Metabolic cancer biology: structural-based analysis of cancer as a metabolic disease, new sights and opportunities for disease treatment

Immunometabolism in cancer: basic mechanisms and new targeting strategy

Maturing immunometabolic research empowers immune regulation novel approaches. Progressive metabolic adaptation of tumor cells permits a thriving tumor microenvironment (TME) in which immune cells always lose the initial killing capacity, which remains an unsolved dilemma even with the development of immune checkpoint therapies. In recent years, many studies on tumor immunometabolism have been reported. The development of immunometabolism may facilitate anti-tumor immunotherapy from the recurrent crosstalk between metabolism and immunity. Here, we discuss clinical studies of the core signaling pathways of immunometabolism and their inhibitors or agonists, as well as the specific functions of these pathways in regulating immunity and metabolism, and discuss some of the identified immunometabolic checkpoints. Understanding the comprehensive advances in immunometabolism helps to revise the status quo of cancer treatment. An overview of the new landscape of immunometabolism. The PI3K pathway promotes anabolism and inhibits catabolism. The LKB1 pathway inhibits anabolism and promotes catabolism. Overactivation of PI3K/AKT/mTOR pathway and IDO, IL4I1, ACAT, Sirt2, and MTHFD2 promote immunosuppression of TME formation, as evidenced by increased Treg and decreased T-cell proliferation. The LKBI-AMPK pathway promotes the differentiation of naive T cells to effector T cells and memory T cells and promotes anti-tumor immunity in DCs.

Collision cross section measurement and prediction methods in omics

Omics studies such as metabolomics, lipidomics, and proteomics have become important for understanding the mechanisms in living organisms. However, the compounds detected are structurally different and contain isomers, with each structure or isomer leading to a different result in terms of the role they play in the cell or tissue in the organism. Therefore, it is important to detect, characterize, and elucidate the structures of these compounds. Liquid chromatography and mass spectrometry have been utilized for decades in the structure elucidation of key compounds. While prediction models of parameters (such as retention time and fragmentation pattern) have also been developed for these separation techniques, they have some limitations. Moreover, ion mobility has become one of the most promising techniques to give a fingerprint to these compounds by determining their collision cross section (CCS) values, which reflect their shape and size. Obtaining accurate CCS enables its use as a filter for potential analyte structures. These CCS values can be measured experimentally using calibrant-independent and calibrant-dependent approaches. Identification of compounds based on experimental CCS values in untargeted analysis typically requires CCS references from standards, which are currently limited and, if available, would require a large amount of time for experimental measurements. Therefore, researchers use theoretical tools to predict CCS values for untargeted and targeted analysis. In this review, an overview of the different methods for the experimental and theoretical estimation of CCS values is given where theoretical prediction tools include computational and machine modeling type approaches. Moreover, the limitations of the current experimental and theoretical approaches and their potential mitigation methods were discussed.

Integrating a dynamic central metabolism model of cancer cells with a hybrid 3D multiscale model for vascular hepatocellular carcinoma growth

We develop here a novel modelling approach with the aim of closing the conceptual gap between tumour-level metabolic processes and the metabolic processes occurring in individual cancer cells. In particular, the metabolism in hepatocellular carcinoma derived cell lines (HEPG2 cells) has been well characterized but implementations of multiscale models integrating this known metabolism have not been previously reported. We therefore extend a previously published multiscale model of vascular tumour growth, and integrate it with an experimentally verified network of central metabolism in HEPG2 cells. This resultant combined model links spatially heterogeneous vascular tumour growth with known metabolic networks within tumour cells and accounts for blood flow, angiogenesis, vascular remodelling and nutrient/growth factor transport within a growing tumour, as well as the movement of, and interactions between normal and cancer cells. Model simulations report for the first time, predictions of spatially resolved time courses of core metabolites in HEPG2 cells. These simulations can be performed at a sufficient scale to incorporate clinically relevant features of different tumour systems using reasonable computational resources. Our results predict larger than expected temporal and spatial heterogeneity in the intracellular concentrations of glucose, oxygen, lactate pyruvate, f16bp and Acetyl-CoA. The integrated multiscale model developed here provides an ideal quantitative framework in which to study the relationship between dosage, timing, and scheduling of anti-neoplastic agents and the physiological effects of tumour metabolism at the cellular level. Such models, therefore, have the potential to inform treatment decisions when drug response is dependent on the metabolic state of individual cancer cells.

Emerging Roles of Energy Metabolism in Ferroptosis Regulation of Tumor Cells

Ferroptosis is a new form of regulated cell death, which is characterized by the iron-dependent accumulation of lethal lipid peroxides and involved in many critical diseases. Recent reports revealed that cellular energy metabolism activities such as glycolysis, pentose phosphate pathway (PPP), and tricarboxylic acid cycle are involved in the regulation of key ferroptosis markers such as reduced nicotinamide adenine dinucleotide phosphate (NADPH), glutathione (GSH), and reactive oxygen species (ROS), therefore imposing potential regulatory roles in ferroptosis. Remarkably, tumor cells can activate adaptive metabolic responses to inhibit ferroptosis for self-preservation such as the upregulation of glycolysis and PPP. Due to the rapid proliferation of tumor cells and the intensified metabolic rate, tumor energy metabolism has become a target for disrupting the redox homeostasis and induce ferroptosis. Based on these emerging insights, regulatory impact of those-tumor specific metabolic aberrations is systematically characterized, such as rewired glucose metabolism and metabolic compensation through glutamine utilization on ferroptosis and analyzed the underlying molecular mechanisms. Additionally, those ferroptosis-based therapeutic strategies are also discussed by exploiting those metabolic vulnerabilities, which may open up new avenues for tumor treatment in a clinical context.

Developing and studying the dynamical behavior of a nonlinear mathematical model for cancers with tumor by considering immune system role

We are constrained by widespread cancerous diseases to improve treatment methods which save patients and provide better living conditions during and after the treatment period. Because of the complexity of the treatment process, mathematical models need to be used in order to have a better understanding of the process. However, deriving an adequate complex model that can capture the disease pattern which could be confirmed by simulations and experiments has its own barriers. In this paper, a new mathematical model is developed concerning immune system effect on cancer. The model is introduced using nonlinear ordinary differential equations. Also, the qualitative behavior of the proposed system is studied in order to examine the extent of the model with respect to the nature of tumor evolution. Thus, number and status of equilibria points in line with the existence of limit cycles are obtained for sub-systems and the whole system. Meanwhile, possible bifurcations are mentioned, and the consequent evolutions are described. It is shown that the model conforms well to natural possibilities, cancer growth or remission. Thus, the model would be fit for further studies for prediction and contemplating treatment method, especially for immune stimulating drugs and immunotherapy.

Specific activation of glycolytic enzyme enolase 2 in BRAF V600E-mutated colorectal cancer

The BRAF V600E mutation occurs in approximately 10% of patients with metastatic colorectal cancer (CRC) and constitutes a distinct subtype of the disease with extremely poor prognosis. To address this refractory disease, we investigated the unique metabolic gene profile of BRAF V600E-mutated tumors via in silico analysis using a large-scale clinical database. We found that BRAF V600E-mutated tumors exhibited a specific metabolic gene expression signature, including some genes that are associated with poor prognosis in CRC. We discovered that BRAF V600E-mutated tumors expressed high levels of glycolytic enzyme enolase 2 (ENO2), which is mainly expressed in neuronal tissues under physiological conditions. In vitro experiments using CRC cells demonstrated that BRAF V600E-mutated cells exhibited enhanced dependency on ENO2 compared to BRAF wild-type cancer cells and that knockdown of ENO2 led to the inhibition of proliferation and migration of BRAF V600E-mutated cancer cells. Moreover, inhibition of ENO2 resulted in enhanced sensitivity to vemurafenib, a selective inhibitor of BRAF V600E. We identified AP-1 transcription factor subunit (FOSL1) as being involved in the transcription of ENO2 in CRC cells. In addition, both MAPK and PI3K/Akt signaling were suppressed upon inhibition of ENO2, implying an additional oncogenic role of ENO2. These results suggest the crucial role of ENO2 in the progression of BRAF V600E-mutated CRC and indicate the therapeutic implications of targeting this gene.

Omics Integration Analysis Unravel the Landscape of Driving Mechanisms of Colorectal Cancer

Colorectal cancer (CRC) is one of the most malignant cancers and results in a substantial rate of morbidity and mortality. Diagnosis of this malignancy in early stages increases the chance of effective treatment. High-throughput data analyses reveal omics signatures and also provide the possibility of developing computational models for early detection of this disease. Such models would be able to use as complementary tools for early detection of different types of cancers including CRC. In this study, using gene expression data, the Flux balance analysis (FBA) applied to decode metabolic fluxes in cancer and normal cells. Moreover, transcriptome and genome analyses revealed driver agents of CRC in a biological network scheme. By applying comprehensive publicly available data from TCGA, different aspect of CRC regulome including the regulatory effect of gene expression, methylation, microRNA, copy number aberration and point mutation profile over protein levels investigated and the results provide a regulatory picture underlying CRC. Compiling omics profiles indicated snapshots of changes in different omics levels and flux rate of CRC. In conclusion, considering obtained CRC signatures and their role in biological operating systems of cells, the results suggest reliable driver regulatory modules that could potentially serve as biomarkers and therapeutic targets and furthermore expand our understanding of driving mechanisms of this disease.
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Low Entropy Sub-Networks Prevent the Integration of Metabolomic and Transcriptomic Data

The constantly and rapidly increasing amount of the biological data gained from many different high-throughput experiments opens up new possibilities for data- and model-driven inference. Yet, alongside, emerges a problem of risks related to data integration techniques. The latter are not so widely taken account of. Especially, the approaches based on the flux balance analysis (FBA) are sensitive to the structure of a metabolic network for which the low-entropy clusters can prevent the inference from the activity of the metabolic reactions. In the following article, we set forth problems that may arise during the integration of metabolomic data with gene expression datasets. We analyze common pitfalls, provide their possible solutions, and exemplify them by a case study of the renal cell carcinoma (RCC). Using the proposed approach we provide a metabolic description of the known morphological RCC subtypes and suggest a possible existence of the poor-prognosis cluster of patients, which are commonly characterized by the low activity of the drug transporting enzymes crucial in the chemotherapy. This discovery suits and extends the already known poor-prognosis characteristics of RCC. Finally, the goal of this work is also to point out the problem that arises from the integration of high-throughput data with the inherently nonuniform, manually curated low-throughput data. In such cases, the over-represented information may potentially overshadow the non-trivial discoveries.

Detection of Barrett's oesophagus through exhaled breath using an electronic nose device

Timely detection of oesophageal adenocarcinoma (OAC) and even more so its precursor Barrett's oesophagus (BO) could contribute to decrease OAC incidence and mortality. An accurate, minimally-invasive screening method for BO for widespread use is currently not available. In a proof-of-principle study in 402 patients, we developed and cross-validated a BO prediction model using volatile organic compounds (VOCs) analysis with an electronic nose device. This electronic nose was able to distinguish between patients with and without BO with good diagnostic accuracy (sensitivity 91% specificity 74%) and seemed to be independent of proton pump inhibitor use, the presence of hiatal hernia, and reflux. This technique may enable an efficient, well-tolerated, and sensitive and specific screening method to select high-risk individuals to undergo upper endoscopy.

The high-resolution proteomic analysis of protein composition of rat spleen lymphocytes stimulated by Concanavalin A; a comparison with morphine-treated cells

Morphine- and Concanavalin A-induced changes of protein composition of rat spleen lymphocytes were determined by high-resolution proteomic analysis, gel-free, label-free quantification, MaxLFQ. Stimulation by Con A resulted in a major reorganization of spleen cell protein composition evidenced by increased expression level of 94 proteins; 101 proteins were down-regulated (>2-fold). Interestingly, among proteins that were up-regulated to the largest extent were the prototypical brain proteins as a neuron specific enolase, synapsin-1, brain acid-soluble protein-1 and myelin basic protein. Morphine-induced change was limited to no more than 5 up-regulated and 18 down-regulated proteins (>2-fold).

GSTZ1-1 downregulates Wnt/β-catenin signalling in hepatocellular carcinoma cells

Glutathione S-transferase Zeta 1-1 (GSTZ1-1), an enzyme involved in the catabolism of phenylalanine and the detoxification of xenobiotics, plays a tumour suppressor role in hepatocellular carcinoma (HCC), but the underlying mechanism remains largely unknown. Here, we further explored the function of GSTZ1-1 in HCC through transcriptome analysis by RNA sequencing. The analysis revealed that 223 genes were upregulated and 290 genes were downregulated in GSTZ1-1-overexpressing Huh7 cells. Gene Ontology analysis showed that these differentially expressed genes (DEGs) were highly enriched for protein phosphorylation, cell cycle arrest and metabolic processes. Pathway analysis revealed that metabolic pathways were the predominant enriched pathways among the upregulated genes, while the TGF-β and Wnt/β-catenin signalling pathways were prominent in the downregulated clusters. Pathway interaction networks also showed that the Wnt/β-catenin pathway was located in the centre of the cluster. The expression levels of selected DEGs were validated by qRT-PCR, and Wnt/β-catenin involvement was validated by luciferase assays, western blotting and immunohistochemical analysis in vitro and in vivo. These results provide a comprehensive overview of the transcriptome in GSTZ1-1-overexpressing Huh7 cells and indicate that GSTZ1-1 may play a tumour suppressor role by inactivating the Wnt/β-catenin signalling pathway.

Transcriptomic changes associated with PCK1 overexpression in hepatocellular carcinoma cells detected by RNA-seq

Phosphoenolpyruvate carboxykinase 1 (PCK1), a step limiting enzyme of gluconeogenesis, is downregulated in hepatocellular carcinoma (HCC). Overexpression of PCK1 has been shown to suppress hepatoma cell growth, but the underlying mechanism remains unclear. We used recombinant adenovirus overexpressing PCK1 or GFP in Huh7 cells, and the differentially expressed genes (DEGs) were identified by RNA-Seq. 180 were upregulated by PCK1 overexpression, whereas 316 were downregulated. Pathway analysis illustrated that PCK1 was closely correlated with Wnt signaling pathway and TGF-beta signaling pathway. Hence, Wnt signaling pathway and its downstream component, FZD2, FZD6, FZD7 and β-catenin were confirmed by qRT-PCR and Western blot. In vivo we also observed that PCK1 had restrained tumor growth as a result of decreasing expression of β-catenin. Whole-transcriptomic profile analysis discovered that overexpression of PCK1 downregulates several oncogenic signaling pathways in HCC, providing potential therapeutic targets for improving HCC therapy.

Nano-Sampling and Reporter Tools to Study Metabolic Regulation in Zebrafish

In the past years, evidence has emerged that hallmarks of human metabolic disorders can be recapitulated in zebrafish using genetic, pharmacological or dietary interventions. An advantage of modeling metabolic diseases in zebrafish compared to other "lower organisms" is the presence of a vertebrate body plan providing the possibility to study the tissue-intrinsic processes preceding the loss of metabolic homeostasis. While the small size of zebrafish is advantageous in many aspects, it also has shortcomings such as the difficulty to obtain sufficient amounts for biochemical analyses in response to metabolic challenges. A workshop at the European Zebrafish Principal Investigator meeting in Trento, Italy, was dedicated to discuss the advantages and disadvantages of zebrafish to study metabolic disorders. This perspective article by the participants highlights strategies to achieve improved tissue-resolution for read-outs using "nano-sampling" approaches for metabolomics as well as live imaging of zebrafish expressing fluorescent reporter tools that inform on cellular or subcellular metabolic processes. We provide several examples, including the use of reporter tools to study the heterogeneity of pancreatic beta-cells within their tissue environment. While limitations exist, we believe that with the advent of new technologies and more labs developing methods that can be applied to minimal amounts of tissue or single cells, zebrafish will further increase their utility to study energy metabolism.

Exploring candidate biomarkers for lung and prostate cancers using gene expression and flux variability analysis

Genome-scale metabolic models have provided valuable resources for exploring changes in metabolism under normal and cancer conditions. However, metabolism itself is strongly linked to gene expression, so integration of gene expression data into metabolic models might improve the detection of genes involved in the control of tumor progression. Herein, we considered gene expression data as extra constraints to enhance the predictive powers of metabolic models. We reconstructed genome-scale metabolic models for lung and prostate, under normal and cancer conditions to detect the major genes associated with critical subsystems during tumor development. Furthermore, we utilized gene expression data in combination with an information theory-based approach to reconstruct co-expression networks of the human lung and prostate in both cohorts. Our results revealed 19 genes as candidate biomarkers for lung and prostate cancer cells. This study also revealed that the development of a complementary approach (integration of gene expression and metabolic profiles) could lead to proposing novel biomarkers and suggesting renovated cancer treatment strategies which have not been possible to detect using either of the methods alone.

Screening for Barrett's Esophagus: Are New High-Volume Methods Feasible?

New improved methods are required for the early detection of esophageal adenocarcinoma in order to reduce mortality from this aggressive cancer. In this review we discuss different screening methods which are currently under evaluation ranging from image-based methods to cell collection devices coupled with biomarkers. As Barrett's esophagus is a low prevalence disease, potential screening tests must be applied to an enriched population to reduce the false-positive rate and improve the cost-effectiveness of the program.

A side-effect free method for identifying cancer drug targets

Identifying effective drug targets, with little or no side effects, remains an ever challenging task. A potential pitfall of failing to uncover the correct drug targets, due to side effect of pleiotropic genes, might lead the potential drugs to be illicit and withdrawn. Simplifying disease complexity, for the investigation of the mechanistic aspects and identification of effective drug targets, have been done through several approaches of protein interactome analysis. Of these, centrality measures have always gained importance in identifying candidate drug targets. Here, we put forward an integrated method of analysing a complex network of cancer and depict the importance of k-core, functional connectivity and centrality (KFC) for identifying effective drug targets. Essentially, we have extracted the proteins involved in the pathways leading to cancer from the pathway databases which enlist real experimental datasets. The interactions between these proteins were mapped to build an interactome. Integrative analyses of the interactome enabled us to unearth plausible reasons for drugs being rendered withdrawn, thereby giving future scope to pharmaceutical industries to potentially avoid them (e.g. ESR1, HDAC2, F2, PLG, PPARA, RXRA, etc). Based upon our KFC criteria, we have shortlisted ten proteins (GRB2, FYN, PIK3R1, CBL, JAK2, LCK, LYN, SYK, JAK1 and SOCS3) as effective candidates for drug development.

Development of a Reference Image Collection Library for Histopathology Image Processing, Analysis and Decision Support Systems Research

Histopathology image processing, analysis and computer-aided diagnosis have been shown as effective assisting tools towards reliable and intra-/inter-observer invariant decisions in traditional pathology. Especially for cancer patients, decisions need to be as accurate as possible in order to increase the probability of optimal treatment planning. In this study, we propose a new image collection library (HICL-Histology Image Collection Library) comprising 3831 histological images of three different diseases, for fostering research in histopathology image processing, analysis and computer-aided diagnosis. Raw data comprised 93, 116 and 55 cases of brain, breast and laryngeal cancer respectively collected from the archives of the University Hospital of Patras, Greece. The 3831 images were generated from the most representative regions of the pathology, specified by an experienced histopathologist. The HICL Image Collection is free for access under an academic license at http://medisp.bme.teiath.gr/hicl/ . Potential exploitations of the proposed library may span over a board spectrum, such as in image processing to improve visualization, in segmentation for nuclei detection, in decision support systems for second opinion consultations, in statistical analysis for investigation of potential correlations between clinical annotations and imaging findings and, generally, in fostering research on histopathology image processing and analysis. To the best of our knowledge, the HICL constitutes the first attempt towards creation of a reference image collection library in the field of traditional histopathology, publicly and freely available to the scientific community.

LY294002 inhibits the Warburg effect in gastric cancer cells by downregulating pyruvate kinase M2

The ‘Warburg effect’ is considered a vital hallmark of cancer cells, characterized by an altered metabolism, in which cells rely on aerobic glycolysis. As a key enzyme of aerobic glycolysis, pyruvate kinase M2 (PKM2) serves a crucial role in tumorigenesis. Accumulating studies have indicated that PKM2 is a potential target for cancer therapy. The aim of the present study was to assess the anticancer effects of LY294002, a specific phosphatidylinositol-3-kinase inhibitor, on gastric cancer (GC) cells and further explore its possible mechanism in vitro. The present study revealed that LY294002 inhibited GC cell proliferation, induced early apoptosis and significantly decreased lactate dehydrogenase activity and lactate production, in part through inhibiting PKM2 expression. In summary, LY294002 exhibits anticancer effects on GC, partly via the downregulation of PKM2.

Effects of targeting SLC1A5 on inhibiting gastric cancer growth and tumor development in vitro and in vivo

Aims

To investigate the oncogenic effects of SLC1A5 on gastric cancer development in vitro and in vivo.

Methods

The expression level of SLC1A5 was detected in 70 gastric cancer paraffin-embedded tissues by immunohistochemistry and also was detected in gastric cancer cell lines by qRT-PCR and western blotting analysis. The effects of knockdown SLC1A5 were analyzed on cell proliferation, cell cycle, the ability of cell migration and invasion and growth signaling pathway in vitro. By using subcutaneous xenograft mouse, the importance of SLC1A5 expression was assessed for both successful engraftment and growth of gastric cancer cells in vivo.

Results

SLC1A5 was up-regulated in gastric cancer tissues and was correlated with malignant features such as deeper local invasion, higher lymph node metastasis, advanced TNM stages and higher Ki-67 expression. Knockdown SLC1A5 in gastric cancer cells suppressed cell proliferation, caused G0/G1 arrest and inhibited cell invasion as well as migration partly by inactivated mTOR/p-70S6K1 signaling pathway in vitro. Furthermore, in vivo experiments indicated that suppression of SLC1A5 could inhibit relative volume of xenografted tumor.

Conclusions

Our results suggested that SLC1A5 might be considered as a new biomarker and also as a potential therapeutic target in gastric cancer.

Unravelling the complexity of signalling networks in cancer: A review of the increasing role for computational modelling

Cancer induction is a highly complex process involving hundreds of different inducers but whose eventual outcome is the same. Clearly, it is essential to understand how signalling pathways and networks generated by these inducers interact to regulate cell behaviour and create the cancer phenotype. While enormous strides have been made in identifying key networking profiles, the amount of data generated far exceeds our ability to understand how it all "fits together". The number of potential interactions is astronomically large and requires novel approaches and extreme computation methods to dissect them out. However, such methodologies have high intrinsic mathematical and conceptual content which is difficult to follow. This review explains how computation modelling is progressively finding solutions and also revealing unexpected and unpredictable nano-scale molecular behaviours extremely relevant to how signalling and networking are coherently integrated. It is divided into linked sections illustrated by numerous figures from the literature describing different approaches and offering visual portrayals of networking and major conceptual advances in the field. First, the problem of signalling complexity and data collection is illustrated for only a small selection of known oncogenes. Next, new concepts from biophysics, molecular behaviours, kinetics, organisation at the nano level and predictive models are presented. These areas include: visual representations of networking, Energy Landscapes and energy transfer/dissemination (entropy); diffusion, percolation; molecular crowding; protein allostery; quinary structure and fractal distributions; energy management, metabolism and re-examination of the Warburg effect. The importance of unravelling complex network interactions is then illustrated for some widely-used drugs in cancer therapy whose interactions are very extensive. Finally, use of computational modelling to develop micro- and nano- functional models ("bottom-up" research) is highlighted. The review concludes that computational modelling is an essential part of cancer research and is vital to understanding network formation and molecular behaviours that are associated with it. Its role is increasingly essential because it is unravelling the huge complexity of cancer induction otherwise unattainable by any other approach.

Energy-based analysis of biomolecular pathways

Decomposition of biomolecular reaction networks into pathways is a powerful approach to the analysis of metabolic and signalling networks. Current approaches based on analysis of the stoichiometric matrix reveal information about steady-state mass flows (reaction rates) through the network. In this work, we show how pathway analysis of biomolecular networks can be extended using an energy-based approach to provide information about energy flows through the network. This energy-based approach is developed using the engineering-inspired bond graph methodology to represent biomolecular reaction networks. The approach is introduced using glycolysis as an exemplar; and is then applied to analyse the efficiency of free energy transduction in a biomolecular cycle model of a transporter protein [sodium-glucose transport protein 1 (SGLT1)]. The overall aim of our work is to present a framework for modelling and analysis of biomolecular reactions and processes which considers energy flows and losses as well as mass transport.

ATP citrate lyase is increased in human breast cancer, depletion of which promotes apoptosis

Breast cancer is a malignant tumor that is harmful to women’s health around the world. Investigating the biological mechanism is, therefore, of pivotal importance to improve patients’ prognoses. Compared to non-neoplastic tissues, enhanced glucose and lipid metabolism is one of the most common properties of malignant breast cancer. Adenosine triphosphate (ATP) citrate lyase is a key enzyme linking aerobic glycolysis and fatty acid synthesis and is of high biological and prognostic significance in breast cancer. In our clinical study, fresh clinical tissues were used to analyze ATP citrate lyase expression by western blotting, and paraffin archived samples from 62 breast cancer patients were used to analyze ATP citrate lyase expression by immunohistochemistry. In the cellular study, following small interfering RNA–mediated inhibition of ATP citrate lyase in MCF-7 cells, cell viability and apoptosis were measured using the Cell Counting Kit-8 and flow cytometry, respectively. Breast cancer tissues showed strong expression of ATP citrate lyase, whereas adjacent normal tissues showed weak expression. Silencing of endogenous ATP citrate lyase expression by small interfering RNA in MCF-7 cells suppressed cell viability and increased cell apoptosis. Collectively, our study revealed that expression of ATP citrate lyase was significantly increased in breast cancer tissue compared with normal tissue. In addition, we found that depletion of ATP citrate lyase suppressed tumor growth, which suggests that ATP citrate lyase–related inhibitors might be potential therapeutic approaches for breast cancer.

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.

Modular bond-graph modelling and analysis of biomolecular systems

Bond graphs can be used to build thermodynamically-compliant hierarchical models of biomolecular systems. As bond graphs have been widely used to model, analyse and synthesise engineering systems, this study suggests that they can play the same rôle in the modelling, analysis and synthesis of biomolecular systems. The particular structure of bond graphs arising from biomolecular systems is established and used to elucidate the relation between thermodynamically closed and open systems. Block diagram representations of the dynamics implied by these bond graphs are used to reveal implicit feedback structures and are linearised to allow the application of control-theoretical methods. Two concepts of modularity are examined: computational modularity where physical correctness is retained and behavioural modularity where module behaviour (such as ultrasensitivity) is retained. As well as providing computational modularity, bond graphs provide a natural formulation of behavioural modularity and reveal the sources of retroactivity. A bond graph approach to reducing retroactivity, and thus inter-module interaction, is shown to require a power supply such as that provided by the ATP ⇌ ADP + Pi reaction. The mitogen-activated protein kinase cascade (Raf-MEK-ERK pathway) is used as an illustrative example.

Epithelial Mesenchymal Transition Induces Aberrant Glycosylation through Hexosamine Biosynthetic Pathway Activation

Deregulated cellular metabolism is a hallmark of tumors. Cancer cells increase glucose and glutamine flux to provide energy needs and macromolecular synthesis demands. Several studies have been focused on the importance of glycolysis and pentose phosphate pathway. However, a neglected but very important branch of glucose metabolism is the hexosamine biosynthesis pathway (HBP). The HBP is a branch of the glucose metabolic pathway that consumes ∼2-5% of the total glucose, generating UDP-GlcNAc as the end product. UDP-GlcNAc is the donor substrate used in multiple glycosylation reactions. Thus, HBP links the altered metabolism with aberrant glycosylation providing a mechanism for cancer cells to sense and respond to microenvironment changes. Here, we investigate the changes of glucose metabolism during epithelial mesenchymal transition (EMT) and the role of O-GlcNAcylation in this process. We show that A549 cells increase glucose uptake during EMT, but instead of increasing the glycolysis and pentose phosphate pathway, the glucose is shunted through the HBP. The activation of HBP induces an aberrant cell surface glycosylation and O-GlcNAcylation. The cell surface glycans display an increase of sialylation α2-6, poly-LacNAc, and fucosylation, all known epitopes found in different tumor models. In addition, modulation of O-GlcNAc levels was demonstrated to be important during the EMT process. Taken together, our results indicate that EMT is an applicable model to study metabolic and glycophenotype changes during carcinogenesis, suggesting that cell glycosylation senses metabolic changes and modulates cell plasticity.

Gamma-enolase: a well-known tumour marker, with a less-known role in cancer

Background

Gamma-enolase, known also as neuron-specific enolase (NSE), is an enzyme of the glycolytic pathway, which is expressed predominantly in neurons and cells of the neuroendocrine system. As a tumour marker it is used in diagnosis and prognosis of cancer; however, the mechanisms enrolling it in malignant progression remain elusive. As a cytoplasmic enzyme gamma-enolase is involved in increased aerobic glycolysis, the main source of energy in cancer cells, supporting cell proliferation. However, different cellular localisation at pathophysiological conditions, proposes other cellular engagements.

Conclusions

The C-terminal part of the molecule, which is not related to glycolytic pathway, was shown to promote survival of neuronal cells by regulating neuronal growth factor receptor dependent signalling pathways, resulting also in extensive actin cytoskeleton remodelling. This additional function could be important also in cancer cells either to protect cells from stressful conditions and therapeutic agents or to promote tumour cell migration and invasion. Gamma-enolase might therefore have a multifunctional role in cancer progression: it supports increased tumour cell metabolic demands, protects tumour cells from stressful conditions and promotes their invasion and migration.

Autofluorescence spectroscopy and imaging: a tool for biomedical research and diagnosis

Native fluorescence, or autofluorescence (AF), consists in the emission of light in the UV-visible, near-IR spectral range when biological substrates are excited with light at suitable wavelength. This is a well-known phenomenon, and the strict relationship of many endogenous fluorophores with morphofunctional properties of the living systems, influencing their AF emission features, offers an extremely powerful resource for directly monitoring the biological substrate condition. Starting from the last century, the technological progresses in microscopy and spectrofluorometry were convoying attention of the scientific community to this phenomenon. In the future, the interest in the autofluorescence will certainly continue. Current instrumentation and analytical procedures will likely be overcome by the unceasing progress in new devices for AF detection and data interpretation, while a progress is expected in the search and characterization of endogenous fluorophores and their roles as intrinsic biomarkers.

Oxidative stress and mitochondrial dysfunction across broad-ranging pathologies: toward mitochondria-targeted clinical strategies

Beyond the disorders recognized as mitochondrial diseases, abnormalities in function and/or ultrastructure of mitochondria have been reported in several unrelated pathologies. These encompass ageing, malformations, and a number of genetic or acquired diseases, as diabetes and cardiologic, haematologic, organ-specific (e.g., eye or liver), neurologic and psychiatric, autoimmune, and dermatologic disorders. The mechanistic grounds for mitochondrial dysfunction (MDF) along with the occurrence of oxidative stress (OS) have been investigated within the pathogenesis of individual disorders or in groups of interrelated disorders. We attempt to review broad-ranging pathologies that involve mitochondrial-specific deficiencies or rely on cytosol-derived prooxidant states or on autoimmune-induced mitochondrial damage. The established knowledge in these subjects warrants studies aimed at elucidating several open questions that are highlighted in the present review. The relevance of OS and MDF in different pathologies may establish the grounds for chemoprevention trials aimed at compensating OS/MDF by means of antioxidants and mitochondrial nutrients.

Genome scale modeling in systems biology: algorithms and resources

In recent years, in silico studies and trial simulations have complemented experimental procedures. A model is a description of a system, and a system is any collection of interrelated objects; an object, moreover, is some elemental unit upon which observations can be made but whose internal structure either does not exist or is ignored. Therefore, any network analysis approach is critical for successful quantitative modeling of biological systems. This review highlights some of most popular and important modeling algorithms, tools, and emerging standards for representing, simulating and analyzing cellular networks in five sections. Also, we try to show these concepts by means of simple example and proper images and graphs. Overall, systems biology aims for a holistic description and understanding of biological processes by an integration of analytical experimental approaches along with synthetic computational models. In fact, biological networks have been developed as a platform for integrating information from high to low-throughput experiments for the analysis of biological systems. We provide an overview of all processes used in modeling and simulating biological networks in such a way that they can become easily understandable for researchers with both biological and mathematical backgrounds. Consequently, given the complexity of generated experimental data and cellular networks, it is no surprise that researchers have turned to computer simulation and the development of more theory-based approaches to augment and assist in the development of a fully quantitative understanding of cellular dynamics.