Cancer cell metabolism is exemplified by high glucose consumption and lactate production. Pyruvate kinase (PK), which catalyzes the final step of glycolysis, has emerged as a potential regulator of ...this metabolic phenotype. The M2 isoform of PK (PKM2) is highly expressed in cancer cells. However, the mechanisms by which PKM2 coordinates high energy requirements with high anabolic activities to support cancer cell proliferation are still not completely understood. Current research has elucidated novel regulatory mechanisms for PKM2, contributing to its important role in cancer. This review summarizes the current understanding and explores future directions in the field, highlighting controversies regarding the activity and specificity of PKM2 in cancer. In light of this knowledge, the potential therapeutic implications and strategies are critically discussed.
Increased glucose metabolism in cancer cells is a phenomenon that has been known for over 90 years, allowing maximal cell growth through faster ATP production and redistribution of carbons towards ...nucleotide, protein and fatty acid synthesis. Recently, metabolites that can promote tumorigeneis by altering the epigenome have been identified. These ‘oncometabolites’ include the tricarboxylic acid cycle metabolites succinate and fumarate, whose levels are elevated in rare tumours with succinate dehydrogenase and fumarate hydratase mutations, respectively. 2‐Hydroxyglutarate is another oncometabolite; it is produced de novo as a result of the mutation of isocitrate dehydrogenase, and is commonly found in gliomas and acute myeloid leukaemia. Interestingly, the structural similarity of these oncometabolites to their precursor metabolite, α‐ketoglutarate, explains the tumorigenic potential of these metabolites, by competitive inhibition of a superfamily of enzymes called the α‐ketoglutarate‐dependent dioxygenases. These enzymes utilize α‐ketoglutarate as a cosubstrate, and are involved in fatty acid metabolism, oxygen sensing, collagen biosynthesis, and modulation of the epigenome. They include enzymes that are involved in regulating gene expression via DNA and histone tail demethylation. In this review, we will focus on the link between metabolism and epigenetics, and how we may target oncometabolite‐induced tumorigenesis in the future.
Recently, metabolites which can promote tumorigenesis by altering the epigenome have been identified. These ‘oncometabolites’ include succinate, fumarate and 2‐hydroxyglutarate (2HG), which can competitively inhibit α‐ketoglutarate dependent dioxygenases. These enzymes are important in the regulation of gene expression via DNA and histone tail demethylation. This review focused on the link between metabolism and epigenetics.
Reactive oxygen species (ROS) are increasingly recognised as important signalling molecules through oxidation of protein cysteine residues. Comprehensive identification of redox-regulated proteins ...and pathways is crucial to understand ROS-mediated events. Here, we present stable isotope cysteine labelling with iodoacetamide (SICyLIA), a mass spectrometry-based workflow to assess proteome-scale cysteine oxidation. SICyLIA does not require enrichment steps and achieves unbiased proteome-wide sensitivity. Applying SICyLIA to diverse cellular models and primary tissues provides detailed insights into thiol oxidation proteomes. Our results demonstrate that acute and chronic oxidative stress causes oxidation of distinct metabolic proteins, indicating that cysteine oxidation plays a key role in the metabolic adaptation to redox stress. Analysis of mouse kidneys identifies oxidation of proteins circulating in biofluids, through which cellular redox stress can affect whole-body physiology. Obtaining accurate peptide oxidation profiles from complex organs using SICyLIA holds promise for future analysis of patient-derived samples to study human pathologies.
Treatment of chronic myeloid leukemia (CML) with imatinib mesylate and other second- and/or third-generation c-Abl-specific tyrosine kinase inhibitors (TKIs) has substantially extended patient ...survival. However, TKIs primarily target differentiated cells and do not eliminate leukemic stem cells (LSCs). Therefore, targeting minimal residual disease to prevent acquired resistance and/or disease relapse requires identification of new LSC-selective target(s) that can be exploited therapeutically. Considering that malignant transformation involves cellular metabolic changes, which may in turn render the transformed cells susceptible to specific assaults in a selective manner, we searched for such vulnerabilities in CML LSCs. We performed metabolic analyses on both stem cell-enriched (CD34
and CD34
CD38
) and differentiated (CD34
) cells derived from individuals with CML, and we compared the signature of these cells with that of their normal counterparts. Through combination of stable isotope-assisted metabolomics with functional assays, we demonstrate that primitive CML cells rely on upregulated oxidative metabolism for their survival. We also show that combination treatment with imatinib and tigecycline, an antibiotic that inhibits mitochondrial protein translation, selectively eradicates CML LSCs both in vitro and in a xenotransplantation model of human CML. Our findings provide a strong rationale for investigation of the use of TKIs in combination with tigecycline to treat patients with CML with minimal residual disease.
Cancer metabolism at a glance Vazquez, Alexei; Kamphorst, Jurre J; Markert, Elke K ...
Journal of cell science,
09/2016, Letnik:
129, Številka:
18
Journal Article
Recenzirano
Odprti dostop
A defining hallmark of cancer is uncontrolled cell proliferation. This is initiated once cells have accumulated alterations in signaling pathways that control metabolism and proliferation, wherein ...the metabolic alterations provide the energetic and anabolic demands of enhanced cell proliferation. How these metabolic requirements are satisfied depends, in part, on the tumor microenvironment, which determines the availability of nutrients and oxygen. In this Cell Science at a Glance paper and the accompanying poster, we summarize our current understanding of cancer metabolism, emphasizing pathways of nutrient utilization and metabolism that either appear or have been proven essential for cancer cells. We also review how this knowledge has contributed to the development of anticancer therapies that target cancer metabolism.
Cancer therapy has long relied on the rapid proliferation of tumour cells for effective treatment. However, the lack of specificity in this approach often leads to undesirable side effects. Many ...reports have described various 'metabolic transformation' events that enable cancer cells to survive, suggesting that metabolic pathways might be good targets. There are currently several drugs under development or in clinical trials that are based on specifically targeting the altered metabolic pathways of tumours. This Review highlights pathways against which there are already drugs in different stages of development and also discusses additional druggable targets.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Activated macrophages undergo metabolic reprogramming, which drives their pro-inflammatory phenotype, but the mechanistic basis for this remains obscure. Here, we demonstrate that upon ...lipopolysaccharide (LPS) stimulation, macrophages shift from producing ATP by oxidative phosphorylation to glycolysis while also increasing succinate levels. We show that increased mitochondrial oxidation of succinate via succinate dehydrogenase (SDH) and an elevation of mitochondrial membrane potential combine to drive mitochondrial reactive oxygen species (ROS) production. RNA sequencing reveals that this combination induces a pro-inflammatory gene expression profile, while an inhibitor of succinate oxidation, dimethyl malonate (DMM), promotes an anti-inflammatory outcome. Blocking ROS production with rotenone by uncoupling mitochondria or by expressing the alternative oxidase (AOX) inhibits this inflammatory phenotype, with AOX protecting mice from LPS lethality. The metabolic alterations that occur upon activation of macrophages therefore repurpose mitochondria from ATP synthesis to ROS production in order to promote a pro-inflammatory state.
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•LPS induces mitochondrial repurposing from ATP synthesis to ROS production•Oxidation of succinate and mitochondrial hyperpolarization drive ROS production•Blocking LPS-induced ROS production or hyperpolarization inhibits IL-1β•SDH is critical for the inflammatory response
To support their pro-inflammatory function, activated macrophages repurpose their mitochondria, switching from ATP production to ROS generation.
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.
Synopsis
During tumor development, cancer cells modify their metabolism to meet the requirements of cellular proliferation, thus facilitating the uptake and conversion of nutrients into biomass. Many key metabolic alterations are similar across tumor cells, including changes in glucose metabolism that give rise to the Warburg effect, and an increase in biosynthetic activities (such as nucleotide, lipids and amino‐acid synthesis) (DeBerardinis et al, 2008; Tennant et al, 2009; Vander Heiden et al, 2009). The observation that many types of cancer cells adapt their metabolism toward increased proliferation makes flux balance analysis (FBA), a constraint‐based modeling (CBM) approach, suitable for modeling cancer metabolism as it assumes that cells are under selective pressure to increase their growth rate (Price et al, 2003).
Building on previous reconstructions of a generic (non‐tissue specific) human metabolic network (Duarte et al, 2007; Ma et al, 2007), we develop here the first large‐scale FBA model of cancer metabolism that aims to capture the main metabolic alterations that are common across many cancer types. The model reconstruction is based on our recent computational method for the automatic reconstruction of human tissue metabolic models (Jerby et al, 2010), integrating the human metabolic model with cancer gene expression data. The construction of the cancer model focuses on activating a core set of metabolic enzyme‐coding genes that are highly expressed across cancer cell lines in the NCI‐60 collection, with additional reactions enabling their activation and the biosynthesis of a set of biomass compounds required for cellular proliferation. This generic cancer model enables the successful prediction of the metabolic state of cancer cells across different gene knockdowns and modeling the effects of drug applications on a large scale. As a first demonstration of the predictive performance of the cancer model, we applied it to predict 199 growth‐supporting genes whose knockdown is expected to inhibit cellular proliferation, showing that the model predictions indeed match results of shRNA gene silencing experiments (Luo et al, 2008).
To identify viable anticancer drug targets, we predicted whether the knockdown of the growth‐supporting genes is likely to be toxic to normal cells. Out of the 199 genes that are predicted to be growth supporting in the cancer model, 52 are predicted to have negligible effects on energy production in normal cells. However, the knockdown of the majority of the latter is yet predicted to potentially cause damage to proliferation of normal cells, suggesting that the targeting of these genes would cause similar side effects to those observed with current cytostatic drugs (Partridge et al, 2001). Next, we predicted 342 synthetic lethal drug targets, whose predicted synergy was validated based on (i) comparison with genetic interactions between the corresponding yeast orthologs (Costanzo et al, 2010) and (ii) by analyzing the efficacy of metabolic drugs targeting these genes, finding that drugs that target a single gene (participating in a predicted synthetic lethal pair) indeed have higher efficacy in cell lines in which the synergistic gene is lowly expressed. In contrast to the single targets described above, the knockdown of a third of these synergistic pairs is predicted to leave the proliferation of normal cells intact. Most importantly, the specific targeting of a gene participating in a synergistic pair is especially appealing in tumors in which its interacting gene is specifically inactivated—the targeting of such a gene solely is likely to selectively damage the tumor, without affecting the function of healthy tissues in which the interacting gene in the pair is active. We utilized genomic and transcriptomic data to infer gene inactivation across an array of cancers, which has led to the identification of cancer type‐specific targets based on the intersection of this data with our predicted synergistic gene pairs.
In summary, the model presented here lays down a fundamental computational approach for interpreting the rapidly accumulating proteomics (Bichsel et al, 2001) and metabolomics (Fan et al, 2009) data characterizing cancer metabolic alterations. We hope that the publication of this first step will spur further studies aimed at obtaining a systems level understanding of cancer metabolism and at designing new therapeutic means that selectively target them.
The first genome‐scale network model of cancer metabolism is developed and validated by successfully 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.
Combinations of synthetic lethal drug targets are predicted, whose synergy is validated using available drug efficacy and gene expression measurements across the NCI‐60 cancer cell line collection.
Potential selective treatments for specific cancers that depend on cancer type‐specific downregulation of gene expression and somatic mutations are compiled.
The Warburg effect--a classical hallmark of cancer metabolism--is a counter-intuitive phenomenon in which rapidly proliferating cancer cells resort to inefficient ATP production via glycolysis ...leading to lactate secretion, instead of relying primarily on more efficient energy production through mitochondrial oxidative phosphorylation, as most normal cells do. The causes for the Warburg effect have remained a subject of considerable controversy since its discovery over 80 years ago, with several competing hypotheses. Here, utilizing a genome-scale human metabolic network model accounting for stoichiometric and enzyme solvent capacity considerations, we show that the Warburg effect is a direct consequence of the metabolic adaptation of cancer cells to increase biomass production rate. The analysis is shown to accurately capture a three phase metabolic behavior that is observed experimentally during oncogenic progression, as well as a prominent characteristic of cancer cells involving their preference for glutamine uptake over other amino acids.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Amino acids control cell growth via activation of the highly conserved kinase TORC1. Glutamine is a particularly important amino acid in cell growth control and metabolism. However, the role of ...glutamine in TORC1 activation remains poorly defined. Glutamine is metabolized through glutaminolysis to produce α-ketoglutarate. We demonstrate that glutamine in combination with leucine activates mammalian TORC1 (mTORC1) by enhancing glutaminolysis and α-ketoglutarate production. Inhibition of glutaminolysis prevented GTP loading of RagB and lysosomal translocation and subsequent activation of mTORC1. Constitutively active Rag heterodimer activated mTORC1 in the absence of glutaminolysis. Conversely, enhanced glutaminolysis or a cell-permeable α-ketoglutarate analog stimulated lysosomal translocation and activation of mTORC1. Finally, cell growth and autophagy, two processes controlled by mTORC1, were regulated by glutaminolysis. Thus, mTORC1 senses and is activated by glutamine and leucine via glutaminolysis and α-ketoglutarate production upstream of Rag. This may provide an explanation for glutamine addiction in cancer cells.
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► Inhibition of glutaminolysis prevents mTORC1 activation by leucine and glutamine ► Enhanced glutaminolysis stimulates mTORC1 ► RagB mediates glutaminolysis-dependent activation of mTORC1 ► Activation of mTORC1 by glutaminolysis blocks autophagy and increases cell size
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