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.     

A rational design approach for amino acid supplementation in hepatocyte culture

Improvement of culture media for mammalian cells is conducted via empirical adjustments, sometimes aided by statistical design methodologies. Here, we demonstrate a proof of principle for the use of constraints‐based modeling to achieve enhanced performance of liver‐specific functions of cultured hepatocytes during plasma exposure by adjusting amino acid supplementation and hormone levels in the medium. Flux balance analysis (FBA) is used to determine an amino acid flux profile consistent with a desired output; this is used to design an amino acid supplementation. Under conditions of no supplementation, empirical supplementation, and designed supplementation, hepatocytes were exposed to plasma and their morphology, specific cell functions (urea, albumin production) and lipid metabolism were measured. Urea production under the designed amino acid supplementation was found to be increased compared with previously reported (empirical) amino acid supplementation. Not surprisingly, the urea production attained was less than the theoretical value, indicating the existence of pathways or constraints not present in the current model. Although not an explicit design objective, albumin production was also increased by designed amino acid supplementation, suggesting a functional linkage between these outputs. In conjunction with traditional approaches to improving culture conditions, the rational design approach described herein provides a novel means to tune the metabolic outputs of cultured hepatocytes. Biotechnol. Bioeng. 2009;103: 1176–1191. © 2009 Wiley Periodicals, Inc.     

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.     

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.     

Application of multivariate analysis to optimize function of cultured hepatocytes

Understanding the metabolic and regulatory pathways of hepatocytes is important for biotechnological applications involving liver cells, including the development of bioartificial liver (BAL) devices. To characterize intermediary metabolism in the hepatocytes, metabolic flux analysis (MFA) was applied to elucidate the changes in intracellular pathway fluxes of primary rat hepatocytes exposed to human plasma and to provide a comprehensive snapshot of the hepatic metabolic profile. In the current study, the combination of preconditioning and plasma supplementation produced distinct metabolic states. Combining the metabolic flux distribution obtained by MFA with methodologies such as Fisher discriminant analysis (FDA) and partial least squares or projection to latent structures (PLS) provided insights into the underlying structure and causal relationship within the data. With the aid of these analyses, patterns in the cellular response of the hepatocytes that contributed to the separation of the different hepatic states were identified. Of particular interest was the recognition of distal pathways that strongly correlated with a particular hepatic function. The hepatic functions investigated were intracellular triglyceride accumulation and urea production. This study illustrates a framework for optimizing hepatic function and a possibility of identifying potential targets for improving hepatic functions.     

Metabolic profiling based quantitative evaluation of hepatocellular metabolism in presence of adipocyte derived extracellular matrix

The elucidation of the effect of extracellular matrices on hepatocellular metabolism is critical to understand the mechanism of functional upregulation. We have developed a system using natural extracellular matrices [Adipogel] for enhanced albumin synthesis of rat hepatocyte cultures for a period of 10 days as compared to collagen sandwich cultures. Primary rat hepatocytes isolated from livers of female Lewis rats recover within 4 days of culture from isolation induced injury while function is stabilized at 7 days post-isolation. Thus, the culture period can be classified into three distinct stages viz. recovery stage [day 0-4], pre-stable stage [day 5-7] and the stable stage [day 8-10]. A Metabolic Flux Analysis of primary rat hepatocytes cultured in Adipogel was performed to identify the key metabolic pathways modulated as compared to collagen sandwich cultures. In the recovery stage [day 4], the collagen-soluble Adipogel cultures shows an increase in TriCarboxylic Acid [TCA] cycle fluxes; in the pre-stable stage [day 7], there is an increase in PPP and TCA cycle fluxes while in the stable stage [day 10], there is a significant increase in TCA cycle, urea cycle fluxes and amino acid uptake rates concomitant with increased albumin synthesis rate as compared to collagen sandwich cultures throughout the culture period. Metabolic analysis of the collagen-soluble Adipogel condition reveals significantly higher transamination reaction fluxes, amino acid uptake and albumin synthesis rates for the stable vs. recovery stages of culture. The identification of metabolic pathways modulated for hepatocyte cultures in presence of Adipogel will be a useful step to develop an optimization algorithm to further improve hepatocyte function for Bioartificial Liver Devices. The development of this framework for upregulating hepatocyte function in Bioartificial Liver Devices will facilitate the utilization of an integrated experimental and computational approach for broader applications of Adipogel in tissue e engineering and regenerative medicine.     

Metabolic pre-conditioning of cultured cells in physiological levels of insulin: generating resistance to the lipid-accumulating effects of plasma in hepatocytes

Understanding the regulation of hepatocyte lipid metabolism is important for several biotechnological applications involving liver cells. During exposure of hepatocytes to plasma, as is the case in extracorporeal bioartificial liver assist devices, it has been reported that hepatic-specific functions, e.g., albumin and urea synthesis and diazepam removal, are dramatically compromised and hepatocytes progressively accumulate cytoplasmic lipid droplets. We hypothesized that the composition of hepatocyte culture medium significantly affects lipid metabolism during subsequent plasma exposure. Rat hepatocytes were cultured in medium containing either physiological (50 microU/mL) or supra-physiological (500 mU/mL) insulin levels for 1 week and then exposed to human plasma supplemented with or without amino acids. We found that insulin's anabolic effects, such as stimulation of triglyceride storage, were carried over from the pre-conditioning to the plasma exposure period. While hepatocytes cultured in high insulin medium accumulated large quantities of triglycerides during subsequent plasma exposure, culture in low insulin medium largely prevented lipid accumulation. Urea and albumin secretion, as well as the ammonia removal rate, were largely unaffected by insulin but increased with amino acid supplementation. Thus, hepatocyte metabolism during plasma exposure can be modulated by medium pre-conditioning and supplements added to plasma.     

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. Biotechnol. Bioeng. 2010;107: 347–356. © 2010 Wiley Periodicals, Inc.     

Identification of metabolic fluxes in hepatic cells from transient 13C-labeling experiments: Part I. Experimental observations

An experimental set-up for acquiring metabolite and transient (13)C-labeling data in mammalian cells is presented. An efficient sampling procedure was established for hepatic cells cultured in six-well plates as a monolayer attached to collagen, which allowed simultaneous quenching of metabolism and extraction of the intracellular intermediates of interest. Extracellular concentrations of glucose, amino acids, lactate, pyruvate, and urea were determined by GC-MS procedures and were used for estimation of metabolic uptake and excretion rates. Sensitive LC-MS and GC-MS methods were used to quantify the intracellular intermediates of tricarboxylic acid cycle, glycolysis, and pentose phosphate pathway and for the determination of isotopomer fractions of the respective metabolites. Mass isotopomer fractions were determined in a transient (13)C-labeling experiment using (13)C-labeled glucose as substrate. The absolute amounts of intracellular metabolites were obtained from a non-labeled experiment carried out in exactly the same way as the (13)C-labeling experiment, except that the media contained naturally labeled glucose only. Estimation of intracellular metabolic fluxes from the presented data is addressed in part II of this contribution.     

Genome-scale model for Clostridium acetobutylicum: Part I. Metabolic network resolution and analysis

A genome-scale metabolic network reconstruction for Clostridium acetobutylicum (ATCC 824) was carried out using a new semi-automated reverse engineering algorithm. The network consists of 422 intracellular metabolites involved in 552 reactions and includes 80 membrane transport reactions. The metabolic network illustrates the reliance of clostridia on the urea cycle, intracellular L-glutamate solute pools, and the acetylornithine transaminase for amino acid biosynthesis from the 2-oxoglutarate precursor. The semi-automated reverse engineering algorithm identified discrepancies in reaction network databases that are major obstacles for fully automated network-building algorithms. The proposed semi-automated approach allowed for the conservation of unique clostridial metabolic pathways, such as an incomplete TCA cycle. A thermodynamic analysis was used to determine the physiological conditions under which proposed pathways (e.g., reverse partial TCA cycle and reverse arginine biosynthesis pathway) are feasible. The reconstructed metabolic network was used to create a genome-scale model that correctly characterized the butyrate kinase knock-out and the asolventogenic M5 pSOL1 megaplasmid degenerate strains. Systematic gene knock-out simulations were performed to identify a set of genes encoding clostridial enzymes essential for growth in silico.     

Effect of amino acid supplementation on titer and glycosylation distribution in hybridoma cell cultures—Systems biology‐based interpretation using genome‐scale metabolic flux balance model and multivariate data analysis

Genome‐scale flux balance analysis (FBA) is a powerful systems biology tool to characterize intracellular reaction fluxes during cell cultures. FBA estimates intracellular reaction rates by optimizing an objective function, subject to the constraints of a metabolic model and media uptake/excretion rates. A dynamic extension to FBA, dynamic flux balance analysis (DFBA), can calculate intracellular reaction fluxes as they change during cell cultures. In a previous study by Read et al. (2013), a series of informed amino acid supplementation experiments were performed on twelve parallel murine hybridoma cell cultures, and this data was leveraged for further analysis (Read et al., Biotechnol Prog. 2013;29:745–753). In order to understand the effects of media changes on the model murine hybridoma cell line, a systems biology approach is applied in the current study. Dynamic flux balance analysis was performed using a genome‐scale mouse metabolic model, and multivariate data analysis was used for interpretation. The calculated reaction fluxes were examined using partial least squares and partial least squares discriminant analysis. The results indicate media supplementation increases product yield because it raises nutrient levels extending the growth phase, and the increased cell density allows for greater culture performance. At the same time, the directed supplementation does not change the overall metabolism of the cells. This supports the conclusion that product quality, as measured by glycoform assays, remains unchanged because the metabolism remains in a similar state. Additionally, the DFBA shows that metabolic state varies more at the beginning of the culture but less by the middle of the growth phase, possibly due to stress on the cells during inoculation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1163–1173, 2016     

Genome-scale metabolic flux analysis of Streptomyces lividans growing on a complex medium

Constraint-based metabolic modeling comprises various excellent tools to assess experimentally observed phenotypic behavior of micro-organisms in terms of intracellular metabolic fluxes. In combination with genome-scale metabolic networks, micro-organisms can be investigated in much more detail and under more complex environmental conditions. Although complex media are ubiquitously applied in industrial fermentations and are often a prerequisite for high protein secretion yields, such multi-component conditions are seldom investigated using genome-scale flux analysis. In this paper, a systematic and integrative approach is presented to determine metabolic fluxes in Streptomyces lividans TK24 grown on a nutritious and complex medium. Genome-scale flux balance analysis and randomized sampling of the solution space are combined to extract maximum information from exometabolome profiles. It is shown that biomass maximization cannot predict the observed metabolite production pattern as such. Although this cellular objective commonly applies to batch fermentation data, both input and output constraints are required to reproduce the measured biomass production rate. Rich media hence not necessarily lead to maximum biomass growth. To eventually identify a unique intracellular flux vector, a hierarchical optimization of cellular objectives is adopted. Out of various tested secondary objectives, maximization of the ATP yield per flux unit returns the closest agreement with the maximum frequency in flux histograms. This unique flux estimation is hence considered as a reasonable approximation for the biological fluxes. Flux maps for different growth phases show no active oxidative part of the pentose phosphate pathway, but NADPH generation in the TCA cycle and NADPH transdehydrogenase activity are most important in fulfilling the NADPH balance. Amino acids contribute to biomass growth by augmenting the pool of available amino acids and by boosting the TCA cycle, particularly when using glutamate and aspartate. Depletion of glutamate and aspartate causes a distinct shift in fluxes of the central carbon and nitrogen metabolism. In the current work, hurdles encountered in flux analysis at a genome-scale level are addressed using hierarchical flux balance analysis and uniform sampling of the constrained solution space. This general framework can now be adopted in further studies of S. lividans, e.g., as a host for heterologous protein production.     

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.     

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.     

Metabolic flux analysis for serine alkaline protease fermentation by Bacillus licheniformis in a defined medium: effects of the oxygen transfer rate.

The metabolic fluxes through the central carbon pathways in the bioprocess for serine alkaline protease (SAP) production by Bacillus licheniformis were calculated by the metabolic flux-based stoichiometric model based on the proposed metabolic network that contains 102 metabolites and 133 reaction fluxes using the time profiles of citrate, dry cell, organic acids, amino acids, and SAP as the constraints. The model was solved by minimizing the SAP accumulation rate in the cell. The effects of the oxygen-transfer rate (OTR) on the metabolic fluxes were investigated in a defined medium where citrate was used as the sole carbon source. The central pathways were active for the growth and the SAP synthesis in all the periods of the bioprocess at low (LOT), medium (MOT), and high (HOT) oxygen-transfer conditions. The flux partitioning in the TCA cycle at alpha-ketoglutarate towards glutamate group and at oxalacetate (OA) toward aspartic acid group amino acids were dependent on the OTR. The flux of the anaplerotic reaction that connects the TCA cycle either from malate or OA to the gluconeogenesis pathway via the main branch point pyruvate (Pyr) was also influenced by the OTR. With the decrease in the OTR, the intracellular flux values after glycerate 3-phosphate (PG3) in the gluconeogenesis pathway and the specific growth rate decreased. The total ATP-generation rate increased with the increase in OTR. The pathway towards the aspartic acid family amino acids which is important for sporulation that precedes the SAP synthesis were all active throughout the bioprocess. Metabolic flux analysis results at LOT, MOT, and HOT conditions encourage the design of an oxygen-transfer strategy in the bioreactor; moreover, asparagine synthetase or aspartate kinase could be the potential metabolic engineering sites due to the low value of the flux from the branch point aspartate toward asparagine.     

System-level insights into yeast metabolism by thermodynamic analysis of elementary flux modes

One of the most obvious phenotypes of a cell is its metabolic activity, which is defined by the fluxes in the metabolic network. Although experimental methods to determine intracellular fluxes are well established, only a limited number of fluxes can be resolved. Especially in eukaryotes such as yeast, compartmentalization and the existence of many parallel routes render exact flux analysis impossible using current methods. To gain more insight into the metabolic operation of S. cerevisiae we developed a new computational approach where we characterize the flux solution space by determining elementary flux modes (EFMs) that are subsequently classified as thermodynamically feasible or infeasible on the basis of experimental metabolome data. This allows us to provably rule out the contribution of certain EFMs to the in vivo flux distribution. From the 71 million EFMs in a medium size metabolic network of S. cerevisiae, we classified 54% as thermodynamically feasible. By comparing the thermodynamically feasible and infeasible EFMs, we could identify reaction combinations that span the cytosol and mitochondrion and, as a system, cannot operate under the investigated glucose batch conditions. Besides conclusions on single reactions, we found that thermodynamic constraints prevent the import of redox cofactor equivalents into the mitochondrion due to limits on compartmental cofactor concentrations. Our novel approach of incorporating quantitative metabolite concentrations into the analysis of the space of all stoichiometrically feasible flux distributions allows generating new insights into the system-level operation of the intracellular fluxes without making assumptions on metabolic objectives of the cell.     

Control by pH of urea synthesis in isolated rat hepatocytes

Control by pH of urea synthesis has been studied in isolated rat hepatocytes incubated with a physiological mixture of amino acids. Inhibition of urea synthesis by decreasing the pH of the medium was caused by diminished production of ammonia and not, as suggested in the literature, by inhibition of entry of ammonia into the ornithine cycle. The decrease by low pH of the rate of degradation of the added amino acids, that of alanine being quantitatively the most important, was accompanied by a decrease in their intracellular concentration. It is concluded that inhibited transport of amino acids across the plasma membrane of the hepatocyte is responsible, at least in part, for the fall in urea synthesis with decreasing pH. It is proposed that inhibition by low pH of other steps in the ureogenic pathway, distal to the production of ammonia, does not affect flux through the ornithine cycle per se, but rather contributes to the buffering of the intrahepatic concentration of ammonia.     

Hepatocyte heterogeneity in the metabolism of amino acids and ammonia.

With respect to hepatocyte heterogeneity in ammonia and amino acid metabolism, two different patterns of sublobular gene expression are distinguished: 'gradient-type' and 'strict- or compartment-type' zonation. An example for strict-type zonation is the reciprocal distribution of carbamoylphosphate synthase and glutamine synthase in the liver lobule. The mechanisms underlying the different sublobular gene expressions are not yet settled but may involve the development of hepatic architecture, innervation, blood-borne hormonal and metabolic factors. The periportal zone is characterized by a high capacity for uptake and catabolism of amino acids (except glutamate and aspartate) as well as for urea synthesis and gluconeogenesis. On the other hand, glutamine synthesis, ornithine transamination and the uptake of vascular glutamate, aspartate, malate and alpha-ketoglutarate are restricted to a small perivenous hepatocyte population. Accordingly, in the intact liver lobule the major pathways for ammonia detoxication, urea and glutamine synthesis, are anatomically switched behind each other and represent in functional terms the sequence of the periportal low affinity system (urea synthesis) and a previous high affinity system (glutamine synthesis) for ammonia detoxication. Perivenous glutamine synthase-containing hepatocytes ('scavenger cells') act as a high affinity scavenger for the ammonia, which escapes the more upstream urea-synthesizing compartment. Periportal glutaminase acts as a pH- and hormone-modulated ammonia-amplifying system in the mitochondria of periportal hepatocytes. The activity of this amplifying system is one crucial determinant for flux through the urea cycle in view of the high Km (ammonia) of carbamoylphosphate synthase, the rate-controlling enzyme of the urea cycle. The structural and functional organization of glutamine and ammonia-metabolizing pathways in the liver lobule provides one basis for the understanding of a hepatic role in systemic acid base homeostasis. Urea synthesis is a major pathway for irreversible removal of metabolically generated bicarbonate. The lobular organization enables the adjustment of the urea cycle flux and accordingly the rate of irreversible hepatic bicarbonate elimination to the needs of the systemic acid base situation, without the threat of hyperammonemia.     

Inclusion of maintenance energy improves the intracellular flux predictions of CHO

Chinese hamster ovary (CHO) cells are the leading platform for the production of biopharmaceuticals with human-like glycosylation. The standard practice for cell line generation relies on trial and error approaches such as adaptive evolution and high-throughput screening, which typically take several months. Metabolic modeling could aid in designing better producer cell lines and thus shorten development times. The genome-scale metabolic model (GSMM) of CHO can accurately predict growth rates. However, in order to predict rational engineering strategies it also needs to accurately predict intracellular fluxes. In this work we evaluated the agreement between the fluxes predicted by parsimonious flux balance analysis (pFBA) using the CHO GSMM and a wide range of ¹³C metabolic flux data from literature. While glycolytic fluxes were predicted relatively well, the fluxes of tricarboxylic acid (TCA) cycle were vastly underestimated due to too low energy demand. Inclusion of computationally estimated maintenance energy significantly improved the overall accuracy of intracellular flux predictions. Maintenance energy was therefore determined experimentally by running continuous cultures at different growth rates and evaluating their respective energy consumption. The experimentally and computationally determined maintenance energy were in good agreement. Additionally, we compared alternative objective functions (minimization of uptake rates of seven nonessential metabolites) to the biomass objective. While the predictions of the uptake rates were quite inaccurate for most objectives, the predictions of the intracellular fluxes were comparable to the biomass objective function.     

Quantitative analysis of metabolic fluxes in Escherichia coli, using two-dimensional NMR spectroscopy and complete isotopomer models.

Complete isotopomer models that simulate distribution of label in 13C tracer experiments are applied to the quantification of metabolic fluxes in the primary carbon metabolism of E. coli under aerobic and anaerobic conditions. The concept of isotopomer mapping matrices (IMMs) is used to simplify the formulation of isotopomer mass balances by expressing all isotopomer mass balances of a metabolite pool in a single matrix equation. A numerically stable method to calculate the steady-state isotopomer distribution in metabolic networks in introduced. Net values of intracellular fluxes and the degree of reversibility of enzymatic steps are estimated by minimization of the deviations between experimental and simulated measurements. The metabolic model applied includes the Embden-Meyerhof-Parnas and the pentose phosphate pathway, the tricarboxylic acid cycle, anaplerotic reaction sequences and pathways involved in amino acid synthesis. The study clearly demonstrates the value of complete isotopomer models for maximizing the information obtainable from 13C tracer experiments. The approach applied here offers a completely general and comprehensive analysis of carbon tracer experiments where any set of experimental data on the labeling state and extracellular fluxes can be used for the quantification of metabolic fluxes in complex metabolic networks.