The potential of urinary metabolites for diagnosing multiple sclerosis

A definitive diagnostic test for multiple sclerosis (MS) does not exist; instead physicians use a combination of medical history, magnetic resonance imaging, and cerebrospinal fluid analysis (CSF). Significant effort has been employed to identify biomarkers from CSF to facilitate MS diagnosis; however none of the proposed biomarkers have been successful to date. Urine is a proven source of metabolite biomarkers and has the potential to be a rapid, non-invasive, inexpensive, and efficient diagnostic tool for various human diseases. Nevertheless, urinary metabolites have not been extensively explored as a source of biomarkers for MS. Instead, we demonstrate that urinary metabolites have significant promise for monitoring disease-progression, and response to treatment in MS patients. NMR analysis of urine permitted the identification of metabolites that differentiate experimental autoimmune encephalomyelitis (EAE)-mice (prototypic disease model for MS) from healthy and MS drug-treated EAE mice

MUC1 mucin stabilizes and activates hypoxia-inducible factor 1 alpha to regulate metabolism in pancreatic cancer

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

The potential of urinary metabolites for diagnosing multiple sclerosis

A definitive diagnostic test for multiple sclerosis (MS) does not exist; instead physicians use a combination of medical history, magnetic resonance imaging, and cerebrospinal fluid analysis (CSF). Significant effort has been employed to identify biomarkers from CSF to facilitate MS diagnosis; however none of the proposed biomarkers have been successful to date. Urine is a proven source of metabolite biomarkers and has the potential to be a rapid, non-invasive, inexpensive, and efficient diagnostic tool for various human diseases. Nevertheless, urinary metabolites have not been extensively explored as a source of biomarkers for MS. Instead, we demonstrate that urinary metabolites have significant promise for monitoring disease-progression, and response to treatment in MS patients. NMR analysis of urine permitted the identification of metabolites that differentiate experimental autoimmune encephalomyelitis (EAE)-mice (prototypic disease model for MS) from healthy and MS drug-treated EAE mice

MUC1 mucin stabilizes and activates hypoxia-inducible factor 1 alpha to regulate metabolism in pancreatic cancer

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

Explicit–implicit mapping approach to nonlinear blind separation of sparse nonnegative dependent sources from a single mixture: pure component extraction from nonlinear mixture mass spectra

The nonlinear, nonnegative single-mixture blind source separation (BSS) problem consists of decomposing observed nonlinearly mixed multicomponent signal into nonnegative dependent component (source) signals. The problem is difficult and is a special case of the underdetermined BSS problem. However, it is practically relevant for the contemporary metabolic profiling of biological samples when only one sample is available for acquiring mass spectra ; afterwards, the pure components are extracted. Herein, we present a method for the blind separation of nonnegative dependent sources from a single, nonlinear mixture. First, an explicit feature map is used to map a single mixture into a pseudo multi-mixture. Second, an empirical kernel map is used for implicit mapping of a pseudo multi-mixture into a high-dimensional reproducible kernel Hilbert space (RKHS). Under sparse probabilistic conditions that were previously imposed on sources, the single-mixture nonlinear problem is converted into an equivalent linear, multiple-mixture problem that consists of the original sources and their higher order monomials. These monomials are suppressed by robust principal component analysis, hard-, soft- and trimmed thresholding. Sparseness constrained nonnegative matrix factorizations in RKHS yield sets of separated components. Afterwards, separated components are annotated with the pure components from the library using the maximal correlation criterion. The proposed method is depicted with a numerical example that is related to the extraction of 8 dependent components from 1 nonlinear mixture. The method is further demonstrated on 3 nonlinear chemical reactions of peptide synthesis in which 25, 19 and 28 dependent analytes are extracted from 1 nonlinear mixture mass spectra. The goal application of the proposed method is, in combination with other separation techniques, mass spectrometry-based non-targeted metabolic profiling, such as biomarker identification studies

Application of NMR metabolomics for human disease

Metabolomics is the measurement and analysis of metabolite perturbations in a biological system caused by disease, medication or environmental stress. Metabolites are more proximal to a phenotype or disease than either genetic or proteomics information. As a result, metabolomics gives a very immediate chemical interpretation of the phenotypical variations. NMR based metabolomics approach has a great potential to study disease pathogenesis and monitor treatments, both in pre-clinical and clinical settings. This dissertation describes the application of NMR metabolomics in two human diseases: multiple sclerosis and pancreatic cancer. The first part of the dissertation describes the work done on Multiple sclerosis (MS). MS is a very challenging disease to diagnose where misdiagnosis and late diagnosis are common. Currently the investigation of MS metabolite biomarkers has primarily focused on the analysis of cerebrospinal fluid and serum. Little attention has been given to the analysis of urine. In an effort to address this gap, NMR based metabolomics was employed to find urinary markers of MS. Initially a pre-clinical study was conducted using the MS mouse model. The result demonstrated that urine metabolites could be used to differentiate diseased and healthy animals. Later on a proof of concept clinical study was conducted and the result demonstrated the potential of NMR metabolomics in MS diagnosis. The second part of the dissertation is about the work done on pancreatic cancer. Pancreatic cancer is the fourth leading cause of cancer related mortalities in the United States. It has a very low 5% five-year survival rate. In this dissertation NMR metabolomics approach was applied to study pancreatic cancer metabolism. Specifically, the approach was used to discover metabolic responses of MUC1 overexpressing pancreatic cancer cells, to understand the chemotherapeutic response of gemcitabine resistance, and study the metabolic impact of ketone body treatment in pancreatic cancer. The results metabolically characterize MUC1 overexpressing pancreatic cancer cells, highlight potential therapeutic targets to enhance gemcitabine therapeutic response, and elaborate the metabolite response of ketone bodies treatment in pancreatic cancer

BIOMARKERS USED TO DETECT AND MONITOR NEUROLOGICAL AUTOIMMUNE DISEASES

Biomarkers of neurological autoimmune diseases are described, and methods of using such biomarkers also are described. Autoimmune diseases arise from an inappropriate immune response by the body against Substances or tissues normally found in the body. Neurological autoimmune diseases are those autoimmune diseases that affect some aspect of the neurological system (e.g., the central nervous system or the peripheral nervous system). Biomarkers of one or more neu rological autoimmune diseases, especially those biomarkers that can be evaluated non-invasively, are useful in the art. Biomarkers of neurological autoimmune diseases are pro vided, and methods of using Such biomarkers also are pro vided. In one aspect, a method of determining if a patient is Suffering from, or is at risk of Suffering from, a neurological autoimmune disease is provided. Such a method includes collecting a urine sample from a patient; and determining the levels of one or more biomarkers in the patient’s urine, wherein the one or more biomarkers are selected from the group consisting of 3-ureidopropionic acid, guanidinoacetate and indoxyl sulfate. Generally, an increase in the level of the one or more biomarkers in the patient’s urine is indicative of the presence of a neurological autoimmune disease in the patient. A representative neurological autoimmune disease is multiple Sclerosis. In some embodiments, the biomarkers are 3-ureidopropi onic acid and guanidinoacetate. In some embodiments, the biomarkers are 3-ureidopropionic acid and indoxyl Sulfate. In Some embodiments, the biomarkers are guanidinoacetate and indoxyl Sulfate. In some embodiments, the biomarkers are 3-ureidopropionic acid, guanidinoacetate and indoxyl Sul fate. The levels of the one or more biomarkers can be deter mined, for example, using an immunoassay, chromatography, spectroscopy or NMR

BIOMARKERS USED TO DETECT AND MONITOR NEUROLOGICAL AUTOIMMUNE DISEASES

Biomarkers of neurological autoimmune diseases are described, and methods of using such biomarkers also are described. Autoimmune diseases arise from an inappropriate immune response by the body against Substances or tissues normally found in the body. Neurological autoimmune diseases are those autoimmune diseases that affect some aspect of the neurological system (e.g., the central nervous system or the peripheral nervous system). Biomarkers of one or more neu rological autoimmune diseases, especially those biomarkers that can be evaluated non-invasively, are useful in the art. Biomarkers of neurological autoimmune diseases are pro vided, and methods of using Such biomarkers also are pro vided. In one aspect, a method of determining if a patient is Suffering from, or is at risk of Suffering from, a neurological autoimmune disease is provided. Such a method includes collecting a urine sample from a patient; and determining the levels of one or more biomarkers in the patient’s urine, wherein the one or more biomarkers are selected from the group consisting of 3-ureidopropionic acid, guanidinoacetate and indoxyl sulfate. Generally, an increase in the level of the one or more biomarkers in the patient’s urine is indicative of the presence of a neurological autoimmune disease in the patient. A representative neurological autoimmune disease is multiple Sclerosis. In some embodiments, the biomarkers are 3-ureidopropi onic acid and guanidinoacetate. In some embodiments, the biomarkers are 3-ureidopropionic acid and indoxyl Sulfate. In Some embodiments, the biomarkers are guanidinoacetate and indoxyl Sulfate. In some embodiments, the biomarkers are 3-ureidopropionic acid, guanidinoacetate and indoxyl Sul fate. The levels of the one or more biomarkers can be deter mined, for example, using an immunoassay, chromatography, spectroscopy or NMR

A Urinary Metabolic Signature for Multiple Sclerosis and Neuromyelitis Optica

Urine is a metabolite-rich biofluid that reflects the body’s effort to maintain chemical and osmotic homeostasis. Clinical diagnosis routinely relies on urine samples because the collection process is easy and noninvasive. Despite these advantages, urine is an under-investigated source of biomarkers for multiple sclerosis (MS). Nuclear magnetic resonance spectroscopy (NMR) has become a common approach for analyzing urinary metabolites for disease diagnosis and biomarker discovery. For illustration of the potential of urinary metabolites for diagnosing and treating MS patients, and for differentiating between MS and other illnesses, 38 urine samples were collected from healthy controls, MS patients, and neuromyelitis optica-spectrum disorder (NMO-SD) patients and analyzed with NMR, multivariate statistics, one-way ANOVA, and univariate statistics. Urine from MS patients exhibited a statistically distinct metabolic signature from healthy and NMO-SD controls. A total of 27 metabolites were differentially altered in the urine from MS and NMO-SD patients and were associated with synthesis and degradation of ketone bodies, amino acids, propionate and pyruvate metabolism, tricarboxylic acid cycle, and glycolysis. Metabolites altered in urine from MS patients were shown to be related to known pathogenic processes relevant to MS, including alterations in energy and fatty acid metabolism, mitochondrial activity, and the gut microbiota

Metabolic reprogramming induced by ketone bodies diminishes pancreatic cancer cachexia

Background: Aberrant energy metabolism is a hallmark of cancer. To fulfill the increased energy requirements, tumor cells secrete cytokines/factors inducing muscle and fat degradation in cancer patients, a condition known as cancer cachexia. It accounts for nearly 20% of all cancer-related deaths. However, the mechanistic basis of cancer cachexia and therapies targeting cancer cachexia thus far remain elusive. A ketogenic diet, a high-fat and low-carbohydrate diet that elevates circulating levels of ketone bodies (i.e., acetoacetate, β-hydroxybutyrate, and acetone), serves as an alternative energy source. It has also been proposed that a ketogenic diet leads to systemic metabolic changes. Keeping in view the significant role of metabolic alterations in cancer, we hypothesized that a ketogenic diet may diminish glycolytic flux in tumor cells to alleviate cachexia syndrome and, hence, may provide an efficient therapeutic strategy. Results: We observed reduced glycolytic flux in tumor cells upon treatment with ketone bodies. Ketone bodies also diminished glutamine uptake, overall ATP content, and survival in multiple pancreatic cancer cell lines, while inducing apoptosis. A decrease in levels of c-Myc, a metabolic master regulator, and its recruitment on glycolytic gene promoters, was in part responsible for the metabolic phenotype in tumor cells. Ketone body-induced intracellular metabolomic reprogramming in pancreatic cancer cells also leads to a significantly diminished cachexia in cell line models. Our mouse orthotopic xenograft models further confirmed the effect of a ketogenic diet in diminishing tumor growth and cachexia. Conclusions: Thus, our studies demonstrate that the cachectic phenotype is in part due to metabolic alterations in tumor cells, which can be reverted by a ketogenic diet, causing reduced tumor growth and inhibition of muscle and body weight loss