A comprehensive genome‐scale reconstruction of Escherichia coli metabolism—2011

The initial genome‐scale reconstruction of the metabolic network of Escherichia coli K‐12 MG1655 was assembled in 2000. It has been updated and periodically released since then based on new and curated genomic and biochemical knowledge. An update has now been built, named iJO1366, which accounts for 1366 genes, 2251 metabolic reactions, and 1136 unique metabolites. iJO1366 was (1) updated in part using a new experimental screen of 1075 gene knockout strains, illuminating cases where alternative pathways and isozymes are yet to be discovered, (2) compared with its predecessor and to experimental data sets to confirm that it continues to make accurate phenotypic predictions of growth on different substrates and for gene knockout strains, and (3) mapped to the genomes of all available sequenced E. coli strains, including pathogens, leading to the identification of hundreds of unannotated genes in these organisms. Like its predecessors, the iJO1366 reconstruction is expected to be widely deployed for studying the systems biology of E. coli and for metabolic engineering applications.     

High-quality genome-scale metabolic modelling of Pseudomonas putida highlights its broad metabolic capabilities

Genome-scale reconstructions of metabolism are computational species-specific knowledge bases able to compute systemic metabolic properties. We present a comprehensive and validated reconstruction of the biotechnologically relevant bacterium Pseudomonas putida KT2440 that greatly expands computable predictions of its metabolic states. The reconstruction represents a significant reactome expansion over available reconstructed bacterial metabolic networks. Specifically, iJN1462 (i) incorporates several hundred additional genes and associated reactions resulting in new predictive capabilities, including new nutrients supporting growth; (ii) was validated by in vivo growth screens that included previously untested carbon (48) and nitrogen (41) sources; (iii) yielded gene essentiality predictions showing large accuracy when compared with a knock-out library and Bar-seq data; and (iv) allowed mapping of its network to 82 P. putida sequenced strains revealing functional core that reflect the large metabolic versatility of this species, including aromatic compounds derived from lignin. Thus, this study provides a thoroughly updated metabolic reconstruction and new computable phenotypes for P. putida, which can be leveraged as a first step toward understanding the pan metabolic capabilities of Pseudomonas.     

Systems metabolic engineering: Genome-scale models and beyond

The advent of high throughput genome-scale bioinformatics has led to an exponential increase in available cellular system data. Systems metabolic engineering attempts to use data-driven approaches – based on the data collected with high throughput technologies – to identify gene targets and optimize phenotypical properties on a systems level. Current systems metabolic engineering tools are limited for predicting and defining complex phenotypes such as chemical tolerances and other global, multigenic traits. The most pragmatic systems-based tool for metabolic engineering to arise is the in silico genome-scale metabolic reconstruction. This tool has seen wide adoption for modeling cell growth and predicting beneficial gene knockouts, and we examine here how this approach can be expanded for novel organisms. This review will highlight advances of the systems metabolic engineering approach with a focus on de novo development and use of genome-scale metabolic reconstructions for metabolic engineering applications. We will then discuss the challenges and prospects for this emerging field to enable model-based metabolic engineering. Specifically, we argue that current state-of-the-art systems metabolic engineering techniques represent a viable first step for improving product yield that still must be followed by combinatorial techniques or random strain mutagenesis to achieve optimal cellular systems.     

Methanogenesis from furfural by defined mixed cultures

Methanogenesis from furfural by defined mixed cultures was studied. Under sulfate-reducing conditions, a Desulfovibrio strain was used as the furfural-degrading species producing acetic acid. This sulfate-reducing bacterium (SRB) Desulfovibrio strain B is an incomplete oxidizer, unable to carry out the terminal oxidation of organic substrates, leaving acetic acid as the end product. Introduction of acetate-utilizing methanogenic archaeon Methanosarcina barkeri 227 converted acetic acid to methane. This well-defined mixed consortium used furfural as its sole source of carbon and converted it to methane and CO₂. In the mixed culture, when a methanogen inhibitor was used in the culture medium, furfural was converted to acetic acid by the Desulfovibrio strain B, but acetic acid did not undergo further metabolism. On the other hand, when the growth of Desulfovibrio in the consortium was suppressed with a specific SRB inhibitor, namely molybdate, furfural was not degraded. Thus, the metabolic activities of both Desulfovibrio strain B and M. barkeri 227 were essential for the complete degradation of furfural. Received: 15 August 2001 / Accepted: 20 September 2001     

A Method to Constrain Genome-Scale Models with 13C Labeling Data

Current limitations in quantitatively predicting biological behavior hinder our efforts to engineer biological systems to produce biofuels and other desired chemicals. Here, we present a new method for calculating metabolic fluxes, key targets in metabolic engineering, that incorporates data from ¹³C labeling experiments and genome-scale models. The data from ¹³C labeling experiments provide strong flux constraints that eliminate the need to assume an evolutionary optimization principle such as the growth rate optimization assumption used in Flux Balance Analysis (FBA). This effective constraining is achieved by making the simple but biologically relevant assumption that flux flows from core to peripheral metabolism and does not flow back. The new method is significantly more robust than FBA with respect to errors in genome-scale model reconstruction. Furthermore, it can provide a comprehensive picture of metabolite balancing and predictions for unmeasured extracellular fluxes as constrained by ¹³C labeling data. A comparison shows that the results of this new method are similar to those found through ¹³C Metabolic Flux Analysis (¹³C MFA) for central carbon metabolism but, additionally, it provides flux estimates for peripheral metabolism. The extra validation gained by matching 48 relative labeling measurements is used to identify where and why several existing COnstraint Based Reconstruction and Analysis (COBRA) flux prediction algorithms fail. We demonstrate how to use this knowledge to refine these methods and improve their predictive capabilities. This method provides a reliable base upon which to improve the design of biological systems.     

Stoichiometric model and flux balance analysis for a mixed culture of Leptospirillum ferriphilum and Ferroplasma acidiphilum

The oxidation process of sulfide minerals in natural environments is achieved by microbial communities from the Archaea and Bacteria domains. A metabolic reconstruction of two dominant species, Leptospirillum ferriphilum and Ferroplasma acidiphilum, which are always found together as a mixed culture in this natural environments, was made. The metabolic model, composed of 152 internal reactions and 29 transport reactions, describes the main interactions between these species, assuming that both use ferrous iron as energy source, and F. acidiphilum takes advantage of the organic compounds secreted by L. ferriphilum for chemomixotrophic growth. A first metabolic model for a mixed culture used in bacterial leaching is proposed in this article, which pretends to represent the characteristics of the mixed culture in a simplified manner. It was evaluated with experimental data through flux balance analysis (FBA) using as objective function the maximization of biomass. The growth yields on ferrous iron obtained for each microorganism are consistent with experimental data, and the flux distribution obtained allows understanding of the metabolic capabilities of both microorganisms growing together in a bioleaching process. The model was used to simulate the growth of F. acidiphilum on different substrates, to determine in silico which compounds maximize cell growth, and which are essential. Knockout simulations were carried out for L. ferriphilum and F. acidiphilum metabolic models, predicting key enzymes of central metabolism. The results of this analysis are consistent with experimental data from literature, showing a robust behavior of the metabolic model. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 31:307–315, 2015     

Flux balance analysis of primary metabolism in the diatom Phaeodactylum tricornutum

Diatoms (Bacillarophyceae) are photosynthetic unicellular microalgae that have risen to ecological prominence in oceans over the past 30 million years. They are of interest as potential feedstocks for sustainable biofuels. Maximizing production of these feedstocks will require genetic modifications and an understanding of algal metabolism. These processes may benefit from genome‐scale models, which predict intracellular fluxes and theoretical yields, as well as the viability of knockout and knock‐in transformants. Here we present a genome‐scale metabolic model of a fully sequenced and transformable diatom: Phaeodactylum tricornutum. The metabolic network was constructed using the P. tricornutum genome, biochemical literature, and online bioinformatic databases. Intracellular fluxes in P. tricornutum were calculated for autotrophic, mixotrophic and heterotrophic growth conditions, as well as knockout conditions that explore the in silico role of glycolytic enzymes in the mitochondrion. The flux distribution for lower glycolysis in the mitochondrion depended on which transporters for TCA cycle metabolites were included in the model. The growth rate predictions were validated against experimental data obtained using chemostats. Two published studies on this organism were used to validate model predictions for cyclic electron flow under autotrophic conditions, and fluxes through the phosphoketolase, glycine and serine synthesis pathways under mixotrophic conditions. Several gaps in annotation were also identified. The model also explored unusual features of diatom metabolism, such as the presence of lower glycolysis pathways in the mitochondrion, as well as differences between P. tricornutum and other photosynthetic organisms.     

A regulatory network modeled from wild-type gene expression data guides functional predictions in Caenorhabditis elegans development

Background

The iJO1366 reconstruction of the metabolic network of Escherichia coli is one of the most complete and accurate metabolic reconstructions available for any organism. Still, because our knowledge of even well-studied model organisms such as this one is incomplete, this network reconstruction contains gaps and possible errors. There are a total of 208 blocked metabolites in iJO1366, representing gaps in the network.

Results

A new model improvement workflow was developed to compare model based phenotypic predictions to experimental data to fill gaps and correct errors. A Keio Collection based dataset of E. coli gene essentiality was obtained from literature data and compared to model predictions. The SMILEY algorithm was then used to predict the most likely missing reactions in the reconstructed network, adding reactions from a KEGG based universal set of metabolic reactions. The feasibility of these putative reactions was determined by comparing updated versions of the model to the experimental dataset, and genes were predicted for the most feasible reactions.

Conclusions

Numerous improvements to the iJO1366 metabolic reconstruction were suggested by these analyses. Experiments were performed to verify several computational predictions, including a new mechanism for growth on myo-inositol. The other predictions made in this study should be experimentally verifiable by similar means. Validating all of the predictions made here represents a substantial but important undertaking.     

Microbial Community Metabolic Modeling: A Community Data‐Driven Network Reconstruction

Metabolic network modeling of microbial communities provides an in‐depth understanding of community‐wide metabolic and regulatory processes. Compared to single organism analyses, community metabolic network modeling is more complex because it needs to account for interspecies interactions. To date, most approaches focus on reconstruction of high‐quality individual networks so that, when combined, they can predict community behaviors as a result of interspecies interactions. However, this conventional method becomes ineffective for communities whose members are not well characterized and cannot be experimentally interrogated in isolation. Here, we tested a new approach that uses community‐level data as a critical input for the network reconstruction process. This method focuses on directly predicting interspecies metabolic interactions in a community, when axenic information is insufficient. We validated our method through the case study of a bacterial photoautotroph–heterotroph consortium that was used to provide data needed for a community‐level metabolic network reconstruction. Resulting simulations provided experimentally validated predictions of how a photoautotrophic cyanobacterium supports the growth of an obligate heterotrophic species by providing organic carbon and nitrogen sources. J. Cell. Physiol. 231: 2339–2345, 2016. © 2016 The Authors. Journal of Cellular Physiology Published by Wiley Periodicals, Inc.     

Quantification of corrinoids in methanogenic bacteria

Corrinoids in several diverse species of methanogens were quantified by a bioassay utilizingEscherichia coli 113–3, a corrinoid auxotroph. All five species examined contained >0.65 nmol corrinoid/mg dry cells when grown on H₂/CO₂ as carbon and energy source. The highest corrinoid levels (4.1 nmol/mg cells) were found inMethanosarcina barkeri grown on methanol. The amount of corrinoids found in this species was dependent on growth conditions, but, regardless of energy source, metabolized levels inMethanosarcina barkeri were higher than those found in theMethanobacterium species examined (M. arbophilicum, M. formicium, M. ruminantium, andM. thermoautotrophicum).     

Effect of trace elements and vitamins on the growth of Methanosarcina barkeri

Growth of Methanosarcina barkeri on methanol as energy source was found to be dependent on cobalt and molybdenum. In the presence of 10^?6 M Co and 5 × 10^?7M Mo optimal growth occurred. Furthermore it could be demonstrated that nickel and selenium each in a concentration of 10^?7 M stimulated the growth of this methanogenic bacterium while the following elements tested in the range of 10^?7 M to 10^?3 had no influence: B, Cr, Cu, Mn, Pb. The requirement of Co and Ni for optimal growth are in accordance with the results that the cells contain the Co containing corrinoid Factor III (0.1 – 0.2 mg 5-hydroxylbenzimidazolylcyanocobamide per g wet cells) and Factor F₄₃₀, a nickel component. Studies on the vitamin dependency of M. barkeri showed that this strain needs only the vitamin riboflavin for the growth in a defined medium. Under these conditions a cell density of 2.6 g dry cells/l could be obtained in a fed batch culture.     

Optimization of metabolic pathways for bioconversion of lignocellulose to ethanol through genetic engineering

The optimization of metabolic pathways is of fundamental importance for strategies aimed at improving the economics and yield of the lignocellulose-to-ethanol processes. Although Escherichia coli is capable of metabolizing a wide variety of substrates including hexoses and pentoses, its hexose metabolism is inferior to that of Zymomonas mobilis, an obligate, ethanologenic bacterium. We therefore inserted and expressed Z. mobilis genes encoding essential enzymes involved in the fermentation pathway, alcohol dehydrogenase II (adh II) and pyruvate decarboxylase (pdc), into E. coli, resulting in increased cell growth and ethanol production. Ethanol concentrations of > 30 g/L were obtained on 10% glucose. Additionally, since pyruvate is mainly assimilated through pyruvate formate lyase (pfl) and forms formic acid and acetyl coenzyme A, metabolic redirection was attempted through gene knockout by Red-mediated recombination to decrease the byproducts of pyruvate metabolism. Under microaerobic conditions, pflA- and pflB-mutants produced more ethanol (163% and 207%, respectively) relative to the parent strain, using glucose as a carbon source.     

Metabolism of halophilic archaea

In spite of their common hypersaline environment, halophilic archaea are surprisingly different in their nutritional demands and metabolic pathways. The metabolic diversity of halophilic archaea was investigated at the genomic level through systematic metabolic reconstruction and comparative analysis of four completely sequenced species: Halobacterium salinarum, Haloarcula marismortui, Haloquadratum walsbyi, and the haloalkaliphile Natronomonas pharaonis. The comparative study reveals different sets of enzyme genes amongst halophilic archaea, e.g. in glycerol degradation, pentose metabolism, and folate synthesis. The carefully assessed metabolic data represent a reliable resource for future system biology approaches as it also links to current experimental data on (halo)archaea from the literature. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Uncovering the gene knockout landscape for improved lycopene production in E. coli

Systematic and combinatorial genetic approaches for the identification of gene knockout and overexpression targets have been effectively employed in the improvement of cellular phenotypes. Previously, we demonstrated how two of these tools, metabolic modeling and transposon mutagenesis, can be combined to identify strains of interest spanning the metabolic landscape of recombinant lycopene production in Escherichia coli. However, it is unknown how to best select multiple-gene knockout targets. Hence, this study seeks to understand how the overall order of gene selection, or search trajectory, biases the exploration and topology of the metabolic landscape. In particular, transposon mutagenesis and selection were employed in the background of eight different knockout genotypes. Collectively, 800,000 mutants were analyzed in hopes of exhaustively identifying all advantageous gene knockout targets. Several interesting observations, including clusters of gene functions, recurrence, and divergent genotypes, demonstrate the complexity of mapping only one genotype to one phenotype. One particularly interesting mutant, the ΔhnrΔyliE genotype, exhibited a drastically improved lycopene production capacity in basic minimal medium in comparison to the best strains identified in previous studies.     

Conversion of lactose to methane by defined bacterial cocultures

The conversion of lactose — the main constituent of whey — to methane and carbon dioxide was studied using different defined constructed cultures, imploying strains of Methanosarcina barkeri, Methanobacterium bryantii, Escherichia coli, Acetobacterium woodii, Lactobacillus casei, and Lactobacillus plantarum. The following combinations of strains (food chains) were studied with respect to efficiency and yield of lactose conversion (methane yield in parentheses): E. coli and M. barkeri (4.5–7.6%), E. coli and M. bryantii (13.3%),E. coli, M. barkeri and M. bryantii (54%), L. casei, A. woodii and M. barkeri (93.3%). These conversions were carried out in pH controlled batch fermentations. A very efficient coculture was a combination of L. plantarum with A. woodii and M. barkeri: in chemostat cultures lactose was converted to methane and carbon dioxide with a yield of about 90%, at dilution rates of 0.27 d^-1to 0.37 d^-1.     

Sulfate-Dependent Interspecies H2 Transfer between Methanosarcina barkeri and Desulfovibrio vulgaris during Coculture Metabolism of Acetate or Methanol

We compared the metabolism of methanol and acetate when Methanosarcina barkeri was grown in the presence and absence of Desulfovibrio vulgaris. The sulfate reducer was not able to utilize methanol or acetate as the electron donor for energy metabolism in pure culture, but was able to grow in coculture. Pure cultures of M. barkeri produced up to 10 μmol of H₂ per liter in the culture headspace during growth on acetate or methanol. In coculture with D. vulgaris, the gaseous H₂ concentration was ≤2 μmol/liter. The fractions of ¹⁴CO₂ produced from [¹⁴C]methanol and 2-[¹⁴C]acetate increased from 0.26 and 0.16, respectively, in pure culture to 0.59 and 0.33, respectively, in coculture. Under these conditions, approximately 42% of the available electron equivalents derived from methanol or acetate were transferred and were utilized by D. vulgaris to reduce approximately 33 μmol of sulfate per 100 μmol of substrate consumed. As a direct consequence, methane formation in cocultures was two-thirds that observed in pure cultures. The addition of 5.0 mM sodium molybdate or exogenous H₂ decreased the effects of D. vulgaris on the metabolism of M. barkeri. An analysis of growth and carbon and electron flow patterns demonstrated that sulfate-dependent interspecies H₂ transfer from M. barkeri to D. vulgaris resulted in less methane production, increased CO₂ formation, and sulfide formation from substrates not directly utilized by the sulfate reducer as electron donors for energy metabolism and growth.     

A comparison of muscle energy models for simulating human walking in three dimensions

The popular Hill model for muscle activation and contractile dynamics has been extended with several different formulations for predicting the metabolic energy expenditure of human muscle actions. These extended models differ considerably in their approach to computing energy expenditure, particularly in their treatment of active lengthening and eccentric work, but their predictive abilities have never been compared. In this study, we compared the predictions of five different Hill-based muscle energy models in 3D forward dynamics simulations of normal human walking. In a data-tracking simulation that minimized muscle fatigue, the energy models predicted metabolic costs that varied over a three-fold range (2.45–7.15 J/m/kg), with the distinction arising from whether or not eccentric work was subtracted from the net heat rate in the calculation of the muscle metabolic rate. In predictive simulations that optimized neuromuscular control to minimize the metabolic cost, all five models predicted similar speeds, step lengths, and stance phase durations. However, some of the models predicted a hip circumduction strategy to minimize metabolic cost, while others did not, and the accuracy of the predicted knee and ankle angles and ground reaction forces also depended on the energy model used. The results highlights the need to clarify how eccentric work should be treated when calculating muscle energy expenditure, the difficulty in predicting realistic metabolic costs in simulated walking even with a detailed 3D musculoskeletal model, the potential for using such models to predict energetically-optimal gait modifications, and the room for improvement in existing muscle energy models and locomotion simulation frameworks.     

Purposive behavior and cognitive mapping: a neural network model

This study presents a real-time, biologically plausible neural network approach to purposive behavior and cognitive mapping. The system is composed of (a) an action system, consisting of a goal-seeking neural mechanism controlled by a motivational system; and (b) a cognitive system, involving a neural cognitive map. The goal-seeking mechanism displays exploratory behavior until either (a) the goal is found or (b) an adequate prediction of the goal is generated. The cognitive map built by the network is a top logical map, i.e., it represents only the adjacency, but not distances or directions, between places. The network has recurrent and non-recurrent properties that allow the reading of the cognitive map without modifying it. Two types of predictions are introduced: fast-time and real-time predictions. Fast-time predictions are produced in advance of what occurs in real time, when the information stored in the cognitive map is used to predict the remote future. Real-time predictions are generated simultaneously with the occurrence of environmental events, when the information stored in the cognitive map is being updated. Computer simulations show that the network successfully describes latent learning and detour behavior in rats. In addition, simulations demonstrate that the network can be applied to problem-solving paradigms such as the Tower of Hanoi puzzle.     

Flux Balance Analysis of Cyanobacterial Metabolism: The Metabolic Network of Synechocystis sp. PCC 6803

Cyanobacteria are versatile unicellular phototrophic microorganisms that are highly abundant in many environments. Owing to their capability to utilize solar energy and atmospheric carbon dioxide for growth, cyanobacteria are increasingly recognized as a prolific resource for the synthesis of valuable chemicals and various biofuels. To fully harness the metabolic capabilities of cyanobacteria necessitates an in-depth understanding of the metabolic interconversions taking place during phototrophic growth, as provided by genome-scale reconstructions of microbial organisms. Here we present an extended reconstruction and analysis of the metabolic network of the unicellular cyanobacterium Synechocystis sp. PCC 6803. Building upon several recent reconstructions of cyanobacterial metabolism, unclear reaction steps are experimentally validated and the functional consequences of unknown or dissenting pathway topologies are discussed. The updated model integrates novel results with respect to the cyanobacterial TCA cycle, an alleged glyoxylate shunt, and the role of photorespiration in cellular growth. Going beyond conventional flux-balance analysis, we extend the computational analysis to diurnal light/dark cycles of cyanobacterial metabolism.     

Formaldehyde activating enzyme (Fae) and hexulose-6-phosphate synthase (Hps) in <Emphasis Type="Italic">Methanosarcina barkeri</Emphasis>: a possible function in ribose-5-phosphate biosynthesis

Formaldehyde activating enzyme (Fae) was first discovered in methylotrophic bacteria, where it is involved in the oxidation of methanol to CO₂ and in formaldehyde detoxification. The 18 kDa protein catalyzes the condensation of formaldehyde with tetrahydromethanopterin (H₄MPT) to methylene-H₄MPT. We describe here that Fae is also present and functional in the methanogenic archaeon Methanosarcina barkeri. The faeA homologue in the genome of M. barkeri was heterologously expressed in Escherichia coli and the overproduced purified protein shown to actively catalyze the condensation reaction: apparent V _max=13 U/mg protein (1 U=μmol/min); apparent Km for H₄MPT=30 μM; apparent Km for formaldehyde=0.1 mM. By Western blot analysis the concentration of Fae in cell extracts of M. barkeri was determined to be in the order of 0.1% of the soluble cell proteins. Besides the faeA gene the genome of M. barkeri harbors a second gene, faeB-hpsB, which is shown to code for a 42 kDa protein with both Fae activity (3.6 U/mg) and hexulose-6-phosphate synthase (Hps) activity (4.4 U/mg). The results support the recent proposal that in methanogenic archaea Fae and Hps could have a function in ribose phosphate synthesis.