Alterations of metabolic genes and metabolites in cancer

Altered metabolic regulation has long been observed in human cancer and broadly used in the clinic for tumor detection. Two recent findings--the direct regulation of metabolic enzymes by frequently mutated cancer genes and frequent mutations of several metabolic enzymes themselves in cancer--have renewed interest in cancer metabolism. Supporting a causative role of altered metabolic enzymes in tumorigenesis, abnormal levels of several metabolites have been found to play a direct role in cancer development. The alteration of metabolic genes and metabolites offer not only new biomarkers for diagnosis and prognosis, but also potential new targets for cancer therapy.     

Therapeutic targets in cancer cell metabolism and autophagy

The metabolism of cancer cells is reprogrammed both by oncogene signaling and by dysregulation of metabolic enzymes. The resulting altered metabolism supports cellular proliferation and survival but leaves cancer cells dependent on a continuous supply of nutrients. Thus, many metabolic enzymes have become targets for new cancer therapies. Recently, two processes—expression of specific isoforms of metabolic enzymes and autophagy—have been shown to be crucial for the adaptation of tumor cells to changes in nutrient availability. An increasing number of approved and experimental therapeutics target these two processes. A better understanding of the molecular basis of cancer-associated metabolic changes may lead to improved cancer therapies.     

Organization of Enzyme Concentration across the Metabolic Network in Cancer Cells

Rapid advances in mass spectrometry have allowed for estimates of absolute concentrations across entire proteomes, permitting the interrogation of many important biological questions. Here, we focus on a quantitative aspect of human cancer cell metabolism that has been limited by a paucity of available data on the abundance of metabolic enzymes. We integrate data from recent measurements of absolute protein concentration to analyze the statistics of protein abundance across the human metabolic network. At a global level, we find that the enzymes in glycolysis comprise approximately half of the total amount of metabolic proteins and can constitute up to 10% of the entire proteome. We then use this analysis to investigate several outstanding problems in cancer metabolism, including the diversion of glycolytic flux for biosynthesis, the relative contribution of nitrogen assimilating pathways, and the origin of cellular redox potential. We find many consistencies with current models, identify several inconsistencies, and find generalities that extend beyond current understanding. Together our results demonstrate that a relatively simple analysis of the abundance of metabolic enzymes was able to reveal many insights into the organization of the human cancer cell metabolic network.     

Targeting cellular metabolism to improve cancer therapeutics

The metabolic properties of cancer cells diverge significantly from those of normal cells. Energy production in cancer cells is abnormally dependent on aerobic glycolysis. In addition to the dependency on glycolysis, cancer cells have other atypical metabolic characteristics such as increased fatty acid synthesis and increased rates of glutamine metabolism. Emerging evidence shows that many features characteristic to cancer cells, such as dysregulated Warburg-like glucose metabolism, fatty acid synthesis and glutaminolysis are linked to therapeutic resistance in cancer treatment. Therefore, targeting cellular metabolism may improve the response to cancer therapeutics and the combination of chemotherapeutic drugs with cellular metabolism inhibitors may represent a promising strategy to overcome drug resistance in cancer therapy. Recently, several review articles have summarized the anticancer targets in the metabolic pathways and metabolic inhibitor-induced cell death pathways, however, the dysregulated metabolism in therapeutic resistance, which is a highly clinical relevant area in cancer metabolism research, has not been specifically addressed. From this unique angle, this review article will discuss the relationship between dysregulated cellular metabolism and cancer drug resistance and how targeting of metabolic enzymes, such as glucose transporters, hexokinase, pyruvate kinase M2, lactate dehydrogenase A, pyruvate dehydrogenase kinase, fatty acid synthase and glutaminase can enhance the efficacy of common therapeutic agents or overcome resistance to chemotherapy or radiotherapy.     

Sirtuins in Cancer: a Balancing Act between Genome Stability and Metabolism

Genomic instability and altered metabolism are key features of most cancers. Recent studies suggest that metabolic reprogramming is part of a systematic response to cellular DNA damage. Thus, defining the molecules that fine-tune metabolism in response to DNA damage will enhance our understanding of molecular mechanisms of tumorigenesis and have profound implications for the development of strategies for cancer therapy. Sirtuins have been established as critical regulators in cellular homeostasis and physiology. Here, we review the emerging data revealing a pivotal function of sirtuins in genome maintenance and cell metabolism, and highlight current advances about the phenotypic consequences of defects in these critical regulators in tumorigenesis. While many questions should be addressed about the regulation and context-dependent functions of sirtuins, it appears clear that sirtuins may provide a promising, exciting new avenue for cancer therapy.     

Review of metabolic pathways activated in cancer cells as determined through isotopic labeling and network analysis

Cancer metabolism has emerged as an indispensable part of contemporary cancer research. During the past 10 years, the use of stable isotopic tracers and network analysis have unveiled a number of metabolic pathways activated in cancer cells. Here, we review such pathways along with the particular tracers and labeling observations that led to the discovery of their rewiring in cancer cells. The list of such pathways comprises the reductive metabolism of glutamine, altered glycolysis, serine and glycine metabolism, mutant isocitrate dehydrogenase (IDH) induced reprogramming and the onset of acetate metabolism. Additionally, we demonstrate the critical role of isotopic labeling and network analysis in identifying these pathways. The alterations described in this review do not constitute a complete list, and future research using these powerful tools is likely to discover other cancer-related pathways and new metabolic targets for cancer therapy.     

Predicting selective drug targets in cancer through metabolic networks

The interest in studying metabolic alterations in cancer and their potential role as novel targets for therapy has been rejuvenated in recent years. Here, we report the development of the first genome‐scale network model of cancer metabolism, validated by correctly identifying genes essential for cellular proliferation in cancer cell lines. The model predicts 52 cytostatic drug targets, of which 40% are targeted by known, approved or experimental anticancer drugs, and the rest are new. It further predicts combinations of synthetic lethal drug targets, whose synergy is validated using available drug efficacy and gene expression measurements across the NCI‐60 cancer cell line collection. Finally, potential selective treatments for specific cancers that depend on cancer type‐specific downregulation of gene expression and somatic mutations are compiled.     

Predicting metabolic biomarkers of human inborn errors of metabolism

Early diagnosis of inborn errors of metabolism is commonly performed through biofluid metabolomics, which detects specific metabolic biomarkers whose concentration is altered due to genomic mutations. The identification of new biomarkers is of major importance to biomedical research and is usually performed through data mining of metabolomic data. After the recent publication of the genome‐scale network model of human metabolism, we present a novel computational approach for systematically predicting metabolic biomarkers in stochiometric metabolic models. Applying the method to predict biomarkers for disruptions of red‐blood cell metabolism demonstrates a marked correlation with altered metabolic concentrations inferred through kinetic model simulations. Applying the method to the genome‐scale human model reveals a set of 233 metabolites whose concentration is predicted to be either elevated or reduced as a result of 176 possible dysfunctional enzymes. The method's predictions are shown to significantly correlate with known disease biomarkers and to predict many novel potential biomarkers. Using this method to prioritize metabolite measurement experiments to identify new biomarkers can provide an order of a 10‐fold increase in biomarker detection performance.     

Thematic Review Series: Calorie Restriction and Ketogenic Diets: The ketogenic diet for the treatment of malignant glioma

Advances in our understanding of glioma biology has led to an increase in targeted therapies in preclinical and clinical trials; however, cellular heterogeneity often precludes the targeted molecules from being found on all glioma cells, thus reducing the efficacy of these treatments. In contrast, one trait shared by virtually all tumor cells is altered (dysregulated) metabolism. Tumor cells have an increased reliance on glucose, suggesting that treatments affecting cellular metabolism may be an effective method to improve current therapies. Indeed, metabolism has been a focus of cancer research in the last few years, as many pathways long associated with tumor growth have been found to intersect metabolic pathways in the cell. The ketogenic diet (high fat, low carbohydrate and protein), caloric restriction, and fasting all cause a metabolic change, specifically, a reduction in blood glucose and an increase in blood ketones. We, and others, have demonstrated that these metabolic changes improve survival in animal models of malignant gliomas and can potentiate the anti-tumor effect of chemotherapies and radiation treatment. In this review we discuss the use of metabolic alteration for the treatment of malignant brain tumors.     

The relationship between phospholipids and insulin resistance: From clinical to experimental studies

Insulin resistance induced by high‐fat diet and impropriate life style is a major contributor to the pathogenesis of metabolic disease. However, the underlying molecular mechanisms remain unclear. Recent studies in metabolic dysfunction have extended this beyond simply elevated cholesterol and triglycerides levels and have identified a key role for lipid metabolism. For example, altered phospholipid metabolism has now become central in the pathogenesis of metabolic disease. In this review, we discuss the association between insulin sensitivity and phospholipid metabolism and highlight the most significant discoveries generated over the last several decades. Finally, we summarize the current knowledge surrounding the molecular mechanisms related to phospholipids and insulin resistance and provide new insight for future research into their relationship.     

The tumor suppressor WW domain-containing oxidoreductase modulates cell metabolism

The WW domain-containing oxidoreductase (WWOX) encodes a tumor suppressor that is frequently altered in cancer. WWOX binds several proteins and thus is postulated to be involved in a variety of cellular processes. Interestingly, Wwox-knockout mice develop normally in utero but succumb to hypoglycemia and other metabolic defects early in life resulting in their death by 3–4 weeks of age. Cumulative evidence has linked WWOX with cellular metabolism including steroid metabolism, high-density lipoprotein cholesterol (HDL-C) metabolism, bone metabolism and, more recently, glucose metabolism. In this review, we discuss these evolving functions for WWOX and how its deletion affects cellular metabolism and neoplastic progression.     

Quercetin modulates keratoconus metabolism in vitro

Corneal scarring is the result of a disease, infection or injury. The resulting scars cause significant loss of vision or even blindness. To‐date, the most successful treatment is corneal transplantation, but it does not come without side effects. One of the corneal dystrophies that are correlated with corneal scarring is keratoconus (KC). The onset of the disease is still unknown; however, altered cellular metabolism has been linked to promoting the fibrotic phenotype and therefore scarring. We have previously shown that human keratoconus cells (HKCs) have altered metabolic activity when compared to normal human corneal fibroblasts (HCFs). In our current study, we present evidence that quercetin, a natural flavonoid, is a strong candidate for regulating metabolic activity of both HCFs and HKCs in vitro and therefore a potential therapeutic to target the altered cellular metabolism characteristic of HKCs. Targeted mass spectrometry‐based metabolomics was performed on HCFs and HKCs with and without quercetin treatment in order to identify variations in metabolite flux. Overall, our study reveals a novel therapeutic target OF Quercetin on corneal stromal cell metabolism in both healthy and diseased states. Clearly, further studies are necessary in order to dissect the mechanism of action of quercetin. Copyright © 2015 John Wiley & Sons, Ltd.     

Comparing methods for metabolic network analysis and an application to metabolic engineering

Bioinformatics tools have facilitated the reconstruction and analysis of cellular metabolism of various organisms based on information encoded in their genomes. Characterization of cellular metabolism is useful to understand the phenotypic capabilities of these organisms. It has been done quantitatively through the analysis of pathway operations. There are several in silico approaches for analyzing metabolic networks, including structural and stoichiometric analysis, metabolic flux analysis, metabolic control analysis, and several kinetic modeling based analyses. They can serve as a virtual laboratory to give insights into basic principles of cellular functions. This article summarizes the progress and advances in software and algorithm development for metabolic network analysis, along with their applications relevant to cellular physiology, and metabolic engineering with an emphasis on microbial strain optimization. Moreover, it provides a detailed comparative analysis of existing approaches under different categories.     

基于酶约束的代谢网络模型研究进展及其应用

基因组尺度代谢网络模型已经成功地应用于指导代谢工程改造,但由于传统通量平衡分析法仅考虑化学计量学和反应方向约束,模拟得到的是理论最优结果,对一些现象如代谢溢流、底物层级利用等无法准确描述。近年来人们通过在代谢网络模型中引入新的蛋白量、热力学等约束发展了新的约束优化计算方法,可以更准确真实地模拟细胞在不同条件下的代谢行为。文中主要对近年来提出的多种酶约束模型进行评述,对酶约束引入的基本思路、酶约束的数学方程表示及优化目标设定、引入酶约束后对代谢通量计算结果的影响及酶约束模型在代谢工程菌种改造中的应用等进行了全面深入的介绍,并提出了已有各种方法存在的主要问题,展望了相关方法的未来发展方向。通过引入新的约束,代谢网络模型能够更精确模拟和预测细胞在环境和基因扰动下的代谢行为,为代谢工程菌种改造提供更准确可靠的指导。     

Unfolding the role of autophagy in the cancer metabolism

Autophagy is considered an indispensable process that scavenges toxins, recycles complex macromolecules, and sustains the essential cellular functions. In addition to its housekeeping role, autophagy plays a substantial role in many pathophysiological processes such as cancer. Certainly, it adapts cancer cells to thrive in the stress conditions such as hypoxia and starvation. Cancer cells indeed have also evolved by exploiting the autophagy process to fulfill energy requirements through the production of metabolic fuel sources and fundamentally altered metabolic pathways. Occasionally autophagy as a foe impedes tumorigenesis and promotes cell death. The complex role of autophagy in cancer makes it a potent therapeutic target and has been actively tested in clinical trials. Moreover, the versatility of autophagy has opened new avenues of effective combinatorial therapeutic strategies. Thereby, it is imperative to comprehend the specificity of autophagy in cancer-metabolism. This review summarizes the recent research and conceptual framework on the regulation of autophagy by various metabolic pathways, enzymes, and their cross-talk in the cancer milieu, including the implementation of altered metabolism and autophagy in clinically approved and experimental therapeutics.     

Computational identification of altered metabolism using gene expression and metabolic pathways

Understanding altered metabolism is an important issue because altered metabolism is often revealed as a cause or an effect in pathogenesis. It has also been shown to be an important factor in the manipulation of an organism's metabolism in metabolic engineering. Unfortunately, it is not yet possible to measure the concentration levels of all metabolites in the genome‐wide scale of a metabolic network; consequently, a method that infers the alteration of metabolism is beneficial. The present study proposes a computational method that identifies genome‐wide altered metabolism by analyzing functional units of KEGG pathways. As control of a metabolic pathway is accomplished by altering the activity of at least one rate‐determining step enzyme, not all gene expressions of enzymes in the pathway demonstrate significant changes even if the pathway is altered. Therefore, we measure the alteration levels of a metabolic pathway by selectively observing expression levels of significantly changed genes in a pathway. The proposed method was applied to two strains of Saccharomyces cerevisiae gene expression profiles measured in very high‐gravity (VHG) fermentation. The method identified altered metabolic pathways whose properties are related to ethanol and osmotic stress responses which had been known to be observed in VHG fermentation because of the high sugar concentration in growth media and high ethanol concentration in fermentation products. With the identified altered pathways, the proposed method achieved best accuracy and sensitivity rates for the Red Star (RS) strain compared to other three related studies (gene‐set enrichment analysis (GSEA), significance analysis of microarray to gene set (SAM‐GS), reporter metabolite), and for the CEN.PK 113‐7D (CEN) strain, the proposed method and the GSEA method showed comparably similar performances. Biotechnol. Bioeng. 2009;103: 835–843. © 2009 Wiley Periodicals, Inc.     

Predicting synthetic rescues in metabolic networks

An important goal of medical research is to develop methods to recover the loss of cellular function due to mutations and other defects. Many approaches based on gene therapy aim to repair the defective gene or to insert genes with compensatory function. Here, we propose an alternative, network‐based strategy that aims to restore biological function by forcing the cell to either bypass the functions affected by the defective gene, or to compensate for the lost function. Focusing on the metabolism of single‐cell organisms, we computationally study mutants that lack an essential enzyme, and thus are unable to grow or have a significantly reduced growth rate. We show that several of these mutants can be turned into viable organisms through additional gene deletions that restore their growth rate. In a rather counterintuitive fashion, this is achieved via additional damage to the metabolic network. Using flux balance‐based approaches, we identify a number of synthetically viable gene pairs, in which the removal of one enzyme‐encoding gene results in a non‐viable phenotype, while the deletion of a second enzyme‐encoding gene rescues the organism. The systematic network‐based identification of compensatory rescue effects may open new avenues for genetic interventions.