Abstract
Many cancers have long been thought to primarily metabolize glucose for energy production—a phenomenon known as the Warburg Effect, after the classic studies of Otto Warburg in the early twentieth century. Yet cancer cells also utilize other substrates, such as amino acids and fatty acids, to produce raw materials for cellular maintenance and energetic currency to accomplish cellular tasks. The contribution of these substrates is increasingly appreciated in the context of glioma, the most common form of malignant brain tumor. Multiple catabolic pathways are used for energy production within glioma cells, and are linked in many ways to anabolic pathways supporting cellular function. For example: glycolysis both supports energy production and provides carbon skeletons for the synthesis of nucleic acids; meanwhile fatty acids are used both as energetic substrates and as raw materials for lipid membranes. Furthermore, bio-energetic pathways are connected to pro-oncogenic signaling within glioma cells. For example: AMPK signaling links catabolism with cell cycle progression; mTOR signaling contributes to metabolic flexibility and cancer cell survival; the electron transport chain produces ATP and reactive oxygen species (ROS) which act as signaling molecules; Hypoxia Inducible Factors (HIFs) mediate interactions with cells and vasculature within the tumor environment. Mutations in the tumor suppressor p53, and the tricarboxylic acid cycle enzymes Isocitrate Dehydrogenase 1 and 2 have been implicated in oncogenic signaling as well as establishing metabolic phenotypes in genetically-defined subsets of malignant glioma. These pathways critically contribute to tumor biology. The aim of this review is two-fold. Firstly, we present the current state of knowledge regarding the metabolic strategies employed by malignant glioma cells, including aerobic glycolysis; the pentose phosphate pathway; one-carbon metabolism; the tricarboxylic acid cycle, which is central to amino acid metabolism; oxidative phosphorylation; and fatty acid metabolism, which significantly contributes to energy production in glioma cells. Secondly, we highlight processes (including the Randle Effect, AMPK signaling, mTOR activation, etc.) which are understood to link bio-energetic pathways with oncogenic signals, thereby allowing the glioma cell to achieve a pro-malignant state.
Highlights
Specialty section: This article was submitted to Molecular and Cellular Oncology, a section of the journal Frontiers in Cell and Developmental
For example: AMPK signaling links catabolism with cell cycle progression; mechanistic target of rapamycin (mTOR) signaling contributes to metabolic flexibility and cancer cell survival; the electron transport chain produces adenosine triphosphate (ATP) and reactive oxygen species (ROS) which act as signaling molecules; Hypoxia Inducible Factors (HIFs) mediate interactions with cells and vasculature within the tumor environment
We present the current state of knowledge regarding the metabolic strategies employed by malignant glioma cells, including aerobic glycolysis; the pentose phosphate pathway; one-carbon metabolism; the tricarboxylic acid cycle, which is central to amino acid metabolism; oxidative phosphorylation; and fatty acid metabolism, which significantly contributes to energy production in glioma cells
Summary
Reviewed by: Eric Chevet, INSERM, France Anna Maria Porcelli, University of Bologna, Italy. Since these cancer models do not reproduce the complex interactions of glioma cells with surrounding brain tissue and immune factors, such systems may not accurately reflect human tumor biology For this reason, immunocompetent murine models of glioma which are orthotopic and do not utilize serum-exposed cell lines are increasingly considered necessary for testing novel therapeutics prior to clinical investigation in patients (Oh et al, 2014). Immunocompetent murine models of glioma which are orthotopic and do not utilize serum-exposed cell lines are increasingly considered necessary for testing novel therapeutics prior to clinical investigation in patients (Oh et al, 2014) Bypassing these issues may improve efforts to predict therapeutic response to therapies targeting either cellular bio-energetics or immune factors in animal models. Where possible and relevant in this review, we will note the type of model system used for experimentation
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