Computational approaches for understanding energy metabolism

Warburg Effect Revisited: Embodiment of Classical Biochemistry and Organic Chemistry. Current State and Prospects

The Nobel Prize Winner (1931) Dr. Otto H. Warburg had established that the primary energy source of the cancer cell is aerobic glycolysis (the Warburg effect). He also postulated the hypothesis about "the prime cause of cancer", which is a matter of debate nowadays. Contrary to the hypothesis, his discovery was recognized entirely. However, the discovery had almost vanished in the heat of battle about the hypothesis. The prime cause of cancer is essential for the prevention and diagnosis, yet the effects that influence tumor growth are more important for cancer treatment. Due to the Warburg effect, a large amount of data has been accumulated on biochemical changes in the cell and the organism as a whole. Due to the Warburg effect, the recovery of normal biochemistry and oxygen respiration and the restoration of the work of mitochondria of cancer cells can inhibit tumor growth and lead to remission. Here, we review the current knowledge on the inhibition of abnormal glycolysis, neutralization of its consequences, and normalization of biochemical parameters, as well as recovery of oxygen respiration of a cancer cell and mitochondrial function from the point of view of classical biochemistry and organic chemistry.

Multi-scale computational study of the Warburg effect, reverse Warburg effect and glutamine addiction in solid tumors

Cancer metabolism has received renewed interest as a potential target for cancer therapy. In this study, we use a multi-scale modeling approach to interrogate the implications of three metabolic scenarios of potential clinical relevance: the Warburg effect, the reverse Warburg effect and glutamine addiction. At the intracellular level, we construct a network of central metabolism and perform flux balance analysis (FBA) to estimate metabolic fluxes; at the cellular level, we exploit this metabolic network to calculate parameters for a coarse-grained description of cellular growth kinetics; and at the multicellular level, we incorporate these kinetic schemes into the cellular automata of an agent-based model (ABM), iDynoMiCS. This ABM evaluates the reaction-diffusion of the metabolites, cellular division and motion over a simulation domain. Our multi-scale simulations suggest that the Warburg effect provides a growth advantage to the tumor cells under resource limitation. However, we identify a non-monotonic dependence of growth rate on the strength of glycolytic pathway. On the other hand, the reverse Warburg scenario provides an initial growth advantage in tumors that originate deeper in the tissue. The metabolic profile of stromal cells considered in this scenario allows more oxygen to reach the tumor cells in the deeper tissue and thus promotes tumor growth at earlier stages. Lastly, we suggest that glutamine addiction does not confer a selective advantage to tumor growth with glutamine acting as a carbon source in the tricarboxylic acid (TCA) cycle, any advantage of glutamine uptake must come through other pathways not included in our model (e.g., as a nitrogen donor). Our analysis illustrates the importance of accounting explicitly for spatial and temporal evolution of tumor microenvironment in the interpretation of metabolic scenarios and hence provides a basis for further studies, including evaluation of specific therapeutic strategies that target metabolism.

Cancer metabolism is an emerging hallmark of cancer. In the past decade, a renewed focus on cancer metabolism has led to several distinct hypotheses describing the role of metabolism in cancer. To complement experimental efforts in this field, a scale-bridging computational framework is needed to allow rapid evaluation of emerging hypotheses in cancer metabolism. In this study, we present a multi-scale modeling platform and demonstrate the distinct outcomes in population-scale growth dynamics under different metabolic scenarios: the Warburg effect, the reverse Warburg effect and glutamine addiction. Within this modeling framework, we confirmed population-scale growth advantage enabled by the Warburg effect, provided insights into the symbiosis between stromal cells and tumor cells in the reverse Warburg effect and argued that the anaplerotic role of glutamine is not exploited by tumor cells to gain growth advantage under resource limitations. We point to the opportunity for this framework to help understand tissue-scale response to therapeutic strategies that target cancer metabolism while accounting for the tumor complexity at multiple scales.

Understanding metabolism with flux analysis: From theory to application

Quantitative and qualitative knowledge of metabolic rates (i.e. fluxes) over a metabolic network and in specific cellular compartments gives insights into the regulation of metabolism and helps to understand the contribution of metabolic alterations to pathology. In this review we introduce methodology to resolve metabolic fluxes from stable isotope labeling and relevant techniques in model development, model simplification, flux uncertainty analysis and experimental design that together is termed metabolic flux analysis. Finally we discuss applications using metabolic flux analysis to elucidate mechanisms pertinent to tumor cell metabolism. We hope that this review gives the readers a brief introduction of how flux analysis is conducted, how technical issues related to it are addressed, and how its application has contributed to our knowledge of tumor cell metabolism.

A Flux Balance of Glucose Metabolism Clarifies the Requirements of the Warburg Effect

The Warburg effect, or aerobic glycolysis, is marked by the increased metabolism of glucose to lactate in the presence of oxygen. Despite its widespread prevalence in physiology and cancer biology, the causes and consequences remain incompletely understood. Here, we show that a simple balance of interacting fluxes in glycolysis creates constraints that impose the necessary conditions for glycolytic flux to generate lactate as opposed to entering into the mitochondria. These conditions are determined by cellular redox and energy demands. By analyzing the constraints and sampling the feasible region of the model, we further study how cell proliferation rate and mitochondria-associated NADH oxidizing and ATP producing fluxes are interlinked. Together this analysis illustrates the simplicity of the origins of the Warburg effect by identifying the flux distributions that are necessary for its instantiation.

Metabolomics: A Primer

Metabolomics generates a profile of small molecules that are derived from cellular metabolism and can directly reflect the outcome of complex networks of biochemical reactions, thus providing insights into multiple aspects of cellular physiology. Technological advances have enabled rapid and increasingly expansive data acquisition with samples as small as single cells; however, substantial challenges in the field remain. In this primer we provide an overview of metabolomics, especially mass spectrometry (MS)-based metabolomics, which uses liquid chromatography (LC) for separation, and discuss its utilities and limitations. We identify and discuss several areas at the frontier of metabolomics. Our goal is to give the reader a sense of what might be accomplished when conducting a metabolomics experiment, now and in the near future.

(13)C MRS and LC-MS Flux Analysis of Tumor Intermediary Metabolism

We present the first validated metabolic network model for analysis of flux through key pathways of tumor intermediary metabolism, including glycolysis, the oxidative and non-oxidative arms of the pentose pyrophosphate shunt, the TCA cycle as well as its anaplerotic pathways, pyruvate-malate shuttling, glutaminolysis, and fatty acid biosynthesis and oxidation. The model that is called Bonded Cumomer Analysis for application to (13)C magnetic resonance spectroscopy ((13)C MRS) data and Fragmented Cumomer Analysis for mass spectrometric data is a refined and efficient form of isotopomer analysis that can readily be expanded to incorporate glycogen, phospholipid, and other pathways thereby encompassing all the key pathways of tumor intermediary metabolism. Validation was achieved by demonstrating agreement of experimental measurements of the metabolic rates of oxygen consumption, glucose consumption, lactate production, and glutamate pool size with independent measurements of these parameters in cultured human DB-1 melanoma cells. These cumomer models have been applied to studies of DB-1 melanoma and DLCL2 human diffuse large B-cell lymphoma cells in culture and as xenografts in nude mice at 9.4 T. The latter studies demonstrate the potential translation of these methods to in situ studies of human tumor metabolism by MRS with stable (13)C isotopically labeled substrates on instruments operating at high magnetic fields (≥7 T). The melanoma studies indicate that this tumor line obtains 51% of its ATP by mitochondrial metabolism and 49% by glycolytic metabolism under both euglycemic (5 mM glucose) and hyperglycemic conditions (26 mM glucose). While a high level of glutamine uptake is detected corresponding to ~50% of TCA cycle flux under hyperglycemic conditions, and ~100% of TCA cycle flux under euglycemic conditions, glutaminolysis flux and its contributions to ATP synthesis were very small. Studies of human lymphoma cells demonstrated that inhibition of mammalian target of rapamycin (mTOR) signaling produced changes in flux through the glycolytic, pentose shunt, and TCA cycle pathways that were evident within 8 h of treatment and increased at 24 and 48 h. Lactate was demonstrated to be a suitable biomarker of mTOR inhibition that could readily be monitored by (1)H MRS and perhaps also by FDG-PET and hyperpolarized (13)C MRS methods.

Bonded Cumomer Analysis of Human Melanoma Metabolism Monitored by 13C NMR Spectroscopy of Perfused Tumor Cells

A network model for the determination of tumor metabolic fluxes from (13)C NMR kinetic isotopomer data has been developed and validated with perfused human DB-1 melanoma cells carrying the BRAF V600E mutation, which promotes oxidative metabolism. The model generated in the bonded cumomer formalism describes key pathways of tumor intermediary metabolism and yields dynamic curves for positional isotopic enrichment and spin-spin multiplets. Cells attached to microcarrier beads were perfused with 26 mm [1,6-(13)C2]glucose under normoxic conditions at 37 °C and monitored by (13)C NMR spectroscopy. Excellent agreement between model-predicted and experimentally measured values of the rates of oxygen and glucose consumption, lactate production, and glutamate pool size validated the model. ATP production by glycolytic and oxidative metabolism were compared under hyperglycemic normoxic conditions; 51% of the energy came from oxidative phosphorylation and 49% came from glycolysis. Even though the rate of glutamine uptake was ∼ 50% of the tricarboxylic acid cycle flux, the rate of ATP production from glutamine was essentially zero (no glutaminolysis). De novo fatty acid production was ∼ 6% of the tricarboxylic acid cycle flux. The oxidative pentose phosphate pathway flux was 3.6% of glycolysis, and three non-oxidative pentose phosphate pathway exchange fluxes were calculated. Mass spectrometry was then used to compare fluxes through various pathways under hyperglycemic (26 mm) and euglycemic (5 mm) conditions. Under euglycemic conditions glutamine uptake doubled, but ATP production from glutamine did not significantly change. A new parameter measuring the Warburg effect (the ratio of lactate production flux to pyruvate influx through the mitochondrial pyruvate carrier) was calculated to be 21, close to upper limit of oxidative metabolism.

Mitochondrial Respiration Chain Enzymatic Activities in the Human Brain: Methodological Implications for Tissue Sampling and Storage

Mitochondrial respiratory chain complexes enzymatic (MRCCE) activities were successfully evaluated in frozen brain samples. Epilepsy surgery offers an ethical opportunity to study human brain tissue surgically removed to treat drug resistant epilepsies. Epilepsy surgeries are done with hemodynamic and laboratory parameters to maintain physiology, but there are no studies analyzing the association among these parameters and MRCCE activities in the human brain tissue. We determined the intra-operative parameters independently associated with MRCCE activities in middle temporal neocortex (Cx), amygdala (AMY) and head of hippocampus (HIP) samples of patients (n = 23) who underwent temporal lobectomy using multiple linear regressions. MRCCE activities in Cx, AMY and HIP are differentially associated to trans-operative mean arterial blood pressure, O2 saturation, hemoglobin, and anesthesia duration to time of tissue sampling. The time-course between the last seizure occurrence and tissue sampling as well as the sample storage to biochemical assessments were also associated with enzyme activities. Linear regression models including these variables explain 13-17 % of MRCCE activities and show a moderate to strong effect (r = 0.37-0.82). Intraoperative hemodynamic and laboratory parameters as well as the time from last seizure to tissue sampling and storage time are associated with MRCCE activities in human samples from the Cx, AMYG and HIP. Careful control of these parameters is required to minimize confounding biases in studies using human brain samples collected from elective neurosurgery.

A robust and efficient method for estimating enzyme complex abundance and metabolic flux from expression data

A major theme in constraint-based modeling is unifying experimental data, such as biochemical information about the reactions that can occur in a system or the composition and localization of enzyme complexes, with high-throughput data including expression data, metabolomics, or DNA sequencing. The desired result is to increase predictive capability and improve our understanding of metabolism. The approach typically employed when only gene (or protein) intensities are available is the creation of tissue-specific models, which reduces the available reactions in an organism model, and does not provide an objective function for the estimation of fluxes. We develop a method, flux assignment with LAD (least absolute deviation) convex objectives and normalization (FALCON), that employs metabolic network reconstructions along with expression data to estimate fluxes. In order to use such a method, accurate measures of enzyme complex abundance are needed, so we first present an algorithm that addresses quantification of complex abundance. Our extensions to prior techniques include the capability to work with large models and significantly improved run-time performance even for smaller models, an improved analysis of enzyme complex formation, the ability to handle large enzyme complex rules that may incorporate multiple isoforms, and either maintained or significantly improved correlation with experimentally measured fluxes. FALCON has been implemented in MATLAB and ATS, and can be downloaded from: https://github.com/bbarker/FALCON. ATS is not required to compile the software, as intermediate C source code is available. FALCON requires use of the COBRA Toolbox, also implemented in MATLAB.

Systems biology of host-microbe metabolomics

The human gut microbiota performs essential functions for host and well‐being, but has also been linked to a variety of disease states, e.g., obesity and type 2 diabetes. The mammalian body fluid and tissue metabolomes are greatly influenced by the microbiota, with many health‐relevant metabolites being considered ‘mammalian–microbial co‐metabolites’. To systematically investigate this complex host–microbial co‐metabolism, a systems biology approach integrating high‐throughput data and computational network models is required. Here, we review established top‐down and bottom‐up systems biology approaches that have successfully elucidated relationships between gut microbiota‐derived metabolites and host health and disease. We focus particularly on the constraint‐based modeling and analysis approach, which enables the prediction of mechanisms behind metabolic host–microbe interactions on the molecular level. We illustrate that constraint‐based models are a useful tool for the contextualization of metabolomic measurements and can further our insight into host–microbe interactions, yielding, e.g., in potential novel drugs and biomarkers. WIREs Syst Biol Med 2015, 7:195–219. doi: 10.1002/wsbm.1301

For further resources related to this article, please visit the WIREs website.

Conflict of interest: The authors have declared no conflicts of interest for this article.

Redox control of glutamine utilization in cancer

Glutamine utilization promotes enhanced growth of cancer cells. We propose a new concept map of cancer metabolism in which mitochondrial NADH and NADPH, in the presence of a dysfunctional electron transfer chain, promote reductive carboxylation from glutamine. We also discuss why nicotinamide nucleotide transhydrogenase (NNT) is required in vivo for glutamine utilization by reductive carboxylation. Moreover, NADPH, generated by both the pentose phosphate pathway and the cancer-specific serine glycolytic diversion, appears to sustain glutamine utilization for amino-acid synthesis, lipid synthesis, and for ROS quenching. The fact that the supply of NAD⁺ precursors reduces tumor aggressiveness suggests experimental approaches to clarify the role of the NADH-driven redox network in cancer.

Quantitative determinants of aerobic glycolysis identify flux through the enzyme GAPDH as a limiting step

Aerobic glycolysis or the Warburg Effect (WE) is characterized by the increased metabolism of glucose to lactate. It remains unknown what quantitative changes to the activity of metabolism are necessary and sufficient for this phenotype. We developed a computational model of glycolysis and an integrated analysis using metabolic control analysis (MCA), metabolomics data, and statistical simulations. We identified and confirmed a novel mode of regulation specific to aerobic glycolysis where flux through GAPDH, the enzyme separating lower and upper glycolysis, is the rate-limiting step in the pathway and the levels of fructose (1,6) bisphosphate (FBP), are predictive of the rate and control points in glycolysis. Strikingly, negative flux control was found and confirmed for several steps thought to be rate-limiting in glycolysis. Together, these findings enumerate the biochemical determinants of the WE and suggest strategies for identifying the contexts in which agents that target glycolysis might be most effective.

DOI:http://dx.doi.org/10.7554/eLife.03342.001

Cells generate energy from a sugar called glucose via a process called glycolysis. This process involves many enzymes that catalyze 10 different chemical reactions, and it essentially converts glucose step-by-step into a simpler chemical called pyruvate.

Pyruvate is then normally transported into structures within the cell called mitochondria, where it is further broken down using oxygen to release more energy. However, in cells that are rapidly dividing, pyruvate is converted into another chemical called lactate—which releases energy more quickly, but releases less energy overall. Cancer cells often convert most of their glucose into lactate, rather than breaking down pyruvate in their mitochondria: an observation known as the ‘Warburg effect’. And while many factors affect how a cell releases energy from pyruvate, it remains unclear what regulates which of these biochemical processes is most common in a living cell.

In this study, Shestov et al. have developed a computational model for the process of glycolysis and used this to investigate the causes of the Warburg Effect. The model was based on the known characteristics of the enzymes and chemical reactions involved at each step. It predicted that the activity of the enzyme called GAPDH, which carries out the sixth step in glycolysis, in many cases affects how much lactate is produced. This suggests that this enzyme represents a bottleneck in the pathway.

Next, Shestov et al. performed experiments where they used drugs to block different stages of the glycolysis pathway, and confirmed that the GAPDH enzyme is important for regulating this pathway in living cancer cells too. In these treated cells, the levels of a chemical called fructose-1,6-biphosphate (which is made in a step in the pathway between glucose and pyruvate) were either very high or very low. Shestov et al. proposed that the flow of chemicals through the glycolysis pathway is controlled by the GAPDH enzyme when the chemicals used by the enzymes upstream of GAPDH in the pathway (which includes fructose-1,6-biphosphate) are plentiful. However, if these chemicals are limited, other enzymes that are involved in earlier steps of the pathway regulate the process instead.

The findings of Shestov et al. reveal that the regulation of glycolysis is more complex than previously thought, and is also very different when cells are undergoing the Warburg Effect. In the future, these findings might help to identify the types of cancer that could be effectively treated using drugs that target the glycolysis process, which are currently being tested in pre-clinical studies.

DOI:http://dx.doi.org/10.7554/eLife.03342.002