Automated metabolic reconstruction for Methanococcus jannaschii

Genome-scale metabolic modelling of extremophiles and its applications in astrobiological environments

Metabolic modelling approaches have become the powerful tools in modern biology. These mathematical models are widely used to predict metabolic phenotypes of the organisms or communities of interest, and to identify metabolic targets in metabolic engineering. Apart from a broad range of industrial applications, the possibility of using metabolic modelling in the contexts of astrobiology are poorly explored. In this mini-review, we consolidated the concepts and related applications of applying metabolic modelling in studying organisms in space-related environments, specifically the extremophilic microbes. We recapitulated the current state of the art in metabolic modelling approaches and their advantages in the astrobiological context. Our review encompassed the applications of metabolic modelling in the theoretical investigation of the origin of life within prebiotic environments, as well as the compilation of existing uses of genome-scale metabolic models of extremophiles. Furthermore, we emphasize the current challenges associated with applying this technique in extreme environments, and conclude this review by discussing the potential implementation of metabolic models to explore theoretically optimal metabolic networks under various space conditions. Through this mini-review, our aim is to highlight the potential of metabolic modelling in advancing the study of astrobiology.

Optimizing Strategies for Bio-Based Ethanol Production Using Genome-Scale Metabolic Modeling of the Hyperthermophilic Archaeon, Pyrococcus furiosus

A genome-scale metabolic model, encompassing a total of 623 genes, 727 reactions, and 865 metabolites, was developed for Pyrococcus furiosus, an archaeon that grows optimally at 100°C by carbohydrate and peptide fermentation. The model uses subsystem-based genome annotation, along with extensive manual curation of 237 gene-reaction associations including those involved in central carbon metabolism, amino acid metabolism, and energy metabolism. The redox and energy balance of P. furiosus was investigated through random sampling of flux distributions in the model during growth on disaccharides. The core energy balance of the model was shown to depend on high acetate production and the coupling of a sodium-dependent ATP synthase and membrane-bound hydrogenase, which generates a sodium gradient in a ferredoxin-dependent manner, aligning with existing understanding of P. furiosus metabolism. The model was utilized to inform genetic engineering designs that favor the production of ethanol over acetate by implementing an NADPH and CO-dependent energy economy. The P. furiosus model is a powerful tool for understanding the relationship between generation of end products and redox/energy balance at a systems-level that will aid in the design of optimal engineering strategies for production of bio-based chemicals and fuels. IMPORTANCE The bio-based production of organic chemicals provides a sustainable alternative to fossil-based production in the face of today's climate challenges. In this work, we present a genome-scale metabolic reconstruction of Pyrococcus furiosus, a well-established platform organism that has been engineered to produce a variety of chemicals and fuels. The metabolic model was used to design optimal engineering strategies to produce ethanol. The redox and energy balance of P. furiosus was examined in detail, which provided useful insights that will guide future engineering designs.

Diversity of gut methanogens and functional enzymes associated with methane metabolism in smallholder dairy cattle

Methane is a greenhouse gas with disastrous consequences when released to intolerable levels. Ruminants produce methane during gut fermentation releasing it through belching and/or flatulence. To better understand the diversity of methanogens and functional enzymes associated with methane metabolism in dairy cows, 48 samples; 6 rumen fluid and 42 dung samples were collected from Kenyan and Tanzanian farms and were analyzed using shotgun metagenomic approach. Statistical analysis for species frequency, relative abundance, percentages, and P values were undertaken using MS Excel and IBM SPSS statistics 20. The results showed archaea from 5 phyla, 9 classes, 16 orders, 25 families, 59 genera, and 87 species. Gut sites significantly contributed to the presence and distribution of various methanogens (P < 0.01). The class Methanomicrobia was abundant in the rumen samples (~ 39%) and dung (~ 44%). The most abundant (~ 17%) methanogen species identified was Methanocorpusculum labreanum. However, some taxonomic class data were unclassified (~ 6% in the rumen and ~ 4% in the dung). Five functional enzymes: Glycine/Serine hydroxymethyltransferase, Formylmethanofuran-tetrahydromethanopterin N-formyltransferase, Formate dehydrogenase, anaerobic carbon monoxide dehydrogenase, and catalase-peroxidase associated with methane metabolism were identified. KEGG functional metabolic analysis for the enzymes identified during this study was significant (P < 0.05) for five metabolism processes. The methanogen species abundances from this study in numbers/kind can be utilized exclusively or jointly as indirect selection criteria for methane mitigation. When targeting functional genes of the microbes/animal for better performance, the concern not to affect the host animal's functionality should be undertaken. Future studies should consider taxonomically categorizing unclassified species.

Mini-Intein Structures from Extremophiles Suggest a Strategy for Finding Novel Robust Inteins

Inteins are prevalent among extremophiles. Mini-inteins with robust splicing properties are of particular interest for biotechnological applications due to their small size. However, biochemical and structural characterization has still been limited to a small number of inteins, and only a few serve as widely used tools in protein engineering. We determined the crystal structure of a naturally occurring Pol-II mini-intein from Pyrococcus horikoshii and compared all three mini-inteins found in the genome of P. horikoshii. Despite their similar sizes, the comparison revealed distinct differences in the insertions and deletions, implying specific evolutionary pathways from distinct ancestral origins. Our studies suggest that sporadically distributed mini-inteins might be more promising for further protein engineering applications than highly conserved mini-inteins. Structural investigations of additional inteins could guide the shortest path to finding novel robust mini-inteins suitable for various protein engineering purposes.

Multiobjective optimization to reconstruct biological networks

Automated methods for reconstructing biological networks are becoming increasingly important in computational systems biology. Public databases containing information on biological processes for hundreds of organisms are assisting in the inference of such networks. This paper proposes a multiobjective genetic algorithm method to reconstruct networks related to metabolism and protein interaction. Such a method utilizes structural properties of scale-free networks and known biological information about individual genes and proteins to reconstruct metabolic networks represented as enzyme graph and protein interaction networks. We test our method on four commonly-used protein networks in yeast. Two are networks related to the metabolism of the yeast: KEGG and BioCyc. The other two datasets are networks from protein-protein interaction: Krogan and BioGrid. Experimental results show that the proposed method is capable of reconstructing biological networks by combining different omics data and structural characteristics of scale-free networks. However, the proposed method to reconstruct the network is time-consuming because several evaluations must be performed. We parallelized this method on GPU to overcome this limitation by parallelizing the objective functions of the presented method. The parallel method shows a significant reduction in the execution time over the GPU card which yields a 492-fold speedup.

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.

Genome-Scale Modeling of Thermophilic Microorganisms

Thermophilic microorganisms are of increasing interest for many industries as their enzymes and metabolisms are highly efficient at elevated temperatures. However, their metabolic processes are often largely different from their mesophilic counterparts. These differences can lead to metabolic engineering strategies that are doomed to fail. Genome-scale metabolic modeling is an effective and highly utilized way to investigate cellular phenotypes and to test metabolic engineering strategies. In this review we chronicle a number of thermophilic organisms that have recently been studied with genome-scale models. The microorganisms spread across archaea and bacteria domains, and their study gives insights that can be applied in a broader context than just the species they describe. We end with a perspective on the future development and applications of genome-scale models of thermophilic organisms.

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.

Exploring the structure and function of Thermotoga maritima CorA reveals the mechanism of gating and ion selectivity in Co2+/Mg2+ transport

The CorA family of divalent cation transporters utilizes Mg²⁺ and Co²⁺ as primary substrates. The molecular mechanism of its function, including ion selectivity and gating, has not been fully characterized. Recently we reported a new structure of a CorA homologue from Methanocaldococcus jannaschii, which provided novel structural details that offered the conception of a unique gating mechanism involving conversion of an open hydrophilic gate into a closed hydrophobic one. In the present study we report functional evidence for this novel gating mechanism in the Thermotoga maritima CorA together with an improved crystal structure of this CorA to 2.7 Å (1 Å=0.1 nm) resolution. The latter reveals the organization of the selectivity filter to be similar to that of M. jannaschii CorA and also the previously unknown organization of the second signature motif of the CorA family. The proposed gating is achieved by a helical rotation upon the binding of a metal ion substrate to the regulatory binding sites. Additionally, our data suggest that the preference of this CorA for Co²⁺ over Mg²⁺ is controlled by the presence of threonine side chains in the channel. Finally, the roles of the intracellular metal-binding sites have been assigned to increased thermostability and regulation of the gating. These mechanisms most likely apply to the entire CorA family as they are regulated by the highly conserved amino acids.

Changes in the quaternary structure and function of MjHSP16.5 attributable to deletion of the IXI motif and introduction of the substitution, R107G, in the α-crystallin domain

The archael small heat-shock protein (sHSP), MjHSP16.5, forms a 24-subunit oligomer with octahedral symmetry. Here, we demonstrate that the IXI motif present in the C-terminal domain is necessary for the oligomerization of MjHSP16.5. Removal increased the in vitro chaperone activity with citrate synthase as the client protein. Less predictable were the effects of the R107G substitution in MjHSP16.5 because of the differences in the oligomerization of metazoan and non-metazoan sHSPs. We present the crystal structure for MjHSP16.5 R107G and compare this with an improved (2.5 Å) crystal structure for wild-type (WT) MjHSP16.5. Although no significant structural differences were found in the crystal, using cryo-electron microscopy, we identified two 24mer species with octahedral symmetry for the WT MjHSP16.5 both at room temperature and at 60°C, all showing two major species with the same diameter of 12.4 nm. Similarly, at room temperature, there are also two kinds of 12.4 nm oligomers for R107G MjHSP16.5, but in the 60°C sample, a larger 24mer species with a diameter of 13.6 nm was observed with significant changes in the fourfold symmetry axis and dimer–dimer interface. This highly conserved arginine, therefore, contributes to the quaternary organization of non-metazoan sHSP oligomers. Potentially, the R107G substitution has functional consequences as R107G MjHSP16.5 was far superior to the WT protein in protecting β_L-crystallin against heat-induced aggregation.

Structural insights into the mechanisms of Mg2+ uptake, transport, and gating by CorA

Despite the importance of Mg(2+) for numerous cellular activities, the mechanisms underlying its import and homeostasis are poorly understood. The CorA family is ubiquitous and is primarily responsible for Mg(2+) transport. However, the key questions-such as, the ion selectivity, the transport pathway, and the gating mechanism-have remained unanswered for this protein family. We present a 3.2 Å resolution structure of the archaeal CorA from Methanocaldococcus jannaschii, which is a unique complete structure of a CorA protein and reveals the organization of the selectivity filter, which is composed of the signature motif of this family. The structure reveals that polar residues facing the channel coordinate a partially hydrated Mg(2+) during the transport. Based on these findings, we propose a unique gating mechanism involving a helical turn upon the binding of Mg(2+) to the regulatory intracellular binding sites, and thus converting a polar ion passage into a narrow hydrophobic pore. Because the amino acids involved in the uptake, transport, and gating are all conserved within the entire CorA family, we believe this mechanism is general for the whole family including the eukaryotic homologs.

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

Methanosarcina acetivorans strain C2A is a marine methanogenic archaeon notable for its substrate utilization, genetic tractability, and novel energy conservation mechanisms. To help probe the phenotypic implications of this organism's unique metabolism, we have constructed and manually curated a genome-scale metabolic model of M. acetivorans, iMB745, which accounts for 745 of the 4,540 predicted protein-coding genes (16%) in the M. acetivorans genome. The reconstruction effort has identified key knowledge gaps and differences in peripheral and central metabolism between methanogenic species. Using flux balance analysis, the model quantitatively predicts wild-type phenotypes and is 96% accurate in knockout lethality predictions compared to currently available experimental data. The model was used to probe the mechanisms and energetics of by-product formation and growth on carbon monoxide, as well as the nature of the reaction catalyzed by the soluble heterodisulfide reductase HdrABC in M. acetivorans. The genome-scale model provides quantitative and qualitative hypotheses that can be used to help iteratively guide additional experiments to further the state of knowledge about methanogenesis.

The complete genome sequence of Thermoproteus tenax: a physiologically versatile member of the Crenarchaeota

Here, we report on the complete genome sequence of the hyperthermophilic Crenarchaeum Thermoproteus tenax (strain Kra1, DSM 2078^T) a type strain of the crenarchaeotal order Thermoproteales. Its circular 1.84-megabase genome harbors no extrachromosomal elements and 2,051 open reading frames are identified, covering 90.6% of the complete sequence, which represents a high coding density. Derived from the gene content, T. tenax is a representative member of the Crenarchaeota. The organism is strictly anaerobic and sulfur-dependent with optimal growth at 86°C and pH 5.6. One particular feature is the great metabolic versatility, which is not accompanied by a distinct increase of genome size or information density as compared to other Crenarchaeota. T. tenax is able to grow chemolithoautotrophically (CO₂/H₂) as well as chemoorganoheterotrophically in presence of various organic substrates. All pathways for synthesizing the 20 proteinogenic amino acids are present. In addition, two presumably complete gene sets for NADH:quinone oxidoreductase (complex I) were identified in the genome and there is evidence that either NADH or reduced ferredoxin might serve as electron donor. Beside the typical archaeal A₀A₁-ATP synthase, a membrane-bound pyrophosphatase is found, which might contribute to energy conservation. Surprisingly, all genes required for dissimilatory sulfate reduction are present, which is confirmed by growth experiments. Mentionable is furthermore, the presence of two proteins (ParA family ATPase, actin-like protein) that might be involved in cell division in Thermoproteales, where the ESCRT system is absent, and of genes involved in genetic competence (DprA, ComF) that is so far unique within Archaea.

High-throughput generation, optimization and analysis of genome-scale metabolic models

Genome-scale metabolic models have proven to be valuable for predicting organism phenotypes from genotypes. Yet efforts to develop new models are failing to keep pace with genome sequencing. To address this problem, we introduce the Model SEED, a web-based resource for high-throughput generation, optimization and analysis of genome-scale metabolic models. The Model SEED integrates existing methods and introduces techniques to automate nearly every step of this process, taking approximately 48 h to reconstruct a metabolic model from an assembled genome sequence. We apply this resource to generate 130 genome-scale metabolic models representing a taxonomically diverse set of bacteria. Twenty-two of the models were validated against available gene essentiality and Biolog data, with the average model accuracy determined to be 66% before optimization and 87% after optimization.

Industrial systems biology

The chemical industry is currently undergoing a dramatic change driven by demand for developing more sustainable processes for the production of fuels, chemicals, and materials. In biotechnological processes different microorganisms can be exploited, and the large diversity of metabolic reactions represents a rich repository for the design of chemical conversion processes that lead to efficient production of desirable products. However, often microorganisms that produce a desirable product, either naturally or because they have been engineered through insertion of heterologous pathways, have low yields and productivities, and in order to establish an economically viable process it is necessary to improve the performance of the microorganism. Here metabolic engineering is the enabling technology. Through metabolic engineering the metabolic landscape of the microorganism is engineered such that there is an efficient conversion of the raw material, typically glucose, to the product of interest. This process may involve both insertion of new enzymes activities, deletion of existing enzyme activities, but often also deregulation of existing regulatory structures operating in the cell. In order to rapidly identify the optimal metabolic engineering strategy the industry is to an increasing extent looking into the use of tools from systems biology. This involves both x-ome technologies such as transcriptome, proteome, metabolome, and fluxome analysis, and advanced mathematical modeling tools such as genome-scale metabolic modeling. Here we look into the history of these different techniques and review how they find application in industrial biotechnology, which will lead to what we here define as industrial systems biology.

Uncovering metabolic pathways relevant to phenotypic traits of microbial genomes

Identifying the biochemical basis of microbial phenotypes is a main objective of comparative genomics. Here we present a novel method using multivariate machine learning techniques for comparing automatically derived metabolic reconstructions of sequenced genomes on a large scale. Applying our method to 266 genomes directly led to testable hypotheses such as the link between the potential of microorganisms to cause periodontal disease and their ability to degrade histidine, a link also supported by clinical studies.

PFP: Automated prediction of gene ontology functional annotations with confidence scores using protein sequence data

Protein function prediction is a central problem in bioinformatics, increasing in importance recently due to the rapid accumulation of biological data awaiting interpretation. Sequence data represents the bulk of this new stock and is the obvious target for consideration as input, as newly sequenced organisms often lack any other type of biological characterization. We have previously introduced PFP (Protein Function Prediction) as our sequence-based predictor of Gene Ontology (GO) functional terms. PFP interprets the results of a PSI-BLAST search by extracting and scoring individual functional attributes, searching a wide range of E-value sequence matches, and utilizing conventional data mining techniques to fill in missing information. We have shown it to be effective in predicting both specific and low-resolution functional attributes when sufficient data is unavailable. Here we describe (1) significant improvements to the PFP infrastructure, including the addition of prediction significance and confidence scores, (2) a thorough benchmark of performance and comparisons to other related prediction methods, and (3) applications of PFP predictions to genome-scale data. We applied PFP predictions to uncharacterized protein sequences from 15 organisms. Among these sequences, 60-90% could be annotated with a GO molecular function term at high confidence (>or=80%). We also applied our predictions to the protein-protein interaction network of the Malaria plasmodium (Plasmodium falciparum). High confidence GO biological process predictions (>or=90%) from PFP increased the number of fully enriched interactions in this dataset from 23% of interactions to 94%. Our benchmark comparison shows significant performance improvement of PFP relative to GOtcha, InterProScan, and PSI-BLAST predictions. This is consistent with the performance of PFP as the overall best predictor in both the AFP-SIG '05 and CASP7 function (FN) assessments. PFP is available as a web service at http://dragon.bio.purdue.edu/pfp/.

The hidden universal distribution of amino acid biosynthetic networks: a genomic perspective on their origins and evolution

A core of widely distributed network branches biosynthesizing at least 16 out of the 20 standard amino acids is predicted using comparative genomics.

Background

Twenty amino acids comprise the universal building blocks of proteins. However, their biosynthetic routes do not appear to be universal from an Escherichia coli-centric perspective. Nevertheless, it is necessary to understand their origin and evolution in a global context, that is, to include more 'model' species and alternative routes in order to do so. We use a comparative genomics approach to assess the origins and evolution of alternative amino acid biosynthetic network branches.

Results

By tracking the taxonomic distribution of amino acid biosynthetic enzymes, we predicted a core of widely distributed network branches biosynthesizing at least 16 out of the 20 standard amino acids, suggesting that this core occurred in ancient cells, before the separation of the three cellular domains of life. Additionally, we detail the distribution of two types of alternative branches to this core: analogs, enzymes that catalyze the same reaction (using the same metabolites) and belong to different superfamilies; and 'alternologs', herein defined as branches that, proceeding via different metabolites, converge to the same end product. We suggest that the origin of alternative branches is closely related to different environmental metabolite sources and life-styles among species.

Conclusion

The multi-organismal seed strategy employed in this work improves the precision of dating and determining evolutionary relationships among amino acid biosynthetic branches. This strategy could be extended to diverse metabolic routes and even other biological processes. Additionally, we introduce the concept of 'alternolog', which not only plays an important role in the relationships between structure and function in biological networks, but also, as shown here, has strong implications for their evolution, almost equal to paralogy and analogy.

Design and application of genome-scale reconstructed metabolic models

In this chapter, the process for the reconstruction of genome-scale metabolic networks is described, and some of the main applications of such models are illustrated. The reconstruction process can be viewed as an iterative process where information obtained from several sources is combined to construct a preliminary set of reactions and constraints. This involves steps such as genome annotation; identification of the reactions from the annotated genome sequence and available literature; determination of the reaction stoichiometry; definition of compartmentation and assignment of localization; determination of the biomass composition; measurement, calculation, or fitting of energy requirements; and definition of additional constraints. The reaction and constraint sets, after debugging, may be integrated into a stoichiometric model that can be used for simulation using tools such as Flux Balance Analysis (Section 3.8). From the flux distributions obtained, physiologic parameters such as growth yields or minimal medium components can be calculated, and their distance from similar experimental data provides a basis from where the model may need to be improved.

Towards multidimensional genome annotation

Our information about the gene content of organisms continues to grow as more genomes are sequenced and gene products are characterized. Sequence-based annotation efforts have led to a list of cellular components, which can be thought of as a one-dimensional annotation. With growing information about component interactions, facilitated by the advancement of various high-throughput technologies, systemic, or two-dimensional, annotations can be generated. Knowledge about the physical arrangement of chromosomes will lead to a three-dimensional spatial annotation of the genome and a fourth dimension of annotation will arise from the study of changes in genome sequences that occur during adaptive evolution. Here we discuss all four levels of genome annotation, with specific emphasis on two-dimensional annotation methods.

Modeling methanogenesis with a genome-scale metabolic reconstruction of Methanosarcina barkeri

We present a genome-scale metabolic model for the archaeal methanogen Methanosarcina barkeri. We characterize the metabolic network and compare it to reconstructions from the prokaryotic, eukaryotic and archaeal domains. Using the model in conjunction with constraint-based methods, we simulate the metabolic fluxes and resulting phenotypes induced by different environmental and genetic conditions. This represents the first large-scale simulation of either a methanogen or an archaeal species. Model predictions are validated by comparison to experimental growth measurements and phenotypes of M. barkeri on different substrates. The predicted growth phenotypes for wild type and mutants of the methanogenic pathway have a high level of agreement with experimental findings. We further examine the efficiency of the energy-conserving reactions in the methanogenic pathway, specifically the Ech hydrogenase reaction, and determine a stoichiometry for the nitrogenase reaction. This work demonstrates that a reconstructed metabolic network can serve as an analysis platform to predict cellular phenotypes, characterize methanogenic growth, improve the genome annotation and further uncover the metabolic characteristics of methanogenesis.

From genomes to in silico cells via metabolic networks

Genome-scale metabolic models are the focal point of systems biology as they allow the collection of various data types in a form suitable for mathematical analysis. High-quality metabolic networks and metabolic networks with incorporated regulation have been successfully used for the analysis of phenotypes from phenotypic arrays and in gene-deletion studies. They have also been used for gene expression analysis guided by metabolic network structure, leading to the identification of commonly regulated genes. Thus, genome-scale metabolic modeling currently stands out as one of the most promising approaches to obtain an in silico prediction of cellular function based on the interaction of all of the cellular components.