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

Genome-scale models in human metabologenomics

Metabologenomics integrates metabolomics with other omics data types to comprehensively study the genetic and environmental factors that influence metabolism. These multi-omics data can be incorporated into genome-scale metabolic models (GEMs), which are highly curated knowledge bases that explicitly account for genes, transcripts, proteins and metabolites. By including all known biochemical reactions catalysed by enzymes and transporters encoded in the human genome, GEMs analyse and predict the behaviour of complex metabolic networks. Continued advancements to the scale and scope of GEMs - from cells and tissues to microbiomes and the whole body - have helped to design effective treatments and develop better diagnostic tools for metabolic diseases. Furthermore, increasing amounts of multi-omics data are incorporated into GEMs to better identify the underlying mechanisms, biomarkers and potential drug targets of metabolic diseases.

Exosomal miR-21-5p from glioma associated mesenchymal stem cells promotes the progression and glycolysis of glioblastoma via PDHA1

Glioblastoma (GBM) is highly malignant and grows rapidly, and there is currently a lack of effective treatments. Metabolism provides the basis for the occurrence and development of GBM. Pyruvate dehydrogenase A1 (PDHA1) is a key component in both the tricarboxylic acid cycle and glycolysis, playing an important role in the metabolic processes related to cancer, but its role in GBM remains unclear. Glioma associated mesenchymal stem cells (GaMSC) play a significant role in the development of glioma. This study aims to explore the relationship between GaMSC derived exosomes (GAMSC-EXO) and PDHA1, as well as the effects and mechanisms on GBM glucose metabolism. In this study, human GaMSC-derived exosomes were isolated and identified. The role of GAMSC-EXO in GBM proliferation, migration, invasion and glucose metabolism was investigated. The upstream miRNA of PDHA1 was predicted and the relationship between miR-21-5p and PDHA1 in GAMSC-EXO and its effect on GBM glucose metabolism was investigated. We found that GAMSCs promote GBM cell proliferation, migration, invasion and glycolysis by releasing exosomes. After inhibiting GBM glycolysis, GBM proliferation, migration and invasion abilities were weakened. MiR-21-5p in exosomes was identified as the miRNA that affects the above biological behaviors. Mechanismly, miR-21-5p directly binds to the mRNA of PDHA1 and downregulates its transcription, thereby promoting GBM glycolysis. Together, this study demonstrated that exosomal miR-21-5p from GAMSC promoted GBM proliferation, migration, invasion, and glycolysis by targeting PDHA1, which provided novel insights into the metabolic interactions between GAMSCs and GBM cells, emphasizing the importance of exosome-mediated communication in tumor progression.

Unveiling the Molecular Mechanisms of Glioblastoma through an Integrated Network-Based Approach

Background/Objectives: Despite current treatments extending the lifespan of Glioblastoma (GBM) patients, the average survival time is around 15-18 months, underscoring the fatality of GBM. This study aims to investigate the impact of sample heterogeneity on gene expression in GBM, identify key metabolic pathways and gene modules, and explore potential therapeutic targets. Methods: In this study, we analysed GBM transcriptome data derived from The Cancer Genome Atlas (TCGA) using genome-scale metabolic models (GEMs) and co-expression networks. We examine transcriptome data incorporating tumour purity scores (TPSs), allowing us to assess the impact of sample heterogeneity on gene expression profiles. We analysed the metabolic profile of GBM by generating condition-specific GEMs based on the TPS group. Results: Our findings revealed that over 90% of genes showing brain and glioma specificity in RNA expression demonstrate a high positive correlation, underscoring their expression is dominated by glioma cells. Conversely, negatively correlated genes are strongly associated with immune responses, indicating a complex interaction between glioma and immune pathways and non-tumorigenic cell dominance on gene expression. TPS-based metabolic profile analysis was supported by reporter metabolite analysis, highlighting several metabolic pathways, including arachidonic acid, kynurenine and NAD pathway. Through co-expression network analysis, we identified modules that significantly overlap with TPS-correlated genes. Notably, SOX11 and GSX1 are upregulated in High TPS, show a high correlation with TPS, and emerged as promising therapeutic targets. Additionally, NCAM1 exhibits a high centrality score within the co-expression module, which shows a positive correlation with TPS. Moreover, LILRB4, an immune-related gene expressed in the brain, showed a negative correlation and upregulated in Low TPS, highlighting the importance of modulating immune responses in the GBM mechanism. Conclusions: Our study uncovers sample heterogeneity's impact on gene expression and the molecular mechanisms driving GBM, and it identifies potential therapeutic targets for developing effective treatments for GBM patients.

Review of Current Human Genome-Scale Metabolic Models for Brain Cancer and Neurodegenerative Diseases

Brain disorders represent 32% of the global disease burden, with 169 million Europeans affected. Constraint-based metabolic modelling and other approaches have been applied to predict new treatments for these and other diseases. Many recent studies focused on enhancing, among others, drug predictions by generating generic metabolic models of brain cells and on the contextualisation of the genome-scale metabolic models with expression data. Experimental flux rates were primarily used to constrain or validate the model inputs. Bi-cellular models were reconstructed to study the interaction between different cell types. This review highlights the evolution of genome-scale models for neurodegenerative diseases and glioma. We discuss the advantages and drawbacks of each approach and propose improvements, such as building bi-cellular models, tailoring the biomass formulations for glioma and refinement of the cerebrospinal fluid composition.

Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer

The influence of metabolism on signaling, epigenetic markers, and transcription is highly complex yet important for understanding cancer physiology. Despite the development of high-resolution multi-omics technologies, it is difficult to infer metabolic activity from these indirect measurements. Fortunately, genome-scale metabolic models and constraint-based modeling provide a systems biology framework to investigate the metabolic states and define the genotype-phenotype associations by integrations of multi-omics data. Constraint-Based Reconstruction and Analysis (COBRA) methods are used to build and simulate metabolic networks using mathematical representations of biochemical reactions, gene-protein reaction associations, and physiological and biochemical constraints. These methods have led to advancements in metabolic reconstruction, network analysis, perturbation studies as well as prediction of metabolic state. Most computational tools for performing these analyses are written for MATLAB, a proprietary software. In order to increase accessibility and handle more complex datasets and models, community efforts have started to develop similar open-source tools in Python. To date there is a comprehensive set of tools in Python to perform various flux analyses and visualizations; however, there are still missing algorithms in some key areas. This review summarizes the availability of Python software for several components of COBRA methods and their applications in cancer metabolism. These tools are evolving rapidly and should offer a readily accessible, versatile way to model the intricacies of cancer metabolism for identifying cancer-specific metabolic features that constitute potential drug targets.

Cancer Recurrence and Omics: Metabolic Signatures of Cancer Dormancy Revealed by Transcriptome Mapping of Genome-Scale Networks

A major problem in medicine and oncology is cancer recurrence through the activation of dormant cancer cells. A system scale examination of metabolic dysregulations associated with the cancer dormancy offers promise for the discovery of novel molecular targets for cancer precision medicine, and importantly, for the prevention of cancer recurrence. In this study, we mapped the total mRNA sequencing-based transcriptomic data from dormant cancer cell lines and nondormant cancer controls onto a human genome-scale metabolic network by using a graph-based approach, and two mass balance-based approaches with one based on reaction activity/inactivity and the other one on flux changes. The gene expression datasets were accessed from Gene Expression Omnibus (GSE83142 and GSE114012). This analysis included two diverse cancer types, a liquid and a solid cancer, namely, acute lymphoblastic leukemia and colorectal cancer. For the dormant cancer state, we observed changes in major adenosine triphosphate-producing pathways, including the citric acid cycle, oxidative phosphorylation, and glycolysis/gluconeogenesis, indicating a reprogramming in the metabolism of dormant cells away from Warburg-based energy metabolism. All three computational approaches unanimously predicted that folate metabolism, pyruvate metabolism, and glutamate metabolism, as well as valine/leucine/isoleucine metabolism are likely dysregulated in cancer dormancy. These findings provide new insights and molecular pathway targets on cancer dormancy, comprehensively catalog dormancy-associated metabolic pathways, and inform future research aimed at prevention of cancer recurrence in particular.

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.

The genotypic and phenotypic impact of hypoxia microenvironment on glioblastoma cell lines

BACKGROUND

Glioblastoma is a fatal brain tumour with a poor patient survival outcome. Hypoxia has been shown to reprogram cells towards a stem cell phenotype associated with self-renewal and drug resistance properties. Activation of hypoxia-inducible factors (HIFs) helps in cellular adaptation mechanisms under hypoxia. Similarly, miRNAs are known to be dysregulated in GBM have been shown to act as critical mediators of the hypoxic response and to regulate key processes involved in tumorigenesis.

METHODS

Glioblastoma (GBM) cells were exposed to oxygen deprivation to mimic a tumour microenvironment and different cell aspects were analysed such as morphological changes and gene expression of miRNAs and survival genes known to be associated with tumorigenesis.

RESULTS

It was observed that miR-128a-3p, miR-34-5p, miR-181a/b/c, were down-regulated in 6 GBM cell lines while miR-17-5p and miR-221-3p were upregulated when compared to a non-GBM control. When the same GBM cell lines were cultured under hypoxic microenvironment, a further 4-10-fold downregulation was observed for miR-34-5p, miR-128a-3p and 181a/b/c while a 3-6-fold upregulation was observed for miR-221-3p and 17-5p for most of the cells. Furthermore, there was an increased expression of SOX2 and Oct4, GLUT-1, VEGF, Bcl-2 and survivin, which are associated with a stem-like state, increased metabolism, altered angiogenesis and apoptotic escape, respectively.

CONCLUSION

This study shows that by mimicking a tumour microenvironment, miRNAs are dysregulated, stemness factors are induced and alteration of the survival genes necessary for the cells to adapt to the micro-environmental factors occurs. Collectively, these results might contribute to GBM aggressiveness.

Glioma Stem-Like Cells and Metabolism: Potential for Novel Therapeutic Strategies

Glioma stem-like cells (GSCs) were first described as a population which may in part be resistant to traditional chemotherapeutic therapies and responsible for tumour regrowth. Knowledge of the underlying metabolic complexity governing GSC growth and function may point to potential differences between GSCs and the tumour bulk which could be harnessed clinically. There is an increasing interest in the direct/indirect targeting or reprogramming of GSC metabolism as a potential novel therapeutic approach in the adjuvant or recurrent setting to help overcome resistance which may be mediated by GSCs. In this review we will discuss stem-like models, interaction between metabolism and GSCs, and potential current and future strategies for overcoming GSC resistance.

Metabolic Drivers of Invasion in Glioblastoma

Glioblastoma is a primary malignant brain tumor with a median survival under 2 years. The poor prognosis glioblastoma caries is largely due to cellular invasion, which enables escape from resection, and drives inevitable recurrence. While most studies to date have focused on pathways that enhance the invasiveness of tumor cells in the brain microenvironment as the primary driving forces behind GBM’s ability to invade adjacent tissues, more recent studies have identified a role for adaptations in cellular metabolism in GBM invasion. Metabolic reprogramming allows invasive cells to generate the energy necessary for colonizing surrounding brain tissue and adapt to new microenvironments with unique nutrient and oxygen availability. Historically, enhanced glycolysis, even in the presence of oxygen (the Warburg effect) has dominated glioblastoma research with respect to tumor metabolism. More recent global profiling experiments, however, have identified roles for lipid, amino acid, and nucleotide metabolism in tumor growth and invasion. A thorough understanding of the metabolic traits that define invasive GBM cells may provide novel therapeutic targets for this devastating disease. In this review, we focus on metabolic alterations that have been characterized in glioblastoma, the dynamic nature of tumor metabolism and how it is shaped by interaction with the brain microenvironment, and how metabolic reprogramming generates vulnerabilities that may be ripe for exploitation.

Current Perspective on the Natural Compounds and Drug Delivery Techniques in Glioblastoma Multiforme

Simple Summary

Glioblastoma multiforme (GBM) is one of the belligerent neoplasia that metastasize to other brain regions and invade nearby healthy tissues. However, the treatments available are associated with some limitations, such as high variations in solid tumors and deregulation of multiple cellular pathways. The heterogeneity of the GBM tumor and its aggressive infiltration into the nearby tissues makes it difficult to treat. Hence, the development of multimodality therapy that can be more effective, novel, with fewer side effects, improving the prognosis for GBM is highly desired. This review evaluated the use of natural phytoconstituents as an alternative for the development of a new therapeutic strategy. The key aspects of GBM and the potential of drug delivery techniques were also assessed, for tumor site delivery with limited side-effects. These efforts will help to provide better therapeutic options to combat GBM in future.

Abstract

Glioblastoma multiforme (GBM) is one of the debilitating brain tumors, being associated with extremely poor prognosis and short median patient survival. GBM is associated with complex pathogenesis with alterations in various cellular signaling events, that participate in cell proliferation and survival. The impairment in cellular redox pathways leads to tumorigenesis. The current standard pharmacological regimen available for glioblastomas, such as radiotherapy and surgical resection following treatment with chemotherapeutic drug temozolomide, remains fatal, due to drug resistance, metastasis and tumor recurrence. Thus, the demand for an effective therapeutic strategy for GBM remains elusive. Hopefully, novel products from natural compounds are suggested as possible solutions. They protect glial cells by reducing oxidative stress and neuroinflammation, inhibiting proliferation, inducing apoptosis, inhibiting pro-oncogene events and intensifying the potent anti-tumor therapies. Targeting aberrant cellular pathways in the amelioration of GBM could promote the development of new therapeutic options that improve patient quality of life and extend survival. Consequently, our review emphasizes several natural compounds in GBM treatment. We also assessed the potential of drug delivery techniques such as nanoparticles, Gliadel wafers and drug delivery using cellular carriers which could lead to a novel path for the obliteration of GBM.

Brain lipidomics as a rising field in neurodegenerative contexts: Perspectives with Machine Learning approaches

Lipids are essential for cellular functioning considering their role in membrane composition, signaling, and energy metabolism. The brain is the second most abundant organ in terms of lipid concentration and diversity only after adipose tissue. However, in the central system (CNS) lipid dysregulation has been linked to the etiology, progression, and severity of neurodegenerative diseases such as Alzheimeŕs, Parkinson, and Multiple Sclerosis. Advances in the human genome and subsequent sequencing technologies allowed us the study of lipidomics as a promising approach to diagnosis and treatment of neurodegeneration. Lipidomics advances rapidly increased the amount and quality of data allowing the integration with other omic types as well as implementing novel bioinformatic and quantitative tools such as machine learning (ML). Integration of lipidomics data with ML, as a powerful quantitative predictive approach, led to improvements in diagnostic biomarker prediction, clinical data integration, network, and systems approaches for neural behavior, novel etiology markers for inflammation, and neurodegeneration progression and even Mass Spectrometry image analysis. In this sense, by exploiting lipidomics data with ML is possible to improve the identification of new biomarkers or unveil new molecular mechanisms associated with lipid impairment across neurodegeneration. In this review, we present the lipidomic neurobiology state-of-the-art highlighting its potential applications to study neurodegenerative conditions. Also, we present theoretical background, applications, and advances in the integration of lipidomics with ML. This review opens the door to new approaches in this rising field.

Systematic investigation of mouse models of Parkinson's disease by transcriptome mapping on a brain-specific genome-scale metabolic network

Genome-scale metabolic networks enable systemic investigation of metabolic alterations caused by diseases by providing interpretation of omics data. Although Mus musculus (mouse) is one of the most commonly used model organisms for neurodegenerative diseases, a brain-specific metabolic network model of mice has not yet been reconstructed. Here we reconstructed the first brain-specific metabolic network model of mice, iBrain674-Mm, by a homology-based approach, which consisted of 992 reactions controlled by 674 genes and distributed over 48 pathways. We validated the newly reconstructed network model by showing that it predicts healthy resting-state metabolic phenotypes of mouse brain compatible with the literature. We later used iBrain674-Mm to interpret various experimental mouse models of Parkinson's Disease (PD) at the transcriptome level. To this end, we applied a constraint-based modelling based biomarker prediction method called TIMBR (Transcriptionally Inferred Metabolic Biomarker Response) to predict altered metabolite production from transcriptomic data. Systemic analysis of seven different PD mouse models by TIMBR showed that the neuronal levels of glutamate, lactate, creatine phosphate, neuronal acetylcholine, bilirubin and formate increased in most of the PD mouse models, whereas the levels of melatonin, epinephrine, astrocytic formate and astrocytic bilirubin decreased. Although most of the predictions were consistent with the literature, there were some inconsistencies among different PD mouse models, signifying that there is no perfect experimental model to reflect PD metabolism. The newly reconstructed brain-specific genome-scale metabolic network model of mice can make important contributions to the interpretation and development of experimental mouse models of PD and other neurodegenerative diseases.

Towards the routine use of in silico screenings for drug discovery using metabolic modelling

Currently, the development of new effective drugs for cancer therapy is not only hindered by development costs, drug efficacy, and drug safety but also by the rapid occurrence of drug resistance in cancer. Hence, new tools are needed to study the underlying mechanisms in cancer. Here, we discuss the current use of metabolic modelling approaches to identify cancer-specific metabolism and find possible new drug targets and drugs for repurposing. Furthermore, we list valuable resources that are needed for the reconstruction of cancer-specific models by integrating various available datasets with genome-scale metabolic reconstructions using model-building algorithms. We also discuss how new drug targets can be determined by using gene essentiality analysis, an in silico method to predict essential genes in a given condition such as cancer and how synthetic lethality studies could greatly benefit cancer patients by suggesting drug combinations with reduced side effects.

A systems biology approach for studying neurodegenerative diseases

Neurodegenerative diseases (NDDs), such as Alzheimer's (AD) and Parkinson's (PD), are among the leading causes of lost years of healthy life and exert a great strain on public healthcare systems. Despite being first described more than a century ago, no effective cure exists for AD or PD. Although extensively characterised at the molecular level, traditional neurodegeneration research remains marred by narrow-sense approaches surrounding amyloid β (Aβ), tau, and α-synuclein (α-syn). A systems biology approach enables the integration of multi-omics data and informs discovery of biomarkers, drug targets, and treatment strategies. Here, we present a comprehensive timeline of high-throughput data collection, and associated biotechnological advancements and computational analysis related to AD and PD. We hereby propose that a philosophical change in the definitions of AD and PD is now needed.

Genome-Scale Metabolic Modeling of Glioblastoma Reveals Promising Targets for Drug Development

Glioblastoma (GBM) is an aggressive type of brain cancer with a poor prognosis for affected patients. The current line of treatment only gives the patients a survival time of on average 15 months. In this work, we use genome-scale metabolic models (GEMs) together with other systems biology tools to examine the global transcriptomics-data of GBM-patients obtained from The Cancer Genome Atlas (TCGA). We reveal the molecular mechanisms underlying GBM and identify potential therapeutic targets for effective treatment of patients. The work presented consists of two main parts. The first part stratifies the patients into two groups, high and low survival, and compares their gene expression. The second part uses GBM and healthy brain tissue GEMs to simulate gene knockout in a GBM cell model to find potential therapeutic targets and predict their side effect in healthy brain tissue. We (1) find that genes upregulated in the patients with low survival are linked to various stages of the glioma invasion process, and (2) identify five essential genes for GBM, whose inhibition is non-toxic to healthy brain tissue, therefore promising to investigate further as therapeutic targets.

Human Systems Biology and Metabolic Modelling: A Review-From Disease Metabolism to Precision Medicine

In cell and molecular biology, metabolism is the only system that can be fully simulated at genome scale. Metabolic systems biology offers powerful abstraction tools to simulate all known metabolic reactions in a cell, therefore providing a snapshot that is close to its observable phenotype. In this review, we cover the 15 years of human metabolic modelling. We show that, although the past five years have not experienced large improvements in the size of the gene and metabolite sets in human metabolic models, their accuracy is rapidly increasing. We also describe how condition-, tissue-, and patient-specific metabolic models shed light on cell-specific changes occurring in the metabolic network, therefore predicting biomarkers of disease metabolism. We finally discuss current challenges and future promising directions for this research field, including machine/deep learning and precision medicine. In the omics era, profiling patients and biological processes from a multiomic point of view is becoming more common and less expensive. Starting from multiomic data collected from patients and N-of-1 trials where individual patients constitute different case studies, methods for model-building and data integration are being used to generate patient-specific models. Coupled with state-of-the-art machine learning methods, this will allow characterizing each patient's disease phenotype and delivering precision medicine solutions, therefore leading to preventative medicine, reduced treatment, and in silico clinical trials.

Differential Metabolism of Medium-Chain Fatty Acids in Differentiated Human-Induced Pluripotent Stem Cell-Derived Astrocytes

Medium-chain triglyceride (MCT) ketogenic diets increase ketone bodies, which are believed to act as alternative energy substrates in the injured brain. Octanoic (C8:0) and decanoic (C10:0) acids, which produce ketone bodies through β-oxidation, are used as part of MCT ketogenic diets. Although the ketogenic role of MCT is well-established, it remains unclear how the network metabolism underlying β-oxidation of these medium-chain fatty acids (MCFA) differ. We aim to elucidate basal β-oxidation of these commonly used MCFA at the cellular level. Human-induced pluripotent stem cell-derived (iPSC) astrocytes were incubated with [U-¹³C]-C8:0 or [U-¹³C]-C10:0, and the fractional enrichments (FE) of the derivatives were used for metabolic flux analysis. Data indicate higher extracellular concentrations and faster secretion rates of β-hydroxybutyrate (βHB) and acetoacetate (AcAc) with C8:0 than C10:0, and an important contribution from unlabeled substrates. Flux analysis indicates opposite direction of metabolic flux between the MCFA intermediates C6:0 and C8:0, with an important contribution of unlabeled sources to the elongation in the C10:0 condition, suggesting different β-oxidation pathways. Finally, larger intracellular glutathione concentrations and secretions of 3-OH-C10:0 and C6:0 were measured in C10:0-treated astrocytes. These findings reveal MCFA-specific ketogenic properties. Our results provide insights into designing different MCT-based ketogenic diets to target specific health benefits.

Discovery of therapeutic agents for prostate cancer using genome-scale metabolic modeling and drug repositioning

BACKGROUND

Genome-scale metabolic models (GEMs) offer insights into cancer metabolism and have been used to identify potential biomarkers and drug targets. Drug repositioning is a time- and cost-effective method of drug discovery that can be applied together with GEMs for effective cancer treatment.

METHODS

In this study, we reconstruct a prostate cancer (PRAD)-specific GEM for exploring prostate cancer metabolism and also repurposing new therapeutic agents that can be used in development of effective cancer treatment. We integrate global gene expression profiling of cell lines with >1000 different drugs through the use of prostate cancer GEM and predict possible drug-gene interactions.

FINDINGS

We identify the key reactions with altered fluxes based on the gene expression changes and predict the potential drug effect in prostate cancer treatment. We find that sulfamethoxypyridazine, azlocillin, hydroflumethiazide, and ifenprodil can be repurposed for the treatment of prostate cancer based on an in silico cell viability assay. Finally, we validate the effect of ifenprodil using an in vitro cell assay and show its inhibitory effect on a prostate cancer cell line.

INTERPRETATION

Our approach demonstate how GEMs can be used to predict therapeutic agents for cancer treatment based on drug repositioning. Besides, it paved a way and shed a light on the applicability of computational models to real-world biomedical or pharmaceutical problems.

Mitochondrial energy metabolism and signalling in human glioblastoma cell lines with different PTEN gene status

Glioblastomas epidemiology and aggressiveness demand for a well characterization of biochemical mechanisms of the cells. The discovery of oxidative tumours related to chemoresistance is changing the prevalent view of dysfunctional mitochondria in cancer cells. Thus, glioblastomas metabolism is now an area of intense research, wherein was documented a high heterogeneity in energy metabolism and in particular in mitochondrial OxPhos. We report results gained by investigating mitochondrial OxPhos and bioenergetics, in a model of three human glioblastoma cell lines characterized by a different PTEN gene status. Functional data are analysed in relation to the expression levels of some main transcription factors and signalling proteins, which can be involved in the regulation of mitochondrial biogenesis and activity. Collectively, our observations indicate for the three cell lines a similar bioenergetic phenotype maintaining a certain degree of mitochondrial oxidative activity, with some difference for PTEN-wild type SF767 cells respect to PTEN-deleted A172 and U87MG characterized by a loss-of-function point mutation of PTEN. SF767 has lower ATP content and higher ADP/ATP ratio, higher AMPK activating-phosphorylation evoking energy impairment, higher OxPhos complexes and PGC1α-Sirt3-p53 protein abundance, in line with a higher respiration. Finally, SF767 shows a similar mitochondrial energy supply, but higher non-phosphorylating respiration linked to dissipation of protonmotive force. Intriguingly, it is now widely accepted that a regulated mitochondrial proton leak attenuate ROS generation and in tumours may be at the base of pro-survival advantage and chemoresistance.

Metabolic Reprogramming in Glioma

Many cancers have long been thought to primarily metabolize glucose for energy production—a phenomenon known as the Warburg Effect, after the classic studies of Otto Warburg in the early twentieth century. Yet cancer cells also utilize other substrates, such as amino acids and fatty acids, to produce raw materials for cellular maintenance and energetic currency to accomplish cellular tasks. The contribution of these substrates is increasingly appreciated in the context of glioma, the most common form of malignant brain tumor. Multiple catabolic pathways are used for energy production within glioma cells, and are linked in many ways to anabolic pathways supporting cellular function. For example: glycolysis both supports energy production and provides carbon skeletons for the synthesis of nucleic acids; meanwhile fatty acids are used both as energetic substrates and as raw materials for lipid membranes. Furthermore, bio-energetic pathways are connected to pro-oncogenic signaling within glioma cells. For example: AMPK signaling links catabolism with cell cycle progression; mTOR signaling contributes to metabolic flexibility and cancer cell survival; the electron transport chain produces ATP and reactive oxygen species (ROS) which act as signaling molecules; Hypoxia Inducible Factors (HIFs) mediate interactions with cells and vasculature within the tumor environment. Mutations in the tumor suppressor p53, and the tricarboxylic acid cycle enzymes Isocitrate Dehydrogenase 1 and 2 have been implicated in oncogenic signaling as well as establishing metabolic phenotypes in genetically-defined subsets of malignant glioma. These pathways critically contribute to tumor biology. The aim of this review is two-fold. Firstly, we present the current state of knowledge regarding the metabolic strategies employed by malignant glioma cells, including aerobic glycolysis; the pentose phosphate pathway; one-carbon metabolism; the tricarboxylic acid cycle, which is central to amino acid metabolism; oxidative phosphorylation; and fatty acid metabolism, which significantly contributes to energy production in glioma cells. Secondly, we highlight processes (including the Randle Effect, AMPK signaling, mTOR activation, etc.) which are understood to link bio-energetic pathways with oncogenic signals, thereby allowing the glioma cell to achieve a pro-malignant state.

Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network

Astrocytes are the most abundant cells of the central nervous system; they have a predominant role in maintaining brain metabolism. In this sense, abnormal metabolic states have been found in different neuropathological diseases. Determination of metabolic states of astrocytes is difficult to model using current experimental approaches given the high number of reactions and metabolites present. Thus, genome-scale metabolic networks derived from transcriptomic data can be used as a framework to elucidate how astrocytes modulate human brain metabolic states during normal conditions and in neurodegenerative diseases. We performed a Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network with the purpose of elucidating a significant portion of the metabolic map of the astrocyte. This is the first global high-quality, manually curated metabolic reconstruction network of a human astrocyte. It includes 5,007 metabolites and 5,659 reactions distributed among 8 cell compartments, (extracellular, cytoplasm, mitochondria, endoplasmic reticle, Golgi apparatus, lysosome, peroxisome and nucleus). Using the reconstructed network, the metabolic capabilities of human astrocytes were calculated and compared both in normal and ischemic conditions. We identified reactions activated in these two states, which can be useful for understanding the astrocytic pathways that are affected during brain disease. Additionally, we also showed that the obtained flux distributions in the model, are in accordance with literature-based findings. Up to date, this is the most complete representation of the human astrocyte in terms of inclusion of genes, proteins, reactions and metabolic pathways, being a useful guide for in-silico analysis of several metabolic behaviors of the astrocyte during normal and pathologic states.

Advances in the targeting of HIF-1α and future therapeutic strategies for glioblastoma multiforme (Review)

Cell metabolism can be reprogrammed by tissue hypoxia leading to cell transformation and glioblastoma multiforme (GBM) progression. In response to hypoxia, GBM cells are able to express a transcription factor called hypoxia inducible factor-1 (HIF-1). HIF-1 belongs to a family of heterodimeric proteins that includes HIF-1α and HIF-1β subunits. HIF-1α has been reported to play a pivotal role in GBM development and progression. In the present review, we discuss the role of HIF-1α in glucose uptake, cancer proliferation, cell mobility and chemoresistance in GBM. Evidence from previous studies indicates that HIF-1α regulates angiogenesis, metabolic and transcriptional signaling pathways. Examples of such are the EGFR, PI3K/Akt and MAPK/ERK pathways. It affects cell migration and invasion by regulating glucose metabolism and growth in GBM cells. The present review focuses on the strategies through which to target HIF-1α and the related downstream genes highlighting their regulatory roles in angiogenesis, apoptosis, migration and glucose metabolism for the development of future GBM therapeutics. Combined treatment with inhibitors of HIF-1α and glycolysis may enhance antitumor effects in clinical settings.