Cancer Metabolism: A Modeling Perspective

Identification of Anticancer Enzymes and Biomarkers for Hepatocellular Carcinoma through Constraint-Based Modeling

Hepatocellular carcinoma (HCC) results in the abnormal regulation of cellular metabolic pathways. Constraint-based modeling approaches can be utilized to dissect metabolic reprogramming, enabling the identification of biomarkers and anticancer targets for diagnosis and treatment. In this study, two genome-scale metabolic models (GSMMs) were reconstructed by employing RNA sequencing expression patterns of hepatocellular carcinoma (HCC) and their healthy counterparts. An anticancer target discovery (ACTD) framework was integrated with the two models to identify HCC targets for anticancer treatment. The ACTD framework encompassed four fuzzy objectives to assess both the suppression of cancer cell growth and the minimization of side effects during treatment. The composition of a nutrient may significantly affect target identification. Within the ACTD framework, ten distinct nutrient media were utilized to assess nutrient uptake for identifying potential anticancer enzymes. The findings revealed the successful identification of target enzymes within the cholesterol biosynthetic pathway using a cholesterol-free cell culture medium. Conversely, target enzymes in the cholesterol biosynthetic pathway were not identified when the nutrient uptake included a cholesterol component. Moreover, the enzymes PGS1 and CRL1 were detected in all ten nutrient media. Additionally, the ACTD framework comprises dual-group representations of target combinations, pairing a single-target enzyme with an additional nutrient uptake reaction. Additionally, the enzymes PGS1 and CRL1 were identified across the ten-nutrient media. Furthermore, the ACTD framework encompasses two-group representations of target combinations involving the pairing of a single-target enzyme with an additional nutrient uptake reaction. Computational analysis unveiled that cell viability for all dual-target combinations exceeded that of their respective single-target enzymes. Consequently, integrating a target enzyme while adjusting an additional exchange reaction could efficiently mitigate cell proliferation rates and ATP production in the treated cancer cells. Nevertheless, most dual-target combinations led to lower side effects in contrast to their single-target counterparts. Additionally, differential expression of metabolites between cancer cells and their healthy counterparts were assessed via parsimonious flux variability analysis employing the GSMMs to pinpoint potential biomarkers. The variabilities of the fluxes and metabolite flow rates in cancer and healthy cells were classified into seven categories. Accordingly, two secretions and thirteen uptakes (including eight essential amino acids and two conditionally essential amino acids) were identified as potential biomarkers. The findings of this study indicated that cancer cells exhibit a higher uptake of amino acids compared with their healthy counterparts.

A highly selective probe engineered to detect polarity and distinguish normal cells and tumor cells in tissue sections

Early diagnostics and therapies for diseases such as cancer are limited by the fact that the inducing factors for the development of cytopathies are not clear. The stable polarity of lipid droplets is a potential biomarker for tumor cells; however, the complex intracellular biological environment poses great difficulties for specific detection of the polarity. Therefore, to meet this pressing challenge, we designed a highly selective fluorescent probe, DCI-Cou-polar, which used the ICT mechanism to differentiate normal cells and tumor cells in tissue sections by detecting changes in the polarities of intracellular lipid droplets. The introduction of a cyclic amine at the 7-position of coumarin (benzoquinolizine coumarin) reduced its ability to donate electrons compared with the diethylamino group, which increased the probe selectivity while retaining the sensitivity to polarity. With NIR emission and large Stokes shifts, DCI-Cou-polar has high sensitivity to polarity, excellent photostability, and biocompatibility, and it tracks lipid droplets with high fidelity. Therefore, we believe that this polarity-sensitive probe provides information on the connection between the polarity of lipid droplets and tumors while improving the development of highly selective polarity probes.

Nano-energy interference: A novel strategy for blunting tumor adaptation and metastasis

Blunting the tumor's stress-sensing ability is an effective strategy for controlling tumor adaptive survival and metastasis. Here, we have designed a cyclically amplified nano-energy interference device based on lipid nanoparticles (LNP), focused on altering cellular energy metabolism. This innovative nano device efficiently targets and monitors the tumor's status while simultaneously inhibiting mitochondrial respiration, biogenesis and ribosome production. To this end, we first identified azelaic acid (AA), a binary acid capable of disrupting the mitochondrial respiratory chain. Upon encapsulation in LNP and linkage to mitochondrial-targeting molecules, this disruptive effect is further augmented. Consequently, tumors exhibit a substantial upregulation of the glycolytic pathway, intensifying their glucose demand and worsening the tumor's energy-deprived microenvironment. Then, the glucose analog, 2-Deoxy-D-glucose (2-DG), linked to the LNP, efficiently targets tumors and competitively inhibits the tumor's normal glucose uptake. The synergetic results of combining AA with 2-DG induce comprehensive energy deficiency within tumors, blocking the generation of energy-sensitive ribosomes. Ultimately, the disruption of both mitochondria and ribosomes depletes energy supply and new protein-generating capacity, weakening tumor's ability to adapt to environmental stress and thereby inhibiting growth and metastasis. Comprehensively, this nano-energy interference device, by controlling the tumor's stress-sensing ability, provides a novel therapeutic strategy for refractory tumors.

Modeling blood metabolite homeostatic levels reduces sample heterogeneity across cohorts

Blood metabolite levels are affected by numerous factors, including preanalytical factors such as collection methods and geographical sites. These perturbations have caused deleterious consequences for many metabolomics studies and represent a major challenge in the metabolomics field. It is important to understand these factors and develop models to reduce their perturbations. However, to date, the lack of suitable mathematical models for blood metabolite levels under homeostasis has hindered progress. In this study, we develop quantitative models of blood metabolite levels in healthy adults based on multisite sample cohorts that mimic the current challenge. Five cohorts of samples obtained across four geographically distinct sites were investigated, focusing on approximately 50 metabolites that were quantified using 1H NMR spectroscopy. More than one-third of the variation in these metabolite profiles is due to cross-cohort variation. A dramatic reduction in the variation of metabolite levels (90%), especially their site-to-site variation (95%), was achieved by modeling each metabolite using demographic and clinical factors and especially other metabolites, as observed in the top principal components. The results also reveal that several metabolites contribute disproportionately to such variation, which could be explained by their association with biological pathways including biosynthesis and degradation. The study demonstrates an intriguing network effect of metabolites that can be utilized to better define homeostatic metabolite levels, which may have implications for improved health monitoring. As an example of the potential utility of the approach, we show that modeling gender-related metabolic differences retains the interesting variance while reducing unwanted (site-related) variance.

Mathematical reconstruction of the metabolic network in an in-vitro multiple myeloma model

It is increasingly apparent that cancer cells, in addition to remodelling their metabolism to survive and proliferate, adapt and manipulate the metabolism of other cells. This property may be a telling sign that pre-clinical tumour metabolism studies exclusively utilising in-vitro mono-culture models could prove to be limited for uncovering novel metabolic targets able to translate into clinical therapies. Although this is increasingly recognised, and work towards addressing the issue is becoming routinary much remains poorly understood. For instance, knowledge regarding the biochemical mechanisms through which cancer cells manipulate non-cancerous cell metabolism, and the subsequent impact on their survival and proliferation remains limited. Additionally, the variations in these processes across different cancer types and progression stages, and their implications for therapy, also remain largely unexplored. This study employs an interdisciplinary approach that leverages the predictive power of mathematical modelling to enrich experimental findings. We develop a functional multicellular in-silico model that facilitates the qualitative and quantitative analysis of the metabolic network spawned by an in-vitro co-culture model of bone marrow mesenchymal stem- and myeloma cell lines. To procure this model, we devised a bespoke human genome constraint-based reconstruction workflow that combines aspects from the legacy mCADRE & Metabotools algorithms, the novel redHuman algorithm, along with 13C-metabolic flux analysis. Our workflow transforms the latest human metabolic network matrix (Recon3D) into two cell-specific models coupled with a metabolic network spanning a shared growth medium. When cross-validating our in-silico model against the in-vitro model, we found that the in-silico model successfully reproduces vital metabolic behaviours of its in-vitro counterpart; results include cell growth predictions, respiration rates, as well as support for observations which suggest cross-shuttling of redox-active metabolites between cells.

A Mathematical Model of the Disruption of Glucose Homeostasis in Cancer Patients

In this paper, we investigate the disruption of the glucose homeostasis at the whole-body level by the presence of cancer disease. Of particular interest are the potentially different responses of patients with or without hyperglycemia (including diabetes mellitus) to the cancer challenge, and how tumor growth, in turn, responds to hyperglycemia and its medical management. We propose a mathematical model that describes the competition between cancer cells and glucose-dependent healthy cells for a shared glucose resource. We also include the metabolic reprogramming of healthy cells by cancer-cell-initiated mechanism to reflect the interplay between the two cell populations. We parametrize this model and carry out numerical simulations of various scenarios, with growth of tumor mass and loss of healthy body mass as endpoints. We report sets of cancer characteristics that show plausible disease histories. We investigate parameters that change cancer cells' aggressiveness, and we exhibit differing responses in diabetic and non-diabetic, in the absence or presence of glycemic control. Our model predictions are in line with observations of weight loss in cancer patients and the increased growth (or earlier onset) of tumor in diabetic individuals. The model will also aid future studies on countermeasures such as the reduction of circulating glucose in cancer patients.

Targeting cancer metabolic vulnerabilities for advanced therapeutic efficacy

Cancer metabolism is how cancer cells utilize nutrients and energy to support their growth and proliferation. Unlike normal cells, cancer cells have a unique metabolic profile that allows them to generate energy and the building blocks they need for rapid growth and division. This metabolic profile is marked by an increased reliance on glucose and glutamine as energy sources and changes in how cancer cells use and make key metabolic intermediates like ATP, NADH, and NADPH. This script analyzes a comprehensive overview of the latest advances in tumor metabolism, identifying the key unresolved issues, elaborates on how tumor cells differ from normal cells in their metabolism of nutrients, and explains how tumor cells conflate growth signals and nutrients to proliferate. The metabolic interaction of tumorigenesis and lipid metabolism within the tumor microenvironment and the role of ROS as an anti-tumor agent by mediating various signaling pathways for clinical cancer therapeutic targeting are outlined. Cancer metabolism is highly dynamic and heterogeneous; thus, advanced technologies to better investigate metabolism at the unicellular level without altering tumor tissue are necessary for better research and clinical transformation. The study of cancer metabolism is an area of active research, as scientists seek to understand the underlying metabolic changes that drive cancer growth and to identify potential therapeutic targets.

A mathematical model of the disruption of glucose homeostasis in cancer patients

In this paper we investigate the disruption of the glucose homeostasis at the whole-body level by the presence of cancer disease. Of particular interest are the potentially different responses of patients with or without hyperglycemia (including Diabetes Mellitus) to the cancer challenge, and how tumor growth, in turn, responds to hyperglycemia and its medical management. We propose a mathematical model that describes the competition between cancer cells and glucosedependent healthy cells for a shared glucose resource. We also include the metabolic reprogramming of healthy cells by cancer-cell-initiated mechanism to reflect the interplay between the two cell populations. We parametrize this model and carry out numerical simulations of various scenarios, with growth of tumor mass and loss of healthy body mass as endpoints. We report sets of cancer characteristics that show plausible disease histories. We investigate parameters that change cancer cells’ aggressiveness, and we exhibit differing responses in diabetic and non-diabetic, in the absence or presence of glycemic control. Our model predictions are in line with observations of weight loss in cancer patients and the increased growth (or earlier onset) of tumor in diabetic individuals. The model will also aid future studies on countermeasures such as the reduction of circulating glucose in cancer patients.

Identifying essential genes in genome-scale metabolic models of consensus molecular subtypes of colorectal cancer

Identifying essential targets in the genome-scale metabolic networks of cancer cells is a time-consuming process. The present study proposed a fuzzy hierarchical optimization framework for identifying essential genes, metabolites and reactions. On the basis of four objectives, the present study developed a framework for identifying essential targets that lead to cancer cell death and evaluating metabolic flux perturbations in normal cells that have been caused by cancer treatment. Through fuzzy set theory, a multiobjective optimization problem was converted into a trilevel maximizing decision-making (MDM) problem. We applied nested hybrid differential evolution to solve the trilevel MDM problem to identify essential targets in genome-scale metabolic models for five consensus molecular subtypes (CMSs) of colorectal cancer. We used various media to identify essential targets for each CMS and discovered that most targets affected all five CMSs and that some genes were CMS-specific. We obtained experimental data on the lethality of cancer cell lines from the DepMap database to validate the identified essential genes. The results reveal that most of the identified essential genes were compatible with the colorectal cancer cell lines obtained from DepMap and that these genes, with the exception of EBP, LSS, and SLC7A6, could generate a high level of cell death when knocked out. The identified essential genes were mostly involved in cholesterol biosynthesis, nucleotide metabolisms, and the glycerophospholipid biosynthetic pathway. The genes involved in the cholesterol biosynthetic pathway were also revealed to be determinable, if a cholesterol uptake reaction was not induced when the cells were in the culture medium. However, the genes involved in the cholesterol biosynthetic pathway became non-essential if such a reaction was induced. Furthermore, the essential gene CRLS1 was revealed as a medium-independent target for all CMSs.

Long noncoding RNA UCA1 promotes glutamine-driven anaplerosis of bladder cancer by interacting with hnRNP I/L to upregulate GPT2 expression

Long noncoding RNA urothelial cancer associated 1 (UCA1), initially identified in bladder cancer, is associated with multiple cellular processes, including metabolic reprogramming. However, its characteristics in the anaplerosis context of bladder cancer (BLCA) remain elusive. We identified UCA1 as a binding partner of heterogeneous nuclear ribonucleoproteins (hnRNPs) I and L, RNA-binding proteins (RBPs) with no previously known role in metabolic reprogramming. UCA1 and hnRNP I/L profoundly affected glycolysis, TCA cycle, glutaminolysis, and proliferation of BLCA. Importantly, UCA1 specifically bound to and facilitated the combination of hnRNP I/L to the promoter of glutamic pyruvate transaminase 2 (GPT2), an enzyme transferring glutamate to α-ketoglutarate, resulting in upregulated expression of GPT2 and enhanced glutamine-derived carbons in the TCA cycle. We also systematically confirmed the influence of UCA1 and hnRNP I/L on metabolism and proliferation via glutamine-driven anaplerosis in BLCA. Our study revealed the critical role of UCA1-mediated mechanisms involved in glutamine-driven anaplerosis and provided novel evidence that lncRNA regulates metabolic reprogramming in tumor cells.

Exploring the Metabolic Landscape of AML: From Haematopoietic Stem Cells to Myeloblasts and Leukaemic Stem Cells

Despite intensive chemotherapy regimens, up to 60% of adults with acute myeloid leukaemia (AML) will relapse and eventually succumb to their disease. Recent studies suggest that leukaemic stem cells (LSCs) drive AML relapse by residing in the bone marrow niche and adapting their metabolic profile. Metabolic adaptation and LSC plasticity are novel hallmarks of leukemogenesis that provide important biological processes required for tumour initiation, progression and therapeutic responses. These findings highlight the importance of targeting metabolic pathways in leukaemia biology which might serve as the Achilles' heel for the treatment of AML relapse. In this review, we highlight the metabolic differences between normal haematopoietic cells, bulk AML cells and LSCs. Specifically, we focus on four major metabolic pathways dysregulated in AML; (i) glycolysis; (ii) mitochondrial metabolism; (iii) amino acid metabolism; and (iv) lipid metabolism. We then outline established and emerging drug interventions that exploit metabolic dependencies of leukaemic cells in the treatment of AML. The metabolic signature of AML cells alters during different biological conditions such as chemotherapy and quiescence. Therefore, targeting the metabolic vulnerabilities of these cells might selectively eradicate them and improve the overall survival of patients with AML.

Systems Biology Approaches to Decipher the Underlying Molecular Mechanisms of Glioblastoma Multiforme

Glioblastoma multiforme (GBM) is one of the most malignant central nervous system tumors, showing a poor prognosis and low survival rate. Therefore, deciphering the underlying molecular mechanisms involved in the progression of the GBM and identifying the key driver genes responsible for the disease progression is crucial for discovering potential diagnostic markers and therapeutic targets. In this context, access to various biological data, development of new methodologies, and generation of biological networks for the integration of multi-omics data are necessary for gaining insights into the appearance and progression of GBM. Systems biology approaches have become indispensable in analyzing heterogeneous high-throughput omics data, extracting essential information, and generating new hypotheses from biomedical data. This review provides current knowledge regarding GBM and discusses the multi-omics data and recent systems analysis in GBM to identify key biological functions and genes. This knowledge can be used to develop efficient diagnostic and treatment strategies and can also be used to achieve personalized medicine for GBM.

Let-7a induces metabolic reprogramming in breast cancer cells via targeting mitochondrial encoded ND4

BACKGROUND AND OBJECTIVES

MicroRNA (miRNA) that translocate from the nucleus to mitochondria are referred to as mitochondrial microRNA (mitomiR). Albeit mitomiRs have been shown to modulate gene expression, their functional impact within mitochondria is unknown. The main objective of this study is to investigate whether the mitochondrial genome is regulated by miR present inside the mitochondria.

METHODS AND RESULTS

Here, we report mitomiR let-7a regulates mitochondrial transcription in breast cancer cells and reprogram the metabolism accordingly. These effects were mediated through the interaction of let-7a with mtDNA, as studied by RNA pull-down assays, altering the activity of Complex I in a cell line-specific manner. Our study, for the first time, identifies the role of mitomiR (let-7a) in regulating the mitochondrial genome by transcriptional repression and its contribution to regulating mitochondrial metabolism of breast cancer cells.

CONCLUSION

These findings uncover a novel mechanism by which mitomiR regulates mitochondrial transcription.

Computer-Aided Design for Identifying Anticancer Targets in Genome-Scale Metabolic Models of Colon Cancer

Simple Summary

Discovery of anticancer targets with minimal side effects is a major challenge in drug discovery and development. This study developed a fuzzy optimization framework for identifying anticancer targets. The framework was applied to identify not only gene regulator targets but also metabolite- and reaction-centric targets. The computational results show that the combination of a carbon metabolism target and any one-target gene that participates in the sphingolipid, glycerophospholipid, nucleotide, cholesterol biosynthesis, or pentose phosphate pathways is more effective for treatment than one-target inhibition is, and a two-target combination of 5-FU and folate supplement can improve cell viability, reduce metabolic deviation, and reduce side effects of normal cells.

Abstract

The efficient discovery of anticancer targets with minimal side effects is a major challenge in drug discovery and development. Early prediction of side effects is key for reducing development costs, increasing drug efficacy, and increasing drug safety. This study developed a fuzzy optimization framework for Identifying AntiCancer Targets (IACT) using constraint-based models. Four objectives were established to evaluate the mortality of treated cancer cells and to minimize side effects causing toxicity-induced tumorigenesis on normal cells and smaller metabolic perturbations. Fuzzy set theory was applied to evaluate potential side effects and investigate the magnitude of metabolic deviations in perturbed cells compared with their normal counterparts. The framework was applied to identify not only gene regulator targets but also metabolite- and reaction-centric targets. A nested hybrid differential evolution algorithm with a hierarchical fitness function was applied to solve multilevel IACT problems. The results show that the combination of a carbon metabolism target and any one-target gene that participates in the sphingolipid, glycerophospholipid, nucleotide, cholesterol biosynthesis, or pentose phosphate pathways is more effective for treatment than one-target inhibition is. A clinical antimetabolite drug 5-fluorouracil (5-FU) has been used to inhibit synthesis of deoxythymidine-5′-triphosphate for treatment of colorectal cancer. The computational results reveal that a two-target combination of 5-FU and a folate supplement can improve cell viability, reduce metabolic deviation, and reduce side effects of normal cells.

Robust metabolic transcriptional components in 34,494 patient-derived cancer-related samples and cell lines

Background

Patient-derived bulk expression profiles of cancers can provide insight into the transcriptional changes that underlie reprogrammed metabolism in cancer. These profiles represent the average expression pattern of all heterogeneous tumor and non-tumor cells present in biopsies of tumor lesions. Hence, subtle transcriptional footprints of metabolic processes can be concealed by other biological processes and experimental artifacts. However, consensus independent component analyses (c-ICA) can capture statistically independent transcriptional footprints of both subtle and more pronounced metabolic processes.

Methods

We performed c-ICA with 34,494 bulk expression profiles of patient-derived tumor biopsies, non-cancer tissues, and cell lines. Gene set enrichment analysis with 608 gene sets that describe metabolic processes was performed to identify the transcriptional components enriched for metabolic processes (mTCs). The activity of these mTCs was determined in all samples to create a metabolic transcriptional landscape.

Results

A set of 555 mTCs was identified of which many were robust across different datasets, platforms, and patient-derived tissues and cell lines. We demonstrate how the metabolic transcriptional landscape defined by the activity of these mTCs in samples can be used to explore the associations between the metabolic transcriptome and drug sensitivities, patient outcomes, and the composition of the immune tumor microenvironment.

Conclusions

To facilitate the use of our transcriptional metabolic landscape, we have provided access to all data via a web portal (www.themetaboliclandscapeofcancer.com). We believe this resource will contribute to the formulation of new hypotheses on how to metabolically engage the tumor or its (immune) microenvironment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40170-021-00272-7.

Ketogenic Diet in Cancer Prevention and Therapy: Molecular Targets and Therapeutic Opportunities

Although cancer is still one of the most significant global challenges facing public health, the world still lacks complementary approaches that would significantly enhance the efficacy of standard anticancer therapies. One of the essential strategies during cancer treatment is following a healthy diet program. The ketogenic diet (KD) has recently emerged as a metabolic therapy in cancer treatment, targeting cancer cell metabolism rather than a conventional dietary approach. The ketogenic diet (KD), a high-fat and very-low-carbohydrate with adequate amounts of protein, has shown antitumor effects by reducing energy supplies to cells. This low energy supply inhibits tumor growth, explaining the ketogenic diet's therapeutic mechanisms in cancer treatment. This review highlights the crucial mechanisms that explain the ketogenic diet's potential antitumor effects, which probably produces an unfavorable metabolic environment for cancer cells and can be used as a promising adjuvant in cancer therapy. Studies discussed in this review provide a solid background for researchers and physicians to design new combination therapies based on KD and conventional therapies.

Metastatic TNBC is closely associated with a fused mitochondrial morphology and a glycolytic and lipogenic metabolism

Mitochondria modify their function and morphology to satisfy the bioenergetic demand of the cells. Cancer cells take advantage of these features to sustain their metabolic, proliferative, metastatic, and survival necessities. Understanding the morphological changes to mitochondria in the different grades of triple-negative breast cancer (TNBC) could help to design new treatments. Consequently, this research explored mitochondrial morphology and the gene expression of some proteins related to mitochondrial dynamics, as well as proteins associated with oxidative and non-oxidative metabolism in metastatic and non-metastatic TNBC. We found that mitochondrial morphology and metabolism are different in metastatic and non-metastatic TNBC. In metastatic TNBC, there is overexpression of genes related to mitochondrial dynamics, fatty-acid metabolism, and glycolysis. These features are accompanied by a fused mitochondrial morphology. By comparison, in non-metastatic TNBC, there is a stress-associated mitochondrial morphology with hyperfragmented mitochondria, accompanied by the upregulated expression of genes associated with the biogenesis of mitochondria; both of which are characteristics related to the higher production of reactive oxygen species observed in this cell line. These differences between metastatic and non-metastatic TNBC should provide a better understanding of metastasis and contribute to the development of improved specific and personalized therapies for TNBC.

Acute myeloid leukemia sensitivity to metabolic inhibitors: glycolysis showed to be a better therapeutic target

Cancer cells alter their metabolism by switching from glycolysis to oxidative phosphorylation (OXPHOS), regardless of oxygen availability. Metabolism may be a molecular target in acute myeloid leukemia (AML), where mutations in metabolic genes have been described. This study evaluated glycolysis and OXPHOS as therapeutic targets. The sensitivity to 2-deoxy-D-glucose (2-DG; glycolysis inhibitor) and oligomycin (OXPHOS inhibitor) was tested in six AML cell lines (HEL, HL-60, K-562, KG-1, NB-4, THP-1). These cells were characterized for IDH1/2 exon 4 mutations, reactive oxygen species, and mitochondrial membrane potential. Metabolic activity was assessed by resazurin assay, whereas cell death and cell cycle were assessed by flow cytometry. Glucose uptake and metabolism-related gene expression were analyzed by ¹⁸F-FDG and RT-PCR/qPCR, respectively. No IDH1/2 exon 4 mutations were detected. HEL cells had the highest ¹⁸F-FDG uptake and peroxides/superoxide anion levels, whereas THP-1 showed the lowest. 2-DG reduced metabolic activity in all cell lines with HEL, KG-1, and NB-4 being the most sensitive cells. Oligomycin decreased metabolic activity in a cell line-dependent manner, the THP-1 resistant and HL-60 being the most sensitive. Both inhibitors induced apoptosis and cell cycle arrest in a cell line- and compound-dependent manner. 2-DG decreased ¹⁸F-FDG uptake in HEL, HL-60, KG-1, and NB-4, while oligomycin increased the uptake in K-562. Metabolism gene expression had different responses to treatments. In conclusion, HEL and KG-1 show to be more glycolytic, whereas HL-60 was more OXPHOS dependent. Results suggest that AML cells reprogram their metabolism to overcome OXPHOS inhibition suggesting that glycolysis may be a better therapeutic target.

A Non-canonical Function of BMAL1 Metabolically Limits Obesity-Promoted Triple-Negative Breast Cancer

The epidemiological association between disrupted circadian rhythms and metabolic diseases is implicated in increased risk of human breast cancer and poor therapeutic outcomes. To define a metabolic phenotype and the underlying molecular mechanism, we applied chronic insulin treatment (CIT) to an in vitro model of triple-negative breast cancer to directly address how BMAL1, a key circadian transcription factor, regulates cancer cell respiration and governs tumor progression. At the cellular level, BMAL1 suppresses the flexibility of mitochondrial substrate usage and the pyruvate-dependent mitochondrial respiration induced by CIT. We established an animal model of diet-induced obesity/hyperinsulinemia and observed that BMAL1 functions as a tumor suppressor in obese, but not lean, mice. Downregulation of BMAL1 is associated with higher risk of metastasis in human breast tumors. In summary, loss of BMAL1 in tumors confers advantages to cancer cells in both intrinsic mitochondrial metabolism and extrinsic inflammatory tumor microenvironment during pre-diabetic obesity/hyperinsulinemia.

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Circadian regulator BMAL1 rewires metabolism in a chronic insulin-treated TNBC model

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Pyruvate links BMAL1 to mitochondrial bioenergetics

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BMAL1 suppresses tumor proliferation and metastasis in hyperinsulinemic obese mice

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BMAL1 influences tumor microenvironment in high-fat-diet-fed mice

Elucidating the Reprograming of Colorectal Cancer Metabolism Using Genome-Scale Metabolic Modeling

Colorectal cancer is the third most incidental cancer worldwide, and the response rate of current treatment for colorectal cancer is very low. Genome-scale metabolic models (GEMs) are systems biology platforms, and they had been used to assist researchers in understanding the metabolic alterations in different types of cancer. Here, we reconstructed a generic colorectal cancer GEM by merging 374 personalized GEMs from the Human Pathology Atlas and used it as a platform for systematic investigation of the difference between tumor and normal samples. The reconstructed model revealed the metabolic reprogramming in glutathione as well as the arginine and proline metabolism in response to tumor occurrence. In addition, six genes including ODC1, SMS, SRM, RRM2, SMOX, and SAT1 associated with arginine and proline metabolism were found to be key players in this metabolic alteration. We also investigated these genes in independent colorectal cancer patients and cell lines and found that many of these genes showed elevated level in colorectal cancer and exhibited adverse effect in patients. Therefore, these genes could be promising therapeutic targets for treatment of a specific colon cancer patient group.

Opposite Effects of Garcinol on Tumor Energy Metabolism in Oral Squamous Cell Carcinoma Cells

Garcinol is a natural polyisoprenylated benzophenone extracted from the dried fruit rind of Garcinia indica. The aim of this study was to investigate the roles of garcinol in oral squamous cell carcinoma (OSCC) cells and its action on cancer cell energy metabolisms. Cell cycle, apoptosis, migration and invasion assays were detected, and oxygen consumption and extracellular acidification rates were also measured with Extracellular Flux Analyzer. Our studies showed that garcinol represses OSCC cells proliferation, cell cycle, migration and invasion, and colony formation. Of note, garcinol directly targeted cancer cell energy producing pathway mitochondrial respiration by significantly inhibiting ATP production, maximal respiration, spare respiration capacity and basal respiration in a dose-dependent manner. But garcinol treatment reflexively boosted glycolysis presented by increased glycolysis and glycolytic capacity. The promotion of garcinol on glycolytic pathway is also confirmed presented by elevated lactic acid content and the activity of pyruvate kinase. Furthermore, the expression of glucose transporter1 and 4, and several important genes related to the glycolysis pathway, including HIF-1α, AKT, and PTEN, was also upregulated after garcinol treatment. Taken together, our results revealed that garcinol has opposite effects on tumor energy metabolism through inhibiting mitochondrial oxidative phosphorylation significantly, and reflexively enhancing glycolysis in OSCC cells. Abbreviations OSCC oral squamous cell carcinoma DMBA dimethylbenzanthracene OCR oxygen consumption rate OXPHOS oxidative phosphorylation ECAR extracellular acidification rate.

Metabolic reprogramming in breast cancer results in distinct mitochondrial bioenergetics between luminal and basal subtypes

Mitochondrial dysfunction is a key feature of cancer and is frequently associated with increased aggressiveness and metastatic potential. Recent evidence has brought to light a metabolic rewiring that takes place during the epithelial-to-mesenchymal transition (EMT), a process that drives the invasive capability of malignant tumors, and highlights a mechanistic link between mitochondrial dysfunction and EMT that has been only partially investigated. In this study, we characterized mitochondrial function and bioenergetic status of cultured human breast cancer cell lines, including luminal-like and basal-like subtypes. Through a combination of biochemical and functional studies, we demonstrated that basal-like cell lines exhibit impaired, but not completely inactive, mitochondrial function, and rely on a consequent metabolic switch to glycolysis to support their ATP demand. These altered metabolic activities are linked to modifications of key electron transport chain proteins and a significant increase in levels of reactive oxygen species compared to luminal cells. Furthermore, we observed that the stable knockdown of EMT markers caused functional changes in mitochondria that result in acquisition of a hybrid glycolysis/OXPHOS phenotype in cancer cells as a means to sustain their metabolic demand.

The peroxisome: an update on mysteries 2.0

Peroxisomes are key metabolic organelles, which contribute to cellular lipid metabolism, e.g. the β-oxidation of fatty acids and the synthesis of myelin sheath lipids, as well as cellular redox balance. Peroxisomal dysfunction has been linked to severe metabolic disorders in man, but peroxisomes are now also recognized as protective organelles with a wider significance in human health and potential impact on a large number of globally important human diseases such as neurodegeneration, obesity, cancer, and age-related disorders. Therefore, the interest in peroxisomes and their physiological functions has significantly increased in recent years. In this review, we intend to highlight recent discoveries, advancements and trends in peroxisome research, and present an update as well as a continuation of two former review articles addressing the unsolved mysteries of this astonishing organelle. We summarize novel findings on the biological functions of peroxisomes, their biogenesis, formation, membrane dynamics and division, as well as on peroxisome-organelle contacts and cooperation. Furthermore, novel peroxisomal proteins and machineries at the peroxisomal membrane are discussed. Finally, we address recent findings on the role of peroxisomes in the brain, in neurological disorders, and in the development of cancer.

Model-Supported Radiotherapy Personalization: In silico Test of Hyper- and Hypo-Fractionation Effects

The need for radiotherapy personalization is now widely recognized, however, it would require considerations not only on the probability of control and survival of the tumor, but also on the possible toxic effects, on the quality of the expected life and the economic efficiency of the treatment. In this paper, we propose a simulation tool that can be integrated into a decision support system that allows selection of the most suitable irradiation regimen. We used a macroscale mathematical model, which includes active and necrotic tumor dynamics and the role of oxygenation to simulate the effects of different hypo-/hyper-fractional regimens using retrospective data of seven virtual patients from as many cervical cancer patients used for its training in a previous study. The results confirmed the heterogeneous response across the patients as a function of treatment regimen and suggested the tumor growth rate as a main factor in the final tumor regression. In addition to the maximum regression, another criterion was suggested to select the most suitable regimen (minimum number of fractions to achieve a regression of 80%) minimizing the toxicity and maximizing the cost-effectiveness ratio. Despite the lack of direct validation, the simulation results are in agreement with the literature findings that suggest the need for hypo-fractionated regimens in case of aggressive tumor phenotypes. Finally, the paper suggests a possible exploitation of the model within a tool to support clinical decisions.

AKR1B10 activates diacylglycerol (DAG) second messenger in breast cancer cells

Aldo-keto reductase 1B10 (AKR1B10) is upregulated in breast cancer and promotes tumor growth and metastasis. However, little is known of the molecular mechanisms of action. Herein we report that AKR1B10 activates lipid second messengers to stimulate cell proliferation. Our data showed that ectopic expression of AKR1B10 in breast cancer cells MCF-7 promoted lipogenesis and enhanced levels of lipid second messengers, including phosphatidylinositol bisphosphate (PIP2), diacylglycerol (DAG), and inositol triphosphate (IP3). In contrast, silencing of AKR1B10 in breast cancer cells BT-20 and colon cancer cells HCT-8 led to decrease of these lipid messengers. Qualitative analyses by liquid chromatography-mass spectrum (LC-MS) revealed that AKR1B10 regulated the cellular levels of total DAG and majority of subspecies. This in turn modulated the phosphorylation of protein kinase C (PKC) isoforms PKCδ (Thr505), PKCµ (Ser744/748), and PKCα/βII (Thr638/641) and activity of the PKC-mediated c-Raf/MEK/ERK signaling cascade. A pan inhibitor of PKC (Go6983) blocked ERK1/2 activation by AKR1B10. In these cells, phospho-p90RSK, phospho-MSK, and Cyclin D1 expression was increased by AKR1B10, and pharmacological inhibition of the ERK signaling cascade with MEK1/2 inhibitors U0126 and PD98059 eradicated induction of phospho-p90RSK, phospho-MSK, and Cyclin D1. In breast cancer cells, AKR1B10 promoted the clonogenic growth and proliferation of breast cancer cells in two-dimension (2D) and three-dimension (3D) cultures and tumor growth in immunodeficient female nude mice through activation of the PKC/ERK pathway. These data suggest that AKR1B10 stimulates breast cancer cell growth and proliferation through activation of DAG-mediated PKC/ERK signaling pathway.

Systems biology in hepatology: approaches and applications

Detailed insights into the biological functions of the liver and an understanding of its crosstalk with other human tissues and the gut microbiota can be used to develop novel strategies for the prevention and treatment of liver-associated diseases, including fatty liver disease, cirrhosis, hepatocellular carcinoma and type 2 diabetes mellitus. Biological network models, including metabolic, transcriptional regulatory, protein-protein interaction, signalling and co-expression networks, can provide a scaffold for studying the biological pathways operating in the liver in connection with disease development in a systematic manner. Here, we review studies in which biological network models were used to integrate multiomics data to advance our understanding of the pathophysiological responses of complex liver diseases. We also discuss how this mechanistic approach can contribute to the discovery of potential biomarkers and novel drug targets, which might lead to the design of targeted and improved treatment strategies. Finally, we present a roadmap for the successful integration of models of the liver and other human tissues with the gut microbiota to simulate whole-body metabolic functions in health and disease.

A pathology atlas of the human cancer transcriptome

Cancer is one of the leading causes of death, and there is great interest in understanding the underlying molecular mechanisms involved in the pathogenesis and progression of individual tumors. We used systems-level approaches to analyze the genome-wide transcriptome of the protein-coding genes of 17 major cancer types with respect to clinical outcome. A general pattern emerged: Shorter patient survival was associated with up-regulation of genes involved in cell growth and with down-regulation of genes involved in cellular differentiation. Using genome-scale metabolic models, we show that cancer patients have widespread metabolic heterogeneity, highlighting the need for precise and personalized medicine for cancer treatment. All data are presented in an interactive open-access database (www.proteinatlas.org/pathology) to allow genome-wide exploration of the impact of individual proteins on clinical outcomes.

Exosomal miRNAs and miRNA dysregulation in cancer-associated fibroblasts

Purpose

The present review aimed to assess the role of exosomal miRNAs in cancer-associated fibroblasts (CAFs), normal fibroblasts (NFs), and cancer cells. The roles of exosomal miRNAs and miRNA dysregulation in CAF formation and activation were summarized.

Methods

All relevant publications were retrieved from the PubMed database, with key words such as CAFs, CAF, stromal fibroblasts, cancer-associated fibroblasts, miRNA, exosomal, exosome, and similar terms.

Results

Recent studies have revealed that CAFs, NFs, and cancer cells can secrete exosomal miRNAs to affect each other. Dysregulation of miRNAs and exosomal miRNAs influence the formation and activation of CAFs. Furthermore, miRNA dysregulation in CAFs is considered to be associated with a secretory phenotype change, tumor invasion, tumor migration and metastasis, drug resistance, and poor prognosis.

Conclusions

Finding of exosomal miRNA secretion provides novel insights into communication among CAFs, NFs, and cancer cells. MicroRNA dysregulation is also involved in the whole processes of CAF formation and function. Dysregulation of miRNAs in CAFs can affect the secretory phenotype of the latter cells.

Deciphering Epithelial-Mesenchymal Transition Regulatory Networks in Cancer through Computational Approaches

Epithelial–mesenchymal transition (EMT), the process by which epithelial cells can convert into motile mesenchymal cells, plays an important role in development and wound healing but is also involved in cancer progression. It is increasingly recognized that EMT is a dynamic process involving multiple intermediate or “hybrid” phenotypes rather than an “all-or-none” process. However, the role of EMT in various cancer hallmarks, including metastasis, is debated. Given the complexity of EMT regulation, computational modeling has proven to be an invaluable tool for cancer research, i.e., to resolve apparent conflicts in experimental data and to guide experiments by generating testable hypotheses. In this review, we provide an overview of computational modeling efforts that have been applied to regulation of EMT in the context of cancer progression and its associated tumor characteristics. Moreover, we identify possibilities to bridge different modeling approaches and point out outstanding questions in which computational modeling can contribute to advance our understanding of pathological EMT.

New Challenges to Study Heterogeneity in Cancer Redox Metabolism

Reactive oxygen species (ROS) are important pathophysiological molecules involved in vital cellular processes. They are extremely harmful at high concentrations because they promote the generation of radicals and the oxidation of lipids, proteins, and nucleic acids, which can result in apoptosis. An imbalance of ROS and a disturbance of redox homeostasis are now recognized as a hallmark of complex diseases. Considering that ROS levels are significantly increased in cancer cells due to mitochondrial dysfunction, ROS metabolism has been targeted for the development of efficient treatment strategies, and antioxidants are used as potential chemotherapeutic drugs. However, initial ROS-focused clinical trials in which antioxidants were supplemented to patients provided inconsistent results, i.e., improved treatment or increased malignancy. These different outcomes may result from the highly heterogeneous redox responses of tumors in different patients. Hence, population-based treatment strategies are unsuitable and patient-tailored therapeutic approaches are required for the effective treatment of patients. Moreover, due to the crosstalk between ROS, reducing equivalents [e.g., NAD(P)H] and central metabolism, which is heterogeneous in cancer, finding the best therapeutic target requires the consideration of system-wide approaches that are capable of capturing the complex alterations observed in all of the associated pathways. Systems biology and engineering approaches may be employed to overcome these challenges, together with tools developed in personalized medicine. However, ROS- and redox-based therapies have yet to be addressed by these methodologies in the context of disease treatment. Here, we review the role of ROS and their coupled redox partners in tumorigenesis. Specifically, we highlight some of the challenges in understanding the role of hydrogen peroxide (H₂O₂), one of the most important ROS in pathophysiology in the progression of cancer. We also discuss its interplay with antioxidant defenses, such as the coupled peroxiredoxin/thioredoxin and glutathione/glutathione peroxidase systems, and its reducing equivalent metabolism. Finally, we highlight the need for system-level and patient-tailored approaches to clarify the roles of these systems and identify therapeutic targets through the use of the tools developed in personalized medicine.

Computational modelling of genome-scale metabolic networks and its application to CHO cell cultures

Genome-scale metabolic models (GEMs) have become increasingly important in recent years. Currently, GEMs are the most accurate in silico representation of the genotype-phenotype link. They allow us to study complex networks from the systems perspective. Their application may drastically reduce the amount of experimental and clinical work, improve diagnostic tools and increase our understanding of complex biological phenomena. GEMs have also demonstrated high potential for the optimisation of bio-based production of recombinant proteins. Herein, we review the basic concepts, methods, resources and software tools used for the reconstruction and application of GEMs. We overview the evolution of the modelling efforts devoted to the metabolism of Chinese Hamster Ovary (CHO) cells. We present a case study on CHO cell metabolism under different amino acid depletions. This leads us to the identification of the most influential as well as essential amino acids in selected CHO cell lines.

The Evolving Role of Succinate in Tumor Metabolism: An 18F-FDG-Based Study

In recent years, inherited and acquired mutations in the tricarboxylic acid (TCA) cycle enzymes have been reported in diverse cancers. Pheochromocytomas and paragangliomas often exhibit dysregulation of glucose metabolism, which is also driven by mutations in genes encoding the TCA cycle enzymes or by activation of hypoxia signaling. Pheochromocytomas and paragangliomas associated with succinate dehydrogenase (SDH) deficiency are characterized by high ¹⁸F-FDG avidity. This association is currently only partially explained. Therefore, we hypothesized that accumulation of succinate due to the TCA cycle defect could be the major connecting hub between SDH-mutated tumors and the ¹⁸F-FDG uptake profile. Methods: To test whether succinate modifies the ¹⁸F-FDG metabolic profile of tumors, we performed in vitro and in vivo (small-animal PET/CT imaging and autoradiography) experiments in the presence of succinate, fumarate, and phosphate-buffered saline (PBS) in different cell models. As a control, we also evaluated the impact of succinate on ¹⁸F-fluorocholine uptake and retention. Glucose transporter 1 (GLUT1) immunohistochemistry was performed to assess whether ¹⁸F-FDG uptake correlates with GLUT1 staining. Results: Intratumoral injection of succinate significantly increased ¹⁸F-FDG uptake at 24 h on small-animal PET/CT imaging and autoradiography. No effect of succinate was observed on cancer cells in vitro, but interestingly, we found that succinate caused increased ¹⁸F-FDG uptake by human umbilical vein endothelial cells in a concentration-dependent manner. No significant effect was observed after intratumoral injection of fumarate or PBS. Succinate, fumarate, and PBS have no effect on cell viability, regardless of cell lineage. Intramuscular injection of succinate also significantly increases ¹⁸F-FDG uptake by muscle when compared with either PBS or fumarate, highlighting the effect of succinate on connective tissues. No difference was observed between PBS and succinate on ¹⁸F-fluorocholine uptake in the tumor and muscle and on hind limb blood flow. GLUT1 expression quantification did not significantly differ between the study groups. Conclusion: The present study shows that succinate stimulates ¹⁸F-FDG uptake by endothelial cells, a finding that partially explains the ¹⁸F-FDG metabotype observed in tumors with SDH deficiency. Although this study is an ¹⁸F-FDG-based approach, it provides an impetus to better characterize the determinants of ¹⁸F-FDG uptake in various tumors and their surrounding microenvironment, with a special emphasis on the role of tumor-specific oncometabolites.

Personal model-assisted identification of NAD+ and glutathione metabolism as intervention target in NAFLD

To elucidate the molecular mechanisms underlying non‐alcoholic fatty liver disease (NAFLD), we recruited 86 subjects with varying degrees of hepatic steatosis (HS). We obtained experimental data on lipoprotein fluxes and used these individual measurements as personalized constraints of a hepatocyte genome‐scale metabolic model to investigate metabolic differences in liver, taking into account its interactions with other tissues. Our systems level analysis predicted an altered demand for NAD⁺ and glutathione (GSH) in subjects with high HS. Our analysis and metabolomic measurements showed that plasma levels of glycine, serine, and associated metabolites are negatively correlated with HS, suggesting that these GSH metabolism precursors might be limiting. Quantification of the hepatic expression levels of the associated enzymes further pointed to altered de novo GSH synthesis. To assess the effect of GSH and NAD⁺ repletion on the development of NAFLD, we added precursors for GSH and NAD⁺ biosynthesis to the Western diet and demonstrated that supplementation prevents HS in mice. In a proof‐of‐concept human study, we found improved liver function and decreased HS after supplementation with serine (a precursor to glycine) and hereby propose a strategy for NAFLD treatment.

Metabolic synthetic lethality in cancer therapy

Our understanding of cancer has recently seen a major paradigm shift resulting in it being viewed as a metabolic disorder, and altered cellular metabolism being recognised as a hallmark of cancer. This concept was spurred by the findings that the oncogenic mutations driving tumorigenesis induce a reprogramming of cancer cell metabolism that is required for unrestrained growth and proliferation. The recent discovery that mutations in key mitochondrial enzymes play a causal role in tumorigenesis suggested that dysregulation of metabolism could also be a driver of tumorigenesis. These mutations induce profound adaptive metabolic alterations that are a prerequisite for the survival of the mutated cells. Because these metabolic events are specific to cancer cells, they offer an opportunity to develop new therapies that specifically target tumour cells without affecting healthy tissue. Here, we will describe recent developments in metabolism-based cancer therapy, in particular focusing on the concept of metabolic synthetic lethality. This article is part of a Special Issue entitled Mitochondria in Cancer, edited by Giuseppe Gasparre, Rodrigue Rossignol and Pierre Sonveaux.

Genome scale metabolic modeling of cancer

Cancer cells reprogram metabolism to support rapid proliferation and survival. Energy metabolism is particularly important for growth and genes encoding enzymes involved in energy metabolism are frequently altered in cancer cells. A genome scale metabolic model (GEM) is a mathematical formalization of metabolism which allows simulation and hypotheses testing of metabolic strategies. It has successfully been applied to many microorganisms and is now used to study cancer metabolism. Generic models of human metabolism have been reconstructed based on the existence of metabolic genes in the human genome. Cancer specific models of metabolism have also been generated by reducing the number of reactions in the generic model based on high throughput expression data, e.g. transcriptomics and proteomics. Targets for drugs and bio markers for diagnostics have been identified using these models. They have also been used as scaffolds for analysis of high throughput data to allow mechanistic interpretation of changes in expression. Finally, GEMs allow quantitative flux predictions using flux balance analysis (FBA). Here we critically review the requirements for successful FBA simulations of cancer cells and discuss the symmetry between the methods used for modeling of microbial and cancer metabolism. GEMs have great potential for translational research on cancer and will therefore become of increasing importance in the future.

Effect of Intermittent versus Chronic Calorie Restriction on Tumor Incidence: A Systematic Review and Meta-Analysis of Animal Studies

Both chronic calorie restriction (CCR) and intermittent calorie restriction (ICR) have shown anticancer effects. However, the direct evidence comparing ICR to CCR with respect to cancer prevention is controversial and inconclusive. PubMed and Web of Science were searched on November 25, 2015. The relative risk (RR) [95% confidence interval (CI)] was calculated for tumor incidence, and the standardised mean difference (95% CI) was computed for levels of serum insulin-like growth factor-1 (IGF-1), leptin, and adiponectin using a random-effects meta-analysis. Sixteen studies were identified, including 11 using genetically engineered mouse models (908 animals with 38-76 weeks of follow-up) and 5 using chemically induced rat models (379 animals with 7-18 weeks of follow-up). Compared to CCR, ICR decreased tumor incidence in genetically engineered models (RR = 0.57; 95% CI: 0.37, 0.88) but increased the risk in chemically induced models (RR = 1.53, 95% CI: 1.13, 2.06). It appears that ICR decreases IGF-1 and leptin and increases adiponectin in genetically engineered models. Thus, the evidence suggests that ICR exerts greater anticancer effect in genetically engineered mouse models but weaker cancer prevention benefit in chemically induced rat models as compared to CCR. Further studies are warranted to confirm our findings and elucidate the mechanisms responsible for these effects.

Integrative analysis of human omics data using biomolecular networks

High-throughput '-omics' technologies have given rise to an increasing abundance of genome-scale data detailing human biology at the molecular level. Although these datasets have already made substantial contributions to a more comprehensive understanding of human physiology and diseases, their interpretation becomes increasingly cryptic and nontrivial as they continue to expand in size and complexity. Systems biology networks offer a scaffold upon which omics data can be integrated, facilitating the extraction of new and physiologically relevant information from the data. Two of the most prevalent networks that have been used for such integrative analyses of omics data are genome-scale metabolic models (GEMs) and protein-protein interaction (PPI) networks, both of which have demonstrated success among many different omics and sample types. This integrative approach seeks to unite 'top-down' omics data with 'bottom-up' biological networks in a synergistic fashion that draws on the strengths of both strategies. As the volume and resolution of high-throughput omics data continue to grow, integrative network-based analyses are expected to play an increasingly important role in their interpretation.

Reconstructed Metabolic Network Models Predict Flux-Level Metabolic Reprogramming in Glioblastoma

Developments in genome scale metabolic modeling techniques and omics technologies have enabled the reconstruction of context-specific metabolic models. In this study, glioblastoma multiforme (GBM), one of the most common and aggressive malignant brain tumors, is investigated by mapping GBM gene expression data on the growth-implemented brain specific genome-scale metabolic network, and GBM-specific models are generated. The models are used to calculate metabolic flux distributions in the tumor cells. Metabolic phenotypes predicted by the GBM-specific metabolic models reconstructed in this work reflect the general metabolic reprogramming of GBM, reported both in in-vitro and in-vivo experiments. The computed flux profiles quantitatively predict that major sources of the acetyl-CoA and oxaloacetic acid pool used in TCA cycle are pyruvate dehydrogenase from glycolysis and anaplerotic flux from glutaminolysis, respectively. Also, our results, in accordance with recent studies, predict a contribution of oxidative phosphorylation to ATP pool via a slightly active TCA cycle in addition to the major contributor aerobic glycolysis. We verified our results by using different computational methods that incorporate transcriptome data with genome-scale models and by using different transcriptome datasets. Correct predictions of flux distributions in glycolysis, glutaminolysis, TCA cycle and lipid precursor metabolism validate the reconstructed models for further use in future to simulate more specific metabolic patterns for GBM.