Aberrant glucose metabolism is one of the hallmarks of cancer that facilitates cancer cell survival and proliferation. Here, we demonstrate that MUC1, a large, type I transmembrane protein that is overexpressed in several carcinomas including pancreatic adenocarcinoma, modulates cancer cell metabolism to facilitate growth properties of cancer cells. MUC1 occupies the promoter elements of multiple genes directly involved in glucose metabolism and regulates their expression. Furthermore, MUC1 expression enhances glycolytic activity in pancreatic cancer cells. We also demonstrate that MUC1 expression enhances in vivo glucose uptake and expression of genes involved in glucose uptake and metabolism in orthotopic implantation models of pancreatic cancer. The MUC1 cytoplasmic tail is known to activate multiple signaling pathways through its interactions with several transcription factors/coregulators at the promoter elements of various genes. Our results indicate that MUC1 acts as a modulator of the hypoxic response in pancreatic cancer cells by regulating the expression/stability and activity of hypoxia-inducible factor-1α (HIF-1α). MUC1 physically interacts with HIF-1α and p300 and stabilizes the former at the protein level. By using a ChIP assay, we demonstrate that MUC1 facilitates recruitment of HIF-1α and p300 on glycolytic gene promoters in a hypoxia-dependent manner. Also, by metabolomic studies, we demonstrate that MUC1 regulates multiple metabolite intermediates in the glucose and amino acid metabolic pathways. Thus, our studies indicate that MUC1 acts as a master regulator of the metabolic program and facilitates metabolic alterations in the hypoxic environments that help tumor cells survive and proliferate under such conditions. cancer metabolism | glutamine accumulation | pentose phosphate pathway | 2-ketoglutarate M UC1, a type I transmembrane protein, plays a significant role in the progression of cancer, particularly pancreatic adenocarcinoma (1-4). Although expressed in the normal pancreas, its expression is elevated in pancreatic adenocarcinoma and its expression pattern changes from a strictly apical localization on normal polarized epithelial cells to a broad distribution across the cell surface membrane of nonpolarized tumor cells (2). This results in aberrant signaling that enhances tumor progression and metastasis. MUC1 protein is expressed in >90% of pancreatic tumors (5), and MUC1 expression in tumors and its serum levels are associated with a poor prognosis and recurrence in patients with resected tumors (6). Much of the oncogenic role of MUC1 can be attributed to the participation of the small, cytoplasmic tail of MUC1 (MUC1.CT) in signal transduction and transcriptional events (2). ChIP-chip analyses have demonstrated that MUC1 occupies a plethora of promoter elements in which MUC1 modulates the recruitment and activity of transcription factors, thus regulating transcription of the corresponding genes (7).Several studies have established a role for MUC1 in tumor growth, invasion and metastas...

This review discusses strategies for the identification of metabolites in complex biological mixtures, as encountered in metabolomics, which have emerged in the recent past. These include NMR database-assisted approaches for the identification of commonly known metabolites as well as novel combinations of NMR and MS analysis methods for the identification of unknown metabolites. The use of certain chemical additives to the NMR tube can permit identification of metabolites with specific physical chemical properties. A fundamental characteristic of all living systems is their extraordinarily high complexity at the molecular level [1,2]. This includes both large and small molecules, most of which are part of complex biochemical reaction networks [3][4][5]. The footprint of all small biological molecules, or metabolites, provides unique information about the state of a living organism, which is a prerequisite for the understanding of the activity of biochemical pathways and their consequences for homeostasis, health and disease, aging, as well as for elucidation of the effect of mutations and other biological, chemical or physical perturbations [6][7][8][9]. Over the past few years, the field of metabolomics (also referred to as 'metabonomics') has assumed a critical role in the comprehensive characterization of the metabolites of biological systems and their relationship to the biological state of an organism [10][11][12][13]. Specifically, metabolomics is providing new insights into the metabolite makeup of biofluids, such as serum and urine, cells, tissues and organs and their role in biochemical pathways [14][15][16]. Metabolomics allows the identification of biomarkers that are characteristic for particular phenotypes, such as a specific disease, even before the 'classical' symptoms occur [17][18][19]. Metabolomics also promises to be useful for monitoring the treatment of many different health conditions and opens up the prospect for new approaches to wellness and personalized medicine [20,21]. Therefore, the biomedical implications of metabolomics are of paramount significance and are expected to continue to rapidly grow in importance due to the high likelihood within this decade of routine applications for diagnosis and treatment of various conditions and diseases based on a wide range of metabolomics tools [22,23].MS and NMR spectroscopy are the two major experimental analysis techniques in metabolomics [24,25]. This is primarily because of the exceptional resolution power of both of these techniques to detect individual metabolites in complex mixtures while requiring little or no purification or physical separation of mixture components [26][27][28] H TOCSY trace displayed as green cross-section is extracted (upper panel). Next, its cross-peaks are queried against the database using the webserver [61]. The query correctly and exclusively assigned the trace to the nicotinamide ring portion of NADP + (see lower panel depicting a snapshot of the web server).future science groupEmerging new strategies for succ...

Identification of metabolites in complex mixtures represents a key step in metabolomics. A new strategy is introduced, which is implemented in a new public web server, COLMARm, that permits the co-analysis of up to three 2D NMR spectra, namely 13C-1H HSQC, 1H-1H TOCSY, and 13C-1H HSQC-TOCSY for the comprehensive, accurate, and efficient performance of this task. The highly versatile and interactive nature of COLMARm permits its application to a wide range of metabolomics samples independent of the magnetic field. Database query is performed using the HSQC spectrum and the top metabolite hits are then validated against the TOCSY-type experiment(s) by superimposing the expected cross-peaks on the mixture spectrum. In this way the user can directly accept or reject candidate metabolites by taking advantage of the complementary spectral information offered by these experiments and their different sensitivities. The power of COLMARm is demonstrated for a human serum sample uncovering the existence of 14 metabolites that hitherto were not identified by NMR.

Staphylococcus epidermidis is a skin-resident bacterium and a major cause of biomaterial-associated infections. The transition from residing on the skin to residing on an implanted biomaterial is accompanied by regulatory changes that facilitate bacterial survival in the new environment. These regulatory changes are dependent upon the ability of bacteria to "sense" environmental changes. In S. epidermidis, disparate environmental signals can affect synthesis of the biofilm matrix polysaccharide intercellular adhesin (PIA). Previously, we demonstrated that PIA biosynthesis is regulated by tricarboxylic acid (TCA) cycle activity. The observations that very different environmental signals result in a common phenotype (i.e. increased PIA synthesis) and that TCA cycle activity regulates PIA biosynthesis led us to hypothesize that S. epidermidis is "sensing" disparate environmental signals through the modulation of TCA cycle activity. In this study, we used NMR metabolomics to demonstrate that divergent environmental signals are transduced into common metabolomic changes that are "sensed" by metabolite-responsive regulators, such as CcpA, to affect PIA biosynthesis. These data clarify one mechanism by which very different environmental signals cause common phenotypic changes. In addition, due to the frequency of the TCA cycle in diverse genera of bacteria and the intrinsic properties of TCA cycle enzymes, it is likely the TCA cycle acts as a signal transduction pathway in many bacteria.Staphylococcus epidermidis is a skin-resident, opportunistic pathogen that is the leading cause of hospital-associated infections (1). Although the type and severity of diseases produced by this bacterium varies, its most common infectious manifestation is associated with implanted biomaterials. The dramatic environmental changes that occur during the transition from being skin-resident to residing on implanted biomaterials necessitates the need for changes in the expression of genes coding for enzymes required for growth in the new environment. This environmental adaptation often includes activating transcription of virulence genes; hence, most virulence genes are regulated by environmental and nutritional signals (2). Accordingly, a major area of interest in microbiology is determining how bacteria "sense" and respond to environmental signals. Given the tremendous diversity of microbial life, it is not surprising that the mechanisms bacteria employ are equally diverse. These mechanisms include two-component regulatory systems, alternative factors, mechanosensors, small RNAs, riboswitches, and many others. Although remarkable advances have been made in identifying the response regulators, our knowledge of signaling mechanisms has lagged behind, the exception being cell-density signaling.The tricarboxylic acid (TCA) cycle has been implicated as regulating or affecting staphylococcal virulence and/or virulence determinant biosynthesis (3-9). The TCA cycle has three primary functions: (i) to provide biosynthetic intermediates, (ii) to gen...

We previously hypothesized that Staphylococcus epidermidis senses a diverse set of environmental and nutritional factors associated with biofilm formation through a modulation in the activity of the tricarboxylic acid (TCA) cycle. Herein, we report our further investigation of the impact of additional environmental stress factors on TCA cycle activity and provide a detailed description of our NMR methodology. S. epidermidis wild-type strain 1457 was treated with stressors that are associated with biofilm formation, a sub-lethal dose of tetracycline, 5% NaCl, 2% glucose and autoinducer-2 (AI-2). As controls and to integrate our current data with our previous study, 4% ethanol stress and iron-limitation were also used. Consistent with our prior observations, the effect of many environmental stress factors on the S. epidermidis metabolome was essentially identical to the effect of TCA cycle inactivation in the aconitase mutant strain 1457-acnA::tetM. A detailed quantitative analysis of metabolite concentration changes using 2D 1H-13C HSQC and 1H-1H TOCSY spectra identified a network of 37 metabolites uniformly affected by the stressors and TCA cycle inactivation. We postulate that the TCA cycle acts as the central pathway in a metabolic signaling network.

Staphylococcus aureus is a prominent nosocomial pathogen and a major cause of biomaterial-associated infections. The success of S. aureus as a pathogen is due in part to its ability to adapt to stressful environments. As an example, the transition from residing in the nares to residing in the blood or deeper tissues is accompanied by changes in the availability of nutrients and elements such as oxygen and iron. As such, nutrients, oxygen, and iron are important determinants of virulence factor synthesis in S. aureus. In addition to influencing virulence factor synthesis, oxygen and iron are critical cofactors in enzymatic and electron transfer reactions; thus, a change in iron or oxygen availability alters the bacterial metabolome. Changes in metabolism create intracellular signals that alter the activity of metabolite-responsive regulators such as CodY, RpiRc, and CcpA. To assess the extent of metabolomic changes associated with oxygen and iron limitation, S. aureus cells were cultivated in iron-limited medium and/or with decreasing aeration, and the metabolomes were examined by nuclear magnetic resonance (NMR) spectroscopy. As expected, oxygen and iron limitation dramatically decreased tricarboxylic acid (TCA) cycle activity, creating a metabolic block and significantly altering the metabolome. These changes were most prominent during post-exponential-phase growth, when TCA cycle activity was maximal. Importantly, many of the effects of iron limitation were obscured by aeration limitation. Aeration limitation not only obscured the metabolic effects of iron limitation but also overrode the transcription of iron-regulated genes. Finally, in contrast to previous speculation, we confirmed that acidification of the culture medium occurs independent of the availability of iron.

Infectious diseases can be difficult to cure, especially if the pathogen forms a biofilm. After decades of extensive research into the morphology, physiology and genomics of biofilm formation, attention has recently been directed toward the analysis of the cellular metabolome in order to understand the transformation of a planktonic cell to a biofilm. Metabolomics can play an invaluable role in enhancing our understanding of the underlying biological processes related to the structure, formation and antibiotic resistance of biofilms. A systematic view of metabolic pathways or processes responsible for regulating this ‘social structure’ of microorganisms may provide critical insights into biofilm-related drug resistance and lead to novel treatments. This review will discuss the development of NMR-based metabolomics as a technology to study medically relevant biofilms. Recent advancements from case studies reviewed in this manuscript have shown the potential of metabolomics to shed light on numerous biological problems related to biofilms.

Large amounts of data from high throughput metabolomic experiments are commonly visualized using a principal component analysis (PCA) 2D scores plot. The question of the similarity or difference between multiple metabolic states then becomes a question of the degree of overlap between their respective data point clusters in PC scores space. A qualitative visual inspection of the clustering pattern in PCA score plots is a common protocol. This report describes the application of tree diagrams and bootstrapping techniques for an improved quantitative analysis of metabolic PCA data clustering. Our PCAtoTree program creates a distance matrix with 100 bootstrap steps that describes the separation of all clusters in a metabolic dataset. Using accepted phylogenetic software, the distance matrix resulting from the various metabolic states is organized into a phylogenetic-like tree format, where bootstrap values ≥ 50 indicate a statistically relevant branch separation. PCAtoTree analysis of two previously published data sets demonstrates the improved resolution of metabolic state differences using tree diagrams. In addition, for metabolomic studies of large numbers of different metabolic states, the tree format provides a better description of similarities and differences between each metabolic state. The approach is also tolerant of sample size variations between different metabolic states.