Genomic Science Program
U.S. Department of Energy | Office of Science | Biological and Environmental Research Program

Unraveling Metabolic Interactions Within a Rhizosphere Microbial Community


Manish Kumar1* (, Maxwell Neal1, Deepan Thiruppathy1, Oriane Moyne1, Gabriela Canto Encalada1, Anurag Passi1, Rodrigo Santibañez1, Diego Tec-Campos1, Karsten Zengler1,2,3


Department of Pediatrics, University of California–San Diego; 2Department of Bioengineering, University of California–San Diego; 3Center for Microbiome Innovation, University of California–San Diego


This project couples novel laboratory and field studies to develop the first predictive model of grass microbiomes based on new mechanistic insights into dynamic plant-microbe interactions in the grasses Sorghum bicolor and Brachypodium distachyon that improve plant nitrogen (N)-use efficiency (NUE). The results will be used to predict plant mutants and microbial amendments, which improve low-input biomass production for validation in laboratory and field studies. To achieve this goal, researchers will determine the mechanistic basis of dynamic exudate exchange in the grass rhizosphere with a specific focus on the identification of plant transporters and proteins that regulate root-exudate composition, and how specific exudates select for beneficial microbes that increase plant biomass and NUE. Researchers will further develop a predictive plant-microbe model for advancing sustainable bioenergy crops and will predictively shift plant-microbe interactions to enhance plant biomass production and N acquisition from varied N forms.


This research delves into the rich microbial diversity present in soils, particularly in the rhizosphere, where a myriad of bacteria influences soil properties through nutrient transformations, including carbon (C) and N pools that are directly linked to plant growth. To unravel the intricate web of metabolite exchanges among soil microbes and their dynamic interactions with the host plant, the team adopted a computational approach and utilized multiomics data (Kumar et al. 2019). Specifically, researchers focused on a synthetic microbial community (SynCom) composed of 16 rhizosphere bacteria isolated from switchgrass (Coker et al. 2022).

First, researchers constructed and manually curated genome-scale metabolic models for each of these rhizosphere bacteria, representing from various genera such as Arthrobacter, Bacillus, Bosea, Bradyrhizobium, Brevibacillus, Burkholderia, Chitinophaga, Lysobacter, Methylobacterium, Mucilaginibacter, Mycobacterium, Niastella, Paenibacillus, Rhizobium, Rhodococcus, Sphingomonas, and Variovorax. Integrating individual models with metatranscriptomic (RNA-Seq) and metatranslatomic (Ribo-Seq) data, researcherse constructed condition-specific community metabolic models (CM-models). Throughout this investigation, the team systematically evaluated the impact of removing individual microbes from the SynCom, shedding light on the specific contributions of each member. These CM-models predict the response of the SynCom to perturbation with very high accuracy. Furthermore, the CM-models played a crucial role in predicting metabolic exchanges between community members, unveiling the intricate nature of interactions, including competition and cooperation, among rhizosphere microbes.

These models have predicted substantial interactions involving the exchange of short-chain organic acids, carbohydrates, amino acids, and purine and pyrimidine derivatives among rhizosphere bacteria. Furthermore, the predictions suggest shifts in the nature of these metabolic exchanges when specific community members are removed. These model-driven hypotheses propose that such metabolic shifts confer nutritional advantages to select members, while concurrently suppressing the growth of others. Illuminating the intricate mechanisms of interaction among plant-associated microorganisms offers invaluable insights into the development of strategies for engineering microbial communities capable of enhancing plant growth and bolstering resilience against diseases.


Kumar, M., et al. 2019. “Modelling Approaches for Studying the Microbiome,” Nature Microbiology 4, 1253–67.

Coker, J., et al. 2022. “A Reproducible and Tunable Synthetic Soil Microbial Community Provides New Insights into Microbial Ecology,” mSystems 7.

Funding Information

This work is supported by the DOE, Office of Science, BER program under Award DESC0021234.