(516a) Semi-Structured Kinetic Modeling of a Methanotroph-Cyanobacteria Coculture Can Quantify the Effect of Unknown Metabolic Interactions on Enhancing Coculture Growth

Existing research, including our own^1–4, has demonstrated that methanotroph-photoautotroph (M-P) cocultures provide a highly effective biological platform for biogas valorization to produce high density fuels and valuable bioproducts. The advantages of the platform can be largely attributed to the metabolic coupling of methane oxidation and photosynthesis oxygenation. In general, microbial communities, including the synthetic M-P coculture, offer a number of advantages and hold great potential for future biotechnology development^5,6. However, the utilization of microbial communities for biotechnological applications in bioenergy and related areas have been limited. This is mainly due to the highly complex dynamics of the community, largely unknown interdependency among different species, and the lack of the tools to efficiently characterize the community.

In this work, we discuss two major challenges that have to be addressed in order to developed M-P coculture-based biotechnology: one is how to accurately characterize the coculture in real-time; the other is how to develop a kinetic model for the coculture, which lays the foundation for the design and scale-up of the bioreactor, as well as the optimization of operation conditions.

Currently how to quickly and accurately characterize microbial communities in real-time is an unsolved problem, which is also one major obstacle in developing kinetic models for the community. For the M-P coculture, besides tracking the individual biomass concentrations, an added difficulty is how to determine the substrate (i.e., O₂ for the methanotroph and CO₂ for the photoautotroph) consumption rates and product (i.e., CO₂ for the methanotroph and O₂ for the photoautotroph) excretion rates for each strain. In the M-P coculture, due to the cross-feeding of O₂ and CO₂, both strains contribute to the overall consumption and production of O₂ and CO₂ that can be directly measured. However, how to infer or estimate the individual consumption or production rate of O₂ and CO₂ remains an unsolved problem.

To address these challenges, we recently developed an experimental-computational (E-C) protocol based on the gas consumption rates, overall mass balance, and each organism’s growth stoichiometry. The E-C protocol only requires commonly measured variables such as gas composition, total optical density of the coculture and the inorganic carbon in the liquid broth. The E-C protocol not only determines the individual biomass concentrations, but also determines the in situ consumption and production rates of O₂ and CO₂ for the methanotroph and photoautotroph in the coculture. The accuracy of the E-C protocol was experimentally validated to outperform flow cytometry based approach⁷.

With the real-time characterization of the M-P coculture, in the work we report a semi-structured kinetic model that can accurately predict the growth rate, as well as the consumption/production rates of O₂ and CO₂ for the methanotroph and photoautotroph in the coculture under wide cultivation conditions. For the M-P coculture, besides the exchange of in situ produced O₂ and CO₂, there may exist other cross-feeding mechanisms that further enhance the growth of the M-P coculture. To capture the effect of these unknown metabolic exchanges, in the semi-structured kinetic model, we explicitly model the known cross-species interactions (i.e., exchange of in situ produced O₂ and CO₂), while using the lumped parameters (i.e., the maximum cell growth rates, , in the Monod model) captured the effect of unknown “emergent interactions”^3,5. In addition, the mass transfer from the gas phase to the liquid phase is directly modeled, so that gas phase concentration can be linked to the microbial growth in the liquid phase. Such integrated modeling provides additional routes to validate the model.

To validate the semi-structured model, we use Methylomicrobium buryatense 5GB1– Arthrosipira platensis as the model system and conducted a series of experiments under different conditions, which covered the effect of light intensity, feed gas composition, as well as the initial inoculum ratio. Figure 1 provides a few examples that compare the model prediction with experimental measurements under selected growth conditions. Fig. 1 (a) compares the biomass concentration of the model coculture under different light intensities; Fig. 1 (b) plots the change of concentration of different gas components over time. Both figures show that the model predictions agree very well with experimental measurements, confirming the accuracy of the model.

Besides providing the necessary foundation to guide the design, optimization and scale up of bioreactors, the semi-structured kinetic model also enables the quantification of effect from the metabolic interactions within the M-P coculture, albeit unknown, on coculture growth. This was validated for a model coculture, where we compared coculture with single cultures. Fig. 1 (c) and (d) compares the single culture and coculture biomass over time, which clearly demonstrate that both species in the coculture show significantly enhanced growth than the single culture. As shown in Table 1, for both species in the coculture, their ’ showed 42% and 48% increases compared to those of the single cultures respectively, which confirms the existence of additional mutualistic interactions besides O₂/CO₂ exchange.

References:

1- Badr K, Hilliard M, Roberts N, He QP, Wang J. Photoautotroph-Methanotroph Coculture–A Flexible Platform for Efficient Biological CO2-CH4 Co-utilization. IFAC-PapersOnLine. 2019;52(1):916-921.

2- Roberts N, Hilliard M, He QP, Wang J. A Microalgae-Methanotroph Coculture is a Promising Platform for Fuels and Chemical Production From Wastewater. Front Energy Res. 2020;8(September):1-14. doi:10.3389/fenrg.2020.563352

3- Badr K, Whelan W, He QP, Wang J. Fast and easy quantitative characterization of methanotroph–photoautotroph cocultures. Biotechnol Bioeng. 2021;118 (2). doi:https://doi.org/10.1002/bit.27603

4- Roberts N, Hilliard M, Bahr K, He QP, Wang J. Efficient and robust biological CH4/CO2 co-utilization through coculture of methanotroph and microalgae. 40th Symp Biotechnol Fuels Chem. Published online October 2018.

5- Bernstein HC, Carlson RP. Microbial consortia engineering for cellular factories: in vitro to in silico systems. Comput Struct Biotechnol J. 2012;3(4):e201210017.

6- Brenner K, You L, Arnold FH. Engineering microbial consortia: a new frontier in synthetic biology. Trends Biotechnol. 2008;26(9):483-489. http://www.sciencedirect.com/science/article/pii/S0167779908001716

7- Badr K., Whelan W., He Q.P., Wang J. “Quantitative Characterization of Methanotroph-Photoautotroph Cocultures” Biotechnology and bioengineering, 2020, 118 (2), 703-714.