Designing a Synthetic Microbial Community through Genome Metabolic Modeling to enhance Plant-Microbe Interaction
Creators
- 1. Grupo de Genômica Eco-evolutiva Microbiana, Laboratório de Genética Molecular de Microrganismos, Departamento de Microbiologia, Universidade Federal de Viçosa, Brazil
Description
Supplementary data 1 - Reconstructed genome-scale metabolic networks from MAGs and Hosts
Supplementary data 2 - Plant growth-promoting traits among members of the minimal community
Manipulating the rhizosphere microbial community through beneficial microorganism inoculation has gained interest in improving crop productivity and stress resistance. Synthetic microbial communities, known as SynCom, mimic natural microbial compositions while reducing the number of components. However, achieving this goal requires a comprehensive understanding of natural microbial communities and a careful selection of compatible microorganisms with colonization traits, which still pose challenges. In this study, we employed an in-silico approach using genome metabolic modeling to design a synthetic microbial community aimed at improving the yield of important crop plants. We used a targeted approach to select a minimal community (MinCom) encompassing essential compounds for microbial metabolism and compounds relevant to plant interactions. This resulted in a reduction of the initial community size by approximately 4.5-fold. Notably, the MinCom retained crucial genes associated with essential plant growth-promoting traits, such as iron acquisition, EPS production, potassium solubilization, nitrogen fixation, GABA production, and IAA-related tryptophan metabolism. Furthermore, our selection process for the SymCom, based on a comprehensive understanding of microbe-microbe-plant interactions, yielded a set of six hub species that displayed notable taxonomic novelty, including members of the Eremiobacterota and Verrucomicrobiota phyla. Our study contributes to the growing body of research on synthetic microbial communities and their potential to enhance agricultural practices. The insights gained from our in-silico approach and the selection of hub species pave the way for further investigations into the development of tailored microbial communities that can optimize crop productivity and improve stress resilience in agricultural systems.
Files
Supplementary data1_m2m.zip
Files
(31.6 MB)
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