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1.  Edelfosine-induced metabolic changes in cancer cells that precede the overproduction of reactive oxygen species and apoptosis 
BMC Systems Biology  2010;4:135.
Metabolic flux profiling based on the analysis of distribution of stable isotope tracer in metabolites is an important method widely used in cancer research to understand the regulation of cell metabolism and elaborate new therapeutic strategies. Recently, we developed software Isodyn, which extends the methodology of kinetic modeling to the analysis of isotopic isomer distribution for the evaluation of cellular metabolic flux profile under relevant conditions. This tool can be applied to reveal the metabolic effect of proapoptotic drug edelfosine in leukemia Jurkat cell line, uncovering the mechanisms of induction of apoptosis in cancer cells.
The study of 13C distribution of Jukat cells exposed to low edelfosine concentration, which induces apoptosis in ≤5% of cells, revealed metabolic changes previous to the development of apoptotic program. Specifically, it was found that low dose of edelfosine stimulates the TCA cycle. These metabolic perturbations were coupled with an increase of nucleic acid synthesis de novo, which indicates acceleration of biosynthetic and reparative processes. The further increase of the TCA cycle fluxes, when higher doses of drug applied, eventually enhance reactive oxygen species (ROS) production and trigger apoptotic program.
The application of Isodyn to the analysis of mechanism of edelfosine-induced apoptosis revealed primary drug-induced metabolic changes, which are important for the subsequent initiation of apoptotic program. Initiation of such metabolic changes could be exploited in anticancer therapy.
PMCID: PMC2984393  PMID: 20925932
2.  Large-scale 13C-flux analysis reveals distinct transcriptional control of respiratory and fermentative metabolism in Escherichia coli 
The authors analyze the role transcription plays in regulating bacterial metabolic flux. Of 91 transcriptional regulators studied, 2/3 affect absolute fluxes, but only a small number of regulators control the partitioning of flux between different metabolic pathways.
In contrast to the canonical respiro-fermentative glucose metabolism, fully respiratory galactose metabolism depends exclusively on the PEP-glyoxylate cycle.Of 91 transcription factors, 2/3 affect absolute fluxes, but only one controls the distribution of fluxes on galactose and nine on glucose.Transcriptional control of hexose flux distributions is confined to the acetyl-CoA branch point.The PEP-glyoxylate cycle is controlled by cAMP-Crp in a hexose uptake rate-dependent manner.
Focusing on central carbon metabolism of Escherichia coli, we aim here to systematically identify transcriptional regulators that control the distribution of metabolic fluxes during aerobic growth on hexoses. To assess the condition dependence of transcriptional control of flux, we selected glucose and galactose as two substrates that are highly similar, yet lead to distinct growth rates (Soupene et al, 2003), overall metabolic rates (De Anda et al, 2006; Samir El et al, 2009) and levels of catabolite repression (Hogema et al, 1998; Bettenbrock et al, 2007).
Experimentally determined fluxes (Fischer and Sauer, 2003a) during growth on glucose and galactose reveal two distinct metabolic states. On glucose, high metabolic rates lead to high overflow metabolism and respiratory fluxes through the TCA cycle. On galactose, in contrast, metabolism was much slower without overflow metabolism and respiratory fluxes exclusively through the PEP-glyoxylate cycle (Fischer and Sauer, 2003b).
To determine which transcriptional events controlled these two distinct metabolic states, we determined intracellular fluxes in 91 transcription factor mutants. These genetic perturbations primarily affected absolute fluxes but not the distribution of fluxes. The distribution of flux between glycolysis and pentose–phosphate pathway in upper metabolism, e.g., remained constant in all mutants under all conditions. Transcriptional control of the flux distribution was exclusively seen at the acetyl-CoA branch point. On glucose, nine transcription factors controlled the distribution of fluxes at this branch point, five of which (ArcA, IHFA, IHFB, PdhR, Fur) did so presumably directly through their known targets in TCA cycle and/or respiration. Without known targets in the relevant pathways, the remaining four transcription factors (GlpR, QseB, HdfR, GlcC) may act either indirectly or directly through unknown targets. On galactose, transcriptional control focused exclusively on the PEP-glyoxylate cycle. While deletion of six transcription factors (Cra, Crp, IHFA, IHFB, Mlc, NagC) abolished or reduced the PEP-glyoxylate cycle flux, we demonstrate by substrate-limited chemostat experiments, derepression of galactose uptake and show by metabolomics that five of these transcription factors act indirectly through increased cAMP concentrations that allosterically activate Crp, the only direct transcription factors that controls the PEP-glyoxylate cycle (Nanchen et al, 2008).
Overall, our absolute flux data demonstrate that control of flux splitting during growth on hexoses was confined to the acetyl-CoA branch point in E. coli. Of the 36 transcription factors known to target genes in pathways that diverge from the acetyl-CoA branch point, only one transcription factor on galactose and five plus potentially four others on glucose showed altered flux splitting. The primary focus of steady state transcriptional control on the acetyl-CoA branch point, and thus the metabolic decision between the energetically efficient respiration and the less efficient but more rapid fermentation, was recently also demonstrated with only relative flux data for Saccharomyces cerevisiae (Fendt et al, 2010).
In contrast to glucose-grown Bacillus subtilis (Fischer et al, 2005), for batch glucose-grown E. coli, none of the investigated transcription factor mutants exhibited improved biomass productivity. However, the mutants Cra, IHF A, IHF B and NagC with increased uptake rates grew much faster at almost unaltered biomass yields. As the removal of the glucose PTS-based repression with a Crr mutant also resulted in increased galactose uptake, we provide evidence that E. coli actively represses its galactose uptake at the expense of otherwise possible rapid growth.
Despite our increasing topological knowledge on regulation networks in model bacteria, it is largely unknown which of the many co-occurring regulatory events actually control metabolic function and the distribution of intracellular fluxes. Here, we unravel condition-dependent transcriptional control of Escherichia coli metabolism by large-scale 13C-flux analysis in 91 transcriptional regulator mutants on glucose and galactose. In contrast to the canonical respiro-fermentative glucose metabolism, fully respiratory galactose metabolism depends exclusively on the phosphoenol-pyruvate (PEP)-glyoxylate cycle. While 2/3 of the regulators directly or indirectly affected absolute flux rates, the partitioning between different pathways remained largely stable with transcriptional control focusing primarily on the acetyl-CoA branch point. Flux distribution control was achieved by nine transcription factors on glucose, including ArcA, Fur, PdhR, IHF A and IHF B, but was exclusively mediated by the cAMP-dependent Crp regulation of the PEP-glyoxylate cycle flux on galactose. Five further transcription factors affected this flux only indirectly through cAMP and Crp by increasing the galactose uptake rate. Thus, E. coli actively limits its galactose catabolism at the expense of otherwise possible faster growth.
PMCID: PMC3094070  PMID: 21451587
central metabolism; fermentative growth; gene regulatory networks; respiratory growth; transcriptional regulation
3.  A novel deconvolution method for modeling UDP-N-acetyl-D-glucosamine biosynthetic pathways based on 13C mass isotopologue profiles under non-steady-state conditions 
BMC Biology  2011;9:37.
Stable isotope tracing is a powerful technique for following the fate of individual atoms through metabolic pathways. Measuring isotopic enrichment in metabolites provides quantitative insights into the biosynthetic network and enables flux analysis as a function of external perturbations. NMR and mass spectrometry are the techniques of choice for global profiling of stable isotope labeling patterns in cellular metabolites. However, meaningful biochemical interpretation of the labeling data requires both quantitative analysis and complex modeling. Here, we demonstrate a novel approach that involved acquiring and modeling the timecourses of 13C isotopologue data for UDP-N-acetyl-D-glucosamine (UDP-GlcNAc) synthesized from [U-13C]-glucose in human prostate cancer LnCaP-LN3 cells. UDP-GlcNAc is an activated building block for protein glycosylation, which is an important regulatory mechanism in the development of many prominent human diseases including cancer and diabetes.
We utilized a stable isotope resolved metabolomics (SIRM) approach to determine the timecourse of 13C incorporation from [U-13C]-glucose into UDP-GlcNAc in LnCaP-LN3 cells. 13C Positional isotopomers and isotopologues of UDP-GlcNAc were determined by high resolution NMR and Fourier transform-ion cyclotron resonance-mass spectrometry. A novel simulated annealing/genetic algorithm, called 'Genetic Algorithm for Isotopologues in Metabolic Systems' (GAIMS) was developed to find the optimal solutions to a set of simultaneous equations that represent the isotopologue compositions, which is a mixture of isotopomer species. The best model was selected based on information theory. The output comprises the timecourse of the individual labeled species, which was deconvoluted into labeled metabolic units, namely glucose, ribose, acetyl and uracil. The performance of the algorithm was demonstrated by validating the computed fractional 13C enrichment in these subunits against experimental data. The reproducibility and robustness of the deconvolution were verified by replicate experiments, extensive statistical analyses, and cross-validation against NMR data.
This computational approach revealed the relative fluxes through the different biosynthetic pathways of UDP-GlcNAc, which comprises simultaneous sequential and parallel reactions, providing new insight into the regulation of UDP-GlcNAc levels and O-linked protein glycosylation. This is the first such analysis of UDP-GlcNAc dynamics, and the approach is generally applicable to other complex metabolites comprising distinct metabolic subunits, where sufficient numbers of isotopologues can be unambiguously resolved and accurately measured.
PMCID: PMC3126751  PMID: 21627825
4.  Mechanism of liver glycogen repletion in vivo by nuclear magnetic resonance spectroscopy. 
Journal of Clinical Investigation  1985;76(3):1229-1236.
In order to quantitate the pathways by which liver glycogen is repleted, we administered [1-13C]glucose by gavage into awake 24-h fasted rats and examined the labeling pattern of 13C in hepatic glycogen. Two doses of [1-13C]glucose, 1 and 6 mg/g body wt, were given to examine whether differences in the plasma glucose concentration altered the metabolic pathways via which liver glycogen was replenished. After 1 and 3 h (high-dose group) and after 1 and 2 h (low-dose group), the animals were anesthetized and the liver was quickly freeze-clamped. Liver glycogen was extracted and the purified glycogen hydrolyzed to glucose with amyloglucosidase. The distribution of the 13C-label was subsequently determined by 13C-nuclear magnetic resonance spectroscopy. The percent 13C enrichment of the glucosyl units in glycogen was: 15.1 +/- 0.8%(C-1), 1.5 +/- 0.1%(C-2), 1.2 +/- 0.1%(C-3), 1.1 +/- 0.1%(C-4), 1.6 +/- 0.1%(C-5), and 2.2 +/- 0.1%(C-6) for the high-dose study (n = 4, at 3 h); 16.5 +/- 0.5%(C-1), 2.0 +/- 0.1%(C-2), 1.3 +/- 0.1%(C-3), 1.1 +/- 0.1%(C-4), 2.2 +/- 0.1%(C-5), and 2.4 +/- 0.1%(C-6) in the low-dose study (n = 4, at 2 h). The average 13C-enrichment of C-1 glucose in the portal vein was found to be 43 +/- 1 and 40 +/- 2% in the high- and low-dose groups, respectively. Therefore, the amount of glycogen that was synthesized from the direct pathway (i.e., glucose----glucose-6-phosphate----glucose-1-phosphate----UDP-glucose---- glycogen) was calculated to be 31 and 36% in the high- and low-dose groups, respectively. The 13C-enrichments of portal vein lactate and alanine were 14 and 14%, respectively, in the high-dose group and 11 and 8%, respectively, in the low-dose group. From these enrichments, the minimum contribution of these gluconeogenic precursors to glycogen repletion can be calculated to be 7 and 20% in the high- and low-dose groups, respectively. The maximum contribution of glucose recycling at the triose isomerase step to glycogen synthesis (i.e., glucose----triose-phosphates----glycogen) was estimated to be 3 and 1% in the high- and low-dose groups, respectively. In conclusion, our results demonstrate that (a) only one-third of liver glycogen repletion occurs via the direct conversion of glucose to glycogen, and that (b) only a very small amount of glycogen synthesis can be accounted for by the conversion of glucose to triose phosphates and back to glycogen; this suggests that futile cycling between fructose-6-phosphate and fructose-1,6-diphosphate under these conditions is minimal. Our results also show that (c) alanine and lactate account for a minimum of between 7 and 20% of the glycogen synthesized, and that (d) the three pathways through which the labeled flux is measured account for a total of only 50% of the total glycogen synthesized. These results suggest that either there is a sizeable amount of glycogen synthesis via pathway(s) that were not examined in the present experiment or that there is a much greater dilution of labeled alanine/lactate in the oxaloacetate pool than previously appreciated, or some combination of these two explanations.
PMCID: PMC424028  PMID: 4044833
5.  Non-stationary 13C metabolic flux analysis of Chinese hamster ovary cells in batch culture using extracellular labeling highlights metabolic reversibility and compartmentation 
BMC Systems Biology  2014;8:50.
Mapping the intracellular fluxes for established mammalian cell lines becomes increasingly important for scientific and economic reasons. However, this is being hampered by the high complexity of metabolic networks, particularly concerning compartmentation.
Intracellular fluxes of the CHO-K1 cell line central carbon metabolism were successfully determined for a complex network using non-stationary 13C metabolic flux analysis. Mass isotopomers of extracellular metabolites were determined using [U-13C6] glucose as labeled substrate. Metabolic compartmentation and extracellular transport reversibility proved essential to successfully reproduce the dynamics of the labeling patterns. Alanine and pyruvate reversibility changed dynamically even if their net production fluxes remained constant. Cataplerotic fluxes of cytosolic phosphoenolpyruvate carboxykinase and mitochondrial malic enzyme and pyruvate carboxylase were successfully determined. Glycolytic pyruvate channeling to lactate was modeled by including a separate pyruvate pool. In the exponential growth phase, alanine, glycine and glutamate were excreted, and glutamine, aspartate, asparagine and serine were taken up; however, all these amino acids except asparagine were exchanged reversibly with the media. High fluxes were determined in the pentose phosphate pathway and the TCA cycle. The latter was fueled mainly by glucose but also by amino acid catabolism.
The CHO-K1 central metabolism in controlled batch culture proves to be robust. It has the main purpose to ensure fast growth on a mixture of substrates and also to mitigate oxidative stress. It achieves this by using compartmentation to control NADPH and NADH availability and by simultaneous synthesis and catabolism of amino acids.
PMCID: PMC4022241  PMID: 24773761
Chinese hamster ovary cells; Metabolic flux analysis; Mitochondria; Compartmentation; Mammalian metabolism; CHO; Mammalian cell culture; Metabolic transport; Reversibility
6.  OpenMebius: An Open Source Software for Isotopically Nonstationary 13C-Based Metabolic Flux Analysis 
BioMed Research International  2014;2014:627014.
The in vivo measurement of metabolic flux by 13C-based metabolic flux analysis (13C-MFA) provides valuable information regarding cell physiology. Bioinformatics tools have been developed to estimate metabolic flux distributions from the results of tracer isotopic labeling experiments using a 13C-labeled carbon source. Metabolic flux is determined by nonlinear fitting of a metabolic model to the isotopic labeling enrichment of intracellular metabolites measured by mass spectrometry. Whereas 13C-MFA is conventionally performed under isotopically constant conditions, isotopically nonstationary 13C metabolic flux analysis (INST-13C-MFA) has recently been developed for flux analysis of cells with photosynthetic activity and cells at a quasi-steady metabolic state (e.g., primary cells or microorganisms under stationary phase). Here, the development of a novel open source software for INST-13C-MFA on the Windows platform is reported. OpenMebius (Open source software for Metabolic flux analysis) provides the function of autogenerating metabolic models for simulating isotopic labeling enrichment from a user-defined configuration worksheet. Analysis using simulated data demonstrated the applicability of OpenMebius for INST-13C-MFA. Confidence intervals determined by INST-13C-MFA were less than those determined by conventional methods, indicating the potential of INST-13C-MFA for precise metabolic flux analysis. OpenMebius is the open source software for the general application of INST-13C-MFA.
PMCID: PMC4071984  PMID: 25006579
7.  Natural Isotopic Signatures of Variations in Body Nitrogen Fluxes: A Compartmental Model Analysis 
PLoS Computational Biology  2014;10(10):e1003865.
Body tissues are generally 15N-enriched over the diet, with a discrimination factor (Δ15N) that varies among tissues and individuals as a function of their nutritional and physiopathological condition. However, both 15N bioaccumulation and intra- and inter-individual Δ15N variations are still poorly understood, so that theoretical models are required to understand their underlying mechanisms. Using experimental Δ15N measurements in rats, we developed a multi-compartmental model that provides the first detailed representation of the complex functioning of the body's Δ15N system, by explicitly linking the sizes and Δ15N values of 21 nitrogen pools to the rates and isotope effects of 49 nitrogen metabolic fluxes. We have shown that (i) besides urea production, several metabolic pathways (e.g., protein synthesis, amino acid intracellular metabolism, urea recycling and intestinal absorption or secretion) are most probably associated with isotope fractionation and together contribute to 15N accumulation in tissues, (ii) the Δ15N of a tissue at steady-state is not affected by variations of its P turnover rate, but can vary according to the relative orientation of tissue free amino acids towards oxidation vs. protein synthesis, (iii) at the whole-body level, Δ15N variations result from variations in the body partitioning of nitrogen fluxes (e.g., urea production, urea recycling and amino acid exchanges), with or without changes in nitrogen balance, (iv) any deviation from the optimal amino acid intake, in terms of both quality and quantity, causes a global rise in tissue Δ15N, and (v) Δ15N variations differ between tissues depending on the metabolic changes involved, which can therefore be identified using simultaneous multi-tissue Δ15N measurements. This work provides proof of concept that Δ15N measurements constitute a new promising tool to investigate how metabolic fluxes are nutritionally or physiopathologically reorganized or altered. The existence of such natural and interpretable isotopic biomarkers promises interesting applications in nutrition and health.
Author Summary
Body proteins ensure vital functions, and their constancy is maintained through the tight coordination of many nitrogen metabolic fluxes, but our understanding of how this flux system is regulated, and sometimes dysregulated, remains fragmentary and incomplete. Besides, body tissues are generally naturally enriched in the heavier stable nitrogen isotope (15N) over the diet: this 15N bioaccumulation (Δ15N) varies depending on tissues and metabolic orientations, likely as the result of isotope effects associated to some metabolic pathways. We used a novel approach, combining multi-tissue Δ15N measurements and their analysis using modeling, to understand how body Δ15N values relate to nitrogen fluxes. The multi-tissue model we have developed provides a clearer understanding of the metabolic processes that generate isotopic fractionation, and of how tissue Δ15N values are modulated in response to changes in the body distribution of specific nitrogen fluxes. We show that Δ15N values tend to rise when the amino acids intake does not optimally fit the metabolic demand, and that Δ15N values constitute natural and interpretable signatures of nutritionally-induced variations in nitrogen fluxes. This approach constitutes a new promising tool to investigate how nitrogen metabolism is nutritionally or physiopathologically reorganized or altered, and promises interesting applications in many areas (nutrition, pathology, ecology, paleontology, etc).
PMCID: PMC4183419  PMID: 25275306
8.  Glucose-methanol co-utilization in Pichia pastoris studied by metabolomics and instationary 13C flux analysis 
BMC Systems Biology  2013;7:17.
Several studies have shown that the utilization of mixed carbon feeds instead of methanol as sole carbon source is beneficial for protein production with the methylotrophic yeast Pichia pastoris. In particular, growth under mixed feed conditions appears to alleviate the metabolic burden related to stress responses triggered by protein overproduction and secretion. Yet, detailed analysis of the metabolome and fluxome under mixed carbon source metabolizing conditions are missing. To obtain a detailed flux distribution of central carbon metabolism, including the pentose phosphate pathway under methanol-glucose conditions, we have applied metabolomics and instationary 13C flux analysis in chemostat cultivations.
Instationary 13C-based metabolic flux analysis using GC-MS and LC-MS measurements in time allowed for an accurate mapping of metabolic fluxes of glycolysis, pentose phosphate and methanol assimilation pathways. Compared to previous results from NMR-derived stationary state labelling data (proteinogenic amino acids, METAFoR) more fluxes could be determined with higher accuracy. Furthermore, using a thermodynamic metabolic network analysis the metabolite measurements and metabolic flux directions were validated. Notably, the concentration of several metabolites of the upper glycolysis and pentose phosphate pathway increased under glucose-methanol feeding compared to the reference glucose conditions, indicating a shift in the thermodynamic driving forces. Conversely, the extracellular concentrations of all measured metabolites were lower compared with the corresponding exometabolome of glucose-grown P. pastoris cells.
The instationary 13C flux analysis resulted in fluxes comparable to previously obtained from NMR datasets of proteinogenic amino acids, but allowed several additional insights. Specifically, i) in vivo metabolic flux estimations were expanded to a larger metabolic network e.g. by including trehalose recycling, which accounted for about 1.5% of the glucose uptake rate; ii) the reversibility of glycolytic/gluconeogenesis, TCA cycle and pentose phosphate pathways reactions was estimated, revealing a significant gluconeogenic flux from the dihydroxyacetone phosphate/glyceraldehydes phosphate pool to glucose-6P. The origin of this finding could be carbon recycling from the methanol assimilatory pathway to the pentose phosphate pool. Additionally, high exchange fluxes of oxaloacetate with aspartate as well as malate indicated amino acid pool buffering and the activity of the malate/Asp shuttle; iii) the ratio of methanol oxidation vs utilization appeared to be lower (54 vs 79% assimilated methanol directly oxidized to CO2).
In summary, the application of instationary 13C-based metabolic flux analysis to P. pastoris provides an experimental framework with improved capabilities to explore the regulation of the carbon and energy metabolism of this yeast, particularly for the case of methanol and multicarbon source metabolism.
PMCID: PMC3626722  PMID: 23448228
Pichia pastoris; Instationary 13C-metabolic flux analysis; GC-MS; LC-MS
9.  Sensitivity Analysis of Flux Determination in Heart by H218O -provided Labeling Using a Dynamic Isotopologue Model of Energy Transfer Pathways 
PLoS Computational Biology  2012;8(12):e1002795.
To characterize intracellular energy transfer in the heart, two organ-level methods have frequently been employed: inversion and saturation transfer, and dynamic labeling. Creatine kinase (CK) fluxes obtained by following oxygen labeling have been considerably smaller than the fluxes determined by saturation transfer. It has been proposed that dynamic labeling determines net flux through CK shuttle, whereas saturation transfer measures total unidirectional flux. However, to our knowledge, no sensitivity analysis of flux determination by oxygen labeling has been performed, limiting our ability to compare flux distributions predicted by different methods. Here we analyze oxygen labeling in a physiological heart phosphotransfer network with active CK and adenylate kinase (AdK) shuttles and establish which fluxes determine the labeling state. A mathematical model consisting of a system of ordinary differential equations was composed describing enrichment in each phosphoryl group and inorganic phosphate. By varying flux distributions in the model and calculating the labeling, we analyzed labeling sensitivity to different fluxes in the heart. We observed that the labeling state is predominantly sensitive to total unidirectional CK and AdK fluxes and not to net fluxes. We conclude that measuring dynamic incorporation of into the high-energy phosphotransfer network in heart does not permit unambiguous determination of energetic fluxes with a higher magnitude than the ATP synthase rate when the bidirectionality of fluxes is taken into account. Our analysis suggests that the flux distributions obtained using dynamic labeling, after removing the net flux assumption, are comparable with those from inversion and saturation transfer.
Author Summary
In heart, the movement of energy metabolites between force-producing myosin, other ATPases, and mitochondria is vital for its function and closely related to heart pathologies. In addition to diffusion, transport of ATP, ADP, Pi, and phosphocreatine occurs along parallel pathways such as the adenylate kinase and creatine kinase shuttles. Two organ-level methods have been developed to study the relative flux through these pathways. However, their results differ. It was recently demonstrated that studies often suffer from the exclusion of compartmentation from their metabolic models. One study overcame this limitation by using compartmental models and statistical methods on multiple experiments. Here, we analyzed the sensitivity of the other method - dynamic labeling of phosphoryl groups and inorganic phosphate. For that, we composed a mathematical model tracking enrichment of the metabolites and evaluated sensitivity of labeling to different flux distribution scenarios. Our study shows that the dynamic method provides a measure of total flux, and not net flux as presumed previously, making the fluxes predicted from both methods consistent. Importantly, conclusions derived on the basis of labeling analysis, particularly those regarding the net flux through the shuttles in control and pathological cases, need to be reevaluated.
PMCID: PMC3516558  PMID: 23236266
10.  Bacterial adaptation through distributed sensing of metabolic fluxes 
We present a large-scale differential equation model of E. coli's central metabolism and its enzymatic, transcriptional, and posttranslational regulation. This model reproduces E. coli's known physiological behavior.We found that the interplay of known interactions in E. coli's central metabolism can indirectly recognize the presence of extracellular carbon sources through measuring intracellular metabolic flux patterns.We found that E. coli's system-level adaptations between glycolytic and gluconeogenic carbon sources are realized on the molecular level by global feedback architectures that overarch the enzymatic and transcriptional regulatory layers.We found that the capability for closed-loop self-regulation can emerge within metabolism itself and therefore, metabolic operation may adapt itself autonomously to changing carbon sources (not requiring upstream sensing and signaling).
Adaptations to fluctuating carbon source availability are of particular importance for bacteria. To understand these adaptations, it needs to be understood how a system's behavior emerges from the interactions between the characterized molecules (Kitano, 2002b). To attain such a system understanding of bacterial metabolic adaptations to carbon source availability, the coupling between the recognition and adjustment aspects and between the enzymatic and genetic regulatory layers must be understood. For many carbon sources, neither transmembrane sensors nor regulatory proteins with sensing function have been identified. Also, it remains unclear how multiple local regulations work together to accomplish a coherent adjustment on the systems level. In this paper, we show that (1) the interplay of the known interactions in E. coli's central metabolism is capable of recognizing carbon sources indirectly, and that (2) these molecular interactions can adjust E. coli's metabolic operation between growth on glycolytic and gluconeogenic carbon sources, and that (3) this adaptation is governed by general principles.
We hypothesized that the system-level adaptations between growth on glycolytic and gluconeogenic carbon sources are accomplished by a system-wide regulation architecture that emerges when the known enzymatic and transcriptional regulations become coupled through five transcription factor (TF)–metabolite interactions. To (1) assess whether such coupled molecular interactions can indeed work together to adapt metabolic operation, and if yes, (2) to understand this system-level adaptation in molecular-level detail, we constructed a large-scale differential equation model. The model topology comprises the Embden–Meyerhoff pathway, the tricarboxylic acid (TCA) cycle, the glyoxylate (GLX) shunt, the anaplerotic reactions, the diversion of carbon flux to the GLX shunt, the uptake of glucose, the uptake and excretion of acetate, enzymatic regulation, transcriptional regulation by four TFs, and the regulation of these TFs' activities through TF–metabolite interactions. We translated the topology into differential equations by assigning the most appropriate rate law to each interaction. The kinetic model comprises 47 ordinary differential equations and 193 parameters. Parameter values were estimated through application of the ‘divide-and-conquer approach' (Kotte and Heinemann, 2009) on published experimental steady state-omics data sets.
Model simulations reproduce E. coli's known physiological behavior in an environment with fluctuating carbon source availability. But how does the in silico cell recognize acetate without a transmembrane sensor for extracellular acetate or a TF binding to intracellular acetate? Similarly, it is unclear whether the glucose sensing function of the phosphotransferase system is the exclusive mechanism to recognize glucose, or whether this sensing function is integrated into a larger sensing architecture. The model suggests that the recognition is performed indirectly through a mechanism we termed distributed sensing of intracellular metabolic fluxes. This mechanism uses two distinct motifs, which we termed pathway usage and flux direction, to establish defined correlations between metabolic fluxes and the levels of certain, here termed flux-signaling metabolites. The binding of these metabolites to TFs propagates the flux information to the transcriptional regulatory layer. A molecular sensor for intracellular metabolic flux is thus defined as a system of regulations and enzyme kinetics, comprising (1) either of the two motifs pathway usage or flux direction and (2) the binding of the thus established flux-signaling metabolites to TF(s).
As the in silico cell establishes and uses sensors for several intracellular metabolic fluxes, the overall sensing architecture infers the present carbon sources from a pattern of metabolic fluxes and is as such of a distributed nature. The core of this sensing architecture is formed not by transmembrane sensors but by four flux sensors, which establish flux-signaling metabolites according to the two proposed general motifs. These flux sensors use intracellular metabolic flux as a means to correlate the presence of extracellular carbon sources with the levels of intracellular metabolites. The recognition of glucose through the PTS transmembrane complex is embedded as one flux sensor in this distributed sensing architecture; the other three flux sensors function without the help of transmembrane complexes.
The in silico cell achieves the coupling between recognition and adjustment through its TFs, whose activities respond to the available carbon sources and at the same time regulate the expression of target genes. This combined recognition and adjustment, centered on the four TFs, closes four global feedback loops that overarch the metabolic and genetic layers as illustrated in Figure 6. The adaptation of the in silico cell arises from the global feedback loop-embedded, flux sensor-adjusted transcriptional regulation of the four TFs, with each TF performing one part of the overall adaptation. This adaptation incorporates both the influence of the metabolic on the genetic layer, achieved through TF–metabolite interactions, and of the genetic on the metabolic layer, achieved through the impact of adjusted enzyme levels on metabolic fluxes.
The existence of the global feedback architectures challenges the conventional view that top-level regulatory proteins recognize environmental conditions and adjust downstream metabolic operation. It suggests that the capability for closed-loop self-regulation can emerge within metabolism itself and therefore, metabolic operation may adapt itself autonomously (not requiring upstream sensing and regulation) to changing carbon sources.
To conclude, the presented differential equation model of E. coli's central metabolism offers a consistent explanation of how a multitude of known molecular interactions fit into a coherent systems picture; the interactions work together like gear wheels that mesh with one another to adapt central metabolism between growth on the glycolytic substrate glucose and the gluconeogenic substrate acetate. The deduced general functional principles provide the missing link to understand system-level adaptations to carbon sources in molecular-level detail. The proposed principles fall under the umbrella of distributed flux sensing. The flux sensing mechanism entails the binding of TFs to flux-signaling metabolites, which are established through the motifs signaling of pathway usage and signaling of flux direction, and are embedded in global feedback loop architectures. These principles allow an autonomous adaptation of metabolic operation to growth in fluctuating environments.
The recognition of carbon sources and the regulatory adjustments to recognized changes are of particular importance for bacterial survival in fluctuating environments. Despite a thorough knowledge base of Escherichia coli's central metabolism and its regulation, fundamental aspects of the employed sensing and regulatory adjustment mechanisms remain unclear. In this paper, using a differential equation model that couples enzymatic and transcriptional regulation of E. coli's central metabolism, we show that the interplay of known interactions explains in molecular-level detail the system-wide adjustments of metabolic operation between glycolytic and gluconeogenic carbon sources. We show that these adaptations are enabled by an indirect recognition of carbon sources through a mechanism we termed distributed sensing of intracellular metabolic fluxes. This mechanism uses two general motifs to establish flux-signaling metabolites, whose bindings to transcription factors form flux sensors. As these sensors are embedded in global feedback loop architectures, closed-loop self-regulation can emerge within metabolism itself and therefore, metabolic operation may adapt itself autonomously (not requiring upstream sensing and signaling) to fluctuating carbon sources.
PMCID: PMC2858440  PMID: 20212527
computational model; metabolism; regulation; sensing; systems biology
11.  Rhabdomyosarcoma cells show an energy producing anabolic metabolic phenotype compared with primary myocytes 
Molecular Cancer  2008;7:79.
The functional status of a cell is expressed in its metabolic activity. We have applied stable isotope tracing methods to determine the differences in metabolic pathways in proliferating Rhabdomysarcoma cells (Rh30) and human primary myocytes in culture. Uniformly 13C-labeled glucose was used as a source molecule to follow the incorporation of 13C into more than 40 marker metabolites using NMR and GC-MS. These include metabolites that report on the activity of glycolysis, Krebs' cycle, pentose phosphate pathway and pyrimidine biosynthesis.
The Rh30 cells proliferated faster than the myocytes. Major differences in flux through glycolysis were evident from incorporation of label into secreted lactate, which accounts for a substantial fraction of the glucose carbon utilized by the cells. Krebs' cycle activity as determined by 13C isotopomer distributions in glutamate, aspartate, malate and pyrimidine rings was considerably higher in the cancer cells than in the primary myocytes. Large differences were also evident in de novo biosynthesis of riboses in the free nucleotide pools, as well as entry of glucose carbon into the pyrimidine rings in the free nucleotide pool. Specific labeling patterns in these metabolites show the increased importance of anaplerotic reactions in the cancer cells to maintain the high demand for anabolic and energy metabolism compared with the slower growing primary myocytes. Serum-stimulated Rh30 cells showed higher degrees of labeling than serum starved cells, but they retained their characteristic anabolic metabolism profile. The myocytes showed evidence of de novo synthesis of glycogen, which was absent in the Rh30 cells.
The specific 13C isotopomer patterns showed that the major difference between the transformed and the primary cells is the shift from energy and maintenance metabolism in the myocytes toward increased energy and anabolic metabolism for proliferation in the Rh30 cells. The data further show that the mitochondria remain functional in Krebs' cycle activity and respiratory electron transfer that enables continued accelerated glycolysis. This may be a common adaptive strategy in cancer cells.
PMCID: PMC2577687  PMID: 18939998
12.  Metabolic flux analysis in a nonstationary system: Fed-batch fermentation of a high yielding strain of E. coli producing 1,3-propanediol 
Metabolic engineering  2007;9(3):277-292.
Metabolic fluxes estimated from stable-isotope studies provide a key to understanding cell physiology and regulation of metabolism. A limitation of the classical method for metabolic flux analysis (MFA) is the requirement for isotopic steady state. To extend the scope of flux determination from stationary to nonstationary systems, we present a novel modeling strategy that combines key ideas from isotopomer spectral analysis (ISA) and stationary MFA. Isotopic transients of the precursor pool and the sampled products are described by two parameters, D and G parameters, respectively, which are incorporated into the flux model. The G value is the fraction of labeled product in the sample, and the D value is the fractional contribution of the feed for the production of labeled products. We illustrate the novel modeling strategy with a nonstationary system that closely resembles industrial production conditions, i.e. fed-batch fermentation of E. coli that produces 1,3-propanediol (PDO). Metabolic fluxes and the D and G parameters were estimated by fitting labeling distributions of biomass amino acids measured by GC/MS to a model of E. coli metabolism. We obtained highly consistent fits from the data with 82 redundant measurements. Metabolic fluxes were estimated for 20 time points during course of the fermentation. As such we established, for the first time, detailed time profiles of in vivo fluxes. We found that intracellular fluxes changed significantly during the fed-batch. The intracellular flux associated with PDO pathway increased by 10%. Concurrently, we observed a decrease in the split ratio between glycolysis and pentose phosphate pathway from 70/30 to 50/50 as a function of time. The TCA cycle flux, on the other hand, remained constant throughout the fermentation. Furthermore, our flux results provided additional insight in support of the assumed genotype of the organism.
PMCID: PMC2048574  PMID: 17400499
Instationary fluxes; 13C flux analysis; elementary metabolite units (EMU); gas chromatography mass spectrometry (GC/MS); statistical analysis
13.  Diverse mechanisms of growth inhibition by luteolin, resveratrol, and quercetin in MIA PaCa-2 cells: a comparative glucose tracer study with the fatty acid synthase inhibitor C75 
The rationale of this dose matching/dose escalating study was to compare a panel of flavonoids—luteolin, resveratrol, and quercetin—against the metabolite flux-controlling properties of a synthetic targeted fatty acid synthase inhibitor drug C75 on multiple macromolecule synthesis pathways in pancreatic tumor cells using [1,2-13C2]-d-glucose as the single precursor metabolic tracer. MIA PaCa-2 pancreatic adenocarcinoma cells were cultured for 48 h in the presence of 0.1% DMSO (control), or 50 or 100 μM of each test compound, while intracellular glycogen, RNA ribose, palmitate and cholesterol as well as extra cellular 13CO2, lactate and glutamate production patterns were measured using gas chromatography/mass spectrometry (GC/MS) and stable isotope-based dynamic metabolic profiling (SiDMAP). The use of 50% [1,2-13C2]-d-glucose as tracer resulted in an average of 24 excess 13CO2 molecules for each 1,000 CO2 molecule in the culture media, which was decreased by 29 and 33% (P < 0.01) with 100 μM C75 and luteolin treatments, respectively. Extracellular tracer glucose-derived 13C-labeled lactate fractions (Σm) were between 45.52 and 47.49% in all cultures with a molar ratio of 2.47% M + 1/Σm lactate produced indirectly by direct oxidation of glucose in the pentose cycle in control cultures; treatment with 100 μM C75 and luteolin decreased this figure to 1.80 and 1.67%. The tracer glucose-derived 13C labeled fraction (Σm) of ribonucleotide ribose was 34.73% in controls, which was decreased to 20.58 and 8.45% with C75, 16.15 and 6.86% with luteolin, 27.66 and 19.25% with resveratrol, and 30.09 and 25.67% with quercetin, respectively. Luteolin effectively decreased nucleotide precursor synthesis pentose cycle flux primarily via the oxidative branch, where we observed a 41.74% flux (M + 1/Σm) in control cells, in comparison with only a 37.19%, 32.74%, or a 26.57%, 25.47% M + 1/Σm flux (P < 0.001) after 50 or 100 μM C75 or luteolin treatment. Intracellular de novo fatty acid palmitate (C16:0) synthesis was severely and equally blocked by C75 and luteolin treatments indicated by the 5.49% (control), 2.29 or 2.47% (C75) and 2.21 or 2.73% (luteolin) tracer glucose-derived 13C-labeled fractions, respectively. On the other hand there was a significant 192 and 159% (P < 0.001), and a 103 and 117% (P < 0.01) increase in tracer glucose-derived cholesterol after C75 or luteolin treatment. Only resveratrol and quercetin at 100 μM inhibited tracer glucose-derived glycogen labeling (Σm) and turnover by 34.8 and 23.8%, respectively. The flavonoid luteolin possesses equal efficacy to inhibit fatty acid palmitate de novo synthesis as well as nucleotide RNA ribose turnover via the oxidative branch of the pentose cycle in comparison with the targeted fatty acid synthase inhibitor synthetic compound C75. Luteolin is also effective in stringently controlling glucose entry and anaplerosis in the TCA cycle, while it promotes less glucose flux towards cholesterol synthesis than that of C75. In contrast, quercetin and resveratrol inhibit glycogen synthesis and turnover as their underlying mechanism of controlling tumor cell proliferation. Therefore the flavonoid luteolin controls fatty and nucleic acid syntheses as well as energy production with pharmacological strength, which can be explored as a non-toxic natural treatment modality for pancreatic cancer.
PMCID: PMC3383678  PMID: 22754424
Metabolic profile; Phytochemicals; Pancreatic cancer; Fatty acid synthase; Lipogenesis; Luteolin; Resveratrol; Quercetin; C75
14.  Kinetic isotope effects significantly influence intracellular metabolite 13C labeling patterns and flux determination 
Biotechnology journal  2013;8(9):1080-1089.
Rigorous mathematical modeling of carbon-labeling experiments allows estimation of fluxes through the pathways of central carbon metabolism, yielding powerful information for basic scientific studies as well as for a wide range of applications. However, the mathematical models that have been developed for flux determination from 13C labeling data have commonly neglected the influence of kinetic isotope effects on the distribution of 13C label in intracellular metabolites, as these effects have often been assumed to be inconsequential. We have used measurements of the 13C isotope effects on the pyruvate dehydrogenase enzyme from the literature to model isotopic fractionation at the pyruvate node and quantify the modeling errors expected to result from the assumption that isotope effects are negligible. We show that under some conditions kinetic isotope effects have a significant impact on the 13C labeling patterns of intracellular metabolites, and the errors associated with neglecting isotope effects in 13C-metabolic flux analysis models can be comparable in size to measurement errors associated with GC–MS. Thus, kinetic isotope effects must be considered in any rigorous assessment of errors in 13C labeling data, goodness-of-fit between model and data, confidence intervals of estimated metabolic fluxes, and statistical significance of differences between estimated metabolic flux distributions.
PMCID: PMC4045492  PMID: 23828762
Isotope Effects; Isotopomer Modeling; Metabolic Engineering; Metabolic Flux Analysis; Modeling errors
15.  Hepatic gluconeogenic fluxes and glycogen turnover during fasting in humans. A stable isotope study. 
Journal of Clinical Investigation  1997;100(5):1305-1319.
Fluxes through intrahepatic glucose-producing metabolic pathways were measured in normal humans during overnight or prolonged (60 h) fasting. The glucuronate probe was used to measure the turnover and sources of hepatic UDP-glucose; mass isotopomer distribution analysis from [2-13C1]glycerol for gluconeogenesis and UDP-gluconeogenesis; [U-13C6]glucose for glucose production (GP) and the direct UDP-glucose pathway; and [1-2H1]galactose for UDP-glucose flux and retention in hepatic glycogen. After overnight fasting, GP (fluxes in milligram per kilogram per minute) was 2.19+/-0.09, of which 0.79 (36%) was from gluconeogenesis, 1.40 was from glycogenolysis, 0.30 was retained in glycogen via UDP-gluconeogenesis, and 0.17 entered hepatic UDP-glucose by the direct pathway. Thus, total flux through the gluconeogenic pathway (1.09) represented 54% of extrahepatic glucose disposal (2.02) and the net hepatic glycogen depletion rate was 0.93 (46%). Prolonging [2-13C1]glycerol infusion slowly increased measured fractional gluconeogenesis. In response to prolonged fasting, GP was lower (1. 43+/-0.06) and fractional and absolute gluconeogenesis were higher (78+/-2% and 1.11+/-0.07, respectively). The small but nonzero glycogen input to plasma glucose (0.32+/-0.03) was completely balanced by retained UDP-gluconeogenesis (0.31+/-0.02). Total gluconeogenic pathway flux therefore accounted for 99+/-2% of GP, but with a glycogen cycle interposed. Prolonging isotope infusion to 10 h increased measured fractional gluconeogenesis and UDP-gluconeogenesis to 84-96%, implying replacement of glycogen by gluconeogenic-labeled glucose. Moreover, after glucagon administration, GP (1.65), recovery of [1-2H1]galactose label in plasma glucose (25%) and fractional gluconeogenesis (91%) increased, such that 78% (0.45/0.59) of glycogen released was labeled (i.e., of recent gluconeogenic origin). In conclusion, hepatic gluconeogenic flux into glycogen and glycogen turnover persist during fasting in humans, reconciling inconsistencies in the literature and interposing another locus of control in the normal pathway of GP.
PMCID: PMC508308  PMID: 9276749
16.  Understanding the physiology of Lactobacillus plantarum at zero growth 
The physiology of Lactobacillus plantarum at extremely low growth rates, through cultivation in retentostats, is much closer to carbon-limited growth than to stationary phase, as evidenced from transcriptomics data, metabolic fluxes, and biomass composition and viability.Using a genome-scale metabolic model and constraint-based computational analyses, amino-acid fluxes—in particular, the rather paradoxical excretion of Asp, Arg, Met, and Ala—could be rationalized as a means to allow extensive metabolism of other amino acids, that is, that of branched-chain and aromatic amino acids.Catabolic products from aromatic amino acids are known to have putative plant-hormone action. The metabolism of amino acids, as well as transcription data, strongly suggested a plant environment-like response in slow-growing L. plantarum, which was confirmed by significant effects of fermented medium on plant root formation.
Natural ecosystems are usually characterized by extremely low and fluctuating nutrient availability. Hence, microorganisms in these environments live a ‘feast-and-famine' existence, with famine the most habitual state. As a result, extremely slow or no growth is the most common state of bacteria, and maintenance processes dominate their life.
In the present study, Lactobacillus plantarum was used as a model microorganism to investigate the physiology of slow growth. Besides fermented foods, this microorganism can be observed in a variety of environmental niches, including plants and lakes, in which nutrient supply is limited. To mimic these conditions, L. plantarum was grown in a glucose-limited chemostat with complete biomass retention (retentostat). During cultivation, biomass progressively accumulated, resulting in steadily decreasing specific substrate availability. Less energy was thus available for growth, and the specific growth rate decreased accordingly, with a final calculated doubling time greater than one year. Detailed measurements of metabolic fluxes were used as constraints in a genome-scale metabolic model to precisely calculate the amount of energy used for net biomass synthesis and for maintenance purposes: at the lowest growth rate investigated (μ=0.0002 h−1), maintenance accounted for 94% of all energy expenses.
Genome-scale metabolic analysis was used in combination with transcriptomics to study the adaptation of L. plantarum to extremely slow growth under limited carbon and energy supply. Importantly, slow growth as investigated here was fundamentally different from the widely studied carbon starvation-induced stationary phase: non-growing cells in retentostat conditions were glucose limited rather than starved, and the transition from a growing to a non-growing state under retentostat conditions was progressive, in contrast with the abrupt transition in batch cultures. These differences were reflected in various aspects of the cell physiology.
The metabolic behavior was remarkably stable during adaptation to slow growth. Although carbon catabolite repression was clearly relieved, as indicated by the upregulation of genes for the utilization of alternative carbohydrates, the metabolism remained largely based on the conversion of glucose to lactate.
Stress resistance mechanisms were also not massively induced. In particular, analysis of the biomass composition—which remained similar to fast-growing cells even under virtually non-growing conditions—and of the gene expression profile, failed to reveal clear stringent or general stress responses, which are generally triggered in glucose-starved cells. The observation that genes involved in growth-associated processes were not downregulated suggested that active synthesis of biomass components (RNA, proteins, and membranes) was required to account for the observed stable biomass and that turnover of macromolecules was high in slow-growing cells. Biomass viability or morphology was also not affected, compared with faster growth conditions. The only typical stress response was the induction of an SOS response—in particular, the upregulation of the two error-prone DNA polymerases—suggesting an increased potential for genetic diversity under adverse conditions. Although diversity was not apparent under the conditions studied here, such mechanisms of increased rates of mutagenesis are likely to have an important role in the adaptation of L. plantarum to slow growth.
A surprising response of L. plantarum during adaptation to slow growth was the production of several amino acids (Arg, Asp, Met, and Ala). A priori, this metabolic behavior seemed inefficient in a context of energy limitation. However, reduced cost analysis using the genome-scale metabolic model indicated that it had a positive effect on energy generation. In-depth analysis of metabolic flux distributions showed that biosynthesis of these amino acids was connected to the catabolism of branched-chain and aromatic amino acids (BCAAs and AAAs), under conditions of limited ammonium efflux. At a fixed ammonium efflux—fixed at the measured value—flux balance analysis indicated that BCAAs and AAAs were expensive to metabolize, because the regeneration of 2-ketoglutarate through glutamate dehydrogenase was limited by ammonium dissipation. Therefore, alternative pathways had to be active to supply the necessary pool of 2-ketoglutarate. At low growth rates, amino-acid production (Arg, Asp, Ala, and Met) accounted for most of the 2-ketoglutarate regeneration. Although it came at the expense of ATP, this metabolic alternative to glutamate dehydrogenase was less energy costly than other solutions such as purine biosynthesis. This is thus an excellent example in which precise, quantitative modeling results in new insights in physiology that intuition would never have achieved. It also shows that flux balance analysis can be used to accurately predict energetically inefficient metabolism, provided the appropriate fluxes are constrained (here, ammonium efflux).
The observation that BCAAs and AAAs were catabolized at the expense of energy was intriguing. However, several end products of these catabolic pathways can serve as signaling molecules for interactions with other organisms. In particular, precursors of plant hormones were predicted as possible end products in the model simulations. Accordingly, the production of compounds interfering with plant root development was demonstrated in slow-growing L. plantarum. The metabolic analysis thus suggested that slow-growing L. plantarum produced plant hormones—or precursors thereof—as a strategy to divert the plant metabolism towards its own interest. In support of this view, transcriptome analysis indicated the upregulation of genes involved in the catabolism of β-glucosides—typical sugars from plant cell wall—as well as a very high induction of six gene clusters encoding cell-surface protein complexes predicted to have a role in the utilization of plant polysaccharides (csc clusters). In such a plant context, limited ammonium production would also make sense, because of the well-documented toxicity of ammonium for plants: production of amino acids could represent an alternative to ammonium excretion while keeping both parties satisfied.
In conclusion, the physiology of L. plantarum at extremely low growth rates, as studied by genome-scale metabolic modeling and transcriptomics, is fundamentally different from that of starvation-induced stationary phase cells. Excitingly, these conditions seem to trigger responses that favor interactions with the environment, more specifically with plants. The reported observations were made in the absence of any plant-derived material, suggesting that this response might constitute a hardwired behavior.
Situations of extremely low substrate availability, resulting in slow growth, are common in natural environments. To mimic these conditions, Lactobacillus plantarum was grown in a carbon-limited retentostat with complete biomass retention. The physiology of extremely slow-growing L. plantarum—as studied by genome-scale modeling and transcriptomics—was fundamentally different from that of stationary-phase cells. Stress resistance mechanisms were not massively induced during transition to extremely slow growth. The energy-generating metabolism was remarkably stable and remained largely based on the conversion of glucose to lactate. The combination of metabolic and transcriptomic analyses revealed behaviors involved in interactions with the environment, more particularly with plants: production of plant hormones or precursors thereof, and preparedness for the utilization of plant-derived substrates. Accordingly, the production of compounds interfering with plant root development was demonstrated in slow-growing L. plantarum. Thus, conditions of slow growth and limited substrate availability seem to trigger a plant environment-like response, even in the absence of plant-derived material, suggesting that this might constitute an intrinsic behavior in L. plantarum.
PMCID: PMC2964122  PMID: 20865006
Lactobacillus plantarum; metabolic modeling; retentostat; slow growth; transcriptome analysis
17.  A Comprehensive Metabolic Profile of Cultured Astrocytes Using Isotopic Transient Metabolic Flux Analysis and 13C-Labeled Glucose 
Metabolic models have been used to elucidate important aspects of brain metabolism in recent years. This work applies for the first time the concept of isotopic transient 13C metabolic flux analysis (MFA) to estimate intracellular fluxes in primary cultures of astrocytes. This methodology comprehensively explores the information provided by 13C labeling time-courses of intracellular metabolites after administration of a 13C-labeled substrate. Cells were incubated with medium containing [1-13C]glucose for 24 h and samples of cell supernatant and extracts collected at different time points were then analyzed by mass spectrometry and/or high performance liquid chromatography. Metabolic fluxes were estimated by fitting a carbon labeling network model to isotopomer profiles experimentally determined. Both the fast isotopic equilibrium of glycolytic metabolite pools and the slow labeling dynamics of TCA cycle intermediates are described well by the model. The large pools of glutamate and aspartate which are linked to the TCA cycle via reversible aminotransferase reactions are likely to be responsible for the observed delay in equilibration of TCA cycle intermediates. Furthermore, it was estimated that 11% of the glucose taken up by astrocytes was diverted to the pentose phosphate pathway. In addition, considerable fluxes through pyruvate carboxylase [PC; PC/pyruvate dehydrogenase (PDH) ratio = 0.5], malic enzyme (5% of the total pyruvate production), and catabolism of branched-chained amino acids (contributing with ∼40% to total acetyl-CoA produced) confirmed the significance of these pathways to astrocytic metabolism. Consistent with the need of maintaining cytosolic redox potential, the fluxes through the malate–aspartate shuttle and the PDH pathway were comparable. Finally, the estimated glutamate/α-ketoglutarate exchange rate (∼0.7 μmol mg prot−1 h−1) was similar to the TCA cycle flux. In conclusion, this work demonstrates the potential of isotopic transient MFA for a comprehensive analysis of energy metabolism.
PMCID: PMC3171112  PMID: 21941478
metabolic fluxes; intracellular pools; 13C-labeling time-courses; [1-13C]glucose; mass spectrometry
18.  Metabolic Flux Elucidation for Large-Scale Models Using 13C Labeled Isotopes 
Metabolic engineering  2007;9(5-6):387-405.
A key consideration in metabolic engineering is the determination of fluxes of the metabolites within the cell. This determination provides an unambiguous description of metabolism before and/or after engineering interventions. Here, we present a computational framework that combines a constraint-based modeling framework with isotopic label tracing on a large-scale. When cells are fed a growth substrate with certain carbon positions labeled with 13C, the distribution of this label in the intracellular metabolites can be calculated based on the known biochemistry of the participating pathways. Most labeling studies focus on skeletal representations of central metabolism and ignore many flux routes that could contribute to the observed isotopic labeling patterns. In contrast, our approach investigates the importance of carrying out isotopic labeling studies using a more comprehensive reaction network consisting of 350 fluxes and 184 metabolites in Escherichia coli including global metabolite balances on cofactors such as ATP, NADH, and NADPH. The proposed procedure is demonstrated on an E. coli strain engineered to produce amorphadiene, a precursor to the anti-malarial drug artemisinin. The cells were grown in continuous culture on glucose containing 20% [U-13C]glucose; the measurements are made using GC-MS performed on 13 amino acids extracted from the cells. We identify flux distributions for which the calculated labeling patterns agree well with the measurements alluding to the accuracy of the network reconstruction. Furthermore, we explore the robustness of the flux calculations to variability in the experimental MS measurements, as well as highlight the key experimental measurements necessary for flux determination. Finally, we discuss the effect of reducing the model, as well as shed light onto the customization of the developed computational framework to other systems.
PMCID: PMC2121621  PMID: 17632026
Metabolic flux analysis; Isotope labeling; Constraint-based modeling; Nonlinear optimization; Statistical analysis
19.  Oncogenic K-Ras decouples glucose and glutamine metabolism to support cancer cell growth 
A systems approach using 13C metabolic flux analysis (MFA), non-targeted tracer fate detection (NTFD), and transcriptional profiling was applied to investigate the role of oncogenic K-Ras in metabolic transformation.K-Ras transformed cells exhibit an increased glycolytic rate and lower flux through the oxidative tricarboxylic acid (TCA) cycle.K-Ras transformed cells show a relative increase in glutamine anaplerosis and reductive TCA metabolism.Transcriptional changes driven by oncogenic K-Ras suggest control nodes associated with the metabolic reprogramming of cancer cells.
The ras and myc oncogenes drive pleiotropic changes in cell signaling, nutrient uptake, and intracellular metabolism (Chiaradonna et al, 2006b; Yuneva et al, 2007; Wise et al, 2008; Vander Heiden et al, 2009). Mutated ras proteins, identified in 25% of human cancers (Bos, 1989; Downward, 2003), correlate with an increased rate of glucose consumption, lactate accumulation, altered expression of mitochondrial genes, increased ROS production, and reduced mitochondrial activity (Bos, 1989; Downward, 2003; Vizan et al, 2005; Chiaradonna et al, 2006a; Yun et al, 2009; Baracca et al, 2010; Weinberg et al, 2010). Furthermore, K-Ras transformed cancer cells are dependent upon glucose and glutamine availability, since their withdrawal induces apoptosis and cell-cycle arrest, respectively (Ramanathan et al, 2005; Telang et al, 2006; Yun et al, 2009). However, the precise metabolic effects downstream of oncogenic Ras signaling as well as the mechanisms by which intracellular glucose and glutamine metabolism change have not been completely elucidated.
In this report, we have investigated the reprogramming of central carbon metabolism in cancer cells and its regulation by the K-ras oncogene, applying a systems level approach using 13C metabolic flux analysis (MFA), non-targeted tracer fate detection (NTFD), and transcriptional profiling. These data reveal a coordinated decoupling of glycolysis and the tricarboxylic acid (TCA) cycle. K-Ras transformed mouse and human cells exhibited a high glucose to lactate flux and relatively lower oxidative metabolism of pyruvate. Such changes were supported by increased expression of glycolytic genes as well as several pyruvate dehydrogenase kinases. In contrast to glucose, the contribution of glutamine carbon to TCA cycle intermediates through both oxidative and reductive metabolism was significantly increased upon K-Ras transformation. Despite this increase in glutamine anaplerosis, oxidative TCA flux was significantly decreased. Additionally, we observed elevated levels of glutamine-derived nitrogen in various biosynthetic metabolites in transformed cells, including amino acids, 5-oxoproline, and the nucleobase adenine. Consistent with these changes, we detected increased transcription of genes associated with glutamine metabolism and nucleotide biosynthesis in cells expressing oncogenic K-Ras.
Taken together, these findings indicate an important role of oncogenic K-Ras in cancer cell metabolism. The observed decoupling of glucose and glutamine metabolism enables the efficient utilization of both carbon and nitrogen from glutamine for biosynthetic processes. In accord with these alterations, oncogenic K-Ras induces gene expression changes that may drive this metabolic reprogramming. Finally, these results may enable the identification of metabolic and transcriptional targets throughout the network and allow more effective cancer therapies.
Oncogenes such as K-ras mediate cellular and metabolic transformation during tumorigenesis. To analyze K-Ras-dependent metabolic alterations, we employed 13C metabolic flux analysis (MFA), non-targeted tracer fate detection (NTFD) of 15N-labeled glutamine, and transcriptomic profiling in mouse fibroblast and human carcinoma cell lines. Stable isotope-labeled glucose and glutamine tracers and computational determination of intracellular fluxes indicated that cells expressing oncogenic K-Ras exhibited enhanced glycolytic activity, decreased oxidative flux through the tricarboxylic acid (TCA) cycle, and increased utilization of glutamine for anabolic synthesis. Surprisingly, a non-canonical labeling of TCA cycle-associated metabolites was detected in both transformed cell lines. Transcriptional profiling detected elevated expression of several genes associated with glycolysis, glutamine metabolism, and nucleotide biosynthesis upon transformation with oncogenic K-Ras. Chemical perturbation of enzymes along these pathways further supports the decoupling of glycolysis and TCA metabolism, with glutamine supplying increased carbon to drive the TCA cycle. These results provide evidence for a role of oncogenic K-Ras in the metabolic reprogramming of cancer cells.
PMCID: PMC3202795  PMID: 21847114
cancer; metabolic flux analysis; metabolism; Ras; transcriptional analysis
20.  Comparative Metabolic Flux Analysis of Lysine-Producing Corynebacterium glutamicum Cultured on Glucose or Fructose 
A comprehensive approach to 13C tracer studies, labeling measurements by gas chromatography-mass spectrometry, metabolite balancing, and isotopomer modeling, was applied for comparative metabolic network analysis of lysine-producing Corynebacterium glutamicum on glucose or fructose. Significantly reduced yields of lysine and biomass and enhanced formation of dihydroxyacetone, glycerol, and lactate in comparison to those for glucose resulted on fructose. Metabolic flux analysis revealed drastic differences in intracellular flux depending on the carbon source applied. On fructose, flux through the pentose phosphate pathway (PPP) was only 14.4% of the total substrate uptake flux and therefore markedly decreased compared to that for glucose (62.0%). This result is due mainly to (i) the predominance of phosphoenolpyruvate-dependent phosphotransferase systems for fructose uptake (PTSFructose) (92.3%), resulting in a major entry of fructose via fructose 1,6-bisphosphate, and (ii) the inactivity of fructose 1,6-bisphosphatase (0.0%). The uptake of fructose during flux via PTSMannose was only 7.7%. In glucose-grown cells, the flux through pyruvate dehydrogenase (70.9%) was much less than that in fructose-grown cells (95.2%). Accordingly, flux through the tricarboxylic acid cycle was decreased on glucose. Normalized to that for glucose uptake, the supply of NADPH during flux was only 112.4% on fructose compared to 176.9% on glucose, which might explain the substantially lower lysine yield of C. glutamicum on fructose. Balancing NADPH levels even revealed an apparent deficiency of NADPH on fructose, which is probably overcome by in vivo activity of malic enzyme. Based on these results, potential targets could be identified for optimization of lysine production by C. glutamicum on fructose, involving (i) modification of flux through the two PTS for fructose uptake, (ii) amplification of fructose 1,6-bisphosphatase to increase flux through the PPP, and (iii) knockout of a not-yet-annotated gene encoding dihydroxyacetone phosphatase or kinase activity to suppress overflow metabolism. Statistical evaluation revealed high precision of the estimates of flux, so the observed differences for metabolic flux are clearly substrate specific.
PMCID: PMC321251  PMID: 14711646
21.  Integrating Flux Balance Analysis into Kinetic Models to Decipher the Dynamic Metabolism of Shewanella oneidensis MR-1 
PLoS Computational Biology  2012;8(2):e1002376.
Shewanella oneidensis MR-1 sequentially utilizes lactate and its waste products (pyruvate and acetate) during batch culture. To decipher MR-1 metabolism, we integrated genome-scale flux balance analysis (FBA) into a multiple-substrate Monod model to perform the dynamic flux balance analysis (dFBA). The dFBA employed a static optimization approach (SOA) by dividing the batch time into small intervals (i.e., ∼400 mini-FBAs), then the Monod model provided time-dependent inflow/outflow fluxes to constrain the mini-FBAs to profile the pseudo-steady-state fluxes in each time interval. The mini-FBAs used a dual-objective function (a weighted combination of “maximizing growth rate” and “minimizing overall flux”) to capture trade-offs between optimal growth and minimal enzyme usage. By fitting the experimental data, a bi-level optimization of dFBA revealed that the optimal weight in the dual-objective function was time-dependent: the objective function was constant in the early growth stage, while the functional weight of minimal enzyme usage increased significantly when lactate became scarce. The dFBA profiled biologically meaningful dynamic MR-1 metabolisms: 1. the oxidative TCA cycle fluxes increased initially and then decreased in the late growth stage; 2. fluxes in the pentose phosphate pathway and gluconeogenesis were stable in the exponential growth period; and 3. the glyoxylate shunt was up-regulated when acetate became the main carbon source for MR-1 growth.
Author Summary
This study integrates two modeling approaches, a Monod kinetic model and genome-scale flux balance analysis, to analyze the dynamic metabolism of an environmentally important bacterium (S. oneidensis MR-1). The modeling results reveal that MR-1 metabolism is suboptimal for biomass growth, while MR-1 continuously reprograms the intracellular flux distributions in adaption to nutrient conditions. This innovative dFBA framework can be widely used to investigate transient cell metabolisms in response to environmental variations. Furthermore, the dFBA is able to simulate metabolite-labeling dynamics in 13C-tracer experiments, and thus can serve as a springboard to advanced 13C-assisted dynamic metabolic flux analysis by using labeled proteinogenic amino acids to improve flux results.
PMCID: PMC3271021  PMID: 22319437
22.  FiatFlux – a software for metabolic flux analysis from 13C-glucose experiments 
BMC Bioinformatics  2005;6:209.
Quantitative knowledge of intracellular fluxes is important for a comprehensive characterization of metabolic networks and their functional operation. In contrast to direct assessment of metabolite concentrations, in vivo metabolite fluxes must be inferred indirectly from measurable quantities in 13C experiments. The required experience, the complicated network models, large and heterogeneous data sets, and the time-consuming set-up of highly controlled experimental conditions largely restricted metabolic flux analysis to few expert groups. A conceptual simplification of flux analysis is the analytical determination of metabolic flux ratios exclusively from MS data, which can then be used in a second step to estimate absolute in vivo fluxes.
Here we describe the user-friendly software package FiatFlux that supports flux analysis for non-expert users. In the first module, ratios of converging fluxes are automatically calculated from GC-MS-detected 13C-pattern in protein-bound amino acids. Predefined fragmentation patterns are automatically identified and appropriate statistical data treatment is based on the comparison of redundant information in the MS spectra. In the second module, absolute intracellular fluxes may be calculated by a 13C-constrained flux balancing procedure that combines experimentally determined fluxes in and out of the cell and the above flux ratios. The software is preconfigured to derive flux ratios and absolute in vivo fluxes from [1-13C] and [U-13C]glucose experiments and GC-MS analysis of amino acids for a variety of microorganisms.
FiatFlux is an intuitive tool for quantitative investigations of intracellular metabolism by users that are not familiar with numerical methods or isotopic tracer experiments. The aim of this open source software is to enable non-specialists to adapt the software to their specific scientific interests, including other 13C-substrates, labeling mixtures, and organisms.
PMCID: PMC1199586  PMID: 16122385
23.  Rational design of 13C-labeling experiments for metabolic flux analysis in mammalian cells 
BMC Systems Biology  2012;6:43.
13C-Metabolic flux analysis (13C-MFA) is a standard technique to probe cellular metabolism and elucidate in vivo metabolic fluxes. 13C-Tracer selection is an important step in conducting 13C-MFA, however, current methods are restricted to trial-and-error approaches, which commonly focus on an arbitrary subset of the tracer design space. To systematically probe the complete tracer design space, especially for complex systems such as mammalian cells, there is a pressing need for new rational approaches to identify optimal tracers.
Recently, we introduced a new framework for optimal 13C-tracer design based on elementary metabolite units (EMU) decomposition, in which a measured metabolite is decomposed into a linear combination of so-called EMU basis vectors. In this contribution, we applied the EMU method to a realistic network model of mammalian metabolism with lactate as the measured metabolite. The method was used to select optimal tracers for two free fluxes in the system, the oxidative pentose phosphate pathway (oxPPP) flux and anaplerosis by pyruvate carboxylase (PC). Our approach was based on sensitivity analysis of EMU basis vector coefficients with respect to free fluxes. Through efficient grouping of coefficient sensitivities, simple tracer selection rules were derived for high-resolution quantification of the fluxes in the mammalian network model. The approach resulted in a significant reduction of the number of possible tracers and the feasible tracers were evaluated using numerical simulations. Two optimal, novel tracers were identified that have not been previously considered for 13C-MFA of mammalian cells, specifically [2,3,4,5,6-13C]glucose for elucidating oxPPP flux and [3,4-13C]glucose for elucidating PC flux. We demonstrate that 13C-glutamine tracers perform poorly in this system in comparison to the optimal glucose tracers.
In this work, we have demonstrated that optimal tracer design does not need to be a pure simulation-based trial-and-error process; rather, rational insights into tracer design can be gained through the application of the EMU basis vector methodology. Using this approach, rational labeling rules can be established a priori to guide the selection of optimal 13C-tracers for high-resolution flux elucidation in complex metabolic network models.
PMCID: PMC3490712  PMID: 22591686
Metabolic flux analysis; Stable-isotope tracers; Experiment design; Pathway analysis; Statistical analysis; Confidence intervals; Mammalian cells; Free fluxes; Mass spectrometry
24.  13C and 1H Nuclear Magnetic Resonance Study of Glycogen Futile Cycling in Strains of the Genus Fibrobacter 
We investigated the carbon metabolism of three strains of Fibrobacter succinogenes and one strain of Fibrobacter intestinalis. The four strains produced the same amounts of the metabolites succinate, acetate, and formate in approximately the same ratio (3.7/1/0.3). The four strains similarly stored glycogen during all growth phases, and the glycogen-to-protein ratio was close to 0.6 during the exponential growth phase. 13C nuclear magnetic resonance (NMR) analysis of [1-13C]glucose utilization by resting cells of the four strains revealed a reversal of glycolysis at the triose phosphate level and the same metabolic pathways. Glycogen futile cycling was demonstrated by 13C NMR by following the simultaneous metabolism of labeled [13C]glycogen and exogenous unlabeled glucose. The isotopic dilutions of the CH2 of succinate and the CH3 of acetate when the resting cells were metabolizing [1-13C]glucose and unlabeled glycogen were precisely quantified by using 13C-filtered spin-echo difference 1H NMR spectroscopy. The measured isotopic dilutions were not the same for succinate and acetate; in the case of succinate, the dilutions reflected only the contribution of glycogen futile cycling, while in the case of acetate, another mechanism was also involved. Results obtained in complementary experiments are consistent with reversal of the succinate synthesis pathway. Our results indicated that for all of the strains, from 12 to 16% of the glucose entering the metabolic pathway originated from prestored glycogen. Although genetically diverse, the four Fibrobacter strains studied had very similar carbon metabolism characteristics.
PMCID: PMC124674  PMID: 12033219
25.  Bidirectionality and Compartmentation of Metabolic Fluxes Are Revealed in the Dynamics of Isotopomer Networks 
Isotope labeling is one of the few methods of revealing the in vivo bidirectionality and compartmentalization of metabolic fluxes within metabolic networks. We argue that a shift from steady state to dynamic isotopomer analysis is required to deal with these cellular complexities and provide a review of dynamic studies of compartmentalized energy fluxes in eukaryotic cells including cardiac muscle, plants, and astrocytes. Knowledge of complex metabolic behaviour on a molecular level is prerequisite for the intelligent design of genetically modified organisms able to realize their potential of revolutionizing food, energy, and pharmaceutical production. We describe techniques to explore the bidirectionality and compartmentalization of metabolic fluxes using information contained in the isotopic transient, and discuss the integration of kinetic models with MFA. The flux parameters of an example metabolic network were optimized to examine the compartmentalization of metabolites and and the bidirectionality of fluxes in the TCA cycle of Saccharomyces uvarum for steady-state respiratory growth.
PMCID: PMC2680642  PMID: 19468334
Metabolic network; isotopomer dynamics; MFA; mathematical modeling; compartmentalization; 13C NMR

Results 1-25 (906238)