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

Discovery of substrate cycles in large scale metabolic networks using hierarchical modularity

BACKGROUND

A substrate cycle is a set of metabolic reactions, arranged in a loop, which does not result in net consumption or production of the metabolites. The cycle operates by transforming a cofactor, e.g. oxidizing a reducing equivalent. Substrate cycles have been found experimentally in many parts of metabolism; however, their physiological roles remain unclear. As genome-scale metabolic models become increasingly available, there is now the opportunity to comprehensively catalogue substrate cycles, and gain additional insight into this potentially important motif of metabolic networks.

RESULTS

We present a method to identify substrate cycles in the context of metabolic modules, which facilitates functional analysis. This method utilizes elementary flux mode (EFM) analysis to find potential substrate cycles in the form of cyclical EFMs, and combines this analysis with network partition based on retroactive (cyclical) interactions between reactions. In addition to providing functional context, partitioning the network into modules allowed exhaustive EFM calculations on smaller, tractable subnetworks that are enriched in metabolic cycles. Applied to a large-scale model of human liver metabolism (HepatoNet1), our method found not only well-known substrate cycles involving ATP hydrolysis, but also potentially novel substrate cycles involving the transformation of other cofactors. A key characteristic of the substrate cycles identified in this study is that the lengths are relatively short (2-13 reactions), comparable to many experimentally observed substrate cycles.

CONCLUSIONS

EFM computation for large scale networks remains computationally intractable for exhaustive substrate cycle enumeration. Our algorithm utilizes a 'divide and conquer' strategy where EFM analysis is performed on systematically identified network modules that are designed to be enriched in cyclical interactions. We find that several substrate cycles uncovered using our approach are not identified when the network is partitioned in a more generic manner based solely on connectivity rather than cycles, demonstrating the value of targeting motif searches to sub-networks replete with a topological feature that resembles the desired motif itself.

Recent advances in 2D and 3D in vitro systems using primary hepatocytes, alternative hepatocyte sources and non-parenchymal liver cells and their use in investigating mechanisms of hepatotoxicity, cell signaling and ADME

This review encompasses the most important advances in liver functions and hepatotoxicity and analyzes which mechanisms can be studied in vitro. In a complex architecture of nested, zonated lobules, the liver consists of approximately 80 % hepatocytes and 20 % non-parenchymal cells, the latter being involved in a secondary phase that may dramatically aggravate the initial damage. Hepatotoxicity, as well as hepatic metabolism, is controlled by a set of nuclear receptors (including PXR, CAR, HNF-4α, FXR, LXR, SHP, VDR and PPAR) and signaling pathways. When isolating liver cells, some pathways are activated, e.g., the RAS/MEK/ERK pathway, whereas others are silenced (e.g. HNF-4α), resulting in up- and downregulation of hundreds of genes. An understanding of these changes is crucial for a correct interpretation of in vitro data. The possibilities and limitations of the most useful liver in vitro systems are summarized, including three-dimensional culture techniques, co-cultures with non-parenchymal cells, hepatospheres, precision cut liver slices and the isolated perfused liver. Also discussed is how closely hepatoma, stem cell and iPS cell-derived hepatocyte-like-cells resemble real hepatocytes. Finally, a summary is given of the state of the art of liver in vitro and mathematical modeling systems that are currently used in the pharmaceutical industry with an emphasis on drug metabolism, prediction of clearance, drug interaction, transporter studies and hepatotoxicity. One key message is that despite our enthusiasm for in vitro systems, we must never lose sight of the in vivo situation. Although hepatocytes have been isolated for decades, the hunt for relevant alternative systems has only just begun.

Effect of fasting on the metabolic response of liver to experimental burn injury

Liver metabolism is altered after systemic injuries such as burns and trauma. These changes have been elucidated in rat models of experimental burn injury where the liver was isolated and perfused ex vivo. Because these studies were performed in fasted animals to deplete glycogen stores, thus simplifying quantification of gluconeogenesis, these observations reflect the combined impact of fasting and injury on liver metabolism. Herein we asked whether the metabolic response to experimental burn injury is different in fed vs. fasted animals. Rats were subjected to a cutaneous burn covering 20% of the total body surface area, or to similar procedures without administering the burn, hence a sham-burn. Half of the animals in the burn and sham-burn groups were fasted starting on postburn day 3, and the others allowed to continue ad libitum. On postburn day 4, livers were isolated and perfused for 1 hour in physiological medium supplemented with 10% hematocrit red blood cells. The uptake/release rates of major carbon and nitrogen sources, oxygen, and carbon dioxide were measured during the perfusion and the data fed into a mass balance model to estimate intracellular fluxes. The data show that in fed animals, injury increased glucose output mainly from glycogen breakdown and minimally impacted amino acid metabolism. In fasted animals, injury did not increase glucose output but increased urea production and the uptake of several amino acids, namely glutamine, arginine, glycine, and methionine. Furthermore, sham-burn animals responded to fasting by triggering gluconeogenesis from lactate; however, in burned animals the preferred gluconeogenic substrate was amino acids. Taken together, these results suggest that the fed state prevents the burn-induced increase in hepatic amino acid utilization for gluconeogenesis. The role of glycogen stores and means to increase and/or maintain internal sources of glucose to prevent increased hepatic amino acid utilization warrant further studies.

Minimal cut sets and the use of failure modes in metabolic networks

A minimal cut set is a minimal set of reactions whose inactivation would guarantee a failure in a certain network function or functions. Minimal cut sets (MCSs) were initially developed from the metabolic pathway analysis method (MPA) of elementary modes (EMs); they provide a way of identifying target genes for eliminating a certain objective function from a holistic perspective that takes into account the structure of the whole metabolic network. The concept of MCSs is fairly new and still being explored and developed; the initial concept has developed into a generalized form and its similarity to other network characterizations are discussed. MCSs can be used in conjunction with other constraints-based methods to get a better understanding of the capability of metabolic networks and the interrelationship between metabolites and enzymes/genes. The concept could play an important role in systems biology by contributing to fields such as metabolic and genetic engineering where it could assist in finding ways of producing industrially relevant compounds from renewable resources, not only for economical, but also for sustainability, reasons.

Metabolomics-on-a-chip and metabolic flux analysis for label-free modeling of the internal metabolism of HepG2/C3A cells

In vitro microfluidic systems are increasingly used as an alternative to standard Petri dishes in bioengineering and metabolomic investigations, as they are expected to provide cellular environments close to the in vivo conditions. In this work, we combined the recently developed "metabolomics-on-a-chip" approach with metabolic flux analysis to model the metabolic network of the hepatoma HepG2/C3A cell line and to infer the distribution of intracellular metabolic fluxes in standard Petri dishes and microfluidic biochips. A high pyruvate reduction to lactate was observed in both systems, suggesting that the cells operate in oxygen-limited environments. Our results also indicate that HepG2/C3A cells in the biochip are characterized by a higher consumption rate of oxygen, presumably due to a higher oxygenation rate in the microfluidic environment. This leads to a higher entry of the ultimate glycolytic product, acetyl-CoA, into the Krebs cycle. These findings are supported by the transcriptional activity of HepG2/C3A cells in both systems since we observed that genes regulated by a HIF-1 (hypoxia-regulated factor-1) transcriptional factor were over expressed under the Petri conditions, but to a lesser extent in the biochip.

Advanced stoichiometric analysis of metabolic networks of mammalian systems

Metabolic engineering tools have been widely applied to living organisms to gain a comprehensive understanding about cellular networks and to improve cellular properties. Metabolic flux analysis (MFA), flux balance analysis (FBA), and metabolic pathway analysis (MPA) are among the most popular tools in stoichiometric network analysis. Although application of these tools into well-known microbial systems is extensive in the literature, various barriers prevent them from being utilized in mammalian cells. Limited experimental data, complex regulatory mechanisms, and the requirement of more complex nutrient media are some major obstacles in mammalian cell systems. However, mammalian cells have been used to produce therapeutic proteins, to characterize disease states or related abnormal metabolic conditions, and to analyze the toxicological effects of some medicinally important drugs. Therefore, there is a growing need for extending metabolic engineering principles to mammalian cells in order to understand their underlying metabolic functions. In this review article, advanced metabolic engineering tools developed for stoichiometric analysis including MFA, FBA, and MPA are described. Applications of these tools in mammalian cells are discussed in detail, and the challenges and opportunities are highlighted.

Stoichiometry based steady-state hepatic flux analysis: computational and experimental aspects

: The liver has many complex physiological functions, including lipid, protein and carbohydrate metabolism, as well as bile and urea production. It detoxifies toxic substances and medicinal products. It also plays a key role in the onset and maintenance of abnormal metabolic patterns associated with various disease states, such as burns, infections and major traumas. Liver cells have been commonly used in in vitro experiments to elucidate the toxic effects of drugs and metabolic changes caused by aberrant metabolic conditions, and to improve the functions of existing systems, such as bioartificial liver. More recently, isolated liver perfusion systems have been increasingly used to characterize intrinsic metabolic changes in the liver caused by various perturbations, including systemic injury, hepatotoxin exposure and warm ischemia. Metabolic engineering tools have been widely applied to these systems to identify metabolic flux distributions using metabolic flux analysis or flux balance analysis and to characterize the topology of the networks using metabolic pathway analysis. In this context, hepatic metabolic models, together with experimental methodologies where hepatocytes or perfused livers are mainly investigated, are described in detail in this review. The challenges and opportunities are also discussed extensively.

Metabolic network analysis of perfused livers under fed and fasted states: incorporating thermodynamic and futile-cycle-associated regulatory constraints

Isolated liver perfusion systems have been extensively used to characterize intrinsic metabolic changes in liver under various conditions, including systemic injury, hepatotoxin exposure, and warm ischemia. Most of these studies were performed utilizing fasted animals prior to perfusion so that a simplified metabolic network could be used in order to determine intracellular fluxes. However, fasting induced metabolic alterations might interfere with disease related changes. Therefore, there is a need to develop a "unified" metabolic flux analysis approach that could be similarly applied to both fed and fasted states. In this study we explored a methodology based on elementary mode analysis in order to determine intracellular fluxes and active pathways simultaneously. In order to decrease the solution space, thermodynamic constraints, and enzymatic regulatory properties for the formation of futile cycles were further considered in the model, resulting in a mixed integer quadratic programming problem. Given the published experimental observations describing the perfused livers under fed and fasted states, the proposed approach successfully determined that gluconeogenesis, glycogenolysis and fatty acid oxidation were active in both states. However, fasting increased the fluxes in gluconeogenic reactions whereas it decreased fluxes associated with glycogenolysis, TCA cycle, fatty acid oxidation and electron transport reactions. This analysis further identified that more pathways were found to be active in fed state while their weight values were relatively lower compared to fasted state. Glucose, lactate, glutamine, glutamate and ketone bodies were also found to be important external metabolites whose extracellular fluxes should be used in the hepatic metabolic network analysis. In conclusion, the mathematical formulation explored in this study is an attractive tool to analyze the metabolic network of perfused livers under various disease conditions. This approach could be simultaneously applied to both fasted and fed data sets.

Thermodynamic calculations for biochemical transport and reaction processes in metabolic networks

Thermodynamic analysis of metabolic networks has recently generated increasing interest for its ability to add constraints on metabolic network operation, and to combine metabolic fluxes and metabolite measurements in a mechanistic manner. Concepts for the calculation of the change in Gibbs energy of biochemical reactions have long been established. However, a concept for incorporation of cross-membrane transport in these calculations is still missing, although the theory for calculating thermodynamic properties of transport processes is long known. Here, we have developed two equivalent equations to calculate the change in Gibbs energy of combined transport and reaction processes based on two different ways of treating biochemical thermodynamics. We illustrate the need for these equations by showing that in some cases there is a significant difference between the proposed correct calculation and using an approximative method. With the developed equations, thermodynamic analysis of metabolic networks spanning over multiple physical compartments can now be correctly described.

The application of flux balance analysis in systems biology

An increasing number of genome-scale reconstructions of intracellular biochemical networks are being generated. Coupled with these stoichiometric models, several systems-based approaches for probing these reconstructions in silico have been developed. One such approach, called flux balance analysis (FBA), has been effective at predicting systemic phenotypes in the form of fluxes through a reaction network. FBA employs a linear programming (LP) strategy to generate a flux distribution that is optimized toward a particular 'objective,' subject to a set of underlying physicochemical and thermodynamic constraints. Although classical FBA assumes steady-state conditions, several extensions have been proposed in recent years to constrain the allowable flux distributions and enable characterization of dynamic profiles even with minimal kinetic information. Furthermore, FBA coupled with techniques for measuring fluxes in vivo has facilitated integration of computational and experimental approaches, and is allowing pursuit of rational hypothesis-driven research. Ultimately, as we will describe in this review, studying intracellular reaction fluxes allows us to understand network structure and function and has broad applications ranging from metabolic engineering to drug discovery.

Pathway analysis of liver metabolism under stressed condition

Pathway analysis is a useful tool which reveals important metabolic network properties. However, the big challenge is to propose an objective function for estimating active pathways, which represent the actual state of network. In order to provide weight values for all possible pathways within the metabolic network, this study presents different approaches, considering the structural and physiological properties of the metabolic network, aiming at a unique decomposition of the flux vector into pathways. These methods were used to analyze the hepatic metabolism considering available data sets obtained from the perfused livers of fasted rats receiving burn injury. Utilizing unique decomposition techniques and different fluxes revealed that higher weights were always attributed to short pathways. Specific pathways, including pyruvate, glutamate and oxaloacetate pools, and urea production from arginine, were found to be important or essential in all methods and experimental conditions. Moreover the pathways, including serine production from glycine and conversion between acetoacetate and B-OH-butyrate, were assigned higher weights. Pathway analysis was also used to identify the main sources for the production of certain products in the hepatic metabolic network to gain a better understanding of the effects of burn injury on liver metabolism.

Effects of glucose and insulin on HepG2-C3A cell metabolism

HepG2, hepatocellular carcinoma cells, are used in drug toxicity studies and have also been explored for bioartificial livers. For these applications, the cells are under variable levels of nutrients and hormones, the effects of which on metabolism are poorly understood. In this study, HepG2-C3A cells were cultured under varying levels of glucose (high, low, and glucose-free) and insulin (without and with physiological levels of insulin) for 5 days. Cell growth was found to be comparable between high and low glucose media and lowest for glucose-free medium. Several features of central metabolism were affected profoundly by the medium glucose levels. Glucose consumption was greater for low glucose medium compared to high glucose medium, consistent with known glucose feedback regulation mechanisms. Urea productivity was highest in glucose-free medium. Further, it was seen that lactate acted as an alternative carbon source in the absence of glucose, whereas it acted as a sink for the high and low glucose media. Using a metabolic network flexibility analysis (MNFA) framework with stoichiometric and thermodynamic constraints, intracellular fluxes under varying levels of glucose and insulin were evaluated. The analysis indicates that urea production in HepG2-C3A cells arises via the arginase II pathway rather than from ammonia detoxification. Further, involvement of the putrescine metabolism with glutamine metabolism caused higher urea production in glucose-free medium consistent with higher glutamine uptake. MNFA indicated that in high and low glucose media, glycolysis, glutaminolysis, and oxidative phosphorylation were the main sources of energy (NADH, NADPH, and ATP). In the glucose-free medium, due to very low glycolytic flux, higher malate to pyruvate glutaminolytic flux and TCA cycle contributed more significantly to energy metabolism. The presence of insulin lowered glycerol uptake and corresponding fluxes involved in lipid metabolism for all glucose levels but otherwise exerted negligible effect on metabolism. HepG2-C3A cells thus show distinct differences from primary hepatocytes in terms of energy metabolism and urea production. This knowledge can be used to design media supplements and metabolically engineer cells to restore necessary hepatic functions to HepG2-C3A cells for a range of applications.

Transcriptional and metabolic flux profiling of triadimefon effects on cultured hepatocytes

Conazoles are a class of azole fungicides used to prevent fungal growth in agriculture, for treatment of fungal infections, and are found to be tumorigenic in rats and/or mice. In this study, cultured primary rat hepatocytes were treated to two different concentrations (0.3 and 0.15 mM) of triadimefon, which is a tumorigenic conazole in rat and mouse liver, on a temporal basis with daily media change. Following treatment, cells were harvested for microarray data ranging from 6 to 72 h. Supernatant was collected daily for three days, and the concentrations of various metabolites in the media and supernatant were quantified. Gene expression changes were most significant following exposure to 0.3 mM triadimefon and were characterized mainly by metabolic pathways related to carbohydrate, lipid and amino acid metabolism. Correspondingly, metabolic network flexibility analysis demonstrated a switch from fatty acid synthesis to fatty acid oxidation in cells exposed to triadimefon. It is likely that fatty acid oxidation is active in order to supply energy required for triadimefon detoxification. In 0.15 mM triadimefon treatment, the hepatocytes are able to detoxify the relatively low concentration of triadimefon with less pronounced changes in hepatic metabolism.

Soft constraints-based multiobjective framework for flux balance analysis

The current state of the art for linear optimization in Flux Balance Analysis has been limited to single objective functions. Since mammalian systems perform various functions, a multiobjective approach is needed when seeking optimal flux distributions in these systems. In most of the available multiobjective optimization methods, there is a lack of understanding of when to use a particular objective, and how to combine and/or prioritize mutually competing objectives to achieve a truly optimal solution. To address these limitations we developed a soft constraints based linear physical programming-based flux balance analysis (LPPFBA) framework to obtain a multiobjective optimal solutions. The developed framework was first applied to compute a set of multiobjective optimal solutions for various pairs of objectives relevant to hepatocyte function (urea secretion, albumin, NADPH, and glutathione syntheses) in bioartificial liver systems. Next, simultaneous analysis of the optimal solutions for three objectives was carried out. Further, this framework was utilized to obtain true optimal conditions to improve the hepatic functions in a simulated bioartificial liver system. The combined quantitative and visualization framework of LPPFBA is applicable to any large-scale metabolic network system, including those derived by genomic analyses.

Which metabolic pathways generate and characterize the flux space? A comparison among elementary modes, extreme pathways and minimal generators

Important efforts are being done to systematically identify the relevant pathways in a metabolic network. Unsurprisingly, there is not a unique set of network-based pathways to be tagged as relevant, and at least four related concepts have been proposed: extreme currents, elementary modes, extreme pathways, and minimal generators. Basically, there are two properties that these sets of pathways can hold: they can generate the flux space--if every feasible flux distribution can be represented as a nonnegative combination of flux through them--or they can comprise all the nondecomposable pathways in the network. The four concepts fulfill the first property, but only the elementary modes fulfill the second one. This subtle difference has been a source of errors and misunderstandings. This paper attempts to clarify the intricate relationship between the network-based pathways performing a comparison among them.

Utilizing elementary mode analysis, pathway thermodynamics, and a genetic algorithm for metabolic flux determination and optimal metabolic network design

BACKGROUND

Microbial hosts offer a number of unique advantages when used as production systems for both native and heterologous small-molecules. These advantages include high selectivity and benign environmental impact; however, a principal drawback is low yield and/or productivity, which limits economic viability. Therefore a major challenge in developing a microbial production system is to maximize formation of a specific product while sustaining cell growth. Tools to rationally reconfigure microbial metabolism for these potentially conflicting objectives remain limited. Exhaustively exploring combinations of genetic modifications is both experimentally and computationally inefficient, and can become intractable when multiple gene deletions or insertions need to be considered. Alternatively, the search for desirable gene modifications may be solved heuristically as an evolutionary optimization problem. In this study, we combine a genetic algorithm and elementary mode analysis to develop an optimization framework for evolving metabolic networks with energetically favorable pathways for production of both biomass and a compound of interest.

RESULTS

Utilization of thermodynamically-weighted elementary modes for flux reconstruction of E. coli central metabolism revealed two clusters of EMs with respect to their Delta Gp degrees. For proof of principle testing, the algorithm was applied to ethanol and lycopene production in E. coli. The algorithm was used to optimize product formation, biomass formation, and product and biomass formation simultaneously. Predicted knockouts often matched those that have previously been implemented experimentally for improved product formation. The performance of a multi-objective genetic algorithm showed that it is better to couple the two objectives in a single objective genetic algorithm.

CONCLUSION

A computationally tractable framework is presented for the redesign of metabolic networks for maximal product formation combining elementary mode analysis (a form of convex analysis), pathway thermodynamics, and a genetic algorithm to optimize the production of two industrially-relevant products, ethanol and lycopene, from E. coli. The designed algorithm can be applied to any small-scale model of cellular metabolism theoretically utilizing any substrate and applied towards the production of any product.

Metabolic flux analysis of mitochondrial uncoupling in 3T3-L1 adipocytes

BACKGROUND

Increasing energy expenditure at the cellular level offers an attractive option to limit adiposity and improve whole body energy balance. In vivo and in vitro observations have correlated mitochondrial uncoupling protein-1 (UCP1) expression with reduced white adipose tissue triglyceride (TG) content. The metabolic basis for this correlation remains unclear.

METHODOLOGY/PRINCIPAL FINDINGS

This study tested the hypothesis that mitochondrial uncoupling requires the cell to compensate for the decreased oxidation phosphorylation efficiency by up-regulating lactate production, thus redirecting carbon flux away from TG synthesis. Metabolic flux analysis was used to characterize the effects of non-lethal, long-term mitochondrial uncoupling (up to 18 days) on the pathways of intermediary metabolism in differentiating 3T3-L1 adipocytes. Uncoupling was induced by forced expression of UCP1 and chemical (FCCP) treatment. Chemical uncoupling significantly decreased TG content by ca. 35%. A reduction in the ATP level suggested diminished oxidative phosphorylation efficiency in the uncoupled adipocytes. Flux analysis estimated significant up-regulation of glycolysis and down-regulation of fatty acid synthesis, with chemical uncoupling exerting quantitatively larger effects.

CONCLUSIONS/SIGNIFICANCE

The results of this study support our hypothesis regarding uncoupling-induced redirection of carbon flux into glycolysis and lactate production, and suggest mitochondrial proton translocation as a potential target for controlling adipocyte lipid metabolism.

Impact of perturbed pyruvate metabolism on adipocyte triglyceride accumulation

This study aimed to test the hypothesis that adipocyte TG accumulation could be altered by specifically perturbing pyruvate metabolism. We treated cultured 3T3-L1 adipocytes with chemical inhibitors of lactate dehydrogenase (LDH) and pyruvate carboxylase (PC), and characterized their global effects on intermediary metabolism using metabolic flux and isotopomer analysis. Inhibiting the enzymes over several days did not alter the adipocyte differentiation program as assessed by the expression levels of peroxisome proliferator-activated receptor-gamma and glycerol-3-phosphate dehydrogenase. The main metabolic effects were to up-regulate intracellular lipolysis and decrease TG accumulation. Inhibiting PC also up-regulated glycolysis. Flux estimates indicated that the reduction in TG was due to decreased de novo fatty acid synthesis. Exogenous addition of free fatty acids dose-dependently increased the cellular TG level in the inhibitor-treated adipocytes, but not in untreated control cells. The results of this study support our hypothesis regarding the critical role of pyruvate reactions in TG synthesis.

Spontaneous reaction silencing in metabolic optimization

Metabolic reactions of single-cell organisms are routinely observed to become dispensable or even incapable of carrying activity under certain circumstances. Yet, the mechanisms as well as the range of conditions and phenotypes associated with this behavior remain very poorly understood. Here we predict computationally and analytically that any organism evolving to maximize growth rate, ATP production, or any other linear function of metabolic fluxes tends to significantly reduce the number of active metabolic reactions compared to typical nonoptimal states. The reduced number appears to be constant across the microbial species studied and just slightly larger than the minimum number required for the organism to grow at all. We show that this massive spontaneous reaction silencing is triggered by the irreversibility of a large fraction of the metabolic reactions and propagates through the network as a cascade of inactivity. Our results help explain existing experimental data on intracellular flux measurements and the usage of latent pathways, shedding new light on microbial evolution, robustness, and versatility for the execution of specific biochemical tasks. In particular, the identification of optimal reaction activity provides rigorous ground for an intriguing knockout-based method recently proposed for the synthetic recovery of metabolic function.

Cellular growth and other integrated metabolic functions are manifestations of the coordinated interconversion of a large number of chemical compounds. But what is the relation between such whole-cell behaviors and the activity pattern of the individual biochemical reactions? In this study, we have used flux balance-based methods and reconstructed networks of Helicobacter pylori, Staphylococcus aureus, Escherichia coli, and Saccharomyces cerevisiae to show that a cell seeking to optimize a metabolic objective, such as growth, has a tendency to spontaneously inactivate a significant number of its metabolic reactions, while all such reactions are recruited for use in typical suboptimal states. The mechanisms governing this behavior not only provide insights into why numerous genes can often be disabled without affecting optimal growth but also lay a foundation for the recently proposed synthetic rescue of metabolic function in which the performance of suboptimally operating cells can be enhanced by disabling specific metabolic reactions. Our findings also offer explanation for another experimentally observed behavior, in which some inactive reactions are temporarily activated following a genetic or environmental perturbation. The latter is of utmost importance given that nonoptimal and transient metabolic behaviors are arguably common in natural environments.

Effect of uncoupling protein-1 expression on 3T3-L1 adipocyte gene expression

The mitochondrial respiratory uncoupling protein 1 (UCP1) partially uncouples substrate oxidation and oxidative phosphorylation to promote the dissipation of cellular biochemical energy as heat in brown adipose tissue. We have recently shown that expression of UCP1 in 3T3-L1 white adipocytes reduces the accumulation of triglycerides. Here, we investigated the molecular basis underlying UCP1 expression in 3T3-L1 adipocytes. Gene expression data showed that forced UCP1 expression down-regulated several energy metabolism pathways; but ATP levels were constant. A metabolic flux analysis model was used to reflect the gene expression changes onto metabolic processes and concordance was observed in the down-regulation of energy consuming pathways. Our data suggest that adipocytes respond to long-term mitochondrial uncoupling by minimizing ATP utilization.

A computer model of gluconeogenesis and lipid metabolism in the perfused liver

A mathematical model of the perfused rat liver was developed to predict intermediate metabolite concentrations and fluxes in response to changes in various substrate concentrations in the perfusion medium. The model simulates gluconeogenesis in the liver perfused separately with lactate and pyruvate and the combination of these substrates with fatty acids (oleate). The model consists of key reactions representing gluconeogenesis, glycolysis, fatty acid metabolism, tricarboxylic acid cycle, oxidative phosphorylation, and ketogenesis. Michaelis-Menten-type kinetic expressions, with control by ATP/ADP, are used for many of the reactions. For key regulated reactions (fructose-1,6-bisphosphatase, phosphofructokinase, pyruvate carboxylase, pyruvate dehydrogenase complex, and pyruvate kinase), rate expressions were developed that incorporate allosteric effectors, specific substrate relationships (e.g., cooperative binding), and/or phosphorylation/dephosphorylation using in vitro enzyme activity data and knowledge of the specific mechanisms. The model was independently validated by comparing model predictions with 10 sets of experimental data from 7 different published works, with no parameter adjustments. The simulations predict the same trends, in terms of stimulation of substrate uptake by fatty acid addition, as observed experimentally. In general, the major metabolic indicators calculated by the model are in good agreement with experimental results. For example, the simulated glucose/pyruvate mass yield is 43% compared with the average of 45% reported in the literature. The model accurately predicts the specific time constants of the glucose response (2.5-4 min) and the dynamic behavior of substrate and product fluxes. It is expected that this model will be a useful tool for analyzing the complex relationships between carbohydrate and fat metabolism.

Investigation of metabolic objectives in cultured hepatocytes

Using optimization based methods to predict fluxes in metabolic flux balance models has been a successful approach for some microorganisms, enabling construction of in silico models and even inference of some regulatory motifs. However, this success has not been translated to mammalian cells. The lack of knowledge about metabolic objectives in mammalian cells is a major obstacle that prevents utilization of various metabolic engineering tools and methods for tissue engineering and biomedical purposes. In this work, we investigate and identify possible metabolic objectives for hepatocytes cultured in vitro. To achieve this goal, we present a special data-mining procedure for identifying metabolic objective functions in mammalian cells. This multi-level optimization based algorithm enables identifying the major fluxes in the metabolic objective from MFA data in the absence of information about critical active constraints of the system. Further, once the objective is determined, active flux constraints can also be identified and analyzed. This information can be potentially used in a predictive manner to improve cell culture results or clinical metabolic outcomes. As a result of the application of this method, it was found that in vitro cultured hepatocytes maximize oxygen uptake, coupling of urea and TCA cycles, and synthesis of serine and urea. Selection of these fluxes as the metabolic objective enables accurate prediction of the flux distribution in the system given a limited amount of flux data; thus presenting a workable in silico model for cultured hepatocytes. It is observed that an overall homeostasis picture is also emergent in the findings.

Modular decomposition of metabolic reaction networks based on flux analysis and pathway projection

MOTIVATION

The rational decomposition of biochemical networks into sub-structures has emerged as a useful approach to study the design of these complex systems. A biochemical network is characterized by an inhomogeneous connectivity distribution, which gives rise to several organizational features, including modularity. To what extent the connectivity-based modules reflect the functional organization of the network remains to be further explored. In this work, we examine the influence of physiological perturbations on the modular organization of cellular metabolism.

RESULTS

Modules were characterized for two model systems, liver and adipocyte primary metabolism, by applying an algorithm for top-down partition of directed graphs with non-uniform edge weights. The weights were set by the engagement of the corresponding reactions as expressed by the flux distribution. For the base case of the fasted rat liver, three modules were found, carrying out the following biochemical transformations: ketone body production, glucose synthesis and transamination. This basic organization was further modified when different flux distributions were applied that describe the liver's metabolic response to whole body inflammation. For the fully mature adipocyte, only a single module was observed, integrating all of the major pathways needed for lipid storage. Weaker levels of integration between the pathways were found for the early stages of adipocyte differentiation. Our results underscore the inhomogeneous distribution of both connectivity and connection strengths, and suggest that global activity data such as the flux distribution can be used to study the organizational flexibility of cellular metabolism.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.