forming distinct clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-F). PCoA based on Bray-Curtis distance revealed significant β-diversity differences in bacterial communities among treatments (PERMANOVA: R\u0026sup2; = 0.669, p\u0026thinsp;=\u0026thinsp;0.001), with PC1 and PC2 collectively explaining 24.15% of the variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). \u003cem\u003eB. cinerea\u003c/em\u003e-treated groups (B and BE) separated from E and CK groups along PC1, while ES2-4 effects differentiated BE from Group B along PC2. Fungal communities showed similar β-diversity differences (R\u0026sup2; = 0.5676, p\u0026thinsp;=\u0026thinsp;0.001), with PC1 and PC2 explaining 24.48% of the variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH), mirroring bacterial clustering patterns. In conclusion, \u003cem\u003eB. velezensis\u003c/em\u003e ES2-4 alleviated \u003cem\u003eB. cinerea\u003c/em\u003e-induced dysbiosis in the soybean rhizosphere by differentially regulating bacterial and fungal community diversity and structure.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eStrain ES2-4 alters taxonomic composition and enriches beneficial microbes in soybean rhizosphere under pathogen stress\u003c/p\u003e\u003cp\u003eTaxonomic annotation based on the NR database revealed that bacteria dominated the soybean rhizosphere microbiota (mean relative abundance: 99.61%), followed by viruses (0.16%), eukaryotes (0.13%), and archaea (0.09%). Bacterial communities consisted of 162 phyla, 269 classes, 482 orders, 998 families, 3,906 genera, and 27,342 species, while fungal communities included 10 phyla, 39 classes, 100 orders, 258 families, 436 genera, and 739 species (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Genus-level Venn analysis identified 3,497 bacterial genera shared across all treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The dominant bacterial genera included \u003cem\u003eRugosimonospora\u003c/em\u003e, \u003cem\u003eReticulibacter\u003c/em\u003e, \u003cem\u003eActinomadura\u003c/em\u003e, \u003cem\u003ePseudonocardia\u003c/em\u003e, \u003cem\u003eAlphaproteobacteria\u003c/em\u003e, \u003cem\u003eBradyrhizobium\u003c/em\u003e, \u003cem\u003eStreptomyces\u003c/em\u003e, and \u003cem\u003eNitrobacteraceae\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Treatment with strain ES2-4 significantly increased the abundance of \u003cem\u003eActinomadura\u003c/em\u003e and \u003cem\u003eKtedonobacter\u003c/em\u003e in healthy plants, while reducing the abundance of \u003cem\u003eEdaphobacter\u003c/em\u003e. In infected plants, ES2-4 pretreatment decreased the abundance of \u003cem\u003eRugosimonospora\u003c/em\u003e, \u003cem\u003eReticulibacter\u003c/em\u003e, \u003cem\u003eActinomadura\u003c/em\u003e, and \u003cem\u003ePseudonocardia\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Differential analysis further revealed higher abundances of \u003cem\u003eAlphaproteobacteria\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eAcidobacteria\u003c/em\u003e, \u003cem\u003ePseudolabrys\u003c/em\u003e, \u003cem\u003eGemmatimonadetes\u003c/em\u003e, \u003cem\u003eBetaproteobacteria\u003c/em\u003e, and \u003cem\u003eOpitutus\u003c/em\u003e in the BE group compared to the B group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD-E). For fungal communities, 324 genera were shared across treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF), with dominant genera including \u003cem\u003eTulasnella\u003c/em\u003e, \u003cem\u003ePseudogymnoascus\u003c/em\u003e, \u003cem\u003eAspergillus\u003c/em\u003e, \u003cem\u003eTulasnellaceae\u003c/em\u003e, \u003cem\u003eRhizopus\u003c/em\u003e, \u003cem\u003eMycoblastus\u003c/em\u003e, and \u003cem\u003eAmanita\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). Strain ES2-4 treatment significantly increased the abundance of \u003cem\u003eAspergillus\u003c/em\u003e and \u003cem\u003eRhizopus\u003c/em\u003e in infected plants, while the BE group exhibited higher abundances of \u003cem\u003eMycoblastus\u003c/em\u003e, \u003cem\u003eAmanita\u003c/em\u003e, \u003cem\u003eSuillus\u003c/em\u003e, and \u003cem\u003eCeratobasidium\u003c/em\u003e compared to other groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH). Differential analysis confirmed that the BE group had significantly higher abundances of \u003cem\u003eAspergillus\u003c/em\u003e, \u003cem\u003eDaldinia\u003c/em\u003e, \u003cem\u003eMycoblastus\u003c/em\u003e, \u003cem\u003eAnaeromyces\u003c/em\u003e, \u003cem\u003eRhizopus\u003c/em\u003e, \u003cem\u003eAmanita\u003c/em\u003e, and \u003cem\u003eTrichoderma\u003c/em\u003e compared to the B group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH-I). In conclusion, strain ES2-4 reshapes microbial community composition by selectively enriching beneficial taxa (e.g., \u003cem\u003eAlphaproteobacteria\u003c/em\u003e, \u003cem\u003eAspergillus\u003c/em\u003e) and suppressing pathogen-associated genera under biotic stress.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eStrain ES2-4 \u003cem\u003ecoordinates metabolic rewiring and defense activation in the rhizosphere to suppress B. cinerea\u003c/em\u003e\u003c/p\u003e\u003cp\u003eKEGG functional annotation revealed distinct metabolic potentials across the treatment groups. The metabolic category accounted for the highest proportion of functional annotations in all groups: 51.84% in the B group, 51.41% in the BE group, 50.71% in the CK group, and 50.72% in the E group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Environmental information processing represented 16.90% (B), 16.79% (BE), 16.45% (CK), and 16.50% (E), while cellular processes made up 11.55% (B), 11.76% (BE), 11.75% (CK), and 11.69% (E). Genetic information processing accounted for 10.74% (B), 10.96% (BE), 11.51% (CK), and 11.59% (E), with organismal systems showing the lowest values: 3.66% (B), 3.71% (BE), 3.86% (CK), and 3.83% (E). Comparative analysis of KEGG pathways demonstrated significant treatment-specific effects. In ES2-4-treated healthy plants (E vs CK), starch/sucrose metabolism (p\u0026thinsp;=\u0026thinsp;0.01219) and the PI3K-Akt signaling pathway (p\u0026thinsp;=\u0026thinsp;0.02157) showed increased gene abundance (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). For pathogen-infected plants pretreated with ES2-4 (BE vs B), several metabolic pathways were significantly downregulated, including metabolic pathways (p\u0026thinsp;=\u0026thinsp;0.01219), microbial metabolism in diverse environments (p\u0026thinsp;=\u0026thinsp;0.01219), cofactor biosynthesis (p\u0026thinsp;=\u0026thinsp;0.01219), nucleotide sugar biosynthesis, fatty acid metabolism (p\u0026thinsp;=\u0026thinsp;0.01219), and butanoate metabolism (p\u0026thinsp;=\u0026thinsp;0.03671). In contrast, pathways such as two-component systems (p\u0026thinsp;=\u0026thinsp;0.01219), purine metabolism (p\u0026thinsp;=\u0026thinsp;0.01219), nucleotide metabolism, and glycan biosynthesis/metabolism (p\u0026thinsp;=\u0026thinsp;0.01219) were upregulated in the BE group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCOG functional classification grouped microbial proteins into three major categories. Metabolic functions dominated across treatments: 44.29% (B), 44.29% (BE), 44.04% (CK), and 43.88% (E), followed by cellular processes/signaling: 23.93% (B), 24.30% (BE), 25.75% (CK), and 25.54% (E). Information storage/processing had the lowest proportions: 18.16% (B), 17.94% (BE), 17.43% (CK), and 17.69% (E) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Differential analysis revealed significant enrichment in the BE group compared to the B group in pathways such as signal transduction mechanisms (p\u0026thinsp;=\u0026thinsp;0.03671), translation/ribosome biogenesis (p\u0026thinsp;=\u0026thinsp;0.01219), DNA replication/recombination/repair (p\u0026thinsp;=\u0026thinsp;0.02157), defense mechanisms (p\u0026thinsp;=\u0026thinsp;0.03671), cell cycle control/division (p\u0026thinsp;=\u0026thinsp;0.03671), cellular motility (p\u0026thinsp;=\u0026thinsp;0.03671), and extracellular structure formation (p\u0026thinsp;=\u0026thinsp;0.03671) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003eIn conclusion, strain ES2-4 reprograms rhizosphere functional processes by suppressing pathogen-promoting metabolic pathways and enhancing stress-responsive and defense-associated functions, thereby establishing a microbially-driven defense network against \u003cem\u003eB. cinerea\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eStrain ES2-4 modulates microbial interaction networks to enhance pathogen resistance in soybean rhizosphere\u003c/p\u003e\u003cp\u003eSpearman correlation networks (|r| \u0026ge; 0.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) revealed distinct interaction patterns between bacterial and fungal communities at the genus level. For bacterial networks, the CK group exhibited the highest complexity (nodes\u0026thinsp;=\u0026thinsp;49, edges\u0026thinsp;=\u0026thinsp;388, average degree\u0026thinsp;=\u0026thinsp;15.837, clustering coefficient\u0026thinsp;=\u0026thinsp;0.753), with a balanced distribution of positive and negative edges (50.77% vs 49.23%). In contrast, strain ES2-4 treatment in healthy plants (E group) slightly reduced network complexity (nodes\u0026thinsp;=\u0026thinsp;48, edges\u0026thinsp;=\u0026thinsp;379, average degree\u0026thinsp;=\u0026thinsp;15.792, clustering coefficient\u0026thinsp;=\u0026thinsp;0.736), but increased the proportion of positive edges to 54.35% (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B). Pathogen infection (B group) drastically simplified bacterial networks (nodes\u0026thinsp;=\u0026thinsp;47, edges\u0026thinsp;=\u0026thinsp;139, average degree\u0026thinsp;=\u0026thinsp;5.915), while ES2-4 pretreatment (BE group) partially restored network complexity (nodes\u0026thinsp;=\u0026thinsp;50, edges\u0026thinsp;=\u0026thinsp;154, average degree\u0026thinsp;=\u0026thinsp;6.16) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC-D). Keystone genus analysis (Table S3, S5) demonstrated that ES2-4 enhanced the synergism between beneficial bacteria (e.g., Bradyrhizobium-Streptomyces) while suppressing pathogen-associated genera (e.g., Ralstonia) in network centrality. Fungal networks exhibited contrasting dynamics. ES2-4 treatment in healthy plants (E group) reduced the number of edges from 180 (CK) to 108, with a lower average degree (4.408 vs 7.347), but a higher clustering coefficient (0.645) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE-F). Pathogen challenge (B group) increased fungal connectivity (edges\u0026thinsp;=\u0026thinsp;159, average degree\u0026thinsp;=\u0026thinsp;6.49), whereas ES2-4 pretreatment (BE group) weakened the interaction intensity (edges\u0026thinsp;=\u0026thinsp;104, average degree\u0026thinsp;=\u0026thinsp;4.426) and elevated the proportion of positive edges to 68.27% (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG-H). Fungal keystone analysis (Table S4, S6) indicated that ES2-4 reduced the network centrality of pathogenic fungi (e.g., Fusarium) while strengthening antagonistic genera (e.g., Trichoderma), thereby optimizing community stability. In summary, strain ES2-4 enhances soybean's ecological defense against \u003cem\u003eB. cinerea\u003c/em\u003e by differentially restructuring bacterial synergy networks and fungal antagonism networks, establishing a host-beneficial microbiome equilibrium.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study integrates metabolomic and metagenomic approaches to uncover the dual defense mechanisms of \u003cem\u003eB. velezensis\u003c/em\u003e ES2-4 against soybean gray mold caused by \u003cem\u003eB. cinerea\u003c/em\u003e. Metabolomic profiling revealed that ES2-4 reprogrammed soybean root exudates, inducing antifungal metabolites (e.g., oxalic acid and eicosane) that directly suppressed pathogen growth while promoting the chemotaxis of beneficial bacteria. Metagenomic analysis demonstrated that ES2-4-mediated microbiome remodeling was characterized by an enrichment of \u003cem\u003eAlphaproteobacteria\u003c/em\u003e and \u003cem\u003eStreptomyces\u003c/em\u003e, alongside the activation of stress-responsive pathways. Notably, ES2-4 synergistically suppressed pathogen-associated metabolic networks while fostering microbial cooperation through reconstructed co-occurrence networks. This work innovatively deciphers the cascade interactions between root exudates and microbiome engineering in plant immunity, bridging the knowledge gap in PGPR-mediated biocontrol. These findings advance sustainable agriculture by proposing rhizosphere microbiome manipulation as a targeted strategy against soil-borne diseases, offering both theoretical and practical insights into ecological plant protection.\u003c/p\u003e\u003cp\u003ePlants undergo metabolic reprogramming to adapt to biotic and abiotic stresses, with PGPR enhancing this process to improve stress resistance\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. This study systematically elucidates the regulatory mechanism of \u003cem\u003eB. velezensis\u003c/em\u003e ES2-4 on soybean root metabolic reprogramming and its role in inducing systemic resistance (ISR) through metabolomics. Principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) revealed that ES2-4 treatment significantly reshaped the metabolic profiles of healthy and infected soybean roots. Differential metabolite analysis identified 12 metabolites (including oxalic acid, eicosane, and stearic acid) upregulated in healthy plants, and 22 metabolites accumulated in diseased plants, with eicosane, oxalic acid, stearic acid, and 9-octadecenamide being common key functional metabolites. Notably, eicosane, a crucial effector molecule in PGPR-plant interactions, has been validated for its antimicrobial activity in various plant-microbe systems\u003csup\u003e\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. This study first demonstrates that \u003cem\u003eBacillus\u003c/em\u003e can enhance disease resistance by inducing plant self-secretion of this metabolite, offering new insights into rhizosphere microbial regulation of plant immunity. KEGG pathway enrichment analysis revealed that ES2-4 primarily regulated starch/sucrose metabolism, galactose metabolism, and unsaturated fatty acid synthesis in healthy plants, while significantly affecting 11 metabolic pathways in diseased plants. The upregulation of unsaturated fatty acid biosynthesis was particularly critical, as its products participate in membrane construction and signal transduction\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, with enhancement closely related to PGPR-mediated plant disease resistance\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Concurrently, the activation of valine/leucine/isoleucine pathways through jasmonic acid (JA) signaling, with isoleucine serving as a JA-Ile precursor, activated core JA signaling to enhance resistance against \u003cem\u003eB. cinerea\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Additionally, ES2-4-induced reconstruction of carbohydrate metabolism (starch/sucrose and galactose metabolism) displayed dual functions: providing energy substrates for defense reactions and acting as signaling molecules to activate immune responses\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, sharing mechanistic similarities with \u003cem\u003eTrichoderma\u003c/em\u003e-plant interactions\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. These findings demonstrate that ES2-4 establishes a multi-dimensional defense system through metabolic reprogramming, including direct induction of antimicrobial metabolites, JA/ET-mediated ISR activation, and reinforcement of metabolic networks. Further studies should employ gene silencing to validate the spatiotemporal dynamics and molecular mechanisms of key metabolites.\u003c/p\u003e\u003cp\u003eIn vitro experiments revealed that ES2-4 chemotaxis was regulated by specific root exudates. Chemotaxis assays demonstrated that 0.05-1 mM concentrations of oxalic acid, eicosane, 9-octadecenamide, and stearic acid significantly enhanced bacterial chemotaxis, with the efficacy in the following order: oxalic acid\u0026thinsp;\u0026gt;\u0026thinsp;9-octadecenamide\u0026thinsp;\u0026gt;\u0026thinsp;stearic acid\u0026thinsp;\u0026gt;\u0026thinsp;eicosane. Notably, higher concentrations (1 mM) exhibited a stronger chemotactic attraction. Biofilm formation analysis showed that 0.05-1 mM oxalic acid and 0.5-1 mM stearic acid significantly promoted ES2-4 biofilm development, with 1 mM stearic acid doubling biofilm biomass. This suggests that stearic acid enhances rhizosphere colonization, likely through exopolysaccharide synthesis or quorum sensing regulation. This phenomenon is consistent with the findings of Ankati et al.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, who observed that \u003cem\u003ePseudomonas\u003c/em\u003e sp. RP2 utilized peanut-derived fatty acids to enrich the rhizosphere. Similar mechanisms were also noted in \u003cem\u003eBacillus amyloliquefaciens\u003c/em\u003e SQR9 (cucumber root tryptophan induction)\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eTsukamurella tyrosinosolvens P9 (peanut oxalate-activated siderogenesis)\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, and banana root-\u003cem\u003eB. amyloliquefaciens\u003c/em\u003e NJN-6 interactions\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, confirming the universality of root exudates in regulating PGPR behavior. Strain ES2-4 likely establishes chemical gradients through the induced secretion of eicosane and 9-octadecenamide to drive directional migration and stable colonization. Importantly, strain ES2-4-induced stearic acid, oxalic acid, and eicosane exhibited significant antifungal effects against \u003cem\u003eB. cinerea\u003c/em\u003e. The antifungal properties of eicosane have been documented in \u003cem\u003eStreptomyces griseus\u003c/em\u003e VSG4 metabolites\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, n-eicosane in \u003cem\u003eStreptomyces KX852460\u003c/em\u003e fermentation broth\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, and oxalic acid from \u003cem\u003eBacillus cereus\u003c/em\u003e AR156 [51]. However, oxalic acid\u0026rsquo;s role in plant-microbe interactions is concentration-dependent, showing dual effects in some contexts\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSoil microbial diversity plays a crucial role in maintaining ecological functions and suppressing pathogens\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Metagenomic analysis revealed that ES2-4 significantly enhanced bacterial diversity and richness in the healthy soybean rhizosphere, while reversing the pathogen-induced decline in diversity, suggesting pathogen suppression via optimized microenvironments \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Notably, \u003cem\u003eB. cinerea\u003c/em\u003e infection increased fungal diversity, whereas ES2-4 reduced fungal diversity in both healthy and diseased plants, indicating microbial antagonism against pathogenic fungi. At the genus level, ES2-4 pretreatment enriched beneficial taxa, including \u003cem\u003eAlphaproteobacteria\u003c/em\u003e, \u003cem\u003eAcidobacteriaceae\u003c/em\u003e, and \u003cem\u003ePseudolabrys\u003c/em\u003e. \u003cem\u003eAlphaproteobacteria\u003c/em\u003e plays a role in carbon/nitrogen cycling and enhances antioxidant capacity\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, while \u003cem\u003eAcidobacteriaceae\u003c/em\u003e is associated with Fusarium head blight suppression\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, aligning with the findings of You et al.\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e on \u003cem\u003eBacillus subtilis\u003c/em\u003e-mediated rhizosphere regulation. KEGG annotation revealed that ES2-4 upregulated starch/sucrose metabolism genes in healthy plants and activated two-component systems in diseased plants. As core prokaryotic environmental sensors, two-component systems enhance microbial stress adaptation through biofilm regulation\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, with functional reinforcement consistent with Pham et al.\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e on histidine kinase-mediated disease resistance. COG analysis further revealed that strain ES2-4 significantly increased defense mechanisms and signal transduction proteins, indicating a coordinated establishment of microbial-host defense.\u003c/p\u003e\u003cp\u003eMicrobial network analysis demonstrated that strain ES2-4 enhanced bacterial mutualism by increasing positive interactions. Pathogen invasion reduced network complexity, whereas strain ES2-4 pretreatment (BE group) reconstructed highly connected networks through strengthened \u003cem\u003eAcidobacteria\u003c/em\u003e-\u003cem\u003eProteobacteria\u003c/em\u003e interactions\u0026mdash;a structure known to reduce pathogen colonization success\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. For fungal communities, ES2-4 optimized functionality by reducing network complexity and suppressing pathogenic \u003cem\u003eTulasnella\u003c/em\u003e. Notably, \u003cem\u003eChloroflexi\u003c/em\u003e, as a potential pathogen-associated taxon, exhibited significant negative connectivity in BE networks, suggesting beneficial microbiome competition. This resembles \u003cem\u003eAcidobacteria\u003c/em\u003e-mediated pathogen suppression in maize rotation systems\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn conclusion, \u003cem\u003eBacillus velezensis\u003c/em\u003e ES2-4 combats soybean gray mold through a dual defense mechanism: (1) reprogramming root exudates (e.g., oxalic acid, eicosane) to directly inhibit \u003cem\u003eB. cinerea\u003c/em\u003e while enhancing the recruitment of beneficial microbes, and (2) restoring bacterial diversity (e.g., \u003cem\u003eAlphaproteobacteria\u003c/em\u003e, \u003cem\u003eStreptomyces\u003c/em\u003e) while suppressing fungal pathogens. This study establishes a novel link between PGPR-induced metabolic shifts and microbiome remodeling, revealing microbial synergy in stress-response pathways, such as two-component signaling systems. Future research should focus on field validation, elucidating molecular signaling mechanisms (e.g., JA/ET pathways), and investigating the colonization dynamics of strain ES2-4. Optimizing strain-plant-microbiome interactions will contribute to the development of eco-friendly and sustainable biocontrol strategies.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eBacterial strain, and fungal inoculum\u003c/h2\u003e\u003cp\u003e\u003cem\u003eBacillus velezensis\u003c/em\u003e ES2-4, originally isolated from soil and preserved at the Laboratory of Applied Botany, Sichuan Normal University, was maintained in Luria-Bertani (LB) broth supplemented with 25% glycerol and stored at \u0026minus;\u0026thinsp;80\u0026deg;C. Prior to experimental use, the strain was reactivated by culturing in LB broth at 37\u0026deg;C for 24 h. \u003cem\u003eBotrytis cinerea\u003c/em\u003e was cultured on potato dextrose agar (PDA) plates at 28\u0026deg;C for 7 days. Spore suspensions were prepared by flooding the culture surface with sterile distilled water, followed by stirring and filtration through four layers of sterile degreased gauze. Spore concentrations were determined using a hemocytometer and adjusted to the required density with sterile water before use in experiments.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePreparation of Soybean Root Exudates\u003c/h3\u003e\n\u003cp\u003eSoybean seeds were surface-sterilized with 0.1% (w/v) sodium hypochlorite for 5 min, rinsed 5\u0026ndash;6 times with sterile distilled water, and placed on moist filter paper in Petri dishes. Germination was induced in darkness at 25\u0026deg;C using a germination chamber. Uniformly germinated seedlings were transferred to culture bottles containing one-quarter-strength Hoagland nutrient solution for hydroponic cultivation, with roots shielded from light. After two weeks of cultivation, root irrigation treatments were initiated.\u003c/p\u003e\u003cp\u003eThe experiment included four treatment groups: (1) B group (\u003cem\u003eB. cinerea\u003c/em\u003e-infected soybean plants); (2) E group (\u003cem\u003eB. velezensis\u003c/em\u003e ES2-4-treated soybean plants); (3) BE group (soybean plants pre-treated with \u003cem\u003eB. velezensis\u003c/em\u003e ES2-4 followed by \u003cem\u003eB. cinerea\u003c/em\u003e infection); and (4) CK group (control plants treated with sterile distilled water). In the BE and E groups, 20 mL of \u003cem\u003eB. velezensis\u003c/em\u003e ES2-4 suspension (1\u0026times;10⁷ CFU\u0026middot;mL⁻\u0026sup1;) was applied to the rhizosphere 20 days after transplanting. Five days later, the BE and B groups were inoculated with 20 mL of \u003cem\u003eB. cinerea\u003c/em\u003e spore suspension (1\u0026times;10⁷ CFU\u0026middot;mL⁻\u0026sup1;). Five days post-inoculation, plant roots were thoroughly rinsed with ultrapure water to remove residual metabolites of \u003cem\u003eB. velezensis\u003c/em\u003e ES2-4 and \u003cem\u003eB. cinerea\u003c/em\u003e, followed by hydroponic cultivation in ultrapure water for two days. The culture solution was then collected, filtered, and freeze-dried\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Each treatment included five biological replicates.\u003c/p\u003e\n\u003ch3\u003eRoot exudates metabolome assay\u003c/h3\u003e\n\u003cp\u003eSample pretreatment was performed following the method described by Chen et al.\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, with minor modifications. Briefly, 3 mg of freeze-dried root exudate powder was dissolved in 1 mL of methanol, and 50 \u0026micro;L of L-2-chloro-phenylalanine (0.3 mg/mL) was added as an internal standard. The mixture was vortexed for 30 s and sonicated on ice for 15 min. The solution was then filtered through a 0.22 \u0026micro;m membrane and centrifuged at 16,000 r/min for 15 min at 4\u0026deg;C. The supernatant was completely dried under nitrogen gas, derivatized, and homogenized prior to instrumental analysis. Gas chromatography-mass spectrometry (GC-MS) parameters were set according to reference\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMass spectrometric data were matched against the National Institute of Standards and Technology (NIST) Mass Spectral Library. Metabolites were identified by comparing their electron ionization (EI) mass spectra, including fragment patterns, with reference standards in the database. Final metabolite confirmation was achieved using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Human Metabolome Database (HMDB), with reliability further validated against published literature. Semi-quantitative metabolite analysis was performed using the internal standard method, followed by chromatographic peak area normalization, data transformation, and scaling. Normalized datasets were subjected to multivariate statistical analyses, including Principal Component Analysis (PCA), Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), volcano plot analysis, and hierarchical clustering. Functional annotation and enrichment analysis of metabolites were conducted using KEGG pathway mapping. All statistical analyses were performed using MetaboAnalyst 5.0.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEffect of soybean rhizosphere exudates on the chemotactic response of\u003c/b\u003e \u003cb\u003eB. velezensis\u003c/b\u003e \u003cb\u003eES2-4 and\u003c/b\u003e \u003cb\u003eB. cinerea\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe qualitative assessment of microbial inhibition was performed following the method described by Soo-Young et al.\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, with modifications. Briefly, sterile filter paper discs (6 mm in diameter) were placed at the center of semi-solid agar plates containing gradient concentrations of differential metabolites. A 10 \u0026micro;L aliquot of bacterial suspension was applied to each disc, with three biological replicates per treatment. For fungal inhibition assays, Potato Dextrose Agar (PDA) medium supplemented with varying concentrations of differential metabolites was prepared and poured into 90 mm sterile Petri dishes. Mycelial plugs (6 mm in diameter) were excised from the periphery of 7-day-old \u003cem\u003eB. cinerea\u003c/em\u003e colonies and transferred to the center of each PDA plate. Plates were incubated at 25\u0026deg;C, and colony diameters were measured at 12-hour intervals using a digital caliper. Three biological replicates were included for each treatment.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eBiocontrol agent and pathogen treatments and experimental design\u003c/h2\u003e\u003cp\u003eSoybean seeds were initially germinated in seedling trays and then transferred into pots for further cultivation. After 20 days of growth under controlled conditions, the experimental treatments were initiated as follows: standard watering without inoculation (CK), inoculation with the biocontrol bacterium ES2-4 (E), application of \u003cem\u003eES2-4\u003c/em\u003e followed by \u003cem\u003eB. cinerea\u003c/em\u003e inoculation (BE), and inoculation with \u003cem\u003eB. cinerea\u003c/em\u003e alone (B). In the E and BE groups, a 20 mL suspension of \u003cem\u003eES2-4\u003c/em\u003e (1\u0026times;10⁷ CFU\u0026middot;mL⁻\u0026sup1;) was applied to the rhizosphere of soybean seedlings 20 days after transplantation. Five days later, the BE and B groups received a 20 mL suspension of \u003cem\u003eB. cinerea\u003c/em\u003e spores (1\u0026times;10⁷ CFU\u0026middot;mL⁻\u0026sup1;). Rhizosphere soil samples were collected five days post-fungal inoculation for metagenomic sequencing\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Each experimental condition was replicated five times biologically.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDNA extraction, PCR amplification, and high-throughput sequencing\u003c/h3\u003e\n\u003cp\u003eGenomic DNA was extracted from six soil samples using the TIANamp Soil DNA Kit (TIANGEN, Beijing, China) following the manufacturer\u0026rsquo;s protocol. The quality and concentration of the extracted DNA were evaluated via 1% agarose gel electrophoresis and NanoDrop\u0026reg; ND-2000 spectrophotometry (Thermo Scientific, Wilmington, NC, USA) before storage at -80\u0026deg;C. To amplify bacterial 16S rDNA and fungal ITS rDNA, the following specific primer pairs were used: 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3') for 16S rDNA, and ITS1-F (CTTGGTCATTTAGAGGAAGTAA) and ITS4-R (TCCTCCGCTTATTGATATGC) for the ITS rDNA gene. PCR amplification was performed using a T100 Thermal Cycler (Bio-Rad, California, USA) with 2\u0026times; TransStart\u0026reg; Fast Pfu PCR SuperMix, according to the manufacturer's instructions\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. The thermal cycling conditions were as follows: initial denaturation at 95\u0026deg;C for 3 min, followed by 27 cycles of 95\u0026deg;C for 30 s, 55\u0026deg;C for 30 s, and 72\u0026deg;C for 45 s, with a final extension at 72\u0026deg;C for 10 min and a hold at 4\u0026deg;C. Each sample was amplified in triplicate. PCR products were separated on a 2% agarose gel, purified using the TIANgel Purification Kit (TIANGEN, Beijing, China), and quantified using a Quantus\u0026trade; Fluorometer (Promega, Beijing, China). The purified amplicons were sequenced on the Illumina NovaSeq PE250 platform by Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China) following standard protocols.\u003c/p\u003e\n\u003ch3\u003eData Processing and Bioinformatics Analysis\u003c/h3\u003e\n\u003cp\u003eRaw FASTQ files were demultiplexed using a custom Perl script developed in-house. Quality filtering and merging of paired-end reads were performed using fastp v0.19.6 and FLASH v1.2.7, respectively, with the following criteria: (1) reads with an average quality score below 20 over a 50 bp sliding window were trimmed, and reads shorter than 50 bp or containing ambiguous bases were discarded; (2) overlapping sequences of at least 10 bp with a maximum mismatch ratio of 0.2 in the overlap region were merged; and (3) samples were demultiplexed based on barcode and primer sequences, allowing exact barcode matches and up to two nucleotide mismatches in the primers.\u003c/p\u003e\u003cp\u003eOperational taxonomic units (OTUs) were identified from the processed sequences using UPARSE v7.1\u003csup\u003e58,59\u003c/sup\u003e, with a 97% sequence similarity threshold. The most abundant sequence within each OTU was selected as its representative. To standardize sequencing depth, 16S rRNA gene sequences were rarefied to 44,980 reads, while ITS rDNA sequences were rarefied to 84,995 reads, yielding an average Good\u0026rsquo;s coverage of 99.09%.\u003c/p\u003e\u003cp\u003eTaxonomic classification of OTU representative sequences was conducted using the RDP Classifier v2.2, referencing the SILVA database (v138.1) for bacterial identification and the UNITE database (v138.1) for fungal classification, with a confidence threshold of 0.7. The functional potential of the metagenome was inferred using PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States)\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, utilizing its integrated pipeline, which includes HMMER for sequence alignment, EPA-NG and Gappa for phylogenetic placement, castor for 16S gene copy normalization, and MinPath for gene family and pathway predictions, all following the standard PICRUSt2 protocol.\u003c/p\u003e\u003cp\u003eBioinformatic analyses of the soil microbiota were performed on the Majorbio Cloud platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cloud.majorbio.com\u003c/span\u003e\u003cspan address=\"https://cloud.majorbio.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Based on OTU data, rarefaction curves and alpha diversity indices\u0026mdash;including observed OTUs, Chao1 richness, Shannon index, and Good\u0026rsquo;s coverage\u0026mdash;were calculated using Mothur v1.30.1\u003csup\u003e61\u003c/sup\u003e. Principal coordinate analysis (PCoA) based on Bray-Curtis dissimilarity was performed using the Vegan v2.5-3 package to assess microbial community similarities among samples. Significant differences between treatments were determined using the Welch t-test in STAMP, while variations in OTU relative abundance across treatments were analyzed using likelihood ratio tests in the \u0026ldquo;EdgeR\u0026rdquo; package. A Manhattan plot was generated for visualization using the \u0026ldquo;ggplot2\u0026rdquo; package.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCo-Occurrence Network Analysis\u003c/h2\u003e\u003cp\u003eCo-occurrence networks were constructed to examine shifts in microbial community interactions for both bacterial and fungal populations. These networks were generated in R (v4.3.1) using Spearman correlation coefficients, incorporating only significant correlations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with an absolute correlation coefficient (|R|) greater than 0.6 \u003csup\u003e62\u003c/sup\u003e. The resulting networks were visualized using the Fruchterman-Reingold layout in Gephi.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll quantitative data are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error (SE). Statistical analyses were performed using GraphPad Prism 9.5.0. Analysis of variance (ANOVA) was conducted to assess significant differences, followed by Duncan\u0026rsquo;s multiple range test for mean separation. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that there are no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProject supported by the National Natural Science Foundation of China (NO. 32100240).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSequence data that support the findings of this study have been deposited in the NCBI with the primary accession code PRJNA1303063.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSiwei Chen made contribution to the conception and design; Xinpeng Guo and Maohua Wu analyzed and interpreted data; Rui Chen\u0026nbsp;drafted the article; Bing He and Ting Zheng revisied it critically for important intellectual content; All authors approved the final version to be published.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSavary, S.\u003cem\u003e et al.\u003c/em\u003e The global burden of pathogens and pests on major food crops. \u003cem\u003eNature Ecology \u0026amp; Evolution\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 430-439 (2019). https://doi.org:10.1038/s41559-018-0793-y\u003c/li\u003e\n\u003cli\u003eZhao, Y.\u003cem\u003e et al.\u003c/em\u003e Comparison of Nutritional Diversity in Five Fresh Legumes Using Flavonoids Metabolomics and Postharvest Botrytis cinerea Defense Analysis of Peas Mediated by Sakuranetin. \u003cem\u003eJ Agric Food Chem\u003c/em\u003e \u003cstrong\u003e72\u003c/strong\u003e, 6053-6063 (2024). https://doi.org:10.1021/acs.jafc.3c08968\u003c/li\u003e\n\u003cli\u003eZhao, Y.\u003cem\u003e et al.\u003c/em\u003e Root Exudates Modulate Rhizosphere Microbial Communities during the Interaction of Pseudomonas chlororaphis, \u0026beta;-Aminobutyric Acid, and Botrytis Cinerea in Tomato Plants. \u003cem\u003eJournal of Plant Growth Regulation\u003c/em\u003e \u003cstrong\u003e43\u003c/strong\u003e, 701-714 (2024). https://doi.org:10.1007/s00344-023-11128-3\u003c/li\u003e\n\u003cli\u003eHua, L.\u003cem\u003e et al.\u003c/em\u003e Pathogenic mechanisms and control strategies of Botrytis cinerea causing post-harvest decay in fruits and vegetables. \u003cem\u003eFood Quality and Safety\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 111-119 (2018). https://doi.org:10.1093/fqsafe/fyy016\u003c/li\u003e\n\u003cli\u003eShao, W., Zhao, Y. \u0026amp; Ma, Z. 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Using network analysis to explore co-occurrence patterns in soil microbial communities. \u003cem\u003eIsme j\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 343-351 (2012). https://doi.org:10.1038/ismej.2011.119\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Soybean, Bacillus velezensis, Gray mold, Root exudates, Rhizosphere microbiome","lastPublishedDoi":"10.21203/rs.3.rs-7283311/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7283311/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGray mold, caused by \u003cem\u003eBotrytis cinerea\u003c/em\u003e, represents a significant threat to soybean productivity, while conventional chemical control strategies raise concerns regarding long-term sustainability. Plant-associated beneficial microbes, such as \u003cem\u003eBacillus velezensis\u003c/em\u003e, have been proposed as environmentally sustainable alternatives; however, their specific roles in modulating root-microbe interactions remain insufficiently characterized. This study investigated the mechanisms by which \u003cem\u003eB. velezensis\u003c/em\u003e ES2-4 enhances soybean resistance by modulating root exudate composition and restructuring rhizosphere microbial communities. Metabolomic and metagenomic analyses indicated that ES2-4 inoculation led to the upregulation of antifungal metabolites (e.g., oxalic acid, eicosane) in root exudates, which facilitated the recruitment of beneficial bacteria while inhibiting \u003cem\u003eB. cinerea\u003c/em\u003e proliferation. Pathogen infection was associated with disruptions in rhizosphere microbial diversity; however, ES2-4 application restored bacterial richness, particularly within the \u003cem\u003eAlphaproteobacteria\u003c/em\u003e and \u003cem\u003eStreptomyces\u003c/em\u003e lineages, while reducing the relative abundance of fungal pathogens. Co-occurrence network analysis further demonstrated that ES2-4 inoculation promoted microbial interactions associated with stress-responsive pathways, including two-component signaling systems and fatty acid metabolism, while downregulating pathogen-associated metabolic functions. These findings elucidate a dual mechanism through which ES2-4 enhances plant immunity via metabolite-mediated microbiome modulation, highlighting its potential as a sustainable biocontrol agent against soybean gray mold.\u003c/p\u003e","manuscriptTitle":"Bacillus velezensis ES2-4 Modulates Root Exudation and Microbiome Remodeling to Enhance Soybean Resistance Against Gray Mold","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-21 13:05:58","doi":"10.21203/rs.3.rs-7283311/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-10T11:44:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-04T07:48:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-14T09:53:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157743492813434687622348948614135221351","date":"2025-08-14T09:33:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66708741157301642739105940950343860126","date":"2025-08-14T07:43:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-14T01:56:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-14T01:41:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-13T12:24:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-10T13:49:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-10T13:43:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"880a02fc-1335-41f0-982d-4465a15be431","owner":[],"postedDate":"August 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":53161816,"name":"Biological sciences/Biotechnology"},{"id":53161817,"name":"Biological sciences/Microbiology"},{"id":53161818,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2025-10-27T16:23:39+00:00","versionOfRecord":{"articleIdentity":"rs-7283311","link":"https://doi.org/10.1038/s41598-025-21135-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-10-23 16:16:25","publishedOnDateReadable":"October 23rd, 2025"},"versionCreatedAt":"2025-08-21 13:05:58","video":"","vorDoi":"10.1038/s41598-025-21135-x","vorDoiUrl":"https://doi.org/10.1038/s41598-025-21135-x","workflowStages":[]},"version":"v1","identity":"rs-7283311","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7283311","identity":"rs-7283311","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}