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Abstract Prebiotics are compounds that selectively stimulate the growth and activity of beneficial microorganisms. The use of prebiotics is a well-established strategy for managing human gut health. This concept can also be extended to plants where plant rhizosphere microbiomes can improve the nutrient acquisition and disease resistance. However, we lack effective strategies for choosing metabolites to elicit the desired impacts on plant health. In this study, we target the rhizosphere of tomato (Solanum lycopersicum) suffering from wilt disease (caused by Ralstonia solanacearum) as source for potential prebiotic metabolites. We identify metabolites (ribose, lactic acid, xylose, mannose, maltose, gluconolactone, and ribitol) exclusively used by soil commensal bacteria (not positively correlated with R. solanacearum) but not efficiently used by the pathogen in vitro. Metabolites application in the soil with 1 µmol g−1 soil effectively protects tomato and other Solanaceae crops, pepper (Capsicum annuum) and eggplant (Solanum melongena), from pathogen invasion. After adding prebiotics, the rhizosphere soil microbiome exhibits enrichment of pathways related to carbon metabolism and autotoxin degradation, which were driven by commensal microbes. Collectively, we propose a novel pathway for mining metabolites from the rhizosphere soil and their use as prebiotics to help control soil-borne bacterial wilt diseases.

Abstract Background Process and function that underlie the assembly of a rhizosphere microbial community may be strongly linked to the maintenance of plant health. However, their assembly processes and functional changes in the deterioration of soilborne disease remain unclear. Here, we investigated features of rhizosphere microbiomes related to Fusarium wilt disease and assessed their assembly by comparison pair of diseased/healthy sequencing data. The untargeted metabolomics was employed to explore potential community assembly drivers, and shotgun metagenome sequencing was used to reveal the mechanisms of metabolite-mediated process after soil conditioning. Results Results showed the deterministic assembly process associated with diseased rhizosphere microbiomes, and this process was significantly correlated to five metabolites (tocopherol acetate, citrulline, galactitol, octadecylglycerol, and behenic acid). Application of the metabolites resulted in a deterministic assembly of microbiome with the high morbidity of watermelon. Furthermore, metabolite conditioning was found to weaken the function of autotoxin degradation undertaken by specific bacterial group (Bradyrhizobium, Streptomyces, Variovorax, Pseudomonas, and Sphingomonas) while promoting the metabolism of small-molecule sugars and acids initiated from another bacterial group (Anaeromyxobacter, Bdellovibrio, Conexibacter, Flavobacterium, and Gemmatimonas). Video Abstract Conclusion These findings strongly suggest that shifts in a metabolite-mediated microbial community assembly process underpin the deterministic establishment of soilborne Fusarium wilt disease and reveal avenues for future research focusing on ameliorating crop loss due to this pathogen.

ABSTRACT Seed-associated microbiomes can impact the later colonization of a plant rhizosphere microbiome. However, there remains little insight into the underlying mechanisms concerning how alterations in the composition of the seed microbiome may intervene in the assembly of a rhizosphere microbiome. In this study, the fungus Trichoderma guizhouense NJAU4742 was introduced to both maize and watermelon seed microbiomes by seed coating. Application was found to significantly promote seed germination and improve plant growth and rhizosphere soil quality. The activities of acid phosphatase, cellulase, peroxidase, sucrase, and α-glucosidase increased significantly in two crops. The introduction of Trichoderma guizhouense NJAU4742 also led to a decrease in the occurrence of disease. Coating with T. guizhouense NJAU4742 did not alter the alpha diversities of the bacterial and fungal communities but formed a key network module that contained both Trichoderma and Mortierella. This key network module comprised of these potentially beneficial microorganisms was positively linked with the belowground biomass and activities of rhizosphere soil enzymes but negatively correlated with disease incidence. Overall, this study provides insights into plant growth promotion and plant health maintenance via seed coating in order to influence the rhizosphere microbiome. IMPORTANCE Seed-associated microbiomes can impact the rhizosphere microbiome assembly and function display. However, there remains little insight into the underlying mechanisms concerning how alterations in the composition of the seed microbiome with the beneficial microbes may intervene in the assembly of a rhizosphere microbiome. Here, we introduced T. guizhouense NJAU4742 to the seed microbiome by seed coating. This introduction led to a decrease in the occurrence of disease and an increase in plant growth; furthermore, it formed a key network module that contained both Trichoderma and Mortierella. Our study provides insights into plant growth promotion and plant health maintenance via seed coating in order to influence the rhizosphere microbiome.

Abstract The network analysis has attracted increasing attention and interest from ecological academics, thus it is of great necessity to develop more convenient and powerful tools. For that reason, we have developed an R package, named “ggClusterNet,” to complete and display the network analysis in an easier manner. In that package, ten network layout algorithms are designed to better display the modules of microbiome network (randomClusterG, PolygonClusterG, PolygonRrClusterG, ArtifCluster, randSNEClusterG, PolygonModsquareG, PolyRdmNotdCirG, model_Gephi.2, model_igraph, and model_maptree). For the convenience of the users, many functions related to microbial network analysis, such as corMicor(), net_properties(), node_properties(), ZiPiPlot(), random_Net_compate(), are integrated to complete the network mining. Furthermore, the pipeline function named network.2() and corBionetwork() are also added for the quick achievement of the network or bipartite network analysis as well as their in‐depth mining. The ggClusterNet is publicly available via GitHub (https://github.com/taowenmicro/ggClusterNet/) or Gitee (https://gitee.com/wentaomicro/ggClusterNet) for users' access. A complete description of the usages can be found on the manuscript's GitHub page (https://github.com/taowenmicro/ggClusterNet/wiki).