A reverse-ecology framework to uncover the potential metabolic interplay among 'Candidatus Liberibacter' species, Citrus hosts and psyllid vector

The Reverse Ecology-Based Approach to Design a Bacterial Consortium as Soybean Bioinoculant

Bioinoculants traditionally rely on selecting efficient microbes from the soil with potential growth-enhancing traits for plants. However, such approaches often neglect microbe-microbe and microbe-plant interactions. In this study, we applied a reverse ecology framework to design and assess a bacterial consortium tailored for soybeans. Our analysis identified Paenibacillus polymyxa, Methylobacterium brachiatum, and Enterobacter sp. as key strains for their synergistic potential in promoting soybean growth. Computational analyses revealed that these selected strains exhibited low competitiveness and metabolic compatibility. Specifically, their complementary metabolic profiles suggested minimal competition for resources and potential for mutualistic interactions. In vitro experiments further supported these findings, demonstrating that the consortium maintained stable growth without inhibitory effects among strains. In addition, greenhouse validation experiments confirmed the efficacy of the microbial consortium in enhancing soybean growth such as root and shoot development and biomass production. Overall, this study underscores the potential of reverse ecology in optimizing microbial consortia design for bioinoculant applications.

Metabolites of pathogenic microorganisms database (MPMdb) and its seed metabolite applications

Seed metabolites are the combination of essential compounds required by an organism across various potential environmental conditions. The seed metabolites screening framework based on the network topology approach can capture important biological information of species. This study aims to identify comprehensively the relationship between seed metabolites and pathogenic bacteria. A large-scale data set was compiled, describing the seed metabolite sets and metabolite sets of 124,192 pathogenic strains from 34 genera, by constructing genome-scale metabolic models. The enrichment analysis method was used to screen the specific seed metabolites of each species/genus of pathogenic bacteria. The metabolites of pathogenic microorganisms database (MPMdb) (http://qyzhanglab.hzau.edu.cn/MPMdb/) was established for browsing, searching, predicting, or downloading metabolites and seed metabolites of pathogenic microorganisms. Based on the MPMdb, taxonomic and phylogenetic analyses of pathogenic bacteria were performed according to the function of seed metabolites and metabolites. The results showed that the seed metabolites could be used as a feature for microorganism chemotaxonomy, and they could mirror the phylogeny of pathogenic bacteria. In addition, our screened specific seed metabolites of pathogenic bacteria can be used not only for further tapping the nutritional resources and identifying auxotrophies of pathogenic bacteria but also for designing targeted bactericidal compounds by combining with existing antimicrobial agents.IMPORTANCEMetabolites serve as key communication links between pathogenic microorganisms and hosts, with seed metabolites being crucial for microbial growth, reproduction, external communication, and host infection. However, the large-scale screening of metabolites and the identification of seed metabolites have always been the main technical bottleneck due to the low throughput and costly analysis. Genome-scale metabolic models have become a recognized research paradigm to investigate the metabolic characteristics of species. The developed metabolites of pathogenic microorganisms database in this study is committed to systematically predicting and identifying the metabolites and seed metabolites of pathogenic microorganisms, which could provide a powerful resource platform for pathogenic bacteria research.