The interplay between root exudates and Cross-kingdom synthetic microbiota enhances the resistance of Vicia faba to Fusarium wilt disease

The interplay between root exudates and Cross-kingdom synthetic microbiota enhances the resistance of Vicia faba to Fusarium wilt disease | 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 The interplay between root exudates and Cross-kingdom synthetic microbiota enhances the resistance of Vicia faba to Fusarium wilt disease Chaowen Zhang, Mengyuan Li, Hongji Wang, Ke Pan, Ruiqi Wang, Xinyan He, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3980679/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-borne Fusarium wilt imposes substantial economic losses on agriculture, with Vicia faba exhibiting pronounced susceptibility to Fusarium disease. However, the mechanisms underlying V. faba 's resistance to Fusarium and the intricate interplay between crucial rhizosphere microbes and root exudates during pathogen attack remain inadequately understood. This study investigates the interaction between faba bean plants and the soil microbiome to elucidate the mechanisms underlying plant Fusarium wilt. Through comprehensive analysis of 16S ribosomal RNA gene and internal transcribed spacer (ITS) sequencing data obtained from the faba bean rhizosphere soil, this research successfully identified key microbial groups that are enriched in the disease-suppressing rhizosphere, namely Bacillus , Pseudomonas , and Trichoderma . The strains displayed significant inhibitory effects on Fusarium oxysporum , notably. A synthetic community was constructed using these strains, which exhibited a remarkable capacity to suppress Fusarium wilt in faba bean seedlings, achieving an impressive inhibition rate of up to 71.76%. Non-targeted metabolomics analysis was employed to uncover the metabolic pathways through which this Synthetic community aids plants in resisting pathogens. Additionally, metagenomic analysis revealed an increased abundance of Antibiotic Resistance Genes (ARGs) in the rhizosphere soil of diseased plants, while the soil associated with healthy plants exhibited enhanced activity in nitrogen fixation, nucleotide metabolism, and carbohydrate metabolism pathways. Soil metabolites and root exudates were analyzed, and a Random Forest model was employed to investigate the impact of exogenous metabolites on Fusarium wilt occurrence. Significantly, compounds such as 10 µM Betaine, Proline, and Racemosin demonstrated remarkable efficacy in reducing the incidence of Fusarium wilt. Furthermore, transcriptomic and non-targeted metabolomics analyses were conducted in this study, revealing substantial enrichment in pathways including jasmonic acid metabolism, alanine metabolism, aspartate metabolism, glutamate metabolism, and unsaturated fatty acid biosynthesis in diseased V. faba . This study not only advances our understanding of plant Fusarium wilt and their impact mechanisms but also provides valuable insights for enhancing soil health and crop disease resistance. Fusarium wilt Rhizosphere Microbiota Vicia faba Root Exudates Multi-Omics Analysis Soil-Borne Disease Suppression Synthetic communities Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction In today’s era of intensive global agriculture, the heavy reliance on fertilizers and herbicides highlights the critical challenge of preserving soil health to maintain sustainable agricultural productivity. Microorganisms associated with plants play a crucial role in plant health. They assist plants in defending against the invasion of pathogenic microbes, effectively reducing the incidence of soil-borne diseases [ 1 ]. Particularly in plant roots, a critical site for the interaction between plants, the microbial communities in the rhizosphere and phyllosphere play a vital role in plant health, not only by directly fighting pathogens and boosting the plant’s immune system but also by embodying a critical element often overlooked in the plant disease triangle model: the significant impact of indigenous microbial communities on plant immunity[ 2 ], despite the fact that some members may become potential pathogens, altering their impact on plant health[ 3 ].Specific rhizosphere microorganisms can directly inhibit soil-borne plant pathogens, serving as the first line of defense against them [ 4 ]. With the advancement of microbial amplicon sequencing and metagenomic sequencing technologies, significant progress has been made in understanding soil core microbiota. For example, the antagonistic effects of soil core microbial communities on specific pathogens have been shown to suppress disease [ 5 ]. Researchers have found significant differences between the root microbiome communities of healthy and diseased tomatoes. Compared to diseased tomatoes, healthy ones exhibit noticeably higher microbial diversity, density, and symbiotic network modularity [ 1 ]. Root exudates influence the microbial communities in the rhizosphere, affecting plant growth and defense through plant-soil feedback effects. In terms of microbial suppression of pathogens, certain metabolites in root exudates have evident antimicrobial activity. For instance, some phenols and aldehyde compounds can directly inhibit the growth of pathogens [ 6 ]. Additionally, some metabolic products in root exudates can indirectly achieve pathogen suppression by altering the rhizosphere microbial community. For example, certain bacteria and fungi with antimicrobial activity can be attracted by specific plant root exudates, recruiting them to inhibit the growth of pathogens or even develop into disease-suppressive soil. Such root exudates are referred to as plant prebiotics. However, some root exudates can attract pathogenic microorganisms, exacerbating pathogen invasion and disease severity. For example, phenolic substances, organic acids, and amino acids in these exudates can promote the growth and infection of plant pathogens, worsening plant diseases. This is particularly concerning for crops like V. faba , a significant dual-purpose crop valued for both human consumption and animal feed, as it could be more susceptible to such disease exacerbation. It is extensively cultivated around the globe, offering high nutritional and economic value [ 7 ]. To combat Fusarium wilt in V. faba , current strategies involve implementing modified cultivation practices and employing pesticides extensively. However, prolonged pesticide usage can lead to heightened pathogen resistance and result in adverse environmental consequences.[ 8 ]. In contrast, biological control agents emerge as a sustainable and effective alternative for disease mitigation. Researchers reported encouraging outcomes in controlling V. faba Fusarium wilt by employing the antimicrobial peptide P852, a byproduct of Bacillus velezensis [ 9 ]. When examined within the framework of the classic disease triangle model, observations of diseased plants reveal inconsistencies in disease suppression, prompting us to propose that plant rhizosphere microorganisms and root exudates play a pivotal role in enhancing plant defenses against external pathogens. To comprehensively investigate this hypothesis, our research focused on analyzing sequencing data obtained from the rhizospheres of both healthy and wilt-afflicted V. faba plants. We isolated and cultivated rhizosphere bacteria and fungi from seedlings of both healthy and diseased V. faba plants, complementing our study with antagonism experiments to elucidate their interactions. Through advanced metagenomic and microbial diversity analysis, we thoroughly investigated the identified synthetic microbial communities. Our goal was to determine the efficacy of these communities in counteracting F. oxysporum f. sp. fabae (FOF) in sterile V. faba seedlings. Additionally, we compared the microbial communities and functions in the rhizospheres of healthy versus infected plants. Beyond this, our research extended to non-targeted metabolomics analysis of V. faba roots and surrounding soil, enhancing our insight into the disparities in organic compounds between healthy and infected plants. Amidst the examination of FOF-induced plant stress, we performed paraffin pathological section analysis on both healthy and diseased V. faba seedlings, assessed antioxidant enzyme activity and undertook comprehensive transcriptome and non-targeted metabolomic analysis. Materials and Methods Assessment of Fusarium Wilt Incidence in Faba beans We conducted a comprehensive survey to determine the prevalence of Fusarium wilt in faba beans. Ten plants were meticulously examined for each treatment group within controlled climate chambers. Simultaneously, three random plots (1 m×1 m) were selected for each identical treatment group under field conditions, and 12 plants per plot were sampled using the five-point sampling method.[ 10 ]. The Fusarium wilt disease of faba beans is characterized based on the previous description by Zhang et al. [ 9 ]. Isolation and Identification of Microorganisms from Faba Bean Rhizosphere Soil In this study conducted in the Beibei and Rongchang districts of Chongqing City, rhizosphere soil samples from faba beans were collected for the purpose of isolating and identifying microorganisms. The collected samples were stored at 4°C. During processing, the samples were initially suspended in PBS buffer (0.1 M phosphate buffer, pH 7.0). Subsequently, the soil suspension was diluted using the dilution plating method, and various culture media were employed to isolate bacteria and fungi. Nutrient-rich Luria-Bertani (LB) medium and Tryptic Soy Agar (TSA) medium were utilized for bacterial isolation, while potato dextrose agar (PDA) medium was employed for fungal cultivation[ 11 ]. Total DNA extraction and amplicon, sequencing of 16S rRNA and ITS amplicons, metagenomic sequencing Sampling was conducted from the rhizosphere of V. faba , including both healthy and Fusarium wilt disease-infected samples. The soil samples were rapidly collected using sterile soil collectors and immediately stored at -80°C for preservation. Subsequently, genomic DNA was extracted from the soil samples using the E.Z.N.A. Soil DNA Kit (Omega Bio-tek, Inc., USA) following the manufacturer's instructions. The quality and concentration of DNA were assessed using the Nanodrop 2000 (Thermo Fisher Scientific, Inc., USA). The extracted DNA samples were stored at -20°C for future experiments. Each isolate was identified using a high-throughput bacterial identification barcoded PCR approach. Specifically, the V3-V4 region of the 16S rRNA gene and the internal transcribed spacer (ITS) region and for bacteria and fungi. Sequencing was performed on the Illumina NovaSeq 6000 platform using a 2×250 bp cycle protocol at Beijing Allwegene Technology Co., Ltd., Beijing, China. Following the sequencing, image analysis, base calling, and error estimation were executed using the Illumina Analysis Software Version 3.0 (Illumina, Inc., USA). Post-sequencing, the two-step barcoded bacterial taxa's raw data were quality-filtered and demultiplexed based on well and plate barcodes, please refer to the supplementary. We conducted a comprehensive metagenomic sequencing study at BMK Biotechnology Co., Ltd., located in Beijing, China. The focus was the rhizosphere soil of V. faba , from which samples were collected for detailed metagenomic sequencing. The sequencing procedure was executed on an Illumina HiSeq 2500 instrumen, for the methods used in metagenomics, refer to the SM file table1. Antagonistic tests against FOF We isolated a pathogenic strain, termed FOF, from diseased samples of faba bean roots at a farming site in the Beibei District, Chongqing in China, affiliated with Southwest University. An exhaustive evaluation was conducted on various isolated fungus and bacterium to assess their antagonistic potential against FOF, aiding in the identification of potent strains. The evaluative criteria encompassed observing the radius of FOF colony and the size of inhibitory zone it formed upon interaction with the test strains, wherein a diminished in colony radius indicated enhanced in FOF resistance. A comprehensive antagonistic test against FOF included 140 bacterium and 36 fungus, with each strain's antagonistic capacity against FOF assessed across three separate rounds. During the fungal strain selection, a 4 mm FOF agar was centrally positioned on a PDA plate, with the test strains inoculated at four equidistant points, each 3 cm from the center. A PDA plate devoid of test fungi was employed as our control (CK treatment). The test fungi’s effectiveness was gauged based on the degree of FOF colonization across the entire PDA plate. For the bacterial strains, each strain was aligned along the central axis of LB plate, with a 4 mm FOF agar strategically situated between the strain and the plate's periphery. LB plate infused only with sterile water along the centerline served as a control. Post a 7-day incubation period, the strains’ efficacy was appraised. The inhibition efficiency percentage = 100% *(FOF radius in control - FOF radius in test strain) /FOF radius in control [ 8 ]. Constitution of different SynComs of faba bean plants Based on the method referenced by Zhou et al. [ 1 ], faba bean sprouts are grown in an artificial climate box in the Grassland microbiome at Southwest University in China, with a seedling age of 17 days. In a sterile ultra-clean workbench, the sprouts are inoculated twice with a microbial inoculum. Seven days later, they are inoculated with a 10 7 spore suspension of F. oxysporum f. sp. (FOF) to observe if they contract Fusarium wilt and to calculate the incidence rate of the disease. In this experiment, bacteria are cultured on LB medium using a shaking flask fermentation method. The entire cultivation process lasts for three days at a controlled temperature of 28°C. Each bacterial fermentation broth is centrifuged at 4,000×g for 5 minutes, then resuspended in phosphate-buffered saline (PBS). The optical density (OD 600 ) is adjusted to 0.02, which corresponds to approximately 10 7 cells/ml. On the other hand, fungi are cultured in PDB medium using the shaking flask fermentation method under the conditions of fermenting for 6 days at 25°C ,180 rpm, and finally diluted to 10 6 conidia /mL[ 2 ]. The experiment designed four different treatment groups, namely BacFOF, FunFOF, CrossKFOF, and FOF. SynComs are composed of both bacteria and fungi, with the volume ratio of the microbial mixture adjusted to 4:1 (bacteria to fungi). After adjusting the OD600 of the bacterial SynComs and the concentration of fungal conidia, these mixtures are poured into the soil. Eventually, the OD 600 of SynComs is adjusted to 10 7 cells /g soil. Seeds are soaked in 60°C warm water and then immersed in 75% ethanol for 60 seconds. The soil for planting faba beans is sterilized at 121.3°C and 103.4 kPa for 30 minutes. The artificial climate box is then sprayed and wiped with 75% alcohol and 0.2% sodium hypochlorite, followed by disinfection with ultraviolet ozone lamps for one hour. Sterile tomato seedlings are transferred into the artificial climate box, with a 13/11 h day/night cycle, daylight illumination of 12,000 LX at 26 ℃, and a nighttime temperature of 22 ℃ [ 12 ]. Transcriptome sequencing analysis Roots from V. faba plants, both healthy and those infected with Fusarium wilt, were thoroughly rinsed thrice with distilled water before freezing and thawing three times in liquid nitrogen at -196°C. Total RNA was extracted from these samples using the Trizol method and subsequently treated with RNase-free DNase I to eliminate potential DNA contamination[ 13 ]. We conducted transcriptome sequencing at Beijing Overson Gene Technology Co., Ltd. The integrity and purity of the RNA were validated by analyzing the samples on 1% agarose gels, while the Agilent 2100 Bioanalyzer and the NanoDrop spectrophotometer facilitated RNA quantification and quality assessment, respectively. Sequencing libraries were crafted from 1.5 µg of RNA per sample, utilizing the NEBNext® Ultra™ RNA Library Prep Kit for Illumina®, in adherence to the manufacturer's instructions. Unique index codes were incorporated to associate sequences with their respective samples. Isolation of mRNA from total RNA was accomplished using poly-T oligo-attached magnetic beads, and fragmentation was carried out with divalent cations at elevated temperatures. This was followed by the synthesis of first and second strand cDNA[ 14 ].The overhangs were modified using exonuclease/polymerase activities to generate blunt ends, and the NEBNext Adaptor was ligated post adenylation of the 3' ends. The AMPure XP system enabled selective purification of cDNA fragments of 200–250 bp. After treatment with USER Enzyme and PCR performance, the resultant PCR products were purified, and library quality was evaluated. These prepared libraries were sequenced on the Illumina Novaseq 6000 platform, generating 150 bp paired-end reads. Initial data processing involved quality control of raw reads, adapter sequence removal, and conversion into clean reads. These clean reads were then mapped to the reference genome, selecting only perfectly matching or single mismatch reads for further analysis. SNP calling was executed using GATK2 software after sorting, deduplicating reads, and merging the BAM alignment results. Gene expression levels were quantified by HTSeq, counting the number of reads mapped to each gene. Differential expression analysis was performed using the DESeq R package, with biological replicate samples. A fold change above 2 in gene expression quantity and a P-value (or FDR) less than 0.05 were set as the threshold for differentially expressed genes (DEGs). Functional information and Pfam function of Unigene were annotated using Diamond and HMMER software, respectively. Pathway analysis was used to determine the distribution of DEGs in the KEGG database. Each procedure was conducted in triplicate[ 15 ], The methods for RT-PCR were referred to the Supplementary file, and the primer designs were referred to SM table 2. Collection and extraction of root exudates Retrieve diseased faba bean plants and healthy faba bean plants as control groups (n = 6 in each group). After carefully excavating the intact root systems, immerse them in sterile ultra-pure water for 48 hours. Subsequently, the preliminary treated plants were removed, and the root systems were thoroughly washed with deionized water and wrapped in moist filter paper[ 16 ]. The wrapped plant root systems were placed in sterilized beakers that had been rinsed with deionized water, sealed with parafilm, and kept away from light [ 17 ]. After 24 hours, we collected the root systems covered with filter paper and temporarily stored them in an icebox. Next, the root systems were subjected to 30 min of shaking extraction using deionized water and ultrasound treatment. After extraction, the extract was transferred to 2 mL centrifuge tubes. Then, the supernatant was transferred to a new 2 mL centrifuge tube and subjected to freeze-drying in a freeze dryer for 48 h until it turned into a dry powder. The extract was then saved in a -80°C freezer after filtration through a 0.45 µm filter, and methanol (3:1 methanol: H 2 O, v/v) was added. The samples are awaiting non-targeted metabolomics analysis [ 18 ]. Metabolite Extraction and Metabolite Profiling Diseased Vicia faba affected by Fusarium wilt disease and healthy Vicia faba from the control group, the roots and soil at the root were collected from the artificial climate chamber at the grassland microbiology laboratory (n = 6). Metabolites were extracted using a slightly modified version of the previously described method[ 9 ]. In brief, fresh plant samples underwent 24 hours of vacuum freeze-drying using a freeze dryer, followed by processing with a 50 Hz grinder to obtain powder. Subsequently, 50 mg of the powder sample, mixed with 0.6 mL of methanol (3:1 methanol: H 2 O, v/v), was stored overnight in a 2 mL EP tube in a refrigerator at 4°C. After 5 min of sonication and centrifugation at 12,000×g for 10 minutes, the supernatant was filtered using a 0.22 µm Biosharp filter membrane. To ensure method stability and data reliability, each sample was mixed with 10 µL of a quality control (QC) sample, QC sample after every five samples for monitoring purposes[ 19 ]. For LC-MS/MS analysis, an ultra-high-performance liquid chromatography (UHPLC) system (UHPLC UltiMate® 3000, Dionex, Sunnyvale, CA, USA) equipped with a UPLC Hypersil GOLD C18 column (2.1 × 100 mm, 1.9 µm particle size) (Thermo Fisher Scientific, Waltham, MA, USA) was used in conjunction with a Q-Exactive Orbitrap mass spectrometer (Thermo Fisher Scientific). The column temperature was set at 35°C, and the flow rate was maintained at 0.2 mL/min. A 2 µL injection volume was used. The mobile phase consisted of solvent A (0.1% formic acid in ultrapure water) and solvent B (0.1% formic acid in methanol) for the positive ion mode, while solvent C (0.1% NH 3 in ultrapure water) and solvent D (0.1% NH 3 in methanol) were used for the negative ion mode. The gradient program was as follows: 0–10 minutes, 5% B and 95% A; 10–12 minutes, 5% A and 95% B; 12–13 minutes, 5% A and 95% B; 13.1–14 minutes, 95% A and 5% B. For the negative ion mode, the gradient program was as follows: 0-2.5 minutes, 95% C and 5% D; 2.5–16.5 minutes, 95% D and 5% C; 16.5–19 minutes, 95% D and 5% C; and 20 minutes, 95% C and 5% D[ 20 ].The Q-Exactive Orbitrap was operated in information-dependent acquisition (IDA) mode using Xcalibur data acquisition software (Thermo Fisher Scientific, Waltham, MA, USA). The HESI source parameters were set as follows: sheath gas and auxiliary gas flow rates at 40 and 10 Arb, respectively; capillary temperature at 320°C; full mass scan range at m/z 70-1050 with a resolution of 70,000. The MS/MS scan mode was set to data-dependent MS 2 (dd-MS 2 ) scan with a resolution of 35,000. Collision energy in the NCE mode was set at 20/40/60 eV. The spray voltage was set to 3.5 kV for the positive ion mode and − 2.5 kV for the negative ion mode[ 21 ]. In order to identify the metabolites that may drive the assembly process of the rhizosphere microbial community associated with diseased and healthy root systems, we used machine learning to differentiate rhizospheric metabolites associated with diseased and healthy root soil. Based on the previous literature[ 22 , 23 ], we constructed a random forest model using the R package 'RandomForest', which consisted of 500 decision trees. The mtry parameter was set to one-third of the number of input features, with a default value of 75. Using our model, we were able to distinguish the root exudates of V. faba plants affected by Fusarium wilt from those of healthy specimens. The model helped us identify key characteristics, particularly specific metabolites, based on their feature importance rankings. For assessing the model's predictive accuracy, we conducted five-fold cross-validation with 100 iterations, utilizing the 'rfcv' function of the 'RandomForest' package, the input data comprised a table detailing the relative abundances of metabolites in the root exudates. Lastly, we employed the 'ggplot2' package for graphical representation, creating bar plots that clearly illustrated the metabolic differences between the root exudates of Fusarium wilt-affected and healthy V. faba plants [ 14 ]. Impact of Small Metabolites Selected Through Metabolomics on the Incidence of Fusarium wilt Disease According to the results of the random forest and referring to relevant literature[ 24 ], small molecule metabolites related to the induction and prevention of wilt disease were selected. These include Abscisic Acid, 2-Hydroxycinnamic Acid, Eicosatetraynoic Acid, Phthalic Acid, Betaine, Proline, DL-Homoserine, Oleanolic Acid, and Racemosin. At the unfolding of the fourth and fifth true leaves of faba bean seedlings, exogenous small molecules (purity ≥ 99%) and a control group (sterile water) were applied in six separate groups, each at a concentration of 10 µM. One week later, a spore suspension of FOF, prepared at a concentration of 1×10 5–6 CFU/mL by filtration through two layers of gauze, was used to inoculate the plants using a basal cut wound method to induce Fusarium wilt infection in faba beans. For each type of soil, sterile deionized water was used to maintain soil moisture at 70% water holding capacity. After nine weeks, the incidence of wilt disease in each treatment was investigated (this experiment was repeated twice). Measurement of Antioxidant Enzyme Activities 24 h after the faba bean showed symptoms of Fusarium wilt disease, faba bean leaves and roots in climate chambers were collected. The antioxidant enzyme activities of POD (Peroxidase, U/g∙min), superoxide dismutase SOD(Superoxide Dismutase,U/g), CAT(Catalase, mg/g∙min) were assayed, using the spectrophotometer as previously described, with some modifications (Wellburn, 1994, Guo et al., 2020). Chitinase activity was determined using the chitinase Assay kit (Solarbio Science & Technology Co., Ltd., Beijing, China), following the instructions from the manufacturer. The amount of enzyme that decomposes chitin to produce 1 µmol N-acetyl‐D‐(+)‐glucosamin per gram of tissue per hour is one enzyme activity (U/g) unit at 37°C. Statistical Analysis In our statistical analysis, we utilized IBM SPSS Statistics 26.0 (SPSS, Chicago, IL, USA) for conducting one-way ANOVA and Duncan's t-test to identify significant differences ( p < 0.05 ). GraphPad Prism software version 8.0 (GraphPad Software Inc., San Diego, CA, USA) was also employed for additional data analysis. For untargeted metabolomic data, we used Compound Discover 2.1 (Thermo Fisher Scientific, Waltham, MA, USA) to perform operations such as peak detection, extraction, and normalization. This process involved using the Mz Cloud and mzVault databases for metabolite matching, resulting in a dataset comprising mass-to-charge ratios, retention times, and peak areas. We then merged the positive and negative datasets and analyzed them using SIMCA-P 14.1 software (Umetrics, Umea, Sweden) for multivariate statistical analysis, including PCA and OPLS-DA. We identified key differentiating metabolites based on criteria such as VIP ≥ 1, p 1.5. MetaboAnalyst 5.0 was instrumental in analyzing these differentially metabolized chemicals and pinpointing critical metabolic pathways, in conjunction with the KEGG database for pathway annotation. The data analysis in this study was conducted using the R programming language (R Core Team, 2022). For data processing and wrangling, the 'tidyverse' package played a crucial role, offering efficient and user-friendly tools for manipulating and analyzing our data sets. Additionally, the 'ggplot2' package was employed to generate sophisticated, publication-quality graphics, enhancing the visual representation of our analytical results. The analysis results of Fig. 4 a,b plots were generated using the CNSknowall ( http://cnsknowall.com/index.html#/HomePage ). Results Insights into the Rhizosphere Microbial Communities in Healthy and Fusarium Wilt-Affected Faba Beans To investigate the correlation between microbial diversity in the rhizospheres of healthy faba beans and those affected by Fusarium wilt, our study encompassed sample collection and symptom analysis conducted in the Beibei and Rongchang districts of Chongqing, China. Additionally, we meticulously observed and identified the plate morphology of F. oxysporum f. sp. fabae (FOF) to further our understanding of its impact (Fig.2 a). In controlled environment settings, from the rhizosphere soil samples of wilt-affected and healthy faba beans, we obtained an average of 40,781 16S rRNA gene reads and 14,0978 ITS1 reads. Using the UNOISE3 algorithm, we identified 3,032 bacterial zero-radius OTUs (OTUs, operational taxonomic units) and 1,833 fungal zOTUs, Rarefaction curves showed that the sequencing coverage in our study adequately represented the bacterial and fungal diversity. A Principal Coordinate Analysis (PCoA) was performed using Bray-Curtis distance, revealing a distinct separation between microbial samples obtained from the rhizosphere of healthy plants (CK Group) and those affected by wilt (F Group) (Fig. 2b). The bacterial and fungal Shannon and Richness values in the F Group were significantly lower than those in the CK Group ( p 0.05). To understand the potential functional differences of microbial communities in healthy and diseased rhizosphere soils, we compared the metagenomes of the F and CK groups from field samples (Fig.2 d). Metagenomic sequencing produced an average of 62,304,728 clean reads for each sample, with the data size for each sample approximately 4.5GB. The PCoA, based on Bray-Curtis distance, indicated a significant difference in microbial profiles between CK and F groups. For the 16S bacterial sequencing, a total of 2,419 bacterial species were shared between the CK and F groups. The CK group exhibited 369 unique species, whereas the F group displayed 221 distinct bacterial strains. In terms of ITS fungal sequencing, both groups demonstrated a commonality of 542 fungi (Fig. 2e); however, the CK group presented with an additional set of 566 unique fungi compared to the F group's count of 625 exclusive fungi. Notably higher relative concentrations of Bacillus and Pseudomonas spp. were observed in the CK group as depicted in Fig. 2f; conversely, there was a significantly elevated relative concentration of FOF in the F group. To discern the differences in dominant microbial strains between the rhizosphere soil of healthy and Fusarium wilt-infected faba beans, we utilized Linear Discriminant Analysis Effect Size (LEfSe) to analyze the contributions of different bacterial species (LDA SCORE > 3.5). The results indicated that at the bacterial level, the number of bacterial biomarkers in the rhizosphere of the healthy group (CK group) was significantly higher than in the Fusarium wilt-infected group (F group) (Fig3. e, f). Within the bacterial taxa, the CK group contained 8 biomarkers, including Actinobacteriota , Micrococcales , Arthrobacter , Verrucomicrobiota , and Pedosphaerales . In contrast, the F group rhizosphere soil contained 20 biomarkers, such as Stenotrophomonas and Flavobacterium . At the fungal level, the number of fungal biomarkers was significantly higher in the CK group compared to the F group. The CK group contained 11 fungi, including Humicolai grisea , Humicola , and Mortierellaceae . In contrast, the F group contained fungi from the Pezizales , Pezizomycetes , Cantharellales , and Ceratobasidiaceae . To understand how the microbial composition and abundance change in the rhizosphere soil of the F and CK groups, we characterized the top 20 or so bacteria and fungi by their family and genus in different treatment groups. In the 16S bacterial stacked chart, the proportion of bacteria in the Phylum Bacteroidota significantly increased in the diseased soil rhizosphere of the F group (compared to CK group), whereas the proportions of bacteria in the Phyla Acidobacteriota , Actinobacteriota , Patescibacteria , Gemmatimonadota , Verrucomicrobiota , Myxococcota , and Bdellovibrionota significantly decreased. At the Genus level (fig3.b), in the F group's diseased soil rhizosphere, the proportions of bacteria in the genera Pseudomonas , Arthrobactor , and Acidovorax significantly decreased, while those in Sphingobacterium , Sphingomonas , Stenotrophomonas , Pedobacter , and Klebsiella significantly increased (fig3.a). In the ITS fungal stacked chart, compared to the CK group, the proportions of fungi in the Phyla Mortierellomycota and Ascomycota significantly decreased in the F group's diseased soil rhizosphere, whereas the proportion of fungi in the Phylum Basidiomycota significantly increased (fig3.c). At the Genus level, the proportions of fungi in the genera Fusarium , Botrytis , Itersonilia , Vishniacozyma , and Cladorrhinum significantly increased in the F group, while those in Humicola , Arthrobotrys , and Sarocladium decreased (fig3.d). Integrated Analysis of Rhizosphere Microbial Efficacy on Fusarium Wilt Suppression and Metabolic Response in Faba Beans To investigate the presence of beneficial bacteria and fungi in the rhizosphere microbial community for biocontrol of Fusarium wilt disease in faba beans ( V. faba ), we independently collected rhizosphere samples from Rongchang and Beibei districts in Chongqing. From the rhizosphere soil, we isolated 2591 bacterial strains and 275 fungal strains. Through extensive plate confrontation culture experiments for antibacterial testing against F. oxysporum f. sp. fabae (FOF), we identified 15 core bacterial and fungal strains that exhibited varying degrees of inhibitory activity against the growth of the FOF strain. Among the bacteria, one strain of Bacillus subtilis SWU CO-6 (gene bank number OR775339) showed an inhibition rate of 65.62%, and a strain of B. velezensis SWU CO-1 (OR807560) reached an inhibition rate of 49.5%, with B. velezensis being isolated at a rate of 94.7% in the control group. B. vallismortis SWU CK7-A (OR924277) achieved an inhibition rate of 51.92%, and P. vranovensis SWU F6-B (OR807566) had an inhibition rate of 36.45%. In the fungal category, Aspergillus welwitschiae SWU CK2-A (OR821800) reached an inhibition rate of 61.1%, Taronnyces purpureogenus SWU G4 (OR921615) had an inhibition rate of 53.3%, and Trichoderma asperellum SWU CK2-F (OR821799) showed an inhibition rate of 58.89%. When the fermentation broths of the 15 bacterial strains were mixed in equal volume and concentration and tested (Similarly, when the fermentation broths of the 15 fungal strains were mixed and tested against FOF, the inhibition rate was 71.16%, and the mixed fungal preparation (Mix-In vivo ) reduced the incidence rate of Fusarium wilt in faba beans by 73% (Fig4 a,b). A synthetic microbial community, formulated by mixing the bacterial and fungal fermentation broths in a 4:1 volume ratio, showed an inhibition rate of 71.76% against FOF (Mix-In vitro) and reduced the incidence rate of Fusarium wilt in faba beans by 88.57% (Mix-In vivo ). To better understand the principle of the biocontrol effect of microbial preparations (Syn community) on Fusarium wilt disease in faba beans, we collected leaf and root samples of faba beans treated with BacFOF, FunFOF, CrossKFOF, and FOF from artificial climate boxes and conducted non-targeted metabolome analysis using ultra-high performance liquid chromatography-quadrupole-orbitrap mass spectrometry (UHPLC-Q-Orbitrap-MS). A total of 951 peaks were detected in the leaves, among which 453 named metabolites were identified. In the roots, a total of 2612 peaks were detected, with identification of 1157 named metabolites. The results from metabolomic analysis revealed a significant upregulation (−log( p ) value = 4.5846) in the flavonoid biosynthesis pathway in the leaves, involving key metabolites such as dihydrokaempferol, naringenin, liquiritigenin, taxifolin, isoliquiritigenin and phloretin (Fig4 e,f), totaling six types. Additionally, notable alterations were observed in the biosynthetic pathways of phenylalanine, tyrosine, and tryptophan, highlighting the potential role of these metabolic pathways in the plant's disease resistance response. The significant upregulation of the jasmonic acid pathway and its metabolites in the leaves underscores the central role of plant hormones in regulating the plant's response to environmental stresses, emphasizing the importance of the jasmonic acid signaling pathway in innate immune responses. Although a milder trend of changes was observed in root samples during metabolic analysis compared to leaves, key metabolites involved in flavonoid biosynthesis and phenylalanine metabolism pathways were also detected, indicating that root metabolic adjustments similarly responded to treatment with a mixed microbial community. Plant Rhizosphere Metagenomic Profiling The stacked bar chart indicates that plants affected by Fusarium wilt exhibit a higher proportion of 'Environment Information Processing' pathways within the 'Functional KEGG Level 1 Pathways', compared to the F group. Conversely, the CK group displays a greater relative abundance in areas such as Cellular Processing, Genetic Information Processing, and Metabolism, particularly when contrasted with the F group (Fig5.a). In terms of "Functional KEGG level 2 pathways", there are some differences in microbial community functions between the CK and F groups. The relative abundance of the F group is significantly higher than that of the CK group, particularly in amino acid metabolism. This disparity may be attributed to the decomposition of plant tissues and subsequent release of amino acids caused by Fusarium wilt infection. Moreover, notable distinctions between these two groups are observed in functions such as energy metabolism and membrane transport. The Venn diagram shows (Fig5.d) that the CK and F groups have 6250 common metabolic pathways. The CK group has 387 unique pathways, while the F group has 602. As seen in Fig5.e, several highly expressed genes in the F group are associated with the branched-chain amino acid transport system. Specifically, these include ATP-binding protein (KO1995), permease (KO1997 and KO1998), and substrate-binding protein (KO1999) of the branched-chain amino acid transport system. Additionally, an increase in gene expression related to iron complex outer membrane receptor protein (KO2014) was observed. In the CK group, highly expressed genes span a variety of functions. These include genes related to metabolic processes like acetyl-CoA C-acetyltransferase (KO0626), dihydrolipoyllysine-residue acetyltransferase component of pyruvate dehydrogenase complex (KO1652), and 5-methyltetrahydrofolate-homocysteine methyltransferase (KO0548). Functions and synthesis processes for RNA and DNA are also highlighted, including RNA polymerase primary σ-factor (KO3086), DNA gyrase subunit A (KO2469), and DNA-directed RNA polymerase subunits β (KO3043 and KO3046). Additionally, there are genes in the CK group related to protein synthesis, degradation, and repair functions, such as molecular chaperone DnaK (KO4043), cytochrome c oxidase subunit I (KO2274), ATP-dependent Lon protease (KO1338), molecular chaperone GroEL (KO4077), elongation factors G (KO2355), and Tu (KO2358). Furthermore, genes related to energy production and transfer, like F-type H+-transporting ATPase subunit α (KO2132), were noted. By employing metagenomic sequencing techniques, we investigated the impact of various treatments on the abundance of microbial functional genes associated with nitrogen cycling in the rhizosphere soil of faba beans (Fig5.f). Genes including napA , napB , nirK , norC , nirS , and nirK are responsible for encoding denitrifying enzymes, cytochrome c-type proteins, and nitrite reductase. In the F group, the increased presence of these genes indicates a higher rate of denitrification, which could result in greater nitrogen loss from the soil. Fusarium wilt's effect on nitrogen cycling is complex, involving numerous factors. Notably, alterations in the rhizosphere environment triggered by the pathogen can lead to varied gene expression, thereby modifying how nitrogen is transformed in the soil. In contrast, in the CK treatment group, another set of enriched genes related to denitrification and nitrogen assimilation pathways were found, such as nrfA , gltD , glnA , and GLUL. These genes encode nitrite reductase, the small chain of glutamate synthase, glutamine synthetase, and glutamate synthase. The presence of these enriched genes indicates both denitrification and nitrogen assimilation processes in the CK group, possibly helping maintain a balanced nitrogen cycle in the soil. The top 20 ARO genes with the highest disease prevalence include adeF , novA , abcA , MuxC , MexD, baeR , OprN , which have antibiotic efflux effects; IND-6 , BEL-1 , FomB which inactivate antibiotics; carA , oleB for antibiotic target protection, and rpoB2 , chrB , Listeria monocytogenes mprF , B. subtilis mprF , etc., for antibiotic target replacement or alteration. Genes adeF , carA , and IND-6 have a higher proportion in the F group, while OXA-192 and novA are higher in the CK group (Fig5.g). Untargeted Metabolomic Analysis of Root System and Exudates of Faba Bean Affected by Fusarium Wilt In order to gain a comprehensive understanding of the Fusarium wilt disease mechanism in Vicia faba , we collected samples of FOF-infected V. faba roots at 3-4 dpi (days post-infection) from controlled artificial climate boxes and conducted non-targeted metabolome analysis using UHPLC-Q-Orbitrap-MS. A total of 468 peaks were detected in the control group, out of which 157 named metabolites were identified. To quantify their relative abundance, we calculated the ratio between the sum of all peak areas and each category's peak area[17]. Principal component analysis (PCA) was performed to detect the variation between and within groups[26]. The results indicated that the first two principal components (PCs) could distinctly separate the two group. PC1 accounted for 38.8% of the total variation, while PC2 accounted for 27.4%, together representing 66.2% of the total variation (Fig 6. a). Moreover, the volcano plot displays the changes in metabolites in the roots of Fusarium wilt-infected V. faba . Red dots represent upregulated metabolites with a statistical significance of p <0.05 and a fold change greater than 1.5. Green dots represent downregulated metabolites with a statistical significance of p <0.05 and a fold change less than <-1.5. In total, we detected 157 compounds. Among them, there were 5 upregulated compounds and 12 downregulated compounds. PLS-DA (Projection to Latent Structures-Discriminant Analysis) is a powerful multivariate statistical analysis method with supervised pattern recognition, aimed at effectively eliminating irrelevant effects in the study and thereby filtering out differential metabolites[27]. PLS-DA score plots were generated using SIMCA software, and they included the groups F and CK. The permutation test was employed to thoroughly evaluate and validate the quality of the PLS-DA model. An R2 value close to 1 and a Q2 value greater than 0.5 indicated that the PLS-DA model was stable and exhibited excellent fitness and predictive capability.[28]. Furthermore, the permutation plots clearly demonstrated that this model exhibited significant discriminatory differences between the F vs. CK groups. After conducting 200 permutations, all Q2 intercept values in the permutation plots were below zero (SM file 1 Figure S3), confirming the model's exceptional fit[29]. As a result, the PLS-DA models proved to be highly effective in identifying differences between the groups (SM1 file1 Figure S3). In our study, we discovered 46 metabolites with a VIP≥1 in Fusarium wilt-infected Vicia faba (F group) Compared to the control group (as shown in Fig c). Among these metabolites, there were 11 belonging to Lipids and lipid-like molecules, 9 belonging to Organic acids and derivatives, 17 belonging to Organic oxygen compounds, 4 belonging to Organoheterocyclic compounds, and 5 belonging to Organonitrogen compounds. Based on VIP values ≥1, p1.5 or FC values <1.5, we employed these criteria as the threshold to screen differential metabolites. Subsequently, we constructed pathway diagrams (Fig 6.d) and pathway enrichment bubble plots (Fig 6. e) based on the KEGG database. In Figure d, the prominently upregulated pathways encompassed Citrate cycle (TCA cycle), Alanine, aspartate, and glutamate metabolism, Glyoxylate and dicarboxylate metabolism, Pyruvate metabolism, Starch and sucrose metabolism, Pentose phosphate pathway, and Fatty acid biosynthesis. In Fig 6.e, the pathway enrichment outcomes were consistent with those depicted in Fig 6.d, specifically including Alanine, aspartate, and glutamate metabolism, Glycine, serine, and threonine metabolism, Biosynthesis of unsaturated fatty acids, Starch and sucrose metabolism, and Citrate cycle (TCA cycle). To better understand the mechanism of Fusarium wilt in faba bean, we collected root exudate samples from faba beans infected with FOF in controlled climate chambers 3-4 days post-infection (dpi). These samples underwent untargeted metabolomics analysis using UHPLC-Q-Orbitrap-MS. A total of 257 different annotated metabolites were identified in the faba bean root exudates (Fig 7. a). Principal component analysis (PCA) indicated a clear distinction between the F and CK treatments, with PC1 explaining 52.2% of the total variance and PC2 accounting for 21.3%, collectively making up 73.5% of the total variation. Using random forests as classifiers, we differentiated the root exudates of diseased and healthy soils. We developed a model to identify key biomarkers. Through rigorous tenfold cross-validation, we found that 30 metabolites consistently showed the lowest error rates, these are now recognized as crucial Differentially Expressed Metabolites (DEMs) (Fig 7.b). These included 9 organic acids, 6 amino acids and derivatives, 3 carbohydrates, 1 lipid, and others. Metabolomic data showed significant expression ( p >0.05) of compounds like Betaine, DL-Homoserine, Phthalic acid, 2-Hydroxycinnamic acid, and Racemosin in the CK group, while in the F group, compounds like Oleanolic acid, Abscisic acid, Proline, and Eicosatetraynoic acid were prominently expressed ( p >0.05). We identified nine critical differential metabolites strongly associated with the induction and control of Fusarium wilt disease, these include Abscisic acid, 2-Hydroxycinnamic acid, Eicosatetraynoic acid, Phthalic acid, Betaine, Proline, DL-Homoserine, Oleanolic acid, and Racemosin. (Fig 7.c). To enhance credibility, exogenous molecules were added based on the metabolomics data. In the validation experiment, according to previous literature, we determined the concentrations of the metabolites[14]. Faba beans treated with Eicosatetraynoic acid and Phthalic acid showed a higher incidence and severity of Fusarium wilt (Fig.7.d). However, applying Betaine, Proline, Racemosin, and Oleanolic acid significantly reduced wilt occurrence and severity. This experiment mainly utilized the faba bean wilt model to validate the metabolomics data. Transcriptome Analysis of Faba Bean Affected by Fusarium Wilt To investigate the effects of Fusarium wilt on faba beans, this study employed RNA-Seq and RT-PCR analyses to examine the alterations in biosynthetic pathways, disease resistance genes, and transcription factors associated with F. oxysporum infection. The transcriptome analysis revealed a total of 2058 differentially expressed genes (log2[FC] > 1.5 and p < 0.05 ) in Fusarium wilt-infected broad beans compared to the control group. Specifically, 1164 genes were upregulated while 894 genes were downregulated in response to F. oxysporum invasion (Fig 8. a). The stressed group and control group exhibited distinct separation at the transcriptomic level. To validate the accuracy of the transcriptome sequencing, a subset of 7 differentially expressed genes was subjected to qRT-PCR quantification experiments. Fusarium wilt exerts a complex influence on metabolic pathways in V. faba , as revealed by KEGG pathway analysis (Fig 8. b). Upregulated pathways encompass Valine, leucine, and isoleucine degradation, Alanine, aspartate, and glutamate metabolism, Beta-alanine metabolism, Glycine, serine, and threonine metabolism, Tyrosine metabolism, Pyruvate metabolism, Carbon fixation in photosynthetic organisms, and Phenylalanine metabolism. These upregulated pathways signify enhanced defense responses, elevated energy production, and increased synthesis of defense-related compounds. Conversely, downregulated pathways involve Ribosome biogenesis in eukaryotes, Biosynthesis of amino acids, Cysteine and methionine metabolism, One carbon pool by folate, Valine, leucine, and isoleucine biosynthesis, Glycine, serine, and threonine metabolism, Purine metabolism, and Carbon metabolism, among others. These downregulated pathways suggest reduced protein synthesis, amino acid biosynthesis, nucleotide metabolism, and carbon utilization. This reallocation of resources likely strengthens defense mechanisms and aids in disease adaptation. The dysregulation of these metabolic pathways underscores the strategic responses of V. faba to Fusarium wilt, bolstering their defense capabilities and optimizing resource allocation to counteract the pathogen. We conducted a comprehensive Gene Ontology (GO) enrichment analysis on the 2058 differentially expressed genes (DEGs) and identified a multitude of enriched pathways (Fig 8. d,e). Among them, notable pathways include oxidation-reduction process, Coenzyme-B sulfoethylthiotransferase activity (GO:005052), cellular respiration（GO:0045333）,energy derivation by oxidation of reduced inorganic compounds, and lysyl-tRNA aminoacylation (GO:0015975), energy derivation by oxidation of reduced inorganic compounds（GO:0015975）. Comprehensive Analysis of Molecular Interactions within the Microbiome, Metabolome, Host Transcriptome, and Metagenome Affected by Faba Bean Fusarium Wilt Using a Multi-Omics Approach To determine the direct interactions between microbes and hosts as well as the indirect interactions mediated by metabolites, a large-scale interaction network spanning five omics measurements was constructed, key interactions were identified between the microbiome, root exudate metabolome, host transcriptome, and metabolome [30]. Using Spearman correlation, DEMs and DEGs were identified as varying with changes in the microbiome(Fig. 9 a). This also includes various DEGs that change with the alteration of metabolites. Overall, 4356 correlations among the five datasets were identified (SM file 2). By integrating the significant Spearman correlations of classified microbial groups, root exudates, key metabolites, DEG, and the macrogenome, a visualized filtered network was generated (fig.9.a). The resulting network contains 337 significant correlations, including positive and negative correlations greater than 0.9 between 337 nodes from the five types of measurements ( p < 0.05 ) (SM file 2). In this study, we discovered significant interactions between Bacillus in the rhizosphere microbiome and specific components of the root exudate metabolome. Specifically, Bacillus showed a strong positive correlation with Betaine and DL-Homoserine in the root exudates. Additionally, Bacillus exhibited a strong negative correlation with the alanine, aspartate, and glutamate metabolism pathways in the root transcriptome. Between the rhizosphere metagenome and the soil metabolome, we also observed significant positive correlations, such as between Carbohydrate metabolism and Sphingosine, as well as Energy metabolism with Asparagine and Lipid metabolism with Glutamine. Notably, a positive correlation between metabolites like Erucamide and differentially expressed genes (DEGs) related to arginine and proline metabolism (e.g., T_1g334280) reveals the impact of microbial communities on host gene expression and metabolic regulation. Furthermore, significant correlations between specific microbes (such as Pseudomonas and Fusarium ) and metabolic pathways, including amino acid and lipid metabolism, along with positive correlations between compounds in root exudates (like 2-Hydroxycinnamic acid and L-Dopa) and specific microbes, emphasize the role of microbial communities in modulating plant physiological responses. Additionally, the association between DEGs involved in alanine, aspartate, and glutamate metabolism (e.g., T_4g066480) and microbes (such as Talaromyces) showcases the complex interactions between the host transcriptome and rhizosphere microbial communities. These findings highlight the importance of considering plant-microbe interactions in plant health and disease management strategies. Discusion Rhizosphere Microbial Dynamics and Biocontrol Strategies in Combatting Fusarium wilt in Faba Beans In our study, we employed advanced high-throughput and isolation-cultivation techniques to investigate the alterations in rhizosphere microorganisms in healthy V. faba plants and those affected by Fusarium wilt. Understanding the composition and functionality of soil microbes is crucial for comprehending and managing soil diseases. We observed significant variations in microbial community structures between healthy and diseased faba bean seedlings, providing valuable insights for the development of biological disease control strategies. Notably, the substantial increase of Bacteroidota (Phylum) bacteria in the F group may be associated with their potential suppressive effects on Fusarium spp. (Fig. 2 .a). Moreover, the notable abundance of Pseudomona s, Bacillus , Actinobacteriota , and Micrococcales in the CK group suggests their pivotal roles in disease suppression. In the healthy faba bean rhizosphere, some known disease-resistant microbes might inhibit pathogens or promote plant health through mechanisms like bio-antagonism, inducing systemic resistance, and secreting bioactive molecules. For instance, Pseudomonas and Bacillus , both more abundant in the CK group, are well-documented beneficial bacteria. They produce antibiotics and other secondary metabolites that inhibit various plant pathogens (Wen et al., 2023). This might explain the increased presence of these beneficial microbes in the rhizosphere of healthy faba beans (CK group). Conversely, the increased abundance of Stenotrophomonas and Flavobacterium in the F group might hint at their potential synergy with Fusarium spp., possibly enhancing disease infectivity. Some strains in Basidiomycota , such as Pezizales and Cantharellales , have been linked to soil-borne plant diseases. The presence of specific microbes unique to the F group soil might create favorable conditions for Fusarium 's invasion and growth. Certain microbes might have a symbiotic relationship with Fusarium , aiding in nutrient acquisition, while others might suppress microbes competing with Fusarium , giving it an ecological advantage. This study emphasizes the pivotal role of rhizosphere microorganisms in the biological control strategy against Fusarium wilt disease in faba beans. The isolation and identification of bacterial and fungal strains capable of inhibiting F. oxysporum f. sp. fabae (FOF) underscores the efficacy of utilizing beneficial microbes as a sustainable alternative to chemical pesticides. The enhanced inhibitory effect of mixed microbial formulations on the incidence rate of Fusarium wilt in faba beans, both in vitro and in vivo, highlights the potential synergistic effects when multiple biocontrol agents are employed together. The significant inhibition rates demonstrated by specific strains of Bacillus , Pseudomonas , Aspergillus welwitschiae , and Trichoderma asperellum against FOF highlight the pivotal role of these microbes in combating Fusarium wilt disease in faba beans. This finding is consistent with previous research conducted by Chowdhury et al.[ 30 ] and Köhl et al.[ 32 ], which suggests that microorganisms belonging to the genera Bacillus and Trichoderma have the ability to produce a diverse range of antimicrobial compounds that effectively target various plant pathogens, including Fusarium species. The high inhibition rates observed in this study further validate these findings, providing additional support for the scientific basis behind utilizing microbial communities in plant disease management. etabolic response analysis of faba beans treated with microbes reveals a significant upregulation in the flavonoid biosynthesis pathway and jasmonic acid signaling pathway, providing valuable insights into plant defense mechanisms. The observed activation of these pathways not only suggests a direct antimicrobial effect but also indicates the induction of the plant's innate immune system, thereby enhancing its resistance against pathogen attack. These findings align with Pieterse et al.'s study[ 33 ], which discusses how beneficial microbes can elicit systemic resistance in plants, an area of research that has gained considerable attention due to its potential for reducing reliance on chemical control measures. Through the integration of amplicon sequencing and culture-based methods, our study on rhizosphere microbial communities in healthy and Fusarium wilt-affected faba beans revealed differences in microbial enrichment between these two states. Amplicon sequencing results indicated that beneficial microbes such as Bacillus and Pseudomonas spp. were enriched in the rhizosphere of healthy faba beans, which enhance plant disease resistance by producing antibiotics and activating plant defense mechanisms[ 34 ]. Conversely, diseased faba beans showed a significant increase in the relative abundance of pathogenic Fusarium oxysporum f. sp. fabae along with other microbes such as Sphingomonas and Klebsiella , reflecting changes in microbial community composition under disease conditions[ 35 ]. Culture-based methods further identified microbes with significant biocontrol potential like Bacillus subtilis and Trichoderma asperellum that exhibited strong inhibitory effects against FOF[ 36 ]. These findings highlight dynamic changes occurring within rhizosphere microbial communities under different health statuses while revealing the potential for utilizing this knowledge to develop effective biocontrol strategies aimed at addressing significant plant diseases. Metagenomic Insights into Rhizosphere Microbial Community Functional Pathways and Antibiotic Resistance Genes(ARGs) Distribution in Healthy and Fusarium wilt-Affected Faba Beans Based on metagenomic data, the bar stacked graph (Fig. 5 ) clearly indicates functional differences between microbial communities of CK and F groups across various Functional KEGG pathways. Notably, within Functional KEGG level 1 pathways, the F group exhibited a higher representation in environmental information processing pathways compared to the CK group. This suggests that wilt disease may induce changes in plant rhizosphere necessitating additional metabolic pathways to cope with these alterations. In contrast, the CK group exhibited higher relative abundance in cellular processes, genetic information processing, and metabolic activities, indicating that healthy plants might maintain a more vibrant and stable microbial community[ 37 ]. Further, in Functional KEGG level 2 pathways, amino acid metabolism was significantly more abundant in the F group, aligning with potential plant tissue decomposition and amino acid release due to wilt disease. This also suggests that infected plants might release more organics, affecting amino acid metabolism. Notably, differences in energy metabolism and membrane transport between CK and F groups suggest potential variations in energy acquisition and substance transfer between healthy and infected plants[ 35 ].In comparison, the healthy CK group showed diverse gene expression patterns. The high expression of genes related to RNA and DNA synthesis, metabolic processes, and protein synthesis and degradation indicates the vitality and diversity of microbes in a healthy soil environment, reflecting a stable and balanced soil ecosystem. Notably, genes related to energy production and transfer in the CK group suggest an active microbial community, while their downregulation in the F group might relate to environmental stress or nutrient limitations in diseased soil(Wen et al., 2023). For the first time, this study explored the distribution of Antibiotics Resistance Gene (ARG) in the rhizosphere soil of V. faba using metagenomic sequencing. Genes deF , carA , and IND-6 . After faba beans are infected with F. oxysporum , causing Fusarium wilt, their rhizosphere microenvironment undergoes a series of complex ecological changes. These changes are not limited to the plant itself but also deeply affect the surrounding soil environment. Firstly, the pathogen's invasion leads to the secretion of different organic substances by the faba bean roots, which can change the chemical composition of the rhizosphere soil, such as pH value, organic matter content, and nutrient availability, thus influencing the structure and function of the rhizosphere microbial community. Furthermore, the development of Fusarium wilt may result in changes in the number and activity of specific microorganisms (including pathogens and non-pathogens) in the faba bean rhizosphere soil. In this new soil environment, the previously balanced microbial community may be disrupted, favoring the proliferation of certain microbes (such as bacteria carrying antibiotic resistance genes like the IND-6 gene). The IND-6 gene encodes a metallo-β-lactamase, an enzyme that can destroy a variety of β-lactam antibiotics, making bacteria resistant to these drugs[ 39 ].The Car A gene is an important factor involved in pyrimidine biosynthesis and bacterial motility, and it may interact with plant pathogens. Therefore, changes in the soil environment may provide a growth advantage to these bacteria carrying antibiotic resistance genes, leading to an increase in their relative abundance in the rhizosphere microbial community. Such changes in the soil microbial community structure caused by plant diseases, especially the increase in antibiotic resistance genes, pose a potential threat to environmental and public health(Wu et al., 2020). Conversely, OXA-192 and novA were more prevalent in the healthy rhizosphere, suggesting they might confer an ecological advantage to their host bacteria in stress-free conditions. This hints at potential antibiotic resistance even in healthy rhizospheres, a topic requiring further exploration. Importantly, while we observed these resistance gene variations, further experiments are needed to confirm if these differences truly result in functional antibiotic resistance variations. Transcriptomic and Metabolomic Adaptations in Faba Beans ( V. faba ) in Response FOF Infection In this study, we utilized RNA-Seq and RT-PCR methodologies to thoroughly investigate the influence of FOF on the pathogenesis of Fusarium wilt disease in Vicia faba . Our research was primarily aimed at understanding how the pathogen modulates the host's biosynthetic pathways, resistance genes, and transcription factors [ 40 ]. Through comprehensive transcriptome analysis, we observed that post FOF infection, 2058 genes in V. faba exhibited significant differential expression in comparison to the control group. Specifically, 1164 genes were upregulated, and 894 genes were downregulated, manifesting a clear segregation at the transcriptomic level. The application of KEGG pathway analysis revealed that FOF induces complex and multi-layered modulations in the metabolic pathways within V. faba . The upregulated pathways include, but are not limited to, Alanine, Aspartate, and Glutamate metabolism, Beta-Alanine metabolism, Glycine, Serine, Threonine metabolism, Tyrosine metabolism, Pyruvate metabolism, Carbon fixation in photosynthetic organisms and Phenylalanine metabolism. These findings are in coherence with previous studies[ 36 , 37 , 38 , 39 ], underpinning the adaptability of Vicia faba in response to FOF infection. The upregulated pathways intimate an enhancement of defensive responses, augmented energy production and escalated synthesis of defense-related compounds. Conversely, the downregulated pathways implicate ribosome biosynthesis in eukaryotes, amino acid biosynthesis, cysteine and methionine metabolism, one-carbon pool by folate, valine, leucine and isoleucine biosynthesis, glycine, serine and threonine metabolism, purine metabolism and carbon metabolism[ 40 , 41 , 42 ]. These attenuated pathways indicate a diminished protein synthesis, amino acid biosynthesis, nucleotide metabolism and carbon utilization during the fungal infection process, which could be a manifestation of the plant's strategic resource redistribution under pathogenic stress, bolstering defensive mechanisms and adapting to the disease. The results from Gene Ontology (GO) enrichment analysis of the 2058 differentially expressed genes (DEGs) compellingly an association with pathways primarily engaged in energy production and Reactive Oxygen Species (ROS) clearance mechanisms (GO:0055114). Notably, these pathways comprise oxidation-reduction processes, Coenzyme-B sulfoethylthiotransferase activity (GO:0050524), cellular respiration (GO:0045333), energy derivation by oxidation of organic compounds (GO:0015975), lysyl-tRNA aminoacylation (GO:0006430), and phosphoenolpyruvate carboxykinase (ATP) activity (GO:0004612). This potent evidence strongly insinuates that Vicia faba mobilizes proactive measures to bolster their energy-generating machinery and amplify ROS scavenging capabilities, strategically countering the intrusion of pathogenic entities. The augmented pathways suggest an intensified defense mechanism, a surge in energy production, and a boost in the biosynthesis of defense-related compounds. Conversely, the pathways experiencing a downturn encompass ribosome biogenesis in eukaryotes, amino acid biosynthesis, cysteine and methionine metabolism, one carbon pool by folate, valine, leucine, and isoleucine biosynthesis, glycine, serine, and threonine metabolism, purine metabolism, and carbon metabolism, among others [ 40 , 41 , 42 ]. These attenuated pathways signify a decrement in protein synthesis, amino acid biosynthesis, nucleotide metabolism, and carbon utilization during the onslaught of fungal infection, potentially representing a shrewd resource reallocation by the plants under pathogenic duress to fortify defense mechanisms and accommodate disease conditions[ 43 , 44 ]. With a skillfully executed analysis of the transcriptomic and metabolomic changes in V. faba when confronted with FOF, we have successfully unveiled the intricate biological mechanisms activated in response to the formidable challenge posed by Fusarium wilt. Specifically, our investigation into the transcriptomic milieu has unveiled a substantial upheaval in the expression profile of a legion of genes in the aftermath of the fungal incursion, this includes not solely the genes orchestrating the metabolic pathways, but also those that fortify the plant's defenses against diseases, alongside pivotal transcription factors. By mapping the differentially expressed genes, we have unraveled a complex network of both upregulated and downregulated metabolic pathways. This critical knowledge provides us with an unprecedented depth of understanding regarding the strategic and potentially sophisticated responses that V. faba employs while navigating the challenging landscape of Fusarium wilt. To comprehensively comprehend the influence exerted by Fusarium wilt on V. faba , we employed a dual approach, evaluating both transcriptomic and metabolomic alterations. This method helped us pinpoint key pathways that the plant uses to react to the disease. We saw major changes in important processes, like the metabolism of amino acids alanine, aspartate, and glutamate, and in how metabolites such as valine, proline, and arginine are made and broken down. These insights match what Alfosea-Simón et al. found in their research[ 50 ]. Conclusion In this study, 16S and ITS amplicon sequencing were employed to identify a total of 1,817,89 OTUs in the rhizospheres of plants. Additionally, bacterial and fungal isolates were obtained from both healthy and diseased soil rhizospheres, resulting in the screening and selection of 15 strains for the construction of synthetic communities. This study demonstrates a significant inhibition of Fusarium wilt in seedlings by the symbiotic microbial community, comprising combined bacteria and fungi (with a remarkable 71.76% suppression of wilt disease). Utilizing UHPLC-Q-Orbitrap-MS non-targeted metabolomics (LC-MS/MS), we elucidated that this microbial community enhances plant resistance against pathogens through upregulation of the flavonoid biosynthesis pathway and activation of the jasmonic acid signaling pathway in both leaves and roots. In a study of the soil rhizosphere of faba beans, it was found that in the soil of diseased plants, there were high levels of Antibiotic Resistance Genes (ARGs) such as IND-6 , CarA , and DeF . Additionally, there was significant activity observed in genes associated with energy metabolism, amino acid metabolism, and the Assimilatory Nitrate Reduction process of the nitrogen cycle. In contrast, healthy plants exhibited an increase in pathways related to Nitrogen Fixation, Nucleotide Metabolism, and Carbohydrate Metabolism. Non-targeted metabolomic analysis (LC-MS/MS) detected 257 root exudates and 65 soil metabolites. Randomforest screening of root metabolites along with validation of exogenous metabolites revealed that betaine, proline, coumarin, and oleanolic acid effectively mitigate wilt dise ase occurrence. The transcriptomic and non-targeted metabolomics analysis revealed significant enrichment in pathways such as alanine, aspartate, and glutamate metabolism, as well as biosynthesis of unsaturated fatty acids in diseased V. faba . This research provides novel insights into the impact of wilt disease on plant immune stress responses, root metabolite secretions, and the composition and function of rhizosphere microbial communities, thereby augmenting soil antibiotic gene levels and nitrogen cycling pathways. Declarations Acknowledgements The authors extend their sincere appreciation for the financial support received for this research. This work was funded by the National Natural Science Foundation of China under Grant No. 31901929. Additional support was provided by the Natural Science Foundation of Chongqing, under Grant Nos. cstc2021jcyj-msxmX1021 and cstc2021jcyj-msxmX10590. The generous contributions from these organizations have been instrumental in facilitating the successful completion of this study. Contributions The conceptualization and design of the experiments were jointly undertaken by Chaowen Zhang, Mengyuan Li, Hongji Wang, and Ke-Pan. The execution of these experiments was carried out by Chaowen Zhang, Mengyuan Li ,Ke-Pan, Ruiqi Wang, Xinyan He, Cong-Hu, Xuanbo Fan, and Yatong Gong. Data analysis was conducted by Chaowen Zhang, Zimei Liu, and Xianyao Li. The guidance on data mining processes and the drafting of the manuscript were provided by Jianjun Zhao, and Yuzhu Han. All authors have critically reviewed the content and have given final approval for the current version of the manuscript to be published. Each contributor has agreed to be accountable for all aspects of the work, ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Competing interests The authors declare no competing interests. Conflict of Interest Statement All authors of this paper hereby declare that there are no commercial or financial conflicts of interest, nor any personal relationships or competitive interests. All authors concur with this statement. Data availability The 16S rRNA amplicon sequencing raw data were deposited in the NCBI BioProject database under the accession numbers PRJNA1077665. ITS gene amplicon sequencing, metagenomics, and transcriptomics have been uploaded to the China National Center for Bioinformation (https://ngdc.cncb.ac.cn/gsub/). The accession numbers are respectively subPRO035184, subPRO035179, and subPRO035181. References Zhou X, Wang J, Liu F, Liang J, Zhao P, Tsui CKM, et al. 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04:48:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3980679/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3980679/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51656014,"identity":"42abe6cc-f7b6-498f-b45e-e09cde440630","added_by":"auto","created_at":"2024-02-26 17:08:46","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":422715,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the analysis pipeline.(\u003cstrong\u003ea\u003c/strong\u003e) Diseased and healthy faba bean samples are collected. (\u003cstrong\u003eb\u003c/strong\u003e) Plant samples including roots, root exudates, and soil samples are collected for UHPLC-Q-Orbitrap-MS un-targeted metabolomic analysis. The root exudates are subject to random forest prediction, and exogenous compounds are used to validate the function of root exudates. (\u003cstrong\u003ec\u003c/strong\u003e) Plant roots are collected for transcriptome sequencing and RT-PCR validation. (\u003cstrong\u003ed\u003c/strong\u003e) The basal part of plant roots and stems are prepared for plant pathological paraffin sections. (\u003cstrong\u003ee\u003c/strong\u003e) The activities of SOD, CAT, POD, and chitinase in plant leaves and roots are measured. (\u003cstrong\u003ef\u003c/strong\u003e) Rhizosphere soil microbes are isolated and cultured, resulting in the separation of 2591 bacteria and 275 fungi. Ultimately, 15 bacteria and 15 fungi are selected to construct bacterial (15) microbial communities (sym), fungal (15) microbial communities (sym), and a combined bacteria + fungi microbial community (sym). \u003cem\u003eIn vitro\u003c/em\u003eexperiments against \u003cem\u003eF. oxysporum\u003c/em\u003e and \u003cem\u003eIn vivo\u003c/em\u003e experiments for wilt disease prevention are conducted. (\u003cstrong\u003eg\u003c/strong\u003e) 16sRNA and ITS RNA amplicon sequencing of the microbial community diversity in the faa bean rhizosphere soil is performed. (\u003cstrong\u003eh\u003c/strong\u003e) Shotgun metagenomic sequencing of the faba bean rhizosphere soil microbes is carried out, followed by KEGG pathway analysis, nitrogen cycle pathway, and antibiotic resistance ontology (ARO) for antibiotic gene abundance. (\u003cstrong\u003ei\u003c/strong\u003e), (\u003cstrong\u003ej\u003c/strong\u003e) Using UHPLC-Q-Orbitrap-MS-untargeted metabolomics to determine the functions of synthesized microbial communities on plants, the leaves and roots of faba beans were collected.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3980679/v1/643bd67bcfb131b980079542.jpg"},{"id":51656015,"identity":"fc9ed5c3-458e-4618-85df-3d844ab4d52c","added_by":"auto","created_at":"2024-02-26 17:08:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":495398,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e presents laboratory and field images of\u003cem\u003e Fusarium\u003c/em\u003ewilt-infected and control groups, as well as images of \u003cem\u003eF. oxysporum f. sp. fabae\u003c/em\u003e (FOF)colonies on PDA plates and spores of\u003cem\u003e \u003c/em\u003eFOF. \u003cstrong\u003e(b)\u003c/strong\u003e, Principal Coordinates Analysis (PCoA) was conducted to analyze bacterial and fungal communities, with PCo1 and PCo2 representing the principal coordinates. The 95% confidence ellipses are depicted around the samples, illustrating the distribution of bacterial and fungal communities in the rhizosphere of\u003cem\u003e Fusarium\u003c/em\u003ewilt-infected and control groups. \u003cstrong\u003e(c)\u003c/strong\u003e Box plots illustrate the Shannon diversity index and richness. The tops and bottoms of the boxes represent the 75th and 25th quartiles, respectively. The upper and lower whiskers extend 1.5 times the interquartile range from the upper and lower edges of the box, respectively. Letters indicate significant differences across compartments. For bacteria, p=0.005, R=0.668; for fungi, p=0.01, R=0.648. In \u003cstrong\u003e(d)\u003c/strong\u003e, Principal Coordinates Analysis (PCoA) was performed to assess the microbial diversity of field-grown\u003cem\u003e Fusarium\u003c/em\u003e wilt-infected\u003cem\u003e V. faba\u003c/em\u003e, representing the diversity of bacteria and fungi. \u003cstrong\u003e(e) \u003c/strong\u003eA Venn diagram showcases the comparison of bacterial and fungal communities in\u003cem\u003e Fusarium \u003c/em\u003ewilt.(\u003cstrong\u003ef\u003c/strong\u003e) The relative concentration levels of \u003cem\u003eBacillus\u003c/em\u003e,\u003cem\u003e Fusarium\u003c/em\u003e, and\u003cem\u003ePseudomonas \u003c/em\u003eare compared in the laboratory (n=5).\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3980679/v1/ebfe0cf70564a17d8409ae40.jpg"},{"id":51656012,"identity":"897f484c-880c-4329-9887-dcb63a5fd610","added_by":"auto","created_at":"2024-02-26 17:08:45","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":743820,"visible":true,"origin":"","legend":"\u003cp\u003eshows the analysis of different microbial species in rhizosphere soil under \u003cem\u003eV. faba \u003c/em\u003e(faba bean)\u003cem\u003e \u003c/em\u003eof CK and F group \u003cstrong\u003e(a, b)\u003c/strong\u003e are bar charts showing the composition of bacteria at the phylum and genus levels, respectively. (\u003cstrong\u003ec, d\u003c/strong\u003e) are bar charts showing the composition of fungi at the phylum and genus levels, respectively. \u003cstrong\u003e(e, f) \u003c/strong\u003eBiological markers related to microbes in the rhizosphere soil under faba bean in CK and F group. Different colored bars indicate species with relatively high abundance in different taxa (LDA Score \u0026gt; 3.5). \u003cstrong\u003e(g, h)\u003c/strong\u003e Annotation dendrogram of different microbial species. Yellow nodes represent species with no significant differences between treatment conditions. Each segment, from inside to outside, represents the phylum, class, order, family, and genus, respectively.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3980679/v1/c348b20497505465d6d6dd21.jpg"},{"id":51656022,"identity":"e2f893ab-7e9f-4e83-a574-7563733fd26d","added_by":"auto","created_at":"2024-02-26 17:08:46","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":896500,"visible":true,"origin":"","legend":"\u003cp\u003eillustrates the inhibition rate of fifteen bacterial strains against \u003cem\u003eFusarium oxysporum \u003c/em\u003ef. sp. \u003cem\u003efabae\u003c/em\u003e (FOF), along with the isolation ratio of these bacteria in the rhizosphere of healthy soil in the control group (CK group). Mix-In vitro refers to the inhibition rate exhibited by a mixed bacterial community consisting of fifteen bacteria against FOF, while Mix-In vivo represents the treatment of faba bean \u003cem\u003eFusarium\u003c/em\u003e wilt using this same mixed bacterial community.\u003cstrong\u003e (b)\u003c/strong\u003e, the inhibition rate of 15 fungi against FOF strains is shown, and is the isolation ratio of fungi in the rhizosphere of healthy soil in the control group (disease-free faba). Mix-\u003cem\u003eIn vitro\u003c/em\u003e refers to the inhibition rate of a mixed fungal community of 15 fungi against FOF, and Mix-\u003cem\u003eIn vivo\u003c/em\u003e refers to the treatment of faba bean\u003cem\u003e Fusarium\u003c/em\u003e wilt by a mixed fungal community of 15 fungi. Mix-\u003cem\u003eIn vitro\u003c/em\u003e (Bacterial and Fungal Cross-Inoculation) refers to the inhibitory treatment against FOF by a mix of 15 bacterial and 15 fungal strains. Mix-\u003cem\u003eIn vivo\u003c/em\u003e (Bacterial +Fungal Cross-Inoculation) refers to the treatment of faba bean\u003cem\u003e Fusarium\u003c/em\u003e wilt by a mix of 15 bacterial and 15 fungal strains.(\u003cstrong\u003ec)\u003c/strong\u003e and\u003cstrong\u003e (d)\u003c/strong\u003e present the plant height, root length, stem thickness, and root thickness, along with the fresh weight of the above-ground parts and roots, following the treatment with bacteria, fungi, and a mixture of bacteria and fungi. (\u003cstrong\u003ee)\u003c/strong\u003e and (\u003cstrong\u003ef)\u003c/strong\u003e illustrate the pathway enrichment analysis of metabolites in the highlighted cluster using MetaboAnalyst 5.0 based on the KEGG database, for the faba beans affected by\u003cem\u003eFusarium\u003c/em\u003e wilt treated with the mixed microbial preparation of bacteria +fungi.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3980679/v1/ccab6771d60888b46dcc6083.jpg"},{"id":51656301,"identity":"0459d9ae-96de-4417-a81a-8a8092c8669a","added_by":"auto","created_at":"2024-02-26 17:16:46","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":517362,"visible":true,"origin":"","legend":"\u003cp\u003ethe functional metagenomic characteristics of rhizosphere soil microbial communities of \u003cem\u003eV. faba\u003c/em\u003ebetween the pathogen (\u003cem\u003eFusarium\u003c/em\u003e wilt) and control are depicted as follows: Figure (\u003cstrong\u003ea)\u003c/strong\u003e display a stacked bar chart of the Functional KEGG level 1 pathway. Figure (\u003cstrong\u003eb)\u003c/strong\u003e presents the top 15 Functional KEGG level 2 pathways, and Figure(\u003cstrong\u003ec)\u003c/strong\u003e illustrates a heatmap of the top 15 Functional KEGG level 3 pathways. Figure (\u003cstrong\u003ed)\u003c/strong\u003e represents a Venn diagram related to KEGG pathways, while Figure (\u003cstrong\u003ee)\u003c/strong\u003e showcases a circular heatmap of the top 30 KEGG pathways. 'F' corresponds to the rhizosphere soil affected by\u003cem\u003e Fusarium\u003c/em\u003ewilt, and 'CK' represents the healthy rhizosphere soil of \u003cem\u003eV. faba\u003c/em\u003e. (\u003cstrong\u003ef) \u003c/strong\u003eis the nitrogen cycle pathway diagram, (\u003cstrong\u003eg) \u003c/strong\u003ecircos plots of antibiotic resistance ontology (ARO) and its group.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3980679/v1/691dcd2597c666f588a06d25.jpg"},{"id":51656017,"identity":"17396b2c-79c9-44c2-bbd4-a4130a463eda","added_by":"auto","created_at":"2024-02-26 17:08:46","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":611978,"visible":true,"origin":"","legend":"\u003cp\u003efor \u003cem\u003eVicia faba Fusarium \u003c/em\u003ewilt (F group) disease compared to the control (CK group) are as follows: (\u003cstrong\u003ea\u003c/strong\u003e)Principal Component Analysis (PCA) of \u003cem\u003eVicia faba\u003c/em\u003e. (\u003cstrong\u003eb\u003c/strong\u003e) Volcano plots were used to filter metabolites of interest based on the log2(FC) and log10(\u003cem\u003ep\u003c/em\u003e) values of metabolites.(\u003cstrong\u003ec\u003c/strong\u003e) The Pie chart represents the classification of compounds, and it indicates that the compounds have a VIP value \u0026gt;1 after PLS-DA analysis.(\u003cstrong\u003ed\u003c/strong\u003e) Pathway impact analysis involving comparisons between F Vs CK . The impact factor indicates the ratio of the number of metabolites mapped to a certain pathway to the total number of metabolites mapped to this pathway. A higher impact factor means a greater intensity of metabolites in the pathway. The P-value was calculated using a hypergeometric test with Bonferroni correction, and a lower p-value indicates a higher level of significance or enrichment in the pathway.(\u003cstrong\u003ee\u003c/strong\u003e)The biological metabolic pathways disturbed by F Vs CK were analyzed using the KEGG database. (\u003cstrong\u003ef\u003c/strong\u003e) Heatmap analysis for identified metabolites with significance selected by t-test（\u003cem\u003ep\u003c/em\u003e\u0026gt;0.05）\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3980679/v1/b592b347385c7f859884edbe.jpg"},{"id":51656013,"identity":"aa303014-c34e-4a5c-811a-a26633a8e80e","added_by":"auto","created_at":"2024-02-26 17:08:45","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":669587,"visible":true,"origin":"","legend":"\u003cp\u003eNontargeted metabolomics is a method used to study the composition and changes of metabolites in organisms. In the nontargeted metabolomics study of\u003cem\u003e V. faba\u003c/em\u003eroot exudates, UHPLC-Q-Orbitrap-MS technology was used to analyze the samples.\u003cstrong\u003e(a) \u003c/strong\u003eshows the principal component analysis (PCA) model constructed based on the abundant metabolites in \u003cem\u003eV. faba\u003c/em\u003e roots. \u003cstrong\u003e(b)\u003c/strong\u003eThe top 30 important metabolites were selected through random forest model as input data.\u003cstrong\u003e(c) \u003c/strong\u003eThe bar chart\u003cstrong\u003e \u003c/strong\u003erepresents the relative abundance of differential metabolites, and different letters (a, b, c) indicate significant differences (\u003cem\u003ep \u0026lt; 0.05\u003c/em\u003e) among these differentially expressed metabolites. \u003cstrong\u003e(d)\u003c/strong\u003eTreat \u003cem\u003eF. oxysporum\u003c/em\u003e with the following compounds at different concentrations: 10, 100, 1000μM of Abscisic acid, 2-Hydroxycinnamic acid, Eicosatetraynoic acid, Phthalic acid, Betaine, Proline, DL-Homoserine, Oleanolic acid, and Racemosin, as well as a mixture of these compounds. Each treatment should be repeated three times. (\u003cstrong\u003ee\u003c/strong\u003e) demonstrates the impact of small molecule metabolites, identified through metabolomic screening, on the incidence rate of\u003cem\u003e Fusarium\u003c/em\u003e wilt. 'Ⅰ' and 'Ⅱ' represent the first and second treatment experiments, respectively. In the disease grading tests, each treatment involved the evaluation of five samples. Statistical analysis indicated that the small molecule metabolites significantly influenced the incidence rate of\u003cem\u003e Fusarium\u003c/em\u003e wilt (\u003cem\u003ep \u0026lt; 0.05\u003c/em\u003e). (\u003cstrong\u003ef\u003c/strong\u003e) illustrates a heatmap that demonstrates the correlation between root exudates and the microbial community of 16S and ITS amplicon sequencing. The heatmap uses Spearman's coefficient (≥±|0.7|, p\u0026lt;0.05) for correlation, with blue indicating negative correlations and red indicating positive correlations. To denote the statistical significance of the observed correlations, markers are used: * indicates a significant difference at the 0.05, and ** signifies a significant difference at the 0.01.\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3980679/v1/2555472aeadcda7029403937.jpg"},{"id":51656020,"identity":"35adc9e1-1e9a-41a7-8065-be09e20a9990","added_by":"auto","created_at":"2024-02-26 17:08:46","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":888325,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential transcriptomic analyses between\u003cem\u003e Fusarium\u003c/em\u003e wilt-infected faba beans and the control group \u003cstrong\u003e(a)\u003c/strong\u003e a volcano plot,\u003cstrong\u003e (b) \u003c/strong\u003ea KEGG enrichment bubble chart, \u003cstrong\u003e(c)\u003c/strong\u003e a heat map,and \u003cstrong\u003e(d)\u003c/strong\u003e a GO enrichment pathway diagram.\u003cstrong\u003e (e)\u003c/strong\u003e RT-PCR expression analysis of seven genes related to Alanine, aspartate, proline, and glutamate metabolism, and the biosynthesis of unsaturated fatty acids in the root system. \u003cstrong\u003e(f)\u003c/strong\u003eThis is a study concerning the response of \u003cem\u003eV. faba \u003c/em\u003eroots to the toxicity of\u003cem\u003e Fusarium\u003c/em\u003ewilt. The research findings are based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.\u003cem\u003e Fusarium\u003c/em\u003e wilt, caused by the FOF, can significantly interfere with the metabolic processes of plants. When severely affected, plant growth may be hindered, manifesting as wilting, and it could even lead to plant death. In this study, we use markers (* and **) to indicate the statistical significance of observed differences. * represents a significant difference at the 0.05 level, which means there is a 5% likelihood that the observed difference is due to randomness. ** represents a significant difference at the 0.01 level, while blue indicates the metabolites that have been identified.\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3980679/v1/e1f895061a29b2530f3010dc.jpg"},{"id":51656026,"identity":"71b65d19-d71e-4fd4-b5aa-e357f9cc81e4","added_by":"auto","created_at":"2024-02-26 17:08:46","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":844008,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) i\u003c/strong\u003entegrates the systemic molecular correlations among the microbiome, root exudate metabolome, and the host transcriptome and metabolome in faba beans afflicted with wilt disease. It represents a host-microbiome interaction network (Spearman coefficient ≥ 0.7). There are significant correlations (\u003cem\u003ep ≤ 0.05\u003c/em\u003e) between metabolites and DEGs (Differentially Expressed Genes). The microbial populations are marked with yellow crosses, the rhizosphere metagenome with red squares, the soil metabolome with green squares, the root transcriptome with blue pentagons, and root exudates with purple stars. Red indicates positive correlations, while blue represents negative correlations. Figure (\u003cstrong\u003eb)\u003c/strong\u003e illustrates a model demonstrating how essential disease-resistant synthetic microbial communities interact with root metabolites, modifying the plant's disease resistance and metabolic pathways. This modification results in a stable and effective suppression of\u003cem\u003e Fusarium\u003c/em\u003e wilt, a disease transmitted through the soil. In this model, red lines symbolize positive correlations, while black lines denote negative correlations. The symbol \"(+)\" indicates the facilitation or enhancement of specific metabolic pathways or the production of certain compounds.\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3980679/v1/f56482e28f7094bddbe33816.jpg"},{"id":53789594,"identity":"11958960-fb65-49fa-8e51-403cbd018020","added_by":"auto","created_at":"2024-03-30 16:29:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1898643,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3980679/v1/7ab19377-1a7f-4e42-8330-b9ab2b90ec68.pdf"},{"id":51656023,"identity":"5516328a-be4a-4deb-8585-25c674e17fe3","added_by":"auto","created_at":"2024-02-26 17:08:46","extension":"docx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":2674610,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterialsfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3980679/v1/b11a60d5415aff0991e69d1a.docx"},{"id":51656027,"identity":"a18448e7-84fa-40d2-912e-441ad6a97bf7","added_by":"auto","created_at":"2024-02-26 17:08:47","extension":"txt","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":577900,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterialsfile2.txt","url":"https://assets-eu.researchsquare.com/files/rs-3980679/v1/9dc65116c6c80addd1c8c3f1.txt"},{"id":51656025,"identity":"929ac57c-40c6-407e-a76b-ee73ec14cbb7","added_by":"auto","created_at":"2024-02-26 17:08:46","extension":"xlsx","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":534568,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterialsfile3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3980679/v1/cecdd1c011db33efd0c77aa9.xlsx"},{"id":51656018,"identity":"b8eb3686-7cec-4ab1-b273-1b6beabab9ff","added_by":"auto","created_at":"2024-02-26 17:08:46","extension":"xlsx","order_by":18,"title":"","display":"","copyAsset":false,"role":"supplement","size":30349,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterialsfile4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3980679/v1/6577aae2061d07444876ed2d.xlsx"},{"id":51656024,"identity":"b68d49df-ae15-4c41-a94b-20bcf344a0b5","added_by":"auto","created_at":"2024-02-26 17:08:46","extension":"xlsx","order_by":19,"title":"","display":"","copyAsset":false,"role":"supplement","size":17376,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterialsfile5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3980679/v1/7dbad36dc463cae7ae2793b9.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The interplay between root exudates and Cross-kingdom synthetic microbiota enhances the resistance of Vicia faba to Fusarium wilt disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn today\u0026rsquo;s era of intensive global agriculture, the heavy reliance on fertilizers and herbicides highlights the critical challenge of preserving soil health to maintain sustainable agricultural productivity. Microorganisms associated with plants play a crucial role in plant health. They assist plants in defending against the invasion of pathogenic microbes, effectively reducing the incidence of soil-borne diseases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Particularly in plant roots, a critical site for the interaction between plants, the microbial communities in the rhizosphere and phyllosphere play a vital role in plant health, not only by directly fighting pathogens and boosting the plant\u0026rsquo;s immune system but also by embodying a critical element often overlooked in the plant disease triangle model: the significant impact of indigenous microbial communities on plant immunity[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], despite the fact that some members may become potential pathogens, altering their impact on plant health[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].Specific rhizosphere microorganisms can directly inhibit soil-borne plant pathogens, serving as the first line of defense against them [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. With the advancement of microbial amplicon sequencing and metagenomic sequencing technologies, significant progress has been made in understanding soil core microbiota. For example, the antagonistic effects of soil core microbial communities on specific pathogens have been shown to suppress disease [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Researchers have found significant differences between the root microbiome communities of healthy and diseased tomatoes. Compared to diseased tomatoes, healthy ones exhibit noticeably higher microbial diversity, density, and symbiotic network modularity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Root exudates influence the microbial communities in the rhizosphere, affecting plant growth and defense through plant-soil feedback effects. In terms of microbial suppression of pathogens, certain metabolites in root exudates have evident antimicrobial activity. For instance, some phenols and aldehyde compounds can directly inhibit the growth of pathogens [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Additionally, some metabolic products in root exudates can indirectly achieve pathogen suppression by altering the rhizosphere microbial community. For example, certain bacteria and fungi with antimicrobial activity can be attracted by specific plant root exudates, recruiting them to inhibit the growth of pathogens or even develop into disease-suppressive soil. Such root exudates are referred to as plant prebiotics. However, some root exudates can attract pathogenic microorganisms, exacerbating pathogen invasion and disease severity. For example, phenolic substances, organic acids, and amino acids in these exudates can promote the growth and infection of plant pathogens, worsening plant diseases. This is particularly concerning for crops like \u003cem\u003eV. faba\u003c/em\u003e, a significant dual-purpose crop valued for both human consumption and animal feed, as it could be more susceptible to such disease exacerbation. It is extensively cultivated around the globe, offering high nutritional and economic value [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. To combat \u003cem\u003eFusarium\u003c/em\u003e wilt in \u003cem\u003eV. faba\u003c/em\u003e, current strategies involve implementing modified cultivation practices and employing pesticides extensively. However, prolonged pesticide usage can lead to heightened pathogen resistance and result in adverse environmental consequences.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In contrast, biological control agents emerge as a sustainable and effective alternative for disease mitigation. Researchers reported encouraging outcomes in controlling \u003cem\u003eV. faba Fusarium\u003c/em\u003e wilt by employing the antimicrobial peptide P852, a byproduct of \u003cem\u003eBacillus velezensis\u003c/em\u003e [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. When examined within the framework of the classic disease triangle model, observations of diseased plants reveal inconsistencies in disease suppression, prompting us to propose that plant rhizosphere microorganisms and root exudates play a pivotal role in enhancing plant defenses against external pathogens. To comprehensively investigate this hypothesis, our research focused on analyzing sequencing data obtained from the rhizospheres of both healthy and wilt-afflicted \u003cem\u003eV. faba\u003c/em\u003e plants. We isolated and cultivated rhizosphere bacteria and fungi from seedlings of both healthy and diseased \u003cem\u003eV. faba\u003c/em\u003e plants, complementing our study with antagonism experiments to elucidate their interactions. Through advanced metagenomic and microbial diversity analysis, we thoroughly investigated the identified synthetic microbial communities. Our goal was to determine the efficacy of these communities in counteracting \u003cem\u003eF. oxysporum f. sp. fabae\u003c/em\u003e (FOF) in sterile \u003cem\u003eV. faba\u003c/em\u003e seedlings. Additionally, we compared the microbial communities and functions in the rhizospheres of healthy versus infected plants. Beyond this, our research extended to non-targeted metabolomics analysis of \u003cem\u003eV. faba\u003c/em\u003e roots and surrounding soil, enhancing our insight into the disparities in organic compounds between healthy and infected plants. Amidst the examination of FOF-induced plant stress, we performed paraffin pathological section analysis on both healthy and diseased \u003cem\u003eV. faba\u003c/em\u003e seedlings, assessed antioxidant enzyme activity and undertook comprehensive transcriptome and non-targeted metabolomic analysis.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e \u003cb\u003eAssessment of\u003c/b\u003e \u003cb\u003eFusarium\u003c/b\u003e \u003cb\u003eWilt Incidence in Faba beans\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe conducted a comprehensive survey to determine the prevalence of \u003cem\u003eFusarium\u003c/em\u003e wilt in faba beans. Ten plants were meticulously examined for each treatment group within controlled climate chambers. Simultaneously, three random plots (1 m\u0026times;1 m) were selected for each identical treatment group under field conditions, and 12 plants per plot were sampled using the five-point sampling method.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The \u003cem\u003eFusarium\u003c/em\u003e wilt disease of faba beans is characterized based on the previous description by Zhang et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eIsolation and Identification of Microorganisms from Faba Bean Rhizosphere Soil\u003c/h2\u003e \u003cp\u003eIn this study conducted in the Beibei and Rongchang districts of Chongqing City, rhizosphere soil samples from faba beans were collected for the purpose of isolating and identifying microorganisms. The collected samples were stored at 4\u0026deg;C. During processing, the samples were initially suspended in PBS buffer (0.1 M phosphate buffer, pH 7.0). Subsequently, the soil suspension was diluted using the dilution plating method, and various culture media were employed to isolate bacteria and fungi. Nutrient-rich Luria-Bertani (LB) medium and Tryptic Soy Agar (TSA) medium were utilized for bacterial isolation, while potato dextrose agar (PDA) medium was employed for fungal cultivation[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eTotal DNA extraction and amplicon, sequencing of 16S rRNA and ITS amplicons, metagenomic sequencing\u003c/h2\u003e \u003cp\u003eSampling was conducted from the rhizosphere of \u003cem\u003eV. faba\u003c/em\u003e, including both healthy and \u003cem\u003eFusarium\u003c/em\u003e wilt disease-infected samples. The soil samples were rapidly collected using sterile soil collectors and immediately stored at -80\u0026deg;C for preservation. Subsequently, genomic DNA was extracted from the soil samples using the E.Z.N.A. Soil DNA Kit (Omega Bio-tek, Inc., USA) following the manufacturer's instructions. The quality and concentration of DNA were assessed using the Nanodrop 2000 (Thermo Fisher Scientific, Inc., USA). The extracted DNA samples were stored at -20\u0026deg;C for future experiments. Each isolate was identified using a high-throughput bacterial identification barcoded PCR approach. Specifically, the V3-V4 region of the 16S rRNA gene and the internal transcribed spacer (ITS) region and for bacteria and fungi. Sequencing was performed on the Illumina NovaSeq 6000 platform using a 2\u0026times;250 bp cycle protocol at Beijing Allwegene Technology Co., Ltd., Beijing, China. Following the sequencing, image analysis, base calling, and error estimation were executed using the Illumina Analysis Software Version 3.0 (Illumina, Inc., USA). Post-sequencing, the two-step barcoded bacterial taxa's raw data were quality-filtered and demultiplexed based on well and plate barcodes, please refer to the supplementary.\u003c/p\u003e \u003cp\u003eWe conducted a comprehensive metagenomic sequencing study at BMK Biotechnology Co., Ltd., located in Beijing, China. The focus was the rhizosphere soil of \u003cem\u003eV. faba\u003c/em\u003e, from which samples were collected for detailed metagenomic sequencing. The sequencing procedure was executed on an Illumina HiSeq 2500 instrumen, for the methods used in metagenomics, refer to the SM file table1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAntagonistic tests against FOF\u003c/h2\u003e \u003cp\u003eWe isolated a pathogenic strain, termed FOF, from diseased samples of faba bean roots at a farming site in the Beibei District, Chongqing in China, affiliated with Southwest University. An exhaustive evaluation was conducted on various isolated fungus and bacterium to assess their antagonistic potential against FOF, aiding in the identification of potent strains. The evaluative criteria encompassed observing the radius of FOF colony and the size of inhibitory zone it formed upon interaction with the test strains, wherein a diminished in colony radius indicated enhanced in FOF resistance. A comprehensive antagonistic test against FOF included 140 bacterium and 36 fungus, with each strain's antagonistic capacity against FOF assessed across three separate rounds. During the fungal strain selection, a 4 mm FOF agar was centrally positioned on a PDA plate, with the test strains inoculated at four equidistant points, each 3 cm from the center. A PDA plate devoid of test fungi was employed as our control (CK treatment). The test fungi\u0026rsquo;s effectiveness was gauged based on the degree of FOF colonization across the entire PDA plate. For the bacterial strains, each strain was aligned along the central axis of LB plate, with a 4 mm FOF agar strategically situated between the strain and the plate's periphery. LB plate infused only with sterile water along the centerline served as a control. Post a 7-day incubation period, the strains\u0026rsquo; efficacy was appraised. The inhibition efficiency percentage\u0026thinsp;=\u0026thinsp;100% *(FOF radius in control - FOF radius in test strain) /FOF radius in control [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eConstitution of different SynComs of faba bean plants\u003c/h2\u003e \u003cp\u003eBased on the method referenced by Zhou et al. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], faba bean sprouts are grown in an artificial climate box in the Grassland microbiome at Southwest University in China, with a seedling age of 17 days. In a sterile ultra-clean workbench, the sprouts are inoculated twice with a microbial inoculum. Seven days later, they are inoculated with a 10\u003csup\u003e7\u003c/sup\u003espore suspension of \u003cem\u003eF. oxysporum\u003c/em\u003e f. sp. (FOF) to observe if they contract \u003cem\u003eFusarium\u003c/em\u003e wilt and to calculate the incidence rate of the disease. In this experiment, bacteria are cultured on LB medium using a shaking flask fermentation method. The entire cultivation process lasts for three days at a controlled temperature of 28\u0026deg;C. Each bacterial fermentation broth is centrifuged at 4,000\u0026times;g for 5 minutes, then resuspended in phosphate-buffered saline (PBS). The optical density (OD\u003csub\u003e600\u003c/sub\u003e) is adjusted to 0.02, which corresponds to approximately 10\u003csup\u003e7\u003c/sup\u003e cells/ml.\u003c/p\u003e \u003cp\u003eOn the other hand, fungi are cultured in PDB medium using the shaking flask fermentation method under the conditions of fermenting for 6 days at 25\u0026deg;C ,180 rpm, and finally diluted to 10\u003csup\u003e6\u003c/sup\u003e conidia /mL[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The experiment designed four different treatment groups, namely BacFOF, FunFOF, CrossKFOF, and FOF. SynComs are composed of both bacteria and fungi, with the volume ratio of the microbial mixture adjusted to 4:1 (bacteria to fungi). After adjusting the OD600 of the bacterial SynComs and the concentration of fungal conidia, these mixtures are poured into the soil. Eventually, the OD\u003csub\u003e600\u003c/sub\u003e of SynComs is adjusted to 10\u003csup\u003e7\u003c/sup\u003e cells /g soil. Seeds are soaked in 60\u0026deg;C warm water and then immersed in 75% ethanol for 60 seconds. The soil for planting faba beans is sterilized at 121.3\u0026deg;C and 103.4 kPa for 30 minutes. The artificial climate box is then sprayed and wiped with 75% alcohol and 0.2% sodium hypochlorite, followed by disinfection with ultraviolet ozone lamps for one hour. Sterile tomato seedlings are transferred into the artificial climate box, with a 13/11 h day/night cycle, daylight illumination of 12,000 LX at 26 ℃, and a nighttime temperature of 22 ℃ [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptome sequencing analysis\u003c/h2\u003e \u003cp\u003eRoots from \u003cem\u003eV. faba\u003c/em\u003e plants, both healthy and those infected with \u003cem\u003eFusarium\u003c/em\u003e wilt, were thoroughly rinsed thrice with distilled water before freezing and thawing three times in liquid nitrogen at -196\u0026deg;C. Total RNA was extracted from these samples using the Trizol method and subsequently treated with RNase-free DNase I to eliminate potential DNA contamination[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. We conducted transcriptome sequencing at Beijing Overson Gene Technology Co., Ltd. The integrity and purity of the RNA were validated by analyzing the samples on 1% agarose gels, while the Agilent 2100 Bioanalyzer and the NanoDrop spectrophotometer facilitated RNA quantification and quality assessment, respectively. Sequencing libraries were crafted from 1.5 \u0026micro;g of RNA per sample, utilizing the NEBNext\u0026reg; Ultra\u0026trade; RNA Library Prep Kit for Illumina\u0026reg;, in adherence to the manufacturer's instructions. Unique index codes were incorporated to associate sequences with their respective samples. Isolation of mRNA from total RNA was accomplished using poly-T oligo-attached magnetic beads, and fragmentation was carried out with divalent cations at elevated temperatures. This was followed by the synthesis of first and second strand cDNA[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].The overhangs were modified using exonuclease/polymerase activities to generate blunt ends, and the NEBNext Adaptor was ligated post adenylation of the 3' ends. The AMPure XP system enabled selective purification of cDNA fragments of 200\u0026ndash;250 bp. After treatment with USER Enzyme and PCR performance, the resultant PCR products were purified, and library quality was evaluated. These prepared libraries were sequenced on the Illumina Novaseq 6000 platform, generating 150 bp paired-end reads. Initial data processing involved quality control of raw reads, adapter sequence removal, and conversion into clean reads. These clean reads were then mapped to the reference genome, selecting only perfectly matching or single mismatch reads for further analysis. SNP calling was executed using GATK2 software after sorting, deduplicating reads, and merging the BAM alignment results. Gene expression levels were quantified by HTSeq, counting the number of reads mapped to each gene. Differential expression analysis was performed using the DESeq R package, with biological replicate samples. A fold change above 2 in gene expression quantity and a P-value (or FDR) less than 0.05 were set as the threshold for differentially expressed genes (DEGs). Functional information and Pfam function of Unigene were annotated using Diamond and HMMER software, respectively. Pathway analysis was used to determine the distribution of DEGs in the KEGG database. Each procedure was conducted in triplicate[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], The methods for RT-PCR were referred to the Supplementary file, and the primer designs were referred to SM table 2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCollection and extraction of root exudates\u003c/h2\u003e \u003cp\u003eRetrieve diseased faba bean plants and healthy faba bean plants as control groups (n\u0026thinsp;=\u0026thinsp;6 in each group). After carefully excavating the intact root systems, immerse them in sterile ultra-pure water for 48 hours. Subsequently, the preliminary treated plants were removed, and the root systems were thoroughly washed with deionized water and wrapped in moist filter paper[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The wrapped plant root systems were placed in sterilized beakers that had been rinsed with deionized water, sealed with parafilm, and kept away from light [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. After 24 hours, we collected the root systems covered with filter paper and temporarily stored them in an icebox. Next, the root systems were subjected to 30 min of shaking extraction using deionized water and ultrasound treatment. After extraction, the extract was transferred to 2 mL centrifuge tubes. Then, the supernatant was transferred to a new 2 mL centrifuge tube and subjected to freeze-drying in a freeze dryer for 48 h until it turned into a dry powder. The extract was then saved in a -80\u0026deg;C freezer after filtration through a 0.45 \u0026micro;m filter, and methanol (3:1 methanol: H\u003csub\u003e2\u003c/sub\u003eO, v/v) was added. The samples are awaiting non-targeted metabolomics analysis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMetabolite Extraction and Metabolite Profiling\u003c/h2\u003e \u003cp\u003eDiseased \u003cem\u003eVicia faba\u003c/em\u003e affected by \u003cem\u003eFusarium\u003c/em\u003e wilt disease and healthy \u003cem\u003eVicia faba\u003c/em\u003e from the control group, the roots and soil at the root were collected from the artificial climate chamber at the grassland microbiology laboratory (n\u0026thinsp;=\u0026thinsp;6). Metabolites were extracted using a slightly modified version of the previously described method[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In brief, fresh plant samples underwent 24 hours of vacuum freeze-drying using a freeze dryer, followed by processing with a 50 Hz grinder to obtain powder. Subsequently, 50 mg of the powder sample, mixed with 0.6 mL of methanol (3:1 methanol: H\u003csub\u003e2\u003c/sub\u003eO, v/v), was stored overnight in a 2 mL EP tube in a refrigerator at 4\u0026deg;C. After 5 min of sonication and centrifugation at 12,000\u0026times;g for 10 minutes, the supernatant was filtered using a 0.22 \u0026micro;m Biosharp filter membrane. To ensure method stability and data reliability, each sample was mixed with 10 \u0026micro;L of a quality control (QC) sample, QC sample after every five samples for monitoring purposes[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. For LC-MS/MS analysis, an ultra-high-performance liquid chromatography (UHPLC) system (UHPLC UltiMate\u0026reg; 3000, Dionex, Sunnyvale, CA, USA) equipped with a UPLC Hypersil GOLD C18 column (2.1 \u0026times; 100 mm, 1.9 \u0026micro;m particle size) (Thermo Fisher Scientific, Waltham, MA, USA) was used in conjunction with a Q-Exactive Orbitrap mass spectrometer (Thermo Fisher Scientific). The column temperature was set at 35\u0026deg;C, and the flow rate was maintained at 0.2 mL/min. A 2 \u0026micro;L injection volume was used. The mobile phase consisted of solvent A (0.1% formic acid in ultrapure water) and solvent B (0.1% formic acid in methanol) for the positive ion mode, while solvent C (0.1% NH\u003csub\u003e3\u003c/sub\u003e in ultrapure water) and solvent D (0.1% NH\u003csub\u003e3\u003c/sub\u003e in methanol) were used for the negative ion mode. The gradient program was as follows: 0\u0026ndash;10 minutes, 5% B and 95% A; 10\u0026ndash;12 minutes, 5% A and 95% B; 12\u0026ndash;13 minutes, 5% A and 95% B; 13.1\u0026ndash;14 minutes, 95% A and 5% B. For the negative ion mode, the gradient program was as follows: 0-2.5 minutes, 95% C and 5% D; 2.5\u0026ndash;16.5 minutes, 95% D and 5% C; 16.5\u0026ndash;19 minutes, 95% D and 5% C; and 20 minutes, 95% C and 5% D[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].The Q-Exactive Orbitrap was operated in information-dependent acquisition (IDA) mode using Xcalibur data acquisition software (Thermo Fisher Scientific, Waltham, MA, USA). The HESI source parameters were set as follows: sheath gas and auxiliary gas flow rates at 40 and 10 Arb, respectively; capillary temperature at 320\u0026deg;C; full mass scan range at m/z 70-1050 with a resolution of 70,000. The MS/MS scan mode was set to data-dependent MS\u003csub\u003e2\u003c/sub\u003e (dd-MS\u003csub\u003e2\u003c/sub\u003e) scan with a resolution of 35,000. Collision energy in the NCE mode was set at 20/40/60 eV. The spray voltage was set to 3.5 kV for the positive ion mode and \u0026minus;\u0026thinsp;2.5 kV for the negative ion mode[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In order to identify the metabolites that may drive the assembly process of the rhizosphere microbial community associated with diseased and healthy root systems, we used machine learning to differentiate rhizospheric metabolites associated with diseased and healthy root soil. Based on the previous literature[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], we constructed a random forest model using the R package 'RandomForest', which consisted of 500 decision trees. The mtry parameter was set to one-third of the number of input features, with a default value of 75. Using our model, we were able to distinguish the root exudates of \u003cem\u003eV. faba\u003c/em\u003e plants affected by \u003cem\u003eFusarium\u003c/em\u003e wilt from those of healthy specimens. The model helped us identify key characteristics, particularly specific metabolites, based on their feature importance rankings. For assessing the model's predictive accuracy, we conducted five-fold cross-validation with 100 iterations, utilizing the 'rfcv' function of the 'RandomForest' package, the input data comprised a table detailing the relative abundances of metabolites in the root exudates. Lastly, we employed the 'ggplot2' package for graphical representation, creating bar plots that clearly illustrated the metabolic differences between the root exudates of \u003cem\u003eFusarium\u003c/em\u003e wilt-affected and healthy \u003cem\u003eV. faba\u003c/em\u003e plants [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eImpact of Small Metabolites Selected Through Metabolomics on the Incidence of\u003c/b\u003e \u003cb\u003eFusarium\u003c/b\u003e \u003cb\u003ewilt Disease\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAccording to the results of the random forest and referring to relevant literature[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], small molecule metabolites related to the induction and prevention of wilt disease were selected. These include Abscisic Acid, 2-Hydroxycinnamic Acid, Eicosatetraynoic Acid, Phthalic Acid, Betaine, Proline, DL-Homoserine, Oleanolic Acid, and Racemosin. At the unfolding of the fourth and fifth true leaves of faba bean seedlings, exogenous small molecules (purity\u0026thinsp;\u0026ge;\u0026thinsp;99%) and a control group (sterile water) were applied in six separate groups, each at a concentration of 10 \u0026micro;M. One week later, a spore suspension of FOF, prepared at a concentration of 1\u0026times;10\u003csup\u003e5\u0026ndash;6\u003c/sup\u003e CFU/mL by filtration through two layers of gauze, was used to inoculate the plants using a basal cut wound method to induce \u003cem\u003eFusarium\u003c/em\u003e wilt infection in faba beans. For each type of soil, sterile deionized water was used to maintain soil moisture at 70% water holding capacity. After nine weeks, the incidence of wilt disease in each treatment was investigated (this experiment was repeated twice).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement of Antioxidant Enzyme Activities\u003c/h2\u003e \u003cp\u003e24 h after the faba bean showed symptoms of \u003cem\u003eFusarium\u003c/em\u003e wilt disease, faba bean leaves and roots in climate chambers were collected. The antioxidant enzyme activities of POD (Peroxidase, U/g∙min), superoxide dismutase SOD(Superoxide Dismutase,U/g), CAT(Catalase, mg/g∙min) were assayed, using the spectrophotometer as previously described, with some modifications (Wellburn, 1994, Guo et al., 2020). Chitinase activity was determined using the chitinase Assay kit (Solarbio Science \u0026amp; Technology Co., Ltd., Beijing, China), following the instructions from the manufacturer. The amount of enzyme that decomposes chitin to produce 1 \u0026micro;mol N-acetyl‐D‐(+)‐glucosamin per gram of tissue per hour is one enzyme activity (U/g) unit at 37\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eIn our statistical analysis, we utilized IBM SPSS Statistics 26.0 (SPSS, Chicago, IL, USA) for conducting one-way ANOVA and Duncan's t-test to identify significant differences (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e). GraphPad Prism software version 8.0 (GraphPad Software Inc., San Diego, CA, USA) was also employed for additional data analysis. For untargeted metabolomic data, we used Compound Discover 2.1 (Thermo Fisher Scientific, Waltham, MA, USA) to perform operations such as peak detection, extraction, and normalization. This process involved using the Mz Cloud and mzVault databases for metabolite matching, resulting in a dataset comprising mass-to-charge ratios, retention times, and peak areas. We then merged the positive and negative datasets and analyzed them using SIMCA-P 14.1 software (Umetrics, Umea, Sweden) for multivariate statistical analysis, including PCA and OPLS-DA. We identified key differentiating metabolites based on criteria such as VIP\u0026thinsp;\u0026ge;\u0026thinsp;1, \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e, and FC\u0026thinsp;\u0026gt;\u0026thinsp;1.5. MetaboAnalyst 5.0 was instrumental in analyzing these differentially metabolized chemicals and pinpointing critical metabolic pathways, in conjunction with the KEGG database for pathway annotation. The data analysis in this study was conducted using the R programming language (R Core Team, 2022). For data processing and wrangling, the 'tidyverse' package played a crucial role, offering efficient and user-friendly tools for manipulating and analyzing our data sets. Additionally, the 'ggplot2' package was employed to generate sophisticated, publication-quality graphics, enhancing the visual representation of our analytical results. The analysis results of Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003ea,b plots were generated using the\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCNSknowall (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cnsknowall.com/index.html#/HomePage\u003c/span\u003e\u003cspan address=\"http://cnsknowall.com/index.html#/HomePage\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003ch3\u003eInsights into the Rhizosphere Microbial Communities in Healthy and\u003cem\u003e\u0026nbsp;Fusarium\u003c/em\u003e Wilt-Affected Faba Beans\u003c/h3\u003e\n\u003cp\u003eTo investigate the correlation between microbial diversity in the rhizospheres of healthy faba beans and those affected by\u003cem\u003e\u0026nbsp;Fusarium\u003c/em\u003e wilt, our study encompassed sample collection and symptom analysis conducted in the Beibei and Rongchang districts of Chongqing, China. Additionally, we meticulously observed and identified the plate morphology of \u003cem\u003eF. oxysporum\u003c/em\u003e f. sp. \u003cem\u003efabae\u003c/em\u003e (FOF) to further our understanding of its impact (Fig.2 a). In controlled environment settings, from the rhizosphere soil samples of wilt-affected and healthy faba beans, we obtained an average of 40,781 16S rRNA gene reads and 14,0978 ITS1 reads. Using the UNOISE3 algorithm, we identified 3,032 bacterial zero-radius OTUs (OTUs,\u0026nbsp;operational taxonomic units) and 1,833 fungal zOTUs, Rarefaction curves showed that the sequencing coverage in our study adequately represented the bacterial and fungal diversity. A Principal Coordinate Analysis (PCoA) was performed using Bray-Curtis distance, revealing a distinct separation between microbial samples obtained from the rhizosphere of healthy plants (CK Group) and those affected by wilt (F Group) (Fig. 2b). The bacterial and fungal Shannon and Richness values in the F Group were significantly lower than those in the CK Group (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05). However, the Shannon value for fungi in the F Group was higher than that in the CK Group (\u003cem\u003ep\u003c/em\u003e\u0026gt;0.05). To understand the potential functional differences of microbial communities in healthy and diseased rhizosphere soils, we compared the metagenomes of the F and CK groups from field samples (Fig.2 d). Metagenomic sequencing produced an average of 62,304,728 clean reads for each sample, with the data size for each sample approximately 4.5GB. The PCoA, based on Bray-Curtis distance, indicated a significant difference in microbial profiles between CK and F groups. For the 16S bacterial sequencing, a total of 2,419 bacterial species were shared between the CK and F groups. The CK group exhibited 369 unique species, whereas the F group displayed 221 distinct bacterial strains. In terms of ITS fungal sequencing, both groups demonstrated a commonality of 542 fungi (Fig. 2e); however, the CK group presented with an additional set of 566 unique fungi compared to the F group\u0026apos;s count of 625 exclusive fungi. Notably higher relative concentrations of \u003cem\u003eBacillus\u003c/em\u003e and Pseudomonas spp. were observed in the CK group as depicted in Fig. 2f; conversely, there was a significantly elevated relative concentration of FOF in the F group.\u003c/p\u003e\n\u003cp\u003eTo discern the differences in dominant microbial strains between the rhizosphere soil of healthy and\u003cem\u003e\u0026nbsp;Fusarium\u003c/em\u003e wilt-infected faba beans, we utilized Linear Discriminant Analysis Effect Size (LEfSe) to analyze the contributions of different bacterial species (LDA SCORE \u0026gt; 3.5). The results indicated that at the bacterial level, the number of bacterial biomarkers in the rhizosphere of the healthy group (CK group) was significantly higher than in the\u003cem\u003e\u0026nbsp;Fusarium\u003c/em\u003e wilt-infected group (F group) (Fig3. e, f). Within the bacterial taxa, the CK group contained 8 biomarkers, including \u003cem\u003eActinobacteriota\u003c/em\u003e, \u003cem\u003eMicrococcales\u003c/em\u003e, \u003cem\u003eArthrobacter\u003c/em\u003e, \u003cem\u003eVerrucomicrobiota\u003c/em\u003e, and \u003cem\u003ePedosphaerales\u003c/em\u003e. In contrast, the F group rhizosphere soil contained 20 biomarkers, such as \u003cem\u003eStenotrophomonas\u003c/em\u003e and \u003cem\u003eFlavobacterium\u003c/em\u003e. At the fungal level, the number of fungal biomarkers was significantly higher in the CK group compared to the F group. The CK group contained 11 fungi, including \u003cem\u003eHumicolai grisea\u003c/em\u003e, \u003cem\u003eHumicola\u003c/em\u003e, and \u003cem\u003eMortierellaceae\u003c/em\u003e. In contrast, the F group contained fungi from the \u003cem\u003ePezizales\u003c/em\u003e, \u003cem\u003ePezizomycetes\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Cantharellales\u003c/em\u003e, and \u003cem\u003eCeratobasidiaceae\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eTo understand how the microbial composition and abundance change in the rhizosphere soil of the F and CK groups, we characterized the top 20 or so bacteria and fungi by their family and genus in different treatment groups. In the 16S bacterial stacked chart, the proportion of bacteria in the Phylum\u003cem\u003e\u0026nbsp;Bacteroidota\u003c/em\u003e significantly increased in the diseased soil rhizosphere of the F group (compared to CK group), whereas the proportions of bacteria in the Phyla \u003cem\u003eAcidobacteriota\u003c/em\u003e, \u003cem\u003eActinobacteriota\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Patescibacteria\u003c/em\u003e, \u003cem\u003eGemmatimonadota\u003c/em\u003e, \u003cem\u003eVerrucomicrobiota\u003c/em\u003e, \u003cem\u003eMyxococcota\u003c/em\u003e, and \u003cem\u003eBdellovibrionota\u003c/em\u003e significantly decreased. At the Genus level (fig3.b), in the F group\u0026apos;s diseased soil rhizosphere, the proportions of bacteria in the genera \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eArthrobactor\u003c/em\u003e, and \u003cem\u003eAcidovorax\u003c/em\u003e significantly decreased, while those in \u003cem\u003eSphingobacterium\u003c/em\u003e, \u003cem\u003eSphingomonas\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Stenotrophomonas\u003c/em\u003e, \u003cem\u003ePedobacter\u003c/em\u003e, and \u003cem\u003eKlebsiella\u003c/em\u003e significantly increased (fig3.a). In the ITS fungal stacked chart, compared to the CK group, the proportions of fungi in the Phyla \u003cem\u003eMortierellomycota\u003c/em\u003e and \u003cem\u003eAscomycota\u003c/em\u003e significantly decreased in the F group\u0026apos;s diseased soil rhizosphere, whereas the proportion of fungi in the Phylum\u003cem\u003e\u0026nbsp;Basidiomycota\u0026nbsp;\u003c/em\u003esignificantly increased (fig3.c). At the Genus level, the proportions of fungi in the genera\u003cem\u003e\u0026nbsp;Fusarium\u003c/em\u003e, \u003cem\u003eBotrytis\u003c/em\u003e, \u003cem\u003eItersonilia\u003c/em\u003e, \u003cem\u003eVishniacozyma\u003c/em\u003e, and \u003cem\u003eCladorrhinum\u003c/em\u003e significantly increased in the F group, while those in \u003cem\u003eHumicola\u003c/em\u003e, \u003cem\u003eArthrobotrys\u003c/em\u003e, and \u003cem\u003eSarocladium\u003c/em\u003e decreased (fig3.d).\u003c/p\u003e\n\u003ch3\u003eIntegrated Analysis of Rhizosphere Microbial Efficacy on\u003cem\u003e\u0026nbsp;Fusarium\u003c/em\u003e Wilt Suppression and Metabolic Response in Faba Beans\u003c/h3\u003e\n\u003cp\u003eTo investigate the presence of beneficial bacteria and fungi in the rhizosphere microbial community for biocontrol of\u003cem\u003e\u0026nbsp;Fusarium\u003c/em\u003e wilt disease in faba beans (\u003cem\u003eV. faba\u003c/em\u003e), we independently collected rhizosphere samples from Rongchang and Beibei districts in Chongqing. From the rhizosphere soil, we isolated 2591 bacterial strains and 275 fungal strains. Through extensive plate confrontation culture experiments for antibacterial testing against \u003cem\u003eF. oxysporum\u003c/em\u003e f. sp. \u003cem\u003efabae\u003c/em\u003e (FOF), we identified 15 core bacterial and fungal strains that exhibited varying degrees of inhibitory activity against the growth of the FOF strain. Among the bacteria, one strain of \u003cem\u003eBacillus subtilis\u003c/em\u003e SWU CO-6 (gene bank number OR775339) showed an inhibition rate of 65.62%, and a strain of \u003cem\u003eB. velezensis\u003c/em\u003e SWU CO-1 (OR807560) reached an inhibition rate of 49.5%, with \u003cem\u003eB. velezensis\u0026nbsp;\u003c/em\u003ebeing isolated at a rate of 94.7% in the control group. \u003cem\u003eB. vallismortis\u003c/em\u003e SWU CK7-A (OR924277) achieved an inhibition rate of 51.92%, and \u003cem\u003eP. vranovensis\u003c/em\u003e SWU F6-B (OR807566) had an inhibition rate of 36.45%. In the fungal category, \u003cem\u003eAspergillus welwitschiae\u003c/em\u003e SWU CK2-A (OR821800) reached an inhibition rate of 61.1%, \u003cem\u003eTaronnyces purpureogenus\u003c/em\u003e SWU G4 (OR921615) had an inhibition rate of 53.3%, and \u003cem\u003eTrichoderma asperellum\u003c/em\u003e SWU CK2-F (OR821799) showed an inhibition rate of 58.89%. When the fermentation broths of the 15 bacterial strains were mixed in equal volume and concentration and tested (Similarly, when the fermentation broths of the 15 fungal strains were mixed and tested against FOF, the inhibition rate was 71.16%, and the mixed fungal preparation (Mix-In \u003cem\u003evivo\u003c/em\u003e) reduced the incidence rate of\u003cem\u003e\u0026nbsp;Fusarium\u003c/em\u003e wilt in faba beans by 73% (Fig4 a,b). A synthetic microbial community, formulated by mixing the bacterial and fungal fermentation broths in a 4:1 volume ratio, showed an inhibition rate of 71.76% against FOF (Mix-In vitro) and reduced the incidence rate of\u003cem\u003e\u0026nbsp;Fusarium\u003c/em\u003e wilt in faba beans by 88.57% (Mix-In \u003cem\u003evivo\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eTo better understand the principle of the biocontrol effect of microbial preparations (Syn community) on\u003cem\u003e\u0026nbsp;Fusarium\u003c/em\u003e wilt disease in faba beans, we collected leaf and root samples of faba beans treated with BacFOF, FunFOF, CrossKFOF, and FOF from artificial climate boxes and conducted non-targeted metabolome analysis using ultra-high performance liquid chromatography-quadrupole-orbitrap mass spectrometry (UHPLC-Q-Orbitrap-MS). A total of 951 peaks were detected in the leaves, among which 453 named metabolites were identified. In the roots, a total of 2612 peaks were detected, with identification of 1157 named metabolites. The results from metabolomic analysis revealed a significant upregulation (\u0026minus;log(\u003cem\u003ep\u003c/em\u003e) value = 4.5846) in the flavonoid biosynthesis pathway in the leaves, involving key metabolites such as dihydrokaempferol, naringenin, liquiritigenin, taxifolin, isoliquiritigenin and phloretin (Fig4 e,f), totaling six types. Additionally, notable alterations were observed in the biosynthetic pathways of phenylalanine, tyrosine, and tryptophan, highlighting the potential role of these metabolic pathways in the plant\u0026apos;s disease resistance response. The significant upregulation of the jasmonic acid pathway and its metabolites in the leaves underscores the central role of plant hormones in regulating the plant\u0026apos;s response to environmental stresses, emphasizing the importance of the jasmonic acid signaling pathway in innate immune responses. Although a milder trend of changes was observed in root samples during metabolic analysis compared to leaves, key metabolites involved in flavonoid biosynthesis and phenylalanine metabolism pathways were also detected, indicating that root metabolic adjustments similarly responded to treatment with a mixed microbial community.\u003c/p\u003e\n\u003ch3\u003ePlant Rhizosphere Metagenomic Profiling\u003c/h3\u003e\n\u003cp\u003eThe stacked bar chart indicates that plants affected by\u003cem\u003e\u0026nbsp;Fusarium\u003c/em\u003e wilt exhibit a higher proportion of \u0026apos;Environment Information Processing\u0026apos; pathways within the \u0026apos;Functional KEGG Level 1 Pathways\u0026apos;, compared to the F group. Conversely, the CK group displays a greater relative abundance in areas such as Cellular Processing, Genetic Information Processing, and Metabolism, particularly when contrasted with the F group (Fig5.a). In terms of \u0026quot;Functional KEGG level 2 pathways\u0026quot;, there are some differences in microbial community functions between the CK and F groups. The relative abundance of the F group is significantly higher than that of the CK group, particularly in amino acid metabolism. This disparity may be attributed to the decomposition of plant tissues and subsequent release of amino acids caused by\u003cem\u003e\u0026nbsp;Fusarium\u003c/em\u003e wilt infection. Moreover, notable distinctions between these two groups are observed in functions such as energy metabolism and membrane transport. The Venn diagram shows (Fig5.d) that the CK and F groups have 6250 common metabolic pathways. The CK group has 387 unique pathways, while the F group has 602. As seen in Fig5.e, several highly expressed genes in the F group are associated with the branched-chain amino acid transport system. Specifically, these include ATP-binding protein (KO1995), permease (KO1997 and KO1998), and substrate-binding protein (KO1999) of the branched-chain amino acid transport system. Additionally, an increase in gene expression related to iron complex outer membrane receptor protein (KO2014) was observed. In the CK group, highly expressed genes span a variety of functions. These include genes related to metabolic processes like acetyl-CoA C-acetyltransferase (KO0626), dihydrolipoyllysine-residue acetyltransferase component of pyruvate dehydrogenase complex (KO1652), and 5-methyltetrahydrofolate-homocysteine methyltransferase (KO0548). Functions and synthesis processes for RNA and DNA are also highlighted, including RNA polymerase primary \u0026sigma;-factor (KO3086), DNA gyrase subunit A (KO2469), and DNA-directed RNA polymerase subunits \u0026beta; (KO3043 and KO3046). Additionally, there are genes in the CK group related to protein synthesis, degradation, and repair functions, such as molecular chaperone DnaK (KO4043), cytochrome c oxidase subunit I (KO2274), ATP-dependent Lon protease (KO1338), molecular chaperone GroEL (KO4077), elongation factors G (KO2355), and Tu (KO2358). Furthermore, genes related to energy production and transfer, like F-type H+-transporting ATPase subunit \u0026alpha; (KO2132), were noted. By employing metagenomic sequencing techniques, we investigated the impact of various treatments on the abundance of microbial functional genes associated with nitrogen cycling in the rhizosphere soil of faba beans (Fig5.f).\u003c/p\u003e\n\u003cp\u003eGenes including \u003cem\u003enapA\u003c/em\u003e, \u003cem\u003enapB\u003c/em\u003e, \u003cem\u003enirK\u003c/em\u003e, \u003cem\u003enorC\u003c/em\u003e, \u003cem\u003enirS\u003c/em\u003e, and \u003cem\u003enirK\u003c/em\u003e are responsible for encoding denitrifying enzymes, cytochrome c-type proteins, and nitrite reductase. In the F group, the increased presence of these genes indicates a higher rate of denitrification, which could result in greater nitrogen loss from the soil.\u003cem\u003e\u0026nbsp;Fusarium\u003c/em\u003e wilt\u0026apos;s effect on nitrogen cycling is complex, involving numerous factors. Notably, alterations in the rhizosphere environment triggered by the pathogen can lead to varied gene expression, thereby modifying how nitrogen is transformed in the soil. In contrast, in the CK treatment group, another set of enriched genes related to denitrification and nitrogen assimilation pathways were found, such as \u003cem\u003enrfA\u003c/em\u003e, \u003cem\u003egltD\u003c/em\u003e, \u003cem\u003eglnA\u003c/em\u003e, and GLUL. These genes encode nitrite reductase, the small chain of glutamate synthase, glutamine synthetase, and glutamate synthase. The presence of these enriched genes indicates both denitrification and nitrogen assimilation processes in the CK group, possibly helping maintain a balanced nitrogen cycle in the soil. The top 20 ARO genes with the highest disease prevalence include \u003cem\u003eadeF\u003c/em\u003e, \u003cem\u003enovA\u003c/em\u003e, \u003cem\u003eabcA\u003c/em\u003e,\u003cem\u003e\u0026nbsp;MuxC\u003c/em\u003e, \u003cem\u003eMexD,\u003c/em\u003e \u003cem\u003ebaeR\u003c/em\u003e, \u003cem\u003eOprN\u003c/em\u003e, which have antibiotic efflux effects; \u003cem\u003eIND-6\u003c/em\u003e, \u003cem\u003eBEL-1\u003c/em\u003e, \u003cem\u003eFomB\u003c/em\u003e which inactivate antibiotics; \u003cem\u003ecarA\u003c/em\u003e, \u003cem\u003eoleB\u003c/em\u003e for antibiotic target protection, and \u003cem\u003erpoB2\u003c/em\u003e, \u003cem\u003echrB\u003c/em\u003e, Listeria monocytogenes \u003cem\u003emprF\u003c/em\u003e, \u003cem\u003eB. subtilis\u003c/em\u003e \u003cem\u003emprF\u003c/em\u003e, etc., for antibiotic target replacement or alteration. Genes \u003cem\u003eadeF\u003c/em\u003e, \u003cem\u003ecarA\u003c/em\u003e, and \u003cem\u003eIND-6\u0026nbsp;\u003c/em\u003ehave a higher proportion in the F group, while \u003cem\u003eOXA-192\u003c/em\u003e and \u003cem\u003enovA\u0026nbsp;\u003c/em\u003eare higher in the CK group (Fig5.g).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eUntargeted Metabolomic Analysis of Root System and Exudates of Faba Bean Affected by\u003cem\u003e\u0026nbsp;Fusarium\u003c/em\u003e Wilt\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eIn order to gain a comprehensive understanding of the \u003cem\u003eFusarium\u003c/em\u003e wilt disease mechanism in \u003cem\u003eVicia faba\u003c/em\u003e, we collected samples of FOF-infected \u003cem\u003eV. faba\u003c/em\u003e roots at 3-4 dpi (days post-infection) from controlled artificial climate boxes and conducted non-targeted metabolome analysis using UHPLC-Q-Orbitrap-MS. A total of 468 peaks were detected in the control group, out of which 157 named metabolites were identified. To quantify their relative abundance, we calculated the ratio between the sum of all peak areas and each category\u0026apos;s peak area[17]. Principal component analysis (PCA) was performed to detect the variation between and within groups[26]. The results indicated that the first two principal components (PCs) could distinctly separate the two group. PC1 accounted for 38.8% of the total variation, while PC2 accounted for 27.4%, together representing 66.2% of the total variation (Fig 6. a). Moreover, the volcano plot displays the changes in metabolites in the roots of\u003cem\u003e\u0026nbsp;Fusarium\u003c/em\u003e wilt-infected \u003cem\u003eV. faba\u003c/em\u003e. Red dots represent upregulated metabolites with a statistical significance of \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05 and a fold change greater than 1.5. Green dots represent downregulated metabolites with a statistical significance of \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05 and a fold change less than \u0026lt;-1.5. In total, we detected 157 compounds. Among them, there were 5 upregulated compounds and 12 downregulated compounds. PLS-DA (Projection to Latent Structures-Discriminant Analysis) is a powerful multivariate statistical analysis method with supervised pattern recognition, aimed at effectively eliminating irrelevant effects in the study and thereby filtering out differential metabolites[27]. PLS-DA score plots were generated using SIMCA software, and they included the groups F and CK. The permutation test was employed to thoroughly evaluate and validate the quality of the PLS-DA model. An R2 value close to 1 and a Q2 value greater than 0.5 indicated that the PLS-DA model was stable and exhibited excellent fitness and predictive capability.[28]. Furthermore, the permutation plots clearly demonstrated that this model exhibited significant discriminatory differences between the F vs. CK groups. After conducting 200 permutations, all Q2 intercept values in the permutation plots were below zero (SM file 1 Figure S3), confirming the model\u0026apos;s exceptional fit[29]. As a result, the PLS-DA models proved to be highly effective in identifying differences between the groups (SM1 file1 Figure S3). In our study, we discovered 46 metabolites with a VIP\u0026ge;1 in\u003cem\u003e\u0026nbsp;Fusarium\u003c/em\u003e wilt-infected\u003cem\u003e\u0026nbsp;Vicia faba\u0026nbsp;\u003c/em\u003e(F group) Compared to the control group (as shown in Fig c). Among these metabolites, there were 11 belonging to Lipids and lipid-like molecules, 9 belonging to Organic acids and derivatives, 17 belonging to Organic oxygen compounds, 4 belonging to Organoheterocyclic compounds, and 5 belonging to Organonitrogen compounds. \u0026nbsp;Based on VIP values \u0026ge;1, p\u0026lt;0.05, and fold change (FC) values \u0026gt;1.5 or FC values \u0026lt;1.5, we employed these criteria as the threshold to screen differential metabolites. Subsequently, we constructed pathway diagrams (Fig 6.d) and pathway enrichment bubble plots (Fig 6. e) based on the KEGG database. In Figure d, the prominently upregulated pathways encompassed Citrate cycle (TCA cycle), Alanine, aspartate, and glutamate metabolism, Glyoxylate and dicarboxylate metabolism, Pyruvate metabolism, Starch and sucrose metabolism, Pentose phosphate pathway, and Fatty acid biosynthesis. In Fig 6.e, the pathway enrichment outcomes were consistent with those depicted in Fig 6.d, specifically including Alanine, aspartate, and glutamate metabolism, Glycine, serine, and threonine metabolism, Biosynthesis of unsaturated fatty acids, Starch and sucrose metabolism, and Citrate cycle (TCA cycle).\u003c/p\u003e\n\u003cp\u003eTo better understand the mechanism of\u003cem\u003e\u0026nbsp;Fusarium\u0026nbsp;\u003c/em\u003ewilt in faba bean, we collected root exudate samples from faba beans infected with FOF in controlled climate chambers 3-4 days post-infection (dpi). These samples underwent untargeted metabolomics analysis using UHPLC-Q-Orbitrap-MS. A total of 257 different annotated metabolites were identified in the faba bean root exudates (Fig 7. a). Principal component analysis (PCA) indicated a clear distinction between the F and CK treatments, with PC1 explaining 52.2% of the total variance and PC2 accounting for 21.3%, collectively making up 73.5% of the total variation. Using random forests as classifiers, we differentiated the root exudates of diseased and healthy soils. We developed a model to identify key biomarkers. Through rigorous tenfold cross-validation, we found that 30 metabolites consistently showed the lowest error rates, these are now recognized as crucial Differentially Expressed Metabolites (DEMs) (Fig 7.b). These included 9 organic acids, 6 amino acids and derivatives, 3 carbohydrates, 1 lipid, and others. Metabolomic data showed significant expression (\u003cem\u003ep\u003c/em\u003e\u0026gt;0.05) of compounds like Betaine, DL-Homoserine, Phthalic acid, 2-Hydroxycinnamic acid, and Racemosin in the CK group, while in the F group, compounds like Oleanolic acid, Abscisic acid, Proline, and Eicosatetraynoic acid were prominently expressed (\u003cem\u003ep\u003c/em\u003e\u0026gt;0.05). We identified nine critical differential metabolites strongly associated with the induction and control of\u003cem\u003e\u0026nbsp;Fusarium\u003c/em\u003e wilt disease, these include Abscisic acid, 2-Hydroxycinnamic acid, Eicosatetraynoic acid, Phthalic acid, Betaine, Proline, DL-Homoserine, Oleanolic acid, and Racemosin. (Fig 7.c). To enhance credibility, exogenous molecules were added based on the metabolomics data. In the validation experiment, according to previous literature, we determined the concentrations of the metabolites[14]. Faba beans treated with Eicosatetraynoic acid and Phthalic acid showed a higher incidence and severity of\u003cem\u003e\u0026nbsp;Fusarium\u0026nbsp;\u003c/em\u003ewilt (Fig.7.d). However, applying Betaine, Proline, Racemosin, and Oleanolic acid significantly reduced wilt occurrence and severity. This experiment mainly utilized the faba bean wilt model to validate the metabolomics data.\u003c/p\u003e\n\u003ch3\u003eTranscriptome Analysis of Faba Bean Affected by\u003cem\u003e\u0026nbsp;Fusarium\u003c/em\u003e Wilt\u003c/h3\u003e\n\u003cp\u003eTo investigate the effects of\u003cem\u003e\u0026nbsp;Fusarium\u003c/em\u003e wilt on faba beans, this study employed RNA-Seq and RT-PCR analyses to examine the alterations in biosynthetic pathways, disease resistance genes, and transcription factors associated with \u003cem\u003eF. oxysporum\u003c/em\u003e infection. The transcriptome analysis revealed a total of 2058 differentially expressed genes (log2[FC] \u0026gt; 1.5 and\u003cem\u003e\u0026nbsp;p \u0026lt; 0.05\u003c/em\u003e) in\u003cem\u003e\u0026nbsp;Fusarium\u0026nbsp;\u003c/em\u003ewilt-infected broad beans compared to the control group. Specifically, 1164 genes were upregulated while 894 genes were downregulated in response to \u003cem\u003eF. oxysporum\u0026nbsp;\u003c/em\u003einvasion (Fig 8. a). The stressed group and control group exhibited distinct separation at the transcriptomic level. To validate the accuracy of the transcriptome sequencing, a subset of 7 differentially expressed genes was subjected to qRT-PCR quantification experiments.\u003cem\u003e\u0026nbsp;Fusarium\u003c/em\u003e wilt exerts a complex influence on metabolic pathways in \u003cem\u003eV. faba\u003c/em\u003e, as revealed by KEGG pathway analysis (Fig 8. b). Upregulated pathways encompass Valine, leucine, and isoleucine degradation, Alanine, aspartate, and glutamate metabolism, Beta-alanine metabolism, Glycine, serine, and threonine metabolism, Tyrosine metabolism, Pyruvate metabolism, Carbon fixation in photosynthetic organisms, and Phenylalanine metabolism. These upregulated pathways signify enhanced defense responses, elevated energy production, and increased synthesis of defense-related compounds. Conversely, downregulated pathways involve Ribosome biogenesis in eukaryotes, Biosynthesis of amino acids, Cysteine and methionine metabolism, One carbon pool by folate, Valine, leucine, and isoleucine biosynthesis, Glycine, serine, and threonine metabolism, Purine metabolism, and Carbon metabolism, among others. These downregulated pathways suggest reduced protein synthesis, amino acid biosynthesis, nucleotide metabolism, and carbon utilization. This reallocation of resources likely strengthens defense mechanisms and aids in disease adaptation. The dysregulation of these metabolic pathways underscores the strategic responses of \u003cem\u003eV. faba\u003c/em\u003e to\u003cem\u003e\u0026nbsp;Fusarium\u003c/em\u003e wilt, bolstering their defense capabilities and optimizing resource allocation to counteract the pathogen. We conducted a comprehensive Gene Ontology (GO) enrichment analysis on the 2058 differentially expressed genes (DEGs) and identified a multitude of enriched pathways (Fig 8. d,e). Among them, notable pathways include oxidation-reduction process, Coenzyme-B sulfoethylthiotransferase activity (GO:005052), cellular respiration（GO:0045333）,energy derivation by oxidation of reduced inorganic compounds, and lysyl-tRNA aminoacylation (GO:0015975), energy derivation by oxidation of reduced inorganic compounds（GO:0015975）.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eComprehensive Analysis of Molecular Interactions within the Microbiome, Metabolome, Host Transcriptome, and Metagenome Affected by Faba Bean\u003cem\u003e\u0026nbsp;Fusarium\u003c/em\u003e Wilt Using a Multi-Omics Approach\u003c/h3\u003e\n\u003cp\u003eTo determine the direct interactions between microbes and hosts as well as the indirect interactions mediated by metabolites, a large-scale interaction network spanning five omics measurements was constructed, key interactions were identified between the microbiome, root exudate metabolome, host transcriptome, and metabolome\u0026nbsp;[30]. Using Spearman correlation, DEMs and DEGs were identified as varying with changes in the microbiome(Fig. 9 a). This also includes various DEGs that change with the alteration of metabolites. Overall, 4356 correlations among the five datasets were identified (SM file 2). By integrating the significant Spearman correlations of classified microbial groups, root exudates, key metabolites, DEG, and the macrogenome, a visualized filtered network was generated (fig.9.a). The resulting network contains 337 significant correlations, including positive and negative correlations greater than 0.9 between 337 nodes from the five types of measurements (\u003cem\u003ep \u0026lt; 0.05\u003c/em\u003e) (SM file 2). In this study, we discovered significant interactions between \u003cem\u003eBacillus\u003c/em\u003e in the rhizosphere microbiome and specific components of the root exudate metabolome. Specifically, \u003cem\u003eBacillus\u003c/em\u003e showed a strong positive correlation with Betaine and DL-Homoserine in the root exudates. Additionally, \u003cem\u003eBacillus\u003c/em\u003e exhibited a strong negative correlation with the alanine, aspartate, and glutamate metabolism pathways in the root transcriptome. Between the rhizosphere metagenome and the soil metabolome, we also observed significant positive correlations, such as between Carbohydrate metabolism and Sphingosine, as well as Energy metabolism with Asparagine and Lipid metabolism with Glutamine.\u003c/p\u003e\n\u003cp\u003eNotably, a positive correlation between metabolites like Erucamide and differentially expressed genes (DEGs) related to arginine and proline metabolism (e.g., T_1g334280) reveals the impact of microbial communities on host gene expression and metabolic regulation. Furthermore, significant correlations between specific microbes (such as \u003cem\u003ePseudomonas\u003c/em\u003e and\u003cem\u003e\u0026nbsp;Fusarium\u003c/em\u003e) and metabolic pathways, including amino acid and lipid metabolism, along with positive correlations between compounds in root exudates (like 2-Hydroxycinnamic acid and L-Dopa) and specific microbes, emphasize the role of microbial communities in modulating plant physiological responses. Additionally, the association between DEGs involved in alanine, aspartate, and glutamate metabolism (e.g., T_4g066480) and microbes (such as Talaromyces) showcases the complex interactions between the host transcriptome and rhizosphere microbial communities. These findings highlight the importance of considering plant-microbe interactions in plant health and disease management strategies.\u003c/p\u003e"},{"header":"Discusion","content":"\u003cp\u003e \u003cb\u003eRhizosphere Microbial Dynamics and Biocontrol Strategies in Combatting\u003c/b\u003e \u003cb\u003eFusarium\u003c/b\u003e \u003cb\u003ewilt in Faba Beans\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn our study, we employed advanced high-throughput and isolation-cultivation techniques to investigate the alterations in rhizosphere microorganisms in healthy \u003cem\u003eV. faba\u003c/em\u003e plants and those affected by \u003cem\u003eFusarium\u003c/em\u003e wilt. Understanding the composition and functionality of soil microbes is crucial for comprehending and managing soil diseases. We observed significant variations in microbial community structures between healthy and diseased faba bean seedlings, providing valuable insights for the development of biological disease control strategies. Notably, the substantial increase of \u003cem\u003eBacteroidota\u003c/em\u003e (Phylum) bacteria in the F group may be associated with their potential suppressive effects on \u003cem\u003eFusarium\u003c/em\u003e spp. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e.a). Moreover, the notable abundance of \u003cem\u003ePseudomona\u003c/em\u003es, \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003eActinobacteriota\u003c/em\u003e, and \u003cem\u003eMicrococcales\u003c/em\u003e in the CK group suggests their pivotal roles in disease suppression. In the healthy faba bean rhizosphere, some known disease-resistant microbes might inhibit pathogens or promote plant health through mechanisms like bio-antagonism, inducing systemic resistance, and secreting bioactive molecules. For instance, \u003cem\u003ePseudomonas\u003c/em\u003e and \u003cem\u003eBacillus\u003c/em\u003e, both more abundant in the CK group, are well-documented beneficial bacteria. They produce antibiotics and other secondary metabolites that inhibit various plant pathogens (Wen et al., 2023). This might explain the increased presence of these beneficial microbes in the rhizosphere of healthy faba beans (CK group). Conversely, the increased abundance of \u003cem\u003eStenotrophomonas\u003c/em\u003e and \u003cem\u003eFlavobacterium\u003c/em\u003e in the F group might hint at their potential synergy with \u003cem\u003eFusarium\u003c/em\u003e spp., possibly enhancing disease infectivity. Some strains in \u003cem\u003eBasidiomycota\u003c/em\u003e, such as \u003cem\u003ePezizales\u003c/em\u003e and \u003cem\u003eCantharellales\u003c/em\u003e, have been linked to soil-borne plant diseases. The presence of specific microbes unique to the F group soil might create favorable conditions for \u003cem\u003eFusarium\u003c/em\u003e's invasion and growth. Certain microbes might have a symbiotic relationship with \u003cem\u003eFusarium\u003c/em\u003e, aiding in nutrient acquisition, while others might suppress microbes competing with \u003cem\u003eFusarium\u003c/em\u003e, giving it an ecological advantage.\u003c/p\u003e \u003cp\u003eThis study emphasizes the pivotal role of rhizosphere microorganisms in the biological control strategy against \u003cem\u003eFusarium\u003c/em\u003e wilt disease in faba beans. The isolation and identification of bacterial and fungal strains capable of inhibiting \u003cem\u003eF. oxysporum\u003c/em\u003e f. sp. \u003cem\u003efabae\u003c/em\u003e (FOF) underscores the efficacy of utilizing beneficial microbes as a sustainable alternative to chemical pesticides. The enhanced inhibitory effect of mixed microbial formulations on the incidence rate of \u003cem\u003eFusarium\u003c/em\u003e wilt in faba beans, both in vitro and in vivo, highlights the potential synergistic effects when multiple biocontrol agents are employed together. The significant inhibition rates demonstrated by specific strains of \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eAspergillus welwitschiae\u003c/em\u003e, and \u003cem\u003eTrichoderma asperellum\u003c/em\u003e against FOF highlight the pivotal role of these microbes in combating \u003cem\u003eFusarium\u003c/em\u003e wilt disease in faba beans. This finding is consistent with previous research conducted by Chowdhury et al.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and K\u0026ouml;hl et al.[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], which suggests that microorganisms belonging to the genera \u003cem\u003eBacillus\u003c/em\u003e and \u003cem\u003eTrichoderma\u003c/em\u003e have the ability to produce a diverse range of antimicrobial compounds that effectively target various plant pathogens, including \u003cem\u003eFusarium\u003c/em\u003e species. The high inhibition rates observed in this study further validate these findings, providing additional support for the scientific basis behind utilizing microbial communities in plant disease management. etabolic response analysis of faba beans treated with microbes reveals a significant upregulation in the flavonoid biosynthesis pathway and jasmonic acid signaling pathway, providing valuable insights into plant defense mechanisms. The observed activation of these pathways not only suggests a direct antimicrobial effect but also indicates the induction of the plant's innate immune system, thereby enhancing its resistance against pathogen attack. These findings align with Pieterse et al.'s study[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], which discusses how beneficial microbes can elicit systemic resistance in plants, an area of research that has gained considerable attention due to its potential for reducing reliance on chemical control measures.\u003c/p\u003e \u003cp\u003eThrough the integration of amplicon sequencing and culture-based methods, our study on rhizosphere microbial communities in healthy and \u003cem\u003eFusarium\u003c/em\u003e wilt-affected faba beans revealed differences in microbial enrichment between these two states. Amplicon sequencing results indicated that beneficial microbes such as \u003cem\u003eBacillus\u003c/em\u003e and Pseudomonas spp. were enriched in the rhizosphere of healthy faba beans, which enhance plant disease resistance by producing antibiotics and activating plant defense mechanisms[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Conversely, diseased faba beans showed a significant increase in the relative abundance of pathogenic \u003cem\u003eFusarium oxysporum\u003c/em\u003e f. sp. \u003cem\u003efabae\u003c/em\u003e along with other microbes such as \u003cem\u003eSphingomonas\u003c/em\u003e and \u003cem\u003eKlebsiella\u003c/em\u003e, reflecting changes in microbial community composition under disease conditions[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Culture-based methods further identified microbes with significant biocontrol potential like \u003cem\u003eBacillus subtilis\u003c/em\u003e and \u003cem\u003eTrichoderma asperellum\u003c/em\u003e that exhibited strong inhibitory effects against FOF[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. These findings highlight dynamic changes occurring within rhizosphere microbial communities under different health statuses while revealing the potential for utilizing this knowledge to develop effective biocontrol strategies aimed at addressing significant plant diseases.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMetagenomic Insights into Rhizosphere Microbial Community Functional Pathways and Antibiotic Resistance Genes(ARGs) Distribution in Healthy and\u003c/b\u003e \u003cb\u003eFusarium\u003c/b\u003e \u003cb\u003ewilt-Affected Faba Beans\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBased on metagenomic data, the bar stacked graph (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) clearly indicates functional differences between microbial communities of CK and F groups across various Functional KEGG pathways. Notably, within Functional KEGG level 1 pathways, the F group exhibited a higher representation in environmental information processing pathways compared to the CK group. This suggests that wilt disease may induce changes in plant rhizosphere necessitating additional metabolic pathways to cope with these alterations. In contrast, the CK group exhibited higher relative abundance in cellular processes, genetic information processing, and metabolic activities, indicating that healthy plants might maintain a more vibrant and stable microbial community[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Further, in Functional KEGG level 2 pathways, amino acid metabolism was significantly more abundant in the F group, aligning with potential plant tissue decomposition and amino acid release due to wilt disease. This also suggests that infected plants might release more organics, affecting amino acid metabolism. Notably, differences in energy metabolism and membrane transport between CK and F groups suggest potential variations in energy acquisition and substance transfer between healthy and infected plants[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].In comparison, the healthy CK group showed diverse gene expression patterns. The high expression of genes related to RNA and DNA synthesis, metabolic processes, and protein synthesis and degradation indicates the vitality and diversity of microbes in a healthy soil environment, reflecting a stable and balanced soil ecosystem. Notably, genes related to energy production and transfer in the CK group suggest an active microbial community, while their downregulation in the F group might relate to environmental stress or nutrient limitations in diseased soil(Wen et al., 2023).\u003c/p\u003e \u003cp\u003eFor the first time, this study explored the distribution of Antibiotics Resistance Gene (ARG) in the rhizosphere soil of \u003cem\u003eV. faba\u003c/em\u003e using metagenomic sequencing. Genes \u003cem\u003edeF\u003c/em\u003e, \u003cem\u003ecarA\u003c/em\u003e, and \u003cem\u003eIND-6\u003c/em\u003e. After faba beans are infected with \u003cem\u003eF. oxysporum\u003c/em\u003e, causing \u003cem\u003eFusarium\u003c/em\u003e wilt, their rhizosphere microenvironment undergoes a series of complex ecological changes. These changes are not limited to the plant itself but also deeply affect the surrounding soil environment. Firstly, the pathogen's invasion leads to the secretion of different organic substances by the faba bean roots, which can change the chemical composition of the rhizosphere soil, such as pH value, organic matter content, and nutrient availability, thus influencing the structure and function of the rhizosphere microbial community. Furthermore, the development of \u003cem\u003eFusarium\u003c/em\u003e wilt may result in changes in the number and activity of specific microorganisms (including pathogens and non-pathogens) in the faba bean rhizosphere soil. In this new soil environment, the previously balanced microbial community may be disrupted, favoring the proliferation of certain microbes (such as bacteria carrying antibiotic resistance genes like the \u003cem\u003eIND-6\u003c/em\u003e gene). The \u003cem\u003eIND-6\u003c/em\u003e gene encodes a metallo-β-lactamase, an enzyme that can destroy a variety of β-lactam antibiotics, making bacteria resistant to these drugs[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].The \u003cem\u003eCar A\u003c/em\u003e gene is an important factor involved in pyrimidine biosynthesis and bacterial motility, and it may interact with plant pathogens. Therefore, changes in the soil environment may provide a growth advantage to these bacteria carrying antibiotic resistance genes, leading to an increase in their relative abundance in the rhizosphere microbial community. Such changes in the soil microbial community structure caused by plant diseases, especially the increase in antibiotic resistance genes, pose a potential threat to environmental and public health(Wu et al., 2020). Conversely, \u003cem\u003eOXA-192\u003c/em\u003e and \u003cem\u003enovA\u003c/em\u003e were more prevalent in the healthy rhizosphere, suggesting they might confer an ecological advantage to their host bacteria in stress-free conditions. This hints at potential antibiotic resistance even in healthy rhizospheres, a topic requiring further exploration. Importantly, while we observed these resistance gene variations, further experiments are needed to confirm if these differences truly result in functional antibiotic resistance variations.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTranscriptomic and Metabolomic Adaptations in Faba Beans (\u003c/b\u003e \u003cb\u003eV. faba\u003c/b\u003e \u003cb\u003e) in Response\u003c/b\u003e \u003cb\u003eFOF\u003c/b\u003e \u003cb\u003eInfection\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn this study, we utilized RNA-Seq and RT-PCR methodologies to thoroughly investigate the influence of FOF on the pathogenesis of \u003cem\u003eFusarium\u003c/em\u003e wilt disease in \u003cem\u003eVicia faba\u003c/em\u003e. Our research was primarily aimed at understanding how the pathogen modulates the host's biosynthetic pathways, resistance genes, and transcription factors [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Through comprehensive transcriptome analysis, we observed that post FOF infection, 2058 genes in \u003cem\u003eV. faba\u003c/em\u003e exhibited significant differential expression in comparison to the control group. Specifically, 1164 genes were upregulated, and 894 genes were downregulated, manifesting a clear segregation at the transcriptomic level. The application of KEGG pathway analysis revealed that FOF induces complex and multi-layered modulations in the metabolic pathways within \u003cem\u003eV. faba\u003c/em\u003e. The upregulated pathways include, but are not limited to, Alanine, Aspartate, and Glutamate metabolism, Beta-Alanine metabolism, Glycine, Serine, Threonine metabolism, Tyrosine metabolism, Pyruvate metabolism, Carbon fixation in photosynthetic organisms and Phenylalanine metabolism. These findings are in coherence with previous studies[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], underpinning the adaptability of \u003cem\u003eVicia faba\u003c/em\u003e in response to FOF infection. The upregulated pathways intimate an enhancement of defensive responses, augmented energy production and escalated synthesis of defense-related compounds. Conversely, the downregulated pathways implicate ribosome biosynthesis in eukaryotes, amino acid biosynthesis, cysteine and methionine metabolism, one-carbon pool by folate, valine, leucine and isoleucine biosynthesis, glycine, serine and threonine metabolism, purine metabolism and carbon metabolism[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. These attenuated pathways indicate a diminished protein synthesis, amino acid biosynthesis, nucleotide metabolism and carbon utilization during the fungal infection process, which could be a manifestation of the plant's strategic resource redistribution under pathogenic stress, bolstering defensive mechanisms and adapting to the disease. The results from Gene Ontology (GO) enrichment analysis of the 2058 differentially expressed genes (DEGs) compellingly an association with pathways primarily engaged in energy production and Reactive Oxygen Species (ROS) clearance mechanisms (GO:0055114). Notably, these pathways comprise oxidation-reduction processes, Coenzyme-B sulfoethylthiotransferase activity (GO:0050524), cellular respiration (GO:0045333), energy derivation by oxidation of organic compounds (GO:0015975), lysyl-tRNA aminoacylation (GO:0006430), and phosphoenolpyruvate carboxykinase (ATP) activity (GO:0004612). This potent evidence strongly insinuates that \u003cem\u003eVicia faba\u003c/em\u003e mobilizes proactive measures to bolster their energy-generating machinery and amplify ROS scavenging capabilities, strategically countering the intrusion of pathogenic entities. The augmented pathways suggest an intensified defense mechanism, a surge in energy production, and a boost in the biosynthesis of defense-related compounds. Conversely, the pathways experiencing a downturn encompass ribosome biogenesis in eukaryotes, amino acid biosynthesis, cysteine and methionine metabolism, one carbon pool by folate, valine, leucine, and isoleucine biosynthesis, glycine, serine, and threonine metabolism, purine metabolism, and carbon metabolism, among others [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. These attenuated pathways signify a decrement in protein synthesis, amino acid biosynthesis, nucleotide metabolism, and carbon utilization during the onslaught of fungal infection, potentially representing a shrewd resource reallocation by the plants under pathogenic duress to fortify defense mechanisms and accommodate disease conditions[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWith a skillfully executed analysis of the transcriptomic and metabolomic changes in \u003cem\u003eV. faba\u003c/em\u003e when confronted with FOF, we have successfully unveiled the intricate biological mechanisms activated in response to the formidable challenge posed by \u003cem\u003eFusarium\u003c/em\u003e wilt. Specifically, our investigation into the transcriptomic milieu has unveiled a substantial upheaval in the expression profile of a legion of genes in the aftermath of the fungal incursion, this includes not solely the genes orchestrating the metabolic pathways, but also those that fortify the plant's defenses against diseases, alongside pivotal transcription factors. By mapping the differentially expressed genes, we have unraveled a complex network of both upregulated and downregulated metabolic pathways. This critical knowledge provides us with an unprecedented depth of understanding regarding the strategic and potentially sophisticated responses that V. faba employs while navigating the challenging landscape of \u003cem\u003eFusarium\u003c/em\u003e wilt. To comprehensively comprehend the influence exerted by \u003cem\u003eFusarium\u003c/em\u003e wilt on \u003cem\u003eV. faba\u003c/em\u003e, we employed a dual approach, evaluating both transcriptomic and metabolomic alterations. This method helped us pinpoint key pathways that the plant uses to react to the disease. We saw major changes in important processes, like the metabolism of amino acids alanine, aspartate, and glutamate, and in how metabolites such as valine, proline, and arginine are made and broken down. These insights match what Alfosea-Sim\u0026oacute;n et al. found in their research[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, 16S and ITS amplicon sequencing were employed to identify a total of 1,817,89 OTUs in the rhizospheres of plants. Additionally, bacterial and fungal isolates were obtained from both healthy and diseased soil rhizospheres, resulting in the screening and selection of 15 strains for the construction of synthetic communities. This study demonstrates a significant inhibition of \u003cem\u003eFusarium\u003c/em\u003e wilt in seedlings by the symbiotic microbial community, comprising combined bacteria and fungi (with a remarkable 71.76% suppression of wilt disease). Utilizing UHPLC-Q-Orbitrap-MS non-targeted metabolomics (LC-MS/MS), we elucidated that this microbial community enhances plant resistance against pathogens through upregulation of the flavonoid biosynthesis pathway and activation of the jasmonic acid signaling pathway in both leaves and roots. In a study of the soil rhizosphere of faba beans, it was found that in the soil of diseased plants, there were high levels of Antibiotic Resistance Genes (ARGs) such as \u003cem\u003eIND-6\u003c/em\u003e, \u003cem\u003eCarA\u003c/em\u003e, and \u003cem\u003eDeF\u003c/em\u003e. Additionally, there was significant activity observed in genes associated with energy metabolism, amino acid metabolism, and the Assimilatory Nitrate Reduction process of the nitrogen cycle. In contrast, healthy plants exhibited an increase in pathways related to Nitrogen Fixation, Nucleotide Metabolism, and Carbohydrate Metabolism. Non-targeted metabolomic analysis (LC-MS/MS) detected 257 root exudates and 65 soil metabolites. Randomforest screening of root metabolites along with validation of exogenous metabolites revealed that betaine, proline, coumarin, and oleanolic acid effectively mitigate wilt dise ase occurrence. The transcriptomic and non-targeted metabolomics analysis revealed significant enrichment in pathways such as alanine, aspartate, and glutamate metabolism, as well as biosynthesis of unsaturated fatty acids in diseased \u003cem\u003eV. faba\u003c/em\u003e. This research provides novel insights into the impact of wilt disease on plant immune stress responses, root metabolite secretions, and the composition and function of rhizosphere microbial communities, thereby augmenting soil antibiotic gene levels and nitrogen cycling pathways.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors extend their sincere appreciation for the financial support received for this research. This work was funded by the National Natural Science Foundation of China under Grant No. 31901929. Additional support was provided by the Natural Science Foundation of Chongqing, under Grant Nos. cstc2021jcyj-msxmX1021 and cstc2021jcyj-msxmX10590. The generous contributions from these organizations have been instrumental in facilitating the successful completion of this study.\u003c/p\u003e\n\u003ch2\u003eContributions\u003c/h2\u003e\n\u003cp\u003eThe conceptualization and design of the experiments were jointly undertaken by Chaowen Zhang, Mengyuan Li, Hongji Wang, and Ke-Pan. The execution of these experiments was carried out by Chaowen Zhang, Mengyuan Li ,Ke-Pan, Ruiqi Wang, Xinyan He, Cong-Hu, Xuanbo Fan, and Yatong Gong. Data analysis was conducted by Chaowen Zhang, Zimei Liu, and Xianyao Li. The guidance on data mining processes and the drafting of the manuscript were provided by Jianjun Zhao, and Yuzhu Han. All authors have critically reviewed the content and have given final approval for the current version of the manuscript to be published. Each contributor has agreed to be accountable for all aspects of the work, ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eConflict of Interest Statement\u003c/h2\u003e\n\u003cp\u003eAll authors of this paper hereby declare that there are no commercial or financial conflicts of interest, nor any personal relationships or competitive interests. All authors concur with this statement.\u003c/p\u003e\n\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eThe 16S rRNA amplicon sequencing raw data were deposited in the NCBI BioProject database under the accession numbers PRJNA1077665. ITS gene amplicon sequencing, metagenomics, and transcriptomics have been uploaded to the China National Center for Bioinformation (https://ngdc.cncb.ac.cn/gsub/). The accession numbers are respectively subPRO035184, subPRO035179, and subPRO035181.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhou X, Wang J, Liu F, Liang J, Zhao P, Tsui CKM, et al. Cross-kingdom synthetic microbiota supports tomato suppression of \u003cem\u003eFusarium\u003c/em\u003e wilt disease. Nat Commun [Internet]. 2022 [cited 2023 Jul 21];13:7890. Available from: https://www.nature.com/articles/s41467-022-35452-6\u003c/li\u003e\n\u003cli\u003eXun W, Ren Y, Yan H, Ma A, Liu Z, Wang L, et al. Sustained Inhibition of Maize Seed-Borne \u003cem\u003eFusarium\u003c/em\u003e Using a \u003cem\u003eBacillus\u003c/em\u003e-Dominated Rhizospheric Stable Core Microbiota with Unique Cooperative Patterns. Advanced Science [Internet]. 2023 [cited 2023 Dec 31];10:2205215. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/advs.202205215\u003c/li\u003e\n\u003cli\u003eGoossens P, Spooren J, Baremans KCM, Andel A, Lapin D, Echobardo N, et al. 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Available from: https://www.frontiersin.org/articles/10.3389/fpls.2020.581234\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Fusarium wilt, Rhizosphere Microbiota, Vicia faba, Root Exudates, Multi-Omics Analysis, Soil-Borne Disease Suppression, Synthetic communities","lastPublishedDoi":"10.21203/rs.3.rs-3980679/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3980679/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSoil-borne \u003cem\u003eFusarium\u003c/em\u003e wilt imposes substantial economic losses on agriculture, with \u003cem\u003eVicia faba\u003c/em\u003e exhibiting pronounced susceptibility to \u003cem\u003eFusarium\u003c/em\u003e disease. However, the mechanisms underlying \u003cem\u003eV. faba\u003c/em\u003e's resistance to \u003cem\u003eFusarium\u003c/em\u003e and the intricate interplay between crucial rhizosphere microbes and root exudates during pathogen attack remain inadequately understood. This study investigates the interaction between faba bean plants and the soil microbiome to elucidate the mechanisms underlying plant \u003cem\u003eFusarium\u003c/em\u003e wilt. Through comprehensive analysis of 16S ribosomal RNA gene and internal transcribed spacer (ITS) sequencing data obtained from the faba bean rhizosphere soil, this research successfully identified key microbial groups that are enriched in the disease-suppressing rhizosphere, namely \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, and \u003cem\u003eTrichoderma\u003c/em\u003e. The strains displayed significant inhibitory effects on \u003cem\u003eFusarium oxysporum\u003c/em\u003e, notably. A synthetic community was constructed using these strains, which exhibited a remarkable capacity to suppress \u003cem\u003eFusarium\u003c/em\u003e wilt in faba bean seedlings, achieving an impressive inhibition rate of up to 71.76%. Non-targeted metabolomics analysis was employed to uncover the metabolic pathways through which this Synthetic community aids plants in resisting pathogens. Additionally, metagenomic analysis revealed an increased abundance of Antibiotic Resistance Genes (ARGs) in the rhizosphere soil of diseased plants, while the soil associated with healthy plants exhibited enhanced activity in nitrogen fixation, nucleotide metabolism, and carbohydrate metabolism pathways. Soil metabolites and root exudates were analyzed, and a Random Forest model was employed to investigate the impact of exogenous metabolites on \u003cem\u003eFusarium\u003c/em\u003e wilt occurrence. Significantly, compounds such as 10 \u0026micro;M Betaine, Proline, and Racemosin demonstrated remarkable efficacy in reducing the incidence of \u003cem\u003eFusarium\u003c/em\u003e wilt. Furthermore, transcriptomic and non-targeted metabolomics analyses were conducted in this study, revealing substantial enrichment in pathways including jasmonic acid metabolism, alanine metabolism, aspartate metabolism, glutamate metabolism, and unsaturated fatty acid biosynthesis in diseased \u003cem\u003eV. faba\u003c/em\u003e. This study not only advances our understanding of plant \u003cem\u003eFusarium\u003c/em\u003e wilt and their impact mechanisms but also provides valuable insights for enhancing soil health and crop disease resistance.\u003c/p\u003e","manuscriptTitle":"The interplay between root exudates and Cross-kingdom synthetic microbiota enhances the resistance of Vicia faba to Fusarium wilt disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-26 17:08:40","doi":"10.21203/rs.3.rs-3980679/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":"7416cb58-a36a-4bd3-a884-5d52eab47417","owner":[],"postedDate":"February 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-30T16:29:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-26 17:08:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3980679","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3980679","identity":"rs-3980679","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}