Microbiome and volatile organic compound profiling of diseased soils and their association with tomato wilt caused by Ralstonia solanacearum

Microbiome and volatile organic compound profiling of diseased soils and their association with tomato wilt caused by Ralstonia solanacearum | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Microbiome and volatile organic compound profiling of diseased soils and their association with tomato wilt caused by Ralstonia solanacearum Yishuo Huang, Jinlong Zhang, Gaofei Jiang, Xiaofang Wang, Zhong Wei, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8281057/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Soil volatile organic compounds (VOCs) are critical in suppressing soil-borne pathogens, yet their microbial origin, functional mechanisms, and contribution to disease suppression remain underexplored. This study investigated the role of VOCs in the disease-suppressive capacity of soils from two tomato monocropping regions against Ralstonia solanacearum , the causative agent of bacterial wilt. Among the disease-conductive and suppressive soils, suppressive soil showed the lowest disease incidence (0.37%) and pathogen load (9.6 × 10³ CFU/g), which was linked to potent soil VOC-mediated suppression of R. solanacearum growth and disease occurrence. GC-MS analysis identified 13 VOCs significantly related to disease index, including naphthalene, 2-undecanone, and humulene, which inhibited pathogen growth in vitro and promoted plant growth and defense enzymes (CAT, SOD). Amplicon sequencing revealed differences in microbial community diversity and composition between conductive and suppressive soils, with Streptomyces as a key disease-suppressive taxon. Isolation of 10 Streptomyces strains from suppressive soil confirmed their role in restoring VOC-mediated suppressiveness in sterilized soil, with strain Stre2 achieving 46.17% pathogen inhibition. Correlation, Procrustes, and variation partitioning analyses (VPA) demonstrated that soil physicochemical and microbial factors jointly shaped soil VOC composition, but bacterial communities also exerted a significant direct influence (11.1% unique contribution). Our findings demonstrate that disease-suppressive soils harness microbiota-derived VOCs to inhibit R. solanacearum and prime plant defenses, offering novel insights for sustainable pathogen management through microbial metabolite engineering. Soil suppressiveness Volatile organic compounds (VOCs) Ralstonia solanacearum Streptomyces Microbial community Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Suppressive soils are a type of soil that has inhibitory effects on plant diseases (Weller et al. 2002 ; Mazzola 2007 ; Exposito et al. 2017 ). After repeated infections by soil-borne pathogens, suppressive soils can recognize and inhibit the invasion of these pathogens through microbial communities, physicochemical properties, and plant interactions, thereby protecting plant health (Schlatter et al. 2017 ). The core mechanism lies in the soil's ability to exhibit "memory" and "response" similar to biological entities, maintaining ecological balance through the synergistic action of multiple factors (Raaijmakers and Mazzola 2016 ). Compared to conducive soils, suppressive soils are rich in antagonistic microorganisms and antagonistic substances (both volatile and non-volatile), which help in maintaining plant development and health (Berendsen et al. 2012 ; Liu et al. 2016 ). Current research has largely focused on the microbial community and non-volatile substances in suppressive soils, while the role of volatile organic compounds (VOCs) in this process has been largely overlooked (Cha et al. 2016 ; Wen et al. 2023 ). The VOCs are low-molecular-weight (< 300 Da) lipophilic biomolecules that primarily originate from both primary and secondary metabolic pathways of soil microorganisms (Raza et al. 2017 ; Schulz-Bohm et al. 2017 ; Weisskopf et al. 2021 ). Microbial VOCs typically consist of alcohols, alkenes, alkanes, aldehydes, carboxylic acids, phenols, ketones, terpenes, and pyrazines (Schmidt et al. 2015 ). Numerous studies have demonstrated that VOCs released by soil microorganisms reduce the incidence of soil-borne diseases by directly suppressing pathogens and indirectly inducing plant defense. For example, VOCs from the bacterium Bacillus amyloliquefaciens T-5 inhibit the growth and virulence traits of bacterial pathogens like R. solanacearum and fungal pathogens like Fusarium oxysporum and Rhizoctonia solani (Raza et al. 2016 ; Ossowicki et al. 2020 ). Long-term treatment with VOCs from beneficial soil bacteria can enhance the biomass, motility, and other survival capabilities of pathogens, while decreasing their pathogenic abilities, such as swarming motility, biofilm formation, and extracellular polysaccharide secretion, eventually leading to a loss of virulence toward tomatoes (Wang et al. 2023 ). In addition, diverse VOCs can induce phenotypic diversity in plants and reduce plant-to-plant competition (Raza et al. 2024 ). Plants can “eavesdrop” on VOC signals emitted by infected plants to adjust their defenses in response to current or future biotic stress risks (Kost and Heil 2006 ). Because of the complex and diverse nature of the soil environment, the soil VOC composition and emission-sink dynamics are influenced by the soil's physicochemical properties, microbial communities, and internal environmental conditions (Raza et al. 2017 ). Therefore, the relationship between soil biotic and abiotic characteristics and the composition and functions of soil VOCs remains unclear, particularly the connection between soil suppressiveness and VOCs. Ralstonia solanacearum is a widely distributed soil-borne plant pathogenic bacterium that causes wilt disease in many crops and poses a serious threat to global agricultural production (Jiang et al. 2017 ; Savary et al. 2019 ). In this study, we selected R. solanacearum soils from two different sites with continuous tomato cropping as research subjects. These soils have distinct VOC compositions due to variations in their physicochemical properties and microbial communities. This paper aims to analyze VOC composition and disease-suppressive functions of these soils through multi-omics analysis, incorporating soil disease indices and microbial community measurements. The main goal is to uncover the potential role of soil VOCs in disease-suppressiveness and their probable link to the soil microbiome and physicochemical properties, offering insights for future research directions while addressing scientific challenges in the field. Results Tomato wilt disease incidence and R. solanacearum abundance of four regions soils We evaluated the incidence of tomato wilt disease and the population density of R. solanacearum in the continuous tomato cropping area soil from four regions. The tomato bacterial wilt disease occurred with soil from all four regions. The highest disease incidence was recorded in Guangxi soil, with an average of 45.24%, followed by Shandong soil (11.35%) and Jiangxi soil (7.93%). The Jiangsu soil exhibited the lowest disease incidence of 0.37% (Fig. S1 a). Regarding the population density of R. solanacearum , Guangxi soil harbored a significantly higher pathogen load (3.85 × 10 5 CFU/g soil) compared to other regions. In contrast, the lowest R. solanacearum population was detected in Jiangsu soil (9.6 × 10 3 CFU/g soil), which aligned with the disease incidence pattern (Fig. S1 b). Therefore, we selected Guangxi soil as the conducive soil and Jiangsu soil as the suppressive soil for subsequent experimental verification. Soil VOCs effects on tomato bacterial wilt and R. solanacearum growth Following five days of soil VOCs exposure, significant inter-regional differences were observed in tomato bacterial wilt incidence. Specifically, suppressive soil VOCs significantly suppressed disease incidence, in contrast to conducive soil (Fig. 1 b). Notably, sterilized soil VOCs lost their suppressive effects on both disease development in tomato (Fig. 1 c). To validate the direct impact of soil VOCs on pathogen growth in vitro , R. solanacearum exposed only to suppressive soil VOCs showed significant growth inhibition (Fig. 1 e); however, this inhibitory effect was diminished with sterilized soil (Fig. 1 f). These results revealed that suppressive soil with low native R. solanacearum abundance and low disease incidence produced VOCs capable of suppressing both pathogen growth and disease development. Soil VOCs composition and key disease-suppressing VOCs We used headspace and liquid extraction methods combined with GC-MS to determine the VOC composition of the soil samples. Using the NIST database, we identified the VOCs and classified them at the class level based on their chemical structure. Interestingly, OPLS-DA analysis showed significant separation among soil VOCs from the two types, with PC1 and PC2 explaining 58.7% of the total variation in VOC composition (Premanova test: R² = 0.604, P = 0.031, Fig. 2 a). When comparing the corrected peak areas of VOCs released by the two types of soils, the VOCs from conducive soil showed a greater variety and higher relative abundance of VOC compounds (Fig. S2 ). We then compared the class-level differences in VOCs between the two soils. Suppressive soil was significantly enriched in alkanes and aromatic compounds, while conducive soil contained more alcohols and indenes (two-sided Student’s t-test; N = 4, Fig. 2 b, Fig. S3). Subsequently, we calculated the differential VOCs between the two types of soils using VIP values and DESeq2. The disease-suppressing soil was significantly enriched in 13 compounds (log2FC > 1, P < 0.05), including 2,6,6-Trimethyl-2-cyclohexene-1,4-dione (TMCHD), umbellulon, naphthalene, beta-phellandrene, pentyl-benzene, pentadecane, humulene, 3-methyl-5-propyl-nonane, geosmin, 2-undecanone, 6-methyl-2-heptanone, 2-methylisoborneol, and 2-decanone, among others (Fig. S4). Using STAMP analysis, we further found significant differences ( P < 0.05, Fig. 2 c) in the abundance of these 13 key compounds between the two types of soils. Subsequently, we analyzed the correlation between these VOCs and the disease index of the pot experiment, which showed a significant negative correlation (Spearman, R = -0.71, P = 1.5 e-08, Fig. 2 d). To assess the biological activity of suppressive soil-derived VOCs, we conducted a screening of all commercially available compounds from the identified set. ​Eleven out of the thirteen identified suppressive VOCs were commercially obtainable and were therefore selected​ for the evaluation of their effects on R. solanacearum growth and Solanum lycopersicum defense-related phenotypes under controlled conditions. The inhibitory effects of different VOCs on R. solanacearum varied considerably. Compounds such as 6-methyl-2-heptanone, 2-decanone, 2-undecanone, and humulene caused significant inhibition of R. solanacearum growth compared with the control, while others showed weak or no inhibition. In Solanum lycopersicum , the responses to these VOCs were compound-specific. Several VOCs promoted plant growth, as reflected by larger biomass and longer roots, whereas other VOCs either did not affect the plant growth or induced visible stress-related phenotypes (Fig. S5). Soil bacterial composition and diversity We analyzed the PCoA differences in bacterial composition among the two types of soils and found that samples from the same region clustered together, with clear separation between groups. For bacteria, the distribution on the PC1 (84.6%) and PC2 (3.8%) axes showed significant regional separation (Premanova: R² = 0.844, P = 0.032; Fig. 3 a). In terms of α-diversity, there were no significant differences in the Shannon and Chao1 indices for bacteria between the two types of soil (Fig. S6, Table S1 ). The amplicon sequencing analysis revealed that the phylum Acidobacteriota was the dominant bacterial group in soil samples, with the highest average relative abundance of 27.73%, followed by the phyla Proteobacteria and Chloroflexi . At the phylum level, the relative abundance of Acidobacteriota (33.87 ± 7.88%, P < 0.05), Gemmatimonadota (8.28 ± 0.52%, P < 0.001), and Verrucomicrobiota (2.26 ± 0.12%, P < 0.01) in suppressive soil was significantly higher than in conducive soil. Conversely, the relative abundance of Firmicutes (13.45 ± 2.09%, P < 0.01) was significantly lower in suppressive soil compared to conducive soil (Fig. 3 b, Table S2 ). Soil microbial genus differential analysis and potential disease-suppressing microbes’ analysis By comparing the microbial communities of disease-suppressive and conducive soils, significant differences at the bacterial genus level were observed. Based on STAMP, we identified 11 bacterial genera significantly enriched in the disease-suppressing soil (Fig. S7a). Using DESeq2, a total of 237 differentially abundant genera were identified (log₂FC > ± 2 and P adj < 0.05). In comparison to the conducive soil, 97 genera were significantly upregulated and 140 genera were significantly downregulated in the disease-suppressing soil (Fig. S7b). Additionally, we used LEfSe to calculate the importance of key genera in both soil types and selected the top 20 key genera, among which 10 were enriched in the disease-suppressing soil (Fig. S7c). Using STAMP (11 genera), Deseq2 (97 genera), and LDA (LEfSe, 10 genera), we determined the microbial genera enriched in the disease-suppressing soil and visualized them in a Venn diagram. We then focused on six key disease-suppressing microbial genera (Fig. 3 c), which included RB41, Luteitalea , MND1, Streptomyces , Lysobacter , and Longimicrobium (Fig. 3 d). Correlation analysis between these 6 key genera and the disease index revealed a significant negative correlation for all 6 genera, with Streptomyces showing the strongest negative correlation (Fig. 3 e). Furthermore, the relative abundance of these key microbial genera was correlated with the 13 key VOCs, revealing a significant positive correlation between Streptomyces and humulene, suggesting that Streptomyces may be an important source of humulene. Isolation, identification of Streptomyces , and functional validation of their VOC s As a key genus identified for its VOC-mediated contribution to soil disease suppressiveness in this study, 10 Streptomyces strains were isolated and identified using 16S rRNA sequencing from suppressive soil (Fig. 4 a). The phylogenetic analysis revealed that most isolated strains clustered closely with Streptomyces taxa identified through amplicon sequencing (Fig. 4 b). The inhibitory effects of VOCs released by these strains on R. solanacearum were tested in vitro . Among the 10 strains, seven exhibited VOC-mediated suppression of R. solanacearum growth. Strain Stre2 showed the strongest effect, with 46.2% inhibition of R. solanacearum growth, compared to the control (Fig. 4 c). A comparative analysis was conducted to identify shared VOCs among suppressive soil VOCs, Streptomyces -derived VOCs, and bioassay-defined key suppressive VOCs. Most compounds were unique to suppressive soils (95, 58.3%) or Streptomyces (51, 31.3%), while 8 VOCs (5.0%) were shared between soils and Streptomyces , indicating that part of the soil volatilome may originate from Streptomyces metabolism (Fig. 4 d, Table S3-S4). Importantly, four VOCs were found in all three groups, representing potential core suppressive compounds. These were identified as 2-methylisoborneol, 2-undecanone, geosmin, and humulene. To further validate their role in soil suppressiveness, Streptomyces strains were introduced into both native and gamma-ray-sterilized Jiangsu soils. The VOC-mediated disease suppression was significantly diminished with sterilized soil, whereas supplementation with Streptomyces partially restored this inhibitory capacity (Fig. 4 e). These results revealed that Streptomyces strains played an important role in inhibiting disease in disease-suppressive soil. Microbial communities exert a stronger influence than soil physicochemical properties on soil VOC composition Suppressive soils displayed a markedly different physicochemical profile compared with conducive soils (Table 1 ). Overall, suppressive soils showed significantly higher pH, mineral nutrient (NO 3− N, AvCa, AvP, AvFe), and organic matter-associated parameters (DOC, DON, TC, and TN) (all P < 0.01). Notably, NO₃ − N and DON concentrations were more than fivefold higher in suppressive soils, and total carbon and nitrogen levels were also substantially enriched. These findings indicate that suppressive soils provide a more nutrient-rich and chemically favorable environment, which may support beneficial microbial communities associated with disease suppression. Principal coordinate analysis (PCoA) based on soil physicochemical properties revealed a clear separation between disease-suppressive and conducive soils along the first two principal coordinates (Fig. S8a). PC1 alone explained 96.0% of the total variance, and the overall separation between soil types was statistically significant (Premanova: R 2 = 0.955, P = 0.037). These results demonstrate that suppressive soils possess a distinct physicochemical profile compared with conducive soils, reflecting substantial differences in their environmental backgrounds. Table 1 Soil physicochemical properties of the two soil types Type Conducive Suppressive P_value Field Capacity 0.20 ± 0.005 0.27 ± 0.014 1.00e-03 pH 4.53 ± 0.11 5.91 ± 0.15 3.30e-05 NO 3− N(mg/L) 4.35 ± 1.20 26.98 ± 9.89 2.10e-03 NH 4+ N(mg/L) 0.15 ± 0.01 0.37 ± 0.09 1.50e-02 DOC(mg/L) 2.51 ± 0.55 5.40 ± 1.12 0.0015 DON(mg/L) 3.70 ± 1.29 25.28 ± 8.78 1.90e-03 AvCa(mg/kg) 7.70 ± 2.77 70.88 ± 6.64 1.30e-06 AvP(mg/kg) 11.71 ± 4.28 22.75 ± 4.73 1.10e-03 AvFe(mg/L) 33.47 ± 3.65 38.50 ± 6.16 8.70e-02 TC(g/kg) 15.44 ± 1.29 13.24 ± 1.18 1.40e-03 TN(g/kg) 1.41 ± 0.17 1.93 ± 0.31 1.80e-03 Note: Values are presented as mean ± SE (n = 4). Significant differences between the two soil types for each property were determined by an independent samples t-test. DOC, dissolved organic carbon; DON, dissolved organic nitrogen; TC, total carbon; TN, total nitrogen. To further explore the environmental and microbial factors associated with VOC variation, we performed correlation analyses between key disease-suppressive VOCs, bacterial genera, and soil physicochemical properties (Fig. 5 ). Most suppressive VOCs, including humulene, geosmin, 2-methylisoborneol (2-MIB), and naphthalene, together with the core bacterial genera Streptomyces , Lysobacter , and Longimicrobium , showed significant positive correlations with soil physicochemical parameters such as pH, AvCa, AvFe, and AvP. Conversely, the disease index (DI) was negatively correlated with these properties. These results indicate that nutrient-rich and well-balanced soils tend to favor the enrichment of beneficial microbes and the accumulation of suppressive VOCs, thereby reducing disease incidence. Variation partitioning analysis (VPA) further quantified the relative contributions of soil physicochemical and microbial factors to VOC variation (Fig. S8b). Together, soil properties and bacterial communities explained 70.2% of the total variation in VOC composition (adjusted R²), including 59.1% shared variation, 11.1% uniquely attributed to bacterial communities, and only 0.046% explained by soil properties, while 29.8% remained unexplained. Consistently, Procrustes analyses demonstrated significant congruence between VOC composition and both soil physicochemical properties (M² = 0.252, P = 0.024, Fig. S8c) and bacterial community composition (M² = 0.211, P = 0.011, Fig. S8d). The stronger alignment with bacterial communities proves that soil VOC patterns are more directly governed by the structure of microbial assemblages than by abiotic soil factors. Discussion R. solanacearum is a soil-borne bacterial wilt pathogen of significant economic importance and is considered one of the top ten most harmful pathogens (Mansfield et al. 2012 ). We selected soils from two typical regions with different tomato bacterial wilt incidence and observed differences in their disease incidence and the population of R. solanacearum . Subsequently, we measured the disease-suppressive ability of VOCs released by these soils. We found that VOCs released from suppressive soil significantly reduced the disease index of tomato bacterial wilt and inhibited the growth of R. solanacearum . Our analysis revealed a negative correlation between the soil disease index of tomato bacterial wilt and the population of R. solanacearum with the disease-suppressive ability of the VOCs. Specifically, soils with lower disease incidence and fewer R. solanacearum populations released VOCs with stronger disease-suppressive properties. These findings highlight the potential involvement of VOC-mediated mechanisms in soil suppressiveness. The VOC profile analysis showed significant differences in the VOC composition among soils from the two types of soils. By performing Spearman correlation analysis between the VOC abundance and the disease index, we identified 13 potential disease-suppressive VOCs. These disease-suppressive VOCs included naphthalene (Raza et al. 2016 ), 2-decanone (Wang et al. 2025 ), 2-undecanone, pentadecane, humulene (Xing et al. 2018 ), among others, have been reported as antimicrobial substances released by microorganisms. Although most of these studies focused on their inhibitory effects on microbes other than R. solanacearum . In this study, most of the potential disease-suppressive VOCs inhibited the growth of R. solanacearum and promoted plant growth, along with inducing the activity of catalase (CAT) and superoxide dismutase (SOD) enzymes, indicating their role in inducing plant systemic resistance. In previous studies, we found that VOCs released by the beneficial bacteria effectively suppressed the growth and virulence traits of R. solanacearum (Raza et al. 2016 ). In this study, we revealed the microbial basis of soil VOCs' disease-suppressive effects, demonstrating that the primary role of soil VOCs is attributed to soil microbiota rather than soil physicochemical properties. Ossowick through soil microbiota transplantation experiments, has shown that the inhibitory effect of soil VOCs on Fusarium culmorum is unrelated to soil physical and chemical parameters, but rather associated with the soil microbiome (Ossowicki et al. 2020 ). To further investigate this, we employed amplicon sequencing to assess the microbial composition and diversity of bacteria in the soil, confirming significant differences in the bacterial communities among the two types of soils. The disease-suppressive bacterial genera identified included RB41, Luteitalea , MND1, Streptomyces , Lysobacter , and Longimicrobium (Gao et al. 2025 ). Streptomyces , as important beneficial soil microbes, can produce various metabolites to inhibit pathogens and enhance plant resistance (Olanrewaju and Babalola 2019 ). They release terpenoid compounds, such as geosmin and 2-methylisoborneol, which are important VOCs attracting soil insects (Becher et al. 2020 ). Additionally, previous studies have indicated that Streptomyces setonii WY228, through VOCs, regulates plant growth and enhances salt stress tolerance (Qin et al. 2024 ). The release of VOCs by microorganisms is a complex process influenced by both biotic and abiotic factors. Previous studies have shown that actinobacteria in disease-suppressive soils are the most active group in inhibiting the fungal root pathogen Rhizoctonia solani , with 300 strains of Streptomyces isolated and analyzed for their VOC compositions and functions (Cordovez et al. 2015 ). In this study, Streptomyces appeared to be a significant driver of VOC distribution, and the addition of Streptomyces to sterilized soil enhanced the disease-suppressive capacity of the soil VOCs, proving that Streptomyces is an important source of disease-suppressive VOCs in soils. We propose that the disease-suppressive potential of soil VOCs may be attributed to compounds such as naphthalene, 2-undecanone, and humulene released by disease-suppressive microorganisms like Streptomyces . These VOCs inhibit the growth of R. solanacearum , promote plant growth, and enhance the synthesis of defense enzymes, thereby reducing the occurrence of the disease. This provides a theoretical basis for further exploring the characteristics of disease-suppressive soils. Conclusion This study reveals the key role of soil VOCs in suppressing tomato bacterial wilt, along with the underlying microbial-driven mechanisms and microbial community analyses. The findings indicate that the disease-suppressive function of soil VOCs is primarily attributed to key antagonistic microbial communities. Thirteen crucial antagonistic VOCs were identified to directly inhibit the proliferation of the wilt pathogen and promote plant growth, and indirectly by modulating the synthesis of defense-related enzymes. Nutrient-rich soils with higher pH, AvCa, AvFe, and AvP further favor the enrichment of suppressive microbes and the synthesis of VOCs. These findings highlight that microbiota-driven VOCs, shaped under favorable soil conditions, are crucial for disease suppression and provide a basis for developing sustainable soil health–based strategies for pathogen management. This research provides a theoretical basis for the agricultural application of microbial VOCs and offers potential avenues for developing green control methods, such as bio-pesticides or soil amendments. Methods Soil Collection and R. solanacearum Quantification To assess the impact of soil VOCs on R. solanacearum -induced tomato bacterial wilt, soil samples were collected from four tomato monoculture farming regions in Guangxi, Jiangsu, Jiangxi, and Shandong provinces, China. Between August and September 2022, soil samples were collected using a five-point sampling method. Approximately 30 cm of surface soil was collected, sieved through a 2 mm mesh, and mixed into a single sample. Four replicates were taken from each region. The samples were then divided into four subsamples and placed in a sterile plastic bag. The first subsample was used to quantify R. solanacearum . Soil samples were serially diluted and plated on the modified semiselective medium (SMSA), a selective medium for R. solanacearum , and incubated at 30°C for 48h for colony counting (Pradhanang et al. 2000 ). The second soil subsample was preserved with 40% glycerol (1:1, v/v) at -80°C for microbial amplicon sequencing and isolation of key microorganisms. The third subsample was used for VOCs analysis and for evaluating their impact on plant disease and R. solanacearum growth. It was stored at 4°C, activated at room temperature (28°C) for 24 hours before use. The fourth subsample was used to measure soil physicochemical properties. All soil samples were transported on ice and stored at 4°C upon arrival at the laboratory (Raza et al. 2017 ). Tomato growth conditions and pathogen inoculation Surface-sterilized tomato seeds ( Solanum lycopersicum , cultivar "Micro-Tom") were germinated on water agar plates for 3 days. The seedlings were then transplanted into 72-well trays filled with gamma-irradiated cultivation substrate (50 g per well, purchased from Jiangsu Xingnong Substrate Technology Co., Ltd.). Once the plants reached the three-leaf-one-heart stage, they were transferred to gamma-irradiated cultivation substrate containing pots (100 g/pot) fitted above a plastic jar (Fig. 1 a). The pots and plastic jars were cleaned first with sterilized water and then with 70% ethanol to remove attached substances and dried. The plastic jar was added with 70 grams of soil to be tested for the effect of released VOCs, sealed with a double layer of parafilm at the junction of pot and plastic jar and placed in greenhouse. The plant pots receiving soil VOCs from below for five days were inoculated with R. solanacearum strain QL-RS1115 (GenBank: GU390462) at a final concentration of 10 6 CFU/g of soil (Wei et al. 2011 ). All greenhouse experiments were conducted under a controlled light environment with a 12h day/night cycle and 60% relative humidity. Watering was performed every other day with sterile water to maintain sufficient moisture without dripping into the lower plastic jar. The disease severity was recorded every 2 days starting from the first appearance of symptoms on tomato plants until no new symptoms were observed. Each region soil sample contained 6 tomato seedlings with 4 repetitions, and gamma-irradiated soil and no soil treatments were used as controls. Effects of soil VOCs on R. solanacearum growth in vitro The VOC-mediated inhibition of R. solanacearum was evaluated using divided petri dishes, with slight modifications of a previous report (Raza et al. 2020 ). Briefly, a single colony of R. solanacearum QL-RS1115 was cultured in CPG medium (1 g casamino acid (BD Bacto, Becton, Dickinson and Company, USA) at 30°C for 24 hours. The bacterial suspension was washed twice with 0.85% NaCl solution and adjusted to 1 × 10 7 CFU/mL. The divided petri dishes (85 mm in diameter) were filled with 15 ml CPG agar medium on one side, and 5 µL of the bacterial suspension was spotted at two positions (5 cm apart). The other side was filled with 10 g of soil (dry weight) from different regions. The Petri dish was sealed with Parafilm and incubated at 30°C (Fig. 1 b). Controls included petri dishes without soil. After 24 hours, agar containing the bacteria was removed, washed with 1 mL sterile water, and the OD 600 was measured to determine bacterial growth. In parallel, commercial VOC compounds were added at concentrations of 10 µM, using DMSO as the solvent, and 1 mm sterile filter paper discs were used as carriers. The petri dishes were sealed and incubated, with DMSO as controls. Measurement of soil physicochemical properties In this study, eleven physicochemical properties of soil were measured. Field capacity (FC) was determined using the gravimetric method. Briefly, air-dried soil was saturated with deionized water and allowed to drain freely for 24 h under laboratory conditions. The moisture content of the soil was then determined after oven-drying at 105°C to a constant weight, and FC was expressed as the percentage of water retained relative to the dry soil weight (Minasny and McBratney 2018 ). For soil pH, soil was mixed with deionized water in a 1:2.5 (w/v) ratio to make a slurry, and pH was measured using a pH meter (Orion Star A211, Thermo Fisher Scientific, USA) (Xu et al. 2003 ). Dissolved organic carbon (DOC) and dissolved organic nitrogen (DON) were determined by extracting with a soil-to-water ratio of 1:10, followed by shaking and centrifugation. The extract was filtered through a 0.45 µm membrane, and DOC and DON were measured using a total organic carbon analyzer (Elementar, Germany). Total carbon (TC) and total nitrogen (TN) were analyzed using a Vario MACRO cube elemental analyzer (VarioMAX, Elementar, Germany) with air-dried and sieved soil samples (Filep and Rekasi 2011 ). Nitrate nitrogen (NO 3- N) and ammonium nitrogen (NH 4+ N) were extracted by mixing fresh soil with 1 M KCl in a 1:5 (soil to solvent mass ratio) ratio, followed by shaking at 200 rpm for 30 minutes at 25°C. The concentration of nitrate nitrogen was measured using a continuous flow analyzer as described by Carlson (Seal AutoAnalyzer AA3, Norderstedt, Germany) (Carlson et al. 1990 ). The available phosphorus (AvP) was extracted by mixing the air-dried soil with 0.5 M NaHCO 3 at a soil-to-solvent ratio of 1:20 (w/v, 30 min), and the microelements were extracted by mixing the air-dried soil with 0.05 M diethylenetriaminepentaacetic acid (DTPA) at a soil-to-solvent ratio of 1:5 (w/v, 1 h) (Cancela et al. 2002 ). The concentrations of available potassium (AvK) and microelements were determined using an inductively coupled plasma optical emission spectrometer (Agilent 710 ICP-OES, Agilent Technologies, USA). The nutrient contents were expressed on a soil dry weight basis. All analyses were performed in triplicate, and standard materials were used for calibration to ensure data accuracy. Identification of soil and microbial VOCs The identification of VOC profiles was performed following previous reports, with slight modifications (Raza et al. 2017 ; Raza et al. 2020 ). For the headspace extraction of VOCs from soil, 10 g of soil samples were weighed and placed into 60 mL brown glass vials, which were then sealed with a Teflon®-backed silicone rubber stopper. The vials without soil were used as controls. All samples were stored at room temperature for 12 hours to activate the soil microorganisms. Subsequently, the internal standard, 10 µl of 5 mM acetic acid nonylester, was added to the vial. A preconditioned (250°C for 5 min) solid-phase microextraction (SPME) fiber (Supelco, Bellefonte, PA, 50/30 µm DVB/CAR on PDMS) was then inserted into the vials and incubated at 30°C for 30 minutes, followed by an additional 30-minute incubation at 50°C. The SPME fibers were then inserted into the injection port of a gas chromatography-mass spectrometry (GC-MS) system (Finnigan Trace DSQ, Austin, USA) and desorbed at 220°C for 1 minute. Analysis was conducted using an RTX-5MS column (30 m, 0.25 mm ID, 0.25 µm film thickness). The following oven temperature program was used: 33°C (hold for 3 minutes), 180°C (10°C/min), 240°C (30°C/min), followed by a 5-minute hold at 240°C. The mass spectrometer was operated in electron impact mode at 70 eV and 220°C, with a scan range of 50 to 500 m/z. Chromatograms were acquired and analyzed using AMDIS 2.73 software (National Institute of Standards and Technology, Gaithersburg, USA). The spectra of deconvoluted VOC peaks were compared using the main and/or secondary libraries in the NIST/EPA/NIH mass spectral library (Agilent Technologies, Santa Clara, CA, USA). The Kovats retention index of each compound was calculated using an alkane calibration mixture and compared with the data in the NIST/EPA/NIH mass spectral library. A compound was considered identified if its mass spectrum matched a listed compound, the match factor was greater than 800, and the calculated retention index did not differ by more than 5 from the listed value for the nonpolar semi-standard column. The VOC profile of soil samples was also determined using the liquid extraction method. The soil samples used for headspace extraction were added with an internal standard, 1, 4-difluorobenzene (20 µg/ml methanol) at a concentration of 50 µg/kg soil. Later, the soil was thoroughly mixed by shaking and placed at room temperature overnight for equilibrium. Later, 30 mL of methanol was added to the bottles containing the soil samples, which were then sealed with unused Teflon® backing silicone rubber caps. The vials were inverted and marked with the level of liquid as a reference for determining any leak. The vials were then placed at 75°C with gentle shaking (60 rpm) to maintain the soil in suspension. To avoid breakage, the vials were placed inside another container. After 24 h, the liquid level was checked, and any vial with a loss in volume was discarded. Later, the soil suspension was centrifuged (1500 rpm for 10 min), and 10 mL supernatant was transferred to a 50 mL flask. To the solvent extract, 30 mL of distilled water and then 10 mL of hexane were added. The flask was shaken vigorously for 1 min and then allowed the phases to separate for 5 min. The volume of the upper hexane layer was almost similar to the volume of hexane added. The hexane layer was collected, filtered by a Millipore membrane filter (0.45 µm), and analyzed using GC–MS by inserting a 2 µL sample in the injector. The rest procedure for GC–MS analysis and VOC identification was the same as described above. To determine the extraction efficiency, the standard compounds were run alone in hexane (10–100 µg/mL). If a VOC was detected by both the SPME method and the liquid extraction method, we sum the corrected peak areas of the VOCs. The peak area of VOCs was expressed as the relative VOC abundance in arbitrary units (a.u.) per gram of soil (dry weight). The recovery efficiency of standard compound 1, 4-difluorobenzene, for the liquid extraction method was found to be 77%. To analyze the VOC profile produced by Streptomyces , a cell suspension (1 × 10 8 CFU/ml) was prepared as described above, and 5 µL of the suspension was inoculated into a 50 mL conical flask containing 20 mL of International Streptomyces Project medium 3 (ISP3) and sealed for incubation at 30°C for 72 hours. Each treatment was performed in triplicate, with bottles without bacterial inoculation serving as controls. After incubation, 10 µL of acetic acid nonylester (5 mM) was added as the internal standard. The VOC components were then identified using SPME and GC-MS, following the same procedure as for the soil samples. The peaks similar to the control treatment (without bacterial inoculation) were not considered for the identification of VOCs. The number of VOCs produced in each treatment was recorded, and the chromatographic peak area was expressed as the relative peak area to the internal standard in arbitrary units (a.u.) as an indirect approach to estimate the relative amount (concentration) of each VOC (Raza et al. 2020 ; Raza et al. 2024 ). Effects of soil VOCs on the growth of R. solanacearum and plant growth and defense enzyme activities Based on the correlation analysis results, VOCs significantly correlated with the disease index were selected and purchased (Table S3). Following the method described above for testing the effect of VOCs released from soil on the growth of R. solanacearum , except that one side of the divided Petri dish was replaced with pure VOC compounds. DMSO was used as the solvent, and 10 µL of the VOCs at a concentration of 5 µM were added on a 1 mm sterile filter paper as a carrier for the droplets. The petri dishes were sealed with parafilm and placed in an incubator, with dishes containing only DMSO and water as controls. After 24 hours of incubation, the agar containing R. solanacearum was removed using a puncher, washed, and suspended with 1 mL of sterile water, and the OD 600 of R. solanacearum was measured. Each VOC was evaluated six times. Later, the effect of the above pure VOC compounds on Solanum lycopersicum growth and the activities of oxidative defense enzymes such as SOD, CAT, and PAL was determined (Raza et al. 2024 ). For this, a divided agar plate experiment was used, with plants placed in one compartment and bacteria in another. In the experiment, Solanum lycopersicum seeds were surface sterilized for 1 minute in 70% (v/v) ethanol, followed by 5 minutes in 5% (v/v) NaOCl. The seeds were then washed three times with sterile water and placed on petri dishes containing ½ strength Murashige and Skoog (MS) agar (0.8%). The seeds were pre-cooled at 4°C for 3 days, then placed in a growth chamber set at 21°C with 40 W fluorescent lights, and a photoperiod of 16 h light: 8 h dark. After germination, three seeds of uniform size were placed in each compartment, which contained 15 mL of ½ strength MS agar medium with 0.5% sucrose and 0.8% agar. The petri dishes were sealed with parafilm and returned to the growth chamber. After the third true leaf emerged, a 10 µL aliquot of the VOC at 5 µM was added to another compartment of the divided petri dish on 1 mm sterile filter paper. The plates were sealed and placed back in the growth chamber. After 15 days of incubation, Solanum lycopersicum plants were removed from the agar, washed with clean water to remove any remaining agar from the roots, blotted dry to remove excess moisture, and the fresh weight of the plants was recorded. The activities of SOD, PAL, and CAT in the plants were measured using commercial kits (Solarbio, Beijing, China). Each compound was evaluated six times, with DMSO as a control. Soil amplicon sequencing and bioinformatics analysis Soil DNA was extracted using the Power Soil DNA Isolation Kit (Mo Bio Laboratories) according to the manufacturer's protocol. DNA concentration and quality were assessed using a NanoDrop 1000 spectrophotometer (Thermo Scientific) and electrophoresis to evaluate DNA degradation. Amplicon sequencing was conducted using 16S rRNA markers to determine the composition and diversity of bacterial communities. The V4 region of the bacterial 16S rRNA gene was amplified using the primers 515F (5’-GTGYCAGCMGCCGCGGTAA-3’) and 806R (5’-GGACTACHVGGGTWTCTAAT-3’) (Cardenas et al. 2010 ). The amplicon library was sequenced in a paired-end manner (2 × 250) on the Illumina MiSeq platform (Shanghai BIOZERON Co., Ltd.) following standard protocols (Gu et al. 2020 ). Sequence reads were processed and dereplicated using the DADA2 algorithm in QIIME 2 (Quast et al. 2013 ). Paired reads were trimmed and filtered, with a maximum expected error of 2 allowed per read (maxEE = 2). After merging paired reads and conducting chimeric filtering, each 16S rRNA gene sequence (referred to as ASVs here) was phylogenetically classified using the UCLUST algorithm ( http://www.drive5.com/usearch/manual/uclust_algo.html ) against the Silva (SSU138.1) 16S rRNA database and an additional database, with a confidence threshold set at 80% (Caporaso et al. 2010 ). Only sequences with total abundance greater than 5 were retained for further analysis. Isolation of Streptomyces and functional validation of their VOCs A total of 10 Streptomyces strains were isolated from suppressive Jiangsu soil using an improved Gauze’s synthetic medium (Wang et al. 2024 ). The 16S rRNA genes of these Streptomyces strains were amplified using the 27F and 1492R primers and sequenced (Heuer et al. 1997 ). To validate the inhibitory effect of VOCs released by Streptomyces on the growth of R. solanacearum , a dual-segment plate method was used, as described above. On one side of the plate, 3 droplets (0.5 µL each) of R. solanacearum were added, and on the other side, 10 µL of Streptomyces suspension (1 × 10 8 CFU/ml) was inoculated. The plates were sealed with Parafilm and incubated at 28°C for 2 days. Afterward, the agar containing R. solanacearum was removed using a puncher, washed, and suspended in 1 mL sterile water, and the OD 600 was measured. The addition of distilled water treatment was used as a blank control, with three replicates for each treatment. Additionally, a pot experiment was performed to validate the ability of Streptomyces to release disease-suppressive VOCs in soil as described above (Fig. 1 a). Gamma-irradiated suppressive soil was inoculated with a mixture of Streptomyces strains at a concentration of 1×10 7 CFU/g soil. VOCs released by the Streptomyces -treated soil were assessed for their impact on R. solanacearum- induced disease incidence, with sterilized soil and natural soil serving as controls. Each treatment consisted of six tomato seedlings with four replicates. Statistical analysis All data analyses were performed using R version 4.3.1. The “ONE Tukey HSD2” function was used to compare differences between treatments. Microbial community diversity was calculated using ASVs. Changes in community composition were assessed using Principal Component Analysis (PCoA), with analysis performed using the "microceo" v1.9.0 R package. Significant differences in community structure between treatments and Procrustes were evaluated using functions from the "vegan" v2.6.4 and " DESeq2 " v1.40.2 package. A heatmap of key microorganisms or VOCs correlated with the disease index was generated using the "ComplexHeatmap" v1.10.2 package. The analytic hierarchy process of important factors in RDA and VPA was conducted using the "rdacca.hp" v1.1 package. Abbreviations AvCa Available calcium AvFe Available Iron AvK Available potassium AvP Available phosphorus CAT Catalase CFU Colony forming unit CPG Casamino acids peptone glucose medium DI Disease index DMSO Dimethylsulfoxide DOC Dissolved organic carbon DON Dissolved organic nitrogen DTPA Diethylenetriaminepentaacetic acid FC Field capacity GC-MS Gas chromatography-mass spectrometry ISP3 International streptomyces project medium 3 LDA Linear discriminant analysis MS Murashige and skoog NH 4+ N Ammonium nitrogen NO 3− N Nitrate nitrogen OPLS-DA Orthogonal partial least squares discriminant analysis PAL Phenylalanine ammonia lyase PC1 Principal component 1 PC2 Principal component 2 PCoA Principal coordinate analysis SMSA Modified semiselective medium SOD Superoxide dismutase SPME Solid-phase microextraction STAMP Statistical analysis of metagenomic profiles TC Total carbon TMCHD 2,6,6-Trimethyl-2-cyclohexene-1,4-dione TN Total nitrogen VIP Variable importance in projection VOCs Volatile organic compounds VPA Variation partitioning analysis Declarations Authors' contributions ZW and WR conceived and supervised this study. YSH and JLZ designed and performed the experiments and were responsible for data analysis. GFJ and XFW assisted with sample processing and data collection. ZW and WR contributed substantially to manuscript revision and editing, and all other authors provided critical comments on the study. YSH and JLZ wrote the manuscript with input from all authors. Funding This work was supported by the National Natural Science Foundation of China 42377124 (WR), 42350610 (WR), and 32571909 (XFW). Availability of data and materials The 16S rRNA amplicon sequence data have been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under accession number PRJNA1264978. The online version contains supplementary material available at Supplementary Material. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Becher PG, Verschut V, Bibb MJ, Bush MJ, Molnar BP, Barane E, et al. Developmentally regulated volatiles geosmin and 2-methylisoborneol attract a soil arthropod to Streptomyces bacteria promoting spore dispersal. Nat Microbiol. 2020; 5:821-9. https://doi.org/10.1038/s41564-020-0697-x. Berendsen RL, Pieterse CMJ, Bakker PAHM. The rhizosphere microbiome and plant health. Trends in Plant Science. 2012; 17:478-86. https://doi.org/10.1016/j.tplants.2012.04.001. Cancela RC, Abreu CA, Paz-Gonzalez A. DTPA and Mehlich-3 micronutrient extractability in natural soils. Commun Soil Sci Plant Anal. 2002; 33:2357-8. https://doi.org/10.1081/CSS-120014488. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010; 7:335-6. https://doi.org/10.1038/nmeth.f.303. Cardenas E, Wu WM, Leigh MB, Carley J, Carroll S, Gentry T, et al. Significant association between sulfate-reducing bacteria and uranium-reducing microbial communities as revealed by a combined massively parallel sequencing-indicator species approach. Appl Environ Microbiol. 2010; 76:6778-86. https://doi.org/10.1128/aem.01097-10. Carlson RM, Cabrera RI, Paul JL, Quick J, Evans RY. Rapid direct determination of ammonium and nitrate in soil and plant-tissue extracts. Commun Soil Sci Plant Anal. 1990; 21:1519-29. https://doi.org/10.1080/00103629009368319. Cha JY, Han S, Hong HJ, Cho H, Kim D, Kwon Y, et al. Microbial and biochemical basis of a Fusarium wilt-suppressive soil. ISME J. 2016; 10:119-29. https://doi.org/10.1038/ismej.2015.95. Cordovez V, Carrion VJ, Etalo DW, Mumm R, Zhu H, van Wezel GP, et al. Diversity and functions of volatile organic compounds produced by Streptomyces from a disease-suppressive soil. Front Microbiol. 2015; 6:1081. https://doi.org/10.5589/fmicb.2015.01081. Exposito RG, de Bruijn I, Postma J, Raaijmakers JM. Current insights into the role of rhizosphere bacteria in disease suppressive soils. Front Microbiol. 2017; 8:2529. https://doi.org/10.3389/fmicb.2017.02529. Filep T, Rekasi M. Factors controlling dissolved organic carbon (DOC), dissolved organic nitrogen (DON) and DOC/DON ratio in arable soils based on a dataset from Hungary. Geoderma. 2011; 162:312-8. https://doi.org/10.1016/j.geoderma.2011.03.002. Gao Z, Zhu N, Yang M, Cui X, Han C, Guo X, et al. Integrating transcriptome, metabolome and microbiome to explore the interaction mechanism between secondary metabolites and root-associated bacteria of Codonopsis pilosula. Ind Crop Prod. 2025; 227:120790. https://doi.org/10.1016/j.indcrop.2025.120790. Gu Y, Wang X, Yang T, Friman V-P, Geisen S, Wei Z, et al. Chemical structure predicts the effect of plant-derived low-molecular weight compounds on soil microbiome structure and pathogen suppression. Funct Ecol. 2020; 34:2158-69. https://doi.org/10.1111/1365-2435.13624. Heuer H, Krsek M, Baker P, Smalla K, Wellington EMH. Analysis of actinomycete communities by specific amplification of genes encoding 16S rRNA and gel-electrophoretic separation in denaturing gradients. Appl Environ Microbiol. 1997; 63:3233-41. https://doi.org/10.1128/aem.63.8.3233-3241.1997. Jiang G, Wei Z, Xu J, Chen H, Zhang Y, She X, et al. Bacterial wilt in China: History, current status, and future perspectives. Front Plant Sci. 2017; 8:1549. https://doi.org/10.3389/fpls.2017.01549. Kost C, Heil M. Herbivore-induced plant volatiles induce an indirect defence in neighbouring plants. J Ecol. 2006; 94:619-28. https://doi.org/10.1111/j.1365-2745.2006.01120.x. Liu X, Zhang S, Jiang Q, Bai Y, Shen G, Li S, et al. Using community analysis to explore bacterial indicators for disease suppression of tobacco bacterial wilt. Sci Rep. 2016; 6:36773. https://doi.org/10.1038/srep36773. Mansfield J, Genin S, Magori S, Citovsky V, Sriariyanum M, Ronald P, et al. Top 10 plant pathogenic bacteria in molecular plant pathology. Mol Plant Pathol. 2012; 13:614-29. https://doi.org/10.1111/j.1364-3703.2012.00804.x. Mazzola M. Manipulation of rhizosphere bacterial communities to induce suppressive soils. J Nematol. 2007; 39:213-20. https://pmc.ncbi.nlm.nih.gov/articles/PMC2586500/. Minasny B, McBratney AB. Limited effect of organic matter on soil available water capacity. Eur J Soil Sci. 2018; 69:39-47. https://doi.org/10.1111/ejss.12475. Olanrewaju OS, Babalola OO. Streptomyces: implications and interactions in plant growth promotion. Appl Microbiol Biotechnol. 2019; 103:1179-88. https://doi.org/10.1007/s00253-018-09577-y. Ossowicki A, Tracanna V, Petrus MLC, Van Wezel G, Raaijmakers JM, Medema MH, et al. Microbial and volatile profiling of soils suppressive to Fusarium culmorum of wheat. Proc R Soc B. 2020; 287:20192527. https://doi.org/10.1098/rspb.2019.2527. Pradhanang PM, Elphinstone JG, Fox RTV. Sensitive detection of Ralstonia solanacearum in soil: a comparison of different detection techniques. Plant Pathol J. 2000; 49:414-22. https://doi.org/10.1046/j.1365-3059.2000.00481.x. Qin Y-Y, Gong Y, Kong S-Y, Wan Z-Y, Liu J-Q, Xing K, et al. Aerial signaling by plant-associated Streptomyces setonii WY228 regulates plant growth and enhances salt stress tolerance. Microbiol Res. 2024; 286:127823. https://doi.org/10.1016/j.micres.2024.127823. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013; 41:D590-D6. https://doi.org/10.1093/nar/gks1219. Raaijmakers JM, Mazzola M. Soil immune responses: Soil microbiomes may be harnessed for plant health. Science. 2016; 352:1392-3. https://doi.org/10.1126/science.aaf3252. Raza W, Jiang G, Eisenhauer N, Huang Y, Wei Z, Shen Q, et al. Microbe-induced phenotypic variation leads to overyielding in clonal plant populations. Nat Ecol Evol. 2024; 8:392–9. https://doi.org/10.1038/s41559-023-02297-1. Raza W, Mei X, Wei Z, Ling N, Yuan J, Wang J, et al. Profiling of soil volatile organic compounds after long-term application of inorganic, organic and organic-inorganic mixed fertilizers and their effect on plant growth. Sci Total Environ. 2017; 607:326-38. https://doi.org/10.1016/j.scitotenv.2017.07.023. Raza W, Wang J, Jousset A, Friman V-P, Mei X, Wang S, et al. Bacterial community richness shifts the balance between volatile organic compound-mediated microbe-pathogen and microbe-plant interactions. Proc R Soc B. 2020; 287:20200403. https://doi.org/10.1098/rspb.2020.0403. Raza W, Wang J, Wu Y, Ling N, Wei Z, Huang Q, et al. Effects of volatile organic compounds produced by Bacillus amyloliquefaciens on the growth and virulence traits of tomato bacterial wilt pathogen Ralstonia solanacearum . Appl Microbiol Biotechnol. 2016; 100:7639-50. https://doi.org/10.1007/s00253-016-7584-7. Savary S, Willocquet L, Pethybridge SJ, Esker P, McRoberts N, Nelson A. The global burden of pathogens and pests on major food crops. Nat Ecol Evol. 2019; 3:430-9. https://doi.org/10.1038/s41559-018-0793-y. Schlatter D, Kinkel L, Thomashow L, Weller D, Paulitz T. Disease suppressive soils: new insights from the soil microbiome. Phytopathology. 2017; 107:1284-97. https://doi.org/10.1094/phyto-03-17-0111-rvw. Schmidt R, Cordovez V, de Boer W, Raaijmakers J, Garbeva P. Volatile affairs in microbial interactions. ISME J. 2015; 9:2329-35. https://doi.org/10.1038/ismej.2015.42. Schulz-Bohm K, Martín-Sánchez L, Garbeva P. Microbial volatiles: Small molecules with an important role in infra- and inter-kingdom interactions. Front Microbiol. 2017; 8:2484. https://doi.org/10.3389/fmicb.2017.02484. Wang J, Raza W, Jiang G, Yi Z, Fields B, Greenrod S, et al. Bacterial volatile organic compounds attenuate pathogen virulence via evolutionary trade-offs. ISME J. 2023; 17:443–52. https://doi.org/10.1038/s41396-023-01356-6. Wang W, Ge Q, Wen J, Zhang H, Guo Y, Li Z, et al. Horizontal gene transfer and symbiotic microorganisms regulate the adaptive evolution of intertidal algae, Porphyra sense lato. Commun Biol. 2024; 7:976. https://doi.org/10.1038/s42003-024-06663-y. Wang XR, Huang MM, Li WH, Shi YY, Tang YN, Zhang H, et al. Antifungal activity of 2-decanone against Monilinia fructicola and its application in combination with boscalid in mitigating brown rot disease in peach fruit. Physiol Mol Plant Pathol. 2025; 138:102665. https://doi.org/10.1016/j.pmpp.2025.102665. Wei Z, Yang X, Yin S, Shen Q, Ran W, Xu Y. Efficacy of Bacillus -fortified organic fertiliser in controlling bacterial wilt of tomato in the field. Appl Soil Ecol. 2011; 48:152-9. https://doi.org/10.1016/j.apsoil.2011.03.013. Weisskopf L, Schulz S, Garbeva P. Microbial volatile organic compounds in intra-kingdom and inter-kingdom interactions. Nat Rev Microbiol. 2021; 19:391-404. https://doi.org/10.1038/s41579-020-00508-1. Weller DM, Raaijmakers JM, Gardener BBM, Thomashow LS. Microbial populations responsible for specific soil suppressiveness to plant pathogens. Annu Rev Phytopathol. 2002; 40:309-48. https://doi.org/10.1146/annurev.phyto.40.030402.110010. Wen T, Ding Z, Thomashow LS, Hale L, Yang S, Xie P, et al. Deciphering the mechanism of fungal pathogen-induced disease-suppressive soil. New Phytol. 2023; 238:2634-50. https://doi.org/10.1111/nph.18886. Xing M, Zheng L, Deng Y, Xu D, Xi P, Li M, et al. Antifungal activity of natural volatile organic compounds against litchi downy blight pathogen Peronophythora litchii. Molecules. 2018; 23:37-46. https://doi.org/10.3390/molecules23020358. Xu RK, Zhao AZ, Li QM, Kong XL, Ji GL. Acidity regime of the red soils in a subtropical region of southern China under field conditions. Geoderma. 2003; 115:75-84. https://doi.org/10.1016/s0016-7061(03)00077-6. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial1.docx SupplementaryMaterial2.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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15:45:55","extension":"xml","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":146421,"visible":true,"origin":"","legend":"","description":"","filename":"df0da0ee466d4c0bb2557444f56ec45f1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8281057/v1/669327b1204477738c156c40.xml"},{"id":97883227,"identity":"84e9b1f0-5345-438a-a546-a76fe0a24bda","added_by":"auto","created_at":"2025-12-10 12:50:01","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":157406,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8281057/v1/aa03012921f82e8c6e50c588.html"},{"id":97883204,"identity":"55f0187f-12c6-4e61-99b5-6388ba3ae4c6","added_by":"auto","created_at":"2025-12-10 12:50:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":259417,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of soil-released VOCs from conductive and suppressive soils in controlling tomato bacterial wilt and inhibiting the growth of \u003cem\u003eRalstonia solanacearum\u003c/em\u003e. (\u003cstrong\u003ea\u003c/strong\u003e) Schematic diagram of the pot experiment. The disease index of tomato bacterial wilt after treatment with (\u003cstrong\u003eb\u003c/strong\u003e) natural soil VOCs and (\u003cstrong\u003ec\u003c/strong\u003e) sterilized soil VOCs (two-sided Student’s t-test; N = 4). (\u003cstrong\u003ed\u003c/strong\u003e) Schematic diagram of the plate experiment. The growth of \u003cem\u003eR. solanacearum\u003c/em\u003e after treatment with (\u003cstrong\u003ee\u003c/strong\u003e) natural soil VOCs and (\u003cstrong\u003ef\u003c/strong\u003e) sterilized soil VOCs (two-sided Student’s t-test; N = 12). The significance levels are indicated by asterisks: \u003cem\u003eP\u0026lt;\u003c/em\u003e0.05 is \" * \", \u003cem\u003eP\u0026lt;\u003c/em\u003e0.01 is “** \", and \u003cem\u003eP\u0026lt;\u003c/em\u003e0.001 is \" *** \".\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8281057/v1/a28e9942fac1745a7ef3c602.png"},{"id":97883205,"identity":"90618c0e-bf42-426e-be22-17bc01bf9443","added_by":"auto","created_at":"2025-12-10 12:50:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":355941,"visible":true,"origin":"","legend":"\u003cp\u003eProfiling of VOCs of conductive and suppressive soils and their correlation with the plant disease index. (\u003cstrong\u003ea\u003c/strong\u003e)Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) was used to analyze differences in soil VOCs from two type soils with premanova testing. (\u003cstrong\u003eb\u003c/strong\u003e) The composition of VOCs at the chemical class level in soil samples from two regions.Upward arrows indicate a positive spearman correlation between the relative abundance of the compound class and the plant disease index, while downward arrows indicate a negative correlation. (\u003cstrong\u003ec\u003c/strong\u003e) Enrichment of 13 key soil VOCs in suppressive soil based on STAMP analysis (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). (\u003cstrong\u003ed\u003c/strong\u003e) Spearman correlation analysis of the peak area of 13 key soil VOCs and the plant disease index. The full name of TMCHD is 2,6,6-Trimethyl-2-cyclohexene-1,4-dione. The significance levels are indicated by asterisks: \u003cem\u003eP\u0026lt;\u003c/em\u003e0.05 is \" * \", \u003cem\u003eP\u0026lt;\u003c/em\u003e0.01 is “** \", and \u003cem\u003eP\u0026lt;\u003c/em\u003e0.001 is \" *** \".\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8281057/v1/0ca40d783d8b3d9f7684d982.png"},{"id":97883206,"identity":"00b83a90-6a6f-4c2a-a84e-9234114361fa","added_by":"auto","created_at":"2025-12-10 12:50:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":412156,"visible":true,"origin":"","legend":"\u003cp\u003eComposition of bacterial community in conductive and suppressive soils and its correlation with soil volatile organic compounds (VOCs). (\u003cstrong\u003ea\u003c/strong\u003e) Principal coordinate analysis (PCoA) of bacterial community at the ASVs level with premanova testing. (\u003cstrong\u003eb\u003c/strong\u003e) The composition of bacteria at the phylum level. (\u003cstrong\u003ec\u003c/strong\u003e) Venn diagram showing the overlap of enriched bacterial genera identified by three statistical approaches (DESeq2, STAMP, and LEfSe). (\u003cstrong\u003ed\u003c/strong\u003e) Relative abundance of the six suppressive soil-enriched genera (two-sided Student’s t-test; N = 4). (\u003cstrong\u003ee\u003c/strong\u003e) Heatmap of the Spearman correlation between the relative abundance of soil bacterial genera and plant disease index. (\u003cstrong\u003ef\u003c/strong\u003e) Heatmap of the correlation between the peak areas of 13 key soil VOCs and the relative abundance of 11 soil bacterial genera. The significance levels are indicated by asterisks: \u003cem\u003eP\u0026lt;\u003c/em\u003e0.05 is \" * \", \u003cem\u003eP\u0026lt;\u003c/em\u003e0.01 is “** \", and \u003cem\u003eP\u0026lt;\u003c/em\u003e0.001 is \" *** \". TMCHD is 2,6,6-Trimethyl-2-cyclohexene-1,4-dione.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8281057/v1/93b3e0462e86eb46f234b15d.png"},{"id":97883211,"identity":"901b4e1f-b9c7-443c-8b06-d309e730eb06","added_by":"auto","created_at":"2025-12-10 12:50:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":369303,"visible":true,"origin":"","legend":"\u003cp\u003eIsolation of \u003cem\u003eStreptomyces\u003c/em\u003estrains, identification of their volatile organic compounds (VOCs), and validation of their antibacterial activity against \u003cem\u003eR. solanacearum\u003c/em\u003e and tomato bacterial wilt. (\u003cstrong\u003ea\u003c/strong\u003e) Plate morphological characteristics of 10 \u003cem\u003eStreptomyces\u003c/em\u003e strains and (\u003cstrong\u003eb\u003c/strong\u003e) phylogenetic tree of the 10 \u003cem\u003eStreptomyces\u003c/em\u003e strains based on 16S rRNA sequencing. (\u003cstrong\u003ec\u003c/strong\u003e) The impact of VOCs released by 10 strains of \u003cem\u003eStreptomyces\u003c/em\u003e on the biomass of\u003cem\u003e R. solanacearum\u003c/em\u003e (one-way ANOVA with correction by Tukey’s HSD test; N = 3). (\u003cstrong\u003ed\u003c/strong\u003e) Comparison of VOC profiles identified from three sources: soil VOCs, VOCs released by isolated \u003cem\u003eStreptomyces\u003c/em\u003estrains, and key suppressive VOCs. The Venn diagram illustrates the overlap among these VOC groups. (\u003cstrong\u003ee\u003c/strong\u003e) The effects of VOCs derived from natural suppressive soil (Nat_Soil), sterilized suppressive soil (Ster_Soil), and sterilized suppressive soil supplemented with \u003cem\u003eStreptomyces \u003c/em\u003e(Ster_Stre) on tomato bacterial wilt in pot experiment (one-way ANOVA with correction by Tukey’s HSD test; N = 4). The significance levels are indicated by asterisks: \u003cem\u003eP\u0026lt;\u003c/em\u003e0.05 is \" * \", \u003cem\u003eP\u0026lt;\u003c/em\u003e0.01 is “** \", and \u003cem\u003eP\u0026lt;\u003c/em\u003e0.001 is \" *** \".\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8281057/v1/0c878234d2298a8fffb7de54.png"},{"id":97900782,"identity":"e135fc6d-ab99-40bb-b613-109bd536503e","added_by":"auto","created_at":"2025-12-10 15:45:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":375893,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships among soil properties, disease suppressive bacterial genera, and volatile organic compounds (VOCs). Spearman correlation heatmap showing associations between soil physicochemical properties, key disease-suppressive VOCs, six core bacterial genera, and disease index (DI). Red and blue colors indicate positive and negative correlations, respectively. The significance levels are indicated by asterisks: \u003cem\u003eP\u0026lt;\u003c/em\u003e0.05 is \" * \", \u003cem\u003eP\u0026lt;\u003c/em\u003e0.01 is “** \", and \u003cem\u003eP\u0026lt;\u003c/em\u003e0.001 is \" *** \".\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8281057/v1/a79509a29365c8485d5b6573.png"},{"id":97903438,"identity":"5773892b-65be-47a2-abb2-79eb29358f92","added_by":"auto","created_at":"2025-12-10 15:55:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2609273,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8281057/v1/3dd074d8-2ced-40f1-a5c4-cb1a09d8cb6f.pdf"},{"id":97900494,"identity":"3cd4c71d-4f66-4226-8100-69db3c490c6e","added_by":"auto","created_at":"2025-12-10 15:45:34","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":6019442,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8281057/v1/eac87c3165163fdb7f375c0d.docx"},{"id":97883210,"identity":"7c8a5d4f-e1fc-403f-a5c8-fed219dcd176","added_by":"auto","created_at":"2025-12-10 12:50:00","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":280185,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8281057/v1/7aa4761203dae4b72dda9ece.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Microbiome and volatile organic compound profiling of diseased soils and their association with tomato wilt caused by Ralstonia solanacearum","fulltext":[{"header":"Background","content":"\u003cp\u003eSuppressive soils are a type of soil that has inhibitory effects on plant diseases (Weller et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Mazzola \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Exposito et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). After repeated infections by soil-borne pathogens, suppressive soils can recognize and inhibit the invasion of these pathogens through microbial communities, physicochemical properties, and plant interactions, thereby protecting plant health (Schlatter et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The core mechanism lies in the soil's ability to exhibit \"memory\" and \"response\" similar to biological entities, maintaining ecological balance through the synergistic action of multiple factors (Raaijmakers and Mazzola \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Compared to conducive soils, suppressive soils are rich in antagonistic microorganisms and antagonistic substances (both volatile and non-volatile), which help in maintaining plant development and health (Berendsen et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Current research has largely focused on the microbial community and non-volatile substances in suppressive soils, while the role of volatile organic compounds (VOCs) in this process has been largely overlooked (Cha et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wen et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe VOCs are low-molecular-weight (\u0026lt;\u0026thinsp;300 Da) lipophilic biomolecules that primarily originate from both primary and secondary metabolic pathways of soil microorganisms (Raza et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Schulz-Bohm et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Weisskopf et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Microbial VOCs typically consist of alcohols, alkenes, alkanes, aldehydes, carboxylic acids, phenols, ketones, terpenes, and pyrazines (Schmidt et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Numerous studies have demonstrated that VOCs released by soil microorganisms reduce the incidence of soil-borne diseases by directly suppressing pathogens and indirectly inducing plant defense. For example, VOCs from the bacterium \u003cem\u003eBacillus amyloliquefaciens\u003c/em\u003e T-5 inhibit the growth and virulence traits of bacterial pathogens like \u003cem\u003eR. solanacearum\u003c/em\u003e and fungal pathogens like \u003cem\u003eFusarium oxysporum\u003c/em\u003e and \u003cem\u003eRhizoctonia solani\u003c/em\u003e (Raza et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ossowicki et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Long-term treatment with VOCs from beneficial soil bacteria can enhance the biomass, motility, and other survival capabilities of pathogens, while decreasing their pathogenic abilities, such as swarming motility, biofilm formation, and extracellular polysaccharide secretion, eventually leading to a loss of virulence toward tomatoes (Wang et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition, diverse VOCs can induce phenotypic diversity in plants and reduce plant-to-plant competition (Raza et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Plants can \u0026ldquo;eavesdrop\u0026rdquo; on VOC signals emitted by infected plants to adjust their defenses in response to current or future biotic stress risks (Kost and Heil \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Because of the complex and diverse nature of the soil environment, the soil VOC composition and emission-sink dynamics are influenced by the soil's physicochemical properties, microbial communities, and internal environmental conditions (Raza et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, the relationship between soil biotic and abiotic characteristics and the composition and functions of soil VOCs remains unclear, particularly the connection between soil suppressiveness and VOCs.\u003c/p\u003e\u003cp\u003e\u003cem\u003eRalstonia solanacearum\u003c/em\u003e is a widely distributed soil-borne plant pathogenic bacterium that causes wilt disease in many crops and poses a serious threat to global agricultural production (Jiang et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Savary et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In this study, we selected \u003cem\u003eR. solanacearum\u003c/em\u003e soils from two different sites with continuous tomato cropping as research subjects. These soils have distinct VOC compositions due to variations in their physicochemical properties and microbial communities. This paper aims to analyze VOC composition and disease-suppressive functions of these soils through multi-omics analysis, incorporating soil disease indices and microbial community measurements. The main goal is to uncover the potential role of soil VOCs in disease-suppressiveness and their probable link to the soil microbiome and physicochemical properties, offering insights for future research directions while addressing scientific challenges in the field.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eTomato wilt disease incidence and\u003c/b\u003e \u003cb\u003eR. solanacearum\u003c/b\u003e \u003cb\u003eabundance of four regions soils\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe evaluated the incidence of tomato wilt disease and the population density of \u003cem\u003eR. solanacearum\u003c/em\u003e in the continuous tomato cropping area soil from four regions. The tomato bacterial wilt disease occurred with soil from all four regions. The highest disease incidence was recorded in Guangxi soil, with an average of 45.24%, followed by Shandong soil (11.35%) and Jiangxi soil (7.93%). The Jiangsu soil exhibited the lowest disease incidence of 0.37% (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea). Regarding the population density of \u003cem\u003eR. solanacearum\u003c/em\u003e, Guangxi soil harbored a significantly higher pathogen load (3.85 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e CFU/g soil) compared to other regions. In contrast, the lowest \u003cem\u003eR. solanacearum\u003c/em\u003e population was detected in Jiangsu soil (9.6 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e CFU/g soil), which aligned with the disease incidence pattern (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb). Therefore, we selected Guangxi soil as the conducive soil and Jiangsu soil as the suppressive soil for subsequent experimental verification.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSoil VOCs effects on tomato bacterial wilt and\u003c/b\u003e \u003cb\u003eR. solanacearum\u003c/b\u003e \u003cb\u003egrowth\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFollowing five days of soil VOCs exposure, significant inter-regional differences were observed in tomato bacterial wilt incidence. Specifically, suppressive soil VOCs significantly suppressed disease incidence, in contrast to conducive soil (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Notably, sterilized soil VOCs lost their suppressive effects on both disease development in tomato (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). To validate the direct impact of soil VOCs on pathogen growth \u003cem\u003ein vitro\u003c/em\u003e, \u003cem\u003eR. solanacearum\u003c/em\u003e exposed only to suppressive soil VOCs showed significant growth inhibition (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee); however, this inhibitory effect was diminished with sterilized soil (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef). These results revealed that suppressive soil with low native \u003cem\u003eR. solanacearum\u003c/em\u003e abundance and low disease incidence produced VOCs capable of suppressing both pathogen growth and disease development.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSoil VOCs composition and key disease-suppressing VOCs\u003c/h2\u003e\u003cp\u003eWe used headspace and liquid extraction methods combined with GC-MS to determine the VOC composition of the soil samples. Using the NIST database, we identified the VOCs and classified them at the class level based on their chemical structure. Interestingly, OPLS-DA analysis showed significant separation among soil VOCs from the two types, with PC1 and PC2 explaining 58.7% of the total variation in VOC composition (Premanova test: \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.604, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). When comparing the corrected peak areas of VOCs released by the two types of soils, the VOCs from conducive soil showed a greater variety and higher relative abundance of VOC compounds (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). We then compared the class-level differences in VOCs between the two soils. Suppressive soil was significantly enriched in alkanes and aromatic compounds, while conducive soil contained more alcohols and indenes (two-sided Student\u0026rsquo;s t-test; N\u0026thinsp;=\u0026thinsp;4, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Fig. S3).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSubsequently, we calculated the differential VOCs between the two types of soils using VIP values and DESeq2. The disease-suppressing soil was significantly enriched in 13 compounds (log2FC\u0026thinsp;\u0026gt;\u0026thinsp;1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), including 2,6,6-Trimethyl-2-cyclohexene-1,4-dione (TMCHD), umbellulon, naphthalene, beta-phellandrene, pentyl-benzene, pentadecane, humulene, 3-methyl-5-propyl-nonane, geosmin, 2-undecanone, 6-methyl-2-heptanone, 2-methylisoborneol, and 2-decanone, among others (Fig. S4). Using STAMP analysis, we further found significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec) in the abundance of these 13 key compounds between the two types of soils. Subsequently, we analyzed the correlation between these VOCs and the disease index of the pot experiment, which showed a significant negative correlation (Spearman, \u003cem\u003eR\u003c/em\u003e = -0.71, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.5 e-08, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). To assess the biological activity of suppressive soil-derived VOCs, we conducted a screening of all commercially available compounds from the identified set. ​Eleven out of the thirteen identified suppressive VOCs were commercially obtainable and were therefore selected​ for the evaluation of their effects on \u003cem\u003eR. solanacearum\u003c/em\u003e growth and \u003cem\u003eSolanum lycopersicum\u003c/em\u003e defense-related phenotypes under controlled conditions. The inhibitory effects of different VOCs on \u003cem\u003eR. solanacearum\u003c/em\u003e varied considerably. Compounds such as 6-methyl-2-heptanone, 2-decanone, 2-undecanone, and humulene caused significant inhibition of \u003cem\u003eR. solanacearum\u003c/em\u003e growth compared with the control, while others showed weak or no inhibition. In \u003cem\u003eSolanum lycopersicum\u003c/em\u003e, the responses to these VOCs were compound-specific. Several VOCs promoted plant growth, as reflected by larger biomass and longer roots, whereas other VOCs either did not affect the plant growth or induced visible stress-related phenotypes (Fig. S5).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSoil bacterial composition and diversity\u003c/h3\u003e\n\u003cp\u003eWe analyzed the PCoA differences in bacterial composition among the two types of soils and found that samples from the same region clustered together, with clear separation between groups. For bacteria, the distribution on the PC1 (84.6%) and PC2 (3.8%) axes showed significant regional separation (Premanova: \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.844, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). In terms of α-diversity, there were no significant differences in the Shannon and Chao1 indices for bacteria between the two types of soil (Fig. S6, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The amplicon sequencing analysis revealed that the phylum \u003cem\u003eAcidobacteriota\u003c/em\u003e was the dominant bacterial group in soil samples, with the highest average relative abundance of 27.73%, followed by the phyla \u003cem\u003eProteobacteria\u003c/em\u003e and \u003cem\u003eChloroflexi\u003c/em\u003e. At the phylum level, the relative abundance of \u003cem\u003eAcidobacteriota\u003c/em\u003e (33.87\u0026thinsp;\u0026plusmn;\u0026thinsp;7.88%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), \u003cem\u003eGemmatimonadota\u003c/em\u003e (8.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and \u003cem\u003eVerrucomicrobiota\u003c/em\u003e (2.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in suppressive soil was significantly higher than in conducive soil. Conversely, the relative abundance of \u003cem\u003eFirmicutes\u003c/em\u003e (13.45\u0026thinsp;\u0026plusmn;\u0026thinsp;2.09%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) was significantly lower in suppressive soil compared to conducive soil (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eSoil microbial genus differential analysis and potential disease-suppressing microbes’ analysis\u003c/h3\u003e\n\u003cp\u003eBy comparing the microbial communities of disease-suppressive and conducive soils, significant differences at the bacterial genus level were observed. Based on STAMP, we identified 11 bacterial genera significantly enriched in the disease-suppressing soil (Fig. S7a). Using DESeq2, a total of 237 differentially abundant genera were identified (log₂FC\u0026thinsp;\u0026gt;\u0026thinsp;\u0026plusmn;\u0026thinsp;2 and \u003cem\u003eP\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e \u0026lt; 0.05). In comparison to the conducive soil, 97 genera were significantly upregulated and 140 genera were significantly downregulated in the disease-suppressing soil (Fig. S7b). Additionally, we used LEfSe to calculate the importance of key genera in both soil types and selected the top 20 key genera, among which 10 were enriched in the disease-suppressing soil (Fig. S7c). Using STAMP (11 genera), Deseq2 (97 genera), and LDA (LEfSe, 10 genera), we determined the microbial genera enriched in the disease-suppressing soil and visualized them in a Venn diagram. We then focused on six key disease-suppressing microbial genera (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), which included RB41, \u003cem\u003eLuteitalea\u003c/em\u003e, MND1, \u003cem\u003eStreptomyces\u003c/em\u003e, \u003cem\u003eLysobacter\u003c/em\u003e, and \u003cem\u003eLongimicrobium\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Correlation analysis between these 6 key genera and the disease index revealed a significant negative correlation for all 6 genera, with \u003cem\u003eStreptomyces\u003c/em\u003e showing the strongest negative correlation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). Furthermore, the relative abundance of these key microbial genera was correlated with the 13 key VOCs, revealing a significant positive correlation between \u003cem\u003eStreptomyces\u003c/em\u003e and humulene, suggesting that \u003cem\u003eStreptomyces\u003c/em\u003e may be an important source of humulene.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIsolation, identification of\u003c/b\u003e \u003cb\u003eStreptomyces\u003c/b\u003e, \u003cb\u003eand functional validation of their VOC\u003c/b\u003es\u003c/p\u003e\u003cp\u003eAs a key genus identified for its VOC-mediated contribution to soil disease suppressiveness in this study, 10 \u003cem\u003eStreptomyces\u003c/em\u003e strains were isolated and identified using 16S rRNA sequencing from suppressive soil (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The phylogenetic analysis revealed that most isolated strains clustered closely with \u003cem\u003eStreptomyces\u003c/em\u003e taxa identified through amplicon sequencing (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). The inhibitory effects of VOCs released by these strains on \u003cem\u003eR. solanacearum\u003c/em\u003e were tested \u003cem\u003ein vitro\u003c/em\u003e. Among the 10 strains, seven exhibited VOC-mediated suppression of \u003cem\u003eR. solanacearum\u003c/em\u003e growth. Strain \u003cem\u003eStre2\u003c/em\u003e showed the strongest effect, with 46.2% inhibition of \u003cem\u003eR. solanacearum\u003c/em\u003e growth, compared to the control (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). A comparative analysis was conducted to identify shared VOCs among suppressive soil VOCs, \u003cem\u003eStreptomyces\u003c/em\u003e-derived VOCs, and bioassay-defined key suppressive VOCs. Most compounds were unique to suppressive soils (95, 58.3%) or \u003cem\u003eStreptomyces\u003c/em\u003e (51, 31.3%), while 8 VOCs (5.0%) were shared between soils and \u003cem\u003eStreptomyces\u003c/em\u003e, indicating that part of the soil volatilome may originate from \u003cem\u003eStreptomyces\u003c/em\u003e metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed, Table S3-S4). Importantly, four VOCs were found in all three groups, representing potential core suppressive compounds. These were identified as 2-methylisoborneol, 2-undecanone, geosmin, and humulene. To further validate their role in soil suppressiveness, \u003cem\u003eStreptomyces\u003c/em\u003e strains were introduced into both native and gamma-ray-sterilized Jiangsu soils. The VOC-mediated disease suppression was significantly diminished with sterilized soil, whereas supplementation with \u003cem\u003eStreptomyces\u003c/em\u003e partially restored this inhibitory capacity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). These results revealed that \u003cem\u003eStreptomyces\u003c/em\u003e strains played an important role in inhibiting disease in disease-suppressive soil.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eMicrobial communities exert a stronger influence than soil physicochemical properties on soil VOC composition\u003c/h3\u003e\n\u003cp\u003eSuppressive soils displayed a markedly different physicochemical profile compared with conducive soils (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Overall, suppressive soils showed significantly higher pH, mineral nutrient (NO\u003csub\u003e3\u0026minus;\u003c/sub\u003eN, AvCa, AvP, AvFe), and organic matter-associated parameters (DOC, DON, TC, and TN) (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Notably, NO₃\u003csub\u003e\u0026minus;\u003c/sub\u003eN and DON concentrations were more than fivefold higher in suppressive soils, and total carbon and nitrogen levels were also substantially enriched. These findings indicate that suppressive soils provide a more nutrient-rich and chemically favorable environment, which may support beneficial microbial communities associated with disease suppression. Principal coordinate analysis (PCoA) based on soil physicochemical properties revealed a clear separation between disease-suppressive and conducive soils along the first two principal coordinates (Fig. S8a). PC1 alone explained 96.0% of the total variance, and the overall separation between soil types was statistically significant (Premanova: \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.955, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037). These results demonstrate that suppressive soils possess a distinct physicochemical profile compared with conducive soils, reflecting substantial differences in their environmental backgrounds.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSoil physicochemical properties of the two soil types\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConducive\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSuppressive\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP_value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eField Capacity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00e-03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e4.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e5.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.30e-05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003csub\u003e3\u0026minus;\u003c/sub\u003eN(mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e4.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e26.98\u0026thinsp;\u0026plusmn;\u0026thinsp;9.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.10e-03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNH\u003csub\u003e4+\u003c/sub\u003eN(mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.50e-02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDOC(mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e2.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e5.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDON(mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e3.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e25.28\u0026thinsp;\u0026plusmn;\u0026thinsp;8.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.90e-03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAvCa(mg/kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e7.70\u0026thinsp;\u0026plusmn;\u0026thinsp;2.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e70.88\u0026thinsp;\u0026plusmn;\u0026thinsp;6.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.30e-06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAvP(mg/kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e11.71\u0026thinsp;\u0026plusmn;\u0026thinsp;4.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e22.75\u0026thinsp;\u0026plusmn;\u0026thinsp;4.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.10e-03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAvFe(mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e33.47\u0026thinsp;\u0026plusmn;\u0026thinsp;3.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e38.50\u0026thinsp;\u0026plusmn;\u0026thinsp;6.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.70e-02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC(g/kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e15.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e13.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.40e-03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTN(g/kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e1.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.80e-03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Values are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE (n\u0026thinsp;=\u0026thinsp;4). Significant differences between the two soil types for each property were determined by an independent samples t-test. DOC, dissolved organic carbon; DON, dissolved organic nitrogen; TC, total carbon; TN, total nitrogen.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo further explore the environmental and microbial factors associated with VOC variation, we performed correlation analyses between key disease-suppressive VOCs, bacterial genera, and soil physicochemical properties (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Most suppressive VOCs, including humulene, geosmin, 2-methylisoborneol (2-MIB), and naphthalene, together with the core bacterial genera \u003cem\u003eStreptomyces\u003c/em\u003e, \u003cem\u003eLysobacter\u003c/em\u003e, and \u003cem\u003eLongimicrobium\u003c/em\u003e, showed significant positive correlations with soil physicochemical parameters such as pH, AvCa, AvFe, and AvP. Conversely, the disease index (DI) was negatively correlated with these properties. These results indicate that nutrient-rich and well-balanced soils tend to favor the enrichment of beneficial microbes and the accumulation of suppressive VOCs, thereby reducing disease incidence.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eVariation partitioning analysis (VPA) further quantified the relative contributions of soil physicochemical and microbial factors to VOC variation (Fig. S8b). Together, soil properties and bacterial communities explained 70.2% of the total variation in VOC composition (adjusted R\u0026sup2;), including 59.1% shared variation, 11.1% uniquely attributed to bacterial communities, and only 0.046% explained by soil properties, while 29.8% remained unexplained. Consistently, Procrustes analyses demonstrated significant congruence between VOC composition and both soil physicochemical properties (M\u0026sup2; = 0.252, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024, Fig. S8c) and bacterial community composition (M\u0026sup2; = 0.211, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011, Fig. S8d). The stronger alignment with bacterial communities proves that soil VOC patterns are more directly governed by the structure of microbial assemblages than by abiotic soil factors.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cem\u003eR. solanacearum\u003c/em\u003e is a soil-borne bacterial wilt pathogen of significant economic importance and is considered one of the top ten most harmful pathogens (Mansfield et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). We selected soils from two typical regions with different tomato bacterial wilt incidence and observed differences in their disease incidence and the population of \u003cem\u003eR. solanacearum\u003c/em\u003e. Subsequently, we measured the disease-suppressive ability of VOCs released by these soils. We found that VOCs released from suppressive soil significantly reduced the disease index of tomato bacterial wilt and inhibited the growth of \u003cem\u003eR. solanacearum\u003c/em\u003e. Our analysis revealed a negative correlation between the soil disease index of tomato bacterial wilt and the population of \u003cem\u003eR. solanacearum\u003c/em\u003e with the disease-suppressive ability of the VOCs. Specifically, soils with lower disease incidence and fewer \u003cem\u003eR. solanacearum\u003c/em\u003e populations released VOCs with stronger disease-suppressive properties. These findings highlight the potential involvement of VOC-mediated mechanisms in soil suppressiveness.\u003c/p\u003e\u003cp\u003eThe VOC profile analysis showed significant differences in the VOC composition among soils from the two types of soils. By performing Spearman correlation analysis between the VOC abundance and the disease index, we identified 13 potential disease-suppressive VOCs. These disease-suppressive VOCs included naphthalene (Raza et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), 2-decanone (Wang et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), 2-undecanone, pentadecane, humulene (Xing et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), among others, have been reported as antimicrobial substances released by microorganisms. Although most of these studies focused on their inhibitory effects on microbes other than \u003cem\u003eR. solanacearum\u003c/em\u003e. In this study, most of the potential disease-suppressive VOCs inhibited the growth of \u003cem\u003eR. solanacearum\u003c/em\u003e and promoted plant growth, along with inducing the activity of catalase (CAT) and superoxide dismutase (SOD) enzymes, indicating their role in inducing plant systemic resistance.\u003c/p\u003e\u003cp\u003eIn previous studies, we found that VOCs released by the beneficial bacteria effectively suppressed the growth and virulence traits of \u003cem\u003eR. solanacearum\u003c/em\u003e (Raza et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In this study, we revealed the microbial basis of soil VOCs' disease-suppressive effects, demonstrating that the primary role of soil VOCs is attributed to soil microbiota rather than soil physicochemical properties. Ossowick through soil microbiota transplantation experiments, has shown that the inhibitory effect of soil VOCs on \u003cem\u003eFusarium culmorum\u003c/em\u003e is unrelated to soil physical and chemical parameters, but rather associated with the soil microbiome (Ossowicki et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). To further investigate this, we employed amplicon sequencing to assess the microbial composition and diversity of bacteria in the soil, confirming significant differences in the bacterial communities among the two types of soils. The disease-suppressive bacterial genera identified included RB41, \u003cem\u003eLuteitalea\u003c/em\u003e, MND1, \u003cem\u003eStreptomyces\u003c/em\u003e, \u003cem\u003eLysobacter\u003c/em\u003e, and \u003cem\u003eLongimicrobium\u003c/em\u003e (Gao et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). \u003cem\u003eStreptomyces\u003c/em\u003e, as important beneficial soil microbes, can produce various metabolites to inhibit pathogens and enhance plant resistance (Olanrewaju and Babalola \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). They release terpenoid compounds, such as geosmin and 2-methylisoborneol, which are important VOCs attracting soil insects (Becher et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, previous studies have indicated that \u003cem\u003eStreptomyces setonii\u003c/em\u003e WY228, through VOCs, regulates plant growth and enhances salt stress tolerance (Qin et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe release of VOCs by microorganisms is a complex process influenced by both biotic and abiotic factors. Previous studies have shown that actinobacteria in disease-suppressive soils are the most active group in inhibiting the fungal root pathogen \u003cem\u003eRhizoctonia solani\u003c/em\u003e, with 300 strains of \u003cem\u003eStreptomyces\u003c/em\u003e isolated and analyzed for their VOC compositions and functions (Cordovez et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In this study, \u003cem\u003eStreptomyces\u003c/em\u003e appeared to be a significant driver of VOC distribution, and the addition of \u003cem\u003eStreptomyces\u003c/em\u003e to sterilized soil enhanced the disease-suppressive capacity of the soil VOCs, proving that \u003cem\u003eStreptomyces\u003c/em\u003e is an important source of disease-suppressive VOCs in soils. We propose that the disease-suppressive potential of soil VOCs may be attributed to compounds such as naphthalene, 2-undecanone, and humulene released by disease-suppressive microorganisms like \u003cem\u003eStreptomyces\u003c/em\u003e. These VOCs inhibit the growth of \u003cem\u003eR. solanacearum\u003c/em\u003e, promote plant growth, and enhance the synthesis of defense enzymes, thereby reducing the occurrence of the disease. This provides a theoretical basis for further exploring the characteristics of disease-suppressive soils.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study reveals the key role of soil VOCs in suppressing tomato bacterial wilt, along with the underlying microbial-driven mechanisms and microbial community analyses. The findings indicate that the disease-suppressive function of soil VOCs is primarily attributed to key antagonistic microbial communities. Thirteen crucial antagonistic VOCs were identified to directly inhibit the proliferation of the wilt pathogen and promote plant growth, and indirectly by modulating the synthesis of defense-related enzymes. Nutrient-rich soils with higher pH, AvCa, AvFe, and AvP further favor the enrichment of suppressive microbes and the synthesis of VOCs. These findings highlight that microbiota-driven VOCs, shaped under favorable soil conditions, are crucial for disease suppression and provide a basis for developing sustainable soil health\u0026ndash;based strategies for pathogen management. This research provides a theoretical basis for the agricultural application of microbial VOCs and offers potential avenues for developing green control methods, such as bio-pesticides or soil amendments.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eSoil Collection and\u003c/b\u003e \u003cb\u003eR. solanacearum\u003c/b\u003e \u003cb\u003eQuantification\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo assess the impact of soil VOCs on \u003cem\u003eR. solanacearum\u003c/em\u003e-induced tomato bacterial wilt, soil samples were collected from four tomato monoculture farming regions in Guangxi, Jiangsu, Jiangxi, and Shandong provinces, China. Between August and September 2022, soil samples were collected using a five-point sampling method. Approximately 30 cm of surface soil was collected, sieved through a 2 mm mesh, and mixed into a single sample. Four replicates were taken from each region. The samples were then divided into four subsamples and placed in a sterile plastic bag. The first subsample was used to quantify \u003cem\u003eR. solanacearum\u003c/em\u003e. Soil samples were serially diluted and plated on the modified semiselective medium (SMSA), a selective medium for \u003cem\u003eR. solanacearum\u003c/em\u003e, and incubated at 30\u0026deg;C for 48h for colony counting (Pradhanang et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The second soil subsample was preserved with 40% glycerol (1:1, v/v) at -80\u0026deg;C for microbial amplicon sequencing and isolation of key microorganisms. The third subsample was used for VOCs analysis and for evaluating their impact on plant disease and \u003cem\u003eR. solanacearum\u003c/em\u003e growth. It was stored at 4\u0026deg;C, activated at room temperature (28\u0026deg;C) for 24 hours before use. The fourth subsample was used to measure soil physicochemical properties. All soil samples were transported on ice and stored at 4\u0026deg;C upon arrival at the laboratory (Raza et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eTomato growth conditions and pathogen inoculation\u003c/h3\u003e\n\u003cp\u003eSurface-sterilized tomato seeds (\u003cem\u003eSolanum lycopersicum\u003c/em\u003e, cultivar \"Micro-Tom\") were germinated on water agar plates for 3 days. The seedlings were then transplanted into 72-well trays filled with gamma-irradiated cultivation substrate (50 g per well, purchased from Jiangsu Xingnong Substrate Technology Co., Ltd.). Once the plants reached the three-leaf-one-heart stage, they were transferred to gamma-irradiated cultivation substrate containing pots (100 g/pot) fitted above a plastic jar (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The pots and plastic jars were cleaned first with sterilized water and then with 70% ethanol to remove attached substances and dried. The plastic jar was added with 70 grams of soil to be tested for the effect of released VOCs, sealed with a double layer of parafilm at the junction of pot and plastic jar and placed in greenhouse. The plant pots receiving soil VOCs from below for five days were inoculated with \u003cem\u003eR. solanacearum\u003c/em\u003e strain QL-RS1115 (GenBank: GU390462) at a final concentration of 10\u003csup\u003e6\u003c/sup\u003e CFU/g of soil (Wei et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). All greenhouse experiments were conducted under a controlled light environment with a 12h day/night cycle and 60% relative humidity. Watering was performed every other day with sterile water to maintain sufficient moisture without dripping into the lower plastic jar. The disease severity was recorded every 2 days starting from the first appearance of symptoms on tomato plants until no new symptoms were observed. Each region soil sample contained 6 tomato seedlings with 4 repetitions, and gamma-irradiated soil and no soil treatments were used as controls.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEffects of soil VOCs on\u003c/b\u003e \u003cb\u003eR. solanacearum\u003c/b\u003e \u003cb\u003egrowth\u003c/b\u003e \u003cb\u003ein vitro\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe VOC-mediated inhibition of \u003cem\u003eR. solanacearum\u003c/em\u003e was evaluated using divided petri dishes, with slight modifications of a previous report (Raza et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Briefly, a single colony of \u003cem\u003eR. solanacearum\u003c/em\u003e QL-RS1115 was cultured in CPG medium (1 g casamino acid (BD Bacto, Becton, Dickinson and Company, USA) at 30\u0026deg;C for 24 hours. The bacterial suspension was washed twice with 0.85% NaCl solution and adjusted to 1 \u0026times; 10\u003csup\u003e7\u003c/sup\u003e CFU/mL. The divided petri dishes (85 mm in diameter) were filled with 15 ml CPG agar medium on one side, and 5 \u0026micro;L of the bacterial suspension was spotted at two positions (5 cm apart). The other side was filled with 10 g of soil (dry weight) from different regions. The Petri dish was sealed with Parafilm and incubated at 30\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Controls included petri dishes without soil. After 24 hours, agar containing the bacteria was removed, washed with 1 mL sterile water, and the OD\u003csub\u003e600\u003c/sub\u003e was measured to determine bacterial growth. In parallel, commercial VOC compounds were added at concentrations of 10 \u0026micro;M, using DMSO as the solvent, and 1 mm sterile filter paper discs were used as carriers. The petri dishes were sealed and incubated, with DMSO as controls.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eMeasurement of soil physicochemical properties\u003c/h2\u003e\u003cp\u003eIn this study, eleven physicochemical properties of soil were measured. Field capacity (FC) was determined using the gravimetric method. Briefly, air-dried soil was saturated with deionized water and allowed to drain freely for 24 h under laboratory conditions. The moisture content of the soil was then determined after oven-drying at 105\u0026deg;C to a constant weight, and FC was expressed as the percentage of water retained relative to the dry soil weight (Minasny and McBratney \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For soil pH, soil was mixed with deionized water in a 1:2.5 (w/v) ratio to make a slurry, and pH was measured using a pH meter (Orion Star A211, Thermo Fisher Scientific, USA) (Xu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Dissolved organic carbon (DOC) and dissolved organic nitrogen (DON) were determined by extracting with a soil-to-water ratio of 1:10, followed by shaking and centrifugation. The extract was filtered through a 0.45 \u0026micro;m membrane, and DOC and DON were measured using a total organic carbon analyzer (Elementar, Germany). Total carbon (TC) and total nitrogen (TN) were analyzed using a Vario MACRO cube elemental analyzer (VarioMAX, Elementar, Germany) with air-dried and sieved soil samples (Filep and Rekasi \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Nitrate nitrogen (NO\u003csub\u003e3-\u003c/sub\u003eN) and ammonium nitrogen (NH\u003csub\u003e4+\u003c/sub\u003eN) were extracted by mixing fresh soil with 1 M KCl in a 1:5 (soil to solvent mass ratio) ratio, followed by shaking at 200 rpm for 30 minutes at 25\u0026deg;C. The concentration of nitrate nitrogen was measured using a continuous flow analyzer as described by Carlson (Seal AutoAnalyzer AA3, Norderstedt, Germany) (Carlson et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). The available phosphorus (AvP) was extracted by mixing the air-dried soil with 0.5 M NaHCO\u003csub\u003e3\u003c/sub\u003e at a soil-to-solvent ratio of 1:20 (w/v, 30 min), and the microelements were extracted by mixing the air-dried soil with 0.05 M diethylenetriaminepentaacetic acid (DTPA) at a soil-to-solvent ratio of 1:5 (w/v, 1 h) (Cancela et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The concentrations of available potassium (AvK) and microelements were determined using an inductively coupled plasma optical emission spectrometer (Agilent 710 ICP-OES, Agilent Technologies, USA). The nutrient contents were expressed on a soil dry weight basis. All analyses were performed in triplicate, and standard materials were used for calibration to ensure data accuracy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eIdentification of soil and microbial VOCs\u003c/h2\u003e\u003cp\u003eThe identification of VOC profiles was performed following previous reports, with slight modifications (Raza et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Raza et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For the headspace extraction of VOCs from soil, 10 g of soil samples were weighed and placed into 60 mL brown glass vials, which were then sealed with a Teflon\u0026reg;-backed silicone rubber stopper. The vials without soil were used as controls. All samples were stored at room temperature for 12 hours to activate the soil microorganisms. Subsequently, the internal standard, 10 \u0026micro;l of 5 mM acetic acid nonylester, was added to the vial. A preconditioned (250\u0026deg;C for 5 min) solid-phase microextraction (SPME) fiber (Supelco, Bellefonte, PA, 50/30 \u0026micro;m DVB/CAR on PDMS) was then inserted into the vials and incubated at 30\u0026deg;C for 30 minutes, followed by an additional 30-minute incubation at 50\u0026deg;C. The SPME fibers were then inserted into the injection port of a gas chromatography-mass spectrometry (GC-MS) system (Finnigan Trace DSQ, Austin, USA) and desorbed at 220\u0026deg;C for 1 minute. Analysis was conducted using an RTX-5MS column (30 m, 0.25 mm ID, 0.25 \u0026micro;m film thickness). The following oven temperature program was used: 33\u0026deg;C (hold for 3 minutes), 180\u0026deg;C (10\u0026deg;C/min), 240\u0026deg;C (30\u0026deg;C/min), followed by a 5-minute hold at 240\u0026deg;C. The mass spectrometer was operated in electron impact mode at 70 eV and 220\u0026deg;C, with a scan range of 50 to 500 m/z. Chromatograms were acquired and analyzed using AMDIS 2.73 software (National Institute of Standards and Technology, Gaithersburg, USA). The spectra of deconvoluted VOC peaks were compared using the main and/or secondary libraries in the NIST/EPA/NIH mass spectral library (Agilent Technologies, Santa Clara, CA, USA). The Kovats retention index of each compound was calculated using an alkane calibration mixture and compared with the data in the NIST/EPA/NIH mass spectral library. A compound was considered identified if its mass spectrum matched a listed compound, the match factor was greater than 800, and the calculated retention index did not differ by more than 5 from the listed value for the nonpolar semi-standard column.\u003c/p\u003e\u003cp\u003eThe VOC profile of soil samples was also determined using the liquid extraction method. The soil samples used for headspace extraction were added with an internal standard, 1, 4-difluorobenzene (20 \u0026micro;g/ml methanol) at a concentration of 50 \u0026micro;g/kg soil. Later, the soil was thoroughly mixed by shaking and placed at room temperature overnight for equilibrium. Later, 30 mL of methanol was added to the bottles containing the soil samples, which were then sealed with unused Teflon\u0026reg; backing silicone rubber caps. The vials were inverted and marked with the level of liquid as a reference for determining any leak. The vials were then placed at 75\u0026deg;C with gentle shaking (60 rpm) to maintain the soil in suspension. To avoid breakage, the vials were placed inside another container. After 24 h, the liquid level was checked, and any vial with a loss in volume was discarded. Later, the soil suspension was centrifuged (1500 rpm for 10 min), and 10 mL supernatant was transferred to a 50 mL flask. To the solvent extract, 30 mL of distilled water and then 10 mL of hexane were added. The flask was shaken vigorously for 1 min and then allowed the phases to separate for 5 min. The volume of the upper hexane layer was almost similar to the volume of hexane added. The hexane layer was collected, filtered by a Millipore membrane filter (0.45 \u0026micro;m), and analyzed using GC\u0026ndash;MS by inserting a 2 \u0026micro;L sample in the injector. The rest procedure for GC\u0026ndash;MS analysis and VOC identification was the same as described above. To determine the extraction efficiency, the standard compounds were run alone in hexane (10\u0026ndash;100 \u0026micro;g/mL). If a VOC was detected by both the SPME method and the liquid extraction method, we sum the corrected peak areas of the VOCs. The peak area of VOCs was expressed as the relative VOC abundance in arbitrary units (a.u.) per gram of soil (dry weight). The recovery efficiency of standard compound 1, 4-difluorobenzene, for the liquid extraction method was found to be 77%.\u003c/p\u003e\u003cp\u003eTo analyze the VOC profile produced by \u003cem\u003eStreptomyces\u003c/em\u003e, a cell suspension (1 \u0026times; 10\u003csup\u003e8\u003c/sup\u003e CFU/ml) was prepared as described above, and 5 \u0026micro;L of the suspension was inoculated into a 50 mL conical flask containing 20 mL of International \u003cem\u003eStreptomyces\u003c/em\u003e Project medium 3 (ISP3) and sealed for incubation at 30\u0026deg;C for 72 hours. Each treatment was performed in triplicate, with bottles without bacterial inoculation serving as controls. After incubation, 10 \u0026micro;L of acetic acid nonylester (5 mM) was added as the internal standard. The VOC components were then identified using SPME and GC-MS, following the same procedure as for the soil samples. The peaks similar to the control treatment (without bacterial inoculation) were not considered for the identification of VOCs. The number of VOCs produced in each treatment was recorded, and the chromatographic peak area was expressed as the relative peak area to the internal standard in arbitrary units (a.u.) as an indirect approach to estimate the relative amount (concentration) of each VOC (Raza et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Raza et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eEffects of soil VOCs on the growth of\u003c/b\u003e \u003cb\u003eR. solanacearum\u003c/b\u003e \u003cb\u003eand plant growth and defense enzyme activities\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBased on the correlation analysis results, VOCs significantly correlated with the disease index were selected and purchased (Table S3). Following the method described above for testing the effect of VOCs released from soil on the growth of \u003cem\u003eR. solanacearum\u003c/em\u003e, except that one side of the divided Petri dish was replaced with pure VOC compounds. DMSO was used as the solvent, and 10 \u0026micro;L of the VOCs at a concentration of 5 \u0026micro;M were added on a 1 mm sterile filter paper as a carrier for the droplets. The petri dishes were sealed with parafilm and placed in an incubator, with dishes containing only DMSO and water as controls. After 24 hours of incubation, the agar containing \u003cem\u003eR. solanacearum\u003c/em\u003e was removed using a puncher, washed, and suspended with 1 mL of sterile water, and the OD\u003csub\u003e600\u003c/sub\u003e of \u003cem\u003eR. solanacearum\u003c/em\u003e was measured. Each VOC was evaluated six times.\u003c/p\u003e\u003cp\u003eLater, the effect of the above pure VOC compounds on \u003cem\u003eSolanum lycopersicum\u003c/em\u003e growth and the activities of oxidative defense enzymes such as SOD, CAT, and PAL was determined (Raza et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For this, a divided agar plate experiment was used, with plants placed in one compartment and bacteria in another. In the experiment, \u003cem\u003eSolanum lycopersicum\u003c/em\u003e seeds were surface sterilized for 1 minute in 70% (v/v) ethanol, followed by 5 minutes in 5% (v/v) NaOCl. The seeds were then washed three times with sterile water and placed on petri dishes containing \u0026frac12; strength Murashige and Skoog (MS) agar (0.8%). The seeds were pre-cooled at 4\u0026deg;C for 3 days, then placed in a growth chamber set at 21\u0026deg;C with 40 W fluorescent lights, and a photoperiod of 16 h light: 8 h dark. After germination, three seeds of uniform size were placed in each compartment, which contained 15 mL of \u0026frac12; strength MS agar medium with 0.5% sucrose and 0.8% agar. The petri dishes were sealed with parafilm and returned to the growth chamber. After the third true leaf emerged, a 10 \u0026micro;L aliquot of the VOC at 5 \u0026micro;M was added to another compartment of the divided petri dish on 1 mm sterile filter paper. The plates were sealed and placed back in the growth chamber. After 15 days of incubation, \u003cem\u003eSolanum lycopersicum\u003c/em\u003e plants were removed from the agar, washed with clean water to remove any remaining agar from the roots, blotted dry to remove excess moisture, and the fresh weight of the plants was recorded. The activities of SOD, PAL, and CAT in the plants were measured using commercial kits (Solarbio, Beijing, China). Each compound was evaluated six times, with DMSO as a control.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eSoil amplicon sequencing and bioinformatics analysis\u003c/h2\u003e\u003cp\u003eSoil DNA was extracted using the Power Soil DNA Isolation Kit (Mo Bio Laboratories) according to the manufacturer's protocol. DNA concentration and quality were assessed using a NanoDrop 1000 spectrophotometer (Thermo Scientific) and electrophoresis to evaluate DNA degradation. Amplicon sequencing was conducted using 16S rRNA markers to determine the composition and diversity of bacterial communities. The V4 region of the bacterial 16S rRNA gene was amplified using the primers 515F (5\u0026rsquo;-GTGYCAGCMGCCGCGGTAA-3\u0026rsquo;) and 806R (5\u0026rsquo;-GGACTACHVGGGTWTCTAAT-3\u0026rsquo;) (Cardenas et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The amplicon library was sequenced in a paired-end manner (2 \u0026times; 250) on the Illumina MiSeq platform (Shanghai BIOZERON Co., Ltd.) following standard protocols (Gu et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Sequence reads were processed and dereplicated using the DADA2 algorithm in QIIME 2 (Quast et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Paired reads were trimmed and filtered, with a maximum expected error of 2 allowed per read (maxEE\u0026thinsp;=\u0026thinsp;2). After merging paired reads and conducting chimeric filtering, each 16S rRNA gene sequence (referred to as ASVs here) was phylogenetically classified using the UCLUST algorithm (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.drive5.com/usearch/manual/uclust_algo.html\u003c/span\u003e\u003cspan address=\"http://www.drive5.com/usearch/manual/uclust_algo.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) against the Silva (SSU138.1) 16S rRNA database and an additional database, with a confidence threshold set at 80% (Caporaso et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Only sequences with total abundance greater than 5 were retained for further analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIsolation of\u003c/b\u003e \u003cb\u003eStreptomyces\u003c/b\u003e \u003cb\u003eand functional validation of their VOCs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 10 \u003cem\u003eStreptomyces\u003c/em\u003e strains were isolated from suppressive Jiangsu soil using an improved Gauze\u0026rsquo;s synthetic medium (Wang et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The 16S rRNA genes of these \u003cem\u003eStreptomyces\u003c/em\u003e strains were amplified using the 27F and 1492R primers and sequenced (Heuer et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). To validate the inhibitory effect of VOCs released by \u003cem\u003eStreptomyces\u003c/em\u003e on the growth of \u003cem\u003eR. solanacearum\u003c/em\u003e, a dual-segment plate method was used, as described above. On one side of the plate, 3 droplets (0.5 \u0026micro;L each) of \u003cem\u003eR. solanacearum\u003c/em\u003e were added, and on the other side, 10 \u0026micro;L of \u003cem\u003eStreptomyces\u003c/em\u003e suspension (1 \u0026times; 10\u003csup\u003e8\u003c/sup\u003e CFU/ml) was inoculated. The plates were sealed with Parafilm and incubated at 28\u0026deg;C for 2 days. Afterward, the agar containing \u003cem\u003eR. solanacearum\u003c/em\u003e was removed using a puncher, washed, and suspended in 1 mL sterile water, and the OD\u003csub\u003e600\u003c/sub\u003e was measured. The addition of distilled water treatment was used as a blank control, with three replicates for each treatment.\u003c/p\u003e\u003cp\u003eAdditionally, a pot experiment was performed to validate the ability of \u003cem\u003eStreptomyces\u003c/em\u003e to release disease-suppressive VOCs in soil as described above (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Gamma-irradiated suppressive soil was inoculated with a mixture of \u003cem\u003eStreptomyces\u003c/em\u003e strains at a concentration of 1\u0026times;10\u003csup\u003e7\u003c/sup\u003e CFU/g soil. VOCs released by the \u003cem\u003eStreptomyces\u003c/em\u003e-treated soil were assessed for their impact on \u003cem\u003eR. solanacearum-\u003c/em\u003einduced disease incidence, with sterilized soil and natural soil serving as controls. Each treatment consisted of six tomato seedlings with four replicates.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll data analyses were performed using R version 4.3.1. The \u0026ldquo;ONE Tukey HSD2\u0026rdquo; function was used to compare differences between treatments. Microbial community diversity was calculated using ASVs. Changes in community composition were assessed using Principal Component Analysis (PCoA), with analysis performed using the \"microceo\" v1.9.0 R package. Significant differences in community structure between treatments and Procrustes were evaluated using functions from the \"vegan\" v2.6.4 and \" DESeq2 \" v1.40.2 package. A heatmap of key microorganisms or VOCs correlated with the disease index was generated using the \"ComplexHeatmap\" v1.10.2 package. The analytic hierarchy process of important factors in RDA and VPA was conducted using the \"rdacca.hp\" v1.1 package.\u003c/p\u003e\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAvCa\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAvailable calcium\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAvFe\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAvailable Iron\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAvK\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAvailable potassium\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAvP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAvailable phosphorus\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCAT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCatalase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCFU\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eColony forming unit\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCPG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCasamino acids peptone glucose medium\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDisease index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDMSO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDimethylsulfoxide\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDOC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDissolved organic carbon\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDON\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDissolved organic nitrogen\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDTPA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDiethylenetriaminepentaacetic acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eField capacity\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGC-MS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGas chromatography-mass spectrometry\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eISP3\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInternational streptomyces project medium 3\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLDA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLinear discriminant analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMurashige and skoog\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNH\u003csub\u003e4+\u003c/sub\u003eN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAmmonium nitrogen\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNO\u003csub\u003e3\u0026minus;\u003c/sub\u003eN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNitrate nitrogen\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOPLS-DA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOrthogonal partial least squares discriminant analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePAL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePhenylalanine ammonia lyase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePC1\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePrincipal component 1\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePC2\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePrincipal component 2\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCoA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePrincipal coordinate analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSMSA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eModified semiselective medium\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSOD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSuperoxide dismutase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSPME\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSolid-phase microextraction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSTAMP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStatistical analysis of metagenomic profiles\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTotal carbon\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTMCHD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e2,6,6-Trimethyl-2-cyclohexene-1,4-dione\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTotal nitrogen\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVIP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eVariable importance in projection\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVOCs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eVolatile organic compounds\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVPA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eVariation partitioning analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZW and WR conceived and supervised this study. YSH and JLZ designed and performed the experiments and were responsible for data analysis. GFJ and XFW assisted with sample processing and data collection. ZW and WR contributed substantially to manuscript revision and editing, and all other authors provided critical comments on the study. YSH and JLZ wrote the manuscript with input from all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China 42377124 (WR), 42350610 (WR), and 32571909 (XFW).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 16S rRNA amplicon sequence data have been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under accession number PRJNA1264978. The online version contains supplementary material available at Supplementary Material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBecher PG, Verschut V, Bibb MJ, Bush MJ, Molnar BP, Barane E, et al. Developmentally regulated volatiles geosmin and 2-methylisoborneol attract a soil arthropod to \u003cem\u003eStreptomyces \u003c/em\u003ebacteria promoting spore dispersal. Nat Microbiol. 2020; 5:821-9. https://doi.org/10.1038/s41564-020-0697-x.\u003c/li\u003e\n\u003cli\u003eBerendsen RL, Pieterse CMJ, Bakker PAHM. The rhizosphere microbiome and plant health. Trends in Plant Science. 2012; 17:478-86. https://doi.org/10.1016/j.tplants.2012.04.001.\u003c/li\u003e\n\u003cli\u003eCancela RC, Abreu CA, Paz-Gonzalez A. DTPA and Mehlich-3 micronutrient extractability in natural soils. Commun Soil Sci Plant Anal. 2002; 33:2357-8. https://doi.org/10.1081/CSS-120014488.\u003c/li\u003e\n\u003cli\u003eCaporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010; 7:335-6. https://doi.org/10.1038/nmeth.f.303.\u003c/li\u003e\n\u003cli\u003eCardenas E, Wu WM, Leigh MB, Carley J, Carroll S, Gentry T, et al. Significant association between sulfate-reducing bacteria and uranium-reducing microbial communities as revealed by a combined massively parallel sequencing-indicator species approach. Appl Environ Microbiol. 2010; 76:6778-86. https://doi.org/10.1128/aem.01097-10.\u003c/li\u003e\n\u003cli\u003eCarlson RM, Cabrera RI, Paul JL, Quick J, Evans RY. Rapid direct determination of ammonium and nitrate in soil and plant-tissue extracts. Commun Soil Sci Plant Anal. 1990; 21:1519-29. https://doi.org/10.1080/00103629009368319.\u003c/li\u003e\n\u003cli\u003eCha JY, Han S, Hong HJ, Cho H, Kim D, Kwon Y, et al. Microbial and biochemical basis of a \u003cem\u003eFusarium\u003c/em\u003e wilt-suppressive soil. ISME J. 2016; 10:119-29. https://doi.org/10.1038/ismej.2015.95.\u003c/li\u003e\n\u003cli\u003eCordovez V, Carrion VJ, Etalo DW, Mumm R, Zhu H, van Wezel GP, et al. Diversity and functions of volatile organic compounds produced by \u003cem\u003eStreptomyces \u003c/em\u003efrom a disease-suppressive soil. Front Microbiol. 2015; 6:1081. https://doi.org/10.5589/fmicb.2015.01081.\u003c/li\u003e\n\u003cli\u003eExposito RG, de Bruijn I, Postma J, Raaijmakers JM. Current insights into the role of rhizosphere bacteria in disease suppressive soils. Front Microbiol. 2017; 8:2529. https://doi.org/10.3389/fmicb.2017.02529.\u003c/li\u003e\n\u003cli\u003eFilep T, Rekasi M. Factors controlling dissolved organic carbon (DOC), dissolved organic nitrogen (DON) and DOC/DON ratio in arable soils based on a dataset from Hungary. Geoderma. 2011; 162:312-8. https://doi.org/10.1016/j.geoderma.2011.03.002.\u003c/li\u003e\n\u003cli\u003eGao Z, Zhu N, Yang M, Cui X, Han C, Guo X, et al. Integrating transcriptome, metabolome and microbiome to explore the interaction mechanism between secondary metabolites and root-associated bacteria of Codonopsis pilosula. Ind Crop Prod. 2025; 227:120790. https://doi.org/10.1016/j.indcrop.2025.120790.\u003c/li\u003e\n\u003cli\u003eGu Y, Wang X, Yang T, Friman V-P, Geisen S, Wei Z, et al. Chemical structure predicts the effect of plant-derived low-molecular weight compounds on soil microbiome structure and pathogen suppression. Funct Ecol. 2020; 34:2158-69. https://doi.org/10.1111/1365-2435.13624.\u003c/li\u003e\n\u003cli\u003eHeuer H, Krsek M, Baker P, Smalla K, Wellington EMH. Analysis of actinomycete communities by specific amplification of genes encoding 16S rRNA and gel-electrophoretic separation in denaturing gradients. Appl Environ Microbiol. 1997; 63:3233-41. https://doi.org/10.1128/aem.63.8.3233-3241.1997.\u003c/li\u003e\n\u003cli\u003eJiang G, Wei Z, Xu J, Chen H, Zhang Y, She X, et al. Bacterial wilt in China: History, current status, and future perspectives. Front Plant Sci. 2017; 8:1549. https://doi.org/10.3389/fpls.2017.01549.\u003c/li\u003e\n\u003cli\u003eKost C, Heil M. Herbivore-induced plant volatiles induce an indirect defence in neighbouring plants. J Ecol. 2006; 94:619-28. https://doi.org/10.1111/j.1365-2745.2006.01120.x.\u003c/li\u003e\n\u003cli\u003eLiu X, Zhang S, Jiang Q, Bai Y, Shen G, Li S, et al. Using community analysis to explore bacterial indicators for disease suppression of tobacco bacterial wilt. Sci Rep. 2016; 6:36773. https://doi.org/10.1038/srep36773.\u003c/li\u003e\n\u003cli\u003eMansfield J, Genin S, Magori S, Citovsky V, Sriariyanum M, Ronald P, et al. Top 10 plant pathogenic bacteria in molecular plant pathology. Mol Plant Pathol. 2012; 13:614-29. https://doi.org/10.1111/j.1364-3703.2012.00804.x.\u003c/li\u003e\n\u003cli\u003eMazzola M. Manipulation of rhizosphere bacterial communities to induce suppressive soils. J Nematol. 2007; 39:213-20. https://pmc.ncbi.nlm.nih.gov/articles/PMC2586500/.\u003c/li\u003e\n\u003cli\u003eMinasny B, McBratney AB. Limited effect of organic matter on soil available water capacity. Eur J Soil Sci. 2018; 69:39-47. https://doi.org/10.1111/ejss.12475.\u003c/li\u003e\n\u003cli\u003eOlanrewaju OS, Babalola OO. Streptomyces: implications and interactions in plant growth promotion. Appl Microbiol Biotechnol. 2019; 103:1179-88. https://doi.org/10.1007/s00253-018-09577-y.\u003c/li\u003e\n\u003cli\u003eOssowicki A, Tracanna V, Petrus MLC, Van Wezel G, Raaijmakers JM, Medema MH, et al. Microbial and volatile profiling of soils suppressive to \u003cem\u003eFusarium culmorum\u003c/em\u003e of wheat. Proc R Soc B. 2020; 287:20192527. https://doi.org/10.1098/rspb.2019.2527.\u003c/li\u003e\n\u003cli\u003ePradhanang PM, Elphinstone JG, Fox RTV. Sensitive detection of \u003cem\u003eRalstonia solanacearum\u003c/em\u003e in soil: a comparison of different detection techniques. Plant Pathol J. 2000; 49:414-22. https://doi.org/10.1046/j.1365-3059.2000.00481.x.\u003c/li\u003e\n\u003cli\u003eQin Y-Y, Gong Y, Kong S-Y, Wan Z-Y, Liu J-Q, Xing K, et al. Aerial signaling by plant-associated \u003cem\u003eStreptomyces setonii \u003c/em\u003eWY228 regulates plant growth and enhances salt stress tolerance. Microbiol Res. 2024; 286:127823. https://doi.org/10.1016/j.micres.2024.127823.\u003c/li\u003e\n\u003cli\u003eQuast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013; 41:D590-D6. https://doi.org/10.1093/nar/gks1219.\u003c/li\u003e\n\u003cli\u003eRaaijmakers JM, Mazzola M. Soil immune responses: Soil microbiomes may be harnessed for plant health. Science. 2016; 352:1392-3. https://doi.org/10.1126/science.aaf3252.\u003c/li\u003e\n\u003cli\u003eRaza W, Jiang G, Eisenhauer N, Huang Y, Wei Z, Shen Q, et al. Microbe-induced phenotypic variation leads to overyielding in clonal plant populations. Nat Ecol Evol. 2024; 8:392–9. https://doi.org/10.1038/s41559-023-02297-1.\u003c/li\u003e\n\u003cli\u003eRaza W, Mei X, Wei Z, Ling N, Yuan J, Wang J, et al. Profiling of soil volatile organic compounds after long-term application of inorganic, organic and organic-inorganic mixed fertilizers and their effect on plant growth. Sci Total Environ. 2017; 607:326-38. https://doi.org/10.1016/j.scitotenv.2017.07.023.\u003c/li\u003e\n\u003cli\u003eRaza W, Wang J, Jousset A, Friman V-P, Mei X, Wang S, et al. Bacterial community richness shifts the balance between volatile organic compound-mediated microbe-pathogen and microbe-plant interactions. Proc R Soc B. 2020; 287:20200403. https://doi.org/10.1098/rspb.2020.0403.\u003c/li\u003e\n\u003cli\u003eRaza W, Wang J, Wu Y, Ling N, Wei Z, Huang Q, et al. Effects of volatile organic compounds produced by \u003cem\u003eBacillus amyloliquefaciens\u003c/em\u003e on the growth and virulence traits of tomato bacterial wilt pathogen \u003cem\u003eRalstonia solanacearum\u003c/em\u003e. Appl Microbiol Biotechnol. 2016; 100:7639-50. https://doi.org/10.1007/s00253-016-7584-7.\u003c/li\u003e\n\u003cli\u003eSavary S, Willocquet L, Pethybridge SJ, Esker P, McRoberts N, Nelson A. The global burden of pathogens and pests on major food crops. Nat Ecol Evol. 2019; 3:430-9. https://doi.org/10.1038/s41559-018-0793-y.\u003c/li\u003e\n\u003cli\u003eSchlatter D, Kinkel L, Thomashow L, Weller D, Paulitz T. Disease suppressive soils: new insights from the soil microbiome. Phytopathology. 2017; 107:1284-97. https://doi.org/10.1094/phyto-03-17-0111-rvw.\u003c/li\u003e\n\u003cli\u003eSchmidt R, Cordovez V, de Boer W, Raaijmakers J, Garbeva P. Volatile affairs in microbial interactions. ISME J. 2015; 9:2329-35. https://doi.org/10.1038/ismej.2015.42.\u003c/li\u003e\n\u003cli\u003eSchulz-Bohm K, Martín-Sánchez L, Garbeva P. Microbial volatiles: Small molecules with an important role in infra- and inter-kingdom interactions. Front Microbiol. 2017; 8:2484. https://doi.org/10.3389/fmicb.2017.02484.\u003c/li\u003e\n\u003cli\u003eWang J, Raza W, Jiang G, Yi Z, Fields B, Greenrod S, et al. Bacterial volatile organic compounds attenuate pathogen virulence via evolutionary trade-offs. ISME J. 2023; 17:443–52. https://doi.org/10.1038/s41396-023-01356-6.\u003c/li\u003e\n\u003cli\u003eWang W, Ge Q, Wen J, Zhang H, Guo Y, Li Z, et al. Horizontal gene transfer and symbiotic microorganisms regulate the adaptive evolution of intertidal algae, Porphyra sense lato. Commun Biol. 2024; 7:976. https://doi.org/10.1038/s42003-024-06663-y.\u003c/li\u003e\n\u003cli\u003eWang XR, Huang MM, Li WH, Shi YY, Tang YN, Zhang H, et al. Antifungal activity of 2-decanone against Monilinia fructicola and its application in combination with boscalid in mitigating brown rot disease in peach fruit. Physiol Mol Plant Pathol. 2025; 138:102665. https://doi.org/10.1016/j.pmpp.2025.102665.\u003c/li\u003e\n\u003cli\u003eWei Z, Yang X, Yin S, Shen Q, Ran W, Xu Y. Efficacy of \u003cem\u003eBacillus\u003c/em\u003e-fortified organic fertiliser in controlling bacterial wilt of tomato in the field. Appl Soil Ecol. 2011; 48:152-9. https://doi.org/10.1016/j.apsoil.2011.03.013.\u003c/li\u003e\n\u003cli\u003eWeisskopf L, Schulz S, Garbeva P. Microbial volatile organic compounds in intra-kingdom and inter-kingdom interactions. Nat Rev Microbiol. 2021; 19:391-404. https://doi.org/10.1038/s41579-020-00508-1.\u003c/li\u003e\n\u003cli\u003eWeller DM, Raaijmakers JM, Gardener BBM, Thomashow LS. Microbial populations responsible for specific soil suppressiveness to plant pathogens. Annu Rev Phytopathol. 2002; 40:309-48. https://doi.org/10.1146/annurev.phyto.40.030402.110010.\u003c/li\u003e\n\u003cli\u003eWen T, Ding Z, Thomashow LS, Hale L, Yang S, Xie P, et al. Deciphering the mechanism of fungal pathogen-induced disease-suppressive soil. New Phytol. 2023; 238:2634-50. https://doi.org/10.1111/nph.18886.\u003c/li\u003e\n\u003cli\u003eXing M, Zheng L, Deng Y, Xu D, Xi P, Li M, et al. Antifungal activity of natural volatile organic compounds against litchi downy blight pathogen Peronophythora litchii. Molecules. 2018; 23:37-46. https://doi.org/10.3390/molecules23020358.\u003c/li\u003e\n\u003cli\u003eXu RK, Zhao AZ, Li QM, Kong XL, Ji GL. Acidity regime of the red soils in a subtropical region of southern China under field conditions. Geoderma. 2003; 115:75-84. https://doi.org/10.1016/s0016-7061(03)00077-6.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Soil suppressiveness, Volatile organic compounds (VOCs), Ralstonia solanacearum, Streptomyces, Microbial community","lastPublishedDoi":"10.21203/rs.3.rs-8281057/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8281057/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSoil volatile organic compounds (VOCs) are critical in suppressing soil-borne pathogens, yet their microbial origin, functional mechanisms, and contribution to disease suppression remain underexplored. This study investigated the role of VOCs in the disease-suppressive capacity of soils from two tomato monocropping regions against \u003cem\u003eRalstonia solanacearum\u003c/em\u003e, the causative agent of bacterial wilt. Among the disease-conductive and suppressive soils, suppressive soil showed the lowest disease incidence (0.37%) and pathogen load (9.6 \u0026times; 10\u0026sup3; CFU/g), which was linked to potent soil VOC-mediated suppression of \u003cem\u003eR. solanacearum\u003c/em\u003e growth and disease occurrence. GC-MS analysis identified 13 VOCs significantly related to disease index, including naphthalene, 2-undecanone, and humulene, which inhibited pathogen growth \u003cem\u003ein vitro\u003c/em\u003e and promoted plant growth and defense enzymes (CAT, SOD). Amplicon sequencing revealed differences in microbial community diversity and composition between conductive and suppressive soils, with \u003cem\u003eStreptomyces\u003c/em\u003e as a key disease-suppressive taxon. Isolation of 10 \u003cem\u003eStreptomyces\u003c/em\u003e strains from suppressive soil confirmed their role in restoring VOC-mediated suppressiveness in sterilized soil, with strain Stre2 achieving 46.17% pathogen inhibition. Correlation, Procrustes, and variation partitioning analyses (VPA) demonstrated that soil physicochemical and microbial factors jointly shaped soil VOC composition, but bacterial communities also exerted a significant direct influence (11.1% unique contribution). Our findings demonstrate that disease-suppressive soils harness microbiota-derived VOCs to inhibit \u003cem\u003eR. solanacearum\u003c/em\u003e and prime plant defenses, offering novel insights for sustainable pathogen management through microbial metabolite engineering.\u003c/p\u003e","manuscriptTitle":"Microbiome and volatile organic compound profiling of diseased soils and their association with tomato wilt caused by Ralstonia solanacearum","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-10 12:49:55","doi":"10.21203/rs.3.rs-8281057/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8a6993fd-0138-4ee7-8564-199c6c862190","owner":[],"postedDate":"December 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T03:38:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-10 12:49:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8281057","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8281057","identity":"rs-8281057","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}