Cancer cell metabolism as new targets for novel designed therapies

A Multi-Level Systems Biology Analysis of Aldrin's Metabolic Effects on Prostate Cancer Cells

Although numerous studies support a dose-effect relationship between Endocrine disruptors (EDs) and the progression and malignancy of tumors, the impact of a chronic exposure to non-lethal concentrations of EDs in cancer remains unknown. More specifically, a number of studies have reported the impact of Aldrin on a variety of cancer types, including prostate cancer. In previous studies, we demonstrated the induction of the malignant phenotype in DU145 prostate cancer (PCa) cells after a chronic exposure to Aldrin (an ED). Proteins are pivotal in the regulation and control of a variety of cellular processes. However, the mechanisms responsible for the impact of ED on PCa and the role of proteins in this process are not yet well understood. Here, two complementary computational approaches have been employed to investigate the molecular processes underlying the acquisition of malignancy in prostate cancer. First, the metabolic reprogramming associated with the chronic exposure to Aldrin in DU145 cells was studied by integrating transcriptomics and metabolomics via constraint-based metabolic modeling. Second, gene set enrichment analysis was applied to determine (i) altered regulatory pathways and (ii) the correlation between changes in the transcriptomic profile of Aldrin-exposed cells and tumor progression in various types of cancer. Experimental validation confirmed predictions revealing a disruption in metabolic and regulatory pathways. This alteration results in the modification of protein levels crucial in regulating triacylglyceride/cholesterol, linked to the malignant phenotype observed in Aldrin-exposed cells.

Genetically personalised organ-specific metabolic models in health and disease

Understanding how genetic variants influence disease risk and complex traits (variant-to-function) is one of the major challenges in human genetics. Here we present a model-driven framework to leverage human genome-scale metabolic networks to define how genetic variants affect biochemical reaction fluxes across major human tissues, including skeletal muscle, adipose, liver, brain and heart. As proof of concept, we build personalised organ-specific metabolic flux models for 524,615 individuals of the INTERVAL and UK Biobank cohorts and perform a fluxome-wide association study (FWAS) to identify 4312 associations between personalised flux values and the concentration of metabolites in blood. Furthermore, we apply FWAS to identify 92 metabolic fluxes associated with the risk of developing coronary artery disease, many of which are linked to processes previously described to play in role in the disease. Our work demonstrates that genetically personalised metabolic models can elucidate the downstream effects of genetic variants on biochemical reactions involved in common human diseases.

Prediction and validation of avascular tumor growth pattern in different metabolic conditions using in silico and in vitro models

Objectives: In recent years, scientists have taken many efforts for in vitro and in silico modeling of cancerous tumors. In fact, three-dimensional (3D) cultures of multicellular tumor spheroids (MCTSs) are good validators for computational results. The goal of this study is to simulate the 3D early growth of avascular tumors using MCTSs and to compare the in vitro models with the results and predictions of a specific computational modeling framework. Using these two types of models, the importance of metabolic condition on tumor growth behavior and necrosis could be predicted. Materials and methods: We took advantage of a previously developed computational model of tumor growth (constructed by integrating a generic metabolic network model of cancer cells with a multiscale agent-based framework). Among the computational predictions is the importance of glucose accessibility on tumor growth behavior. To study the effect of glucose concentration experimentally, MCTSs were grown in high and low glucose culture media. After that, tumor growth pattern was analyzed by MTT assay, cell counting and propidium iodide (PI) staining. Results: We obviously observed that the rate of necrosis increases and the rate of tumor growth and cell activity decreases as the glucose availability reduces, which is in line with the computational model prediction.

DEXOM: Diversity-based enumeration of optimal context-specific metabolic networks

The correct identification of metabolic activity in tissues or cells under different conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome some of these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the generic GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific tissue, cell or condition, containing only the reactions predicted to be active in such context. However, an important limitation is that there are usually many different sub-networks that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic states. In this work we formalize the problem of enumerating optimal metabolic networks and we introduce DEXOM, an unified approach for diversity-based enumeration of context-specific metabolic networks. We developed different strategies for this purpose and we performed an exhaustive analysis using simulated and real data. In order to analyze the extent to which these results are biologically meaningful, we used the alternative solutions obtained with the different methods to measure: 1) the improvement of in silico predictions of essential genes in Saccharomyces cerevisiae using ensembles of metabolic network; and 2) the detection of alternative enriched pathways in different human cancer cell lines. We also provide DEXOM as an open-source library compatible with COBRA Toolbox 3.0, available at https://github.com/MetExplore/dexom.

Rosuvastatin inhibit spheroid formation and epithelial-mesenchymal transition (EMT) in prostate cancer PC-3 cell line

There is a growing body of evidence suggesting antitumor activity of statins. In metastasis and invasion of cancer the Epithelial-Mesenchymal Transition (EMT) of cancerous cells is an important process. Our goal was to understand the effect of Rosuvastatin on the EMT process in human prostate cancer cell line PC-3 cells in adherent 2 dimensional (2D) and spheroid 3 dimensional (3D) culture. PC-3 cells were cultured in adherence and/or spheroid culture system. The cells were treated with different concentrations of Rosuvastatin. After 96 h, the cell proliferation, viability, type and number of spheroids, the expression of E-Cadherin, Vimentin and Zeb-1 were analyzed. The results show that Rosuvastatin inhibit cell proliferation without significant cytotoxicity. The spheroid formation and spheroid sizes were inhibited by Rousavastatin in a dose dependent manner. In 2D culture, expression of the E-Cadherin was increased up to 2.0 fold in a dose dependent linear manner (R² = 0.89). Vimentin and Zeb-1 expressions were decreased up to 40 and 20% of untreated control cells expression level respectively, (R² = 0.99 and 0.92). In 3D system, the expression of E-Cadherin did not show a significant change, but Vimentin and Zeb-1 expressions were decreased up to 70 and 40% of untreated control cells expression level respectively in a dose dependent linear manner in comparison to 2D system (R² = 0.36 and 0.90). Our finding indicates that Rousavastatin inhibit cell proliferation and spheroid formation of PC-3 cells. This inhibition accompanies by inhibition of EMT markers. Therefor, this cholesterol lowering agent could probably have potential in the prevention and suppression of cancer in androgen dependent prostate cancer.

Breast Cancer Cells in Microgravity: New Aspects for Cancer Research

Breast cancer is the leading cause of cancer death in females. The incidence has risen dramatically during recent decades. Dismissed as an "unsolved problem of the last century", breast cancer still represents a health burden with no effective solution identified so far. Microgravity (µg) research might be an unusual method to combat the disease, but cancer biologists decided to harness the power of µg as an exceptional method to increase efficacy and precision of future breast cancer therapies. Numerous studies have indicated that µg has a great impact on cancer cells; by influencing proliferation, survival, and migration, it shifts breast cancer cells toward a less aggressive phenotype. In addition, through the de novo generation of tumor spheroids, µg research provides a reliable in vitro 3D tumor model for preclinical cancer drug development and to study various processes of cancer progression. In summary, µg has become an important tool in understanding and influencing breast cancer biology.

UCP2 Deficiency Increases Colon Tumorigenesis by Promoting Lipid Synthesis and Depleting NADPH for Antioxidant Defenses

Colorectal cancer (CRC) is associated with metabolic and redox perturbation. The mitochondrial transporter uncoupling protein 2 (UCP2) controls cell proliferation in vitro through the modulation of cellular metabolism, but the underlying mechanism in tumors in vivo remains unexplored. Using murine intestinal cancer models and CRC patient samples, we find higher UCP2 protein levels in tumors compared to their non-tumoral counterparts. We reveal the tumor-suppressive role of UCP2 as its deletion enhances colon and small intestinal tumorigenesis in AOM/DSS-treated and Apc^Min/+ mice, respectively, and correlates with poor survival in the latter model. Mechanistically, UCP2 loss increases levels of oxidized glutathione and proteins in tumors. UCP2 deficiency alters glycolytic pathways while promoting phospholipid synthesis, thereby limiting the availability of NADPH for buffering oxidative stress. We show that UCP2 loss renders colon cells more prone to malignant transformation through metabolic reprogramming and perturbation of redox homeostasis and could favor worse outcomes in CRC.

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UCP2 protein expression, but not mRNA, is increased in CRC in both mice and humans

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UCP2 loss promotes AOM/DSS-induced CAC and Apc^Min-dependent intestinal cancer

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UCP2 loss-induced oxidative stress contributes to increased colon tumorigenesis

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UCP2 deficiency drives an imbalance between lipid metabolism and NADPH homeostasis

Effect of simulated microgravity on metabolism of HGC-27 gastric cancer cells

The understanding into the pathogenesis and treatment of gastric cancer has improved in recent years; however, a number of limitations have delayed the development of effective treatment. Cancer cells can undergo glycolysis and inhibit oxidative phosphorylation in the presence of oxygen (Warburg effect). Previous studies have demonstrated that a rotary cell culture system (RCCS) can induce glycolytic metabolism. In addition, the potential of regulating cancer cells by targeting their metabolites has led to the rapid development of metabolomics. In the present study, human HGC-27 gastric cancer cells were cultured in a RCCS bioreactor, simulating weightlessness. Subsequently, liquid chromatography-mass spectrometry was used to examine the effects of simulated microgravity (SMG) on the metabolism of HGC-27 cells. A total of 67 differentially regulated metabolites were identified, including upregulated and downregulated metabolites. Compared with the normal gravity group, phosphatidyl ethanolamine, phosphatidyl choline, arachidonic acid and sphinganine were significantly upregulated in SMG conditions, whereas sphingomyelin, phosphatidyl serine, phosphatidic acid, L-proline, creatine, pantothenic acid, oxidized glutathione, adenosine diphosphate and adenosine triphosphate were significantly downregulated. The Human Metabolome Database compound analysis revealed that lipids and lipid-like metabolites were primarily affected in an SMG environment in the present study. Overall, the findings of the present study may aid our understanding of gastric cancer by identifying the underlying mechanisms of metabolism of the disease under SMG.

p13CMFA: Parsimonious 13C metabolic flux analysis

Deciphering the mechanisms of regulation of metabolic networks subjected to perturbations, including disease states and drug-induced stress, relies on tracing metabolic fluxes. One of the most informative data to predict metabolic fluxes are ¹³C based metabolomics, which provide information about how carbons are redistributed along central carbon metabolism. Such data can be integrated using ¹³C Metabolic Flux Analysis (¹³C MFA) to provide quantitative metabolic maps of flux distributions. However, ¹³C MFA might be unable to reduce the solution space towards a unique solution either in large metabolic networks or when small sets of measurements are integrated. Here we present parsimonious ¹³C MFA (p¹³CMFA), an approach that runs a secondary optimization in the ¹³C MFA solution space to identify the solution that minimizes the total reaction flux. Furthermore, flux minimization can be weighted by gene expression measurements allowing seamless integration of gene expression data with ¹³C data. As proof of concept, we demonstrate how p¹³CMFA can be used to estimate intracellular flux distributions from ¹³C measurements and transcriptomics data. We have implemented p¹³CMFA in Iso2Flux, our in-house developed isotopic steady-state ¹³C MFA software. The source code is freely available on GitHub (https://github.com/cfoguet/iso2flux/releases/tag/0.7.2).

¹³C Metabolic Flux Analysis (¹³C MFA) is a well-established technique that has proven to be a valuable tool in quantifying the metabolic flux profile of central carbon metabolism. When a biological system is incubated with a ¹³C-labeled substrate, ¹³C propagates to metabolites throughout the metabolic network in a flux and pathway-dependent manner. ¹³C MFA integrates measurements of ¹³C enrichment in metabolites to identify the flux distributions consistent with the measured ¹³C propagation. However, there is often a range of flux values that can lead to the observed ¹³C distribution. Indeed, either when the metabolic network is large or a small set of measurements are integrated, the range of valid solutions can be too wide to accurately estimate part of the underlying flux distribution. Here we propose to use flux minimization to select the best flux solution in the¹³C MFA solution space. Furthermore, this approach can integrate gene expression data to give greater weight to the minimization of fluxes through enzymes with low gene expression evidence in order to ensure that the selected solution is biologically relevant. The concept of using flux minimization to select the best solution is widely used in flux balance analysis, but it had never been applied in the framework of ¹³C MFA. We have termed this new approach parsimonious ¹³C MFA (p¹³CMFA).

Stoichiometric gene-to-reaction associations enhance model-driven analysis performance: Metabolic response to chronic exposure to Aldrin in prostate cancer

BACKGROUND

Genome-scale metabolic models (GSMM) integrating transcriptomics have been widely used to study cancer metabolism. This integration is achieved through logical rules that describe the association between genes, proteins, and reactions (GPRs). However, current gene-to-reaction formulation lacks the stoichiometry describing the transcript copies necessary to generate an active catalytic unit, which limits our understanding of how genes modulate metabolism. The present work introduces a new state-of-the-art GPR formulation that considers the stoichiometry of the transcripts (S-GPR). As case of concept, this novel gene-to-reaction formulation was applied to investigate the metabolic effects of the chronic exposure to Aldrin, an endocrine disruptor, on DU145 prostate cancer cells. To this aim we integrated the transcriptomic data from Aldrin-exposed and non-exposed DU145 cells through S-GPR or GPR into a human GSMM by applying different constraint-based-methods.

RESULTS

Our study revealed a significant improvement of metabolite consumption/production predictions when S-GPRs are implemented. Furthermore, our computational analysis unveiled important alterations in carnitine shuttle and prostaglandine biosynthesis in Aldrin-exposed DU145 cells that is supported by bibliographic evidences of enhanced malignant phenotype.

CONCLUSIONS

The method developed in this work enables a more accurate integration of gene expression data into model-driven methods. Thus, the presented approach is conceptually new and paves the way for more in-depth studies of aberrant cancer metabolism and other diseases with strong metabolic component with important environmental and clinical implications.

From the Cover: Inhibitors of Nicotinamide Phosphoribosyltransferase Cause Retinal Damage in Larval Zebrafish

Nicotinamide phosphoribosyltransferase (NAMPT) has been investigated as a target for oncology because it catalyzes a rate-limiting step in cellular energy metabolism to produce nicotinamide adenine dinucleotide. Small molecule inhibitors of NAMPT have been promising drug candidates but preclinical development has been hindered due to associated retinal toxicity. Here we demonstrate that larval zebrafish can predict retinal toxicity associated with this mechanism revealing an attractive alternative method for identifying such toxicities. Zebrafish permit higher throughput testing while using far lower quantities of test article compared with mammalian systems. NAMPT inhibitor-associated toxicity manifested in zebrafish as a loss of response to visual cues compared with auditory cues. Zebrafish retinal damage associated with NAMPT inhibitor treatment was confirmed through histopathology. Ranking 6 NAMPT inhibitors according to their impact on zebrafish vision revealed a positive correlation with their in vitro potencies on human tumor cells. This correlation indicates translatable pharmacodynamics between zebrafish and human NAMPT and is consistent with on-target activity as the cause of retinal toxicity associated with NAMPT inhibition. Together, these data illustrate the utility of zebrafish for identifying compounds that may cause ocular toxicity in mammals, and, likewise, for accelerating development of compounds with improved safety margins.

Thomson T, J. Centelles J, F. Graham S, Cascante M. Untargeted metabolomics reveals distinct metabolic reprogramming in endothelial cells co-cultured with CSC and non-CSC prostate cancer cell subpopulations

Tumour angiogenesis is an important hallmark of cancer and the study of its metabolic adaptations, downstream to any cellular change, can reveal attractive targets for inhibiting cancer growth. In the tumour microenvironment, endothelial cells (ECs) interact with heterogeneous tumour cell types that drive angiogenesis and metastasis. In this study we aim to characterize the metabolic alterations in ECs influenced by the presence of tumour cells with extreme metastatic abilities. Human umbilical vein endothelial cells (HUVECs) were subjected to different microenvironmental conditions, such as the presence of highly metastatic PC-3M and highly invasive PC-3S prostate cancer cell lines, in addition to the angiogenic activator vascular endothelial growth factor (VEGF), under normoxia. Untargeted high resolution liquid chromatography-mass spectrometry (LC-MS) based metabolomics revealed significant metabolite differences among the various conditions and a total of 25 significantly altered metabolites were identified including acetyl L-carnitine, NAD+, hypoxanthine, guanine and oleamide, with profile changes unique to each of the experimental conditions. Biochemical pathway analysis revealed the importance of fatty acid oxidation and nucleotide salvage pathways. These results provide a global metabolic preview that could help in selectively targeting the ECs aiding in either cancer cell invasion or metastasis in the heterogeneous tumour microenvironment.

Model-driven discovery of long-chain fatty acid metabolic reprogramming in heterogeneous prostate cancer cells

Epithelial-mesenchymal-transition promotes intra-tumoral heterogeneity, by enhancing tumor cell invasiveness and promoting drug resistance. We integrated transcriptomic data for two clonal subpopulations from a prostate cancer cell line (PC-3) into a genome-scale metabolic network model to explore their metabolic differences and potential vulnerabilities. In this dual cell model, PC-3/S cells express Epithelial-mesenchymal-transition markers and display high invasiveness and low metastatic potential, while PC-3/M cells present the opposite phenotype and higher proliferative rate. Model-driven analysis and experimental validations unveiled a marked metabolic reprogramming in long-chain fatty acids metabolism. While PC-3/M cells showed an enhanced entry of long-chain fatty acids into the mitochondria, PC-3/S cells used long-chain fatty acids as precursors of eicosanoid metabolism. We suggest that this metabolic reprogramming endows PC-3/M cells with augmented energy metabolism for fast proliferation and PC-3/S cells with increased eicosanoid production impacting angiogenesis, cell adhesion and invasion. PC-3/S metabolism also promotes the accumulation of docosahexaenoic acid, a long-chain fatty acid with antiproliferative effects. The potential therapeutic significance of our model was supported by a differential sensitivity of PC-3/M cells to etomoxir, an inhibitor of long-chain fatty acid transport to the mitochondria.

The coexistence within the same tumor of a variety of subpopulations, featuring different phenotypes (intra-tumoral heterogeneity) represents a challenge for diagnosis, prognosis and targeted therapies. In this work, we have explored the metabolic differences underlying tumor heterogeneity by building cell-type-specific genome-scale metabolic models that integrate transcriptome and metabolome data of two clonal subpopulations derived from the same prostate cancer cell line (PC-3). These subpopulations display either highly proliferative, cancer stem cell (PC-3/M) or highly invasive, epithelial-mesenchymal-transition-like phenotypes (PC-3/S). Our model-driven analysis and experimental validations have unveiled a differential utilization of the long-chain fatty acids pool in both subpopulations. More specifically, our findings show an enhanced entry of long-chain fatty acids into the mitochondria in PC-3/M cells, while in PC-3/S cells, long-chain fatty acids are used as precursors of eicosanoid metabolism. The different utilization of long-chain fatty acids between subpopulations endows PC-3/M cells with a highly proliferative phenotype while enhances PC-3/S invasive phenotype. The present work provides a tool to unveil key metabolic nodes associated with tumor heterogeneity and highlights potential subpopulation-specific targets with important therapeutic implications.

Insulin-like growth factor 1 signaling is essential for mitochondrial biogenesis and mitophagy in cancer cells

Mitochondrial activity and metabolic reprogramming influence the phenotype of cancer cells and resistance to targeted therapy. We previously established that an insulin-like growth factor 1 (IGF-1)-inducible mitochondrial UTP carrier (PNC1/SLC25A33) promotes cell growth. This prompted us to investigate whether IGF signaling is essential for mitochondrial maintenance in cancer cells and whether this contributes to therapy resistance. Here we show that IGF-1 stimulates mitochondrial biogenesis in a range of cell lines. In MCF-7 and ZR75.1 breast cancer cells, IGF-1 induces peroxisome proliferator-activated receptor γ coactivator 1β (PGC-1β) and PGC-1α-related coactivator (PRC). Suppression of PGC-1β and PRC with siRNA reverses the effects of IGF-1 and disrupts mitochondrial morphology and membrane potential. IGF-1 also induced expression of the redox regulator nuclear factor-erythroid-derived 2-like 2 (NFE2L2 alias NRF-2). Of note, MCF-7 cells with acquired resistance to an IGF-1 receptor (IGF-1R) tyrosine kinase inhibitor exhibited reduced expression of PGC-1β, PRC, and mitochondrial biogenesis. Interestingly, these cells exhibited mitochondrial dysfunction, indicated by reactive oxygen species expression, reduced expression of the mitophagy mediators BNIP3 and BNIP3L, and impaired mitophagy. In agreement with this, IGF-1 robustly induced BNIP3 accumulation in mitochondria. Other active receptor tyrosine kinases could not compensate for reduced IGF-1R activity in mitochondrial protection, and MCF-7 cells with suppressed IGF-1R activity became highly dependent on glycolysis for survival. We conclude that IGF-1 signaling is essential for sustaining cancer cell viability by stimulating both mitochondrial biogenesis and turnover through BNIP3 induction. This core mitochondrial protective signal is likely to strongly influence responses to therapy and the phenotypic evolution of cancer.

Identification of extracellular matrix protein 1 as a potential plasma biomarker of ESCC by proteomic analysis using iTRAQ and 2D-LC-MS/MS

PURPOSE

This study was aimed to conduct a proteomics profiling analysis on plasma obtained from ESCC patients with the goal of identifying appropriate plasma protein biomarkers in the progression of ESCC.

EXPERIMENTAL DESIGN

Plasma from 28 ESCC patients and 28 healthy controls (HC) were analyzed by iTRAQ combined with 2D-LC-MS/MS. ProteinPilot software was used to identify the differentially expressed plasma proteins in ESCC compared to HC. Western blot was performed to verify the expression of selected proteins in 37 independent ESCC patients and 37 HC. Transwell and MTT assays were used to detect the biological function of ECM1 protein in vitro.

RESULTS

Nineteen (four upregulated and fifteen downregulated) proteins were identified as differentially expressed between ESCC and HC (p <0.05). Biological functions of these proteins are involved in cell adhesion, cell apoptosis and metabolic processes, visual perception and immune response. Of these, extracellular matrix 1 (ECM1) and lumican (LUM) were selected further confirmation by Western blot (p <0.05), which were consistent with the iTRAQ results. Furthermore, the migration ability of EC9706 cell line after overexpressing ECM1 was increased significantly (p <0.05). The proliferation ability of HUVEC cell was enhanced when treated with the culture supernatants of EC9706 overexpressed ECM1(p <0.05).

CONCLUSION AND CLINICAL RELEVANCE

This proteome analysis indicate that ECM1 is a potential novel plasma protein biomarker for the detection of primary ESCC and evaluation of neoplasms progression.