Elucidating Plant-Microbe-Environment Interactions Through Omics-Enabled Metabolic Modelling Using Synthetic Communities

Abstract

With a growing world population and increasing frequency of climate disturbance events, we are in dire need of methods to improve plant productivity, resilience, and resistance to both abiotic and biotic stressors, both for agriculture and conservation efforts. Microorganisms play an essential role in supporting plant growth, environmental response, and susceptibility to disease. However, understanding the specific mechanisms by which microbes interact with each other and with plants to influence plant phenotypes is a major challenge due to the complexity of natural communities, simultaneous competition and cooperation effects, signalling interactions, and environmental impacts. Synthetic communities are a major asset in reducing the complexity of these systems by simplifying to dominant components and isolating specific variables for controlled experiments, yet there still remains a large gap in our understanding of plant microbiome interactions. This perspectives article presents a brief review discussing ways in which metabolic modelling can be used in combination with synthetic communities to continue progress toward understanding the complexity of plant-microbe-environment interactions. We highlight the utility of metabolic models as applied to a community setting, identify different applications for both flux balance and elementary flux mode simulation approaches, emphasize the importance of ecological theory in guiding data interpretation, and provide ideas for how the integration of metabolic modelling techniques with big data may bridge the gap between simplified synthetic communities and the complexity of natural plant-microbe systems.

Keywords: elementary flux mode analysis; flux balance analysis; metabolic modelling; plant microbial interactions; plant microbiome; synthetic communities.

Figure 1

Schematic illustrating proposed metabolic modelling workflow with synthetic communities as input. The natural microbiome is a complex environment and is simplified to a synthetic community with a tractable number of microorganisms via experimental isolation techniques and/or metagenomics methods. Genomes for this limited number of microorganisms are used to construct metabolic models for each species, which can then be used with pathway simulation tools (flux balance analysis, FBA; elementary flux mode analysis, EFMA) to predict both pairwise and community interactions to observe changes due to additional community members and determine patterns of interaction. In the example shown, the blue microbe secretes a compound that is beneficial to both the red and green microbes and stabilizes their original negative pairwise interaction. Complementarily, culturing experiments can be used to examine the responses of microbial communities (both in planta and in vitro) to environmental variables such as limited nutrients or water stress. Collecting molecular data under specific experimental conditions provides data to integrate with modelling predictions, which can be interpreted with the aid of relevant ecological theory, such as resource ratio theory, resource allocation theory, or the maximum power principle. This process leads to iterative refinement of both models and experimental design, ultimately contributing to broader outcomes relevant to agricultural practice, such as the design of microbial inoculants to promote plant growth and resilience, an improved understanding of plant-microbe response to the environment under changing climate conditions, and the implementation of field-scale trials to further test interaction principles in the natural environment.