Comparative study of oxygen-limited and methane-limited growth phenotypes of Methylomicrobium buryatense 5GB1

Modification and analysis of context-specific genome-scale metabolic models: methane-utilizing microbial chassis as a case study

Context-specific genome-scale model (CS-GSM) reconstruction is becoming an efficient strategy for integrating and cross-comparing experimental multi-scale data to explore the relationship between cellular genotypes, facilitating fundamental or applied research discoveries. However, the application of CS modeling for non-conventional microbes is still challenging. Here, we present a graphical user interface that integrates COBRApy, EscherPy, and RIPTiDe, Python-based tools within the BioUML platform, and streamlines the reconstruction and interrogation of the CS genome-scale metabolic frameworks via Jupyter Notebook. The approach was tested using -omics data collected for Methylotuvimicrobium alcaliphilum 20ZR, a prominent microbial chassis for methane capturing and valorization. We optimized the previously reconstructed whole genome-scale metabolic network by adjusting the flux distribution using gene expression data. The outputs of the automatically reconstructed CS metabolic network were comparable to manually optimized iIA409 models for Ca-growth conditions. However, the CS model questions the reversibility of the phosphoketolase pathway and suggests higher flux via primary oxidation pathways. The model also highlighted unresolved carbon partitioning between assimilatory and catabolic pathways at the formaldehyde-formate node. Only a very few genes and only one enzyme with a predicted function in C1 metabolism, a homolog of the formaldehyde oxidation enzyme (fae1-2), showed a significant change in expression in La-growth conditions. The CS-GSM predictions agreed with the experimental measurements under the assumption that the Fae1-2 is a part of the tetrahydrofolate-linked pathway. The cellular roles of the tungsten (W)-dependent formate dehydrogenase (fdhAB) and fae homologs (fae1-2 and fae3) were investigated via mutagenesis. The phenotype of the fdhAB mutant followed the model prediction. Furthermore, a more significant reduction of the biomass yield was observed during growth in La-supplemented media, confirming a higher flux through formate. M. alcaliphilum 20ZR mutants lacking fae1-2 did not display any significant defects in methane or methanol-dependent growth. However, contrary to fae1, the fae1-2 homolog failed to restore the formaldehyde-activating enzyme function in complementation tests. Overall, the presented data suggest that the developed computational workflow supports the reconstruction and validation of CS-GSM networks of non-model microbes.

IMPORTANCE

The interrogation of various types of data is a routine strategy to explore the relationship between genotype and phenotype. An efficient approach for integrating and cross-comparing experimental multi-scale data in the context of whole-genome-based metabolic network reconstruction becomes a powerful tool that facilitates fundamental and applied research discoveries. The present study describes the reconstruction of a context-specific (CS) model for the methane-utilizing bacterium, Methylotuvimicrobium alcaliphilum 20ZR. M. alcaliphilum 20ZR is becoming an attractive microbial platform for the production of biofuels, chemicals, pharmaceuticals, and bio-sorbents for capturing atmospheric methane. We demonstrate that this pipeline can help reconstruct metabolic models that are similar to manually curated networks. Furthermore, the model is able to highlight previously overlooked pathways, thus advancing fundamental knowledge of non-model microbial systems or promoting their development toward biotechnological or environmental implementations.

Hydrochemical gradients driving extremophile distribution in saline and brine groundwater of southern Poland

Extreme environments, such as highly saline ecosystems, are characterised by a limited presence of microbial communities capable of tolerating and thriving under these conditions. To better understand the limits of life and its chemical and microbiological drivers, highly saline and brine groundwaters of Na-Cl and Na-Ca-Cl types with notably diverse SO4 contents were sampled in water intakes and springs from sedimentary aquifers located in the Outer Carpathians and the Carpathian Foredeep basin and its basement in Poland. Chemical and microbiological methods were used to identify the composition of groundwaters, determine microbial diversity, and indicate processes controlling their distribution using multivariate statistical analyses. DNA sequencing targeting V3-V4 and V4-V5 gene regions revealed a predominance of Proteobacteriota, Methanobacteria, Methanomicrobia, and Nanoarchaea in most of the water samples, irrespective of their geological context. Despite the sample-size constraint, redundancy analysis employing a compositional approach to hydrochemical predictors identified Cl/SO4 and Cl/HCO3 ratios, and specific electrical conductivity, as key gradients shaping microbial communities, depending on the analysed gene regions. Analysis of functional groups revealed that methanogenesis, sulphate oxidation and reduction, and the nitrogen cycle define and distinguish the halotolerant communities in the samples. These communities are characterised by an inverse relationship between methanogens and sulphur-cycling microorganisms.

Utilisation of low methane concentrations by methanotrophs

The growing urgency regarding climate change points to methane as a key greenhouse gas for slowing global warming to allow other mitigation measures to take effect. One approach to both decreasing methane emissions and removing methane from air is aerobic methanotrophic bacteria, those bacteria that grow on methane as sole carbon and energy source and require O2. A subset of these methanotrophs is able to grow on methane levels of 1000 parts per million (ppm) and below, and these present an opportunity for developing both environmental- and bioreactor-based methane treatment systems. However, relatively little is known about the traits of such methanotrophs that allow them to grow on low methane concentrations. This review assesses current information regarding how methanotrophs grow on low methane concentrations in the context of developing treatment strategies that could be applied for both decreasing methane emissions and removing methane from air.

Probing interspecies metabolic interactions within a synthetic binary microbiome using genome-scale modeling

Aim: Metabolic interactions within a microbial community play a key role in determining the structure, function, and composition of the community. However, due to the complexity and intractability of natural microbiomes, limited knowledge is available on interspecies interactions within a community. In this work, using a binary synthetic microbiome, a methanotroph-photoautotroph (M-P) coculture, as the model system, we examined different genome-scale metabolic modeling (GEM) approaches to gain a better understanding of the metabolic interactions within the coculture, how they contribute to the enhanced growth observed in the coculture, and how they evolve over time. Methods: Using batch growth data of the model M-P coculture, we compared three GEM approaches for microbial communities. Two of the methods are existing approaches: SteadyCom, a steady state GEM, and dynamic flux balance analysis (DFBA) Lab, a dynamic GEM. We also proposed an improved dynamic GEM approach, DynamiCom, for the M-P coculture. Results: SteadyCom can predict the metabolic interactions within the coculture but not their dynamic evolutions; DFBA Lab can predict the dynamics of the coculture but cannot identify interspecies interactions. DynamiCom was able to identify the cross-fed metabolite within the coculture, as well as predict the evolution of the interspecies interactions over time. Conclusion: A new dynamic GEM approach, DynamiCom, was developed for a model M-P coculture. Constrained by the predictions from a validated kinetic model, DynamiCom consistently predicted the top metabolites being exchanged in the M-P coculture, as well as the establishment of the mutualistic N-exchange between the methanotroph and cyanobacteria. The interspecies interactions and their dynamic evolution predicted by DynamiCom are supported by ample evidence in the literature on methanotroph, cyanobacteria, and other cyanobacteria-heterotroph cocultures.

Leveraging genome-scale metabolic models to understand aerobic methanotrophs

Genome-scale metabolic models (GEMs) are valuable tools serving systems biology and metabolic engineering. However, GEMs are still an underestimated tool in informing microbial ecology. Since their first application for aerobic gammaproteobacterial methane oxidizers less than a decade ago, GEMs have substantially increased our understanding of the metabolism of methanotrophs, a microbial guild of high relevance for the natural and biotechnological mitigation of methane efflux to the atmosphere. Particularly, GEMs helped to elucidate critical metabolic and regulatory pathways of several methanotrophic strains, predicted microbial responses to environmental perturbations, and were used to model metabolic interactions in cocultures. Here, we conducted a systematic review of GEMs exploring aerobic methanotrophy, summarizing recent advances, pointing out weaknesses, and drawing out probable future uses of GEMs to improve our understanding of the ecology of methane oxidizers. We also focus on their potential to unravel causes and consequences when studying interactions of methane-oxidizing bacteria with other methanotrophs or members of microbial communities in general. This review aims to bridge the gap between applied sciences and microbial ecology research on methane oxidizers as model organisms and to provide an outlook for future studies.

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.