Lars K. Nielsen

Fermentative butanol production by clostridia

Butanol is an aliphatic saturated alcohol having the molecular formula of C4H9OH. Butanol can be used as an intermediate in chemical synthesis and as a solvent for a wide variety of chemical and textile industry applications. Moreover, butanol has been considered as a potential fuel or fuel additive. Biological production of butanol (with acetone and ethanol) was one of the largest industrial fermentation processes early in the 20th century. However, fermentative production of butanol had lost its competitiveness by 1960s due to increasing substrate costs and the advent of more efficient petrochemical processes. Recently, increasing demand for the use of renewable resources as feedstock for the production of chemicals combined with advances in biotechnology through omics, systems biology, metabolic engineering and innovative process developments is generating a renewed interest in fermentative butanol production. This article reviews biotechnological production of butanol by clostridia and some relevant fermentation and downstream processes. The strategies for strain improvement by metabolic engineering and further requirements to make fermentative butanol production a successful industrial process are also discussed. Biotechnol. Bioeng. 2008;101: 209–228.

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.

Microbial hyaluronic acid production

AbstractsHyaluronic acid (HA) is a commercially valuable medical biopolymer increasingly produced through microbial fermentation. Viscosity limits product yield and the focus of research and development has been on improving the key quality parameters, purity and molecular weight. Traditional strain and process optimisation has yielded significant improvements, but appears to have reached a limit. Metabolic engineering is providing new opportunities and HA produced in a heterologous host is about to enter the market. In order to realise the full potential of metabolic engineering, however, greater understanding of the mechanisms underlying chain termination is required.

Towards quantitative metabolomics of mammalian cells: Development of a metabolite extraction protocol

Metabolomics aims to quantify all metabolites within an organism, thereby providing valuable insight into the metabolism of cells. To study intracellular metabolites, they are first extracted from the cells. The ideal extraction procedure should immediately quench metabolism and quantitatively extract all metabolites, a significant challenge given the rapid turnover and physicochemical diversity of intracellular metabolites. We have evaluated several quenching and extraction solutions for their suitability for mammalian cells grown in suspension. Quenching with 60% methanol (buffered or unbuffered) resulted in leakage of intracellular metabolites from the cells. In contrast, quenching with cold isotonic saline (0.9% [w/v] NaCl, 0.5 degrees C) did not damage cells and effectively halted conversion of ATP to ADP and AMP, indicative of metabolic arrest. Of the 12 different extraction methods tested, cold extraction in 50% aqueous acetonitrile was superior to other methods. The recovery of a mixture of standards was excellent, and the concentration of extracted intracellular metabolites was higher than for the other methods tested. The final protocol is easy to implement and can be used to study the intracellular metabolomes of mammalian cells.

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.

Molecular Composition of IMP1 Ribonucleoprotein Granules

Localized mRNAs are transported to sites of local protein synthesis in large ribonucleoprotein (RNP) granules, but their molecular composition is incompletely understood. Insulin-like growth factor II mRNA-binding protein (IMP) zip code-binding proteins participate in mRNA localization, and in motile cells IMP-containing granules are dispersed around the nucleus and in cellular protrusions. We isolated the IMP1-containing RNP granules and found that they represent a unique RNP entity distinct from neuronal hStaufen and/or fragile X mental retardation protein granules, processing bodies, and stress granules. Granules were 100–300 nm in diameter and consisted of IMPs, 40 S ribosomal subunits, shuttling heterologous nuclear RNPs, poly(A)-binding proteins, and mRNAs. Moreover granules contained CBP80 and factors belonging to the exon junction complex and lacked eIF4E, eIF4G, and 60 S ribosomal subunits, indicating that embodied mRNAs are not translated. Granules embodied mRNAs corresponding to about 3% of the human embryonic kidney 293 mRNA transcriptome. Messenger RNAs encoding proteins participating in the secretory pathway and endoplasmic reticulum-associated quality control, as well as ubiquitin-dependent metabolism, were enriched in the granules, reinforcing the concept of RNP granules as post-transcriptional operons.

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.

Modeling hybridoma cell metabolism using a generic genome-scale metabolic model of Mus musculus

The reconstructed cellular metabolic network of Mus musculus, based on annotated genomic data, pathway databases, and currently available biochemical and physiological information, is presented. Although incomplete, it represents the first attempt to collect and characterize the metabolic network of a mammalian cell on the basis of genomic data. The reaction network is generic in nature and attempts to capture the carbon, energy, and nitrogen metabolism of the cell. The metabolic reactions were compartmentalized between the cytosol and the mitochondria, including transport reactions between the compartments and the extracellular medium. The reaction list consists of 872 internal metabolites involved in a total of 1220 reactions, whereof 473 relate to known open reading frames. Initial in silico analysis of the reconstructed model is presented.

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.