Genome-scale metabolic reconstruction and hypothesis testing in the methanogenic archaeon Methanosarcina acetivorans C2A

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.

The effective deficiency of biochemical networks

The deficiency of a (bio)chemical reaction network can be conceptually interpreted as a measure of its ability to support exotic dynamical behavior and/or multistationarity. The classical definition of deficiency relates to the capacity of a network to permit variations of the complex formation rate vector at steady state, irrespective of the network kinetics. However, the deficiency is by definition completely insensitive to the fine details of the directionality of reactions as well as bounds on reaction fluxes. While the classical definition of deficiency can be readily applied in the analysis of unconstrained, weakly reversible networks, it only provides an upper bound in the cases where relevant constraints on reaction fluxes are imposed. Here we propose the concept of effective deficiency, which provides a more accurate assessment of the network's capacity to permit steady state variations at the complex level for constrained networks of any reversibility patterns. The effective deficiency relies on the concept of nonstoichiometric balanced complexes, which we have already shown to be present in real-world biochemical networks operating under flux constraints. Our results demonstrate that the effective deficiency of real-world biochemical networks is smaller than the classical deficiency, indicating the effects of reaction directionality and flux bounds on the variation of the complex formation rate vector at steady state.

Model Organisms To Study Methanogenesis, a Uniquely Archaeal Metabolism

Methanogenic archaea are the only organisms that produce CH4 as part of their energy-generating metabolism. They are ubiquitous in oxidant-depleted, anoxic environments such as aquatic sediments, anaerobic digesters, inundated agricultural fields, the rumen of cattle, and the hindgut of termites, where they catalyze the terminal reactions in the degradation of organic matter. Methanogenesis is the only metabolism that is restricted to members of the domain Archaea. Here, we discuss the importance of model organisms in the history of methanogen research, including their role in the discovery of the archaea and in the biochemical and genetic characterization of methanogenesis. We also discuss outstanding questions in the field and newly emerging model systems that will expand our understanding of this uniquely archaeal metabolism.

A Protocol for the Automatic Construction of Highly Curated Genome-Scale Models of Human Metabolism

Genome-scale metabolic models (GEMs) have emerged as a tool to understand human metabolism from a holistic perspective with high relevance in the study of many diseases and in the metabolic engineering of human cell lines. GEM building relies on either automated processes that lack manual refinement and result in inaccurate models or manual curation, which is a time-consuming process that limits the continuous update of reliable GEMs. Here, we present a novel algorithm-aided protocol that overcomes these limitations and facilitates the continuous updating of highly curated GEMs. The algorithm enables the automatic curation and/or expansion of existing GEMs or generates a highly curated metabolic network based on current information retrieved from multiple databases in real time. This tool was applied to the latest reconstruction of human metabolism (Human1), generating a series of the human GEMs that improve and expand the reference model and generating the most extensive and comprehensive general reconstruction of human metabolism to date. The tool presented here goes beyond the current state of the art and paves the way for the automatic reconstruction of a highly curated, up-to-date GEM with high potential in computational biology as well as in multiple fields of biological science where metabolism is relevant.

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.

The hidden simplicity of metabolic networks is revealed by multireaction dependencies

Understanding the complexity of metabolic networks has implications for manipulation of their functions. The complexity of metabolic networks can be characterized by identifying multireaction dependencies that are challenging to determine due to the sheer number of combinations to consider. Here, we propose the concept of concordant complexes that captures multireaction dependencies and can be efficiently determined from the algebraic structure and operational constraints of metabolic networks. The concordant complexes imply the existence of concordance modules based on which the apparent complexity of 12 metabolic networks of organisms from all kingdoms of life can be reduced by at least 78%. A comparative analysis against an ensemble of randomized metabolic networks shows that the metabolic network of Escherichia coli contains fewer concordance modules and is, therefore, more tightly coordinated than expected by chance. Together, our findings demonstrate that metabolic networks are considerably simpler than what can be perceived from their structure alone.

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.

Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data

Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.

A structural property for reduction of biochemical networks

Large-scale biochemical models are of increasing sizes due to the consideration of interacting organisms and tissues. Model reduction approaches that preserve the flux phenotypes can simplify the analysis and predictions of steady-state metabolic phenotypes. However, existing approaches either restrict functionality of reduced models or do not lead to significant decreases in the number of modelled metabolites. Here, we introduce an approach for model reduction based on the structural property of balancing of complexes that preserves the steady-state fluxes supported by the network and can be efficiently determined at genome scale. Using two large-scale mass-action kinetic models of Escherichia coli, we show that our approach results in a substantial reduction of 99% of metabolites. Applications to genome-scale metabolic models across kingdoms of life result in up to 55% and 85% reduction in the number of metabolites when arbitrary and mass-action kinetics is assumed, respectively. We also show that predictions of the specific growth rate from the reduced models match those based on the original models. Since steady-state flux phenotypes from the original model are preserved in the reduced, the approach paves the way for analysing other metabolic phenotypes in large-scale biochemical networks.

An alternative resource allocation strategy in the chemolithoautotrophic archaeon Methanococcus maripaludis

Most microorganisms in nature spend the majority of time in a state of slow or zero growth and slow metabolism under limited energy or nutrient flux rather than growing at maximum rates. Yet, most of our knowledge has been derived from studies on fast-growing bacteria. Here, we systematically characterized the physiology of the methanogenic archaeon Methanococcus maripaludis during slow growth. M. maripaludis was grown in continuous culture under energy (formate)-limiting conditions at different dilution rates ranging from 0.09 to 0.002 h^-1, the latter corresponding to 1% of its maximum growth rate under laboratory conditions (0.23 h^-1). While the specific rate of methanogenesis correlated with growth rate as expected, the fraction of cellular energy used for maintenance increased and the maintenance energy per biomass decreased at slower growth. Notably, proteome allocation between catabolic and anabolic pathways was invariant with growth rate. Unexpectedly, cells maintained their maximum methanogenesis capacity over a wide range of growth rates, except for the lowest rates tested. Cell size, cellular DNA, RNA, and protein content as well as ribosome numbers also were largely invariant with growth rate. A reduced protein synthesis rate during slow growth was achieved by a reduction in ribosome activity rather than via the number of cellular ribosomes. Our data revealed a resource allocation strategy of a methanogenic archaeon during energy limitation that is fundamentally different from commonly studied versatile chemoheterotrophic bacteria such as E. coli.

Reconstructing organisms in silico: genome-scale models and their emerging applications

Escherichia coli is considered to be the best-known microorganism given the large number of published studies detailing its genes, its genome and the biochemical functions of its molecular components. This vast literature has been systematically assembled into a reconstruction of the biochemical reaction networks that underlie E. coli's functions, a process which is now being applied to an increasing number of microorganisms. Genome-scale reconstructed networks are organized and systematized knowledge bases that have multiple uses, including conversion into computational models that interpret and predict phenotypic states and the consequences of environmental and genetic perturbations. These genome-scale models (GEMs) now enable us to develop pan-genome analyses that provide mechanistic insights, detail the selection pressures on proteome allocation and address stress phenotypes. In this Review, we first discuss the overall development of GEMs and their applications. Next, we review the evolution of the most complete GEM that has been developed to date: the E. coli GEM. Finally, we explore three emerging areas in genome-scale modelling of microbial phenotypes: collections of strain-specific models, metabolic and macromolecular expression models, and simulation of stress responses.

Methanosarcina acetivorans: A Model for Mechanistic Understanding of Aceticlastic and Reverse Methanogenesis

Acetate-utilizing methanogens are responsible for approximately two-thirds of the one billion metric tons of methane produced annually in Earth's anaerobic environments. Methanosarcina acetivorans has emerged as a model organism for the mechanistic understanding of aceticlastic methanogenesis and reverse methanogenesis applicable to understanding the methane and carbon cycles in nature. It has the largest genome in the Archaea, supporting a metabolic complexity that enables a remarkable ability for adapting to environmental opportunities and challenges. Biochemical investigations have revealed an aceticlastic pathway capable of fermentative and respiratory energy conservation that explains how Ms. acetivorans is able to grow and compete in the environment. The mechanism of respiratory energy conservation also plays a role in overcoming endothermic reactions that are key to reversing methanogenesis.

Comparative analysis of differential proteome-wide protein-protein interaction network of Methanobrevibacter ruminantium M1

A proteome-wide protein-protein interaction (PPI) network of Methanobrevibacter ruminantium M1 (MRU), a predominant rumen methanogen, was constructed from its metabolic genes using a gene neighborhood algorithm and then compared with closely related rumen methanogens Using proteome-wide PPI approach, we constructed network encompassed 2194 edges and 637 nodes interacting with 634 genes. Network quality and robustness of functional modules were assessed with gene ontology terms. A structure-function-metabolism mapping for each protein has been carried out with efforts to extract experimental PPI concomitant information from the literature. The results of our study revealed that some topological properties of its network were robust for sharing homologous protein interactions across heterotrophic and hydrogenotrophic methanogens. MRU proteome has shown to establish many PPI sub-networks for associated metabolic subsystems required to survive in the rumen environment. MRU genome found to share interacting proteins from its PPI network involved in specific metabolic subsystems distinct to heterotrophic and hydrogenotrophic methanogens. Across these proteomes, the interacting proteins from differential PPI networks were shared in common for the biosynthesis of amino acids, nucleosides, and nucleotides and energy metabolism in which more fractions of protein pairs shared with Methanosarcina acetivorans. Our comparative study expedites our knowledge to understand a complex proteome network associated with typical metabolic subsystems of MRU and to improve its genome-scale reconstruction in the future.

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.

Energy Conservation and Hydrogenase Function in Methanogenic Archaea, in Particular the Genus Methanosarcina

The biological production of methane is vital to the global carbon cycle and accounts for ca. 74% of total methane emissions. The organisms that facilitate this process, methanogenic archaea, belong to a large and phylogenetically diverse group that thrives in a wide range of anaerobic environments. Two main subgroups exist within methanogenic archaea: those with and those without cytochromes. Although a variety of metabolisms exist within this group, the reduction of growth substrates to methane using electrons from molecular hydrogen is, in a phylogenetic sense, the most widespread methanogenic pathway. Methanogens without cytochromes typically generate methane by the reduction of CO₂ with electrons derived from H₂, formate, or secondary alcohols, generating a transmembrane ion gradient for ATP production via an Na⁺-translocating methyltransferase (Mtr). These organisms also conserve energy with a novel flavin-based electron bifurcation mechanism, wherein the endergonic reduction of ferredoxin is facilitated by the exergonic reduction of a disulfide terminal electron acceptor coupled to either H₂ or formate oxidation. Methanogens that utilize cytochromes have a broader substrate range, and can convert acetate and methylated compounds to methane, in addition to the ability to reduce CO₂ Cytochrome-containing methanogens are able to supplement the ion motive force generated by Mtr with an H⁺-translocating electron transport system. In both groups, enzymes known as hydrogenases, which reversibly interconvert protons and electrons to molecular hydrogen, play a central role in the methanogenic process. This review discusses recent insight into methanogen metabolism and energy conservation mechanisms with a particular focus on the genus Methanosarcina.

A Membrane-Bound Cytochrome Enables Methanosarcina acetivorans To Conserve Energy from Extracellular Electron Transfer

The discovery of a methanogen that can conserve energy to support growth solely from the oxidation of organic carbon coupled to the reduction of an extracellular electron acceptor expands the possible environments in which methanogens might thrive. The potential importance of c-type cytochromes for extracellular electron transfer to syntrophic bacterial partners and/or Fe(III) minerals in some Archaea was previously proposed, but these studies with Methanosarcina acetivorans provide the first genetic evidence for cytochrome-based extracellular electron transfer in Archaea. The results suggest parallels with Gram-negative bacteria, such as Shewanella and Geobacter species, in which multiheme outer-surface c-type cytochromes are an essential component for electrical communication with the extracellular environment. M. acetivorans offers an unprecedented opportunity to study mechanisms for energy conservation from the anaerobic oxidation of one-carbon organic compounds coupled to extracellular electron transfer in Archaea with implications not only for methanogens but possibly also for Archaea that anaerobically oxidize methane.

Extracellular electron exchange in Methanosarcina species and closely related Archaea plays an important role in the global carbon cycle and enhances the speed and stability of anaerobic digestion by facilitating efficient syntrophic interactions. Here, we grew Methanosarcina acetivorans with methanol provided as the electron donor and the humic analogue, anthraquione-2,6-disulfonate (AQDS), provided as the electron acceptor when methane production was inhibited with bromoethanesulfonate. AQDS was reduced with simultaneous methane production in the absence of bromoethanesulfonate. Transcriptomics revealed that expression of the gene for the transmembrane, multiheme, c-type cytochrome MmcA was higher in AQDS-respiring cells than in cells performing methylotrophic methanogenesis. A strain in which the gene for MmcA was deleted failed to grow via AQDS reduction but grew with the conversion of methanol or acetate to methane, suggesting that MmcA has a specialized role as a conduit for extracellular electron transfer. Enhanced expression of genes for methanol conversion to methyl-coenzyme M and the Rnf complex suggested that methanol is oxidized to carbon dioxide in AQDS-respiring cells through a pathway that is similar to methyl-coenzyme M oxidation in methanogenic cells. However, during AQDS respiration the Rnf complex and reduced methanophenazine probably transfer electrons to MmcA, which functions as the terminal reductase for AQDS reduction. Extracellular electron transfer may enable the survival of methanogens in dynamic environments in which oxidized humic substances and Fe(III) oxides are intermittently available. The availability of tools for genetic manipulation of M. acetivorans makes it an excellent model microbe for evaluating c-type cytochrome-dependent extracellular electron transfer in Archaea.

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.

A Genome-Scale Metabolic Model for Methylococcus capsulatus (Bath) Suggests Reduced Efficiency Electron Transfer to the Particulate Methane Monooxygenase

Background: Genome-scale metabolic models allow researchers to calculate yields, to predict consumption and production rates, and to study the effect of genetic modifications in silico, without running resource-intensive experiments. While these models have become an invaluable tool for optimizing industrial production hosts like Escherichia coli and S. cerevisiae, few such models exist for one-carbon (C1) metabolizers.

Results: Here, we present a genome-scale metabolic model for Methylococcus capsulatus (Bath), a well-studied obligate methanotroph, which has been used as a production strain of single cell protein (SCP). The model was manually curated, and spans a total of 879 metabolites connected via 913 reactions. The inclusion of 730 genes and comprehensive annotations, make this model not only a useful tool for modeling metabolic physiology, but also a centralized knowledge base for M. capsulatus (Bath). With it, we determined that oxidation of methane by the particulate methane monooxygenase could be driven both through direct coupling or uphill electron transfer, both operating at reduced efficiency, as either scenario matches well with experimental data and observations from literature.

Conclusion: The metabolic model will serve the ongoing fundamental research of C1 metabolism, and pave the way for rational strain design strategies toward improved SCP production processes in M. capsulatus.

Thermodynamic favorability and pathway yield as evolutionary tradeoffs in biosynthetic pathway choice

The structure of the metabolic network contains myriad organism-specific variations across the tree of life, but the selection basis for pathway choices in different organisms is not well understood. Here, we examined the metabolic capabilities with respect to cofactor use and pathway thermodynamics of all sequenced organisms in the Kyoto Encyclopedia of Genes and Genomes Database. We found that (i) many biomass precursors have alternate synthesis routes that vary substantially in thermodynamic favorability and energy cost, creating tradeoffs that may be subject to selection pressure; (ii) alternative pathways in amino acid synthesis are characteristically distinguished by the use of biosynthetically unnecessary acyl-CoA cleavage; (iii) distinct choices preferring thermodynamic-favorable or cofactor-use-efficient pathways exist widely among organisms; (iv) cofactor-use-efficient pathways tend to have a greater yield advantage under anaerobic conditions specifically; and (v) lysine biosynthesis in particular exhibits temperature-dependent thermodynamics and corresponding differential pathway choice by thermophiles. These findings present a view on the evolution of metabolic network structure that highlights a key role of pathway thermodynamics and cofactor use in determining organism pathway choices.

Methanobacterium formicicum as a target rumen methanogen for the development of new methane mitigation interventions: A review

Methanobacterium formicicum (Methanobacteriaceae family) is an endosymbiotic methanogenic Archaean found in the digestive tracts of ruminants and elsewhere. It has been significantly implicated in global CH₄ emission during enteric fermentation processes. In this review, we discuss current genomic and metabolic aspects of this microorganism for the purpose of the discovery of novel veterinary therapeutics. This microorganism encompasses a typical H₂ scavenging system, which facilitates a metabolic symbiosis across the H₂ producing cellulolytic bacteria and fumarate reducing bacteria. To date, five genome-scale metabolic models (iAF692, iMG746, iMB745, iVS941 and iMM518) have been developed. These metabolic reconstructions revealed the cellular and metabolic behaviors of methanogenic archaea. The characteristics of its symbiotic behavior and metabolic crosstalk with competitive rumen anaerobes support understanding of the physiological function and metabolic fate of shared metabolites in the rumen ecosystem. Thus, systems biological characterization of this microorganism may provide a new insight to realize its metabolic significance for the development of a healthy microbiota in ruminants. An in-depth knowledge of this microorganism may allow us to ensure a long term sustainability of ruminant-based agriculture.

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.

Genome-scale metabolic model analysis indicates low energy production efficiency in marine ammonia-oxidizing archaea

Marine ammonia-oxidizing archaea (AOA) play an important role in the global nitrogen cycle by obtaining energy for biomass production from CO₂ via oxidation of ammonium. The isolation of Candidatus “Nitrosopumilus maritimus” strain SCM1, which represents the globally distributed AOA in the ocean, provided an opportunity for uncovering the contributions of those AOA to carbon and nitrogen cycles in ocean. Although several ammonia oxidation pathways have been proposed for SCM1, little is known about its ATP production efficiency. Here, based on the published genome of Nitrosopumilus maritimus SCM1, a genome-scale metabolic model named NmrFL413 was reconstructed. Based on the model NmrFL413, the estimated ATP/NH₄⁺ yield (0.149–0.276 ATP/NH₄⁺) is tenfold lower than the calculated theoretical yield of the proposed ammonia oxidation pathways in marine AOA (1.5–1.75 ATP/NH₄⁺), indicating a low energy production efficiency of SCM1. Our model also suggested the minor contribution of marine AOA to carbon cycle comparing with their significant contribution to nitrogen cycle in the ocean.

Genomic analysis of methanogenic archaea reveals a shift towards energy conservation

BACKGROUND

The metabolism of archaeal methanogens drives methane release into the environment and is critical to understanding global carbon cycling. Methanogenesis operates at a very low reducing potential compared to other forms of respiration and is therefore critical to many anaerobic environments. Harnessing or altering methanogen metabolism has the potential to mitigate global warming and even be utilized for energy applications.

RESULTS

Here, we report draft genome sequences for the isolated methanogens Methanobacterium bryantii, Methanosarcina spelaei, Methanosphaera cuniculi, and Methanocorpusculum parvum. These anaerobic, methane-producing archaea represent a diverse set of isolates, capable of methylotrophic, acetoclastic, and hydrogenotrophic methanogenesis. Assembly and analysis of the genomes allowed for simple and rapid reconstruction of metabolism in the four methanogens. Comparison of the distribution of Clusters of Orthologous Groups (COG) proteins to a sample of genomes from the RefSeq database revealed a trend towards energy conservation in genome composition of all methanogens sequenced. Further analysis of the predicted membrane proteins and transporters distinguished differing energy conservation methods utilized during methanogenesis, such as chemiosmotic coupling in Msar. spelaei and electron bifurcation linked to chemiosmotic coupling in Mbac. bryantii and Msph. cuniculi.

CONCLUSIONS

Methanogens occupy a unique ecological niche, acting as the terminal electron acceptors in anaerobic environments, and their genomes display a significant shift towards energy conservation. The genome-enabled reconstructed metabolisms reported here have significance to diverse anaerobic communities and have led to proposed substrate utilization not previously reported in isolation, such as formate and methanol metabolism in Mbac. bryantii and CO₂ metabolism in Msph. cuniculi. The newly proposed substrates establish an important foundation with which to decipher how methanogens behave in native communities, as CO₂ and formate are common electron carriers in microbial communities.

BiKEGG: a COBRA toolbox extension for bridging the BiGG and KEGG databases

Development of an interface tool between the Biochemical, Genetic and Genomic (BiGG) and KEGG databases is necessary for simultaneous access to the features of both databases. For this purpose, we present the BiKEGG toolbox, an open source COBRA toolbox extension providing a set of functions to infer the reaction correspondences between the KEGG reaction identifiers and those in the BiGG knowledgebase using a combination of manual verification and computational methods. Inferred reaction correspondences using this approach are supported by evidence from the literature, which provides a higher number of reconciled reactions between these two databases compared to the MetaNetX and MetRxn databases. This set of equivalent reactions is then used to automatically superimpose the predicted fluxes using COBRA methods on classical KEGG pathway maps or to create a customized metabolic map based on the KEGG global metabolic pathway, and to find the corresponding reactions in BiGG based on the genome annotation of an organism in the KEGG database. Customized metabolic maps can be created for a set of pathways of interest, for the whole KEGG global map or exclusively for all pathways for which there exists at least one flux carrying reaction. This flexibility in visualization enables BiKEGG to indicate reaction directionality as well as to visualize the reaction fluxes for different static or dynamic conditions in an animated manner. BiKEGG allows the user to export (1) the output visualized metabolic maps to various standard image formats or save them as a video or animated GIF file, and (2) the equivalent reactions for an organism as an Excel spreadsheet.

Constraint-based metabolic modelling of marine microbes and communities

Constraint-based metabolic modelling (CBMM) consists in the use of computational methods and tools to perform genome-scale simulations and predict metabolic features at the whole cellular level. This approach is rapidly expanding in microbiology, as it combines reliable predictive abilities with conceptually and technically simple frameworks. Among the possible outcomes of CBMM, the capability to i) guide a focused planning of metabolic engineering experiments and ii) provide a system-level understanding of (single or community-level) microbial metabolic circuits also represent primary aims in present-day marine microbiology. In this work we briefly introduce the theoretical formulation behind CBMM and then review the most recent and effective case studies of CBMM of marine microbes and communities. Also, the emerging challenges and possibilities in the use of such methodologies in the context of marine microbiology/biotechnology are discussed. As the potential applications of CBMM have a very broad range, the topics presented in this review span over a large plethora of fields such as ecology, biotechnology and evolution.

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.

Exploring Hydrogenotrophic Methanogenesis: a Genome Scale Metabolic Reconstruction of Methanococcus maripaludis

Hydrogenotrophic methanogenesis occurs in multiple environments, ranging from the intestinal tracts of animals to anaerobic sediments and hot springs. Energy conservation in hydrogenotrophic methanogens was long a mystery; only within the last decade was it reported that net energy conservation for growth depends on electron bifurcation. In this work, we focus on Methanococcus maripaludis, a well-studied hydrogenotrophic marine methanogen. To better understand hydrogenotrophic methanogenesis and compare it with methylotrophic methanogenesis that utilizes oxidative phosphorylation rather than electron bifurcation, we have built iMR539, a genome scale metabolic reconstruction that accounts for 539 of the 1,722 protein-coding genes of M. maripaludis strain S2. Our reconstructed metabolic network uses recent literature to not only represent the central electron bifurcation reaction but also incorporate vital biosynthesis and assimilation pathways, including unique cofactor and coenzyme syntheses. We show that our model accurately predicts experimental growth and gene knockout data, with 93% accuracy and a Matthews correlation coefficient of 0.78. Furthermore, we use our metabolic network reconstruction to probe the implications of electron bifurcation by showing its essentiality, as well as investigating the infeasibility of aceticlastic methanogenesis in the network. Additionally, we demonstrate a method of applying thermodynamic constraints to a metabolic model to quickly estimate overall free-energy changes between what comes in and out of the cell. Finally, we describe a novel reconstruction-specific computational toolbox we created to improve usability. Together, our results provide a computational network for exploring hydrogenotrophic methanogenesis and confirm the importance of electron bifurcation in this process.

IMPORTANCE

Understanding and applying hydrogenotrophic methanogenesis is a promising avenue for developing new bioenergy technologies around methane gas. Although a significant portion of biological methane is generated through this environmentally ubiquitous pathway, existing methanogen models portray the more traditional energy conservation mechanisms that are found in other methanogens. We have constructed a genome scale metabolic network of Methanococcus maripaludis that explicitly accounts for all major reactions involved in hydrogenotrophic methanogenesis. Our reconstruction demonstrates the importance of electron bifurcation in central metabolism, providing both a window into hydrogenotrophic methanogenesis and a hypothesis-generating platform to fuel metabolic engineering efforts.

Genome-wide gene expression and RNA half-life measurements allow predictions of regulation and metabolic behavior in Methanosarcina acetivorans

BACKGROUND

While a few studies on the variations in mRNA expression and half-lives measured under different growth conditions have been used to predict patterns of regulation in bacterial organisms, the extent to which this information can also play a role in defining metabolic phenotypes has yet to be examined systematically. Here we present the first comprehensive study for a model methanogen.

RESULTS

We use expression and half-life data for the methanogen Methanosarcina acetivorans growing on fast- and slow-growth substrates to examine the regulation of its genes. Unlike Escherichia coli where only small shifts in half-lives were observed, we found that most mRNA have significantly longer half-lives for slow growth on acetate compared to fast growth on methanol or trimethylamine. Interestingly, half-life shifts are not uniform across functional classes of enzymes, suggesting the existence of a selective stabilization mechanism for mRNAs. Using the transcriptomics data we determined whether transcription or degradation rate controls the change in transcript abundance. Degradation was found to control abundance for about half of the metabolic genes underscoring its role in regulating metabolism. Genes involved in half of the metabolic reactions were found to be differentially expressed among the substrates suggesting the existence of drastically different metabolic phenotypes that extend beyond just the methanogenesis pathways. By integrating expression data with an updated metabolic model of the organism (iST807) significant differences in pathway flux and production of metabolites were predicted for the three growth substrates.

CONCLUSIONS

This study provides the first global picture of differential expression and half-lives for a class II methanogen, as well as provides the first evidence in a single organism that drastic genome-wide shifts in RNA half-lives can be modulated by growth substrate. We determined which genes in each metabolic pathway control the flux and classified them as regulated by transcription (e.g. transcription factor) or degradation (e.g. post-transcriptional modification). We found that more than half of genes in metabolism were controlled by degradation. Our results suggest that M. acetivorans employs extensive post-transcriptional regulation to optimize key metabolic steps, and more generally that degradation could play a much greater role in optimizing an organism's metabolism than previously thought.

Correlations between bacterial populations and process parameters in four full-scale anaerobic digesters treating sewage sludge

Process parameters and bacterial populations were investigated in four full-scale anaerobic digesters treating sewage sludge. Although the four digesters were operated under similar conditions, digesters A and B had higher pH (7.2-7.4) and lipid removal efficiencies (>50%) than C and D (pH 6.1-6.4; average lipid removal <16%). Bacterial richness, diversity, and evenness were higher in digesters C and D. Among the top-populated genera, ten (group I) were more abundant in digesters A and/or B; they were putative syntrophic fatty acid or protein/amino acid-utilizers. In contrast, fifteen others (group II) were less abundant in A and/or B and included potentially dormant/dead cells originated from activated sludge. Despite the overall richness trend, the presence of the 25 genera in groups I/II was greater in digesters A and B (24) than in C and D (17); this observation suggests that group I bacteria might be essential in AD of sewage sludge.

Conditions for duality between fluxes and concentrations in biochemical networks

Mathematical and computational modelling of biochemical networks is often done in terms of either the concentrations of molecular species or the fluxes of biochemical reactions. When is mathematical modelling from either perspective equivalent to the other? Mathematical duality translates concepts, theorems or mathematical structures into other concepts, theorems or structures, in a one-to-one manner. We present a novel stoichiometric condition that is necessary and sufficient for duality between unidirectional fluxes and concentrations. Our numerical experiments, with computational models derived from a range of genome-scale biochemical networks, suggest that this flux-concentration duality is a pervasive property of biochemical networks. We also provide a combinatorial characterisation that is sufficient to ensure flux-concentration duality.The condition prescribes that, for every two disjoint sets of molecular species, there is at least one reaction complex that involves species from only one of the two sets. When unidirectional fluxes and molecular species concentrations are dual vectors, this implies that the behaviour of the corresponding biochemical network can be described entirely in terms of either concentrations or unidirectional fluxes.

The nutritional status of Methanosarcina acetivorans regulates glycogen metabolism and gluconeogenesis and glycolysis fluxes

Gluconeogenesis is an essential pathway in methanogens because they are unable to use exogenous hexoses as carbon source for cell growth. With the aim of understanding the regulatory mechanisms of central carbon metabolism in Methanosarcina acetivorans, the present study investigated gene expression, the activities and metabolic regulation of key enzymes, metabolite contents and fluxes of gluconeogenesis, as well as glycolysis and glycogen synthesis/degradation pathways. Cells were grown with methanol as a carbon source. Key enzymes were kinetically characterized at physiological pH/temperature. Active consumption of methanol during exponential cell growth correlated with significant methanogenesis, gluconeogenic flux and steady glycogen synthesis. After methanol exhaustion, cells reached the stationary growth phase, which correlated with the rise in glycogen consumption and glycolytic flux, decreased methanogenesis, negligible acetate production and an absence of gluconeogenesis. Elevated activities of carbon monoxide dehydrogenase/acetyl-CoA synthetase complex and pyruvate: ferredoxin oxidoreductase suggested the generation of acetyl-CoA and pyruvate for glycogen synthesis. In the early stationary growth phase, the transcript contents and activities of pyruvate phosphate dikinase, fructose 1,6-bisphosphatase and glycogen synthase decreased, whereas those of glycogen phosphorylase, ADP-phosphofructokinase and pyruvate kinase increased. Therefore, glycogen and gluconeogenic metabolites were synthesized when an external carbon source was provided. Once such a carbon source became depleted, glycolysis and methanogenesis fed by glycogen degradation provided the ATP supply. Weak inhibition of key enzymes by metabolites suggested that the pathways evaluated were mainly transcriptionally regulated. Because glycogen metabolism and glycolysis/gluconeogenesis are not present in all methanogens, the overall data suggest that glycogen storage might represent an environmental advantage for methanosarcinales when carbon sources are scarce. Also, the understanding of the central carbohydrate metabolism in methanosarcinales may help to optimize methane production.

Assessing methanotrophy and carbon fixation for biofuel production by Methanosarcina acetivorans

Background

Methanosarcina acetivorans is a model archaeon with renewed interest due to its unique reversible methane production pathways. However, the mechanism and relevant pathways implicated in (co)utilizing novel carbon substrates in this organism are still not fully understood. This paper provides a comprehensive inventory of thermodynamically feasible routes for anaerobic methane oxidation, co-reactant utilization, and maximum carbon yields of major biofuel candidates by M. acetivorans.

Results

Here, an updated genome-scale metabolic model of M. acetivorans is introduced (iMAC868 containing 868 genes, 845 reactions, and 718 metabolites) by integrating information from two previously reconstructed metabolic models (i.e., iVS941 and iMB745), modifying 17 reactions, adding 24 new reactions, and revising 64 gene-protein-reaction associations based on newly available information. The new model establishes improved predictions of growth yields on native substrates and is capable of correctly predicting the knockout outcomes for 27 out of 28 gene deletion mutants. By tracing a bifurcated electron flow mechanism, the iMAC868 model predicts thermodynamically feasible (co)utilization pathway of methane and bicarbonate using various terminal electron acceptors through the reversal of the aceticlastic pathway.

Conclusions

This effort paves the way in informing the search for thermodynamically feasible ways of (co)utilizing novel carbon substrates in the domain Archaea.

Electronic supplementary material

The online version of this article (doi:10.1186/s12934-015-0404-4) contains supplementary material, which is available to authorized users.

Flux measurements and maintenance energy for carbon dioxide utilization by Methanococcus maripaludis

BACKGROUND

The rapidly growing mesophilic methanogen Methanococcus maripaludis S2 has a unique ability to consume both CO2 and N2, the main components of a flue gas, and produce methane with H2 as the electron donor. The existing literature lacks experimental measurements of CO2 and H2 uptake rates and CH4 production rates on M. maripaludis. Furthermore, it lacks estimates of maintenance energies for use with genome-scale models. In this paper, we performed batch culture experiments on M. maripaludis S2 using CO2 as the sole carbon substrate to quantify three key extracellular fluxes (CO2, H2, and CH4) along with specific growth rates. For precise computation of these fluxes from experimental measurements, we developed a systematic process simulation approach. Then, using an existing genome-scale model, we proposed an optimization procedure to estimate maintenance energy parameters: growth associated maintenance (GAM) and non-growth associated maintenance (NGAM).

RESULTS

The measured extracellular fluxes for M. maripaludis showed excellent agreement with in silico predictions from a validated genome-scale model (iMM518) for NGAM = 7.836 mmol/gDCW/h and GAM = 27.14 mmol/gDCW. M. maripaludis achieved a CO2 to CH4 conversion yield of 70-95 % and a growth yield of 3.549 ± 0.149 g DCW/mol CH4 during the exponential phase. The ATP gain of 0.35 molATP/molCH4 for M. maripaludis, computed using NGAM, is in the acceptable range of 0.3-0.7 mol ATP/molCH4 reported for methanogens. Interestingly, the uptake distribution of amino acids, quantified using iMM518, confirmed alanine to be the most preferred amino acids for growth and methanogenesis.

CONCLUSIONS

This is the first study to report experimental gas consumption and production rates for the growth of M. maripaludis on CO2 and H2 in minimal media. A systematic process simulation and optimization procedure was successfully developed to precisely quantify extracellular fluxes along with cell growth and maintenance energy parameters. Our growth yields, ATP gain, and energy parameters fall within acceptable ranges known in the literature for hydrogenotrophic methanogens.

Cancer cell metabolism as new targets for novel designed therapies

Metabolic processes are altered in cancer cells, which obtain advantages from this metabolic reprogramming in terms of energy production and synthesis of biomolecules that sustain their uncontrolled proliferation. Due to the conceptual progresses in the last decade, metabolic reprogramming was recently included as one of the new hallmarks of cancer. The advent of high-throughput technologies to amass an abundance of omic data, together with the development of new computational methods that allow the integration and analysis of omic data by using genome-scale reconstructions of human metabolism, have increased and accelerated the discovery and development of anticancer drugs and tumor-specific metabolic biomarkers. Here we review and discuss the latest advances in the context of metabolic reprogramming and the future in cancer research.

Rerouting Cellular Electron Flux To Increase the Rate of Biological Methane Production

Methanogens are anaerobic archaea that grow by producing methane, a gas that is both an efficient renewable fuel and a potent greenhouse gas. We observed that overexpression of the cytoplasmic heterodisulfide reductase enzyme HdrABC increased the rate of methane production from methanol by 30% without affecting the growth rate relative to the parent strain. Hdr enzymes are essential in all known methane-producing archaea. They function as the terminal oxidases in the methanogen electron transport system by reducing the coenzyme M (2-mercaptoethane sulfonate) and coenzyme B (7-mercaptoheptanoylthreonine sulfonate) heterodisulfide, CoM-S-S-CoB, to regenerate the thiol-coenzymes for reuse. In Methanosarcina acetivorans, HdrABC expression caused an increased rate of methanogenesis and a decrease in metabolic efficiency on methylotrophic substrates. When acetate was the sole carbon and energy source, neither deletion nor overexpression of HdrABC had an effect on growth or methane production rates. These results suggest that in cells grown on methylated substrates, the cell compensates for energy losses due to expression of HdrABC with an increased rate of substrate turnover and that HdrABC lacks the appropriate electron donor in acetate-grown cells.

Thermodynamics and H2 Transfer in a Methanogenic, Syntrophic Community

Microorganisms in nature do not exist in isolation but rather interact with other species in their environment. Some microbes interact via syntrophic associations, in which the metabolic by-products of one species serve as nutrients for another. These associations sustain a variety of natural communities, including those involved in methanogenesis. In anaerobic syntrophic communities, energy is transferred from one species to another, either through direct contact and exchange of electrons, or through small molecule diffusion. Thermodynamics plays an important role in governing these interactions, as the oxidation reactions carried out by the first community member are only possible because degradation products are consumed by the second community member. This work presents the development and analysis of genome-scale network reconstructions of the bacterium Syntrophobacter fumaroxidans and the methanogenic archaeon Methanospirillum hungatei. The models were used to verify proposed mechanisms of ATP production within each species. We then identified additional constraints and the cellular objective function required to match experimental observations. The thermodynamic S. fumaroxidans model could not explain why S. fumaroxidans does not produce H₂ in monoculture, indicating that current methods might not adequately estimate the thermodynamics, or that other cellular processes (e.g., regulation) play a role. We also developed a thermodynamic coculture model of the association between the organisms. The coculture model correctly predicted the exchange of both H₂ and formate between the two species and suggested conditions under which H₂ and formate produced by S. fumaroxidans would be fully consumed by M. hungatei.

Natural and engineered microbial communities can contain up to hundreds of interacting microbes. These interactions may be positive, negative, or neutral, as well as obligate or facultative. Syntrophy is an obligate, positive interaction, in which one species lives off the metabolic by-products of another. Syntrophic associations play an important role in sustaining a variety of natural communities, including those involved in the breakdown and conversion of short-chain fatty acids (e.g., propionate) to methane. In many syntrophic communities, electrons are transferred from one species to the other through small molecule diffusion. In this work, we expand the study of a two-member syntrophic, methanogenic community through the development and analysis of computational models for both species: the bacterium Syntrophobacter fumaroxidans and the methanogenic archaeon Methanospirillum hungatei. These models were used to analyze energy conservation mechanisms within each species, as well as small molecule exchange between the two organisms in coculture. The coculture model correctly predicted the exchange of both H₂ and formate between the two species and suggested conditions under which these molecules would be fully metabolized within the community.

Genome-scale modeling for metabolic engineering

We focus on the application of constraint-based methodologies and, more specifically, flux balance analysis in the field of metabolic engineering, and enumerate recent developments and successes of the field. We also review computational frameworks that have been developed with the express purpose of automatically selecting optimal gene deletions for achieving improved production of a chemical of interest. The application of flux balance analysis methods in rational metabolic engineering requires a metabolic network reconstruction and a corresponding in silico metabolic model for the microorganism in question. For this reason, we additionally present a brief overview of automated reconstruction techniques. Finally, we emphasize the importance of integrating metabolic networks with regulatory information-an area which we expect will become increasingly important for metabolic engineering-and present recent developments in the field of metabolic and regulatory integration.

Likelihood-based gene annotations for gap filling and quality assessment in genome-scale metabolic models

Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genes and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary to obtain a more accurate network. All described workflows are implemented as part of the DOE Systems Biology Knowledgebase (KBase) and are publicly available via API or command-line web interface.

Carbohydrate metabolism in Archaea: current insights into unusual enzymes and pathways and their regulation

The metabolism of Archaea, the third domain of life, resembles in its complexity those of Bacteria and lower Eukarya. However, this metabolic complexity in Archaea is accompanied by the absence of many "classical" pathways, particularly in central carbohydrate metabolism. Instead, Archaea are characterized by the presence of unique, modified variants of classical pathways such as the Embden-Meyerhof-Parnas (EMP) pathway and the Entner-Doudoroff (ED) pathway. The pentose phosphate pathway is only partly present (if at all), and pentose degradation also significantly differs from that known for bacterial model organisms. These modifications are accompanied by the invention of "new," unusual enzymes which cause fundamental consequences for the underlying regulatory principles, and classical allosteric regulation sites well established in Bacteria and Eukarya are lost. The aim of this review is to present the current understanding of central carbohydrate metabolic pathways and their regulation in Archaea. In order to give an overview of their complexity, pathway modifications are discussed with respect to unusual archaeal biocatalysts, their structural and mechanistic characteristics, and their regulatory properties in comparison to their classic counterparts from Bacteria and Eukarya. Furthermore, an overview focusing on hexose metabolic, i.e., glycolytic as well as gluconeogenic, pathways identified in archaeal model organisms is given. Their energy gain is discussed, and new insights into different levels of regulation that have been observed so far, including the transcript and protein levels (e.g., gene regulation, known transcription regulators, and posttranslational modification via reversible protein phosphorylation), are presented.

MediaDB: a database of microbial growth conditions in defined media

Isolating pure microbial cultures and cultivating them in the laboratory on defined media is used to more fully characterize the metabolism and physiology of organisms. However, identifying an appropriate growth medium for a novel isolate remains a challenging task. Even organisms with sequenced and annotated genomes can be difficult to grow, despite our ability to build genome-scale metabolic networks that connect genomic data with metabolic function. The scientific literature is scattered with information about defined growth media used successfully for cultivating a wide variety of organisms, but to date there exists no centralized repository to inform efforts to cultivate less characterized organisms by bridging the gap between genomic data and compound composition for growth media. Here we present MediaDB, a manually curated database of defined media that have been used for cultivating organisms with sequenced genomes, with an emphasis on organisms with metabolic network models. The database is accessible online, can be queried by keyword searches or downloaded in its entirety, and can generate exportable individual media formulation files. The data assembled in MediaDB facilitate comparative studies of organism growth media, serve as a starting point for formulating novel growth media, and contribute to formulating media for in silico investigation of metabolic networks. MediaDB is freely available for public use at https://mediadb.systemsbiology.net.

Towards a computational model of a methane producing archaeum

Progress towards a complete model of the methanogenic archaeum Methanosarcina acetivorans is reported. We characterized size distribution of the cells using differential interference contrast microscopy, finding them to be ellipsoidal with mean length and width of 2.9 μm and 2.3 μm, respectively, when grown on methanol and 30% smaller when grown on acetate. We used the single molecule pull down (SiMPull) technique to measure average copy number of the Mcr complex and ribosomes. A kinetic model for the methanogenesis pathways based on biochemical studies and recent metabolic reconstructions for several related methanogens is presented. In this model, 26 reactions in the methanogenesis pathways are coupled to a cell mass production reaction that updates enzyme concentrations. RNA expression data (RNA-seq) measured for cell cultures grown on acetate and methanol is used to estimate relative protein production per mole of ATP consumed. The model captures the experimentally observed methane production rates for cells growing on methanol and is most sensitive to the number of methyl-coenzyme-M reductase (Mcr) and methyl-tetrahydromethanopterin:coenzyme-M methyltransferase (Mtr) proteins. A draft transcriptional regulation network based on known interactions is proposed which we intend to integrate with the kinetic model to allow dynamic regulation.

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases

The MetaCyc database (MetaCyc.org) is a comprehensive and freely accessible database describing metabolic pathways and enzymes from all domains of life. MetaCyc pathways are experimentally determined, mostly small-molecule metabolic pathways and are curated from the primary scientific literature. MetaCyc contains >2100 pathways derived from >37 000 publications, and is the largest curated collection of metabolic pathways currently available. BioCyc (BioCyc.org) is a collection of >3000 organism-specific Pathway/Genome Databases (PGDBs), each containing the full genome and predicted metabolic network of one organism, including metabolites, enzymes, reactions, metabolic pathways, predicted operons, transport systems and pathway-hole fillers. Additions to BioCyc over the past 2 years include YeastCyc, a PGDB for Saccharomyces cerevisiae, and 891 new genomes from the Human Microbiome Project. The BioCyc Web site offers a variety of tools for querying and analysis of PGDBs, including Omics Viewers and tools for comparative analysis. New developments include atom mappings in reactions, a new representation of glycan degradation pathways, improved compound structure display, better coverage of enzyme kinetic data, enhancements of the Web Groups functionality, improvements to the Omics viewers, a new representation of the Enzyme Commission system and, for the desktop version of the software, the ability to save display states.

Increments and duplication events of enzymes and transcription factors influence metabolic and regulatory diversity in prokaryotes

In this work, the content of enzymes and DNA-binding transcription factors (TFs) in 794 non-redundant prokaryotic genomes was evaluated. The identification of enzymes was based on annotations deposited in the KEGG database as well as in databases of functional domains (COG and PFAM) and structural domains (Superfamily). For identifications of the TFs, hidden Markov profiles were constructed based on well-known transcriptional regulatory families. From these analyses, we obtained diverse and interesting results, such as the negative rate of incremental changes in the number of detected enzymes with respect to the genome size. On the contrary, for TFs the rate incremented as the complexity of genome increased. This inverse related performance shapes the diversity of metabolic and regulatory networks and impacts the availability of enzymes and TFs. Furthermore, the intersection of the derivatives between enzymes and TFs was identified at 9,659 genes, after this point, the regulatory complexity grows faster than metabolic complexity. In addition, TFs have a low number of duplications, in contrast to the apparent high number of duplications associated with enzymes. Despite the greater number of duplicated enzymes versus TFs, the increment by which duplicates appear is higher in TFs. A lower proportion of enzymes among archaeal genomes (22%) than in the bacterial ones (27%) was also found. This low proportion might be compensated by the interconnection between the metabolic pathways in Archaea. A similar proportion was also found for the archaeal TFs, for which the formation of regulatory complexes has been proposed. Finally, an enrichment of multifunctional enzymes in Bacteria, as a mechanism of ecological adaptation, was detected.

Genomically and biochemically accurate metabolic reconstruction of Methanosarcina barkeri Fusaro, iMG746

Methanosarcina barkeri is an Archaeon that produces methane anaerobically as the primary byproduct of its metabolism. M. barkeri can utilize several substrates for ATP and biomass production including methanol, acetate, methyl amines, and a combination of hydrogen and carbon dioxide. In 2006, a metabolic reconstruction of M. barkeri, iAF692, was generated based on a draft genome annotation. The iAF692 reconstruction enabled the first genome-Scale simulations for Archaea. Since the publication of the first metabolic reconstruction of M. barkeri, additional genomic, biochemical, and phenotypic data have clarified several metabolic pathways. We have used this newly available data to improve the M. barkeri metabolic reconstruction. Modeling simulations using the updated model, iMG746, have led to increased accuracy in predicting gene knockout phenotypes and simulations of batch growth behavior. We used the model to examine knockout lethality data and make predictions about metabolic regulation under different growth conditions. Thus, the updated metabolic reconstruction of M. barkeri metabolism is a useful tool for predicting cellular behavior, studying the methanogenic lifestyle, guiding experimental studies, and making predictions relevant to metabolic engineering applications.