Metabolic flux analysis and visualization

One of the ultimate goals of systems biology research is to obtain a comprehensive understanding of the control mechanisms of complex cellular metabolisms. Metabolic Flux Analysis (MFA) is a important method for the quantitative estimation of intracellular metabolic flows through metabolic pathways and the elucidation of cellular physiology. The primary challenge in the use of MFA is that many biological networks are underdetermined systems; it is therefore difficult to narrow down the solution space from the stoichiometric constraints alone. In this tutorial, we present an overview of Flux Balance Analysis (FBA) and (13)C-Metabolic Flux Analysis ((13)C-MFA), both of which are frequently used to solve such underdetermined systems, and we demonstrate FBA and (13)C-MFA using the genome-scale model and the central carbon metabolism model, respectively. Furthermore, because such comprehensive study of intracellular fluxes is inherently complex, we subsequently introduce various pathway mapping and visualization tools to facilitate understanding of these data in the context of the pathways. Specific visualization of MFA results using the BioCyc Omics Viewer and Pathway Projector are shown as illustrative examples.     

Metabolic flux analysis in biotechnology processes

Metabolic flux analysis (MFA) has become a fundamental tool of metabolic engineering to elucidate the metabolic state of the cell and has been applied to various biotechnological processes. In recent years, considerable technical advances have been made. Developments of analytical instruments allow us to determine ¹³C labeling distribution of intracellular metabolites with high accuracy and sensitivity. Moreover, kinetic information of intracellular label distribution during isotopic instationary enables us to calculate metabolic fluxes with shortened experimental time and decreased amount of labeled substrate. The ¹³C MFA may be one of the most promising approaches for the target estimation to improve strain performances and production processes.     

Metabolic flux analysis and pharmaceutical production

Rational engineering of biological systems is an inherently complex process due to their evolved nature. Metabolic engineering emerged and developed over the past 20 years as a field in which methodologies for the rational engineering of biological systems is now being applied to specific industrial, medical, or scientific problems. Of considerable interest is the determination of metabolic fluxes within the cell itself, called metabolic flux analysis. This special issue and this review have a particular interest in the application of metabolic flux analysis for improving the pharmaceutical production process (for both small and large molecules). Though metabolic flux analysis has been somewhat limited in application towards pharmaceutical production, the overall goal is to: (1) have a better understanding of the organism and/or process in question, and (2) provide a rational basis to further engineer (on both metabolic and process scales) improved pharmaceutical production in these organisms. The focus of this review article is to present how experimental and computational methods of metabolic flux analysis have matured, mirroring the maturation of the metabolic engineering field itself, while highlighting some of the successful applications towards both small- and large-molecule pharmaceuticals.     

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 ¹³C tracer analysis has been used to study small network models and validate assumptions of metabolism. Large-scale ¹³C 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.     

Metabolic flux analysis and metabolic engineering of microorganisms

Recent advances in metabolic flux analysis including genome-scale constraints-based flux analysis and its applications in metabolic engineering are reviewed. Various computational aspects of constraints-based flux analysis including genome-scale stoichiometric models, additional constraints used for the improved accuracy, and several algorithms for identifying the target genes to be manipulated are described. Also, some of the successful applications of metabolic flux analysis in metabolic engineering are reviewed. Finally, we discuss the limitations that need to be overcome to make the results of genome-scale flux analysis more realistically represent the real cell metabolism.     

Metabolic network flux analysis for engineering plant systems

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Highlights► A powerful range of tools has been developed for metabolic network flux analysis. ► These tools yield insights that are used to aid microbial metabolic engineering. ► Plants present great opportunities and special challenges to applying these tools. ► Tool selection and knowledge of plant systems is key to practical success.     

Insulin protects against hepatic damage postburn

Burn injury causes hepatic dysfunction associated with endoplasmic reticulum (ER) stress and induction of the unfolded protein response (UPR). ER stress/UPR leads to hepatic apoptosis and activation of the Jun-N-terminal kinase (JNK) signaling pathway, leading to vast metabolic alterations. Insulin has been shown to attenuate hepatic damage and to improve liver function. We therefore hypothesized that insulin administration exerts its effects by attenuating postburn hepatic ER stress and subsequent apoptosis. Male Sprague Dawley rats received a 60% total body surface area (TBSA) burn injury. Animals were randomized to receive saline (controls) or insulin (2.5 IU/kg q. 24 h) and euthanized at 24 and 48 h postburn. Burn injury induced dramatic changes in liver structure and function, including induction of the ER stress response, mitochondrial dysfunction, hepatocyte apoptosis, and up-regulation of inflammatory mediators. Insulin decreased hepatocyte caspase-3 activation and apoptosis significantly at 24 and 48 h postburn. Furthermore, insulin administration decreased ER stress significantly and reversed structural and functional changes in hepatocyte mitochondria. Finally, insulin attenuated the expression of inflammatory mediators IL-6, MCP-1, and CINC-1. Insulin alleviates burn-induced ER stress, hepatocyte apoptosis, mitochondrial abnormalities, and inflammation leading to improved hepatic structure and function significantly. These results support the use of insulin therapy after traumatic injury to improve patient outcomes.     

Metabolic reconstruction and flux analysis of industrial Pichia yeasts

Pichia yeasts have been recognized as important microbial cell factories in the biotechnological industry. Notably, the Pichia pastoris and Pichia stipitis species have attracted much research interest due to their unique cellular physiology and metabolic capability: P. pastoris has the ability to utilize methanol for cell growth and recombinant protein production, while P. stipitis is capable of assimilating xylose to produce ethanol under oxygen-limited conditions. To harness these characteristics for biotechnological applications, it is highly required to characterize their metabolic behavior. Recently, following the genome sequencing of these two Pichia species, genome-scale metabolic networks have been reconstructed to model the yeasts’ metabolism from a systems perspective. To date, there are three genome-scale models available for each of P. pastoris and P. stipitis. In this mini-review, we provide an overview of the models, discuss certain limitations of previous studies, and propose potential future works that can be conducted to better understand and engineer Pichia yeasts for industrial applications.     

Metabolic flux analysis of cultured hepatocytes exposed to plasma

Hepatic metabolism can be investigated using metabolic flux analysis (MFA), which provides a comprehensive overview of the intracellular metabolic flux distribution. The characterization of intermediary metabolism in hepatocytes is important for all biotechnological applications involving liver cells, including the development of bioartificial liver (BAL) devices. During BAL operation, hepatocytes are exposed to plasma or blood from the patient, at which time they are prone to accumulate intracellular lipids and exhibit poor liver-specific functions. In a prior study, we found that preconditioning the primary rat hepatocytes in culture medium containing physiological levels of insulin, as opposed to the typical supraphysiological levels found in standard hepatocyte culture media, reduced lipid accumulation during subsequent plasma exposure. Furthermore, supplementing the plasma with amino acids restored hepatospecific functions. In the current study, we used MFA to quantify the changes in intracellular pathway fluxes of primary rat hepatocytes in response to low-insulin preconditioning and amino acid supplementation. We found that culturing hepatocytes in medium containing lower physiological levels of insulin decreased the clearance of glucose and glycerol with a concomitant decrease in glycolysis. These findings are consistent with the general notion that low insulin, especially in the presence of high glucagon levels, downregulates glycolysis in favor of gluconeogenesis in hepatocytes. The MFA model shows that, during subsequent plasma exposure, low-insulin preconditioning upregulated gluconeogenesis, with lactate as the primary precursor in unsupplemented plasma, with a greater contribution from deaminated amino acids in amino acid-supplemented plasma. Concomitantly, low-insulin preconditioning increased fatty acid oxidation, an effect that was further enhanced by amino acid supplementation to the plasma. The increase in fatty acid oxidation reduced intracellular triglyceride accumulation. Overall, these findings are consistent with the notion that the insulin level in medium culture presets the metabolic machinery of hepatocytes such that it directly impacts on their metabolic behavior during subsequent plasma culture.     

Metabolic flux analysis of carbon balance in Lactobacillus strains

Metabolic flux analyses were performed based on the carbon balance of six different Lactobacillus strains used in this study. Results confirmed that L. delbrueckii, L. plantarum ATCC 21028, L. plantarum NCIMB 8826 ΔldhL1, L. plantarum NCIMB 8826 ΔldhL1‐pCU‐PxylAB, and L. plantarum NCIMB 8826 ΔldhL1‐pLEM415‐xylAB metabolized glucose via EMP: whereas, L. brevis metabolized glucose via PK pathway. Xylose was metabolized through the PK pathway in L. brevis, L. plantarum NCIMB 8826 ΔldhL1‐pCU‐PxylAB and L. plantarum NCIMB 8826 ΔldhL1‐pLEM415‐xylAB. Operation of both EMP and PK pathways was found in L. brevis, L. plantarum NCIMB 8826 ΔldhL1‐pCU‐PxylAB, and L. plantarum NCIMB 8826 ΔldhL1‐pLEM415‐xylAB when glucose plus xylose were used as carbon source. The information of detailed carbon flow may help the strain and biomass selection in a designed process of lactic acid biosynthesis. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1397–1403, 2016     

Metabolic flux network and analysis of fermentative hydrogen production

Fermentative hydrogen production (FHP) has received a great R & D interest in recent decades, as it offers a potential means of producing H₂ from a variety of renewable resources, even wastewater via a low energy continuous process. Various extracellular metabolites including ethanol, acetate, butyrate and lactate can be produced during the fermentation, building a complex metabolic network of the FHP. Except for the recognition of its complexity, the metabolic flux network has not been well understood. Studies on biochemical reactions and metabolic flux network associated with the FHP in anaerobic fermentation system have only been drawn attention in recent years. This review summarizes the biochemical reactions taking place in the metabolic network of FHP. We discuss how the key operation factors influence metabolism in the FHP process. Recently developed and applied technologies for metabolic flux analysis have been described. Future studies on the metabolic network to enhance fermentative hydrogen production by strict anaerobes are recommended. It is expected that this review can provide useful information in terms of fundamental knowledge and update technology for scientists and research engineers in the field of biological hydrogen production.     

Metabolic flux analysis in plants: coping with complexity

Theory and experience in metabolic engineering both show that metabolism operates at the network level. In plants, this complexity is compounded by a high degree of compartmentation and the synthesis of a very wide array of secondary metabolic products. A further challenge to understanding and predicting plant metabolic function is posed by our ignorance about the structure of metabolic networks even in well-studied systems. Metabolic flux analysis (MFA) provides tools to measure and model the functioning of metabolism, and is making significant contributions to coping with their complexity.
This review gives an overview of different MFA approaches, the measurements required to implement them and the information they yield. The application of MFA methods to plant systems is then illustrated by several examples from the recent literature. Next, the challenges that plant metabolism poses for MFA are discussed together with ways that these can be addressed. Lastly, new developments in MFA are described that can be expected to improve the range and reliability of plant MFA in the coming years.     

Metabolic flux analysis using 13C peptide label measurements

¹³C metabolic flux analysis (MFA) has become the experimental method of choice to investigate the cellular metabolism of microbes, cell cultures and plant seeds. Conventional steady‐state MFA utilizes isotopic labeling measurements of amino acids obtained from protein hydrolysates. To retain spatial information in conventional steady‐state MFA, tissues or subcellular fractions must be dissected or biochemically purified. In contrast, peptides retain their identity in complex protein extracts, and may therefore be associated with a specific time of expression, tissue type and subcellular compartment. To enable ‘single‐sample’ spatially and temporally resolved steady‐state flux analysis, we investigated the suitability of peptide mass distributions (PMDs) as an alternative to amino acid label measurements. PMDs are the discrete convolution of the mass distributions of the constituent amino acids of a peptide. We investigated the requirements for the unique deconvolution of PMDs into amino acid mass distributions (AAMDs), the influence of peptide sequence length on parameter sensitivity, and how AAMD and flux estimates that are determined through deconvolution compare to estimates from a conventional GC–MS measurement‐based approach. Deconvolution of PMDs of the storage protein β–conglycinin of soybean (Glycine max) resulted in good AAMD and flux estimates if fluxes were directly fitted to PMDs. Unconstrained deconvolution resulted in inferior AAMD and flux estimates. PMD measurements do not include amino acid backbone fragments, which increase the information content in GC–MS‐derived analyses. Nonetheless, the resulting flux maps were of comparable quality due to the precision of Orbitrap quantification and the larger number of peptide measurements.     

Metabolic flux analysis ofBeijerinckia indica for PS-7 production

In order to investigate central metabolic changes inBeijerinckia indica, cells were grown on different carbon sources and intracellular flux distributions were studied under varying concentrations of nitrogen. Metabolic fluxes were estimated by combining material balances with extracellular substrate uptake rate, biomass formation rate, and exopolysaccharide (EPS) accumulation rate. Thirty-one metabolic reactions and 30 intracellular metabolites were considered for the flux analysis. The results revealed that most of the carbon source was directed into the Entner-Doudoroff pathway, followed by the recycling of triose-3-phosphate back to Hexose-6-phosphate. The pentose phosphate pathway was operated at a minimal level to supply the precursors for biomass formation. The different metabolic behaviors under varying nitrogen concentrations were observed with flux analysis.     

Metabolic flux analysis of Gluconacetobacter xylinus for bacterial cellulose production

Metabolic flux analysis was used to reveal the metabolic distributions in Gluconacetobacter xylinus (CGMCC no. 2955) cultured on different carbon sources. Compared with other sources, glucose, fructose, and glycerol could achieve much higher bacterial cellulose (BC) yields from G. xylinus (CGMCC no. 2955). The glycerol led to the highest BC production with a metabolic yield of 14.7 g/mol C, which was approximately 1.69-fold and 2.38-fold greater than that produced using fructose and glucose medium, respectively. The highest BC productivity from G. xylinus CGMCC 2955 was 5.97 g BC/L (dry weight) when using glycerol as the sole carbon source. Metabolic flux analysis for the central carbon metabolism revealed that about 47.96 % of glycerol was transformed into BC, while only 19.05 % of glucose and 24.78 % of fructose were transformed into BC. Instead, when glucose was used as the sole carbon source, 40.03 % of glucose was turned into the by-product gluconic acid. Compared with BC from glucose and fructose, BC from the glycerol medium showed the highest tensile strength at 83.5 MPa, with thinner fibers and lower porosity. As a main byproduct of biodiesel production, glycerol holds great potential to produce BC with superior mechanical and microstructural characteristics.     

Metabolic flux analysis in Streptomyces coelicolor under various nutrient limitations

Metabolic flux analysis was applied to Streptomyces coelicolor continuous culture data obtained under nitrogen, phosphate, sulfate, and potassium limitations. The metabolic reaction network involved more than 200 reactions describing the major pathways as well as the secondary metabolism for the production of actinorhodin and excretion of certain metabolites. Linear programming was used for the optimization of specific growth rates and energy requirements. Two types of specific growth rates, stoichiometric and theoretical, were defined, maximized, and compared in order to investigate the microbial potential. Potassium limitation led to the largest and nitrogen limitation to the smallest difference between the stoichiometric and theoretical specific growth rates. Although the value of the maximum theoretical specific growth rate was close to that of the experimental specific growth rate with potassium limitation, this difference was the largest in the case of nitrogen limitation. Energy requirements during different nutrient limitations were also investigated. The model indicated that although the highest actinorhodin production rate was with nitrogen limitation, this was accompanied with the undesired excretion of certain metabolites.     

Metabolic flux analysis as a tool in metabolic engineering of plants

Methods of metabolic flux analysis (MFA) provide insights into the theoretical capabilities of metabolic networks and allow probing the in vivo performance of cellular metabolism. In recent years, an increasing awareness has developed that network analysis methods within the systems biology toolbox are serving to improve our understanding and ability to manipulate metabolism. In this minireview the potential of MFA to increase the chances of success in metabolic engineering of plants is presented, recent progress related to engineering and flux analysis in central metabolism of plants is discussed, and some recent advances in flux analysis methodology are highlighted.     

肌苷发酵过程中后期的代谢流节点分析

应用代谢流分析方法,实验测定副产物的积累速率,将数据输入计算机,应用MArllAB软件计算肌苷发酵中后期代谢流分布。通过分析代谢网络中重要的节点,提出了优化肌苷生物合成途径的建议。     

Metabolic flux analysis of hybridoma continuous culture steady state multiplicity.

Physiological state multiplicity was observed in continuous cultures of the hybridoma cell line ATCC CRL-1606 cultivated in glutamine-limited steady state chemostats. At the same dilution rate (0.04 h-1), two physiologically different cultures were obtained which exhibited similar growth rates and viabilities but drastically different cell concentrations (7.36 x 10(5) and 1.36 x 10(6) cells/mL). Metabolic flux analysis conducted using metabolite and gas exchange rate measurements revealed a more efficient culture for the steady state with the higher cell concentration, as measured by the fraction of pyruvate carbon flux shuttled into the TCA cycle for energy generation. The low-efficiency steady state was achieved after innoculation by growing the cells in a nutrient rich environment, first in batch mode followed by a stepwise increase of the dilution rate to its set point at 0.04 h-1. The high-efficiency steady state was achieved by reducing the dilution rate to progressively lower values to 0.01 h-1 resulting in conditions of stricter nutrient limitation. The high energetic efficiency attained under such conditions was preserved upon increasing the chemostat dilution rate back to 0.04 h-1 with a higher nutrient consumption, resulting in approximate doubling of the steady state cell concentration. This metabolic adaptation is unlikely due to favorable genetic mutations and could be implemented for improving cell culture performance by inducing cellular metabolic shifts to more efficient flux distribution patterns.