18-BBSRC-NSF/BIO - Understanding the origin and evolution of metabolic interactions using synthetic microbial communities

Microbial communities are ubiquitous, and are critical players in mediating host health and disease and in the cycling of elements in ecosystems. Metabolic interactions between species impact community function and stability. However, the emergence and evolution of metabolic interactions is poorly understood. In this project, we will take advantage of tractable synthetic yeast communities and mathematical modelling to experimentally and theoretically study the origin and evolution of metabolic interactions.

We will undertake a fully integrated, collaborative approach that combines the expertise of US and UK groups on metabolic modeling, synthetic biology, and microbial ecology and evolution. First, we will use statistical thermodynamics and differential equations to model metabolic overflows. Next, we will experimentally characterize metabolic overflows as well as key cellular parameters using targeted metabolomics, fluorescence microscopy, and single cell electrochemical measurements. We will use these experimental results to constrain, test, and refine the model. We will then embed the tested model within an in silico evolution framework to simulate evolution, and predict how initial community conditions (e.g. nutrient environment, genotypes, and initial species interactions) will affect the evolution of new metabolic interactions, as we have observed in preliminary work. Finally, we will test model predictions by evolving synthetic yeast communities from these different starting conditions in chemostats and turbidostats, and characterize emerging metabolic interactions.

Microbial communities are important for ecosystem functioning and human health and disease. In communities, species interact where one species alters the physiology of another species. Species interactions govern community-level properties including species composition and spatial patterning, community function, and community stability. Because microbes evolve rapidly, interactions and hence community-level properties can also evolve rapidly. However, this type of ecology-evolution feedback remains poorly understood. Understanding how interactions among species arise and evolve will enable us to better control community functions, predict community stability, and engineer useful communities.

While metabolic overflows (excreted metabolites) are known to mediate many important species interactions, there is currently no unifying theory of metabolism to explain the origin of metabolic interactions, nor to predict how these interactions might evolve. Here, we will investigate why cells secrete metabolites and which ones, how environmental conditions and genotypes affect secreted metabolites, and how initial community conditions might influence the evolution of new interactions?

To address these questions, our objectives are: (1) create a thermodynamic model of the central metabolism, taking into account important processes such as reaction energetics and competition for shared energy and redox carriers; (2) constrain and test the model by growing wild type and engineered Saccharomyces cerevisiae strains in various environments and measuring metabolic overflows and key intracellular metabolic parameters at single cell resolution and in bulk cultures; and (3) monitor and predict the evolution of further metabolic interactions in synthetic yeast communities under different conditions of initial genotypes, initial metabolic interactions, or abiotic nutrient environment.

Understanding metabolic interactions and their environmental and genetic basis holds significant potential for impacting biomedicine and biotechnology. Overflows from microbial cells underpin bioproduction and microbial food making (e.g. bioacetone production, wine and cheese making, etc.). A mechanistic understanding of metabolic overflows would allow us to increase specific product yield, or engineer metabolic interactions to create multi-species bioproduction platforms, thus significantly advancing biotechnology. In the medical domain, several diseases, in particular cancer, relate to metabolic flux changes and the resulting overflows. Again, our work paves the way for a principled understanding of how disease-associated metabolic overflows and interactions might have evolved.

The developed quantitative tools for measuring metabolic overflows and physiological states at single cell and bulk culture resolutions will provide valuable tools for cell biologists, synthetic biologists, and microbial ecologists. In particular, the adaptation of electrochemical measurements to single yeast cells will provide an important tool that currently does not exist. Evolved yeast strains and synthetic communities will also provide a unique resource. These will be accessible to other researchers, who can use them as starting points for engineering more complex synthetic communities.

The developed model of cell metabolism and its extension with in silico evolution will provide excellent tools for both undergraduate (UG) and graduate (GS) teaching. In particular, the thermodynamic constraints and their use to rationalize and understand metabolic design can be incorporated into UG courses, where active engagement of students can be achieved for example by having students create metabolic pathway diagrams and compile thermodynamics values from the literature. The experimental side of the proposal provides ample opportunities to expose high school and undergraduate students to hands-on research.

The focus of this proposal on metabolism and social interactions in microbial communities will also allow us to reach out to the general public. We will collaborate with our existing public outreach teams to create engaging demonstrations to explain to the public about how microbes interact like the humans do. Our work will also help increase public awareness of evolution (versus creationism) by demonstrating rapid evolution of evolutionary novelty (new metabolic interactions).