Elucidating tumor-stromal metabolic crosstalk in colorectal cancer through integration of constraint-based models and LC-MS metabolomics

Colorectal cancer (CRC) is a major cause of morbidity and mortality in the United States. Tumor-stromal metabolic crosstalk in the tumor microenvironment promotes CRC development and progression, but exactly how stromal cells, in particular cancer-associated fibroblasts (CAFs), affect the metabolism of tumor cells remains unknown. Here we take a data-driven approach to investigate the metabolic interactions between CRC cells and CAFs, integrating constraint-based modeling and metabolomic profiling. Using metabolomics data, we perform unsteady-state parsimonious flux balance analysis to infer flux distributions for central carbon metabolism in CRC cells treated with or without CAF-conditioned media. We find that CAFs reprogram CRC metabolism through stimulation of glycolysis, the oxidative arm of the pentose phosphate pathway (PPP), and glutaminolysis, as well as inhibition of the tricarboxylic acid cycle. To identify potential therapeutic targets, we simulate enzyme knockouts and find that CAF-treated CRC cells are especially sensitive to inhibitions of hexokinase and glucose-6-phosphate, the rate limiting steps of glycolysis and oxidative PPP. Our work gives mechanistic insights into the metabolic interactions between CRC cells and CAFs and provides a framework for testing hypotheses towards CRC-targeted therapies.     

The pentose phosphate pathway constitutes a major metabolic hub in pathogenic Francisella

Metabolic pathways are now considered as intrinsic virulence attributes of pathogenic bacteria and thus represent potential targets for antibacterial strategies. Here we focused on the role of the pentose phosphate pathway (PPP) and its connections with other metabolic pathways in the pathophysiology of Francisella novicida. The involvement of the PPP in the intracellular life cycle of Francisella was first demonstrated by studying PPP inactivating mutants. Indeed, we observed that inactivation of the tktA, rpiA or rpe genes severely impaired intramacrophage multiplication during the first 24 hours. However, time-lapse video microscopy demonstrated that rpiA and rpe mutants were able to resume late intracellular multiplication. To better understand the links between PPP and other metabolic networks in the bacterium, we also performed an extensive proteo-metabolomic analysis of these mutants. We show that the PPP constitutes a major bacterial metabolic hub with multiple connections to glycolysis, the tricarboxylic acid cycle and other pathways, such as fatty acid degradation and sulfur metabolism. Altogether our study highlights how PPP plays a key role in the pathogenesis and growth of Francisella in its intracellular niche.     

Rethinking glycolysis: on the biochemical logic of metabolic pathways

Metabolic pathways may seem arbitrary and unnecessarily complex. In many cases, a chemist might devise a simpler route for the biochemical transformation, so why has nature chosen such complex solutions? In this review, we distill lessons from a century of metabolic research and introduce new observations suggesting that the intricate structure of metabolic pathways can be explained by a small set of biochemical principles. Using glycolysis as an example, we demonstrate how three key biochemical constraints--thermodynamic favorability, availability of enzymatic mechanisms and the physicochemical properties of pathway intermediates--eliminate otherwise plausible metabolic strategies. Considering these constraints, glycolysis contains no unnecessary steps and represents one of the very few pathway structures that meet cellular demands. The analysis presented here can be applied to metabolic engineering efforts for the rational design of pathways that produce a desired product while satisfying biochemical constraints.     

Comparative 13C metabolic flux analysis of pyruvate dehydrogenase complex-deficient, L-valine-producing Corynebacterium glutamicum

L-Valine can be formed successfully using C. glutamicum strains missing an active pyruvate dehydrogenase enzyme complex (PDHC). Wild-type C. glutamicum and four PDHC-deficient strains were compared by (13)C metabolic flux analysis, especially focusing on the split ratio between glycolysis and the pentose phosphate pathway (PPP). Compared to the wild type, showing a carbon flux of 69% ± 14% through the PPP, a strong increase in the PPP flux was observed in PDHC-deficient strains with a maximum of 113% ± 22%. The shift in the split ratio can be explained by an increased demand of NADPH for l-valine formation. In accordance, the introduction of the Escherichia coli transhydrogenase PntAB, catalyzing the reversible conversion of NADH to NADPH, into an L-valine-producing C. glutamicum strain caused the PPP flux to decrease to 57% ± 6%, which is below the wild-type split ratio. Hence, transhydrogenase activity offers an alternative perspective for sufficient NADPH supply, which is relevant for most amino acid production systems. Moreover, as demonstrated for L-valine, this bypass leads to a significant increase of product yield due to a concurrent reduction in carbon dioxide formation via the PPP.     

Metabolic flux analysis of CHO cell metabolism in the late non‐growth phase

Chinese hamster ovary (CHO) cell cultures are commonly used for production of recombinant human therapeutic proteins. Often the goal of such a process is to separate the growth phase of the cells, from the non‐growth phase where ideally the cells are diverting resources to produce the protein of interest. Characterizing the way that the cells use nutrients in terms of metabolic fluxes as a function of culture conditions can provide a deeper understanding of the cell biology offering guidance for process improvements. To evaluate the fluxes, metabolic flux analysis of the CHO cell culture in the non‐growth phase was performed by a combination of steady‐state isotopomer balancing and stoichiometric modeling. Analysis of the glycolytic pathway and pentose phosphate pathway (PPP) indicated that almost all of the consumed glucose is diverted towards PPP with a high NADPH production; with even recycle from PPP to G6P in some cases. Almost all of the pyruvate produced from glycolysis entered the TCA cycle with little or no lactate production. Comparison of the non‐growth phase against previously reported fluxes from growth phase cultures indicated marked differences in the fluxes, in terms of the split between glycolysis and PPP, and also around the pyruvate node. Possible reasons for the high NADPH production are also discussed. Evaluation of the fluxes indicated that the medium strength, carbon dioxide level, and temperature with dissolved oxygen have statistically significant impacts on different nodes of the flux network. Biotechnol. Bioeng. 2011; 108:82–92. © 2010 Wiley Periodicals, Inc.     

IDH2 stabilizes HIF‐1α‐induced metabolic reprogramming and promotes chemoresistance in urothelial cancer

Drug resistance contributes to poor therapeutic response in urothelial carcinoma (UC). Metabolomic analysis suggested metabolic reprogramming in gemcitabine‐resistant urothelial carcinoma cells, whereby increased aerobic glycolysis and metabolic stimulation of the pentose phosphate pathway (PPP) promoted pyrimidine biosynthesis to increase the production of the gemcitabine competitor deoxycytidine triphosphate (dCTP) that diminishes its therapeutic effect. Furthermore, we observed that gain‐of‐function of isocitrate dehydrogenase 2 (IDH2) induced reductive glutamine metabolism to stabilize Hif‐1α expression and consequently stimulate aerobic glycolysis and PPP bypass in gemcitabine‐resistant UC cells. Interestingly, IDH2‐mediated metabolic reprogramming also caused cross resistance to CDDP, by elevating the antioxidant defense via increased NADPH and glutathione production. Downregulation or pharmacological suppression of IDH2 restored chemosensitivity. Since the expression of key metabolic enzymes, such as TIGAR, TKT, and CTPS1, were affected by IDH2‐mediated metabolic reprogramming and related to poor prognosis in patients, IDH2 might become a new therapeutic target for restoring chemosensitivity in chemo‐resistant urothelial carcinoma.     

Comparative study on central metabolic fluxes of <Emphasis Type="Italic">Bacillus megaterium</Emphasis> strains in continuous culture using <Superscript>13</Superscript>C labelled substrates

Fluxes of central carbon metabolism [glycolysis, pentose phosphate pathway (PPP), tricarboxylic acid cycle (TCA cycle), biomass formation] were determined for several Bacillus megaterium strains (DSM319, WH320, WH323, MS941) in C- and N-limited chemostat cultures by ¹³C labelling experiments. The labelling patterns of proteinogenic amino acids were analysed by GC/MS and therefrom flux ratios at important nodes within the metabolic network could be calculated. On the basis of a stoichiometric metabolic model flux distributions were estimated for the different B. megaterium strains used at various cultivation conditions. Generally all strains exhibited similar metabolic flux distributions, however, several significant changes were found in (1) the glucose flux entering the PPP via the oxidative branch, (2) the reversibilities within the PPP, (3) the relative fluxes of pyruvate and acetyl-CoA fed to the TCA cycle, (4) the fluxes around the pyruvate node involving a futile cycle.     

Identification of metabolic fluxes in hepatic cells from transient 13C-labeling experiments: Part II. Flux estimation

This contribution addresses the identification of metabolic fluxes and metabolite concentrations in mammalian cells from transient (13)C-labeling experiments. Whilst part I describes experimental set-up and acquisition of required metabolite and (13)C-labeling data, part II focuses on setting up network models and the estimation of intracellular fluxes. Metabolic fluxes were determined in glycolysis, pentose-phosphate pathway (PPP), and citric acid cycle (TCA) in a hepatoma cell line grown in aerobic batch cultures. In glycolytic and PPP metabolite pools isotopic stationarity was observed within 30 min, whereas in the TCA cycle the labeling redistribution did not reach isotopic steady state even within 180 min. In silico labeling dynamics were in accordance with in vivo (13)C-labeling data. Split ratio between glycolysis and PPP was 57%:43%; intracellular glucose concentration was estimated at 101.6 nmol per 10(6) cells. In contrast to isotopic stationary (13)C-flux analysis, transient (13)C-flux analysis can also be applied to industrially relevant mammalian cell fed-batch and batch cultures.     

Parameter estimation and dynamic control analysis of central carbon metabolism in <Emphasis Type="Italic">Escherichia coli</Emphasis>

The central carbon metabolic pathway is the most important among metabolic pathways in all microorganisms since it produces energy and precursors for biosynthesis. In this study, a dynamic model for central carbon metabolism in Escherichia coli (E. coli) consisting of the phosphotransferase (PTS) system, glycolysis, pentose-phosphate pathway (PPP), and storage materials was obtained by ameliorating the model proposed by Chassagnole et al. (2002). In order to improve the performance of the model, principal parameters were estimated through the experimental measurements of intracellular concentrations of metabolites under transient conditions. Through dynamic metabolic control analysis (MCA), the tendencies of the metabolic fluxes at branch points were investigated, and the key parameters and enzyme kinetics that most dominantly affected the productivity of the desired metabolites were determined.     

p53 regulates biosynthesis through direct inactivation of glucose-6-phosphate dehydrogenase

Cancer cells consume large quantities of glucose and primarily use glycolysis for ATP production, even in the presence of adequate oxygen. This metabolic signature (aerobic glycolysis or the Warburg effect) enables cancer cells to direct glucose to biosynthesis, supporting their rapid growth and proliferation. However, both causes of the Warburg effect and its connection to biosynthesis are not well understood. Here we show that the tumour suppressor p53, the most frequently mutated gene in human tumours, inhibits the pentose phosphate pathway (PPP). Through the PPP, p53 suppresses glucose consumption, NADPH production and biosynthesis. The p53 protein binds to glucose-6-phosphate dehydrogenase (G6PD), the first and rate-limiting enzyme of the PPP, and prevents the formation of the active dimer. Tumour-associated p53 mutants lack the G6PD-inhibitory activity. Therefore, enhanced PPP glucose flux due to p53 inactivation may increase glucose consumption and direct glucose towards biosynthesis in tumour cells.     

Mechanism of the oxidative stress-mediated increase in lipid accumulation by the bacterium,R. opacus PD630: Experimental analysis and genome-scale metabolic modeling

Appropriate species of oleaginous bacteria, with their high growth rates and lipid accumulation capabilities, can be good contenders for industrial triacylglycerol (TAG) production, compared to microalgae. Further, oxidative stress (OS) can be used to significantly increase TAG yields in oleaginous microbes, but the mechanism is unexplored. In a first, this study explored the mechanism behind OS-mediated increase in TAG accumulation by the bacterium, Rhodococccus opacus PD630, through experimental analysis and metabolic modelling. Two mechanisms that could increase acetyl-CoA (TAG-precursor) levels were hypothesized based on literature information. One was OS-mediated inactivation of the aconitase (TCA cycle), and another was the inactivation of the triosephosphate isomerase (TPI; glycolysis). The results negated the involvement of aconitase in increased acetyl-CoA levels. Analysis of the metabolic model showed that inactivation of TPI, re-routed the flux through the pentose phosphate pathway (PPP), supplying both NADPH and acetyl-CoA for TAG synthesis. Additionally, inactivation of TPI increased TAG flux by 143%, whereas, inactivating both TPI and aconitase, increased it by 152%. We present experimental evidence for OS-mediated decrease in TPI activity and increase in activity of glucose-6-phosphate dehydrogenase (PPP enzyme). The findings indicate that increased flux through PPP can be explored to improve TAG accumulation on a large-scale.     

Ab initio reconstruction of metabolic pathways

We propose a new formulation for the problem of ab initio metabolic pathway reconstruction. Given a set of biochemical reactions together with their substrates and products, we consider the reactions as transfers of atoms between the chemical compounds and we look for successions of reactions transferring a maximal (or preset) number of atoms between a given source and sink compound. We state this problem as the one of finding a composition of partial injections that maximizes the image size. First, we study the theoretical complexity of this problem, state some related problems and then give a practical algorithm to solve them. Finally, we present two applications of this approach to the reconstruction of the tryptophan biosynthesis pathway and to the glycolysis.     

Molecular intricacies of aerobic glycolysis in cancer: current insights into the classic metabolic phenotype

Aerobic glycolysis is the process of oxidation of glucose into pyruvate followed by lactate production under normoxic condition. Distinctive from its anaerobic counterpart (i.e. glycolysis that occurs under hypoxia), aerobic glycolysis is frequently witnessed in cancers, popularly known as the “Warburg effect”, and it is one of the earliest known evidences of metabolic alteration in neoplasms. Intracellularly, aerobic glycolysis circumvents mitochondrial oxidative phosphorylation (OxPhos), facilitating an increased rate of glucose hydrolysis. This in turn enables cancer cells to successfully compete with normal cells for glucose uptake in order to maintain uninterrupted growth. In addition, evading OxPhos mitigates excessive generation/accumulation of reactive oxygen species that otherwise may be deleterious to cells. Emerging data indicate that aerobic glycolysis in cancer also promotes glutaminolysis to satisfy the precursor requirements of certain biosynthetic processes (e.g. nucleic acids). Next, the metabolic intermediates of aerobic glycolysis also feed the pentose phosphate pathway (PPP) to facilitate macromolecular biosynthesis necessary for cancer cell growth and proliferation. Extracellularly, the extrusion of the end-product of aerobic glycolysis, i.e. lactate, alters the tumor microenvironment, and impacts cancer-associated cells. Collectively, accumulating data unequivocally demonstrate that aerobic glycolysis implicates myriad of molecular and functional processes to support cancer progression. This review, in the light of recent research, dissects the molecular intricacies of its regulation, and also deliberates the emerging paradigms to target aerobic glycolysis in cancer therapy.     

Development of bifunctional biosensors for sensing and dynamic control of glycolysis flux in metabolic engineering

Glycolysis is the primary metabolic pathway in all living organisms. Maintaining the balance of glycolysis flux and biosynthetic pathways is the crucial matter involved in the microbial cell factory. Few regulation systems can address the issue of metabolic flux imbalance in glycolysis. Here, we designed and constructed a bifunctional glycolysis flux biosensor that can dynamically regulate glycolysis flux for overproduction of desired biochemicals. A series of positive-and negative-response biosensors were created and modified for varied thresholds and dynamic ranges. These engineered glycolysis flux biosensors were verified to be able to characterize in vivo fructose-1,6-diphosphate concentration. Subsequently, the biosensors were applied for fine-tuning glycolysis flux to effectively balance the biosynthesis of two chemicals: mevalonate and N-acetylglucosamine. A glycolysis flux-dynamically controlled Escherichia coli strain achieved a 111.3 g/L mevalonate titer in a 1L fermenter.     

Metabolic flux and metabolic network analysis of Penicillium chrysogenum using 2D [13C, 1H] COSY NMR measurements and cumulative bondomer simulation

At present two alternative methods are available for analyzing the fluxes in a metabolic network: (1) combining measurements of net conversion rates with a set of metabolite balances including the cofactor balances, or (2) leaving out the cofactor balances and fitting the resulting free fluxes to measured (13)C-labeling data. In this study these two approaches are applied to the fluxes in the glycolysis and pentose phosphate pathway of Penicillium chrysogenum growing on either ammonia or nitrate as the nitrogen source, which is expected to give different pentose phosphate pathway fluxes. The presented flux analyses are based on extensive sets of 2D [(13)C, (1)H] COSY data. A new concept is applied for simulation of this type of (13)C-labeling data: cumulative bondomer modeling. The outcomes of the (13)C-labeling based flux analysis substantially differ from those of the pure metabolite balancing approach. The fluxes that are determined using (13)C-labeling data are shown to be highly dependent on the chosen metabolic network. Extending the traditional nonoxidative pentose phosphate pathway with additional transketolase and transaldolase reactions, extending the glycolysis with a fructose 6-phosphate aldolase/dihydroxyacetone kinase reaction sequence or adding a phosphoenolpyruvate carboxykinase reaction to the model considerably improves the fit of the measured and the simulated NMR data. The results obtained using the extended version of the nonoxidative pentose phosphate pathway model show that the transketolase and transaldolase reactions need not be assumed reversible to get a good fit of the (13)C-labeling data. Strict statistical testing of the outcomes of (13)C-labeling based flux analysis using realistic measurement errors is demonstrated to be of prime importance for verifying the assumed metabolic model.     

Rewiring and regulation of cross-compartmentalized metabolism in protists

Plastid acquisition, endosymbiotic associations, lateral gene transfer, organelle degeneracy or even organelle loss influence metabolic capabilities in many different protists. Thus, metabolic diversity is sculpted through the gain of new metabolic functions and moderation or loss of pathways that are often essential in the majority of eukaryotes. What is perhaps less apparent to the casual observer is that the sub-compartmentalization of ubiquitous pathways has been repeatedly remodelled during eukaryotic evolution, and the textbook pictures of intermediary metabolism established for animals, yeast and plants are not conserved in many protists. Moreover, metabolic remodelling can strongly influence the regulatory mechanisms that control carbon flux through the major metabolic pathways. Here, we provide an overview of how core metabolism has been reorganized in various unicellular eukaryotes, focusing in particular on one near universal catabolic pathway (glycolysis) and one ancient anabolic pathway (isoprenoid biosynthesis). For the example of isoprenoid biosynthesis, the compartmentalization of this process in protists often appears to have been influenced by plastid acquisition and loss, whereas for glycolysis several unexpected modes of compartmentalization have emerged. Significantly, the example of trypanosomatid glycolysis illustrates nicely how mathematical modelling and systems biology can be used to uncover or understand novel modes of pathway regulation.     

Finding minimal generating set for metabolic network with reversible pathways

Elementary flux modes give a mathematical representation of metabolic pathways in metabolic networks satisfying the constraint of non-decomposability. The large cost of their computation shifts attention to computing a minimal generating set which is a conically independent subset of elementary flux modes. When a metabolic network has reversible reactions and also admits a reversible pathway, the minimal generating set is not unique. A theoretical development and computational framework is provided which outline how to compute the minimal generating set in this case. The method is based on combining existing software to compute the minimal generating set for a “pointed cone” together with standard software to compute the Reduced Row Echelon Form.