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Plasma from patients with Parkinson's disease (PD) is a valuable source of information indicating altered metabolites associated with the risk or progression of the disease. Neurotoxicity of dopaminergic neurons, which is triggered by aggregation of α-synuclein, is the main pathogenic feature of PD. However, a growing body of scientific reports indicates that metabolic changes may precede and directly contribute to neurodegeneration. Identification and characterization of the abnormal metabolic pattern in patients' plasma are therefore crucial for the search for potential PD biomarkers. The aims of the present study were (1) to identify metabolic alterations in plasma metabolome in subjects with PD as compared with the controls; (2) to find new potential markers, some correlations among them; (3) to identify metabolic pathways relevant to the pathophysiology of PD. Plasma samples from patients with PD (n = 25) and control group (n = 12) were collected and the gas chromatography-time-of-flight-mass spectrometry GC-TOFMS-based metabolomics approach was used to evaluate the metabolic changes based on the identified 14 metabolites with significantly altered levels using univariate and multivariate statistical analysis. The panel, including 6 metabolites (L-3-methoxytyrosine, aconitic acid, L-methionine, 13-docosenamide, hippuric acid, 9,12-octadecadienoic acid), was identified to discriminate PD from controls with the area under the curve (AUC) of 0.975, with an accuracy of 92%. We also used statistical criteria to identify the significantly altered level of metabolites. The metabolic pathways involved were associated with linoleic acid metabolism, mitochondrial electron transport chain, glycerolipid metabolism, and bile acid biosynthesis. These abnormal metabolic changes in the plasma of patients with PD were mainly related to the amino acid metabolism, TCA cycle metabolism, and mitochondrial function. [ABSTRACT FROM AUTHOR]

Background: Parkinson's disease (PD) is a progressive, multi-focal neurodegenerative disease for which there is no effective disease modifying treatment. A critical requirement for designing successful clinical trials is the development of robust and reproducible biomarkers identifying PD in preclinical stages. Objective: To investigate the potential for a cluster of biomarkers visualized with multiple analytical platforms to provide a clinically useful tool. Methods: Gas Chromatography-Mass Spectrometry (GC-TOFMS) based metabolomics and immunoassay-based protein/peptide analyses on samples from patients with PD diagnosed in Northern Sweden. Low molecular weight compounds from both plasma and cerebrospinal fluid (CSF) from 20 healthy subjects (controls) and 20 PD patients at the time of diagnosis (baseline) were analyzed. Results: In plasma, we found a significant increase in several amino acids and a decrease in C16-C18 saturated and unsaturated fatty acids in patients as compared to control subjects. We also observed an increase in plasma levels of pyroglutamate and 2-oxoisocaproate (ketoleucine) that may be indicative of increased metabolic stress in patients. In CSF, there was a generally lower level of metabolites in PD as compared to controls, with a specific decrease in 3-hydroxyisovaleric acid, tryptophan and creatinine. Multivariate analysis and modeling of metabolites indicates that while the PD samples can be separated from control samples, the list of detected compounds will need to be expanded in order to define a robust predictive model. CSF biomarker immunoassays of candidate peptide/protein biomarkers revealed a significant decrease in the levels of Aβ-38 and Aβ-42, and an increase in soluble APPα in CSF of patients. Furthermore, these peptides showed significant correlations to each other, and positive correlations to the CSF levels of several 5- and 6-carbon sugars. However, combining these metabolites and proteins/peptides into a single model did not significantly improve the statistical analysis. Conclusions: Together, this metabolomics study has detected significant alterations in plasma and CSF levels of a cluster of amino acids, fatty acids and sugars based on clinical diagnosis and levels of known protein and peptide biomarkers. [ABSTRACT FROM AUTHOR]

A global metabolic profiling was generated with serum samples of patients with B-cell non-Hodgkin's lymphoma (NHL) and healthy controls using two different analytical platforms for metabonomics, UPLC-QTOFMS and GC-TOFMS, in conjunction with multivariate data analysis and ROC analysis. Significant difference in metabolic characteristics was observed between B-cell NHL and healthy control by OPLS-DA. A total of 37 differential metabolites for B-cell NHL were identified. Some significant changes in metabolites were detected, indicating that there were disturbances of key metabolic pathways, including bile acids, glycerophospholipids, fatty acids metabolism, steroid biosynthesis, glycolysis, as well as glycine, serine and threonine metabolism associated with B-cell NHL. A panel of metabolite markers composed of choline, arachidonic acid, LysoPC (17:0), PA (16:0/16:0) and coproporphyrin from UPLC-QTOFMS and another panel of markers composed of benzenebutanoic acid, β-hydroxypyruvic acid, D-2-hydroxyoctanoic acid, pyruvic acid and arachidonic acid derived from GC-TOFMS were selected. A ROC curve analysis of these markers resulted in an AUC of 0.968 and 1.00 for the UPLC-QTOFMS and GC-TOFMS analysis, respectively. These biochemical changes provide a novel molecular diagnostic approach which could be helpful to further understand the pathogenesis and identify the therapeutic target of B-cell NHL. [ABSTRACT FROM AUTHOR]

Medicinal plants have been used by indigenous people across the world for centuries to help individuals preserve their wellbeing and cure diseases. Annona muricata L. (Graviola) which is belonging to the Annonaceae family has been traditionally used due to its medicinal abilities including antimicrobial, anti-inflammatory, antioxidant and cancer cell growth inhibition. Graviola is claimed to be a potential antitumor due to its selective cytotoxicity against several cancer cell lines. However, the metabolic mechanism information underlying the anticancer activity remains limited. This study aimed to investigate the effect of ionic liquid-Graviola fruit pulp extract (IL-GPE) on the metabolomics behavior of colon cancer (HT29) by using an untargeted GC-TOFMS-based metabolic profiling. Multivariate data analysis was used to determine the metabolic profiling, and the ingenuity pathway analysis (IPA) was used to predict the altered canonical pathways after treating the HT29 cells with crude IL-GPE and Taxol (positive control). The principal components analysis (PCA) identified 44 metabolites with the most reliable factor loading, and the cluster analysis (CA) separated three groups of metabolites: metabolites specific to the non-treated HT29 cells, metabolites specific to the treated HT29 cells with the crude IL-GPE and metabolites specific to Taxol treatment. Pathway analysis of metabolomic profiles revealed an alteration of many metabolic pathways, including amino acid metabolism, aerobic glycolysis, urea cycle and ketone bodies metabolism that contribute to energy metabolism and cancer cell proliferation. The crude IL-GPE can be one of the promising anticancer agents due to its selective inhibition of energy metabolism and cancer cell proliferation. Image 1 • GC-TOFMS was successfully used to identify key metabolites in colon cancer HT29 cells treated with IL-GPE and Taxol. • The cluster analysis (CA) separated three groups of metabolites: the non-treated cells, the treated cells with the crude IL-GPE and Taxol. • Pathway analysis revealed an alteration by IL-GPE in metabolic pathway that contribute to energy metabolism, and cancer cell proliferation. • The IL-GPE treatment also altered amino acid metabolism, aerobic glycolysis, urea cycle and ketone bodies metabolism. [ABSTRACT FROM AUTHOR]