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

Lead Research Organisation: University of Warwick
Department Name: School of Life Sciences

Abstract

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.

Technical Summary

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.

Planned Impact

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).
 
Description The work under this award is still ongoing. Key results so far include;
- Development of a thermodynamically correct model for cell growth. This model allowed accounting for population dynamics of cells in a way that was not possible with models that accounts only for kinetics.
- Analysis of population dynamics in a multi-species microbial system under thermodynamic limitations. This allowed to estimate minimal energy requirements of microbial growth.
- Development of a metabolic model that connects host and virus entanglement through metabolism. This model allowed predictions regarding attenuation of COVID19 growth in mammalian lung cells.
Exploitation Route This is early to say, as the award is still active. However, the following outcomes might be expected;
- The use of thermodynamic growth models by other groups working on microbial systems.
- The use of developed model of host-virus and associated approach by others developing inhibition strategies against COVID19 or other viruses.
Sectors Healthcare,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology

 
Description Our work on host-virus modelling, focussing on COVID19 has received relatively high interest from the public and general scientific community. It has been picked up by 26 news outlets and was blogged about by several observers. It is 3rd highest scoring article in the journal it is published in.
First Year Of Impact 2020
Sector Healthcare,Pharmaceuticals and Medical Biotechnology
Impact Types Societal

 
Title Mycodymora 
Description Simulation framework for microbial population dynamics (read related publication here: https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2020.0053) 
Type Of Material Model of mechanisms or symptoms - non-mammalian in vivo 
Year Produced 2020 
Provided To Others? Yes  
Impact N/K 
URL https://github.com/OSS-Lab/micodymora
 
Title FBAhv 
Description This is a Phyton project containing scripts allowing to add a "virus biomass function" to a cell metabolic model (a SBML model), and then to perform an analysis of this "Host-Virus Model" (HVM). This code is used in the implementation of the mammalian lung cell-COVID19 modelling presented in "Delattre et al 2021" (DOI: 10.26508/lsa.202000869). 
Type Of Material Computer model/algorithm 
Year Produced 2020 
Provided To Others? Yes  
Impact none yet. 
URL https://github.com/OSS-Lab/FBAhv
 
Title Host-Virus model (mammalian lung cell - COVID19) 
Description Model to study the host-virus interaction, focusing on mammalian lung cells and Covid19. This model has been submitted to to BioModels but is awaiting release. 
Type Of Material Computer model/algorithm 
Year Produced 2021 
Provided To Others? Yes  
Impact Non. 
URL https://www.ebi.ac.uk/biomodels/MODEL2010280002
 
Title Micodymora 
Description Micodymora is a python package allowing to simulate Ordinary Differential Equations (ODE) models of microbial population dynamics, while providing gas/liquid transfer and acide/base equilibria as additional features 
Type Of Material Computer model/algorithm 
Year Produced 2019 
Provided To Others? Yes  
Impact None as yet 
URL https://github.com/OSS-Lab/micodymora