Metabolic flux analysis of hepatocyte function in hormone- and amino acid-supplemented plasma

Understanding the metabolic and regulatory pathways of hepatocytes is important for biotechnological applications involving liver cells. Previous attempts to culture hepatocytes in plasma yielded poor functional results. Recently we reported that hormone (insulin and hydrocortisone) and amino acid supplementation reduces intracellular lipid accumulation and restores liver-specific function in hepatocytes exposed to heparinized human plasma. In the current study, we performed metabolic flux analysis (MFA) using a simplified metabolic network model of cultured hepatocytes to quantitively estimate the changes in lipid metabolism and relevant intracellular pathways in response to hormone and amino acid supplementation. The model accounts for the majority of central carbon and nitrogen metabolism, and assumes pseudo-steady-state with no metabolic futile cycles. We found that beta-oxidation and tricarboxylic acid (TCA) cycle fluxes were upregulated by both hormone and amino acid supplementation, thus enhancing the rate of lipid oxidation. Concomitantly, hormone and amino acid supplementation increased gluconeogenic fluxes. This, together with an increased rate of glucose clearance, caused an increase in predicted glycogen synthesis. Urea synthesis was primarily derived from ammonia and aspartate generated through transamination reactions, while exogenous ammonia removal accounted for only 3-6% of the urea nitrogen. Amino acid supplementation increased the endogenous synthesis of oxaloacetate, and in turn that of aspartate, a necessary substrate for the urea cycle. These findings from MFA provide cues as to which genes/pathways relevant to fatty acid oxidation, urea production, and gluconeogenesis may be upregulated by plasma supplementation, and are consistent with current knowledge of hepatic amino acid metabolism, which provides further credence to this approach for evaluating the metabolic state of hepatocytes under various environmental conditions.     

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.     

Novel quantitative tools for engineering analysis of hepatocyte cultures in bioartificial liver systems

Extracorporeal bioartificial liver devices (BAL) are perhaps among the most promising technologies for the treatment of liver failure, but significant technical challenges remain in order to develop systems with sufficient processing capacity and of manageable size. One key limitation is that during BAL operation, when the device is exposed to plasma from the patient, hepatocytes are prone to accumulate intracellular lipids and exhibit poor liver-specific functions. Based on hepatic intermediary metabolism, we have utilized mathematical programming techniques to optimize the biochemical environment of hepatocyte cultures towards the desired effect of increased albumin and urea synthesis. To investigate the feasible range of optimal hepatic function, we have obtained a Pareto optimal set of solutions corresponding to liver-specific functions of urea and albumin secretion in the metabolic framework using multiobjective optimization. The importance of amino acids in the supplementation and the criticality of the metabolic pathways have been investigated using logic-based programming techniques. Since the metabolite measurements are bound to be patient specific, and hence subject to variability, uncertainty has to be integrated with system analysis to improve the prediction of hepatic function. We have used the concept of two stage stochastic programming to obtain robust solutions by considering extracellular variability. The proposed analysis represents a new systematic approach to analyze behavior of hepatocyte cultures and optimize different operating parameters for an extracorporeal device based on real-time conditions.     

Hybrid dynamic/static method for large-scale simulation of metabolism

Background  

Many computer studies have employed either dynamic simulation or metabolic flux analysis (MFA) to predict the behaviour of biochemical pathways. Dynamic simulation determines the time evolution of pathway properties in response to environmental changes, whereas MFA provides only a snapshot of pathway properties within a particular set of environmental conditions. However, owing to the large amount of kinetic data required for dynamic simulation, MFA, which requires less information, has been used to manipulate large-scale pathways to determine metabolic outcomes.     

Combining metabolic flux analysis tools and 13C NMR to estimate intracellular fluxes of cultured astrocytes

In this work, brain cell metabolism was investigated by (13)C NMR spectroscopy and metabolic flux analysis (MFA). Monotypic cultures of astrocytes were incubated with labeled glucose for 38 h, and the distribution of the label was analyzed by (13)C NMR spectroscopy. The analysis of the spectra reveals two distinct physiological states characterized by different ratios of pyruvate carboxylase to pyruvate dehydrogenase activities (PC/PDH). Intracellular flux distributions for both metabolic states were estimated by MFA using the isotopic information and extracellular rate measurements as constraints. The model was subsequently checked with the consistency index method. From a biological point of view, the occurrence of the two physiological states appears to be correlated with the presence or absence of extracellular glutamate. Concerning the model, it can be stated that the metabolic network and the set of constraints adopted provide a consistent and robust characterization of the astrocytic metabolism, allowing for the calculation of central intracellular fluxes such as pyruvate recycling, the anaplerotic flux mediated by pyruvate carboxylase, and the glutamine formation through glutamine synthetase.     

应用最大熵原理通过酶控制通量预测突变体的通量分布

在代谢工程和系统生物学领域, 计算机模拟比以往更为有效的应用于生物过程的分析和优化。胞内代谢通量可以用代谢通量分析和基元模式分析来估算。由于测定数据的不足和误差, 以及基元途径的冗余, 经常很难得到准确的代谢通量分布数据。本研究提出一种基于最大熵原理的算法来计算基元模式系数。欠定和不确定条件下, 通过胞外代谢通量数据估算胞内代谢通量分布。为了检验算法的可行性, 对杂交瘤细胞、枯草芽孢杆菌和大肠杆菌的胞内代谢通量分布做了估算。本研究提出的基于最大熵原理的优化算法避免了对细胞状态的生理学假设。与其他目标函数相比, 可以更为可靠和可行的估算胞内代谢通量分布。     

Eukaryotic metabolism: measuring compartment fluxes

Metabolic compartmentation represents a major characteristic of eukaryotic cells. The analysis of compartmented metabolic networks is complicated by separation and parallelization of pathways, intracellular transport, and the need for regulatory systems to mediate communication between interdependent compartments. Metabolic flux analysis (MFA) has the potential to reveal compartmented metabolic events, although it is a challenging task requiring demanding experimental techniques and sophisticated modeling. At present no ready-made solution can be provided to cope with the complexity of compartmented metabolic networks, but new powerful tools are emerging. This review gives an overview of different strategies to approach this issue, focusing on different MFA methods and highlighting the additional information that should be included to improve the outcome of an experiment and associate estimation procedures.     

Modulation of cytochrome P450 gene expression in primary hepatocytes on various artificial extracellular matrices

We studied effect of artificial extracellular matrices (ECMs), such as collagen I, poly (N-p-vinylbenzyl-4-O-β-d-galactopyranosyl-d-gluconamide)(PVLA) and E-cadherin–IgG Fc (E-cad-Fc) on hepatic metabolism to identify the mechanism of in vivo hepatocellular functional and metabolic integrity. mRNA expression of liver function marker, cytochrome P450 (CYP) and transporter genes in hepatocytes were compared among used ECMs using real-time RT-PCR. mRNA expressions of Cyp2c29 and Cyp2d22 among CYP genes in hepatocytes on PVLA were recovered after 3 days due to enhanced liver-specific function by the spheroid formation of hepatocytes whereas mRNA expressions of CYP genes in hepatocytes on collagen and E-cad-Fc drastically decreased with time. mRNA expressions of the Cyp2c29 and Cyp2d22 in hepatocytes on PVLA were more recovered in the presence of epidermal growth factor (EGF) due to the more and bigger spheroid formation of hepatocytes. Multidrug resistance-associated protein 2 (Mrp2) protein was accumulated at intracellular lumen as similar to bile duct in hepatocyte spheroid formed on PVLA, indicating that spheroid formation of hepatocytes is very important for maintaining liver functions.     

Analysis of amino acid supplementation effects on hepatocyte cultures using flux balance analysis

When cultured hepatocytes are exposed to challenging environments such as plasma, they frequently suffer a decline in liver-specific functions. Media supplements are sought to reduce or eliminate this effect. A rational design approach for amino acid supplementation in hepatocyte culture has been developed in our prior work, and designed amino acid supplementation (DAA) was found to increase urea and albumin production. To fully characterize the metabolic state of hepatocytes under different amino acid supplementations, a number of metabolite measurements are performed in this work and used in a metabolic network flexibility analysis framework including thermodynamic constraints to determine the range of values for the intracellular fluxes. A metabolic objective prediction model is used to infer the metabolic objectives of the hepatocytes and to quantify the intracellular flux distribution for three different amino acid supplementations. The results illustrate that DAA leads to greater fluxes in the tricarboxylic acid cycle (TCA) cycle, urea cycle, and fatty acid oxidation concomitant with lower fluxes in intracellular lipid metabolism compared with empirical amino acid and no amino acid supplementation for hepatocytes during plasma exposure. It is also found that hepatocytes exhibit flexibility in their metabolic objectives depending on the composition of the amino acid supplementations. By incorporating both experimental data and thermodynamic constraints into the mathematical model, the proposed approach leads to identification of metabolic objectives and characterization of fluxes' variability and pathway changes due to different cultured conditions.     

A review on metabolic pathway analysis with emphasis on isotope labeling approach

The recent progress on metabolic systems engineering was reviewed based on our recent research results in terms of (1) metabolic signal flow diagram approach, (2) metabolic flux analysis (MFA) in particular with intracellular isotopomer distribution using NMR and/or GC-MS, (3) synthesis and optimization of metabolic flux distribution (MFD), (4) modification of MFD by gene manipulation and by controlling culture environment, (5) metabolic control analysis (MCA), (6) design of metabolic regulation structure, and (7) identification of unknown pathways with isotope tracing by NMR. The main characteristics of metabolic engineering is to treat metabolism as a network or entirety instead of individual reactions. The applications were made for poly-3-hydroxybutyrate (PHB) production usingRalstonia eutropha and recombinantEscherichia coli, lactate production by recombinantSaccharomyces cerevisiae, pyruvate production by vitamin auxotrophic yeastToluropsis glabrata, lysine production usingCorynebacterium glutamicum, and energetic analysis of photosynthesic microorganisms such as Cyanobateria. The characteristics of each approach were reviewed with their applications. The approach based on isotope labeling experiments gives reliable and quantitative results for metabolic flux analysis. It should be recognized that the next stage should be toward the investigation of metabolic flux analysis with gene and protein expressions to uncover the metabolic regulation in relation to genetic modification and/or the change in the culture condition.     

Inverse modeling using multi-block PLS to determine the environmental conditions that provide optimal cellular function

MOTIVATIONS: Tissue engineering constitutes an important field with its potential of addressing the current shortage in organ availability. To successfully develop tissue-engineered organs, it is crucial to understand how to maintain the cells under conditions that maximize their ability to perform their physiological roles, regardless of the environment, whether the cells are part of an extracorporeal system, such as the bioartificial liver assist device, or an implantable tissue-engineered device. Our goals are to (1) provide insight into how cells will behave when confronted with changes in its environment and (2) determine the optimal environmental factors to achieve a desired level of cellular function. RESULTS: Diverse sets of environmental factors were used to systematically perturb the metabolic behavior associated with pre-conditioning and plasma supplementation. To probe metabolic state of hepatocytes, metabolic flux analysis was used to obtain the metabolic profile. We applied a multi-block partial least square (MPLS) model to relate environmental factors and fluxes to levels of intracellular lipids and urea synthesis. The MPLS model identified: (1) the most influential environmental factors and (2) how the metabolic pathways are altered by these factors. Finally, we inverted the MPLS model to determine the concentrations and types of environmental factors required to obtain the most economical solution for achieving optimal levels of cellular function for practical situations.     

Flux profile and modularity analysis of time-dependent metabolic changes of de novo adipocyte formation

White adipose tissue (WAT) mass is the main determinant of obesity and associated health risks. WAT expansion results from increases in white adipocyte cell number and size, which in turn reflect a series of shifts in the cellular metabolic state. To quantitatively profile the metabolic alterations occurring during de novo adipocyte formation, metabolic flux analysis (MFA) was used in conjunction with a novel modularity analysis algorithm on differentiating 3T3-L1 preadipocytes. Use of a type I collagen gel as an effective long-term culture substrate was also assessed. The calculated flux distributions predicted the sequential activation of several intracellular cross-compartmental pathways, including lipogenesis, the pentose phosphate pathway, and the malate cycle, in good agreement with earlier isotopic tracer experiments and gene profiling studies. Partition of the adipocyte metabolic network into highly interacting reaction subgroups suggested a functional reorganization of the major pathways consistent with the lipid-loading phenotype of the adipocyte. Flux and modularity analysis results together point to the flux distribution around pyruvate as a key indicator of adipocyte lipid accumulation.     

Optimization of 13C isotopic tracers for metabolic flux analysis in mammalian cells

Mammalian cells consume and metabolize various substrates from their surroundings for energy generation and biomass synthesis. Glucose and glutamine, in particular, are the primary carbon sources for proliferating cancer cells. While this combination of substrates generates static labeling patterns for use in (13)C metabolic flux analysis (MFA), the inability of single tracers to effectively label all pathways poses an obstacle for comprehensive flux determination within a given experiment. To address this issue we applied a genetic algorithm to optimize mixtures of (13)C-labeled glucose and glutamine for use in MFA. We identified tracer combinations that minimized confidence intervals in an experimentally determined flux network describing central carbon metabolism in tumor cells. Additional simulations were used to determine the robustness of the [1,2-(13)C(2)]glucose/[U-(13)C(5)]glutamine tracer combination with respect to perturbations in the network. Finally, we experimentally validated the improved performance of this tracer set relative to glucose tracers alone in a cancer cell line. This versatile method allows researchers to determine the optimal tracer combination to use for a specific metabolic network, and our findings applied to cancer cells significantly enhance the ability of MFA experiments to precisely quantify fluxes in higher organisms.     

Diabetes and apoptosis: liver

The liver is a central regulator of glucose homeostasis and stores or releases glucose according to metabolic demands. In insulin resistant states or diabetes the dysregulation of hepatic glucose release contributes significantly to the pathophysiology of these conditions. Acute or chronic liver disease can aggravate insulin resistance and the physiological effects of insulin on hepatocytes are disturbed. Insulin resistance has also been recognized as an independent risk factor for the development of liver injury. In the healthy liver tissue homeostasis is achieved through cell turnover by apoptosis and dysregulation of the physiological process resulting in too much or too little cell death can have potentially devastating effects on liver tissue. The delineation of the signaling pathways that mediate apoptosis changed the paradigms of understanding of many liver diseases. These signaling events include cell surface based receptor-ligand systems and intracellular signaling pathways that are regulated through kinases on multiple levels. The dissection of these signaling pathways has shown that the regulators of apoptosis signaling events in hepatocytes can also modulate insulin signaling pathways and that mediators of insulin resistance in turn influence liver cell apoptosis. This review will summarize the potential crosstalk between apoptosis and insulin resistance signaling events and discuss the involved mediators.     

A small metabolic flux model to identify transient metabolic regulations in Saccharomyces cerevisiae

The understanding of dynamic metabolic regulations is important for physiological studies and strain characterization tasks. The present study combined transient experiments with online metabolic flux analysis (MFA) in order to quantify metabolic regulations, namely carbon catabolite repression of respiration and transient acetic-acid production, in Saccharomyces cerevisiae during aerobic growth on glucose. The aim was to investigate which additional information can be gained from using a small metabolic flux model to study transient growth provoked by shift-up and shift-down experiments, compared to online monitoring alone. The MFA model allowed us to propose new correlations between pathways of the central metabolism. A linear correlation between glycolytic flux and respiratory capacity holds for shift-down and shift-up experiments. This confirmed that respiratory functions were subjected to carbon catabolite repression and suggested that respiratory capacity is controlled by the glycolytic flux rather than the glucose influx. Furthermore, the model showed that control of repression of respiration by the glycolytic flux was a dynamic phenomenon. Co-factor balancing within the MFA model showed that transient acetic-acid production indicated a transient limitation in another part of the central metabolism but not in oxidative phosphorylation. However, at super-critical growth rates and when coupling of anabolism and catabolism is resumed, the limitation shifts to oxidative phosphorylation, with the consequence that ethanol is formed. The online application of small metabolic flux models to transient experiments enhanced the physiological insight into transient growth and opens up the use of transient experiments as an efficient tool to understand dynamic metabolic regulations.     

A guide to metabolic flux analysis in metabolic engineering: Methods,tools and applications

The field of metabolic engineering is primarily concerned with improving the biological production of value-added chemicals, fuels and pharmaceuticals through the design, construction and optimization of metabolic pathways, redirection of intracellular fluxes, and refinement of cellular properties relevant for industrial bioprocess implementation. Metabolic network models and metabolic fluxes are central concepts in metabolic engineering, as was emphasized in the first paper published in this journal, “Metabolic fluxes and metabolic engineering” (Metabolic Engineering, 1: 1–11, 1999). In the past two decades, a wide range of computational, analytical and experimental approaches have been developed to interrogate the capabilities of biological systems through analysis of metabolic network models using techniques such as flux balance analysis (FBA), and quantify metabolic fluxes using constrained-based modeling approaches such as metabolic flux analysis (MFA) and more advanced experimental techniques based on the use of stable-isotope tracers, i.e. ¹³C-metabolic flux analysis (¹³C-MFA). In this review, we describe the basic principles of metabolic flux analysis, discuss current best practices in flux quantification, highlight potential pitfalls and alternative approaches in the application of these tools, and give a broad overview of pragmatic applications of flux analysis in metabolic engineering practice.     

Identification of distributed metabolic objectives in the hypermetabolic liver by flux and energy balance analysis

A methodology for inferring distributed metabolic objectives from time series flux data is developed by combining metabolic flux analysis, pathway identification, free energy balances, and nested optimization. This methodology is used to investigate the metabolic response of the rat liver to burn injury-induced whole body inflammation. Gibbs free energy changes were computed for stoichiometrically balanced sequences of reactions, or pathways, rather than individual reactions, to account for energetic coupling between reactions. Systematic enumeration of pathways proceeded by elementary flux mode (EFM) analysis. Together with stoichiometric balances and external metabolite flux measurements, the DeltaG(PATH)(o) criterion provided sufficient constraints to solve a series of nested optimization problems on the metabolic goal functions and associated flux distributions of fasted livers during the first-week time course of burn injury. The optimization results suggest that there is a consistent metabolic goal function for the liver that is insensitive to the changing metabolic burdens experienced by the liver during the first-week time course. As defined by the goal function coefficients, the global metabolic objective was to distribute the metabolic resources between amino acid metabolism and ketone body synthesis. These findings point to a role for the time-invariant structure of the metabolic reaction network, expressed as stoichiometric and thermodynamic constraints, in shaping the cellular metabolic objective.     

A transient isotopic labeling methodology for 13C metabolic flux analysis of photoautotrophic microorganisms

Metabolic flux analysis is increasingly recognized as an integral component of systems biology. However, techniques for experimental measurement of system-wide metabolic fluxes in purely photoautotrophic systems (growing on CO(2) as the sole carbon source) have not yet been developed due to the unique problems posed by such systems. In this paper, we demonstrate that an approach that balances positional isotopic distributions transiently is the only route to obtaining system-wide metabolic flux maps for purely autotrophic metabolism. The outlined transient (13)C-MFA methodology enables measurement of fluxes at a metabolic steady-state, while following changes in (13)C-labeling patterns of metabolic intermediates as a function of time, in response to a step-change in (13)C-label input. We use mathematical modeling of the transient isotopic labeling patterns of central intermediates to assess various experimental requirements for photoautotrophic MFA. This includes the need for intracellular metabolite concentration measurements and isotopic labeling measurements as a function of time. We also discuss photobioreactor design and operation in order to measure fluxes under precise environmental conditions. The transient MFA technique can be used to measure and compare fluxes under different conditions of light intensity, nitrogen sources or compare strains with various mutations or gene deletions and additions.     

Parallel labeling experiments with [U-(13)C]glucose validate E. coli metabolic network model for (13)C metabolic flux analysis

(13)C-metabolic flux analysis (MFA) is a widely used method for measuring intracellular metabolic fluxes in living cells. (13)C MFA relies on several key assumptions: (1) the assumed metabolic network model is complete, in that it accounts for all significant enzymatic and transport reactions; (2) (13)C-labeling measurements are accurate and precise; and (3) enzymes and transporters do not discriminate between (12)C- and (13)C-labeled metabolites. In this study, we tested these inherent assumptions of (13)C MFA for wild-type E. coli by parallel labeling experiments with [U-(13)C]glucose as tracer. Cells were grown in six parallel cultures in custom-constructed mini-bioreactors, starting from the same inoculum, on medium containing different mixtures of natural glucose and fully labeled [U-(13)C]glucose, ranging from 0% to 100% [U-(13)C]glucose. Macroscopic growth characteristics of E. coli showed no observable kinetic isotope effect. The cells grew equally well on natural glucose, 100% [U-(13)C]glucose, and mixtures thereof. (13)C MFA was then used to determine intracellular metabolic fluxes for several metabolic network models: an initial network model from literature; and extended network models that accounted for potential dilution effects of isotopic labeling. The initial network model did not give statistically acceptable fits and produced inconsistent flux results for the parallel labeling experiments. In contrast, an extended network model that accounted for dilution of intracellular CO(2) by exchange with extracellular CO(2) produced statistically acceptable fits, and the estimated metabolic fluxes were consistent for the parallel cultures. This study illustrates the importance of model validation for (13)C MFA. We show that an incomplete network model can produce statistically unacceptable fits, as determined by a chi-square test for goodness-of-fit, and return biased metabolic fluxes. The validated metabolic network model for E. coli from this study can be used in future investigations for unbiased metabolic flux measurements.     

Insights into metabolic efficiency from flux analysis

The efficiency of carbon and energy flows throughout metabolism defines the potential for growth and reproductive success of plants. Understanding the basis for metabolic efficiency requires relevant definitions of efficiency as well as measurements of biochemical functions through metabolism. Here insights into the basis of efficiency provided by (13)C-based metabolic flux analysis (MFA) as well as the uses and limitations of efficiency in predictive flux balance analysis (FBA) are highlighted. (13)C-MFA studies have revealed unusual features of central metabolism in developing green seeds for the efficient use of light to conserve carbon and identified metabolic inefficiencies in plant metabolism due to dissipation of ATP by substrate cycling. Constraints-based FBA has used efficiency to guide the prediction of the growth and actual internal flux distribution of plant systems. Comparisons in a few cases have been made between flux maps measured by (13)C-based MFA and those predicted by FBA assuming one or more maximal efficiency parameters. These studies suggest that developing plant seeds and photoautotrophic microorganisms may indeed have patterns of metabolic flux that maximize efficiency. MFA and FBA are synergistic toolsets for uncovering and explaining the metabolic basis of efficiencies and inefficiencies in plant systems.