Two Experimental Protocols for Accurate Measurement of Gas Component Uptake and Production Rates in Bioconversion Processes

Integrative Genome-Scale Metabolic Modeling Reveals Versatile Metabolic Strategies for Methane Utilization in Methylomicrobium album BG8

Methylomicrobium album BG8 is an aerobic methanotrophic bacterium with promising features as a microbial cell factory for the conversion of methane to value-added chemicals. However, the lack of a genome-scale metabolic model (GEM) of M. album BG8 has hindered the development of systems biology and metabolic engineering of this methanotroph. To fill this gap, a high-quality GEM was constructed to facilitate a system-level understanding of the biochemistry of M. album BG8. Flux balance analysis, constrained with time-series data derived from experiments with various levels of methane, oxygen, and biomass, was used to investigate the metabolic states that promote the production of biomass and the excretion of carbon dioxide, formate, and acetate. The experimental and modeling results indicated that M. album BG8 requires a ratio of ∼1.5:1 between the oxygen- and methane-specific uptake rates for optimal growth. Integrative modeling revealed that at ratios of  >2:1 oxygen-to-methane uptake flux, carbon dioxide and formate were the preferred excreted compounds, while at ratios of <1.5:1 acetate accounted for a larger fraction of the total excreted flux. Our results showed a coupling between biomass production and the excretion of carbon dioxide that was linked to the ratio between the oxygen- and methane-specific uptake rates. In contrast, acetate excretion was experimentally detected during exponential growth only when the initial biomass concentration was increased. A relatively lower growth rate was also observed when acetate was produced in the exponential phase, suggesting a trade-off between biomass and acetate production.

IMPORTANCE A genome-scale metabolic model (GEM) is an integrative platform that enables the incorporation of a wide range of experimental data. It is used to reveal system-level metabolism and, thus, clarify the link between the genotype and phenotype. The lack of a GEM for Methylomicrobium album BG8, an aerobic methane-oxidizing bacterium, has hindered its use in environmental and industrial biotechnology applications. The diverse metabolic states indicated by the GEM developed in this study demonstrate the versatility in the methane metabolic processes used by this strain. The integrative GEM presented here will aid the implementation of the design-build-test-learn paradigm in the metabolic engineering of M. album BG8. This advance will facilitate the development of a robust methane bioconversion platform and help to mitigate methane emissions from environmental systems.

Fast and easy quantitative characterization of methanotroph-photoautotroph cocultures

Recent research has demonstrated that synthetic methanotroph-photoautotroph cocultures offer a highly promising route to convert biogas into value-added products. However, there is a lack of techniques for fast and accurate characterization of cocultures, such as determining the individual biomass concentration of each organism in real-time. To address this unsolved challenge, we propose an experimental-computational protocol for fast, easy, and accurate quantitative characterization of the methanotroph-photoautotroph cocultures. Besides determining the individual biomass concentration of each organism in the coculture, the protocol can also obtain the individual consumption and production rates of O₂ and CO₂ for the methanotroph and photoautotroph, respectively. The accuracy and effectiveness of the proposed protocol was demonstrated using two model coculture pairs, Methylomicrobium alcaliphilum 20ZR-Synechococcus sp. PCC7002 that prefers high pH high salt condition, and Methylococcus capsulatus-Chlorella sorokiniana that prefers low salt and neutral pH medium. The performance of the proposed protocol was compared with a flow cytometry-based cell counting approach. The experimental results show that the proposed protocol is much easier to carry out and delivers faster and more accurate results in measuring individual biomass concentration than the cell counting approach without requiring any special equipment.