Biological methane utilization, one of the main sinks of the greenhouse gas in nature, represents an attractive platform for production of fuels and value-added chemicals. Despite the progress made in our understanding of the individual parts of methane utilization, our knowledge of how the whole-cell metabolic network is organized and coordinated is limited. Attractive growth and methane-conversion rates, a complete and expert-annotated genome sequence, as well as large enzymatic, ¹³C-labeling, and transcriptomic datasets make Methylomicrobium alcaliphilum 20Z^R an exceptional model system for investigating methane utilization networks. Here we present a comprehensive metabolic framework of methane and methanol utilization in M. alcaliphilum 20Z^R. A set of novel metabolic reactions governing carbon distribution across central pathways in methanotrophic bacteria was predicted by in-silico simulations and confirmed by global non-targeted metabolomics and enzymatic evidences. Our data highlight the importance of substitution of ATP-linked steps with PPi-dependent reactions and support the presence of a carbon shunt from acetyl-CoA to the pentose-phosphate pathway and highly branched TCA cycle. The diverged TCA reactions promote balance between anabolic reactions and redox demands. The computational framework of C₁-metabolism in methanotrophic bacteria can represent an efficient tool for metabolic engineering or ecosystem modeling.

Methane utilization by methanotrophic bacteria is an attractive application for biotechnological conversion of natural or biogas into high-added-value products. Haloalcaliphilic methanotrophic bacteria belonging to the genus Methylomicrobium are among the most promising strains for methane-based biotechnology, providing easy and inexpensive cultivation, rapid growth, and the availability of established genetic tools. A number of methane bioconversions using the microbial cultures have been discussed, including the derivation of biodiesel, alkanes, and OMEGA-3 supplements. These compounds are derived from bacterial fatty acid pools. This study investigates fatty acid biosynthesis in Methylomicrobium buryatense 5G(B1). Most of the genes homologous to typical Type II fatty acid biosynthesis pathways could be annotated by bioinformatics analysis, with the exception of FA transport and regulatory elements. Different approaches for improving fatty acid accumulation were investigated. These studies indicate that both fatty acid degradation and acetyl- and malonyl-CoA levels are bottlenecks for higher level fatty acid production. The best strain generated in this study synthesizes 110.7 mg/gDCW of extractable fatty acids, which is approximately 20% more than in original strain. A candidate gene for FA-biosynthesis regulation, farE, was identified and studied. Its deletion results in drastic changes to the FA profile, leading to an increased pool of C18- fatty acid methyl ester. The FarE-regulon was further investigated by RNA-seq analysis of gene expression in farE-knockout mutant and farE-overexpressing strains. These gene profiles highlighted a novel set of enzymes and regulators involved in fatty acid biosynthesis. The gene expression and fatty acid profiles of the different farE-strains support the hypothesis that metabolic fluxes upstream of fatty acid biosynthesis restrict fatty acid production in the methanotroph.

BioUML (homepage: http://www.biouml.org, main public server: https://ict.biouml.org) is a web-based integrated environment (platform) for systems biology and the analysis of biomedical data generated by omics technologies. The BioUML vision is to provide a computational platform to build virtual cell, virtual physiological human and virtual patient. BioUML spans a comprehensive range of capabilities, including access to biological databases, powerful tools for systems biology (visual modelling, simulation, parameters fitting and analyses), a genome browser, scripting (R, JavaScript) and a workflow engine. Due to integration with the Galaxy platform and R/Bioconductor, BioUML provides powerful possibilities for the analyses of omics data. The plug-in-based architecture allows the user to add new functionalities using plug-ins. To facilitate a user focus on a particular task or database, we have developed several predefined perspectives that display only those web interface elements that are needed for a specific task. To support collaborative work on scientific projects, there is a central authentication and authorization system (https://bio-store.org). The diagram editor enables several remote users to simultaneously edit diagrams.

Here, we demonstrate bioconversion of methane to muconic acid, a dicarboxylic acid that can be upgraded to an array of platform chemicals, by three gammaproteobacterial methanotrophs. All engineered methanotrophs expressing a heterologous dihydroxyshikimate dehydratase, protocatechuate decarboxylase, and catechol dioxygenase produced muconic acid from methane, with the highest titer (12.4 mg MA per L), yield (2.8 mg MA per g CH₄), and specific productivity (1.2 mg MA per g dcw, 48 hr) synthesized by Methylotuvimicrobium buryatense, Methylococcus capsulatus, and Methylotuvimicrobium alcaliphilium, respectively. Methylotuvimicrobium alcaliphilum genome-scale model-guided strain engineering predicted that disruption of the pyruvate dehydrogenase or shikimate dehydrogenase would significantly enhance flux to the heterologous muconic acid pathway in this organism. However, knock-out of these targets caused a growth defect, and coupled with similar muconic acid titers (∼1 mg L⁻¹), resulted in minimal flux enhancement to muconic acid in these genetically-modified strains. The shikimate dehydrogenase mutant's ability to grow without aromatic amino acid supplementation revealed that M. alcaliphilum likely encodes an unidentified enzyme or pathway with shikimate biosynthetic capacity, which prevents maximal flux through the synthetic muconic acid pathway. This study expands the suite of products that can be generated from methane using methanotrophic biocatalysts, lays the foundation for green production of muconic acid-derived polymers from methane, and highlights the need for further analysis of methanotroph biosynthetic potential to guide refinement of metabolic models and strain engineering.

Background: Rare Earth Elements (REEs) control methanol utilization in both methane- and methanol-utilizing microbes. It has been established that the addition of REEs leads to the transcriptional repression of MxaFI-MeDH [a two-subunit methanol dehydrogenase (MeDH), calcium-dependent] and the activation of XoxF-MeDH (a one-subunit MeDH, lanthanum-dependent). Both enzymes are pyrroquinoline quinone-dependent alcohol dehydrogenases and show significant homology; however, they display different kinetic properties and substrate specificities. This study investigates the impact of the MxaFI to XoxF switch on the behavior of metabolic networks at a global scale. Results: In this study we investigated the steady-state growth of Methylomicrobium alcaliphilum 20Z^R in media containing calcium (Ca) or lanthanum (La, a REE element). We found that cells supplemented with La show a higher growth rate compared to Ca-cultures; however, the efficiency of carbon conversion, estimated as biomass yield, is higher in cells grown with Ca. Three complementary global-omics approaches–RNA-seq transcriptomics, proteomics, and metabolomics–were applied to investigate the mechanisms of improved growth vs. carbon conversion. Cells grown with La showed the transcriptional activation of the xoxF gene, a homolog of the formaldehyde-activating enzyme (fae2), a putative transporter, genes for hemin-transport proteins, and nitrate reductase. In contrast, genes for mxaFI and associated cytochrome (mxaG) expression were downregulated. Proteomic profiling suggested additional adjustments of the metabolic network at the protein level, including carbon assimilation pathways, electron transport systems, and the tricarboxylic acid (TCA) cycle. Discord between gene expression and protein abundance changes points toward the possibility of post-transcriptional control of the related systems including key enzymes of the TCA cycle and a set of electron-transport carriers. Metabolomic data followed proteomics and showed the reduction of the ribulose-monophosphate (RuMP) pathway intermediates and the increase of the TCA cycle metabolites. Conclusion: Cells exposed to REEs display higher rates of growth but have lower carbon conversion efficiency compared to cells supplemented with Ca. The most plausible explanation for these physiological changes is an increased conversion of methanol into formate by XoxF-MeDH, which further stimulates methane oxidation but limits both the supply of reducing power and flux of formaldehyde into the RuMP pathway.

BioUML (https://www.biouml.org)—is a web-based integrated platform for systems biology and data analysis. It supports visual modelling and construction of hierarchical biological models that allow us to construct the most complex modular models of blood pressure regulation, skeletal muscle metabolism, COVID-19 epidemiology. BioUML has been integrated with git repositories where users can store their models and other data. We have also expanded the capabilities of BioUML for data analysis and visualization of biomedical data: (i) any programs and Jupyter kernels can be plugged into the BioUML platform using Docker technology; (ii) BioUML is integrated with the Galaxy and Galaxy Tool Shed; (iii) BioUML provides two-way integration with R and Python (Jupyter notebooks): scripts can be executed on the BioUML web pages, and BioUML functions can be called from scripts; (iv) using plug-in architecture, specialized viewers and editors can be added. For example, powerful genome browsers as well as viewers for molecular 3D structure are integrated in this way; (v) BioUML supports data analyses using workflows (own format, Galaxy, CWL, BPMN, nextFlow). Using these capabilities, we have initiated a new branch of the BioUML development—u-science—a universal scientific platform that can be configured for specific research requirements.