Bacillus species are core microbiota of highly resistant maize varieties that induce host metabolic defense against corn stalk rot

Bacillus species are core microbiota of highly resistant maize varieties that induce host metabolic defense against corn stalk rot | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Bacillus species are core microbiota of highly resistant maize varieties that induce host metabolic defense against corn stalk rot Wende Liu, Xinyao Xia, Qiuhe Wei, Hanxiang Wu, Xinyu Chen, Chunxia Xiao, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3400607/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 Microbes colonizing each niche of terrestrial plants are indispensable for maintaining crop health. Although corn stalk rot (CSR) is a severe disease infecting maize ( Zea mays ) worldwide, the mechanisms underlying host–microbe interactions across vertical niches in maize plants, which exhibit heterogeneous CSR resistance, remain largely uncharacterized. Here, we investigated the microbial communities associated with CSR-resistant and -susceptible maize cultivars using multi-omics analysis coupled with experimental verification. Maize cultivars resistant to CSR reshaped the microbiota and recruited Bacillus species with three antagonistic phenotypes to alleviate pathogen stress. By inducing the expression of Tyrosine decarboxylase 1 ( TYDC1 ), encoding an enzyme that catalyzes the production of tyramine and dopamine, Bacillus isolates that do not directly suppress pathogen infection facilitated the synthesis of berberine, an isoquinoline alkaloid that inhibits pathogen growth. These beneficial bacteria were recruited from the rhizosphere and transferred to the stems but not grains of the infected resistant plants. Our findings offer insight into how maize plants respond to and interact with their microbiome and provide valuable strategies for controlling soil-borne pathogens. Biological sciences/Plant sciences/Plant stress responses/Biotic Biological sciences/Microbiology/Antimicrobials/Antimicrobial resistance Bacillus Corn stem rot Ecological niches Microbiome Resistance heterogeneity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Corn stalk rot (CSR), an economically destructive soil-borne disease affecting maize ( Zea mays )-growing areas worldwide, is caused by pathogens such as the fungi Fusarium and Pythium , as well as bacterial species 1 . Single or multiple pathogens infect the root vascular bundle via natural openings or wounds, thereby affecting water and nutrient transport and ultimately causing the plant to wilt and die 2 . Due to the wide variety of geographically specific pathogens and local differences in infection routes throughout the growth period, conventional field management measures such as seed coating with chemical fungicides have had limited success in disease control 3 . Although introducing resistance ( R ) genes into a host plant through genetic engineering decreases the incidence of disease 4 , it is challenging to identify appropriate parental resources and stabilize R genes in complex and volatile environments 5 . Therefore, there is a need to identify efficient and more natural disease-suppressive methods from the perspective of agroecosystem management. Microbes are vital to ecosystem health and have effectively colonized plants during the last 400 million years of coevolution with their plant hosts 6 , 7 . Plants enhance their resistance to pathogens by reshaping the composition of their associated microbe communities, resulting in the assembly of a stress-alleviating microbiota 8 – 10 . The plant-mediated ‘cry for help’ hypothesis further highlights the potential for microbes as a significant component of resistance mechanisms, defining an important role for the microbiota in stimulating host resistance to plant diseases and insect pests and tolerance to abiotic stress while promoting growth 11 – 13 . Specifically, Trichoderma fungi colonize the soil and maize roots, thereby reducing the overall number of pathogenic Fusarium species 14 , 15 . Chili pepper ( Capsicum annuum ) plants infected with Fusarium wilt disease recruit beneficial bacteria and mitigate changes in the microbiome in reproductive organs to facilitate the survival of the host and its progeny 16 . These observations suggest that both bacteria and fungi have tremendous potential as biological control agents of CSR 17 . To develop effective disease management strategies, it is imperative to explore the contributions of bacterial and fungal taxa known to improve resistance in plants grown under natural conditions and to identify additional microbes from the ‘microbial dark matter’ 18 . Notably, the genotype-specific characteristics of the endophytic microbiomes in the seed and ‘resistance legacy’ (also known as ‘soil-borne memory’) of the effects of plant–microbe interactions in previous plant generations have been demonstrated 19 , 20 . These cross-generational microbes are located at the two terminals of host biology, pointing to multi-mechanistic microbiome heritability 21 . A comprehensive analysis of microbial communities in distinct ecological niches of maize infected with CSR can provide important information about the sources and heritability of seed endophytic microbes and suggest strategies for CSR prevention. Initially, a range of resistance levels to CSR. were observed among a maize-associated mapping panel of 527 inbred lines 22 with temperate, tropical and subtropical genetic backgrounds representing global maize diversity. We hypothesized that the structural and functional adaptation of the microbial communities would differ in resistant and susceptible maize cultivars. In the current study, using CSR-resistant and -susceptible maize cultivars, we performed a multi-omics analysis of host-associated bacterial and fungal communities in the soil (bulk soil and rhizosphere) and plant endogenous tissues (root, stem and grain). We identified core CSR resistance-related microbiotas in maize and explored the mechanisms underlying host–microbe interactions, laying the foundation for the development of biological control methods against this devastating disease. RESULTS Specific ecological niches and host resistance drive the assembly of associated bacterial communities After inoculating F. graminearum into the root zones of 40 maize cultivars (to avoid injuring the roots) for two consecutive years, we identified pronounced and consistent differences in CSR disease indexes (Supplementary Table 1). Infected maize plants exhibit hollow stems and roots covered with red mycelium, which eventually cause the plant to break (Fig. 1 a). To analyze the bacterial and fungal communities of maize plants that are resistant or susceptible to CSR, we collected samples from four resistant (disease incidence = 0%) and four susceptible (disease incidence > 30%) maize cultivars from five ecological niches: bulk soil (BS), rhizosphere soil (RS), root endosphere (RE), stem endosphere (SE) and grain endosphere (GE; Fig. 1 b). We determined the disease incidence by recording the percentage of diseased seedlings relative to the total number of seedlings. We profiled the compositions of the bacterial and fungal communities based on sequencing of 16S ribosomal RNA (rRNA) gene fragments and internally transcribed spacer (ITS) sequences, followed by clustering into operational taxonomic units (OTUs; 97% identity). We obtained 12,824,344 rRNA and 15,788,680 ITS high-quality reads from 198 samples, ranging from 30,162 to 94,013 reads per sample, with an average of 72,255 reads per sample, representing 4,624 bacterial and 1,471 fungal OTUs. We employed a linear mixed model (LMM) to assess which major factors shape the maize microbiota. To quantify species diversity within a microbial community, we also calculated the Shannon diversity index (SDI) for each community, with high values representing higher species diversity. We observed that ecological niches, together with resistant/susceptible genotypes, have a greater influence on bacterial SDI ( P = 0.0087) than do differences in maize cultivars ( P = 0.629; Supplementary Table 2). We evaluated the assembly process of microbial communities, which is known to be strongly linked to the maintenance of plant health 23 , based on the β-nearest taxon index (βNTI), which measures the mean phylogenetic distance between taxa of a community. We detected marked differences in βNTI in bacterial communities between resistant and susceptible sample pairs among the BS, RS, RE and SE ecological niches. By contrast, we noticed no significant changes for fungal communities, suggesting that resistance has a stronger influence on the assembly of bacterial vs. fungal microbiota (Fig. 1 c, Supplementary Table 3, Wilcoxon rank sum test, P < 0.05). The bacterial communities in soil niches (BS and RS) exhibited a divergent assembly compared to the internal niches (RE, SE and GE), whereas we detected no significant differences in fungal community assembly among the five niches analyzed (Extended Data Fig. 1 a). Furthermore, the proportion of deterministic (HS: homogeneous selection and VS: variable selection, |βNTI| > 2) and stochastic (DDH: dispersal, drift and homogenizing dispersal, |βNTI| 2) of bacterial communities increased in the RE and SE niches of resistant cultivars relative to the other three niches, indicating that the phylogenetic turnover and interaction among microorganisms were higher than expected. Neutral community model analysis showed that the dispersal ability and habitat niche breadth of bacterial communities decrease gradually from soil to internal niches (Extended Data Fig. 1 b, c). In the RS and RE niches, the dispersal limitation on bacterial communities was higher in resistant cultivars than in susceptible samples, as reflected by the lower Nm values (an estimate of dispersal between communities). Collectively, these findings indicate that the RE niche of resistant cultivars undergoes changes in its bacterial, rather than fungal, communities upon F. graminearum infection and that this niche plays a prominent role in host plant resistance. Distinct patterns of microbial diversity characterize different maize niches Assessments of Shannon diversity revealed significant differences between soil and plant niches in both bacterial and fungal communities (Tukey’s multiple comparisons test P < 0.05, Fig. 1 e, Supplementary Table 4). As a whole, the richness of bacterial and fungal species was lower in the maize internal compartments than in the soil, with GE displaying the least diversity. The SDIs of bacterial communities showed no significant differences (Wilcoxon rank sum test, P > 0.05) between resistant and susceptible groups among the five niches. By contrast, the SDIs of the fungal community were lower in the SE than RE niches of susceptible groups, suggesting that the microenvironment is unstable in these two niches in susceptible cultivars (Extended Data Fig. 1 d). Principal coordinates analysis (PCoA) based on Bray–Curtis dissimilarity clearly separated bacterial communities in the soil from the plant internal samples along the first principal coordinate (Fig. 1 f, Supplementary Table 5). In parallel, we observed significant differences in the composition of bacterial communities in the three internal niches along the second principal component. We noticed a similar positional variation pattern of fungal communities, as fungal communities in SE differed markedly from those in the other four niches. Moreover, we identified substantial differences in bacterial and fungal communities between resistant and susceptible samples in each niche, except for bacteria in the GE (Fig. 1 g, Supplementary Table 6). Overall, these results indicate that maize niches and resistance characteristics have strong selective effects on the composition and assembly of microbiome communities. Root-associated co-occurrence networks reveal stronger bacterial–fungal interactions in resistant maize cultivars To investigate whether and how CSR resistance affects the complexity and stability of molecular ecological networks across the ecological niches, we constructed bacterial–fungal co-occurrence networks (Spearman’s correlation coefficient (ρ) was > 0.9 and P < 0.05). We detected a clear shift in the interkingdom network patterns across the five ecological niches. The number of nodes (N), including both bacterial and fungal taxa, was lower in internal samples (RE, SE and GE) than in soil samples (BS and RS; Fig. 2 a, b). This finding was predictable, considering how soil tends to contain a greater diversity of microbes than the internal compartments of plants. Notably, the connectivity index (C) and average degree (D) in the RE, SE and GE niches did not decrease as might have been expected with the shift in nodes and edge, suggesting that the internal niches harbor stronger microbiological interactions. Compared to susceptible cultivars, roots (RE and RS) of resistant samples had more nodes and edges, with higher connectivity and average degree, representing stronger interactions. We observed an opposite pattern in maize stems (SE) compared with roots. Susceptible inbred lines had the most edges, highest connectivity and degree (Supplementary Table 7), and thus showed stronger bacterial–fungal interactions. In addition, the Bray–Curtis dissimilarity of root-associated networks (RS and RE) between resistant and susceptible groups was higher than that in the SE and GE niches, indicating that microbes in root niches were less stable than those in the stem and grain niches (Supplementary Table 8). These divergent network patterns between resistant and susceptible cultivars highlight the importance of root-associated bacteria in suppressing the growth of pathogens such as F. graminearum. We calculated the robustness of each network by simulating species extinction. This analysis revealed marked divergence in robustness between resistant and susceptible groups by removal of either random taxa or targeted module hubs (Fig. 2 c, d). Network vulnerability increased marginally, and the compositional stability and node persistence decreased along the bottom-up niches in both the resistant and susceptible groups (Fig. 2 e-g). These results suggest that niches and resistance characteristics of the host plant greatly influence bacterial–fungal interactions, with the root niches of resistant cultivars conferring the greatest resistance to fungal pathogens. Susceptible cultivars are exposed to rhizosphere microbiomes with functions related to cell wall degradation Because the rhizosphere is a primary ecological niche for microbial, environmental and host interactions, we used metagenomic sequencing data from the rhizosphere microbiome to explore the functional variation of microbiomes associated specifically with resistant or susceptible maize cultivars. PCoA showed that CSR resistance has a pronounced effect on the functional composition (GO, CAZ, COG and KO) of rhizosphere microbes (Fig. 3 a and Extended Data Fig. 2 a, PERMANOVA P < 0.05). However, we did not observe persistent significant differences in functional diversity between data obtained in 2020 and 2021, as reflected by the SDI (Fig. 3 b). We then analyzed the metabolic pathways to explore the above functional alterations. We established that amino acid metabolism pathways (mainly including l-isoleucine, l-arginine and guanosine biosynthesis) are significantly enriched in the rhizosphere microbiome of resistant maize cultivars (Fig. 3 c). Metabolic pathways associated with polysaccharides (such as d-glucosamine biosynthesis, d-glucarate degradation and sucrose degradation) and organic acids (such as fatty acid biosynthesis, unsaturated fatty acid biosynthesis and pantothenate and coenzyme A biosynthesis) were enriched in the susceptible maize cultivars. In addition, we identified pathways associated with the metabolism of amines (thiamine formation and thiamine salvage II) and aromatics (aromatic biogenic amine degradation) in the susceptible group. Further analysis of the rhizosphere microbiome revealed that the abundance of 61 carbohydrate-related enzyme families (CAZy), including six carbohydrate-active enzyme classes, significantly differs between the resistant and susceptible groups (Wilcoxon, P < 0.05, Fig. 3 d). Compared to the microbes associated with resistant samples (15 families), those associated with susceptible samples were enriched for CAZy families (20 families) associated with the degradation of major components of plant cell walls such as cellulose, hemicellulose or pectin. The presence of these enzymes would make it easier for pathogens to breach the cell wall barrier and invade the susceptible host plants. Three peptidoglycan-degrading (GT31, GT49 and GH103) and five chitin-degrading (GH18, GH19, GH23, CBM50 and CBM73) CAZy family members were uniquely enriched in the microbes associated with susceptible samples. The presence of these enzymes would facilitate the degradation of bacterial and fungal cell walls, respectively. Thus, the investigation of CAZy families revealed the more drastic fluctuations of carbohydrate-related family members and stronger cell wall degradation–related interactions among bacteria, fungi and maize roots in the rhizosphere microenvironments of susceptible plants. Bacillus tends to be recruited across bottom-up niches To define the core microbiota associated with CSR-resistant plants, we comprehensively analyzed the taxonomic compositions and relative abundances of their bacterial and fungal communities. Microbial community analysis at the phylum level showed niche specificity (Fig. 4 a). The dominant bacterial phyla were Proteobacteria (63.23%), Acidobacteriota (13.10%) and Actinobacteriota (6.94%). Notably, Acidobacteriota mainly existed in the two soil niches, and the proportion of Actinobacteriota was higher in SE samples. A similar analysis of fungal communities showed that Ascomycota (65.29%) and Basidiomycota (24.93%) are the dominant phyla. Furthermore, measurement of the abundance of F. graminearum from ITS amplicon sequencing data showed that the proliferation of F. graminearum is significantly inhibited in the RS, RE and SE niches of resistant maize inbred lines (Fig. 4 b). We first identified the core taxa in resistant and susceptible groups through differential analysis, which demonstrated that four shared genera, i.e., Bacillus , Granulicella , Mucilaginibacter and Pantoea , showed significant differences among the RS, RE and SE niches (Wilcoxon rank sum test, P < 0.05, Fig. 4 c). Relative abundance measurements revealed that only Bacillus tended to be recruited to the RS, RE and SE niches of CRS-resistant inbred lines (Fig. 4 d). Linear discriminant analysis effect size (LEfSe) with a logarithmic LDA > 2.5 further indicated that Bacillus can serve as a useful biomarker for resistant maize cultivars (Fig. 4 e and Extended Data Fig. 2 b). To identify the core fungal taxa of CRS-resistant maize plants, we investigated fungal communities using the same method; however, the results were non-uniform (Extended Data Fig. 2 c and d). Therefore, we selected Bacillus as a candidate core taxon for CSR-resistant cultivars and subjected it to a series of verification experiments. The presence of Bacillus species enhances plant performance against F. graminearum To explore the effects of the recruited bacteria on plant health, we purified 276 bacterial isolates, accounting for 43% of all the OTUs with more than five reads in RS samples, from the rhizosphere soil of resistant cultivars using gradient dilution and streak plate techniques (Supplementary Table 9). These 276 isolates, including 62 Bacillus isolates, were categorized into 17 families based on 97% identity of 16S rRNA sequencing results. In addition, we obtained 694 Bacillus isolates using the Bacillus -specific method, which clustered into 28 subgroups with 99% identity within a subgroup. We randomly selected 44 isolates from the 28 subgroups for de novo genome sequencing and protein prediction. Based on these results, we assigned accurate taxonomies by reconstructing a neighbor-joining (NJ) phylogenetic tree from 4,215 Bacillus genomes using the CVTree Standalone Version (Fig. 5 a). We identified three models representing the antagonistic phenotype of all the isolates against F. graminearum in dual culture assays, which we characterized as follows: inhibition by niche grabbing, inhibition by secreting antimicrobial compounds and no inhibition. Metagenomic mapping of 23 isolates showed an enrichment in resistant cultivars (marked by * in Fig. 5 a, Wilcoxon rank sum test, P < 0.05) and represented two examples of niche grabbing, nine examples of secreting antimicrobial compounds and 12 isolates with no inhibition. We generated three types of synthetic communities (SCs) by randomly selecting and mixing three isolates from each model in equal-volume suspensions (SC-I: inhibition by niche grabbing; SC-II: inhibition by secreting antimicrobial compounds; SC-III: no inhibition). We investigated the disease suppression activity of these SCs against F. graminearum in greenhouse experiments. Infected maize seedlings were characterized by hyphal diffusion at the base and the apparent yellowing of stems, resulting in wilting within 2 weeks (Fig. 5 b, Extended Data Fig. 3 a). Three individual pot experiments showed that SC-I, SC-II and SC-III significantly decreased CRS incidence in maize seedlings at 2 weeks after F. graminearum inoculation (Fig. 5 c). In addition, F. graminearum treatments reduced the rate of seedling emergence and root growth, while additional inoculation with Bacillus at the same time, especially with SC-II, restored these seedling phenotypes (Extended Data Fig. 3 b-d). To gain insight into the disease resistance mechanisms of the host plant based on their responses to pure Bacillus treatment, we investigated the transcriptomic changes in maize roots treated with these three SCs. We obtained 534,236,556 high-quality reads, with an average of 44,519,713 reads per sample and a 94.6% mapping rate to the maize reference genome (B73-NAM-5.0). Compared to the control groups treated with sterile water, we identified 293, 387 and 171 differentially expressed genes (DEGs, |log 2 (fold-change) | > 1, P adj < 0.05) in maize roots treated with SC-I, SC-II and SC-III, respectively (Fig. 5 d). PCoA based on an expression matrix of all genes showed that SC treatment did not greatly alter gene expression in maize roots (Fig. 5 e), whereas PCoA based on DEGs (Fig. 5 f) and functionally related genes (Fig. 5 g) showed that SC-I and SC-II had similar effects on maize that were different from those observed for SC-III. Gene set enrichment analysis in response to treatment with each SC revealed similar enrichment among DEGs for functional terms such as mitogen-activated protein kinase (MAPK) signaling and plant hormone signal transduction (Fig. 5 h, Extended Data Fig. 4 a, b, Supplementary Table 10). Notably, inoculation with any of the three SCs resulted in lower expression of WRKY33- homologous genes, which facilitate defense-related gene induction (Fig. 6 a). In addition, SC-II and SC-III may enhance the biosynthesis of flavonoids, as evidenced by the induction of flavanone 3-hydroxylase1 ( FHT1 ) expression. We were most interested in the SC-III-specific functional pathway, since SC-III had no inhibitory effect on F. graminearum but significantly decreased the incidence of CSR. Furthermore, SC-III, rather than SC-I or SC-II, specifically facilitated the enrichment of maize genes related to sesquiterpenoid, isoquinoline alkaloid and betalain biosynthesis, as reflected by the normalized enrichment scores (NESs); these compounds are widely considered to be antibacterial metabolites (Fig. 5 h). Both terpene synthase 6 ( TPS6 ) and tyrosine decarboxylase 1 ( TYDC1 ), which participate in the above pathways, were upregulated by treatment with SC-III. The functional terms phenylalanine and tyrosine metabolites, which are biosynthetic precursors of isoquinoline alkaloids, were also enriched by treatment with SC-III (Supplementary Table 10). We validated the expression patterns of the above-mentioned genes by RT-qPCR analysis (Fig. 6 b and Extended Data Fig. 4 c). Isoquinoline alkaloids are enriched in resistant cultivars and suppress CSR To investigate the biochemical composition of the root microenvironments, we determined the identities of metabolites in RS and RE samples collected from resistant and susceptible cultivars treated with F. graminearum in 2021 by liquid chromatography–tandem mass spectrometry (LC-MS/MS). We detected an average of 1,558 metabolites across all samples (Supplementary Tables 11–12). PCoA based on Bray–Curtis distance matrices revealed significant differences in both RS and RE metabolites between resistant and susceptible samples ( P = 0.008, PERMANOVA by Adonis, Fig. 6 c). We identified 947 significantly different metabolites in RE samples and 124 in RS samples (|log2FC| > 1, P < 0.05), demonstrating the presence of distinct chemical microenvironments in these two niches. Consistent with the transcriptomic data (Fig. 5 h), berberine (a natural isoquinoline alkaloid) and its isoquinoline precursor were enriched in the RE niche rather than the RS niche. The biosynthetic precursor l-phenylalanine appeared to be depleted from resistant samples (Fig. 6 d, Extended Data Fig. 4 d). Importantly, l-dopa and tyramine, members of the berberine biosynthesis pathway that are substrates used by TYDC1 to synthesize dopamine, were enriched in RE samples (Extended Data Fig. 4 d). The antagonistic activities of berberine against F. graminearum were demonstrated in vitro . In potato dextrose agar (PDA) plate assays, F. graminearum growth was reduced as the concentration of berberine increased, with growth diameters decreasing by 37%, 52%, 54% and 58% in the presence of 5, 25, 50 and 100 µg/mL berberine, respectively (Fig. 6 e and Extended Data Fig. 4 e). Finally, pre-treatment of seed coats with berberine significantly reduced CSR disease severity (Fig. 6 f). Together, these results indicate that SC-III treatment triggers disease-suppressive activity in the RE niche by inducing the accumulation of antifungal metabolites. DISCUSSION The phenotype of a plant depends primarily on the integration of the plant’s genotype, the associated microbiota and the field microclimate 24 , 25 . Accumulating evidence suggests that plants actively reshape specific communities of microorganisms to alter their phenotypes, promote growth and even inhibit disease occurrence 26 . Our 16S rRNA analysis demonstrated that both the ecological niche and the host resistance genotypes significantly shape the ecological assembly processes of bacterial, rather than fungal, communities. This plasticity in bacterial communities was further verified by diversity analysis. Although our observations do not align with the results of many studies that show that fungal Trichoderma species suppress CSR symptoms 27 , we and others have witnessed an important bacterial community response to CSR 28 , 29 . Host-associated bacteria account for approximately 70 to 90% of total soil microorganisms and harbor 100-fold more functional genes than the host 30 , making them worthy of being the optimal choice to form holobionts with their host. The dispersal ability and habitat niche breadth of bacterial communities decreased gradually from the soil to internal niches, indicating that selection pressure increases during the movement of microbes from belowground to aboveground niches. The plant immune system and associated biochemical barriers are thought to result in strong selective pressure on the microbiota inhabiting inner plant tissues 31 . Our neutral community model analysis further indicated that RS and RE niches in CRS-resistant, rather than CRS-susceptible, maize cultivars provide stronger dispersal limitation and selection pressure. These findings underscore the potential role of maize roots as a physical barrier and selection driver in shaping bacterial communities. The precipitous decline in microbial diversity from soil to internal niches and the clearly separated communities revealed by PCoA also support the role of maize roots in shaping their bacterial communities. In addition, our study demonstrated that the diversity of bacterial communities was much lower in the reproductive organ (grain) than in vegetative organs (root or stem) and that the assembly process of bacterial communities in the grain was not affected by the resistance genotype of the host plant. This pattern of microorganisms within grains can be explained by a life history tradeoff strategy that ensures the survival of the next generation of the host plant at the expense of investing in the susceptible individual in the current generation 16 , 32 . Microbiota do not live in isolation, instead forming complex interactions with living organisms and non-living environments through the exchange of matter, energy and information, as shown by networks with species as nodes and associations as edges 33 . The co-occurrence of species is thought to be mainly driven by three ecological responses: biological interactions, environmental filtration and diffusion restriction. Among these responses, biological interactions may be the main driving force for the overall network 34 , 35 . In this study, the bacterial–fungal interkingdom networks in soil niches showed richer compositions and more complex interactions than those in internal niches, which is in agreement with earlier studies 36 . Importantly, the root-related niches (RS and RE), but not the other niches, showed stronger interactions among bacterial–fungal interkingdom networks in CSR-resistant maize than in CSR-susceptible maize. Thus, we suggest that roots are the key regions where CRS-resistant maize shapes the bacterial community and thereby promotes disease resistance. Fusarium species secrete mycotoxins that cause plant diseases such as stem rot, Fusarium wilt and scab and ear rot, resulting in significant losses in crop production 37 . In contrast to chemical control methods, the use of biological control agents provides a safe, effective, sustainable means of controlling plant diseases caused by Fusarium . Using multiple bioinformatics methods, we illustrated that Bacillus is a prominent feature of the core anti-CSR microbiome; our greenhouse experiments confirmed that Bacillus treatment significantly inhibits CSR. These findings are consistent with those of studies showing that Bacillus can antagonize Fusarium infection in multiple ways: niche competition, the production of antibacterial substances and the induction of plant systemic resistance 38 , 39 . We showed that B. cereus and B. albus antagonize F. graminearum by secreting antimicrobial compounds (Fig. 5 a). A previous study identified hentriacontane and butylphenol as key antifungal volatile organic compounds produced by B. cereus that improve the resistance of tomato ( Solanum lycopersicum ) plants to Fusarium wilt 40 . Although there is no direct evidence that B. albus inhibits pathogen activity, B. albus was reported to produce cellulase, which acts as an antifungal protein capable of rupturing pathogenic cell walls 41 . In the current study, B. subtilis demonstrated strong space competitiveness and monopolized nutritional resources, which is consistent with previous findings 39 , 42 . Surprisingly, F. graminearum growth in dual culture assays was not inhibited by treatment with purified bacterial isolates, but macroscopic disease was suppressed when maize seedlings were treated with B. simplex , B. pumilus , B. safensis or B. altitudinis . Maize root transcriptome sequencing showed that these isolates specifically facilitated the enrichment of molecules involved in sesquiterpenoid, isoquinoline alkaloid and betalain biosynthesis. Although little is known about plant growth–promoting rhizobacteria, these Bacillus strains were reported to improve host performance by increasing the emergence of lateral roots in pea ( Pisum sativum ; B. simplex ), shoot dry mass in chickpea ( Cicer arietinum ; B. pumilus ), root length in Brassica juncea ( B. safensis ) and germination index in rice ( Oryza sativa ; B. altitudinis ) 43 – 46 . Therefore, we suggest that these isolates mainly induce disease resistance by interacting with the host. The two-way production and perception of secretions is a key mechanism through which plants interact with their microbiota 47 . Plants secrete specific exudates at defined stages of their development and under biotic and abiotic stress that shape microbiomes, whereas microbiomes modulate the soil environment by reprogramming root exudation profiles 48 , 49 . Acknowledging the crucial effect of roots on shaping microenvironments, we further explored the specific compositions and differentiation of these root exudates. We demonstrated that the metabolic profiles of CSR-resistant and -susceptible maize cultivars are distinct. This observation is consistent with the expectation that complex root systems and wide genomic diversity of maize result in major physiological differences 50 . Distinct maize genotypes have been shown to differ in their enrichment of specific bacterial taxa 50 . In line with the results of transcriptome functional enrichment, isoquinoline alkaloid pathways, together with the isoquinoline precursor, were enriched in the RE niche of resistant cultivars when treated with SC-III, and they exhibited both in vitro antagonistic activities and in vivo disease suppression effects. This finding is consistent with the previous observations that isoquinoline alkaloid biosynthesis was significantly upregulated in maize seedlings infected with Rice black streaked dwarf virus based on transcriptomic data and with Fusarium verticillioides based on metabolic profiles 51 , 52 . However, these studies did not focus on the associated changes in the microbiota. A previous study of the microbiota–host interaction in fava bean ( Vicia faba ) demonstrated that the antifungal peptide P852 from Bacillus fermentation broth could control Fusarium wilt by promoting the activities of antioxidant enzymes and enhancing isoquinoline alkaloid biosynthesis 53 . Although a clear link between Bacillus treatment and isoquinoline alkaloid biosynthesis was revealed by these findings and the current results, we cannot exclude the possibility that other factors, such as WRKY33-related induction of resistance, contribute to disease suppression. Considering that SC-I inhibited WRKY33 expression but resulted in weaker disease suppression effects compared to SC-III, we suggest that isoquinoline alkaloids play important roles in the resistance of maize cultivars to CSR. It will be interesting to examine whether CSR-resistant maize plants secrete isoquinoline alkaloids, which could potentially inhibit the colonization and invasion of a wider range of pathogens. Together, these findings highlight the important role of Bacillus species as a key microbiota for suppressing Fusarium -induced diseases through multiple mechanisms, including the direct secretion of antimicrobial compounds and the induction of alkaloid biosynthesis by the host (Fig. 6 g). CONCLUSIONS Using multi-omics analyses and experimental validation, we showed that resistant host plants govern the assembly of distinct, beneficial microbial communities in their internal environmental niches. We also demonstrated that Bacillus species are core components of the microbiota recruited by CSR-resistant maize cultivars in the RS, RE and SE ecological niches. In contrast to fungal microbiota, which showed little effect in preventing CSR, several Bacillus species decreased the incidence and symptoms of CSR via the secretion of antimicrobial compounds and the induction of host alkaloid biosynthesis. Although treatment with the SC-III suspension did not inhibit F. graminearum in vitro , it specifically induced endogenous isoquinoline alkaloid biosynthesis in vivo in the RE niche. Overall, these findings improve our understanding of the roles of core microbiotas in plant responses to pathogen challenge and lay the foundation for preventing and treating Fusarium -induced disease. METHODS Experimental design and sampling The experiments were conducted in Gongzhuling City (108° 32′ N, 124° 58′ E), Jilin Province, northeastern China, a site that is considered to be one of the three golden corn belts. The site has a temperate continental monsoon climate with an annual average temperature of 5.6°C, average annual precipitation of 595 mm and a frost-free period of 144 days. Forty maize ( Zea mays ) cultivars (Supplementary Table 1) from a 527 association mapping panel 54 were planted in natural plots and disease nursery plots constructed for disease evaluation in early May 2020 and 2021. For the disease nursery plots, high-temperature-sterilized maize seeds that were co-cultured in conical flask with the mycelium of Fusarium graminearum for 2 weeks were sown in the rhizosphere zone of maize plants at the vegetative 6 (V6) stage without damaging the roots. Maize plants at the reproductive 3 (R3) stage that showed no watery spots on their stems or brown withered stems were defined as healthy; plants with watery spots on their stems and hollow stems with brown xylem were classified as diseased (Fig. 1 a). Surveys for the incidence of CSR (percentage of the total number of plants showing disease symptoms) were performed at the end of September 2020 and 2021. Maize cultivars that lacked CSR symptoms for two consecutive years were defined as resistant; cultivars with an incidence of CSR greater than 30% for two consecutive years were defined as susceptible. Four susceptible and four resistant maize cultivars were selected in October 2020 and again in October 2021 for experimentation. For each of these cultivars, five individual samples were collected from each of six ecological niches: bulk soil (BS), rhizosphere (RS), root endosphere (RE), stem endosphere (SE) and grain endosphere (GE), as illustrated in Fig. 1 b. Bulk soil samples were collected 20 cm away from the plants at a depth of 0–25 cm. Rhizosphere soil was collected by manually shaking uprooted plants after removing large pieces of soil that loosely adhered to the roots. All collected maize tissues were transported to the laboratory in a dry ice box and stored at − 80°C for subsequent experiments. DNA extraction and amplicon sequencing Bulk and rhizosphere soil DNA were extracted using a PowerSoil DNA Isolation Kit (MO BIO Laboratories, Carlsbad, CA, USA) according to the manufacturer’s instructions. For surface sterilization, a minimum of 5 g of roots, stems or seeds was rinsed with 70% (v/v) ethanol for 5 min and then with 6% (w/v) sodium hypochlorite solution for 10 min, followed by three rinses in sterile H 2 O for 15 min. The treated tissues were ground into a powder in liquid nitrogen using a sterile mortar and pestle. Total DNA was extracted from the tissues using a FastDNA SPIN Kit for Soil (MP Biomedicals, Solon, USA) following the manufacturer’s instructions. The V5-V7 hypervariable region (799F/1193R) of the bacterial rRNA gene and the fungal ITS1 region (ITS1F/TIS1R) in the internal tissues were amplified. The V3-V4 region (338F/806R) of the bacterial rRNA gene and the fungal ITS1 region in the bulk and rhizosphere soil samples were also amplified. The amplicon libraries were sequenced using the Illumina NovaSeq PE250 platform (Wekemo Tech Group Co., Ltd. Shenzhen China) with a paired-end protocol. Bioinformatic analysis of amplicon sequencing data QIIME v1.9. 55 and USEARCH v10 56 were used to process the bacterial 16S rRNA gene and fungal ITS sequences. Briefly, FastQC v0.11.5 57 was used to assess read quality. Low-quality reads with scores below Q30 were trimmed in pairs using Trimmomatic v0.39 58 . The corrected reads were merged into a single read and selected at 97% identity (operational taxonomic units, OTUs). The SILVA v138.1 and UNITE v8.2 databases were used to classify the sequences as bacterial or fungal. The OTU tables of bacteria and fungi were rarefied with the script single_rarefaction.py in QIIME for alpha-diversity estimates. The cumulative sum scaling (CSS) normalization method was used for bacterial and fungal β-diversity analyses using beta_diversity.py. The OTUs present in all samples were defined as core taxa. Principal coordinates analysis (PCoA) was performed using the Vegan and Tidyverse packages of R (version 4.1.0) 59 based on the relative abundance of each OTU. Gehpi v0.9.2 60 was used to produce the visualizations of the bacterial–fungal interkingdom networks (Spearman |ρ| > 0.7 and P < 0.01) following a previously described method 61 . To explore the degree of deviation of the co-accordance networks, dissimilarity was calculated using a previously described formula 62 . In addition, weighted robustness, vulnerability, compositional stability and node persistence were evaluated using the ggClusterNet package in R. A random-forest machine-learning model was established using a 10-fold cross-validation error curve in the randomForest package in R. Linear discriminant analysis effect size with permutational multivariate analysis of variance (PERMANOVA) based on 9,999 permutations was employed to identify the key families in the CSR-R and CSR-S groups using the Vegan package in R. Beta nearest taxon index (βNTI) was calculated based on the OTU matrix using the ape, ICAMP, picante and NST packages in R. A neutral community model was conducted to estimate the effects of stochasticity on community assembly, in which the best fit between the frequency of OTU occurrence was generated by applying a nonlinear least-squares method 63 . Metagenomic sequencing and data mining A total of 42 DNA samples (from 3 resistant and 3 susceptible varieties with 3 replicates in 2020, and 4 resistant and 4 susceptible varieties with 3 replicates in 2021) were selected for metagenomic sequencing on an Illumina NovaSeq 6000 instrument (Wekemo Tech Group Co., Ltd. Shenzhen China) with a paired-end protocol. After removing the adaptor and low-quality sequences with Trimmomatic (version 0.39), Bowtie2 (version 2.1.0) 64 was used to eliminate host-derived sequences by mapping the clean data to the maize genome (B73 RedGen_v4 from maizeGDB). The high-quality reads were assembled using Megahit (version 1.2.9) 65 with the parameter meta-large, predicted based on contigs using Prodigal (reads shorter than 300 bp were removed; version 2.6.3) 66 and clustered at a 0.95 identity threshold using CD-hit (version 4.6.2) 67 to generate a nonredundant gene catalog. Taxonomic classification was performed using Kraken2 (version 2.1.2) 68 based on a memory-intensive algorithm that associates short genomic substrings (k-mers) with the lowest common ancestor taxa. The eukaryotic pathogen database EuPathDB was used to classify the pathogenic fungus using Kraken2. Gene quantification was performed by mapping the clean data to the nonredundant gene catalog using Salmon (version 1.9.0) 69 with the quasi-mapping-based model. Functional annotation was accomplished by mapping the genes to the Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology (KO) profiles, Clusters of Orthologous Group of proteins (COG) functional categories and CAZymes (CAZ) using Diamond (version 0.9.14). Python scripts (alpha_diversity.py, and beta_diversity.py) in QIIME (version 1.91) were used to calculate functional diversity. Linear discriminant analysis effect size (LEfSe) was performed to identify the functional capacities of rhizosphere samples with significantly different abundances between resistant and susceptible groups. Validating the role of the soil microbiome in CSR resistance Topsoil (0–25 cm) was collected from the large-scale sampled field, which had no history of maize cultivation. To obtain soil extracts, 1 g of soil was suspended in 9 mL sterile water, and the large soil particles were removed by filtration through Whatman 42 filter paper. The CSR-resistant cultivar GZL185 was planted in sterile substrate that had been incubated for 2 weeks with soil extracts. After five days of growth, the root zone of each maize seedling was irrigated with 10 mL of a spore suspension of F. graminearum (isolated from diseased maize in the field in Gongzhuling City) at a concentration of 2 x 10 5 conidia/mL. Three weeks after F. graminearum inoculation, disease incidence, defined as the percentage of diseased seedlings relative to the total number of seedlings, was monitored. Root collection and LC-MS/MS analysis RS and RE samples from the field maize treated with F. graminearum in 2021 were selected for LC-MS/MS analysis. First, 10 mg portions of soil samples were transferred into centrifuge tubes containing 500 µL ddH 2 O (precooled to 4°C) and vortexed for 60 s. After the addition of 1,000 µL methanol (prechilled to − 20°C), the tubes were placed into an ultrasound machine at room temperature for 10 min and incubated on ice for 30 min. The samples were centrifuged for 10 min at 16,000 g at 4°C, after which 1.2 mL of supernatant was transferred into a new centrifuge tube. Following vacuum concentration, the samples were dissolved in 40 µL 2-chlorobenzalanine (4 ppm) in a methanol:water solution (1:1, v/v, 4°C). Following filtration through a 0.22-µm membrane, the samples were ready for LC-MS/MS detection. To monitor deviations in the analytical results from these pooled mixtures and to compare them to the errors caused by the analytical instrument itself, 20 µL aliquots were removed from each prepared sample and mixed. LC analyses were performed on a Thermo Ultimate 3000 system equipped with an ACQUITY UPLC® HSS T3 (150×2.1 mm, 1.8 µm, Waters) column maintained at 40°C. The temperature of the autosampler was set to 8°C. Gradient elution of analytes was carried out using 0.1% (v/v) formic acid in acetonitrile (C) and 0.1% (v/v) formic acid in water (D) or acetonitrile (A) and 5 mM ammonium formate in water (B) at a flow rate of 0.25 mL/min. Each sample (2 µL) was injected following equilibration. An increasing linear gradient of solvent B (all v/v) was used as follows: 0–1 min, 2% A/C; 1–9 min, 2–50% A/C; 9–12 min, 50–98% A/C; 12–13.5 min, 98% A/C; 13.5–14 min, 98–2% A/C; 14–20 min, 2% A/C-positive mode (14–17 min, 2% A/C, negative mode). The ESI-MSn experiments were performed using a Thermo Q Exactive Focus mass spectrometer with spray voltages of 3.8 kV and − 2.5 kV in positive and negative modes, respectively. Sheath gas and auxiliary gas were set to 45 and 15 arbitrary units, respectively. The capillary temperature was 325°C. The Orbitrap analyzer scanned over a mass/charge range of 81–1,000 for full scans at a mass resolution of 70,000. Data-dependent acquisition (DDA) MS/MS experiments were performed with HCD scan. The normalized collision energy was 30 eV. Dynamic exclusion was implemented to remove unnecessary information in the MS/MS spectra. Isolation and culture of bacterial strains from resistant samples Methods for isolating and cultivating the bacteria in this study were described by Yang Bai et al. 26 . Briefly, 10-fold serial dilutions of the rhizosphere microbiota sample in tryptic soy broth (TSB, Difco Laboratories Inc., Detroit, MI, USA) were prepared in 96-well cell culture plates. The plates were chosen in such a way that < 30% of the wells showed visible bacterial growth after an incubation period. Purified bacterial isolates were obtained by continuous streaking on solid Luria-Bertani (LB) medium, and identities were confirmed by 16S rRNA gene sequencing. All purified bacterial isolates were then placed in 50% (v/v) glycerol for storage at − 80°C for subsequent experiments. The Bacillus strains were further isolated. Briefly, 10 g of soil was placed into 90 mL sterile water, and a 10 –5 soil suspension was prepared by successive 10-fold dilutions in sterile water. The suspension was incubated in a water bath at 80°C for 30 min to kill the non-endospore-forming bacteria. A 0.1-mL aliquot of the diluted suspension was spread onto plates containing LB medium and incubated for 12 h at 37°C. Single isolates were preliminarily classified according to the identity of full-length 16S rRNA sequences mapping to the NCBI rRNA/ITS database, and the single colonies were purified. The surviving strains were verified by endospore staining and stored in a refrigerator at 5°C for future use. Genomic sequencing of Bacillus isolates To obtain accurate classification information, the genomes of 44 isolates were sequenced. Briefly, genomic DNA was extracted from each isolate using a Bacterial Genomic DNA Extraction Kit (Qiagen), and a sequencing library was generated using a VAHTS Universal DNA Library Prep Kit for Illumina (Vazyme, ND604) following the manufacturer’s recommendations. The library preparations were sequenced on the Illumina NovaSeq 6000 platform with the 150-bp paired-end module. After obtaining the raw data, the raw reads were filtered by removing reads containing adaptor, poly-N and low-quality reads (more than 50% of bases with Q < 10). SPAdes (version 3.15.5) 70 was used to perform genome assembly with gradient k values (21, 33, 55, 77, 99 and 127), and annotation was accomplished using Prokka (version 1.13) 71 . To obtain taxonomy information about the isolates, a neighbor-joining (NJ) phylogenetic tree was reconstructed based on the amino acid sequences of 4,215 genomes using the CVTree Standalone Version with k-string = 6 72 . The abundance of these isolates in the rhizosphere was determined by mapping the metagenomic data to these genomes using Bowtie2 (version 2.1.0). Measuring in vitro antagonistic activities of bacteria against F. graminearum The antagonistic activities of bacteria against F. graminearum were measured by in vitro dual culture assays on potato dextrose agar (PDA, Difco) solid medium. Petri dishes containing PDA solid medium inoculated only with F. graminearum in the center served as the negative controls. For the experimental groups, purified isolates were added to the medium edge symmetrically from the center. All Petri dishes were incubated in the dark at 25°C until the PDA medium for the negative controls was completely covered by F. graminearum . The radial growth of F. graminearum in the control (R1) and experimental groups (R2) was then measured with a ruler. The inhibition rate of radial growth of F. graminearum by the isolated bacteria was calculated as follows: 100 × [(R1 − R2)/R1]. Examining the influence of beneficial bacteria on plant disease The purified isolates that showed antagonistic activity against F. graminearum were cultured in LB broth at 28°C for 12 h, centrifuged at 25°C for 2 min at 7000 g and resuspended in sterile water to an OD 600 value of 1.0. Surface-sterilized maize seeds were incubated in Petri dishes with distilled water at 25°C for 3 days for germination. For the negative controls, 2 mL of distilled water was added to the soil around the maize seeds. Maize seeds treated with 2 mL of F. graminearum spore suspension at a concentration of 10 8 conidia/mL served as the positive controls. To examine the effects of beneficial bacteria on CSR development, F. graminearum (2 mL, 2 x 10 5 conidia/mL) and pure beneficial bacteria (2 mL, OD 600 = 1.0) were added simultaneously to the root tips. All maize seeds were planted in plastic cones containing sterile substrate amended with roseite in the greenhouse. Cones with the same treatment were arranged randomly in plastic racks and incubated in a growth chamber (14 h light/10 h dark) at 26°C. The disease severity of the maize plants was assessed based on the number of diseased plants after 2 weeks. RNA-seq of maize roots after inoculation with a synthetic community (SC) of bacteria Three isolates from the three groups (SC-I, SC-II and SC-III) of Bacillus were randomly selected to construct three SCs. A 20 mL aliquot of SC suspension (OD 600 = 1.0) was poured onto the roots of 2-week-old maize seedlings growing in sterile substrate. The control groups received equal amounts of sterile water. Twenty-four hours after inoculation, the plants were uprooted from the substrate soil, and the roots were washed in phosphate-buffered saline (PBS) buffer. Clean roots separated from the rest of the plant with five biological replicates were flash-frozen in liquid nitrogen and stored at − 80°C. TRIzol reagent (1.5 mL) was used to extract total RNA from the root tissues, and the integrity and purity of the RNA were assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies, CA, USA) and a NanoPhotometer Spectrophotometer (IMPLEN, CA, USA), respectively. A 1 µg aliquot of RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using an NEBNext Ultra RNA Library Prep Kit from Illumina (NEB, USA) following the manufacturer’s recommendations, and index codes were added to attribute sequences to particular samples. Following purification and quality assessment, the library was sequenced on the Illumina NovaSeq platform, and 150-bp paired-end reads were generated. Clean reads were obtained by removing reads containing adapters, reads containing poly-N and low-quality reads from the raw data. Clean paired-end reads were aligned to the B73 maize genome (version 5.0), which was downloaded from maizeGDB, using Hisat2 (version 2.0.5) 73 . StringTie (version 2.2.1) 74 was used to count the number of reads mapped to each gene, and the fragments per kilobase of transcript per million mapped reads (FPKM) value of each gene was calculated based on the length of the gene. Differential expression analysis was performed using the DESeq2 R package (1.16.1). Gene Ontology (GO) and KEGG enrichment analysis of differentially expressed genes (DEGs) was performed using the clusterProfiler R package, in which gene length bias was corrected. Functional terms with corrected P -values < 0.05 were considered to be significantly enriched. The expression of genes in core pathways was further verified by reverse-transcription quantitative PCR (RT-qPCR). Briefly, the SC-associated experiment described above including RNA preparation was performed again. A HiScript III 1st Strand cDNA Synthesis Kit (Vazyme, Nanjing, China) and RealStar Green Fast Mixture with ROXII (GenStar, Beijing, China) were employed for cDNA synthesis and qPCR, respectively. The 2 −ΔΔCt method was used to determine the relative expression levels of the target genes. Effects of berberine on pathogen growth and the occurrence of CSR To evaluate the effects of specific root exudates on pathogen growth, four PDA plates containing 5, 25, 50 or 100 µg/mL berberine (Solarbio) were prepared, and 5% (v/v) DMSO was added to the PDA as the mock treatment. Agar disks containing fresh mycelium were inoculated into the centers of the PDA plates with four replicates per treatment, and the plates were incubated at 25°C for 48 h. For greenhouse experiments, maize seeds were surface-sterilized in 3% (v/v) sodium hypochlorite solution for 5 min and rinsed with sterile deionized water. The seeds were soaked in 25 µg/mL berberine solution for 5 min and planted in the sterile substrate. Roots were inoculated by irrigation with F. graminearum (2 mL, 2 x 10 5 conidia/mL) at three days after planting. CSR incidence was scored with the naked eye and recorded every 5 days. Statistical analyses The statistically significant differences in bacterial community composition, plant biomass and SDIs between resistant and susceptible groups were evaluated by a two-tailed unpaired Wilcoxon rank sum test at a threshold P -value < 0.05. Differential abundance of the metabolites in the rhizosphere and root endosphere between resistant and susceptible groups was calculated using the generalized linear model (GLM) approach in the edgeR R package. The major drivers of microbial and fungal alpha-diversity were determined by linear mixed model analysis using the R package lme4. The β-diversity of bacterial and fungal communities and the functional categories were assessed by computing the Bray–Curtis distance matrices and then ordinated using PCoA. PERMANOVA statistical tests were performed to determine the relative contributions of different factors to community dissimilarity using Adonis in the Vegan R package with 1999 permutations. The biomarker bacteria were identified using LEfSe (logarithmic LDA score > 2.5, P -value < 0.05). DATA AVAILABILITY Sequencing data from this study can be found in NCBI under the following BioProject numbers: PRJNA990978, including the 16S rRNA sequencing data (SAMN36289239-SAMN36289436), ITS rRNA sequencing data (SAMN36377919-SAMN36378116), transcriptomic data (SAMN36399084-SAMN36399095) and metagenomic data (SAMN36381305-SAMN36381346). Declarations DATA AVAILABILITY Sequencing data from this study can be found in NCBI under the following BioProject numbers: PRJNA990978, including the 16S rRNA sequencing data (SAMN36289239-SAMN36289436), ITS rRNA sequencing data (SAMN36377919-SAMN36378116), transcriptomic data (SAMN36399084-SAMN36399095) and metagenomic data (SAMN36381305-SAMN36381346). AUTHOR CONTRIBUTIONS Wende Liu conceived and designed the study. Xinyao Xia, Qiuhe Wei, Hanxiang Wu, Xinyu Chen, Chunxia Xiao, Chaotian Liu and Haiyue Yu performed the experiments and analyzed the data. Wende Liu, Hanxiang Wu, Xinyao Xia and Qiuhe Wei wrote the manuscript with contributions from all the other authors. FUNDING This work was financially supported by the Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences. Competing interests The authors declare that they have no competing interests. References Lu, Z.-x. et al. 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Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31 , 1674-1676 (2015). Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC bioinformatics 11 , 1-11 (2010). Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22 , 1658-1659 (2006). Wood, D.E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome biology 20 , 1-13 (2019). Patro, R., Duggal, G., Love, M.I., Irizarry, R.A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nature methods 14 , 417-419 (2017). Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. Journal of computational biology 19 , 455-477 (2012). Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30 , 2068-2069 (2014). Wang, K. et al. Development of an Online Genome Sequence Comparison Resource for Bacillus cereus sensu lato Strains Using the Efficient Composition Vector Method. Toxins 15 , 393 (2023). Kim, D., Paggi, J.M., Park, C., Bennett, C. & Salzberg, S.L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nature biotechnology 37 , 907-915 (2019). Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nature biotechnology 33 , 290-295 (2015). Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryTable.xlsx Supplementary Table 1. Detailed pedigree information and CSR disease indexes over two consecutive years for 40 cultivars obtained from an association mapping panel consisting of 551 diverse inbred lines. Supplementary Table 2. Results of linear mixed model analysis to examine which major factors shape the maize microbiota. Supplementary Table 3. Wilcox test results between resistant and susceptible groups among the five niches. Supplementary Table 4. Tukey’s multiple comparisons test of the SDIs comparing two niches. Supplementary Table 5. Pairwise multilevel comparison between two niches using Adonis. Supplementary Table 6. Pairwise multilevel comparison between resistant and susceptible cultivars in the same niche using Adonis. Supplementary Table 7. Statistical information about the syncretic bacterial–fungal co-occurrence networks. Supplementary Table 8. Number of shared OTUs and unique edges plus their dissimilarity between CSR-R and CSR-S maize inbred lines in the five ecological niches. Supplementary Table 9. Taxonomies of the 276 isolates from two CSR-resistant maize inbred lines based on 16S rRNA gene sequencing. Supplementary Table 10. GSEA enrichment results from maize roots treated with SC-I, SC-II or SC-III. Supplementary Table 11. Metabolites from the root endogenous samples of CSR-R and S maize inbred lines from Jilin Province in 2021, as determined by LC-MS/MS. Supplementary Table 12. Metabolites from the rhizosphere samples of CSR-R and CSR-S maize inbred lines from Jilin Province in 2021, as determined by LC-MS/MS. ExtendedDataFig.3.pdf Extended Data Fig. 3. Bacillus species positively affect plant resistance to F. graminearum . (a) Phenotypic characteristics of healthy and infected maize seedlings at 2 weeks following inoculation with F. graminearum . (b-d) Mean emergence rates (b), dry root weights (c) and fresh root weights are shown as mean ± SD. Different letters indicate significant differences (ordinary one-way ANOVA test, P < 0.05). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3400607","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":239146584,"identity":"a7c66657-9adc-47c1-9903-f46e512ecd0b","order_by":0,"name":"Wende Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYBACPmYwxczAT7QWNpgWyQaitTBAtRgcIFoLO4+ZxMcd1nLGxw8//PiDwS6PCIfxmEnOPJNubHYmzViahyG5mCgtt3nbDiduu8FgIM3AcCCxgSgtf9sO12+ewf755w+itTC2HU4wkAB6ioc4LWzlP3vb0g1nnMkps+YxSCashZ//8GaDn23W8vztxzff/FFhR1gLGjAgUf0oGAWjYBSMAuwAAMaaNAFKu4XcAAAAAElFTkSuQmCC","orcid":"","institution":"Institute of Plant Protection, Chinese Academy of Agricultural Sciences","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Wende","middleName":"","lastName":"Liu","suffix":""},{"id":239146585,"identity":"12f7a714-6939-4b07-b288-7af6e4084044","order_by":1,"name":"Xinyao Xia","email":"","orcid":"","institution":"Institute of Plant Protection, Chinese Academy of Agricultural Science","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xinyao","middleName":"","lastName":"Xia","suffix":""},{"id":239146586,"identity":"c7f2e3f0-e524-49d4-9e08-efc1889b26bf","order_by":2,"name":"Qiuhe Wei","email":"","orcid":"","institution":"Institute of Plant Protection, Chinese Academy of Agricultural Science","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qiuhe","middleName":"","lastName":"Wei","suffix":""},{"id":239146587,"identity":"7f5172f9-ad03-4ba8-844f-63c021608dbb","order_by":3,"name":"Hanxiang Wu","email":"","orcid":"","institution":"Institute of Plant Protection, Chinese Academy of Agricultural Science","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hanxiang","middleName":"","lastName":"Wu","suffix":""},{"id":239146588,"identity":"58d29b4c-b072-4ca8-a194-b090a5606794","order_by":4,"name":"Xinyu Chen","email":"","orcid":"","institution":"Institute of Plant Protection, Chinese Academy of Agricultural Science","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Chen","suffix":""},{"id":239146589,"identity":"c2ec29c0-5d89-4dec-bb1f-bd78963c552f","order_by":5,"name":"Chunxia Xiao","email":"","orcid":"","institution":"Institute of Plant Protection, Chinese Academy of Agricultural Science","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chunxia","middleName":"","lastName":"Xiao","suffix":""},{"id":239146590,"identity":"868527ca-6db8-47c9-904f-bcf60460c472","order_by":6,"name":"Yiping Ye","email":"","orcid":"","institution":"Institute of Plant Protection, Chinese Academy of Agricultural Science","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yiping","middleName":"","lastName":"Ye","suffix":""},{"id":239146591,"identity":"eb87fba4-8655-46e4-b368-6cf3d27f3cc4","order_by":7,"name":"Chaotian Liu","email":"","orcid":"","institution":"Institute of Plant Protection, Chinese Academy of Agricultural Science","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chaotian","middleName":"","lastName":"Liu","suffix":""},{"id":239146592,"identity":"677d627a-3c79-4131-978b-359353bb30d8","order_by":8,"name":"Haiyue Yu","email":"","orcid":"","institution":"China Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Haiyue","middleName":"","lastName":"Yu","suffix":""},{"id":239146593,"identity":"d44cabad-b447-4c04-b058-25c98d9bd87b","order_by":9,"name":"Yuanwen Guo","email":"","orcid":"","institution":"Institute of Plant Protection, Chinese Academy of Agricultural Science","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuanwen","middleName":"","lastName":"Guo","suffix":""},{"id":239146594,"identity":"41b77b28-c29c-4d2f-93b2-5930d3f689d3","order_by":10,"name":"Wenxian Sun","email":"","orcid":"","institution":"Department of Plant Pathology and the Ministry of Agriculture Key Laboratory of Pest Monitoring and Green Management, China Agricultural University, Beijing 100193","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenxian","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2023-09-30 11:55:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3400607/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3400607/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":44433555,"identity":"346a3612-b6f2-41c6-8130-04ee1b132dd5","added_by":"auto","created_at":"2023-10-11 12:40:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":926301,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of resistance heterogeneity on microbiome assembly and diversity in five ecological niches of maize.\u003c/p\u003e\n\u003cp\u003e(a) Phenotypes of the external surfaces and interiors of maize stems in healthy (resistant, R) and infected (susceptible, S) individuals.\u003c/p\u003e\n\u003cp\u003e(b) Diagram showing the sampling niches: BS, bulk soil; RS, rhizosphere soil; RE, root endosphere; SE, stem endosphere; GE, grain endosphere.\u003c/p\u003e\n\u003cp\u003e(c) βNTI calculations of phylogenetic turnover in resistant and susceptible samples in the five ecological niches. *, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001 in the Wilcoxon rank sum test (\u003cem\u003en\u003c/em\u003e = 40).\u003c/p\u003e\n\u003cp\u003e(d) Relative influence of three community assembly processes among resistant and susceptible microbial communities across the five ecological niches. DDH, drift, dispersal limited and homogeneous selection (|βNTI| \u0026lt; 2, stochastic processes); HS, homogenizing dispersal (βNTI \u0026lt; −2, deterministic processes); VS, variable selection (βNTI \u0026gt; 2, deterministic processes).\u003c/p\u003e\n\u003cp\u003e(e) Shannon diversity index (SDI) of bacterial and fungal communities in the five ecological niches.\u003c/p\u003e\n\u003cp\u003e(f) Principal coordinates analysis (PCoA) of bacterial (left) and fungal (right) communities based on Bray–Curtis distances. Significant differences were determined using PERMANOVA (\u003cem\u003en \u003c/em\u003e= 198).\u003c/p\u003e\n\u003cp\u003e(g) PCoA of bacterial (upper panels) and fungal (lower panels) communities in the five ecological niches based on Bray–Curtis distances (\u003cem\u003en \u003c/em\u003e= 40).\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-3400607/v1/0e9b74481350ed1f9d0c9358.png"},{"id":44431715,"identity":"490dc7d8-06d5-42ab-ac0c-e4399aaa94db","added_by":"auto","created_at":"2023-10-11 12:24:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1526975,"visible":true,"origin":"","legend":"\u003cp\u003eBacterial–fungal interkingdom networks in resistant and susceptible maize cultivars across the five ecological niches.\u003c/p\u003e\n\u003cp\u003e(a-b) Bacterial–fungal interkingdom networks in resistant (a) and susceptible (b) maize cultivars. N, number of nodes; E, number of edges; B, number of bacterial OTUs; F, number of fungal OTUs; C, centralization betweenness; D, diameter.\u003c/p\u003e\n\u003cp\u003e(c) Robustness, as measured by randomly removing 50% of the taxa from each network.\u003c/p\u003e\n\u003cp\u003e(d) Robustness, as measured by randomly removing 50% of the module hubs from each network. In c and d, each error bar corresponds to the 95% confidence interval of the mean. Significant differences between R and S groups were calculated using the Wilcoxon rank sum test (*** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e(e) Vulnerability of the network, as measured based on maximum vulnerability across all nodes.\u003c/p\u003e\n\u003cp\u003e(f) Compositional stability from every pair of adjacent niches.\u003c/p\u003e\n\u003cp\u003e(g) Node persistence from every pair of adjacent niches. In e–g, the adjusted r\u003csup\u003e2 \u003c/sup\u003eand \u003cem\u003eP\u003c/em\u003e-values of linear regressions were determined by ANOVA.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-3400607/v1/a5f3ac59c4e42254844e22e2.png"},{"id":44432830,"identity":"98eb7828-e58f-416d-9155-2200055859df","added_by":"auto","created_at":"2023-10-11 12:32:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":456748,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional profiles of the rhizosphere microbiomes.\u003c/p\u003e\n\u003cp\u003e(a) PCoA based on Bray–Curtis distance matrices of GO-, KO-, COG- and CAZy-related genes in resistant and susceptible groups using metagenomic data in 2020.\u003c/p\u003e\n\u003cp\u003e(b) Functional diversity of GO-, KO-, COG- and CAZy-related genes in resistant and susceptible groups using metagenomic data in 2020 and 2021.\u003c/p\u003e\n\u003cp\u003e(c) Heat map showing the normalized abundance of KO terms.\u003c/p\u003e\n\u003cp\u003e(d) Families with significant changes in abundance between resistant and susceptible samples (Wilcox test, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-3400607/v1/cbe105f73019537d9aba714a.png"},{"id":44432833,"identity":"0ec2499e-89c1-4ed8-a437-dfafb468fb15","added_by":"auto","created_at":"2023-10-11 12:32:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":588613,"visible":true,"origin":"","legend":"\u003cp\u003eMicrobial community composition and core disease-preventing microbiota.\u003c/p\u003e\n\u003cp\u003e(a) Relative abundance of the dominant bacterial and fungal phyla in the five ecological niches.\u003c/p\u003e\n\u003cp\u003e(b) Relative abundance of \u003cem\u003eF. graminearum\u003c/em\u003e in \u003cem\u003eITS\u003c/em\u003e rRNA sequencing data.\u003c/p\u003e\n\u003cp\u003e(c) Number of shared and specific families showing differences in abundance between the R and S groups (Wilcox test, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e(d) Relative abundances of \u003cem\u003eBacillus\u003c/em\u003e,\u003cem\u003e Granulicella\u003c/em\u003e,\u003cem\u003e Mucilaginibacter \u003c/em\u003eand\u003cem\u003e Pantoea\u003c/em\u003especies among the five niches.\u003c/p\u003e\n\u003cp\u003e(e) Cladogram indicating the phylogenetic distribution of the bacterial lineages in the resistant (green) and susceptible (red) maize inbred lines. Taxa with linear discriminant analysis (LDA) scores greater than 3 were selected as the biomarkers for the R and S groups.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-3400607/v1/8dc9f042ecc4ea5cebefc25f.png"},{"id":44431723,"identity":"65866472-fcc5-400f-9243-5bbe987cbb6d","added_by":"auto","created_at":"2023-10-11 12:24:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1298188,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram of the \u003cem\u003eBacillus\u003c/em\u003e isolates and suppressive activities against \u003cem\u003eF. graminearum\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e(a) Dendrogram of 44 selected strains, reconstructed using the CVTree method. The three types of antagonistic phenotypes of 44 \u003cem\u003eBacillus\u003c/em\u003e isolates to \u003cem\u003eF. graminearum\u003c/em\u003e in dual culture assays are marked by red, yellow and gray circles, as shown in the key at the top.\u003c/p\u003e\n\u003cp\u003e(b) Overview of the growth status of maize seedlings at 2 weeks after treatment with sterile water as a control (CK), \u003cem\u003eF. graminearum\u003c/em\u003e (Fg), Fg with SC-I, SC-II or SC-III.\u003c/p\u003e\n\u003cp\u003e(c) Mean incidence rates. Values are means ± standard errors; different letters indicate significant differences (Wilcox test, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, n=3).\u003c/p\u003e\n\u003cp\u003e(d) Upregulated and downregulated genes across the three SCs treatments, with circle size corresponding to the FPKM values of genes. The DEGs were selected based on the criteria |log2FC| \u0026gt;1 and \u003cem\u003eP\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e \u0026lt; 0.05. DEGs conforming to these criteria are indicated in red (up) and blue (down), whereas others are indicated in gray.\u003c/p\u003e\n\u003cp\u003e(e-g) PCoA analysis based on the top 500 genes (e), DEGs (f) and functionally related genes (g).\u003c/p\u003e\n\u003cp\u003e(h) Tree map of the GSEA functional terms of maize roots treated with SC-III, with the size of boxes corresponding to the number of core genes related to each functional term. The NES values are shown under the functional pathway names.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-3400607/v1/45c803e823624f620d942613.png"},{"id":44432831,"identity":"5af26041-1d2c-40d1-a8a5-edf2ef3b0a3f","added_by":"auto","created_at":"2023-10-11 12:32:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1000183,"visible":true,"origin":"","legend":"\u003cp\u003eIsoquinoline alkaloids confer benefits in disease suppression.\u003c/p\u003e\n\u003cp\u003e(a) Heatmap showing the expression trends of genes involved in the selected functional pathways. * represents a significant difference (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) compared to the CK group.\u003c/p\u003e\n\u003cp\u003e(b) Relative expression levels of maize \u003cem\u003eTYDC1\u003c/em\u003e, as determined by RT-qPCR. The values are means ± SDs (\u003cem\u003en\u003c/em\u003e = 3). Significant differences (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) were calculated by one-way ANOVA and are shown with different lowercase letters.\u003c/p\u003e\n\u003cp\u003e(c) PCoA based on Bray–Curtis distance matrices of maize RE (left) and RS (right) metabolites in resistant and susceptible groups.\u003c/p\u003e\n\u003cp\u003e(d) Normalized concentrations of berberine, isoquinoline and l-phenylalanine in RE niches, as determined by LC-MS/MS.\u003c/p\u003e\n\u003cp\u003e(e) Inhibitory effects of 5% (v/v) DMSO (CK) and 5, 25, 50 and 100 µg/mL berberine on mycelial growth (upper panels) and 25 µg/mL berberine on disease severity (lower panels).\u003c/p\u003e\n\u003cp\u003e(f) Disease severity (as mean ± SD) of maize seedlings treated with Fg (10\u003csup\u003e5\u003c/sup\u003e spores/mL, CK) and additional berberine (25 µg/mL, BBR).\u003c/p\u003e\n\u003cp\u003e(g) Proposed model of the microbial plasticity across resistance heterogeneity and vertical niches challenged with \u003cem\u003eFusarium\u003c/em\u003e CSR. Maize cultivars resistant to CSR reshape the microbiota and recruit three types of \u003cem\u003eBacillus\u003c/em\u003e species (SC-I: inhibition by niche grabbing; SC-II: inhibition by secreting antimicrobial compounds; and SC-III: no direct inhibition but induction of berberine biosynthesis by the host), conferring benefits in disease suppression. SC-III facilitates berberine biosynthesis by inducing the expression of \u003cem\u003eTYDC\u003c/em\u003e, encoding an enzyme that catalyzes the production of dopamine from l-dopa and tyramine. These beneficial \u003cem\u003eBacillus\u003c/em\u003e can be recruited from the rhizosphere soil (RS) niche via the root endosphere (RE) niche to the stem endosphere (RE) niche, but not to the grain endosphere (GE) niche.\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-3400607/v1/b4cc37fbe6a4909431e35a97.png"},{"id":46217774,"identity":"7d02803b-e37a-42a4-9b73-a711d6f6c7c0","added_by":"auto","created_at":"2023-11-10 11:33:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4519952,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3400607/v1/7a188b62-3726-4fd8-b0d4-78bcb71fdfb8.pdf"},{"id":44431717,"identity":"e8b8cd3b-53be-4647-b5a4-0cbb25ace645","added_by":"auto","created_at":"2023-10-11 12:24:16","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1985932,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table 1. Detailed pedigree information and CSR disease indexes over two consecutive years for 40 cultivars obtained from an association mapping panel consisting of 551 diverse inbred lines.\u003c/p\u003e\n\u003cp\u003eSupplementary Table 2. Results of linear mixed model analysis to examine which major factors shape the maize microbiota.\u003c/p\u003e\n\u003cp\u003eSupplementary Table 3. Wilcox test results between resistant and susceptible groups among the five niches.\u003c/p\u003e\n\u003cp\u003eSupplementary Table 4. Tukey’s multiple comparisons test of the SDIs comparing two niches.\u003c/p\u003e\n\u003cp\u003eSupplementary Table 5. Pairwise multilevel comparison between two niches using Adonis.\u003c/p\u003e\n\u003cp\u003eSupplementary Table 6. Pairwise multilevel comparison between resistant and susceptible cultivars in the same niche using Adonis.\u003c/p\u003e\n\u003cp\u003eSupplementary Table 7. Statistical information about the syncretic bacterial–fungal co-occurrence networks.\u003c/p\u003e\n\u003cp\u003eSupplementary Table 8. Number of shared OTUs and unique edges plus their dissimilarity between CSR-R and CSR-S maize inbred lines in the five ecological niches.\u003c/p\u003e\n\u003cp\u003eSupplementary Table 9. Taxonomies of the 276 isolates from two CSR-resistant maize inbred lines based on \u003cem\u003e16S rRNA\u003c/em\u003e gene sequencing.\u003c/p\u003e\n\u003cp\u003eSupplementary Table 10. GSEA enrichment results from maize roots treated with SC-I, SC-II or SC-III.\u003c/p\u003e\n\u003cp\u003eSupplementary Table 11. Metabolites from the root endogenous samples of CSR-R and S maize inbred lines from Jilin Province in 2021, as determined by LC-MS/MS.\u003c/p\u003e\n\u003cp\u003eSupplementary Table 12. Metabolites from the rhizosphere samples of CSR-R and CSR-S maize inbred lines from Jilin Province in 2021, as determined by LC-MS/MS.\u003c/p\u003e","description":"","filename":"SupplementaryTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3400607/v1/b35bf2a2dacffaadf6412938.xlsx"},{"id":44432835,"identity":"ed4a8eb3-bc7a-43fb-af9c-9e6679b018de","added_by":"auto","created_at":"2023-10-11 12:32:16","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":297056,"visible":true,"origin":"","legend":"\u003cp\u003eExtended Data Fig. 3. \u003cem\u003eBacillus\u003c/em\u003especies positively affect plant resistance to \u003cem\u003eF. graminearum\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e(a) Phenotypic characteristics of healthy and infected maize seedlings at 2 weeks following inoculation with\u003cem\u003e F. graminearum\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e(b-d) Mean emergence rates (b), dry root weights (c) and fresh root weights are shown as mean ± SD. Different letters indicate significant differences (ordinary one-way ANOVA test, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"ExtendedDataFig.3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3400607/v1/603773d6d14316606bb0468d.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Bacillus species are core microbiota of highly resistant maize varieties that induce host metabolic defense against corn stalk rot","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eCorn stalk rot (CSR), an economically destructive soil-borne disease affecting maize (\u003cem\u003eZea mays\u003c/em\u003e)-growing areas worldwide, is caused by pathogens such as the fungi \u003cem\u003eFusarium\u003c/em\u003e and \u003cem\u003ePythium\u003c/em\u003e, as well as bacterial species\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Single or multiple pathogens infect the root vascular bundle via natural openings or wounds, thereby affecting water and nutrient transport and ultimately causing the plant to wilt and die\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Due to the wide variety of geographically specific pathogens and local differences in infection routes throughout the growth period, conventional field management measures such as seed coating with chemical fungicides have had limited success in disease control\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Although introducing resistance (\u003cem\u003eR\u003c/em\u003e) genes into a host plant through genetic engineering decreases the incidence of disease\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, it is challenging to identify appropriate parental resources and stabilize \u003cem\u003eR\u003c/em\u003e genes in complex and volatile environments\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Therefore, there is a need to identify efficient and more natural disease-suppressive methods from the perspective of agroecosystem management.\u003c/p\u003e \u003cp\u003eMicrobes are vital to ecosystem health and have effectively colonized plants during the last 400\u0026nbsp;million years of coevolution with their plant hosts\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Plants enhance their resistance to pathogens by reshaping the composition of their associated microbe communities, resulting in the assembly of a stress-alleviating microbiota\u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The plant-mediated \u0026lsquo;cry for help\u0026rsquo; hypothesis further highlights the potential for microbes as a significant component of resistance mechanisms, defining an important role for the microbiota in stimulating host resistance to plant diseases and insect pests and tolerance to abiotic stress while promoting growth\u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Specifically, \u003cem\u003eTrichoderma\u003c/em\u003e fungi colonize the soil and maize roots, thereby reducing the overall number of pathogenic \u003cem\u003eFusarium\u003c/em\u003e species \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Chili pepper (\u003cem\u003eCapsicum annuum\u003c/em\u003e) plants infected with Fusarium wilt disease recruit beneficial bacteria and mitigate changes in the microbiome in reproductive organs to facilitate the survival of the host and its progeny\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. These observations suggest that both bacteria and fungi have tremendous potential as biological control agents of CSR\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo develop effective disease management strategies, it is imperative to explore the contributions of bacterial and fungal taxa known to improve resistance in plants grown under natural conditions and to identify additional microbes from the \u0026lsquo;microbial dark matter\u0026rsquo;\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Notably, the genotype-specific characteristics of the endophytic microbiomes in the seed and \u0026lsquo;resistance legacy\u0026rsquo; (also known as \u0026lsquo;soil-borne memory\u0026rsquo;) of the effects of plant\u0026ndash;microbe interactions in previous plant generations have been demonstrated\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. These cross-generational microbes are located at the two terminals of host biology, pointing to multi-mechanistic microbiome heritability\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. A comprehensive analysis of microbial communities in distinct ecological niches of maize infected with CSR can provide important information about the sources and heritability of seed endophytic microbes and suggest strategies for CSR prevention.\u003c/p\u003e \u003cp\u003eInitially, a range of resistance levels to CSR. were observed among a maize-associated mapping panel of 527 inbred lines\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e with temperate, tropical and subtropical genetic backgrounds representing global maize diversity. We hypothesized that the structural and functional adaptation of the microbial communities would differ in resistant and susceptible maize cultivars. In the current study, using CSR-resistant and -susceptible maize cultivars, we performed a multi-omics analysis of host-associated bacterial and fungal communities in the soil (bulk soil and rhizosphere) and plant endogenous tissues (root, stem and grain). We identified core CSR resistance-related microbiotas in maize and explored the mechanisms underlying host\u0026ndash;microbe interactions, laying the foundation for the development of biological control methods against this devastating disease.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSpecific ecological niches and host resistance drive the assembly of associated bacterial communities\u003c/h2\u003e \u003cp\u003eAfter inoculating \u003cem\u003eF. graminearum\u003c/em\u003e into the root zones of 40 maize cultivars (to avoid injuring the roots) for two consecutive years, we identified pronounced and consistent differences in CSR disease indexes (Supplementary Table\u0026nbsp;1). Infected maize plants exhibit hollow stems and roots covered with red mycelium, which eventually cause the plant to break (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). To analyze the bacterial and fungal communities of maize plants that are resistant or susceptible to CSR, we collected samples from four resistant (disease incidence\u0026thinsp;=\u0026thinsp;0%) and four susceptible (disease incidence\u0026thinsp;\u0026gt;\u0026thinsp;30%) maize cultivars from five ecological niches: bulk soil (BS), rhizosphere soil (RS), root endosphere (RE), stem endosphere (SE) and grain endosphere (GE; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). We determined the disease incidence by recording the percentage of diseased seedlings relative to the total number of seedlings. We profiled the compositions of the bacterial and fungal communities based on sequencing of 16S ribosomal RNA (rRNA) gene fragments and internally transcribed spacer (ITS) sequences, followed by clustering into operational taxonomic units (OTUs; 97% identity).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe obtained 12,824,344 rRNA and 15,788,680 ITS high-quality reads from 198 samples, ranging from 30,162 to 94,013 reads per sample, with an average of 72,255 reads per sample, representing 4,624 bacterial and 1,471 fungal OTUs. We employed a linear mixed model (LMM) to assess which major factors shape the maize microbiota. To quantify species diversity within a microbial community, we also calculated the Shannon diversity index (SDI) for each community, with high values representing higher species diversity. We observed that ecological niches, together with resistant/susceptible genotypes, have a greater influence on bacterial SDI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0087) than do differences in maize cultivars (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.629; Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eWe evaluated the assembly process of microbial communities, which is known to be strongly linked to the maintenance of plant health\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, based on the β-nearest taxon index (βNTI), which measures the mean phylogenetic distance between taxa of a community. We detected marked differences in βNTI in bacterial communities between resistant and susceptible sample pairs among the BS, RS, RE and SE ecological niches. By contrast, we noticed no significant changes for fungal communities, suggesting that resistance has a stronger influence on the assembly of bacterial vs. fungal microbiota (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, Supplementary Table\u0026nbsp;3, Wilcoxon rank sum test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThe bacterial communities in soil niches (BS and RS) exhibited a divergent assembly compared to the internal niches (RE, SE and GE), whereas we detected no significant differences in fungal community assembly among the five niches analyzed (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Furthermore, the proportion of deterministic (HS: homogeneous selection and VS: variable selection, |βNTI| \u0026gt; 2) and stochastic (DDH: dispersal, drift and homogenizing dispersal, |βNTI| \u0026lt; 2) processes was divergent across niches (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). The proportion of VS (βNTI\u0026thinsp;\u0026gt;\u0026thinsp;2) of bacterial communities increased in the RE and SE niches of resistant cultivars relative to the other three niches, indicating that the phylogenetic turnover and interaction among microorganisms were higher than expected. Neutral community model analysis showed that the dispersal ability and habitat niche breadth of bacterial communities decrease gradually from soil to internal niches (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, c). In the RS and RE niches, the dispersal limitation on bacterial communities was higher in resistant cultivars than in susceptible samples, as reflected by the lower Nm values (an estimate of dispersal between communities). Collectively, these findings indicate that the RE niche of resistant cultivars undergoes changes in its bacterial, rather than fungal, communities upon \u003cem\u003eF. graminearum\u003c/em\u003e infection and that this niche plays a prominent role in host plant resistance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDistinct patterns of microbial diversity characterize different maize niches\u003c/h2\u003e \u003cp\u003eAssessments of Shannon diversity revealed significant differences between soil and plant niches in both bacterial and fungal communities (Tukey\u0026rsquo;s multiple comparisons test \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee, Supplementary Table\u0026nbsp;4). As a whole, the richness of bacterial and fungal species was lower in the maize internal compartments than in the soil, with GE displaying the least diversity. The SDIs of bacterial communities showed no significant differences (Wilcoxon rank sum test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) between resistant and susceptible groups among the five niches. By contrast, the SDIs of the fungal community were lower in the SE than RE niches of susceptible groups, suggesting that the microenvironment is unstable in these two niches in susceptible cultivars (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003ePrincipal coordinates analysis (PCoA) based on Bray\u0026ndash;Curtis dissimilarity clearly separated bacterial communities in the soil from the plant internal samples along the first principal coordinate (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef, Supplementary Table\u0026nbsp;5). In parallel, we observed significant differences in the composition of bacterial communities in the three internal niches along the second principal component. We noticed a similar positional variation pattern of fungal communities, as fungal communities in SE differed markedly from those in the other four niches. Moreover, we identified substantial differences in bacterial and fungal communities between resistant and susceptible samples in each niche, except for bacteria in the GE (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg, Supplementary Table\u0026nbsp;6). Overall, these results indicate that maize niches and resistance characteristics have strong selective effects on the composition and assembly of microbiome communities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eRoot-associated co-occurrence networks reveal stronger bacterial\u0026ndash;fungal interactions in resistant maize cultivars\u003c/h2\u003e \u003cp\u003eTo investigate whether and how CSR resistance affects the complexity and stability of molecular ecological networks across the ecological niches, we constructed bacterial\u0026ndash;fungal co-occurrence networks (Spearman\u0026rsquo;s correlation coefficient (ρ) was \u0026gt;\u0026thinsp;0.9 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). We detected a clear shift in the interkingdom network patterns across the five ecological niches. The number of nodes (N), including both bacterial and fungal taxa, was lower in internal samples (RE, SE and GE) than in soil samples (BS and RS; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, b). This finding was predictable, considering how soil tends to contain a greater diversity of microbes than the internal compartments of plants. Notably, the connectivity index (C) and average degree (D) in the RE, SE and GE niches did not decrease as might have been expected with the shift in nodes and edge, suggesting that the internal niches harbor stronger microbiological interactions. Compared to susceptible cultivars, roots (RE and RS) of resistant samples had more nodes and edges, with higher connectivity and average degree, representing stronger interactions. We observed an opposite pattern in maize stems (SE) compared with roots. Susceptible inbred lines had the most edges, highest connectivity and degree (Supplementary Table\u0026nbsp;7), and thus showed stronger bacterial\u0026ndash;fungal interactions. In addition, the Bray\u0026ndash;Curtis dissimilarity of root-associated networks (RS and RE) between resistant and susceptible groups was higher than that in the SE and GE niches, indicating that microbes in root niches were less stable than those in the stem and grain niches (Supplementary Table\u0026nbsp;8). These divergent network patterns between resistant and susceptible cultivars highlight the importance of root-associated bacteria in suppressing the growth of pathogens such as \u003cem\u003eF. graminearum.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe calculated the robustness of each network by simulating species extinction. This analysis revealed marked divergence in robustness between resistant and susceptible groups by removal of either random taxa or targeted module hubs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, d). Network vulnerability increased marginally, and the compositional stability and node persistence decreased along the bottom-up niches in both the resistant and susceptible groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee-g). These results suggest that niches and resistance characteristics of the host plant greatly influence bacterial\u0026ndash;fungal interactions, with the root niches of resistant cultivars conferring the greatest resistance to fungal pathogens.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSusceptible cultivars are exposed to rhizosphere microbiomes with functions related to cell wall degradation\u003c/h3\u003e\n\u003cp\u003eBecause the rhizosphere is a primary ecological niche for microbial, environmental and host interactions, we used metagenomic sequencing data from the rhizosphere microbiome to explore the functional variation of microbiomes associated specifically with resistant or susceptible maize cultivars. PCoA showed that CSR resistance has a pronounced effect on the functional composition (GO, CAZ, COG and KO) of rhizosphere microbes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, PERMANOVA \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, we did not observe persistent significant differences in functional diversity between data obtained in 2020 and 2021, as reflected by the SDI (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe then analyzed the metabolic pathways to explore the above functional alterations. We established that amino acid metabolism pathways (mainly including l-isoleucine, l-arginine and guanosine biosynthesis) are significantly enriched in the rhizosphere microbiome of resistant maize cultivars (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Metabolic pathways associated with polysaccharides (such as d-glucosamine biosynthesis, d-glucarate degradation and sucrose degradation) and organic acids (such as fatty acid biosynthesis, unsaturated fatty acid biosynthesis and pantothenate and coenzyme A biosynthesis) were enriched in the susceptible maize cultivars. In addition, we identified pathways associated with the metabolism of amines (thiamine formation and thiamine salvage II) and aromatics (aromatic biogenic amine degradation) in the susceptible group.\u003c/p\u003e \u003cp\u003eFurther analysis of the rhizosphere microbiome revealed that the abundance of 61 carbohydrate-related enzyme families (CAZy), including six carbohydrate-active enzyme classes, significantly differs between the resistant and susceptible groups (Wilcoxon, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Compared to the microbes associated with resistant samples (15 families), those associated with susceptible samples were enriched for CAZy families (20 families) associated with the degradation of major components of plant cell walls such as cellulose, hemicellulose or pectin. The presence of these enzymes would make it easier for pathogens to breach the cell wall barrier and invade the susceptible host plants. Three peptidoglycan-degrading (GT31, GT49 and GH103) and five chitin-degrading (GH18, GH19, GH23, CBM50 and CBM73) CAZy family members were uniquely enriched in the microbes associated with susceptible samples. The presence of these enzymes would facilitate the degradation of bacterial and fungal cell walls, respectively. Thus, the investigation of CAZy families revealed the more drastic fluctuations of carbohydrate-related family members and stronger cell wall degradation\u0026ndash;related interactions among bacteria, fungi and maize roots in the rhizosphere microenvironments of susceptible plants.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBacillus\u003c/b\u003e \u003cb\u003etends to be recruited across bottom-up niches\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo define the core microbiota associated with CSR-resistant plants, we comprehensively analyzed the taxonomic compositions and relative abundances of their bacterial and fungal communities. Microbial community analysis at the phylum level showed niche specificity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The dominant bacterial phyla were Proteobacteria (63.23%), Acidobacteriota (13.10%) and Actinobacteriota (6.94%). Notably, Acidobacteriota mainly existed in the two soil niches, and the proportion of Actinobacteriota was higher in SE samples. A similar analysis of fungal communities showed that Ascomycota (65.29%) and Basidiomycota (24.93%) are the dominant phyla. Furthermore, measurement of the abundance of \u003cem\u003eF. graminearum\u003c/em\u003e from ITS amplicon sequencing data showed that the proliferation of \u003cem\u003eF. graminearum\u003c/em\u003e is significantly inhibited in the RS, RE and SE niches of resistant maize inbred lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe first identified the core taxa in resistant and susceptible groups through differential analysis, which demonstrated that four shared genera, i.e., \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003eGranulicella\u003c/em\u003e, \u003cem\u003eMucilaginibacter\u003c/em\u003e and \u003cem\u003ePantoea\u003c/em\u003e, showed significant differences among the RS, RE and SE niches (Wilcoxon rank sum test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Relative abundance measurements revealed that only \u003cem\u003eBacillus\u003c/em\u003e tended to be recruited to the RS, RE and SE niches of CRS-resistant inbred lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). Linear discriminant analysis effect size (LEfSe) with a logarithmic LDA\u0026thinsp;\u0026gt;\u0026thinsp;2.5 further indicated that \u003cem\u003eBacillus\u003c/em\u003e can serve as a useful biomarker for resistant maize cultivars (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee and Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). To identify the core fungal taxa of CRS-resistant maize plants, we investigated fungal communities using the same method; however, the results were non-uniform (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec and d). Therefore, we selected \u003cem\u003eBacillus\u003c/em\u003e as a candidate core taxon for CSR-resistant cultivars and subjected it to a series of verification experiments.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe presence of\u003c/b\u003e \u003cb\u003eBacillus\u003c/b\u003e \u003cb\u003especies enhances plant performance against\u003c/b\u003e \u003cb\u003eF. graminearum\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo explore the effects of the recruited bacteria on plant health, we purified 276 bacterial isolates, accounting for 43% of all the OTUs with more than five reads in RS samples, from the rhizosphere soil of resistant cultivars using gradient dilution and streak plate techniques (Supplementary Table\u0026nbsp;9). These 276 isolates, including 62 \u003cem\u003eBacillus\u003c/em\u003e isolates, were categorized into 17 families based on 97% identity of 16S rRNA sequencing results. In addition, we obtained 694 \u003cem\u003eBacillus\u003c/em\u003e isolates using the \u003cem\u003eBacillus\u003c/em\u003e-specific method, which clustered into 28 subgroups with 99% identity within a subgroup. We randomly selected 44 isolates from the 28 subgroups for \u003cem\u003ede novo\u003c/em\u003e genome sequencing and protein prediction. Based on these results, we assigned accurate taxonomies by reconstructing a neighbor-joining (NJ) phylogenetic tree from 4,215 \u003cem\u003eBacillus\u003c/em\u003e genomes using the CVTree Standalone Version (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe identified three models representing the antagonistic phenotype of all the isolates against \u003cem\u003eF. graminearum\u003c/em\u003e in dual culture assays, which we characterized as follows: inhibition by niche grabbing, inhibition by secreting antimicrobial compounds and no inhibition. Metagenomic mapping of 23 isolates showed an enrichment in resistant cultivars (marked by * in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, Wilcoxon rank sum test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and represented two examples of niche grabbing, nine examples of secreting antimicrobial compounds and 12 isolates with no inhibition.\u003c/p\u003e \u003cp\u003eWe generated three types of synthetic communities (SCs) by randomly selecting and mixing three isolates from each model in equal-volume suspensions (SC-I: inhibition by niche grabbing; SC-II: inhibition by secreting antimicrobial compounds; SC-III: no inhibition). We investigated the disease suppression activity of these SCs against \u003cem\u003eF. graminearum\u003c/em\u003e in greenhouse experiments. Infected maize seedlings were characterized by hyphal diffusion at the base and the apparent yellowing of stems, resulting in wilting within 2 weeks (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Three individual pot experiments showed that SC-I, SC-II and SC-III significantly decreased CRS incidence in maize seedlings at 2 weeks after \u003cem\u003eF. graminearum\u003c/em\u003e inoculation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). In addition, \u003cem\u003eF. graminearum\u003c/em\u003e treatments reduced the rate of seedling emergence and root growth, while additional inoculation with \u003cem\u003eBacillus\u003c/em\u003e at the same time, especially with SC-II, restored these seedling phenotypes (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb-d).\u003c/p\u003e \u003cp\u003eTo gain insight into the disease resistance mechanisms of the host plant based on their responses to pure \u003cem\u003eBacillus\u003c/em\u003e treatment, we investigated the transcriptomic changes in maize roots treated with these three SCs. We obtained 534,236,556 high-quality reads, with an average of 44,519,713 reads per sample and a 94.6% mapping rate to the maize reference genome (B73-NAM-5.0). Compared to the control groups treated with sterile water, we identified 293, 387 and 171 differentially expressed genes (DEGs, |log\u003csub\u003e2\u003c/sub\u003e(fold-change) | \u0026gt; 1, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e \u0026lt; 0.05) in maize roots treated with SC-I, SC-II and SC-III, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). PCoA based on an expression matrix of all genes showed that SC treatment did not greatly alter gene expression in maize roots (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee), whereas PCoA based on DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef) and functionally related genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eg) showed that SC-I and SC-II had similar effects on maize that were different from those observed for SC-III.\u003c/p\u003e \u003cp\u003eGene set enrichment analysis in response to treatment with each SC revealed similar enrichment among DEGs for functional terms such as mitogen-activated protein kinase (MAPK) signaling and plant hormone signal transduction (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eh, Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, b, Supplementary Table\u0026nbsp;10). Notably, inoculation with any of the three SCs resulted in lower expression of \u003cem\u003eWRKY33-\u003c/em\u003ehomologous genes, which facilitate defense-related gene induction (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). In addition, SC-II and SC-III may enhance the biosynthesis of flavonoids, as evidenced by the induction of \u003cem\u003eflavanone 3-hydroxylase1\u003c/em\u003e (\u003cem\u003eFHT1\u003c/em\u003e) expression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe were most interested in the SC-III-specific functional pathway, since SC-III had no inhibitory effect on \u003cem\u003eF. graminearum\u003c/em\u003e but significantly decreased the incidence of CSR. Furthermore, SC-III, rather than SC-I or SC-II, specifically facilitated the enrichment of maize genes related to sesquiterpenoid, isoquinoline alkaloid and betalain biosynthesis, as reflected by the normalized enrichment scores (NESs); these compounds are widely considered to be antibacterial metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eh). Both \u003cem\u003eterpene synthase 6\u003c/em\u003e (\u003cem\u003eTPS6\u003c/em\u003e) and \u003cem\u003etyrosine decarboxylase 1\u003c/em\u003e (\u003cem\u003eTYDC1\u003c/em\u003e), which participate in the above pathways, were upregulated by treatment with SC-III. The functional terms phenylalanine and tyrosine metabolites, which are biosynthetic precursors of isoquinoline alkaloids, were also enriched by treatment with SC-III (Supplementary Table\u0026nbsp;10). We validated the expression patterns of the above-mentioned genes by RT-qPCR analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb and Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e\n\u003ch3\u003eIsoquinoline alkaloids are enriched in resistant cultivars and suppress CSR\u003c/h3\u003e\n\u003cp\u003eTo investigate the biochemical composition of the root microenvironments, we determined the identities of metabolites in RS and RE samples collected from resistant and susceptible cultivars treated with \u003cem\u003eF. graminearum\u003c/em\u003e in 2021 by liquid chromatography\u0026ndash;tandem mass spectrometry (LC-MS/MS). We detected an average of 1,558 metabolites across all samples (Supplementary Tables\u0026nbsp;11\u0026ndash;12). PCoA based on Bray\u0026ndash;Curtis distance matrices revealed significant differences in both RS and RE metabolites between resistant and susceptible samples (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008, PERMANOVA by Adonis, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). We identified 947 significantly different metabolites in RE samples and 124 in RS samples (|log2FC| \u0026gt; 1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), demonstrating the presence of distinct chemical microenvironments in these two niches.\u003c/p\u003e \u003cp\u003eConsistent with the transcriptomic data (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eh), berberine (a natural isoquinoline alkaloid) and its isoquinoline precursor were enriched in the RE niche rather than the RS niche. The biosynthetic precursor l-phenylalanine appeared to be depleted from resistant samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed, Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). Importantly, l-dopa and tyramine, members of the berberine biosynthesis pathway that are substrates used by TYDC1 to synthesize dopamine, were enriched in RE samples (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). The antagonistic activities of berberine against \u003cem\u003eF. graminearum\u003c/em\u003e were demonstrated \u003cem\u003ein vitro\u003c/em\u003e. In potato dextrose agar (PDA) plate assays, \u003cem\u003eF. graminearum\u003c/em\u003e growth was reduced as the concentration of berberine increased, with growth diameters decreasing by 37%, 52%, 54% and 58% in the presence of 5, 25, 50 and 100 \u0026micro;g/mL berberine, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee and Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). Finally, pre-treatment of seed coats with berberine significantly reduced CSR disease severity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef). Together, these results indicate that SC-III treatment triggers disease-suppressive activity in the RE niche by inducing the accumulation of antifungal metabolites.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe phenotype of a plant depends primarily on the integration of the plant\u0026rsquo;s genotype, the associated microbiota and the field microclimate\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Accumulating evidence suggests that plants actively reshape specific communities of microorganisms to alter their phenotypes, promote growth and even inhibit disease occurrence\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Our 16S rRNA analysis demonstrated that both the ecological niche and the host resistance genotypes significantly shape the ecological assembly processes of bacterial, rather than fungal, communities. This plasticity in bacterial communities was further verified by diversity analysis. Although our observations do not align with the results of many studies that show that fungal \u003cem\u003eTrichoderma\u003c/em\u003e species suppress CSR symptoms\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, we and others have witnessed an important bacterial community response to CSR\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHost-associated bacteria account for approximately 70 to 90% of total soil microorganisms and harbor 100-fold more functional genes than the host\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, making them worthy of being the optimal choice to form holobionts with their host. The dispersal ability and habitat niche breadth of bacterial communities decreased gradually from the soil to internal niches, indicating that selection pressure increases during the movement of microbes from belowground to aboveground niches. The plant immune system and associated biochemical barriers are thought to result in strong selective pressure on the microbiota inhabiting inner plant tissues\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur neutral community model analysis further indicated that RS and RE niches in CRS-resistant, rather than CRS-susceptible, maize cultivars provide stronger dispersal limitation and selection pressure. These findings underscore the potential role of maize roots as a physical barrier and selection driver in shaping bacterial communities. The precipitous decline in microbial diversity from soil to internal niches and the clearly separated communities revealed by PCoA also support the role of maize roots in shaping their bacterial communities. In addition, our study demonstrated that the diversity of bacterial communities was much lower in the reproductive organ (grain) than in vegetative organs (root or stem) and that the assembly process of bacterial communities in the grain was not affected by the resistance genotype of the host plant. This pattern of microorganisms within grains can be explained by a life history tradeoff strategy that ensures the survival of the next generation of the host plant at the expense of investing in the susceptible individual in the current generation\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMicrobiota do not live in isolation, instead forming complex interactions with living organisms and non-living environments through the exchange of matter, energy and information, as shown by networks with species as nodes and associations as edges\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. The co-occurrence of species is thought to be mainly driven by three ecological responses: biological interactions, environmental filtration and diffusion restriction. Among these responses, biological interactions may be the main driving force for the overall network\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. In this study, the bacterial\u0026ndash;fungal interkingdom networks in soil niches showed richer compositions and more complex interactions than those in internal niches, which is in agreement with earlier studies\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Importantly, the root-related niches (RS and RE), but not the other niches, showed stronger interactions among bacterial\u0026ndash;fungal interkingdom networks in CSR-resistant maize than in CSR-susceptible maize. Thus, we suggest that roots are the key regions where CRS-resistant maize shapes the bacterial community and thereby promotes disease resistance.\u003c/p\u003e \u003cp\u003e \u003cem\u003eFusarium\u003c/em\u003e species secrete mycotoxins that cause plant diseases such as stem rot, Fusarium wilt and scab and ear rot, resulting in significant losses in crop production\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. In contrast to chemical control methods, the use of biological control agents provides a safe, effective, sustainable means of controlling plant diseases caused by \u003cem\u003eFusarium\u003c/em\u003e. Using multiple bioinformatics methods, we illustrated that \u003cem\u003eBacillus\u003c/em\u003e is a prominent feature of the core anti-CSR microbiome; our greenhouse experiments confirmed that \u003cem\u003eBacillus\u003c/em\u003e treatment significantly inhibits CSR. These findings are consistent with those of studies showing that \u003cem\u003eBacillus\u003c/em\u003e can antagonize \u003cem\u003eFusarium\u003c/em\u003e infection in multiple ways: niche competition, the production of antibacterial substances and the induction of plant systemic resistance\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe showed that \u003cem\u003eB. cereus\u003c/em\u003e and \u003cem\u003eB. albus\u003c/em\u003e antagonize \u003cem\u003eF. graminearum\u003c/em\u003e by secreting antimicrobial compounds (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). A previous study identified hentriacontane and butylphenol as key antifungal volatile organic compounds produced by \u003cem\u003eB. cereus\u003c/em\u003e that improve the resistance of tomato (\u003cem\u003eSolanum lycopersicum\u003c/em\u003e) plants to Fusarium wilt\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Although there is no direct evidence that \u003cem\u003eB. albus\u003c/em\u003e inhibits pathogen activity, \u003cem\u003eB. albus\u003c/em\u003e was reported to produce cellulase, which acts as an antifungal protein capable of rupturing pathogenic cell walls\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. In the current study, \u003cem\u003eB. subtilis\u003c/em\u003e demonstrated strong space competitiveness and monopolized nutritional resources, which is consistent with previous findings\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSurprisingly, \u003cem\u003eF. graminearum\u003c/em\u003e growth in dual culture assays was not inhibited by treatment with purified bacterial isolates, but macroscopic disease was suppressed when maize seedlings were treated with \u003cem\u003eB. simplex\u003c/em\u003e, \u003cem\u003eB. pumilus\u003c/em\u003e, \u003cem\u003eB. safensis\u003c/em\u003e or \u003cem\u003eB. altitudinis\u003c/em\u003e. Maize root transcriptome sequencing showed that these isolates specifically facilitated the enrichment of molecules involved in sesquiterpenoid, isoquinoline alkaloid and betalain biosynthesis. Although little is known about plant growth\u0026ndash;promoting rhizobacteria, these \u003cem\u003eBacillus\u003c/em\u003e strains were reported to improve host performance by increasing the emergence of lateral roots in pea (\u003cem\u003ePisum sativum\u003c/em\u003e; \u003cem\u003eB. simplex\u003c/em\u003e), shoot dry mass in chickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e; \u003cem\u003eB. pumilus\u003c/em\u003e), root length in \u003cem\u003eBrassica juncea\u003c/em\u003e (\u003cem\u003eB. safensis\u003c/em\u003e) and germination index in rice (\u003cem\u003eOryza sativa\u003c/em\u003e; \u003cem\u003eB. altitudinis\u003c/em\u003e)\u003csup\u003e\u003cspan additionalcitationids=\"CR44 CR45\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Therefore, we suggest that these isolates mainly induce disease resistance by interacting with the host.\u003c/p\u003e \u003cp\u003eThe two-way production and perception of secretions is a key mechanism through which plants interact with their microbiota\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Plants secrete specific exudates at defined stages of their development and under biotic and abiotic stress that shape microbiomes, whereas microbiomes modulate the soil environment by reprogramming root exudation profiles \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Acknowledging the crucial effect of roots on shaping microenvironments, we further explored the specific compositions and differentiation of these root exudates. We demonstrated that the metabolic profiles of CSR-resistant and -susceptible maize cultivars are distinct. This observation is consistent with the expectation that complex root systems and wide genomic diversity of maize result in major physiological differences\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Distinct maize genotypes have been shown to differ in their enrichment of specific bacterial taxa \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. In line with the results of transcriptome functional enrichment, isoquinoline alkaloid pathways, together with the isoquinoline precursor, were enriched in the RE niche of resistant cultivars when treated with SC-III, and they exhibited both \u003cem\u003ein vitro\u003c/em\u003e antagonistic activities and \u003cem\u003ein vivo\u003c/em\u003e disease suppression effects. This finding is consistent with the previous observations that isoquinoline alkaloid biosynthesis was significantly upregulated in maize seedlings infected with Rice black streaked dwarf virus based on transcriptomic data and with \u003cem\u003eFusarium verticillioides\u003c/em\u003e based on metabolic profiles\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. However, these studies did not focus on the associated changes in the microbiota.\u003c/p\u003e \u003cp\u003eA previous study of the microbiota\u0026ndash;host interaction in fava bean (\u003cem\u003eVicia faba\u003c/em\u003e) demonstrated that the antifungal peptide P852 from \u003cem\u003eBacillus\u003c/em\u003e fermentation broth could control Fusarium wilt by promoting the activities of antioxidant enzymes and enhancing isoquinoline alkaloid biosynthesis\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Although a clear link between \u003cem\u003eBacillus\u003c/em\u003e treatment and isoquinoline alkaloid biosynthesis was revealed by these findings and the current results, we cannot exclude the possibility that other factors, such as WRKY33-related induction of resistance, contribute to disease suppression. Considering that SC-I inhibited \u003cem\u003eWRKY33\u003c/em\u003e expression but resulted in weaker disease suppression effects compared to SC-III, we suggest that isoquinoline alkaloids play important roles in the resistance of maize cultivars to CSR. It will be interesting to examine whether CSR-resistant maize plants secrete isoquinoline alkaloids, which could potentially inhibit the colonization and invasion of a wider range of pathogens. Together, these findings highlight the important role of \u003cem\u003eBacillus\u003c/em\u003e species as a key microbiota for suppressing \u003cem\u003eFusarium\u003c/em\u003e-induced diseases through multiple mechanisms, including the direct secretion of antimicrobial compounds and the induction of alkaloid biosynthesis by the host (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eg).\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eUsing multi-omics analyses and experimental validation, we showed that resistant host plants govern the assembly of distinct, beneficial microbial communities in their internal environmental niches. We also demonstrated that \u003cem\u003eBacillus\u003c/em\u003e species are core components of the microbiota recruited by CSR-resistant maize cultivars in the RS, RE and SE ecological niches. In contrast to fungal microbiota, which showed little effect in preventing CSR, several \u003cem\u003eBacillus\u003c/em\u003e species decreased the incidence and symptoms of CSR via the secretion of antimicrobial compounds and the induction of host alkaloid biosynthesis. Although treatment with the SC-III suspension did not inhibit \u003cem\u003eF. graminearum in vitro\u003c/em\u003e, it specifically induced endogenous isoquinoline alkaloid biosynthesis \u003cem\u003ein vivo\u003c/em\u003e in the RE niche. Overall, these findings improve our understanding of the roles of core microbiotas in plant responses to pathogen challenge and lay the foundation for preventing and treating \u003cem\u003eFusarium\u003c/em\u003e-induced disease.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eExperimental design and sampling\u003c/h2\u003e \u003cp\u003eThe experiments were conducted in Gongzhuling City (108\u0026deg; 32\u0026prime; N, 124\u0026deg; 58\u0026prime; E), Jilin Province, northeastern China, a site that is considered to be one of the three golden corn belts. The site has a temperate continental monsoon climate with an annual average temperature of 5.6\u0026deg;C, average annual precipitation of 595 mm and a frost-free period of 144 days. Forty maize (\u003cem\u003eZea mays\u003c/em\u003e) cultivars (Supplementary Table\u0026nbsp;1) from a 527 association mapping panel\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e were planted in natural plots and disease nursery plots constructed for disease evaluation in early May 2020 and 2021. For the disease nursery plots, high-temperature-sterilized maize seeds that were co-cultured in conical flask with the mycelium of \u003cem\u003eFusarium graminearum\u003c/em\u003e for 2 weeks were sown in the rhizosphere zone of maize plants at the vegetative 6 (V6) stage without damaging the roots. Maize plants at the reproductive 3 (R3) stage that showed no watery spots on their stems or brown withered stems were defined as healthy; plants with watery spots on their stems and hollow stems with brown xylem were classified as diseased (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Surveys for the incidence of CSR (percentage of the total number of plants showing disease symptoms) were performed at the end of September 2020 and 2021. Maize cultivars that lacked CSR symptoms for two consecutive years were defined as resistant; cultivars with an incidence of CSR greater than 30% for two consecutive years were defined as susceptible.\u003c/p\u003e \u003cp\u003eFour susceptible and four resistant maize cultivars were selected in October 2020 and again in October 2021 for experimentation. For each of these cultivars, five individual samples were collected from each of six ecological niches: bulk soil (BS), rhizosphere (RS), root endosphere (RE), stem endosphere (SE) and grain endosphere (GE), as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb. Bulk soil samples were collected 20 cm away from the plants at a depth of 0\u0026ndash;25 cm. Rhizosphere soil was collected by manually shaking uprooted plants after removing large pieces of soil that loosely adhered to the roots. All collected maize tissues were transported to the laboratory in a dry ice box and stored at \u0026minus;\u0026thinsp;80\u0026deg;C for subsequent experiments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction and amplicon sequencing\u003c/h2\u003e \u003cp\u003eBulk and rhizosphere soil DNA were extracted using a PowerSoil DNA Isolation Kit (MO BIO Laboratories, Carlsbad, CA, USA) according to the manufacturer\u0026rsquo;s instructions. For surface sterilization, a minimum of 5 g of roots, stems or seeds was rinsed with 70% (v/v) ethanol for 5 min and then with 6% (w/v) sodium hypochlorite solution for 10 min, followed by three rinses in sterile H\u003csub\u003e2\u003c/sub\u003eO for 15 min. The treated tissues were ground into a powder in liquid nitrogen using a sterile mortar and pestle. Total DNA was extracted from the tissues using a FastDNA SPIN Kit for Soil (MP Biomedicals, Solon, USA) following the manufacturer\u0026rsquo;s instructions. The V5-V7 hypervariable region (799F/1193R) of the bacterial \u003cem\u003erRNA\u003c/em\u003e gene and the fungal \u003cem\u003eITS1\u003c/em\u003e region (ITS1F/TIS1R) in the internal tissues were amplified. The V3-V4 region (338F/806R) of the bacterial \u003cem\u003erRNA\u003c/em\u003e gene and the fungal \u003cem\u003eITS1\u003c/em\u003e region in the bulk and rhizosphere soil samples were also amplified. The amplicon libraries were sequenced using the Illumina NovaSeq PE250 platform (Wekemo Tech Group Co., Ltd. Shenzhen China) with a paired-end protocol.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatic analysis of amplicon sequencing data\u003c/h2\u003e \u003cp\u003eQIIME v1.9.\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e and USEARCH v10\u003csup\u003e56\u003c/sup\u003e were used to process the bacterial \u003cem\u003e16S rRNA\u003c/em\u003e gene and fungal \u003cem\u003eITS\u003c/em\u003e sequences. Briefly, FastQC v0.11.5\u003csup\u003e57\u003c/sup\u003e was used to assess read quality. Low-quality reads with scores below Q30 were trimmed in pairs using Trimmomatic v0.39\u003csup\u003e58\u003c/sup\u003e. The corrected reads were merged into a single read and selected at 97% identity (operational taxonomic units, OTUs). The SILVA v138.1 and UNITE v8.2 databases were used to classify the sequences as bacterial or fungal. The OTU tables of bacteria and fungi were rarefied with the script single_rarefaction.py in QIIME for alpha-diversity estimates. The cumulative sum scaling (CSS) normalization method was used for bacterial and fungal β-diversity analyses using beta_diversity.py. The OTUs present in all samples were defined as core taxa.\u003c/p\u003e \u003cp\u003ePrincipal coordinates analysis (PCoA) was performed using the Vegan and Tidyverse packages of R (version 4.1.0)\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e based on the relative abundance of each OTU. Gehpi v0.9.2 \u003csup\u003e60\u003c/sup\u003e was used to produce the visualizations of the bacterial\u0026ndash;fungal interkingdom networks (Spearman |ρ| \u0026gt; 0.7 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) following a previously described method \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. To explore the degree of deviation of the co-accordance networks, dissimilarity was calculated using a previously described formula\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. In addition, weighted robustness, vulnerability, compositional stability and node persistence were evaluated using the ggClusterNet package in R. A random-forest machine-learning model was established using a 10-fold cross-validation error curve in the randomForest package in R. Linear discriminant analysis effect size with permutational multivariate analysis of variance (PERMANOVA) based on 9,999 permutations was employed to identify the key families in the CSR-R and CSR-S groups using the Vegan package in R. Beta nearest taxon index (βNTI) was calculated based on the OTU matrix using the ape, ICAMP, picante and NST packages in R. A neutral community model was conducted to estimate the effects of stochasticity on community assembly, in which the best fit between the frequency of OTU occurrence was generated by applying a nonlinear least-squares method\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMetagenomic sequencing and data mining\u003c/h2\u003e \u003cp\u003eA total of 42 DNA samples (from 3 resistant and 3 susceptible varieties with 3 replicates in 2020, and 4 resistant and 4 susceptible varieties with 3 replicates in 2021) were selected for metagenomic sequencing on an Illumina NovaSeq 6000 instrument (Wekemo Tech Group Co., Ltd. Shenzhen China) with a paired-end protocol. After removing the adaptor and low-quality sequences with Trimmomatic (version 0.39), Bowtie2 (version 2.1.0)\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e was used to eliminate host-derived sequences by mapping the clean data to the maize genome (B73 RedGen_v4 from maizeGDB). The high-quality reads were assembled using Megahit (version 1.2.9)\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e with the parameter meta-large, predicted based on contigs using Prodigal (reads shorter than 300 bp were removed; version 2.6.3)\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e and clustered at a 0.95 identity threshold using CD-hit (version 4.6.2)\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e to generate a nonredundant gene catalog. Taxonomic classification was performed using Kraken2 (version 2.1.2)\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e based on a memory-intensive algorithm that associates short genomic substrings (k-mers) with the lowest common ancestor taxa. The eukaryotic pathogen database EuPathDB was used to classify the pathogenic fungus using Kraken2. Gene quantification was performed by mapping the clean data to the nonredundant gene catalog using Salmon (version 1.9.0)\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e with the quasi-mapping-based model. Functional annotation was accomplished by mapping the genes to the Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology (KO) profiles, Clusters of Orthologous Group of proteins (COG) functional categories and CAZymes (CAZ) using Diamond (version 0.9.14). Python scripts (alpha_diversity.py, and beta_diversity.py) in QIIME (version 1.91) were used to calculate functional diversity. Linear discriminant analysis effect size (LEfSe) was performed to identify the functional capacities of rhizosphere samples with significantly different abundances between resistant and susceptible groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eValidating the role of the soil microbiome in CSR resistance\u003c/h2\u003e \u003cp\u003eTopsoil (0\u0026ndash;25 cm) was collected from the large-scale sampled field, which had no history of maize cultivation. To obtain soil extracts, 1 g of soil was suspended in 9 mL sterile water, and the large soil particles were removed by filtration through Whatman 42 filter paper. The CSR-resistant cultivar GZL185 was planted in sterile substrate that had been incubated for 2 weeks with soil extracts. After five days of growth, the root zone of each maize seedling was irrigated with 10 mL of a spore suspension of \u003cem\u003eF. graminearum\u003c/em\u003e (isolated from diseased maize in the field in Gongzhuling City) at a concentration of 2 x 10\u003csup\u003e5\u003c/sup\u003e conidia/mL. Three weeks after \u003cem\u003eF. graminearum\u003c/em\u003e inoculation, disease incidence, defined as the percentage of diseased seedlings relative to the total number of seedlings, was monitored.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eRoot collection and LC-MS/MS analysis\u003c/h2\u003e \u003cp\u003eRS and RE samples from the field maize treated with \u003cem\u003eF. graminearum\u003c/em\u003e in 2021 were selected for LC-MS/MS analysis. First, 10 mg portions of soil samples were transferred into centrifuge tubes containing 500 \u0026micro;L ddH\u003csub\u003e2\u003c/sub\u003eO (precooled to 4\u0026deg;C) and vortexed for 60 s. After the addition of 1,000 \u0026micro;L methanol (prechilled to \u0026minus;\u0026thinsp;20\u0026deg;C), the tubes were placed into an ultrasound machine at room temperature for 10 min and incubated on ice for 30 min. The samples were centrifuged for 10 min at 16,000 \u003cem\u003eg\u003c/em\u003e at 4\u0026deg;C, after which 1.2 mL of supernatant was transferred into a new centrifuge tube. Following vacuum concentration, the samples were dissolved in 40 \u0026micro;L 2-chlorobenzalanine (4 ppm) in a methanol:water solution (1:1, v/v, 4\u0026deg;C). Following filtration through a 0.22-\u0026micro;m membrane, the samples were ready for LC-MS/MS detection.\u003c/p\u003e \u003cp\u003eTo monitor deviations in the analytical results from these pooled mixtures and to compare them to the errors caused by the analytical instrument itself, 20 \u0026micro;L aliquots were removed from each prepared sample and mixed. LC analyses were performed on a Thermo Ultimate 3000 system equipped with an ACQUITY UPLC\u0026reg; HSS T3 (150\u0026times;2.1 mm, 1.8 \u0026micro;m, Waters) column maintained at 40\u0026deg;C. The temperature of the autosampler was set to 8\u0026deg;C. Gradient elution of analytes was carried out using 0.1% (v/v) formic acid in acetonitrile (C) and 0.1% (v/v) formic acid in water (D) or acetonitrile (A) and 5 mM ammonium formate in water (B) at a flow rate of 0.25 mL/min. Each sample (2 \u0026micro;L) was injected following equilibration. An increasing linear gradient of solvent B (all v/v) was used as follows: 0\u0026ndash;1 min, 2% A/C; 1\u0026ndash;9 min, 2\u0026ndash;50% A/C; 9\u0026ndash;12 min, 50\u0026ndash;98% A/C; 12\u0026ndash;13.5 min, 98% A/C; 13.5\u0026ndash;14 min, 98\u0026ndash;2% A/C; 14\u0026ndash;20 min, 2% A/C-positive mode (14\u0026ndash;17 min, 2% A/C, negative mode). The ESI-MSn experiments were performed using a Thermo Q Exactive Focus mass spectrometer with spray voltages of 3.8 kV and \u0026minus;\u0026thinsp;2.5 kV in positive and negative modes, respectively. Sheath gas and auxiliary gas were set to 45 and 15 arbitrary units, respectively. The capillary temperature was 325\u0026deg;C. The Orbitrap analyzer scanned over a mass/charge range of 81\u0026ndash;1,000 for full scans at a mass resolution of 70,000. Data-dependent acquisition (DDA) MS/MS experiments were performed with HCD scan. The normalized collision energy was 30 eV. Dynamic exclusion was implemented to remove unnecessary information in the MS/MS spectra.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eIsolation and culture of bacterial strains from resistant samples\u003c/h2\u003e \u003cp\u003eMethods for isolating and cultivating the bacteria in this study were described by Yang Bai et al.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Briefly, 10-fold serial dilutions of the rhizosphere microbiota sample in tryptic soy broth (TSB, Difco Laboratories Inc., Detroit, MI, USA) were prepared in 96-well cell culture plates. The plates were chosen in such a way that \u0026lt;\u0026thinsp;30% of the wells showed visible bacterial growth after an incubation period. Purified bacterial isolates were obtained by continuous streaking on solid Luria-Bertani (LB) medium, and identities were confirmed by \u003cem\u003e16S rRNA\u003c/em\u003e gene sequencing. All purified bacterial isolates were then placed in 50% (v/v) glycerol for storage at \u0026minus;\u0026thinsp;80\u0026deg;C for subsequent experiments.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eBacillus\u003c/em\u003e strains were further isolated. Briefly, 10 g of soil was placed into 90 mL sterile water, and a 10\u003csup\u003e\u0026ndash;5\u003c/sup\u003e soil suspension was prepared by successive 10-fold dilutions in sterile water. The suspension was incubated in a water bath at 80\u0026deg;C for 30 min to kill the non-endospore-forming bacteria. A 0.1-mL aliquot of the diluted suspension was spread onto plates containing LB medium and incubated for 12 h at 37\u0026deg;C. Single isolates were preliminarily classified according to the identity of full-length 16S rRNA sequences mapping to the NCBI rRNA/ITS database, and the single colonies were purified. The surviving strains were verified by endospore staining and stored in a refrigerator at 5\u0026deg;C for future use.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGenomic sequencing of\u003c/b\u003e \u003cb\u003eBacillus\u003c/b\u003e \u003cb\u003eisolates\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo obtain accurate classification information, the genomes of 44 isolates were sequenced. Briefly, genomic DNA was extracted from each isolate using a Bacterial Genomic DNA Extraction Kit (Qiagen), and a sequencing library was generated using a VAHTS Universal DNA Library Prep Kit for Illumina (Vazyme, ND604) following the manufacturer\u0026rsquo;s recommendations. The library preparations were sequenced on the Illumina NovaSeq 6000 platform with the 150-bp paired-end module.\u003c/p\u003e \u003cp\u003eAfter obtaining the raw data, the raw reads were filtered by removing reads containing adaptor, poly-N and low-quality reads (more than 50% of bases with Q\u0026thinsp;\u0026lt;\u0026thinsp;10). SPAdes (version 3.15.5)\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e was used to perform genome assembly with gradient k values (21, 33, 55, 77, 99 and 127), and annotation was accomplished using Prokka (version 1.13)\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. To obtain taxonomy information about the isolates, a neighbor-joining (NJ) phylogenetic tree was reconstructed based on the amino acid sequences of 4,215 genomes using the CVTree Standalone Version with k-string\u0026thinsp;=\u0026thinsp;6\u003csup\u003e72\u003c/sup\u003e. The abundance of these isolates in the rhizosphere was determined by mapping the metagenomic data to these genomes using Bowtie2 (version 2.1.0).\u003c/p\u003e \u003cp\u003e \u003cb\u003eMeasuring\u003c/b\u003e \u003cb\u003ein vitro\u003c/b\u003e \u003cb\u003eantagonistic activities of bacteria against\u003c/b\u003e \u003cb\u003eF. graminearum\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe antagonistic activities of bacteria against \u003cem\u003eF. graminearum\u003c/em\u003e were measured by \u003cem\u003ein vitro\u003c/em\u003e dual culture assays on potato dextrose agar (PDA, Difco) solid medium. Petri dishes containing PDA solid medium inoculated only with \u003cem\u003eF. graminearum\u003c/em\u003e in the center served as the negative controls. For the experimental groups, purified isolates were added to the medium edge symmetrically from the center. All Petri dishes were incubated in the dark at 25\u0026deg;C until the PDA medium for the negative controls was completely covered by \u003cem\u003eF. graminearum\u003c/em\u003e. The radial growth of \u003cem\u003eF. graminearum\u003c/em\u003e in the control (R1) and experimental groups (R2) was then measured with a ruler. The inhibition rate of radial growth of \u003cem\u003eF. graminearum\u003c/em\u003e by the isolated bacteria was calculated as follows: 100 \u0026times; [(R1\u0026thinsp;\u0026minus;\u0026thinsp;R2)/R1].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eExamining the influence of beneficial bacteria on plant disease\u003c/h2\u003e \u003cp\u003eThe purified isolates that showed antagonistic activity against \u003cem\u003eF. graminearum\u003c/em\u003e were cultured in LB broth at 28\u0026deg;C for 12 h, centrifuged at 25\u0026deg;C for 2 min at 7000 \u003cem\u003eg\u003c/em\u003e and resuspended in sterile water to an OD\u003csub\u003e600\u003c/sub\u003e value of 1.0. Surface-sterilized maize seeds were incubated in Petri dishes with distilled water at 25\u0026deg;C for 3 days for germination. For the negative controls, 2 mL of distilled water was added to the soil around the maize seeds. Maize seeds treated with 2 mL of \u003cem\u003eF. graminearum\u003c/em\u003e spore suspension at a concentration of 10\u003csup\u003e8\u003c/sup\u003e conidia/mL served as the positive controls. To examine the effects of beneficial bacteria on CSR development, \u003cem\u003eF. graminearum\u003c/em\u003e (2 mL, 2 x 10\u003csup\u003e5\u003c/sup\u003e conidia/mL) and pure beneficial bacteria (2 mL, OD\u003csub\u003e600\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.0) were added simultaneously to the root tips. All maize seeds were planted in plastic cones containing sterile substrate amended with roseite in the greenhouse. Cones with the same treatment were arranged randomly in plastic racks and incubated in a growth chamber (14 h light/10 h dark) at 26\u0026deg;C. The disease severity of the maize plants was assessed based on the number of diseased plants after 2 weeks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eRNA-seq of maize roots after inoculation with a synthetic community (SC) of bacteria\u003c/h2\u003e \u003cp\u003eThree isolates from the three groups (SC-I, SC-II and SC-III) of \u003cem\u003eBacillus\u003c/em\u003e were randomly selected to construct three SCs. A 20 mL aliquot of SC suspension (OD\u003csub\u003e600\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.0) was poured onto the roots of 2-week-old maize seedlings growing in sterile substrate. The control groups received equal amounts of sterile water. Twenty-four hours after inoculation, the plants were uprooted from the substrate soil, and the roots were washed in phosphate-buffered saline (PBS) buffer. Clean roots separated from the rest of the plant with five biological replicates were flash-frozen in liquid nitrogen and stored at \u0026minus;\u0026thinsp;80\u0026deg;C. TRIzol reagent (1.5 mL) was used to extract total RNA from the root tissues, and the integrity and purity of the RNA were assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies, CA, USA) and a NanoPhotometer Spectrophotometer (IMPLEN, CA, USA), respectively. A 1 \u0026micro;g aliquot of RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using an NEBNext Ultra RNA Library Prep Kit from Illumina (NEB, USA) following the manufacturer\u0026rsquo;s recommendations, and index codes were added to attribute sequences to particular samples. Following purification and quality assessment, the library was sequenced on the Illumina NovaSeq platform, and 150-bp paired-end reads were generated.\u003c/p\u003e \u003cp\u003eClean reads were obtained by removing reads containing adapters, reads containing poly-N and low-quality reads from the raw data. Clean paired-end reads were aligned to the B73 maize genome (version 5.0), which was downloaded from maizeGDB, using Hisat2 (version 2.0.5)\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. StringTie (version 2.2.1)\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e was used to count the number of reads mapped to each gene, and the fragments per kilobase of transcript per million mapped reads (FPKM) value of each gene was calculated based on the length of the gene. Differential expression analysis was performed using the DESeq2 R package (1.16.1). Gene Ontology (GO) and KEGG enrichment analysis of differentially expressed genes (DEGs) was performed using the clusterProfiler R package, in which gene length bias was corrected. Functional terms with corrected \u003cem\u003eP\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered to be significantly enriched. The expression of genes in core pathways was further verified by reverse-transcription quantitative PCR (RT-qPCR). Briefly, the SC-associated experiment described above including RNA preparation was performed again. A HiScript III 1st Strand cDNA Synthesis Kit (Vazyme, Nanjing, China) and RealStar Green Fast Mixture with ROXII (GenStar, Beijing, China) were employed for cDNA synthesis and qPCR, respectively. The 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method was used to determine the relative expression levels of the target genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eEffects of berberine on pathogen growth and the occurrence of CSR\u003c/h2\u003e \u003cp\u003eTo evaluate the effects of specific root exudates on pathogen growth, four PDA plates containing 5, 25, 50 or 100 \u0026micro;g/mL berberine (Solarbio) were prepared, and 5% (v/v) DMSO was added to the PDA as the mock treatment. Agar disks containing fresh mycelium were inoculated into the centers of the PDA plates with four replicates per treatment, and the plates were incubated at 25\u0026deg;C for 48 h.\u003c/p\u003e \u003cp\u003eFor greenhouse experiments, maize seeds were surface-sterilized in 3% (v/v) sodium hypochlorite solution for 5 min and rinsed with sterile deionized water. The seeds were soaked in 25 \u0026micro;g/mL berberine solution for 5 min and planted in the sterile substrate. Roots were inoculated by irrigation with \u003cem\u003eF. graminearum\u003c/em\u003e (2 mL, 2 x 10\u003csup\u003e5\u003c/sup\u003e conidia/mL) at three days after planting. CSR incidence was scored with the naked eye and recorded every 5 days.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eThe statistically significant differences in bacterial community composition, plant biomass and SDIs between resistant and susceptible groups were evaluated by a two-tailed unpaired Wilcoxon rank sum test at a threshold \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Differential abundance of the metabolites in the rhizosphere and root endosphere between resistant and susceptible groups was calculated using the generalized linear model (GLM) approach in the edgeR R package. The major drivers of microbial and fungal alpha-diversity were determined by linear mixed model analysis using the R package lme4. The β-diversity of bacterial and fungal communities and the functional categories were assessed by computing the Bray\u0026ndash;Curtis distance matrices and then ordinated using PCoA. PERMANOVA statistical tests were performed to determine the relative contributions of different factors to community dissimilarity using Adonis in the Vegan R package with 1999 permutations. The biomarker bacteria were identified using LEfSe (logarithmic LDA score\u0026thinsp;\u0026gt;\u0026thinsp;2.5, \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eDATA AVAILABILITY\u003c/h2\u003e \u003cp\u003eSequencing data from this study can be found in NCBI under the following BioProject numbers: PRJNA990978, including the \u003cem\u003e16S rRNA\u003c/em\u003e sequencing data (SAMN36289239-SAMN36289436), \u003cem\u003eITS\u003c/em\u003e rRNA sequencing data (SAMN36377919-SAMN36378116), transcriptomic data (SAMN36399084-SAMN36399095) and metagenomic data (SAMN36381305-SAMN36381346).\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSequencing data from this study can be found in NCBI under the following BioProject numbers: PRJNA990978, including the \u003cem\u003e16S rRNA\u003c/em\u003e sequencing data (SAMN36289239-SAMN36289436), \u003cem\u003eITS\u003c/em\u003e rRNA sequencing data (SAMN36377919-SAMN36378116), transcriptomic data (SAMN36399084-SAMN36399095) and metagenomic data (SAMN36381305-SAMN36381346).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWende Liu conceived and designed the study. Xinyao Xia, Qiuhe Wei, Hanxiang Wu, Xinyu Chen, Chunxia Xiao, Chaotian Liu and Haiyue Yu performed the experiments and analyzed the data. Wende Liu, Hanxiang Wu, Xinyao Xia and Qiuhe Wei wrote the manuscript with contributions from all the other authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was financially supported by the Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLu, Z.-x.\u003cem\u003e et al.\u003c/em\u003e Screening of antagonistic Trichoderma strains and their application for controlling stalk rot in maize. \u003cem\u003eJournal of Integrative Agriculture\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 145-152 (2020).\u003c/li\u003e\n\u003cli\u003eHaryuni, H., Harahap, A.F.P., Supartini, Priyatmojo, A. \u0026amp; Gozan, M. 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Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. \u003cem\u003eNature biotechnology\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 907-915 (2019).\u003c/li\u003e\n\u003cli\u003ePertea, M.\u003cem\u003e et al.\u003c/em\u003e StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. \u003cem\u003eNature biotechnology\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 290-295 (2015).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Bacillus, Corn stem rot, Ecological niches, Microbiome, Resistance heterogeneity","lastPublishedDoi":"10.21203/rs.3.rs-3400607/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3400607/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMicrobes colonizing each niche of terrestrial plants are indispensable for maintaining crop health. Although corn stalk rot (CSR) is a severe disease infecting maize (\u003cem\u003eZea mays\u003c/em\u003e) worldwide, the mechanisms underlying host\u0026ndash;microbe interactions across vertical niches in maize plants, which exhibit heterogeneous CSR resistance, remain largely uncharacterized. Here, we investigated the microbial communities associated with CSR-resistant and -susceptible maize cultivars using multi-omics analysis coupled with experimental verification. Maize cultivars resistant to CSR reshaped the microbiota and recruited \u003cem\u003eBacillus\u003c/em\u003e species with three antagonistic phenotypes to alleviate pathogen stress. By inducing the expression of \u003cem\u003eTyrosine decarboxylase 1\u003c/em\u003e (\u003cem\u003eTYDC1\u003c/em\u003e), encoding an enzyme that catalyzes the production of tyramine and dopamine, \u003cem\u003eBacillus\u003c/em\u003e isolates that do not directly suppress pathogen infection facilitated the synthesis of berberine, an isoquinoline alkaloid that inhibits pathogen growth. These beneficial bacteria were recruited from the rhizosphere and transferred to the stems but not grains of the infected resistant plants. Our findings offer insight into how maize plants respond to and interact with their microbiome and provide valuable strategies for controlling soil-borne pathogens.\u003c/p\u003e","manuscriptTitle":"Bacillus species are core microbiota of highly resistant maize varieties that induce host metabolic defense against corn stalk rot","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-11 12:24:11","doi":"10.21203/rs.3.rs-3400607/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":"cb1ec754-41ab-496d-8592-5ee18a1f5310","owner":[],"postedDate":"October 11th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":25289112,"name":"Biological sciences/Plant sciences/Plant stress responses/Biotic"},{"id":25289113,"name":"Biological sciences/Microbiology/Antimicrobials/Antimicrobial resistance"}],"tags":[],"updatedAt":"2023-11-10T11:25:31+00:00","versionOfRecord":[],"versionCreatedAt":"2023-10-11 12:24:11","video":{"identity":"3b147c146b92851bcfd8759988236914"},"vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3400607","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3400607","identity":"rs-3400607","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}