Lake-Ee Quek

AraGEM, a Genome-Scale Reconstruction of the Primary Metabolic Network in Arabidopsis

Genome-scale metabolic network models have been successfully used to describe metabolism in a variety of microbial organisms as well as specific mammalian cell types and organelles. This systems-based framework enables the exploration of global phenotypic effects of gene knockouts, gene insertion, and up-regulation of gene expression. We have developed a genome-scale metabolic network model (AraGEM) covering primary metabolism for a compartmentalized plant cell based on the Arabidopsis (Arabidopsis thaliana) genome. AraGEM is a comprehensive literature-based, genome-scale metabolic reconstruction that accounts for the functions of 1,419 unique open reading frames, 1,748 metabolites, 5,253 gene-enzyme reaction-association entries, and 1,567 unique reactions compartmentalized into the cytoplasm, mitochondrion, plastid, peroxisome, and vacuole. The curation process identified 75 essential reactions with respective enzyme associations not assigned to any particular gene in the Kyoto Encyclopedia of Genes and Genomes or AraCyc. With the addition of these reactions, AraGEM describes a functional primary metabolism of Arabidopsis. The reconstructed network was transformed into an in silico metabolic flux model of plant metabolism and validated through the simulation of plant metabolic functions inferred from the literature. Using efficient resource utilization as the optimality criterion, AraGEM predicted the classical photorespiratory cycle as well as known key differences between redox metabolism in photosynthetic and nonphotosynthetic plant cells. AraGEM is a viable framework for in silico functional analysis and can be used to derive new, nontrivial hypotheses for exploring plant metabolism.

OpenFLUX: efficient modelling software for 13C-based metabolic flux analysis

BackgroundThe quantitative analysis of metabolic fluxes, i.e., in vivo activities of intracellular enzymes and pathways, provides key information on biological systems in systems biology and metabolic engineering. It is based on a comprehensive approach combining (i) tracer cultivation on 13C substrates, (ii) 13C labelling analysis by mass spectrometry and (iii) mathematical modelling for experimental design, data processing, flux calculation and statistics. Whereas the cultivation and the analytical part is fairly advanced, a lack of appropriate modelling software solutions for all modelling aspects in flux studies is limiting the application of metabolic flux analysis.ResultsWe have developed OpenFLUX as a user friendly, yet flexible software application for small and large scale 13C metabolic flux analysis. The application is based on the new Elementary Metabolite Unit (EMU) framework, significantly enhancing computation speed for flux calculation. From simple notation of metabolic reaction networks defined in a spreadsheet, the OpenFLUX parser automatically generates MATLAB-readable metabolite and isotopomer balances, thus strongly facilitating model creation. The model can be used to perform experimental design, parameter estimation and sensitivity analysis either using the built-in gradient-based search or Monte Carlo algorithms or in user-defined algorithms. Exemplified for a microbial flux study with 71 reactions, 8 free flux parameters and mass isotopomer distribution of 10 metabolites, OpenFLUX allowed to automatically compile the EMU-based model from an Excel file containing metabolic reactions and carbon transfer mechanisms, showing its user-friendliness. It reliably reproduced the published data and optimum flux distributions for the network under study were found quickly (<20 sec).ConclusionWe have developed a fast, accurate application to perform steady-state 13C metabolic flux analysis. OpenFLUX will strongly facilitate and enhance the design, calculation and interpretation of metabolic flux studies. By providing the software open source, we hope it will evolve with the rapidly growing field of fluxomics.

Metabolic flux analysis in mammalian cell culture

Mammalian cell culture metabolism is characterized by glucoglutaminolysis, that is, high glucose and glutamine uptake combined with a high rate of lactate and non-essential amino acid secretion. Stress associated with acid neutralization and ammonia accumulation necessitates complex feeding schemes and limits cell densities achieved in fed-batch culture. Conventional and constraint-based metabolic flux analysis has been successfully used to study the metabolic phenotype of mammalian cells in culture, while (13)C tracer analysis has been used to study small network models and validate assumptions of metabolism. Large-scale (13)C metabolic flux analysis, which is required to improve confidence in the network models and their predictions, remains a major challenge. Advances in both modeling and analytical techniques are bringing this challenge within sight.

C4GEM, a Genome-Scale Metabolic Model to Study C4 Plant Metabolism

Leaves of C4 grasses (such as maize [Zea mays], sugarcane [Saccharum officinarum], and sorghum [Sorghum bicolor]) form a classical Kranz leaf anatomy. Unlike C3 plants, where photosynthetic CO2 fixation proceeds in the mesophyll (M), the fixation process in C4 plants is distributed between two cell types, the M cell and the bundle sheath (BS) cell. Here, we develop a C4 genome-scale model (C4GEM) for the investigation of flux distribution in M and BS cells during C4 photosynthesis. C4GEM, to our knowledge, is the first large-scale metabolic model that encapsulates metabolic interactions between two different cell types. C4GEM is based on the Arabidopsis (Arabidopsis thaliana) model (AraGEM) but has been extended by adding reactions and transporters responsible to represent three different C4 subtypes (NADP-ME [for malic enzyme], NAD-ME, and phosphoenolpyruvate carboxykinase). C4GEM has been validated for its ability to synthesize 47 biomass components and consists of 1,588 unique reactions, 1,755 metabolites, 83 interorganelle transporters, and 29 external transporters (including transport through plasmodesmata). Reactions in the common C4 model have been associated with well-annotated C4 species (NADP-ME subtypes): 3,557 genes in sorghum, 11,623 genes in maize, and 3,881 genes in sugarcane. The number of essential reactions not assigned to genes is 131, 135, and 156 in sorghum, maize, and sugarcane, respectively. Flux balance analysis was used to assess the metabolic activity in M and BS cells during C4 photosynthesis. Our simulations were consistent with chloroplast proteomic studies, and C4GEM predicted the classical C4 photosynthesis pathway and its major effect in organelle function in M and BS. The model also highlights differences in metabolic activities around photosystem I and photosystem II for three different C4 subtypes. Effects of CO2 leakage were also explored. C4GEM is a viable framework for in silico analysis of cell cooperation between M and BS cells during photosynthesis and can be used to explore C4 plant metabolism.

AlgaGEM - a genome-scale metabolic reconstruction of algae based on the Chlamydomonas reinhardtii genome

BackgroundMicroalgae have the potential to deliver biofuels without the associated competition for land resources. In order to realise the rates and titres necessary for commercial production, however, system-level metabolic engineering will be required. Genome scale metabolic reconstructions have revolutionized microbial metabolic engineering and are used routinely for in silico analysis and design. While genome scale metabolic reconstructions have been developed for many prokaryotes and model eukaryotes, the application to less well characterized eukaryotes such as algae is challenging not at least due to a lack of compartmentalization data.ResultsWe have developed a genome-scale metabolic network model (named AlgaGEM) covering the metabolism for a compartmentalized algae cell based on the Chlamydomonas reinhardtii genome. AlgaGEM is a comprehensive literature-based genome scale metabolic reconstruction that accounts for the functions of 866 unique ORFs, 1862 metabolites, 2249 gene-enzyme-reaction-association entries, and 1725 unique reactions. The reconstruction was compartmentalized into the cytoplasm, mitochondrion, plastid and microbody using available data for algae complemented with compartmentalisation data for Arabidopsis thaliana. AlgaGEM describes a functional primary metabolism of Chlamydomonas and significantly predicts distinct algal behaviours such as the catabolism or secretion rather than recycling of phosphoglycolate in photorespiration. AlgaGEM was validated through the simulation of growth and algae metabolic functions inferred from literature. Using efficient resource utilisation as the optimality criterion, AlgaGEM predicted observed metabolic effects under autotrophic, heterotrophic and mixotrophic conditions. AlgaGEM predicts increased hydrogen production when cyclic electron flow is disrupted as seen in a high producing mutant derived from mutational studies. The model also predicted the physiological pathway for H2 production and identified new targets to further improve H2 yield.ConclusionsAlgaGEM is a viable and comprehensive framework for in silico functional analysis and can be used to derive new, non-trivial hypotheses for exploring this metabolically versatile organism. Flux balance analysis can be used to identify bottlenecks and new targets to metabolically engineer microalgae for production of biofuels.

Metabolite profiling of CHO cells with different growth characteristics

Mammalian cell cultures are the predominant system for the production of recombinant proteins requiring post‐translational modifications. As protein yields are a function of growth performance (among others), and performance varies greatly between culture medium (e.g., different growth rates and peak cell densities), an understanding of the biological mechanisms underpinning this variability would facilitate rational medium and process optimization, increasing product yields, and reducing costs. We employed a metabolomics approach to analyze differences in metabolite concentrations of CHO cells cultivated in three different media exhibiting different growth rates and maximum viable cell densities. Analysis of intra‐ and extracellular metabolite concentrations over the course of the cultures using a combination of HPLC and GC‐MS, readily detected medium specific and time dependent changes. Using multivariate data analysis, we identified a range of metabolites correlating with growth rate, illustrating how metabolomics can be used to relate gross phenotypic changes to the fine details of cellular metabolism. Biotechnol. Bioeng. 2012; 109:1404–1414.

Flux balance analysis of CHO cells before and after a metabolic switch from lactate production to consumption

Mammalian cell cultures typically exhibit an energy inefficient phenotype characterized by the consumption of large quantities of glucose and the concomitant production of large quantities of lactate. Under certain conditions, mammalian cells can switch to a more energy efficient state during which lactate is consumed. Using a metabolic model derived from a mouse genome scale model we performed flux balance analysis of Chinese hamster ovary cells before and after a metabolic switch from lactate production (in the presence of glucose) to lactate consumption (after glucose depletion). Despite a residual degree of freedom after accounting for measurements, the calculated flux ranges and associated errors were narrow enough to enable investigation of metabolic changes across the metabolic switch. Surprisingly, the fluxes through the lower part of the TCA cycle from oxoglutarate to malate were very similar (around 60 µmol/gDW/h) for both phases. A detailed analysis of the energy metabolism showed that cells consuming lactate have an energy efficiency (total ATP produced per total C-mol substrate consumed) six times greater than lactate producing cells.

Recon 2.2: from reconstruction to model of human metabolism

IntroductionThe human genome-scale metabolic reconstruction details all known metabolic reactions occurring in humans, and thereby holds substantial promise for studying complex diseases and phenotypes. Capturing the whole human metabolic reconstruction is an on-going task and since the last community effort generated a consensus reconstruction, several updates have been developed.ObjectivesWe report a new consensus version, Recon 2.2, which integrates various alternative versions with significant additional updates. In addition to re-establishing a consensus reconstruction, further key objectives included providing more comprehensive annotation of metabolites and genes, ensuring full mass and charge balance in all reactions, and developing a model that correctly predicts ATP production on a range of carbon sources.MethodsRecon 2.2 has been developed through a combination of manual curation and automated error checking. Specific and significant manual updates include a respecification of fatty acid metabolism, oxidative phosphorylation and a coupling of the electron transport chain to ATP synthase activity. All metabolites have definitive chemical formulae and charges specified, and these are used to ensure full mass and charge reaction balancing through an automated linear programming approach. Additionally, improved integration with transcriptomics and proteomics data has been facilitated with the updated curation of relationships between genes, proteins and reactions.ResultsRecon 2.2 now represents the most predictive model of human metabolism to date as demonstrated here. Extensive manual curation has increased the reconstruction size to 5324 metabolites, 7785 reactions and 1675 associated genes, which now are mapped to a single standard. The focus upon mass and charge balancing of all reactions, along with better representation of energy generation, has produced a flux model that correctly predicts ATP yield on different carbon sources.ConclusionThrough these updates we have achieved the most complete and best annotated consensus human metabolic reconstruction available, thereby increasing the ability of this resource to provide novel insights into normal and disease states in human. The model is freely available from the Biomodels database (http://identifiers.org/biomodels.db/MODEL1603150001).

A Multi-Omics Analysis of Recombinant Protein Production in Hek293 Cells

Hek293 cells are the predominant hosts for transient expression of recombinant proteins and are used for stable expression of proteins where post-translational modifications performed by CHO cells are inadequate. Nevertheless, there is little information available on the key cellular features underpinning recombinant protein production in Hek293 cells. To improve our understanding of recombinant protein production in Hek293 cells and identify targets for the engineering of an improved host cell line, we have compared a stable, recombinant protein producing Hek293 cell line and its parental cell line using a combination of transcriptomics, metabolomics and fluxomics. Producer cultures consumed less glucose than non-producer cultures while achieving the same growth rate, despite the additional burden of recombinant protein production. Surprisingly, there was no indication that producer cultures compensated for the reduction in glycolytic energy by increasing the efficiency of glucose utilization or increasing glutamine consumption. In contrast, glutamine consumption was lower and the majority of genes involved in oxidative phosphorylation were downregulated in producer cultures. We observed an overall downregulation of a large number of genes associated with broad cellular functions (e.g., cell growth and proliferation) in producer cultures, and therefore speculate that a broad adaptation of the cellular network freed up resources for recombinant protein production while maintaining the same growth rate. Increased abundance of genes associated with endoplasmic reticulum stress indicated a possible bottleneck at the point of protein folding and assembly.

A multi-tissue genome-scale metabolic modeling framework for the analysis of whole plant systems.

Genome scale metabolic modeling has traditionally been used to explore metabolism of individual cells or tissues. In higher organisms, the metabolism of individual tissues and organs is coordinated for the overall growth and well-being of the organism. Understanding the dependencies and rationale for multicellular metabolism is far from trivial. Here, we have advanced the use of AraGEM (a genome-scale reconstruction of Arabidopsis metabolism) in a multi-tissue context to understand how plants grow utilizing their leaf, stem and root systems across the day-night (diurnal) cycle. Six tissue compartments were created, each with their own distinct set of metabolic capabilities, and hence a reliance on other compartments for support. We used the multi-tissue framework to explore differences in the “division-of-labor” between the sources and sink tissues in response to: (a) the energy demand for the translocation of C and N species in between tissues; and (b) the use of two distinct nitrogen sources (NO−3 or NH+4). The “division-of-labor” between compartments was investigated using a minimum energy (photon) objective function. Random sampling of the solution space was used to explore the flux distributions under different scenarios as well as to identify highly coupled reaction sets in different tissues and organelles. Efficient identification of these sets was achieved by casting this problem as a maximum clique enumeration problem. The framework also enabled assessing the impact of energetic constraints in resource (redox and ATP) allocation between leaf, stem, and root tissues required for efficient carbon and nitrogen assimilation, including the diurnal cycle constraint forcing the plant to set aside resources during the day and defer metabolic processes that are more efficiently performed at night. This study is a first step toward autonomous modeling of whole plant metabolism.