Quantifying reductive carboxylation flux of glutamine to lipid in a brown adipocyte cell line

Regulation of substrate utilization by the mitochondrial pyruvate carrier

Pyruvate lies at a central biochemical node connecting carbohydrate, amino acid, and fatty acid metabolism, and the regulation of pyruvate flux into mitochondria represents a critical step in intermediary metabolism impacting numerous diseases. To characterize changes in mitochondrial substrate utilization in the context of compromised mitochondrial pyruvate transport, we applied (13)C metabolic flux analysis (MFA) to cells after transcriptional or pharmacological inhibition of the mitochondrial pyruvate carrier (MPC). Despite profound suppression of both glucose and pyruvate oxidation, cell growth, oxygen consumption, and tricarboxylic acid (TCA) metabolism were surprisingly maintained. Oxidative TCA flux was achieved through enhanced reliance on glutaminolysis through malic enzyme and pyruvate dehydrogenase (PDH) as well as fatty acid and branched-chain amino acid oxidation. Thus, in contrast to inhibition of complex I or PDH, suppression of pyruvate transport induces a form of metabolic flexibility associated with the use of lipids and amino acids as catabolic and anabolic fuels.

(13)C metabolic flux analysis of recombinant expression hosts

Identifying host cell metabolic phenotypes that promote high recombinant protein titer is a major goal of the biotech industry. (13)C metabolic flux analysis (MFA) provides a rigorous approach to quantify these metabolic phenotypes by applying isotope tracers to map the flow of carbon through intracellular metabolic pathways. Recent advances in tracer theory and measurements are enabling more information to be extracted from (13)C labeling experiments. Sustained development of publicly available software tools and standardization of experimental workflows is simultaneously encouraging increased adoption of (13)C MFA within the biotech research community. A number of recent (13)C MFA studies have identified increased citric acid cycle and pentose phosphate pathway fluxes as consistent markers of high recombinant protein expression, both in mammalian and microbial hosts. Further work is needed to determine whether redirecting flux into these pathways can effectively enhance protein titers while maintaining acceptable glycan profiles.

Central metabolic responses to the overproduction of fatty acids in Escherichia coli based on 13C-metabolic flux analysis

We engineered a fatty acid overproducing Escherichia coli strain through overexpressing tesA (“pull”) and fadR (“push”) and knocking out fadE (“block”). This “pull-push-block” strategy yielded 0.17 g of fatty acids (C12–C18) per gram of glucose (equivalent to 48% of the maximum theoretical yield) in batch cultures during the exponential growth phase under aerobic conditions. Metabolic fluxes were determined for the engineered E. coli and its control strain using tracer ([1,2-13C]glucose) experiments and 13C-metabolic flux analysis. Cofactor (NADPH) and energy (ATP) balances were also investigated for both strains based on estimated fluxes. Compared to the control strain, fatty acid overproduction led to significant metabolic responses in the central metabolism: (1) Acetic acid secretion flux decreased 10-fold; (2) Pentose phosphate pathway and Entner–Doudoroff pathway fluxes increased 1.5- and 2.0-fold, respectively; (3) Biomass synthesis flux was reduced 1.9-fold; (4) Anaplerotic phosphoenolpyruvate carboxylation flux decreased 1.7-fold; (5) Transhydrogenation flux converting NADH to NADPH increased by 1.7-fold. Real-time quantitative RT-PCR analysis revealed the engineered strain increased the transcription levels of pntA (encoding the membrane-bound transhydrogenase) by 2.1-fold and udhA (encoding the soluble transhydrogenase) by 1.4-fold, which is in agreement with the increased transhydrogenation flux. Cofactor and energy balances analyses showed that the fatty acid overproducing E. coli consumed significantly higher cellular maintenance energy than the control strain. We discussed the strategies to future strain development and process improvements for fatty acid production in E. coli.

Parallel labeling experiments validate Clostridium acetobutylicum metabolic network model for (13)C metabolic flux analysis

In this work, we provide new insights into the metabolism of Clostridium acetobutylicum ATCC 824 obtained using a systematic approach for quantifying fluxes based on parallel labeling experiments and (13)C-metabolic flux analysis ((13)C-MFA). Here, cells were grown in parallel cultures with [1-(13)C]glucose and [U-(13)C]glucose as tracers and (13)C-MFA was used to quantify intracellular metabolic fluxes. Several metabolic network models were compared: an initial model based on current knowledge, and extended network models that included additional reactions that improved the fits of experimental data. While the initial network model did not produce a statistically acceptable fit of (13)C-labeling data, an extended network model with five additional reactions was able to fit all data with 292 redundant measurements. The model was subsequently trimmed to produce a minimal network model of C. acetobutylicum for (13)C-MFA, which could still reproduce all of the experimental data. The flux results provided valuable new insights into the metabolism of C. acetobutylicum. First, we found that TCA cycle was effectively incomplete, as there was no measurable flux between α-ketoglutarate and succinyl-CoA, succinate and fumarate, and malate and oxaloacetate. Second, an active pathway was identified from pyruvate to fumarate via aspartate. Third, we found that isoleucine was produced exclusively through the citramalate synthase pathway in C. acetobutylicum and that CAC3174 was likely responsible for citramalate synthase activity. These model predictions were confirmed in several follow-up tracer experiments. The validated metabolic network model established in this study can be used in future investigations for unbiased (13)C-flux measurements in C. acetobutylicum.

Anaplerotic metabolism of alloreactive T cells provides a metabolic approach to treat graft-versus-host disease

T-cell activation requires increased ATP and biosynthesis to support proliferation and effector function. Most models of T-cell activation are based on in vitro culture systems and posit that aerobic glycolysis is employed to meet increased energetic and biosynthetic demands. By contrast, T cells activated in vivo by alloantigens in graft-versus-host disease (GVHD) increase mitochondrial oxygen consumption, fatty acid uptake, and oxidation, with small increases of glucose uptake and aerobic glycolysis. Here we show that these differences are not a consequence of alloactivation, because T cells activated in vitro either in a mixed lymphocyte reaction to the same alloantigens used in vivo or with agonistic anti-CD3/anti-CD28 antibodies increased aerobic glycolysis. Using targeted metabolic (13)C tracer fate associations, we elucidated the metabolic pathway(s) employed by alloreactive T cells in vivo that support this phenotype. We find that glutamine (Gln)-dependent tricarboxylic acid cycle anaplerosis is increased in alloreactive T cells and that Gln carbon contributes to ribose biosynthesis. Pharmacological modulation of oxidative phosphorylation rapidly reduces anaplerosis in alloreactive T cells and improves GVHD. On the basis of these data, we propose a model of T-cell metabolism that is relevant to activated lymphocytes in vivo, with implications for the discovery of new drugs for immune disorders.

Nuclear ARRB1 induces pseudohypoxia and cellular metabolism reprogramming in prostate cancer

Tumour cells sustain their high proliferation rate through metabolic reprogramming, whereby cellular metabolism shifts from oxidative phosphorylation to aerobic glycolysis, even under normal oxygen levels. Hypoxia-inducible factor 1A (HIF1A) is a major regulator of this process, but its activation under normoxic conditions, termed pseudohypoxia, is not well documented. Here, using an integrative approach combining the first genome-wide mapping of chromatin binding for an endocytic adaptor, ARRB1, both in vitro and in vivo with gene expression profiling, we demonstrate that nuclear ARRB1 contributes to this metabolic shift in prostate cancer cells via regulation of HIF1A transcriptional activity under normoxic conditions through regulation of succinate dehydrogenase A (SDHA) and fumarate hydratase (FH) expression. ARRB1-induced pseudohypoxia may facilitate adaptation of cancer cells to growth in the harsh conditions that are frequently encountered within solid tumours. Our study is the first example of an endocytic adaptor protein regulating metabolic pathways. It implicates ARRB1 as a potential tumour promoter in prostate cancer and highlights the importance of metabolic alterations in prostate cancer.

OpenMebius: an open source software for isotopically nonstationary 13C-based metabolic flux analysis

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.

Cancer biology in diabetes

Diabetes is a serious metabolic disease that causes multiple organ dysfunctions. Recent evidence suggests that diabetes could contribute to the initiation and progression of certain cancers in addition to the classic diabetic complications. Furthermore, some of the drugs used clinically to treat patients with diabetes might affect cancer initiation, progression and mortality. The recent discovery of the possible anticancer effects of metformin, a classic antidiabetic drug, has led physicians and scientists to reconsider the interaction between diabetes and cancer. In the present review, we analyze recent reports in this field, and explore possible mechanistic links between diabetes and cancer biology.

Glucose and glutamine metabolism in oral squamous cell carcinoma: insight from a quantitative metabolomic approach

OBJECTIVE

To characterize the metabolic system of oral squamous cell carcinoma (OSCC) by metabolome analysis.

STUDY DESIGN

The metabolome profiles, including the Embden-Meyerhof-Parnas pathway (EMPP), the pentose phosphate pathway, the tricarboxylic acid cycle (TCAC), and amino acids, were obtained from OSCC and its surrounding normal tissues (32 patients) using capillary electrophoresis and a time-of-flight mass spectrometer.

RESULTS

Enhancement of glucose consumption and lactate production (Warburg effect) was observed in OSCC tissues. The decrease of glucose along with the decrease of the downstream intermediates in the EMPP suggests that incorporated glucose is mainly consumed for biosynthesis. Glutamine consumption with the increase of the intermediates in the last half of the TCAC suggests the involvement of glutaminolysis, in which glutamine is converted to lactate via the last half of the TCAC.

CONCLUSIONS

It is suggested that OSCC tissues show the Warburg effect, which stems from the combined enhancement of glucose consumption and glutaminolysis.

Role of abnormal lipid metabolism in development, progression, diagnosis and therapy of pancreatic cancer

There is growing evidence that metabolic alterations play an important role in cancer development and progression. The metabolism of cancer cells is reprogrammed in order to support their rapid proliferation. Elevated fatty acid synthesis is one of the most important aberrations of cancer cell metabolism. An enhancement of fatty acids synthesis is required both for carcinogenesis and cancer cell survival, as inhibition of key lipogenic enzymes slows down the growth of tumor cells and impairs their survival. Based on the data that serum fatty acid synthase (FASN), also known as oncoantigen 519, is elevated in patients with certain types of cancer, its serum level was proposed as a marker of neoplasia. This review aims to demonstrate the changes in lipid metabolism and other metabolic processes associated with lipid metabolism in pancreatic ductal adenocarcinoma (PDAC), the most common pancreatic neoplasm, characterized by high mortality. We also addressed the influence of some oncogenic factors and tumor suppressors on pancreatic cancer cell metabolism. Additionally the review discusses the potential role of elevated lipid synthesis in diagnosis and treatment of pancreatic cancer. In particular, FASN is a viable candidate for indicator of pathologic state, marker of neoplasia, as well as, pharmacological treatment target in pancreatic cancer. Recent research showed that, in addition to lipogenesis, certain cancer cells can use fatty acids from circulation, derived from diet (chylomicrons), synthesized in liver, or released from adipose tissue for their growth. Thus, the interactions between de novo lipogenesis and uptake of fatty acids from circulation by PDAC cells require further investigation.

Who controls the ATP supply in cancer cells? Biochemistry lessons to understand cancer energy metabolism

Applying basic biochemical principles, this review analyzes data that contrasts with the Warburg hypothesis that glycolysis is the exclusive ATP provider in cancer cells. Although disregarded for many years, there is increasing experimental evidence demonstrating that oxidative phosphorylation (OxPhos) makes a significant contribution to ATP supply in many cancer cell types and under a variety of conditions. Substrates oxidized by normal mitochondria such as amino acids and fatty acids are also avidly consumed by cancer cells. In this regard, the proposal that cancer cells metabolize glutamine for anabolic purposes without the need for a functional respiratory chain and OxPhos is analyzed considering thermodynamic and kinetic aspects for the reductive carboxylation of 2-oxoglutarate catalyzed by isocitrate dehydrogenase. In addition, metabolic control analysis (MCA) studies applied to energy metabolism of cancer cells are reevaluated. Regardless of the experimental/environmental conditions and the rate of lactate production, the flux-control of cancer glycolysis is robust in the sense that it involves the same steps: glucose transport, hexokinase, hexosephosphate isomerase and glycogen degradation, all at the beginning of the pathway; these steps together with phosphofructokinase 1 also control glycolysis in normal cells. The respiratory chain complexes exert significantly higher flux-control on OxPhos in cancer cells than in normal cells. Thus, determination of the contribution of each pathway to ATP supply and/or the flux-control distribution of both pathways in cancer cells is necessary in order to identify differences from normal cells which may lead to the design of rational alternative therapies that selectively target cancer energy metabolism.

Glucose-dependent de novo lipogenesis in B lymphocytes: a requirement for atp-citrate lyase in lipopolysaccharide-induced differentiation

Bacterially derived lipopolysaccharide (LPS) stimulates naive B lymphocytes to differentiate into immunoglobulin (Ig)-secreting plasma cells. Differentiation of B lymphocytes is characterized by a proliferative phase followed by expansion of the intracellular membrane secretory network to support Ig production. A key question in lymphocyte biology is how naive B cells reprogram metabolism to support de novo lipogenesis necessary for proliferation and expansion of the endomembrane network in response to LPS. We report that extracellularly acquired glucose is metabolized, in part, to support de novo lipogenesis in response to LPS stimulation of splenic B lymphocytes. LPS stimulation leads to increased levels of endogenous ATP-citrate lyase (ACLY), and this is accompanied by increased ACLY enzymatic activity. ACLY produces cytosolic acetyl-CoA from mitochondrially derived citrate. Inhibition of ACLY activity in LPS-stimulated B cells with the selective inhibitor 2-hydroxy-N-arylbenzenesulfonamide (compound-9; C-9) blocks glucose incorporation into de novo lipid biosynthesis, including cholesterol, free fatty acids, and neutral and acidic phospholipids. Moreover, inhibition of ACLY activity in splenic B cells results in inhibition of proliferation and defective endomembrane expansion and reduced expression of CD138 and Blimp-1, markers for plasma-like B cell differentiation. ACLY activity is also required for LPS-induced IgM production in CH12 B lymphoma cells. These data demonstrate that ACLY mediates glucose-dependent de novo lipogenesis in response to LPS signaling and identify a role for ACLY in several phenotypic changes that define plasma cell differentiation.

INCA: a computational platform for isotopically non-stationary metabolic flux analysis

13C flux analysis studies have become an essential component of metabolic engineering research. The scope of these studies has gradually expanded to include both isotopically steady-state and transient labeling experiments, the latter of which are uniquely applicable to photosynthetic organisms and slow-to-label mammalian cell cultures. Isotopomer network compartmental analysis (INCA) is the first publicly available software package that can perform both steady-state metabolic flux analysis and isotopically non-stationary metabolic flux analysis. The software provides a framework for comprehensive analysis of metabolic networks using mass balances and elementary metabolite unit balances. The generation of balance equations and their computational solution is completely automated and can be performed on networks of arbitrary complexity.

Mathematical modeling of isotope labeling experiments for metabolic flux analysis

Isotope labeling experiments (ILEs) offer a powerful methodology to perform metabolic flux analysis. However, the task of interpreting data from these experiments to evaluate flux values requires significant mathematical modeling skills. Toward this, this chapter provides background information and examples to enable the reader to (1) model metabolic networks, (2) simulate ILEs, and (3) understand the optimization and statistical methods commonly used for flux evaluation. A compartmentalized model of plant glycolysis and pentose phosphate pathway illustrates the reconstruction of a typical metabolic network, whereas a simpler example network illustrates the underlying metabolite and isotopomer balancing techniques. We also discuss the salient features of commonly used flux estimation software 13CFLUX2, Metran, NMR2Flux+, FiatFlux, and OpenFLUX. Furthermore, we briefly discuss methods to improve flux estimates. A graphical checklist at the end of the chapter provides a reader a quick reference to the mathematical modeling concepts and resources.

High-throughput data pipelines for metabolic flux analysis in plants

In this chapter we illustrate the methodology for high-throughput metabolic flux analysis. Central to this is developing an end to end data pipeline, crucial for integrating the wet lab experiments and analytics, combining hardware and software automation, and standardizing data representation providing importers and exporters to support third party tools. The use of existing software at the start, data extraction from the chromatogram, and the end, MFA analysis, allows for the most flexibility in this workflow. Developing iMS2Flux provided a standard, extensible, platform independent tool to act as the "glue" between these end points. Most importantly this tool can be easily adapted to support different data formats, data verification and data correction steps allowing it to be central to managing the data necessary for high-throughput MFA. An additional tool was needed to automate the MFA software and in particular to take advantage of the course grained parallel nature of high-throughput analysis and available high performance computing facilities.In combination these methods show the development of high-throughput pipelines that allow metabolic flux analysis to join as a full member of the omics family.

Stable isotope-labeled tracers for metabolic pathway elucidation by GC-MS and FT-MS

Advances in analytical methodologies, principally nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS), over the last decade have made large-scale analysis of the human metabolome a reality. This is leading to the reawakening of the importance of metabolism in human diseases, particularly widespread metabolic diseases such as cancer, diabetes, and obesity. Emerging NMR and MS atom-tracking technologies and informatics are poised to revolutionize metabolomics-based research because they deliver the high information throughput (HIT) that is needed for deciphering systems biochemistry. In particular, stable isotope-resolved metabolomics (SIRM) enables unambiguous tracking of individual atoms through compartmentalized metabolic networks in a wide range of experimental systems, including human subjects. MS offers a wide range of instrumental capabilities involving different levels of initial capital outlay and operating costs, ranging from gas-chromatography (GC) MS that is affordable by many individual laboratories to the HIT-supporting Fourier-transform (FT) class of MS that rivals NMR in cost and infrastructure support. This chapter focuses on sample preparation, instrument, and data processing procedures for these two extremes of MS instrumentation used in SIRM.

Customization of ¹³C-MFA strategy according to cell culture system

(13)C-MFA is far from being a simple assay for quantifying metabolic activity. It requires considerable up-front experimental planning and familiarity with the cell culture system in question, as well as optimized analytics and adequate computation frameworks. The success of a (13)C-MFA experiment is ultimately rated by the ability to accurately quantify the flux of one or more reactions of interest. In this chapter, we describe the different (13)C-MFA strategies that have been developed for the various fermentation or cell culture systems, as well as the limitations of the respective strategies. The strategies are affected by many factors and the (13)C-MFA modeling and experimental strategy must be tailored to conditions. The prevailing philosophy in the computation process is that any metabolic processes that produce significant systematic bias in the labeling pattern of the metabolites being measured must be described in the model. It is equally important to plan a labeling strategy by analytical screening or by heuristics.

PGC-1α supports glutamine metabolism in breast cancer

Background

Glutamine metabolism is a central metabolic pathway in cancer. Recently, reductive carboxylation of glutamine for lipogenesis has been shown to constitute a key anabolic route in cancer cells. However, little is known regarding central regulators of the various glutamine metabolic pathways in cancer cells.

Methods

The impact of PGC-1α and ERRα on glutamine enzyme expression was assessed in ERBB2+ breast cancer cell lines with quantitative RT-PCR, chromatin immunoprecipitation, and immunoblotting experiments. Glutamine flux was quantified using ¹³C-labeled glutamine and GC/MS analyses. Functional assays for lipogenesis were performed using ¹⁴C-labeled glutamine. The expression of glutamine metabolism genes in breast cancer patients was determined by bioinformatics analyses using The Cancer Genome Atlas.

Results

We show that the transcriptional coactivator PGC-1α, along with the transcription factor ERRα, is a positive regulator of the expression of glutamine metabolism genes in ERBB2+ breast cancer. Indeed, ERBB2+ breast cancer cells with increased expression of PGC-1α display elevated expression of glutamine metabolism genes. Furthermore, ERBB2+ breast cancer cells with reduced expression of PGC-1α or when treated with C29, a pharmacological inhibitor of ERRα, exhibit diminished expression of glutamine metabolism genes. The biological relevance of the control of glutamine metabolism genes by the PGC-1α/ERRα axis is demonstrated by consequent regulation of glutamine flux through the citric acid cycle. PGC-1α and ERRα regulate both the canonical citric acid cycle (forward) and the reductive carboxylation (reverse) fluxes; the latter can be used to support de novo lipogenesis reactions, most notably in hypoxic conditions. Importantly, murine and human ERBB2+ cells lines display a significant dependence on glutamine availability for their growth. Finally, we show that PGC-1α expression is positively correlated with that of the glutamine pathway in ERBB2+ breast cancer patients, and high expression of this pathway is associated with reduced patient survival.

Conclusions

These data reveal that the PGC-1α/ERRα axis is a central regulator of glutamine metabolism in ERBB2+ breast cancer. This novel regulatory link, as well as the marked reduction in patient survival time associated with increased glutamine pathway gene expression, suggests that targeting glutamine metabolism may have therapeutic potential in the treatment of ERBB2+ breast cancer.

How does the metabolism of tumour cells differ from that of normal cells

Tumour cells thrive in environments that would be hostile to their normal cell counterparts. Survival depends on the selection of cell lines that harbour modifications of both, gene regulation that shifts the balance between the cell cycle and apoptosis and those that involve the plasticity of the metabolic machinery. With regards to metabolism, the selected phenotypes usually display enhanced anaerobic glycolysis even in the presence of oxygen, the so-called Warburg effect, and anabolic pathways that provide precursors for the synthesis of lipids, proteins and DNA. The review will discuss the original ideas of Otto Warburg and how they initially led to the notion that mitochondria of tumour cells were dysfunctional. Data will be presented to show that not only the organelles are viable and respiring, but that they are key players in tumorigenesis and metastasis. Likewise, interconnecting pathways that stand out in the tumour phenotype and that require intact mitochondria such as glutaminolysis will be addressed. Furthermore, comments will be made as to how the peculiarities of the biochemistry of tumour cells renders them amenable to new forms of treatment by highlighting possible targets for inhibitors. In this respect, a case study describing the effect of a metabolite analogue, the alkylating agent 3BP (3-bromopyruvate), on glycolytic enzyme targets will be presented.

Optimal design of isotope labeling experiments

Stable isotope labeling experiments (ILE) constitute a powerful methodology for estimating metabolic fluxes. An optimal label design for such an experiment is necessary to maximize the precision with which fluxes can be determined. But often, precision gained in the determination of one flux comes at the expense of the precision of other fluxes, and an appropriate label design therefore foremost depends on the question the investigator wants to address. One could liken ILE to shadows that metabolism casts on products. Optimal label design is the placement of the lamp; creating clear shadows for some parts of metabolism and obscuring others.An optimal isotope label design is influenced by: (1) the network structure; (2) the true flux values; (3) the available label measurements; and, (4) commercially available substrates. The first two aspects are dictated by nature and constrain any optimal design. The second two aspects are suitable design parameters. To create an optimal label design, an explicit optimization criterion needs to be formulated. This usually is a property of the flux covariance matrix, which can be augmented by weighting label substrate cost. An optimal design is found by using such a criterion as an objective function for an optimizer. This chapter uses a simple elementary metabolite units (EMU) representation of the TCA cycle to illustrate the process of experimental design of isotope labeled substrates.

Stress eating and tuning out: cancer cells re-wire metabolism to counter stress

Cancer cells reprogram metabolism to maintain rapid proliferation under often stressful conditions. Glycolysis and glutaminolysis are two central pathways that fuel cancer metabolism. Allosteric regulation and metabolite driven post-translational modifications of key metabolic enzymes allow cancer cells glycolysis and glutaminolysis to respond to changes in nutrient availability and the tumor microenvironment. While increased aerobic glycolysis (the Warburg effect) has been a noted part of cancer metabolism for over 80 years, recent work has shown that the elevated levels of glycolytic intermediates are critical to cancer growth and metabolism due to their ability to feed into the anabolic pathways branching off glycolysis such as the pentose phosphate pathway and serine biosynthesis pathway. The key glycolytic enzymes phosphofructokinase-1 (PFK1), pyruvate kinase (PKM2) and phosphoglycerate mutase 1 (PGAM1) are regulated by upstream and downstream metabolites to balance glycolytic flux with flux through anabolic pathways. Glutamine regulation is tightly controlled by metabolic intermediates that allosterically inhibit and activate glutamate dehydrogenase, which fuels the tricarboxylic acid cycle by converting glutamine derived glutamate to α-ketoglutarate. The elucidation of these key allosteric regulatory hubs in cancer metabolism will be essential for understanding and predicting how cancer cells will respond to drugs that target metabolism. Additionally, identification of the structures involved in allosteric regulation will inform the design of anti-metabolism drugs which bypass the off-target effects of substrate mimics. Hence, this review aims to provide an overview of allosteric control of glycolysis and glutaminolysis.

IsoDesign: a software for optimizing the design of 13C-metabolic flux analysis experiments

The growing demand for (13) C-metabolic flux analysis ((13) C-MFA) in the field of metabolic engineering and systems biology is driving the need to rationalize expensive and time-consuming (13) C-labeling experiments. Experimental design is a key step in improving both the number of fluxes that can be calculated from a set of isotopic data and the precision of flux values. We present IsoDesign, a software that enables these parameters to be maximized by optimizing the isotopic composition of the label input. It can be applied to (13) C-MFA investigations using a broad panel of analytical tools (MS, MS/MS, (1) H NMR, (13) C NMR, etc.) individually or in combination. It includes a visualization module to intuitively select the optimal label input depending on the biological question to be addressed. Applications of IsoDesign are described, with an example of the entire (13) C-MFA workflow from the experimental design to the flux map including important practical considerations. IsoDesign makes the experimental design of (13) C-MFA experiments more accessible to a wider biological community. IsoDesign is distributed under an open source license at http://metasys.insa-toulouse.fr/software/isodes/

Publishing 13C metabolic flux analysis studies: a review and future perspectives

(13)C-Metabolic flux analysis ((13)C-MFA) is a powerful model-based analysis technique for determining intracellular metabolic fluxes in living cells. It has become a standard tool in many labs for quantifying cell physiology, e.g., in metabolic engineering, systems biology, biotechnology, and biomedical research. With the increasing number of (13)C-MFA studies published each year, it is now ever more important to provide practical guidelines for performing and publishing (13)C-MFA studies so that quality is not sacrificed as the number of publications increases. The main purpose of this paper is to provide an overview of good practices in (13)C-MFA, which can eventually be used as minimum data standards for publishing (13)C-MFA studies. The motivation for this work is two-fold: (1) currently, there is no general consensus among researchers and journal editors as to what minimum data standards should be required for publishing (13)C-MFA studies; as a result, there are great discrepancies in terms of quality and consistency; and (2) there is a growing number of studies that cannot be reproduced or verified independently due to incomplete information provided in these publications. This creates confusion, e.g. when trying to reconcile conflicting results, and hinders progress in the field. Here, we review current status in the (13)C-MFA field and highlight some of the shortcomings with regards to (13)C-MFA publications. We then propose a checklist that encompasses good practices in (13)C-MFA. We hope that these guidelines will be a valuable resource for the community and allow (13)C-flux studies to be more easily reproduced and accessed by others in the future.

COMPLETE-MFA: complementary parallel labeling experiments technique for metabolic flux analysis

We have developed a novel approach for measuring highly accurate and precise metabolic fluxes in living cells, termed COMPLETE-MFA, short for complementary parallel labeling experiments technique for metabolic flux analysis. The COMPLETE-MFA method is based on combined analysis of multiple isotopic labeling experiments, where the synergy of using complementary tracers greatly improves the precision of estimated fluxes. In this work, we demonstrate the COMPLETE-MFA approach using all singly labeled glucose tracers, [1-(13)C], [2-(13)C], [3-(13)C], [4-(13)C], [5-(13)C], and [6-(13)C]glucose to determine precise metabolic fluxes for wild-type Escherichia coli. Cells were grown in six parallel cultures on defined medium with glucose as the only carbon source. Mass isotopomers of biomass amino acids were measured by gas chromatography-mass spectrometry (GC-MS). The data from all six experiments were then fitted simultaneously to a single flux model to determine accurate intracellular fluxes. We obtained a statistically acceptable fit with more than 300 redundant measurements. The estimated flux map is the most precise flux result obtained thus far for E. coli cells. To our knowledge, this is the first time that six isotopic labeling experiments have been successfully integrated for high-resolution (13)C-flux analysis.

MYC, metabolism, cell growth, and tumorigenesis

The MYC proto-oncogene is frequently activated in human cancers through a variety of mechanisms. Its deregulated expression, unconstrained by inactivation of key checkpoints, such as p53, contributes to tumorigenesis. Unlike its normal counterpart, which is restrained by negative regulators, the unleashed MYC oncogene produces a transcription factor that alters global gene expression through transcriptional regulation, resulting in tumorigenesis. Key genes involved in ribosomal and mitochondrial biogenesis, glucose and glutamine metabolism, lipid synthesis, and cell-cycle progression are robustly activated by MYC, contributing to the acquisition of bioenergetics substrates for the cancer cell to grow and proliferate.

Metformin decreases glucose oxidation and increases the dependency of prostate cancer cells on reductive glutamine metabolism

Metformin inhibits cancer cell proliferation, and epidemiology studies suggest an association with increased survival in patients with cancer taking metformin; however, the mechanism by which metformin improves cancer outcomes remains controversial. To explore how metformin might directly affect cancer cells, we analyzed how metformin altered the metabolism of prostate cancer cells and tumors. We found that metformin decreased glucose oxidation and increased dependency on reductive glutamine metabolism in both cancer cell lines and in a mouse model of prostate cancer. Inhibition of glutamine anaplerosis in the presence of metformin further attenuated proliferation, whereas increasing glutamine metabolism rescued the proliferative defect induced by metformin. These data suggest that interfering with glutamine may synergize with metformin to improve outcomes in patients with prostate cancer.

Metabolic flux rewiring in mammalian cell cultures

Continuous cell lines (CCLs) engage in 'wasteful' glucose and glutamine metabolism that leads to accumulation of inhibitory byproducts, primarily lactate and ammonium. Advances in techniques for mapping intracellular carbon fluxes and profiling global changes in enzyme expression have led to a deeper understanding of the molecular drivers underlying these metabolic alterations. However, recent studies have revealed that CCLs are not necessarily entrenched in a glycolytic or glutaminolytic phenotype, but instead can shift their metabolism toward increased oxidative metabolism as nutrients become depleted and/or growth rate slows. Progress to understand dynamic flux regulation in CCLs has enabled the development of novel strategies to force cultures into desirable metabolic phenotypes, by combining fed-batch feeding strategies with direct metabolic engineering of host cells.

1H-NMR based metabonomic profiling of human esophageal cancer tissue

BACKGROUND

The biomarker identification of human esophageal cancer is critical for its early diagnosis and therapeutic approaches that will significantly improve patient survival. Specially, those that involves in progression of disease would be helpful to mechanism research.

METHODS

In the present study, we investigated the distinguishing metabolites in human esophageal cancer tissues (n = 89) and normal esophageal mucosae (n = 26) using a (1)H nuclear magnetic resonance ((1)H-NMR) based assay, which is a highly sensitive and non-destructive method for biomarker identification in biological systems. Principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least-squares-discriminant analysis (OPLS-DA) were applied to analyse (1)H-NMR profiling data to identify potential biomarkers.

RESULTS

The constructed OPLS-DA model achieved an excellent separation of the esophageal cancer tissues and normal mucosae. Excellent separation was obtained between the different stages of esophageal cancer tissues (stage II = 28; stage III = 45 and stage IV = 16) and normal mucosae. A total of 45 metabolites were identified, and 12 of them were closely correlated with the stage of esophageal cancer. The downregulation of glucose, AMP and NAD, upregulation of formate indicated the large energy requirement due to accelerated cell proliferation in esophageal cancer. The increases in acetate, short-chain fatty acid and GABA in esophageal cancer tissue revealed the activation of fatty acids metabolism, which could satisfy the need for cellular membrane formation. Other modified metabolites were involved in choline metabolic pathway, including creatinine, creatine, DMG, DMA and TMA. These 12 metabolites, which are involved in energy, fatty acids and choline metabolism, may be associated with the progression of human esophageal cancer.

CONCLUSION

Our findings firstly identify the distinguishing metabolites in different stages of esophageal cancer tissues, indicating the attribution of metabolites disturbance to the progression of esophageal cancer. The potential biomarkers provide a promising molecular diagnostic approach for clinical diagnosis of human esophageal cancer and a new direction for the mechanism study.

Cofactor balance by nicotinamide nucleotide transhydrogenase (NNT) coordinates reductive carboxylation and glucose catabolism in the tricarboxylic acid (TCA) cycle

Cancer and proliferating cells exhibit an increased demand for glutamine-derived carbons to support anabolic processes. In addition, reductive carboxylation of α-ketoglutarate by isocitrate dehydrogenase 1 (IDH1) and 2 (IDH2) was recently shown to be a major source of citrate synthesis from glutamine. The role of NAD(P)H/NAD(P)(+) cofactors in coordinating glucose and glutamine utilization in the tricarboxylic acid (TCA) cycle is not well understood, with the source(s) of NADPH for the reductive carboxylation reaction remaining unexplored. Nicotinamide nucleotide transhydrogenase (NNT) is a mitochondrial enzyme that transfers reducing equivalents from NADH to NADPH. Here, we show that knockdown of NNT inhibits the contribution of glutamine to the TCA cycle and activates glucose catabolism in SkMel5 melanoma cells. The increase in glucose oxidation partially occurred through pyruvate carboxylase and rendered NNT knockdown cells more sensitive to glucose deprivation. Importantly, knocking down NNT inhibits reductive carboxylation in SkMel5 and 786-O renal carcinoma cells. Overexpression of NNT is sufficient to stimulate glutamine oxidation and reductive carboxylation, whereas it inhibits glucose catabolism in the TCA cycle. These observations are supported by an impairment of the NAD(P)H/NAD(P)(+) ratios. Our findings underscore the role of NNT in regulating central carbon metabolism via redox balance, calling for other mechanisms that coordinate substrate preference to maintain a functional TCA cycle.

Changes in muscle cell metabolism and mechanotransduction are associated with myopathic phenotype in a mouse model of collagen VI deficiency

This study identifies metabolic and protein phenotypic alterations in gastrocnemius, tibialis anterior and diaphragm muscles of Col6a1^−/− mice, a model of human collagen VI myopathies. All three muscles of Col6a1^−/− mice show some common changes in proteins involved in metabolism, resulting in decreased glycolysis and in changes of the TCA cycle fluxes. These changes lead to a different fate of α-ketoglutarate, with production of anabolic substrates in gastrocnemius and tibialis anterior, and with lipotoxicity in diaphragm. The metabolic changes are associated with changes of proteins involved in mechanotransduction at the myotendineous junction/costameric/sarcomeric level (TN-C, FAK, ROCK1, troponin I fast) and in energy metabolism (aldolase, enolase 3, triose phosphate isomerase, creatine kinase, adenylate kinase 1, parvalbumin, IDH1 and FASN). Together, these change may explain Ca²⁺ deregulation, impaired force development, increased muscle-relaxation-time and fiber damage found in the mouse model as well as in patients. The severity of these changes differs in the three muscles (gastrocnemius<tibialis anterior<diaphragm) and correlates to the mass-to-tendon (myotendineous junction) ratio and to muscle morphology.

Metabolic changes in cancer cells upon suppression of MYC

Background

Cancer cells engage in aerobic glycolysis and glutaminolysis to fulfill their biosynthetic and energetic demands in part by activating MYC. Previous reports have characterized metabolic changes in proliferating cells upon MYC loss or gain of function. However, metabolic differences between MYC-dependent cancer cells and their isogenic differentiated counterparts have not been characterized upon MYC suppression in vitro.

Results

Here we report metabolic changes between MYC-dependent mouse osteogenic sarcomas and differentiated osteoid cells induced upon MYC suppression. While osteogenic sarcoma cells increased oxygen consumption and spare respiratory capacity upon MYC suppression, they displayed minimal changes in glucose and glutamine consumption as well as their respective contribution to the citrate pool. However, glutamine significantly induced oxygen consumption in the presence of MYC which was dependent on aminotransferases. Furthermore, inhibition of aminotransferases selectively diminished cell proliferation and survival of osteogenic sarcoma MYC-expressing cells. There were minimal changes in ROS levels and cell death sensitivity to reactive oxygen species (ROS)-inducing agents between osteoid cells and osteogenic sarcoma cells. Nevertheless, the mitochondrial-targeted antioxidant Mito-Vitamin E still diminished proliferation of MYC-dependent osteogenic sarcoma cells.

Conclusion

These data highlight that aminotransferases and mitochondrial ROS might be attractive targets for cancer therapy in MYC-driven tumors.

Metabolic reprogramming and metabolic dependency in T cells

Upon activation, quiescent naive T cells undergo a growth phase followed by massive clonal expansion and differentiation that are essential for appropriate immune defense and regulation. Accumulation of cell biomass during the initial growth and rapid proliferation during the expansion phase is associated with dramatically increased bioenergetic and biosynthetic demands. This not only requires a metabolic rewiring during the transition between resting and activation but also 'addicts' active T cells to certain metabolic pathways in ways that naive and memory T cells are not. We consider such addiction in terms of the biological effects of deprivation of metabolic substrates or inhibition of specific pathways in T cells. In this review, we illustrate the relevant metabolic pathways revealed by recent metabolic flux analysis and discuss the consequences of metabolic intervention on specific metabolic pathways in T lymphocytes.

Relevance of mitochondrial genetics and metabolism in cancer development

Cancer cells are characterized in general by a decrease of mitochondrial respiration and oxidative phosphorylation, together with a strong enhancement of glycolysis, the so-called Warburg effect. The decrease of mitochondrial activity in cancer cells may have multiple reasons, related either to the input of reducing equivalents to the electron transfer chain or to direct alterations of the mitochondrial respiratory complexes. In some cases, the depression of respiratory activity is clearly the consequence of disruptive mitochondrial DNA (mtDNA) mutations and leads as a consequence to enhanced generation of reactive oxygen species (ROS). By acting both as mutagens and cellular mitogens, ROS may contribute directly to cancer progression. On the basis of our experimental evidence, we suggest a deep implication of the supercomplex organization of the respiratory chain as a missing link between oxidative stress, energy failure, and tumorigenesis. We speculate that under conditions of oxidative stress, a dissociation of mitochondrial supercomplexes occurs, with destabilization of complex I and secondary enhanced generation of ROS, thus leading to a vicious circle amplifying mitochondrial dysfunction. An excellent model to dissect the role of pathogenic, disassembling mtDNA mutations in tumor progression and their contribution to the metabolic reprogramming of cancer cells (glycolysis vs. respiration) is provided by an often underdiagnosed subset of tumors, namely, the oncocytomas, characterized by disruptive mutations of mtDNA, especially of complex I subunits. Such mutations almost completely abolish complex I activity, which slows down the Krebs cycle, favoring a high ratio of α-ketoglutarate/succinate and consequent destabilization of hypoxia inducible factor 1α (HIF1α). On the other hand, if complex I is partially defective, the levels of NAD(+) may be sufficient to implement the Krebs cycle with higher levels of intermediates that stabilize HIF1α, thus favoring tumor malignancy. The threshold model we propose, based on the population-like dynamics of mitochondrial genetics (heteroplasmy vs. homoplasmy), implies that below threshold complex I is present and functioning correctly, thus favoring tumor growth, whereas above threshold, when complex I is not assembled, tumor growth is arrested. We have therefore termed "oncojanus" the mtDNA genes whose disruptive mutations have such a double-edged effect.

Which way does the citric acid cycle turn during hypoxia? The critical role of α-ketoglutarate dehydrogenase complex

The citric acid cycle forms a major metabolic hub and as such it is involved in many disease states involving energetic imbalance. In spite of the fact that it is being branded as a "cycle", during hypoxia, when the electron transport chain does not oxidize reducing equivalents, segments of this metabolic pathway remain operational but exhibit opposing directionalities. This serves the purpose of harnessing high-energy phosphates through matrix substrate-level phosphorylation in the absence of oxidative phosphorylation. In this Mini-Review, these segments are appraised, pointing to the critical importance of the α-ketoglutarate dehydrogenase complex dictating their directionalities.

Reductive glutamine metabolism is a function of the α-ketoglutarate to citrate ratio in cells

Reductively metabolized glutamine is a major cellular carbon source for fatty acid synthesis during hypoxia or when mitochondrial respiration is impaired. Yet, a mechanistic understanding of what determines reductive metabolism is missing. Here we identify several cellular conditions where the α-ketoglutarate/citrate ratio is changed due to an altered acetyl-CoA to citrate conversion, and demonstrate that reductive glutamine metabolism is initiated in response to perturbations that result in an increase in the α-ketoglutarate/citrate ratio. Thus, targeting reductive glutamine conversion for a therapeutic benefit might require distinct modulations of metabolite concentrations rather than targeting the upstream signalling, which only indirectly affects the process.

Isotopically nonstationary 13C metabolic flux analysis

(13)C metabolic flux analysis (MFA) is a powerful approach for quantifying cell physiology based upon a combination of extracellular flux measurements and intracellular isotope labeling measurements. In this chapter, we present the method of isotopically nonstationary (13)C MFA (INST-MFA), which is applicable to systems that are at metabolic steady state, but are sampled during the transient period prior to achieving isotopic steady state following the introduction of a (13)C tracer. We describe protocols for performing the necessary isotope labeling experiments, for quenching and extraction of intracellular metabolites, for mass spectrometry (MS) analysis of metabolite labeling, and for computational flux estimation using INST-MFA. By combining several recently developed experimental and computational techniques, INST-MFA provides an important new platform for mapping carbon fluxes that is especially applicable to animal cell cultures, autotrophic organisms, industrial bioprocesses, high-throughput experiments, and other systems that are not amenable to steady-state (13)C MFA experiments.

Parallel labeling experiments and metabolic flux analysis: Past, present and future methodologies

Radioactive and stable isotopes have been applied for decades to elucidate metabolic pathways and quantify carbon flow in cellular systems using mass and isotope balancing approaches. Isotope-labeling experiments can be conducted as a single tracer experiment, or as parallel labeling experiments. In the latter case, several experiments are performed under identical conditions except for the choice of substrate labeling. In this review, we highlight robust approaches for probing metabolism and addressing metabolically related questions though parallel labeling experiments. In the first part, we provide a brief historical perspective on parallel labeling experiments, from the early metabolic studies when radioisotopes were predominant to present-day applications based on stable-isotopes. We also elaborate on important technical and theoretical advances that have facilitated the transition from radioisotopes to stable-isotopes. In the second part of the review, we focus on parallel labeling experiments for (13)C-metabolic flux analysis ((13)C-MFA). Parallel experiments offer several advantages that include: tailoring experiments to resolve specific fluxes with high precision; reducing the length of labeling experiments by introducing multiple entry-points of isotopes; validating biochemical network models; and improving the performance of (13)C-MFA in systems where the number of measurements is limited. We conclude by discussing some challenges facing the use of parallel labeling experiments for (13)C-MFA and highlight the need to address issues related to biological variability, data integration, and rational tracer selection.

Profiling metabolic networks to study cancer metabolism

Cancer is a disease of unregulated cell growth and survival, and tumors reprogram biochemical pathways to aid these processes. New capabilities in the computational and bioanalytical characterization of metabolism have now emerged, facilitating the identification of unique metabolic dependencies that arise in specific cancers. By understanding the metabolic phenotype of cancers as a function of their oncogenic profiles, metabolic engineering may be applied to design synthetically lethal therapies for some tumors. This process begins with accurate measurement of metabolic fluxes. Here we review advanced methods of quantifying pathway activity and highlight specific examples where these approaches have uncovered potential opportunities for therapeutic intervention.

13CFLUX2--high-performance software suite for (13)C-metabolic flux analysis

Summary:¹³C-based metabolic flux analysis (¹³C-MFA) is the state-of-the-art method to quantitatively determine in vivo metabolic reaction rates in microorganisms. 13CFLUX2 contains all tools for composing flexible computational ¹³C-MFA workflows to design and evaluate carbon labeling experiments. A specially developed XML language, FluxML, highly efficient data structures and simulation algorithms achieve a maximum of performance and effectiveness. Support of multicore CPUs, as well as compute clusters, enables scalable investigations. 13CFLUX2 outperforms existing tools in terms of universality, flexibility and built-in features. Therewith, 13CFLUX2 paves the way for next-generation high-resolution ¹³C-MFA applications on the large scale.

Availability and implementation: 13CFLUX2 is implemented in C++ (ISO/IEC 14882 standard) with Java and Python add-ons to run under Linux/Unix. A demo version and binaries are available at www.13cflux.net.

Contact:info@13cflux.net or k.noeh@fz-juelich.de

Supplementary information:Supplementary data are available at Bioinformatics online.

Parallel labeling experiments with [1,2-(13)C]glucose and [U-(13)C]glutamine provide new insights into CHO cell metabolism

We applied a parallel labeling strategy using two isotopic tracers, [1,2-(13)C]glucose and [U-(13)C]glutamine, to determine metabolic fluxes in Chinese hamster ovary (CHO) cells. CHO cells were grown in parallel cultures over a period of six days with glucose and glutamine feeding. On days 2 and 5, isotopic tracers were introduced and (13)C-labeling of intracellular metabolites was measured by gas chromatography-mass spectrometry (GC-MS). Metabolites in glycolysis pathway reached isotopic steady state for [1,2-(13)C]glucose within 1.5h, and metabolites in the TCA cycle reached isotopic steady state for [U-(13)C]glutamine within 3h. Combined analysis of multiple data sets produced detailed flux maps at two key metabolic phases, exponential growth phase (day 2) and early stationary phase (day 5). Flux results revealed significant rewiring of intracellular metabolism in the transition from growth to non-growth, including changes in oxidative pentose phosphate pathway, anaplerosis, amino acid metabolism, and fatty acid biosynthesis. At the growth phase, de novo fatty acid biosynthesis correlated well with the lipid requirements for cell growth. However, surprisingly, at the non-growth phase the fatty acid biosynthesis flux remained high even though no new lipids were needed for cell growth. Additionally, we identified a discrepancy in the estimated TCA cycle flux obtained using traditional stoichiometric flux balancing and (13)C-metabolic flux analysis. Our results suggested that CHO cells produced additional metabolites from glucose that were not captured in previous metabolic models. Follow-up experiments with [U-(13)C]glucose confirmed that additional metabolites were accumulating in the medium that became M+3 and M+6 labeled.

Alveolate mitochondrial metabolic evolution: dinoflagellates force reassessment of the role of parasitism as a driver of change in apicomplexans

Mitochondrial metabolism is central to the supply of ATP and numerous essential metabolites in most eukaryotic cells. Across eukaryotic diversity, however, there is evidence of much adaptation of the function of this organelle according to specific metabolic requirements and/or demands imposed by different environmental niches. This includes substantial loss or retailoring of mitochondrial function in many parasitic groups that occupy potentially nutrient-rich environments in their metazoan hosts. Infrakingdom Alveolata comprises a well-supported alliance of three disparate eukaryotic phyla-dinoflagellates, apicomplexans, and ciliates. These major taxa represent diverse lifestyles of free-living phototrophs, parasites, and predators and offer fertile territory for exploring character evolution in mitochondria. The mitochondria of apicomplexan parasites provide much evidence of loss or change of function from analysis of mitochondrial protein genes. Much less, however, is known of mitochondrial function in their closest relatives, the dinoflagellate algae. In this study, we have developed new models of mitochondrial metabolism in dinoflagellates based on gene predictions and stable isotope labeling experiments. These data show that many changes in mitochondrial gene content previously only known from apicomplexans are found in dinoflagellates also. For example, loss of the pyruvate dehydrogenase complex and changes in tricarboxylic acid (TCA) cycle enzyme complement are shared by both groups and, therefore, represent ancestral character states. Significantly, we show that these changes do not result in loss of typical TCA cycle activity fueled by pyruvate. Thus, dinoflagellate data show that many changes in alveolate mitochondrial metabolism are independent of the major lifestyle changes seen in these lineages and provide a revised view of mitochondria character evolution during evolution of parasitism in apicomplexans.

Links between metabolism and cancer

Metabolism generates oxygen radicals, which contribute to oncogenic mutations. Activated oncogenes and loss of tumor suppressors in turn alter metabolism and induce aerobic glycolysis. Aerobic glycolysis or the Warburg effect links the high rate of glucose fermentation to cancer. Together with glutamine, glucose via glycolysis provides the carbon skeletons, NADPH, and ATP to build new cancer cells, which persist in hypoxia that in turn rewires metabolic pathways for cell growth and survival. Excessive caloric intake is associated with an increased risk for cancers, while caloric restriction is protective, perhaps through clearance of mitochondria or mitophagy, thereby reducing oxidative stress. Hence, the links between metabolism and cancer are multifaceted, spanning from the low incidence of cancer in large mammals with low specific metabolic rates to altered cancer cell metabolism resulting from mutated enzymes or cancer genes.