Unraveling metabolic interactions between plants and root microbiota through computational modelling

This project will be supervised by Nadine Töpfer at the University of Cologne.

Abstract: 

Plants exist within complex soil ecosystems harboring diverse prokaryotic taxa collectively known as the microbiota. Plant–microbe interactions are crucial for nutrient acquisition, reducing chemical fertilizer dependency, and enhancing plant health, growth, yield, and resistance to biotic and abiotic stresses. To attract and nourish microbes, plants exude up to 40% of their assimilated carbon through roots actively bridging the plant and rhizosphere. Understanding these metabolic interactions is essential for developing sustainable agricultural practices and ensuring food security amid climate change. However, how plants shape these interactions through metabolite exchange remains poorly understood.
Recent advances in genomics, metagenomics, phenotyping, and synthetic biology now enable us to harness genome-scale metabolic models to study plant-microbe interactions at a community level. These models provide a comprehensive mathematical framework capturing entire organism metabolism, facilitating systems-level understanding of genotype-phenotype relationships and multi-omics data integration. They offer powerful predictive capabilities for metabolic fluxes and exchanges, growth conditions, and cellular responses while being computationally efficient and scalable for analyzing large microbial communities. This project will employ genome-scale metabolic modeling based on sequenced microbes to investigate factors driving the establishment and stability of microbial communities on the plant root and along the root axis. We will in silico examine how controlled environmental parameter changes affect community structure and interactions. Furthermore, we will leverage this mechanistic knowledge to design synthetic communities (SynComs). This research will advance our understanding of plant–microbe metabolic interactions and provide a foundation for engineering beneficial rhizosphere communities to enhance agricultural sustainability and crop resilience.

Group homepage: https://ag-toepfer.botanik.uni-koeln.de/

Key publication: Schäfer et al. (2023) Metabolic interaction models recapitulate leaf microbiota ecology. Science Jul 7 2023;381(6653):eadf5121. https://doi.org/10.1126/science.adf5121

Overview of an in silico experimental design to unravel trophic networks i.e. nutrient exchanges and dependency networks, in large plant-associated microbial communities. Here, draft versions of genome-scale metabolic models (GSMMs) for all 194 root-associated microbes and all 224 leaf-associated microbes found in the At-Sphere database are reconstructed and their metabolic interactions are analyzed by constraint-based modeling techniques (Microbial Community Succession Modul (MCSM)).
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