Assessing methanotrophy and carbon fixation for biofuel production by Methanosarcina acetivorans

An integrated systems biology approach reveals differences in formate metabolism in the genus Methanothermobacter

Methanogenesis allows methanogenic archaea to generate cellular energy for their growth while producing methane. Thermophilic hydrogenotrophic species of the genus Methanothermobacter have been recognized as robust biocatalysts for a circular carbon economy and are already applied in power-to-gas technology with biomethanation, which is a platform to store renewable energy and utilize captured carbon dioxide. Here, we generated curated genome-scale metabolic reconstructions for three Methanothermobacter strains and investigated differences in the growth performance of these same strains in chemostat bioreactor experiments with hydrogen and carbon dioxide or formate as substrates. Using an integrated systems biology approach, we identified differences in formate anabolism between the strains and revealed that formate anabolism influences the diversion of carbon between biomass and methane. This finding, together with the omics datasets and the metabolic models we generated, can be implemented for biotechnological applications of Methanothermobacter in power-to-gas technology, and as a perspective, for value-added chemical production.

Uses of Multi-Objective Flux Analysis for Optimization of Microbial Production of Secondary Metabolites

Secondary metabolites are not essential for the growth of microorganisms, but they play a critical role in how microbes interact with their surroundings. In addition to this important ecological role, secondary metabolites also have a variety of agricultural, medicinal, and industrial uses, and thus the examination of secondary metabolism of plants and microbes is a growing scientific field. While the chemical production of certain secondary metabolites is possible, industrial-scale microbial production is a green and economically attractive alternative. This is even more true, given the advances in bioengineering that allow us to alter the workings of microbes in order to increase their production of compounds of interest. This type of engineering requires detailed knowledge of the "chassis" organism's metabolism. Since the resources and the catalytic capacity of enzymes in microbes is finite, it is important to examine the tradeoffs between various bioprocesses in an engineered system and alter its working in a manner that minimally perturbs the robustness of the system while allowing for the maximum production of a product of interest. The in silico multi-objective analysis of metabolism using genome-scale models is an ideal method for such examinations.

Methyl-Based Methanogenesis: an Ecological and Genomic Review

Methyl-based methanogenesis is one of three broad categories of archaeal anaerobic methanogenesis, including both the methyl dismutation (methylotrophic) pathway and the methyl-reducing (also known as hydrogen-dependent methylotrophic) pathway. Methyl-based methanogenesis is increasingly recognized as an important source of methane in a variety of environments. Here, we provide an overview of methyl-based methanogenesis research, including the conditions under which methyl-based methanogenesis can be a dominant source of methane emissions, experimental methods for distinguishing different pathways of methane production, molecular details of the biochemical pathways involved, and the genes and organisms involved in these processes. We also identify the current gaps in knowledge and present a genomic and metagenomic survey of methyl-based methanogenesis genes, highlighting the diversity of methyl-based methanogens at multiple taxonomic levels and the widespread distribution of known methyl-based methanogenesis genes and families across different environments.

A Genome-Scale Metabolic Model of Methanoperedens nitroreducens: Assessing Bioenergetics and Thermodynamic Feasibility

Methane is an abundant low-carbon fuel that provides a valuable energy resource, but it is also a potent greenhouse gas. Therefore, anaerobic oxidation of methane (AOM) is an essential process with central features in controlling the carbon cycle. Candidatus ‘Methanoperedens nitroreducens’ (M. nitroreducens) is a recently discovered methanotrophic archaeon capable of performing AOM via a reverse methanogenesis pathway utilizing nitrate as the terminal electron acceptor. Recently, reverse methanogenic pathways and energy metabolism among anaerobic methane-oxidizing archaea (ANME) have gained significant interest. However, the energetics and the mechanism for electron transport in nitrate-dependent AOM performed by M. nitroreducens is unclear. This paper presents a genome-scale metabolic model of M. nitroreducens, iMN22HE, which contains 813 reactions and 684 metabolites. The model describes its cellular metabolism and can quantitatively predict its growth phenotypes. The essentiality of the cytoplasmic heterodisulfide reductase HdrABC in the reverse methanogenesis pathway is examined by modeling the electron transfer direction and the specific energy-coupling mechanism. Furthermore, based on better understanding electron transport by modeling, a new energy transfer mechanism is suggested. The new mechanism involves reactions capable of driving the endergonic reactions in nitrate-dependent AOM, including the step reactions in reverse canonical methanogenesis and the novel electron-confurcating reaction HdrABC. The genome metabolic model not only provides an in silico tool for understanding the fundamental metabolism of ANME but also helps to better understand the reverse methanogenesis energetics and its thermodynamic feasibility.

GPRuler: Metabolic gene-protein-reaction rules automatic reconstruction

Metabolic network models are increasingly being used in health care and industry. As a consequence, many tools have been released to automate their reconstruction process de novo. In order to enable gene deletion simulations and integration of gene expression data, these networks must include gene-protein-reaction (GPR) rules, which describe with a Boolean logic relationships between the gene products (e.g., enzyme isoforms or subunits) associated with the catalysis of a given reaction. Nevertheless, the reconstruction of GPRs still remains a largely manual and time consuming process. Aiming at fully automating the reconstruction process of GPRs for any organism, we propose the open-source python-based framework GPRuler. By mining text and data from 9 different biological databases, GPRuler can reconstruct GPRs starting either from just the name of the target organism or from an existing metabolic model. The performance of the developed tool is evaluated at small-scale level for a manually curated metabolic model, and at genome-scale level for three metabolic models related to Homo sapiens and Saccharomyces cerevisiae organisms. By exploiting these models as benchmarks, the proposed tool shown its ability to reproduce the original GPR rules with a high level of accuracy. In all the tested scenarios, after a manual investigation of the mismatches between the rules proposed by GPRuler and the original ones, the proposed approach revealed to be in many cases more accurate than the original models. By complementing existing tools for metabolic network reconstruction with the possibility to reconstruct GPRs quickly and with a few resources, GPRuler paves the way to the study of context-specific metabolic networks, representing the active portion of the complete network in given conditions, for organisms of industrial or biomedical interest that have not been characterized metabolically yet.

Sasso Pisano Geothermal Field Environment Harbours Diverse Ktedonobacteria Representatives and Illustrates Habitat-Specific Adaptations

The hydrothermal steam environment of Sasso Pisano (Italy) was selected to investigate the associated microbial community and its metabolic potential. In this context, 16S and 18S rRNA gene partial sequences of thermophilic prokaryotes and eukaryotes inhabiting hot springs and fumaroles as well as mesophilic microbes colonising soil and water were analysed by high-throughput amplicon sequencing. The eukaryotic and prokaryotic communities from hot environments clearly differ from reference microbial communities of colder soil sites, though Ktedonobacteria showed high abundances in various hot spring samples and a few soil samples. This indicates that the hydrothermal steam environments of Sasso Pisano represent not only a vast reservoir of thermophilic but also mesophilic members of this Chloroflexi class. Metabolic functional profiling revealed that the hot spring microbiome exhibits a higher capability to utilise methane and aromatic compounds and is more diverse in its sulphur and nitrogen metabolism than the mesophilic soil microbial consortium. In addition, heavy metal resistance-conferring genes were significantly more abundant in the hot spring microbiome. The eukaryotic diversity at a fumarole indicated high abundances of primary producers (unicellular red algae: Cyanidiales), consumers (Arthropoda: Collembola sp.), and endoparasite Apicomplexa (Gregarina sp.), which helps to hypothesise a simplified food web at this hot and extremely nutrient-deprived acidic environment.

Towards a Systems Biology Approach to Understanding the Lichen Symbiosis: Opportunities and Challenges of Implementing Network Modelling

Lichen associations, a classic model for successful and sustainable interactions between micro-organisms, have been studied for many years. However, there are significant gaps in our understanding about how the lichen symbiosis operates at the molecular level. This review addresses opportunities for expanding current knowledge on signalling and metabolic interplays in the lichen symbiosis using the tools and approaches of systems biology, particularly network modelling. The largely unexplored nature of symbiont recognition and metabolic interdependency in lichens could benefit from applying a holistic approach to understand underlying molecular mechanisms and processes. Together with ‘omics’ approaches, the application of signalling and metabolic network modelling could provide predictive means to gain insights into lichen signalling and metabolic pathways. First, we review the major signalling and recognition modalities in the lichen symbioses studied to date, and then describe how modelling signalling networks could enhance our understanding of symbiont recognition, particularly leveraging omics techniques. Next, we highlight the current state of knowledge on lichen metabolism. We also discuss metabolic network modelling as a tool to simulate flux distribution in lichen metabolic pathways and to analyse the co-dependence between symbionts. This is especially important given the growing number of lichen genomes now available and improved computational tools for reconstructing such models. We highlight the benefits and possible bottlenecks for implementing different types of network models as applied to the study of lichens.

Laboratory scale bioreactor designs in the processes of methane bioconversion: Mini-review

Global methane emissions have been steadily increasing over the past few decades, exerting a negative effect on the environment. Biogas from landfills and sewage treatment plants is the main anthropogenic source of methane. This makes methane bioconversion one of the priority areas of biotechnology. This process involves the production of biochemical compounds from non-food sources through microbiological synthesis. Methanotrophic bacteria are a promising tool for methane bioconversion due to their ability to use this greenhouse gas and to produce protein-rich biomass, as well as a broad range of useful organic compounds. Currently, methane is used not only to produce biomass and chemical compounds, but also to increase the efficiency of water and solid waste treatment. However, the use of gaseous substrates in biotechnological processes is associated with some difficulties. The low solubility of methane in water is one of the major problems. Different approaches have been involved to encounter these challenges, including different bioreactor and gas distribution designs, solid carriers and bulk sorbents, as well as varying air/oxygen supply, the ratio of volumetric flow rate of gas mixture to its consumption rate, etc. The aim of this review was to summarize the current data on different bioreactor designs and the aspects of their applications for methane bioconversion and wastewater treatment. The bioreactors used in these processes must meet a number of requirements such as low methane emission, improved gas exchange surface, and controlled substrate supply to the reaction zone.

Microbial Methane Conversion to Short-Chain Fatty Acids Using Various Electron Acceptors in Membrane Biofilm Reactors

Given our vast methane reserves and the forecasted shortage of crude oil in the not too distant future, the conversion of methane into value-added liquid chemicals or fuels would be beneficial. The generated chemicals or fuels could augment the petroleum-dominated chemical market, and also satisfy the increasing demand for transportation fuels. While methane bioconversion to liquid chemicals has just been reported recently, there is limited understanding of the process. This study aims to clarify the potential electron acceptors that could support the process. Here we operated four membrane biofilm reactors (MBfRs) fed with nitrate, nitrite, oxygen at a relatively low rate, and oxygen at a relatively high rate, respectively, to study if they can support methane bioconversion to short-chain fatty acids (SCFAs) and the associated microbiological features. All tested electron acceptors facilitated methane bioconversion to SCFAs (ranging from 1.1 to 36.7 mg acetate L^-1 d^-1, or 3.4 to 114.6 mg acetate d^-1 m^-2 of biofilm). The carbon efficiency was estimated to be 7.9 ± 1.4% to 148.5 ± 1.3%, with an efficiency higher than 100%, suggesting the assimilation of other carbon, very likely CO₂, into the products. A low oxygen supply rate of 46.4 ± 2.3 mg O₂ d^-1 m^-2 was found to be the most favorable among all the electron conditions provided according to the SCFAs production rate and also the carbon utilization efficiency. Microbial characterization revealed that completely different communities evolved in the respective reactors, suggesting diverse microbial pathways exist for methane bioconversion into value-added chemicals.

Cofactor F420-Dependent Enzymes: An Under-Explored Resource for Asymmetric Redox Biocatalysis

The asymmetric reduction of enoates, imines and ketones are among the most important reactions in biocatalysis. These reactions are routinely conducted using enzymes that use nicotinamide cofactors as reductants. The deazaflavin cofactor F420 also has electrochemical properties that make it suitable as an alternative to nicotinamide cofactors for use in asymmetric reduction reactions. However, cofactor F420-dependent enzymes remain under-explored as a resource for biocatalysis. This review considers the cofactor F420-dependent enzyme families with the greatest potential for the discovery of new biocatalysts: the flavin/deazaflavin-dependent oxidoreductases (FDORs) and the luciferase-like hydride transferases (LLHTs). The characterized F420-dependent reductions that have the potential for adaptation for biocatalysis are discussed, and the enzymes best suited for use in the reduction of oxidized cofactor F420 to allow cofactor recycling in situ are considered. Further discussed are the recent advances in the production of cofactor F420 and its functional analog FO-5′-phosphate, which remains an impediment to the adoption of this family of enzymes for industrial biocatalytic processes. Finally, the prospects for the use of this cofactor and dependent enzymes as a resource for industrial biocatalysis are discussed.

Current status and applications of genome-scale metabolic models

Genome-scale metabolic models (GEMs) computationally describe gene-protein-reaction associations for entire metabolic genes in an organism, and can be simulated to predict metabolic fluxes for various systems-level metabolic studies. Since the first GEM for Haemophilus influenzae was reported in 1999, advances have been made to develop and simulate GEMs for an increasing number of organisms across bacteria, archaea, and eukarya. Here, we review current reconstructed GEMs and discuss their applications, including strain development for chemicals and materials production, drug targeting in pathogens, prediction of enzyme functions, pan-reactome analysis, modeling interactions among multiple cells or organisms, and understanding human diseases.

Kinetic modeling of Stickland reactions-coupled methanogenesis for a methanogenic culture

Studying amino acid catabolism-coupled methanogenesis is the important standpoints to decipher the metabolic behavior of a methanogenic culture. l-Glycine and l-alanine are acted as sole carbon and nitrogen sources for acidogenic bacteria. One amino acid is oxidized and another one is reduced for acetate production via pyruvate by oxidative deamination process in the Stickland reactions. Herein, we have developed a kinetic model for the Stickland reactions-coupled methanogenesis (SRCM) and simulated objectively to maximize the rate of methane production. We collected the metabolic information from enzyme kinetic parameters for amino acid catabolism of Clostridium acetobutylicum ATCC 824 and methanogenesis of Methanosarcina acetivorans C2A. The SRCM model of this study consisted of 18 reactions and 61 metabolites with enzyme kinetic parameters derived experimental data. The internal or external metabolic flux rate of this system found to control the acidogenesis and methanogenesis in a methanogenic culture. Using the SRCM model, flux distributions were calculated for each reaction and metabolite in order to maximize the methane production rate from the glycine–alanine pair. Results of this study, we demonstrated the metabolic behavior, metabolite pairing while mutually interact, and advantages of syntrophic metabolism of amino acid-directed methane production in a methanogenic starter culture.

Transplanting the pathway engineering toolbox to methanogens

Biological methanogenesis evolved early in Earth's history and was likely already a major process by 3.5 Ga. Modern methanogenesis is now a key process in virtually all anaerobic microbial communities, such as marine and lake sediments, wetland and rice soils, and human and cattle digestive tracts. Owing to their long evolution and extensive adaptations to various habitats, methanogens possess enormous metabolic and physiological diversity. Not only does this diversity offers unique opportunities for biotechnology applications, but also reveals their direct impact on the environment, agriculture, and human and animal health. These efforts are facilitated by an advanced genetic toolbox, emerging new molecular tools, and systems-level modelling for methanogens. Further developments and convergence of these technical advancements provide new opportunities for bioengineering methanogens.

A Prospective Study on the Fermentation Landscape of Gaseous Substrates to Biorenewables Using Methanosarcina acetivorans Metabolic Model

The abundance of methane in shale gas and of other gases such as carbon monoxide, hydrogen, and carbon dioxide as chemical process byproducts has motivated the use of gas fermentation for bioproduction. Recent advances in metabolic engineering and synthetic biology allow for engineering of microbes metabolizing a variety of chemicals including gaseous feeds into a number of biorenewables and transportation liquid fuels. New computational tools enable the systematic exploration of all feasible conversion alternatives. Here we computationally assessed all thermodynamically feasible ways of co-utilizing CH₄, CO, and CO₂ using ferric as terminal electron acceptor for the production of all key precursor metabolites. We identified the thermodynamically feasible co-utilization ratio ranges of CH₄, CO, and CO₂ toward production of the target metabolite(s) as a function of ferric uptake. A revised version of the iMAC868 genome-scale metabolic model of Methanosarcina acetivorans was chosen to assess co-utilization of CH₄, CO, and CO₂ and their conversion into selected target products using the optStoic pathway design tool. This revised version contains the latest information on electron flow mechanisms by the methanogen while supplied with methane as the sole carbon source. The interplay between different gas co-utilization ratios and the energetics of reverse methanogenesis were also analyzed using the same metabolic model.

Fine-Tuned Protein Production in Methanosarcina acetivorans C2A

Methanogenic archaea can be integrated into a sustainable, carbon-neutral cycle for producing organic chemicals from C₁ compounds if the rate, yield, and titer of product synthesis can be improved using metabolic engineering. However, metabolic engineering techniques are limited in methanogens by insufficient methods for controlling cellular protein levels. We conducted a systematic approach to tune protein levels in Methanosarcina acetivorans C2A, a model methanogen, by regulating transcription and translation initiation. Rationally designed core promoter and ribosome binding site mutations in M. acetivorans C2A resulted in a predicable change in protein levels over a 60 fold range. The overall range of protein levels was increased an additional 3 fold by introducing the 5' untranslated region of the mcrB transcript. This work demonstrates a wide range of precisely controlled protein levels in M. acetivorans C2A, which will help facilitate systematic metabolic engineering efforts in methanogens.

Microbial Methylotrophic Metabolism: Recent Metabolic Modeling Efforts and Their Applications In Industrial Biotechnology

Developing methylotrophic bacteria into cell factories that meet the chemical demand of the future could be both economical and environmentally friendly. Methane is not only an abundant, low-cost resource but also a potent greenhouse gas, the capture of which could help to reduce greenhouse gas emissions. Rational strain design workflows rely on the availability of carefully combined knowledge often in the form of genome-scale metabolic models to construct high-producer organisms. In this review, the authors present the most recent genome-scale metabolic models in aerobic methylotrophy and their applications. Further, the authors present models for the study of anaerobic methanotrophy through reverse methanogenesis and suggest organisms that may be of interest for expanding one-carbon industrial biotechnology. Metabolic models of methylotrophs are scarce, yet they are important first steps toward rational strain-design in these organisms.

Traceability, reproducibility and wiki-exploration for "à-la-carte" reconstructions of genome-scale metabolic models

Genome-scale metabolic models have become the tool of choice for the global analysis of microorganism metabolism, and their reconstruction has attained high standards of quality and reliability. Improvements in this area have been accompanied by the development of some major platforms and databases, and an explosion of individual bioinformatics methods. Consequently, many recent models result from "à la carte" pipelines, combining the use of platforms, individual tools and biological expertise to enhance the quality of the reconstruction. Although very useful, introducing heterogeneous tools, that hardly interact with each other, causes loss of traceability and reproducibility in the reconstruction process. This represents a real obstacle, especially when considering less studied species whose metabolic reconstruction can greatly benefit from the comparison to good quality models of related organisms. This work proposes an adaptable workspace, AuReMe, for sustainable reconstructions or improvements of genome-scale metabolic models involving personalized pipelines. At each step, relevant information related to the modifications brought to the model by a method is stored. This ensures that the process is reproducible and documented regardless of the combination of tools used. Additionally, the workspace establishes a way to browse metabolic models and their metadata through the automatic generation of ad-hoc local wikis dedicated to monitoring and facilitating the process of reconstruction. AuReMe supports exploration and semantic query based on RDF databases. We illustrate how this workspace allowed handling, in an integrated way, the metabolic reconstructions of non-model organisms such as an extremophile bacterium or eukaryote algae. Among relevant applications, the latter reconstruction led to putative evolutionary insights of a metabolic pathway.

A biochemical framework for anaerobic oxidation of methane driven by Fe(III)-dependent respiration

Consumption of methane by aerobic and anaerobic microbes governs the atmospheric level of this powerful greenhouse gas. Whereas a biochemical understanding of aerobic methanotrophy is well developed, a mechanistic understanding of anaerobic methanotrophy has been prevented by the unavailability of pure cultures. Here we report a biochemical investigation of Methanosarcina acetivorans, a methane-producing species capable of anaerobic methanotrophic growth dependent on reduction of Fe(III). Our findings support a pathway anchored by Fe(III)-dependent mechanisms for energy conservation driving endergonic reactions that are key to methanotrophic growth. The pathway is remarkably similar to pathways hypothesized for uncultured anaerobic methanotrophic archaea. The results contribute to an improved understanding of the methane cycle that is paramount to understanding human interventions influencing Earth’s climate. Finally, the pathway enables advanced development and optimization of biotechnologies converting methane to value-added products through metabolic engineering of M. acetivorans.

The unavailability of pure cultures has prevented a mechanistic understanding of anaerobic methanotrophy. Here the authors report a biochemical investigation of Methanosarcina acetivorans that supports a pathway anchored by Fe(III)-dependent mechanisms for energy conservation and driving endergonic reactions.

Harnessing a methane-fueled, sediment-free mixed microbial community for utilization of distributed sources of natural gas

Harnessing the metabolic potential of uncultured microbial communities is a compelling opportunity for the biotechnology industry, an approach that would vastly expand the portfolio of usable feedstocks. Methane is particularly promising because it is abundant and energy‐rich, yet the most efficient methane‐activating metabolic pathways involve mixed communities of anaerobic methanotrophic archaea and sulfate reducing bacteria. These communities oxidize methane at high catabolic efficiency and produce chemically reduced by‐products at a comparable rate and in near‐stoichiometric proportion to methane consumption. These reduced compounds can be used for feedstock and downstream chemical production, and at the production rates observed in situ they are an appealing, cost‐effective prospect. Notably, the microbial constituents responsible for this bioconversion are most prominent in select deep‐sea sediments, and while they can be kept active at surface pressures, they have not yet been cultured in the lab. In an industrial capacity, deep‐sea sediments could be periodically recovered and replenished, but the associated technical challenges and substantial costs make this an untenable approach for full‐scale operations. In this study, we present a novel method for incorporating methanotrophic communities into bioindustrial processes through abstraction onto low mass, easily transportable carbon cloth artificial substrates. Using Gulf of Mexico methane seep sediment as inoculum, optimal physicochemical parameters were established for methane‐oxidizing, sulfide‐generating mesocosm incubations. Metabolic activity required >∼40% seawater salinity, peaking at 100% salinity and 35 °C. Microbial communities were successfully transferred to a carbon cloth substrate, and rates of methane‐dependent sulfide production increased more than threefold per unit volume. Phylogenetic analyses indicated that carbon cloth‐based communities were substantially streamlined and were dominated by Desulfotomaculum geothermicum. Fluorescence in situ hybridization microscopy with carbon cloth fibers revealed a novel spatial arrangement of anaerobic methanotrophs and sulfate reducing bacteria suggestive of an electronic coupling enabled by the artificial substrate. This system: 1) enables a more targeted manipulation of methane‐activating microbial communities using a low‐mass and sediment‐free substrate; 2) holds promise for the simultaneous consumption of a strong greenhouse gas and the generation of usable downstream products; and 3) furthers the broader adoption of uncultured, mixed microbial communities for biotechnological use.

Genome-Scale Metabolic Modeling of Archaea Lends Insight into Diversity of Metabolic Function

Decades of biochemical, bioinformatic, and sequencing data are currently being systematically compiled into genome-scale metabolic reconstructions (GEMs). Such reconstructions are knowledge-bases useful for engineering, modeling, and comparative analysis. Here we review the fifteen GEMs of archaeal species that have been constructed to date. They represent primarily members of the Euryarchaeota with three-quarters comprising representative of methanogens. Unlike other reviews on GEMs, we specially focus on archaea. We briefly review the GEM construction process and the genealogy of the archaeal models. The major insights gained during the construction of these models are then reviewed with specific focus on novel metabolic pathway predictions and growth characteristics. Metabolic pathway usage is discussed in the context of the composition of each organism's biomass and their specific energy and growth requirements. We show how the metabolic models can be used to study the evolution of metabolism in archaea. Conservation of particular metabolic pathways can be studied by comparing reactions using the genes associated with their enzymes. This demonstrates the utility of GEMs to evolutionary studies, far beyond their original purpose of metabolic modeling; however, much needs to be done before archaeal models are as extensively complete as those for bacteria.

Biocatalysts for methane conversion: big progress on breaking a small substrate

Nature utilizes two groups of enzymes to catalyze methane conversions, methyl-coenzyme M reductases (MCRs) and methane monooxygenases (MMOs). These enzymes have been difficult to incorporate into industrial processes due to their complexity, poor stability, and lack of recombinant tractability. Despite these issues, new ways of preparing and stabilizing these enzymes have recently been discovered, and new mechanistic insight into how MCRs and MMOs break the C-H bond in nature's most inert hydrocarbon have been obtained. This review focuses on recent findings in the methane biocatalysis field, and discusses the impact of these finding on designing MMO and MCR-based biotechnologies.

Methane-Oxidizing Enzymes: An Upstream Problem in Biological Gas-to-Liquids Conversion

Biological conversion of natural gas to liquids (Bio-GTL) represents an immense economic opportunity. In nature, aerobic methanotrophic bacteria and anaerobic archaea are able to selectively oxidize methane using methane monooxygenase (MMO) and methyl coenzyme M reductase (MCR) enzymes. Although significant progress has been made toward genetically manipulating these organisms for biotechnological applications, the enzymes themselves are slow, complex, and not recombinantly tractable in traditional industrial hosts. With turnover numbers of 0.16-13 s(-1), these enzymes pose a considerable upstream problem in the biological production of fuels or chemicals from methane. Methane oxidation enzymes will need to be engineered to be faster to enable high volumetric productivities; however, efforts to do so and to engineer simpler enzymes have been minimally successful. Moreover, known methane-oxidizing enzymes have different expression levels, carbon and energy efficiencies, require auxiliary systems for biosynthesis and function, and vary considerably in terms of complexity and reductant requirements. The pros and cons of using each methane-oxidizing enzyme for Bio-GTL are considered in detail. The future for these enzymes is bright, but a renewed focus on studying them will be critical to the successful development of biological processes that utilize methane as a feedstock.

Achieving Metabolic Flux Analysis for S. cerevisiae at a Genome-Scale: Challenges, Requirements, and Considerations

Recent advances in 13C-Metabolic flux analysis (13C-MFA) have increased its capability to accurately resolve fluxes using a genome-scale model with narrow confidence intervals without pre-judging the activity or inactivity of alternate metabolic pathways. However, the necessary precautions, computational challenges, and minimum data requirements for successful analysis remain poorly established. This review aims to establish the necessary guidelines for performing 13C-MFA at the genome-scale for a compartmentalized eukaryotic system such as yeast in terms of model and data requirements, while addressing key issues such as statistical analysis and network complexity. We describe the various approaches used to simplify the genome-scale model in the absence of sufficient experimental flux measurements, the availability and generation of reaction atom mapping information, and the experimental flux and metabolite labeling distribution measurements to ensure statistical validity of the obtained flux distribution. Organism-specific challenges such as the impact of compartmentalization of metabolism, variability of biomass composition, and the cell-cycle dependence of metabolism are discussed. Identification of errors arising from incorrect gene annotation and suggested alternate routes using MFA are also highlighted.