(661d) Exploring the Interspecies Interactions within a Methanotroph-Cyanobacteria Coculture through Genome-Scale Metabolic Modeling

Biogas is comprised primarily of methane (50%~70%) and carbon dioxide (30% ~50%), which can be produced from various waste sources, including landfill material, animal manure; wastewater; and industrial, institutional, and commercial organic wastes. EPA estimates that currently US biogas production potential is 654 billion cubic feet per year, which could displace 7.5 billion gallons of gasoline [1]. It is clear that biogas has immense potential as a renewable feedstock for producing high-density fuels and commodity chemicals. However, the utilization of biogas represents a significant challenge due to its low pressure and presence of contaminants such as H₂S, ammonia, and volatile organic carbon compounds. To tap into this immense potential, effective biotechnologies that can co-utilize both CO₂ and CH₄ are needed.

In our previous work, we have demonstrated that methanotroph-cyanobacteria coculture offer a highly efficient platform to recover the energy and capture carbon from biogas [1, 2]. Through the metabolic coupling of methane oxidation to oxygenic photosynthesis, we have demonstrated significantly improved growth of both the methanotroph and cyanobacteria strain in the coculture. In order to demonstrate this coculture advantage, we have conducted the comparison experiments for three cases using Arthrosipira platensis - Methylomicrobium buryatense - as the model coculture system. Case A is the coculture; Case B is the sequential culture of cyanobacterium followed by methanotroph, with the amount of O₂ produced by the cyanobacterium injected into the single culture of methanotroph; Case C simulates the effect of the exchange of in situ produced O₂ between the coculture, where the amount of O₂ produced by the cyanobacterium in the coculture was injected into the methanotroph single culture. It clearly showed that both cyanobacterium and methanotroph in coculture (Case A) grew significantly faster than the sequentially operated single cultures (Case B). In addition, the improvement of the methanotroph growth cannot be fully explained by the availability of the extra O₂ produced in coculture (Case C).

In addition, using the model coculture system, we have developed an unstructured model to capture the growth dynamics. Specifically, Monod-like models were developed to capture coculture growth. Two sources of substrate were considered in the model: gas transferred from gas phase and gas (CO₂ and O₂) produced in situ. In the unstructured coculture model, we relied on the increased maximum cell growth rates to capture other potential interactions that enhanced the coculture growth.

In an effort to capture the potential interspecies interaction within the coculture, we also developed a structured, dynamic genome-scale metabolic model to capture the dynamic of the cocutlure [3]. By integrating the available knowledge on each strain with data obtained in our own experiments, we used DFBALab to implement the dynamic GEM for the Coculture [4]. For the dynamic GEM, besides the GEMs for each individual strain, the key inputs were the uptake kinetics for different substrates which were provided from the unstructured dynamic model we already developed. The structured coculture GEM was validated using measured product secretion rates and cell growth rates for each strain over time. However, DFBALab doesn’t support a common compartment for both microorganisms to freely excrete and update any potential metabolites. Therefore, in the dynamic coculture GEM, we had to adjust growth/non-growth associated maintenance energy to capture the synergic effect within the coculture in order to match the enhanced cell growth rate observed in the experiments.

In order to identify potential “metabolic links” between the methanotroph and cyanobacteria in the coculture through modeling, in this work, we explore a different modeling platform – SteadCom. Although SteadCom only captures steady states of multi-species systems. It enables free exchange of any metabolites that are excreted or consumed by different strains in the system. In this way, we could explore different optimization schemes to identify potential “metabolic links” within the coculture, and then manually add such exchange into the dynamic model in DFBALab to further validate the identified interactions. [5,6].

Reference:

1. Badr, K., Hilliard, M., He, Q.P., & Wang, J., “Understanding the Stability and Robustness of a Methanotroph-Cyanobacterium Coculture through Kinetic Modeling and Experimental Validation.” Annual AIChE Meeting. Pittsburgh, PA (2018).
2. Badr K., Hilliard M., Roberts N., He Q.P. and Wang J. (2019), Photoautotroph-Methanotroph Coculture – A Flexible Platform for Efficient Biological CO2-CH4 Co-utilization, IFAC-PapersOnLine, 52 (1), 916-921;
3. Badr, K., Hilliard, M., He, Q.P., & Wang, J., “A Dynamic Genome-Scale Metabolic Network Model for a Novel Methanotroph-Cyanobacteria Coculture” Annual AIChE Meeting. Orlando, FL (2019).
4. Gomez JA, Höffner K, Barton PI. DFBAlab: a fast and reliable MATLAB code for dynamic flux balance analysis. BMC Bioinformatics. (2014),15:409.
5. Chan SHJ, Simons MN, Maranas CD (2017) SteadyCom: Predicting microbial abundances while ensuring community stability. PLoS Comput Biol 13(5): e1005539.
6. Khandelwal RA, Olivier BG, Röling WFM, Teusink B, Bruggeman FJ (2013) Community Flux Balance Analysis for Microbial Consortia at Balanced Growth. PLoS ONE 8(5): e64567.