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Metabolites, Volume 7, Issue 2 (June 2017)

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Research

Jump to: Review, Other

Open AccessArticle Effects of Storage Time on Glycolysis in Donated Human Blood Units
Metabolites 2017, 7(2), 12; doi:10.3390/metabo7020012
Received: 28 December 2016 / Revised: 6 March 2017 / Accepted: 23 March 2017 / Published: 29 March 2017
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Abstract
Background: Donated blood is typically stored before transfusions. During storage, the metabolism of red blood cells changes, possibly causing storage lesions. The changes are storage time dependent and exhibit donor-specific variations. It is necessary to uncover and characterize the responsible molecular mechanisms
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Background: Donated blood is typically stored before transfusions. During storage, the metabolism of red blood cells changes, possibly causing storage lesions. The changes are storage time dependent and exhibit donor-specific variations. It is necessary to uncover and characterize the responsible molecular mechanisms accounting for such biochemical changes, qualitatively and quantitatively; Study Design and Methods: Based on the integration of metabolic time series data, kinetic models, and a stoichiometric model of the glycolytic pathway, a customized inference method was developed and used to quantify the dynamic changes in glycolytic fluxes during the storage of donated blood units. The method provides a proof of principle for the feasibility of inferences regarding flux characteristics from metabolomics data; Results: Several glycolytic reaction steps change substantially during storage time and vary among different fluxes and donors. The quantification of these storage time effects, which are possibly irreversible, allows for predictions of the transfusion outcome of individual blood units; Conclusion: The improved mechanistic understanding of blood storage, obtained from this computational study, may aid the identification of blood units that age quickly or more slowly during storage, and may ultimately improve transfusion management in clinics. Full article
(This article belongs to the Special Issue Metabolomics Modelling)
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Open AccessArticle Sample Preparation Strategies for the Effective Quantitation of Hydrophilic Metabolites in Serum by Multi-Targeted HILIC-MS/MS
Metabolites 2017, 7(2), 13; doi:10.3390/metabo7020013
Received: 30 December 2016 / Revised: 25 February 2017 / Accepted: 23 March 2017 / Published: 30 March 2017
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Abstract
The effect of endogenous interferences of serum in multi-targeted metabolite profiling HILIC-MS/MS analysis was investigated by studying different sample preparation procedures. A modified QuEChERS dispersive SPE protocol, a HybridSPE protocol, and a combination of liquid extraction with protein precipitation were compared to a
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The effect of endogenous interferences of serum in multi-targeted metabolite profiling HILIC-MS/MS analysis was investigated by studying different sample preparation procedures. A modified QuEChERS dispersive SPE protocol, a HybridSPE protocol, and a combination of liquid extraction with protein precipitation were compared to a simple protein precipitation. Evaluation of extraction efficiency and sample clean-up was performed for all methods. SPE sorbent materials tested were found to retain hydrophilic analytes together with endogenous interferences, thus additional elution steps were needed. Liquid extraction was not shown to minimise matrix effects. In general, it was observed that a balance should be reached in terms of recovery, efficient clean-up, and sample treatment time when a wide range of metabolites are analysed. A quick step for removing phospholipids prior to the determination of hydrophilic endogenous metabolites is required, however, based on the results from the applied methods, further studies are needed to achieve high recoveries for all metabolites. Full article
(This article belongs to the Special Issue Metabolomics 2016)
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Open AccessArticle Metabolomic Profiling of the Synergistic Effects of Melittin in Combination with Cisplatin on Ovarian Cancer Cells
Metabolites 2017, 7(2), 14; doi:10.3390/metabo7020014
Received: 17 March 2017 / Revised: 10 April 2017 / Accepted: 12 April 2017 / Published: 14 April 2017
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Abstract
Melittin, the main peptide present in bee venom, has been proposed as having potential for anticancer therapy; the addition of melittin to cisplatin, a first line treatment for ovarian cancer, may increase the therapeutic response in cancer treatment via synergy, resulting in improved
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Melittin, the main peptide present in bee venom, has been proposed as having potential for anticancer therapy; the addition of melittin to cisplatin, a first line treatment for ovarian cancer, may increase the therapeutic response in cancer treatment via synergy, resulting in improved tolerability, reduced relapse, and decreased drug resistance. Thus, this study was designed to compare the metabolomic effects of melittin in combination with cisplatin in cisplatin-sensitive (A2780) and resistant (A2780CR) ovarian cancer cells. Liquid chromatography (LC) coupled with mass spectrometry (MS) was applied to identify metabolic changes in A2780 (combination treatment 5 μg/mL melittin + 2 μg/mL cisplatin) and A2780CR (combination treatment 2 μg/mL melittin + 10 μg/mL cisplatin) cells. Principal components analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) multivariate data analysis models were produced using SIMCA-P software. All models displayed good separation between experimental groups and high-quality goodness of fit (R2) and goodness of prediction (Q2), respectively. The combination treatment induced significant changes in both cell lines involving reduction in the levels of metabolites in the tricarboxylic acid (TCA) cycle, oxidative phosphorylation, purine and pyrimidine metabolism, and the arginine/proline pathway. The combination of melittin with cisplatin that targets these pathways had a synergistic effect. The melittin-cisplatin combination had a stronger effect on the A2780 cell line in comparison with the A2780CR cell line. The metabolic effects of melittin and cisplatin in combination were very different from those of each agent alone. Full article
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Open AccessArticle Analysis of Sub-Lethal Toxicity of Perfluorooctane Sulfonate (PFOS) to Daphnia magna Using 1H Nuclear Magnetic Resonance-Based Metabolomics
Metabolites 2017, 7(2), 15; doi:10.3390/metabo7020015
Received: 24 February 2017 / Revised: 5 April 2017 / Accepted: 12 April 2017 / Published: 14 April 2017
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Abstract
1H nuclear magnetic resonance (NMR)-based metabolomics was used to characterize the response of Daphnia magna after sub-lethal exposure to perfluorooctane sulfonate (PFOS), a commonly found environmental pollutant in freshwater ecosystems. Principal component analysis (PCA) scores plots showed significant separation in the exposed
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1H nuclear magnetic resonance (NMR)-based metabolomics was used to characterize the response of Daphnia magna after sub-lethal exposure to perfluorooctane sulfonate (PFOS), a commonly found environmental pollutant in freshwater ecosystems. Principal component analysis (PCA) scores plots showed significant separation in the exposed samples relative to the controls. Partial least squares (PLS) regression analysis revealed a strong linear correlation between the overall metabolic response and PFOS exposure concentration. More detailed analysis showed that the toxic mode of action is metabolite-specific with some metabolites exhibiting a non-monotonic response with higher PFOS exposure concentrations. Our study indicates that PFOS exposure disrupts various energy metabolism pathways and also enhances protein degradation. Overall, we identified several metabolites that are sensitive to PFOS exposure and may be used as bioindicators of D. magna health. In addition, this study also highlights the important utility of environmental metabolomic methods when attempting to elucidate acute and sub-lethal pollutant stressors on keystone organisms such as D. magna. Full article
(This article belongs to the Special Issue Environmental Metabolomics)
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Open AccessArticle Microscale Quantitative Analysis of Polyhydroxybutyrate in Prokaryotes Using IDMS
Metabolites 2017, 7(2), 19; doi:10.3390/metabo7020019
Received: 22 March 2017 / Revised: 13 May 2017 / Accepted: 15 May 2017 / Published: 17 May 2017
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Abstract
Poly(3-hydroxybutyrate) (PHB) is an interesting biopolymer for replacing petroleum-based plastics, its biological production is performed in natural and engineered microorganisms. Current metabolic engineering approaches rely on high-throughput strain construction and screening. Analytical procedures have to be compatible with the small scale and speed
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Poly(3-hydroxybutyrate) (PHB) is an interesting biopolymer for replacing petroleum-based plastics, its biological production is performed in natural and engineered microorganisms. Current metabolic engineering approaches rely on high-throughput strain construction and screening. Analytical procedures have to be compatible with the small scale and speed of these approaches. Here, we present a method based on isotope dilution mass spectrometry (IDMS) and propanolysis extraction of poly(3-hydroxybutyrate) from an Escherichia coli strain engineered for PHB production. As internal standard (IS), we applied an uniformly labeled 13C-cell suspension, of an E. coli PHB producing strain, grown on U-13C-glucose as C-source. This internal 13C-PHB standard enables to quantify low concentrations of PHB (LOD of 0.01 µg/gCDW) from several micrograms of biomass. With this method, a technical reproducibility of about 1.8% relative standard deviation is achieved. Furthermore, the internal standard is robust towards different sample backgrounds and dilutions. The early addition of the internal standard also enables higher reproducibility and increases sensitivity and throughput by simplified sample preparation steps. Full article
(This article belongs to the Special Issue Isotope Guided Metabolomics and Flux Analysis)
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Open AccessArticle Metabolomics of Therapy Response in Preclinical Glioblastoma: A Multi-Slice MRSI-Based Volumetric Analysis for Noninvasive Assessment of Temozolomide Treatment
Metabolites 2017, 7(2), 20; doi:10.3390/metabo7020020
Received: 31 March 2017 / Revised: 30 April 2017 / Accepted: 15 May 2017 / Published: 18 May 2017
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Abstract
Glioblastoma (GBM) is the most common aggressive primary brain tumor in adults, with a short survival time even after aggressive therapy. Non-invasive surrogate biomarkers of therapy response may be relevant for improving patient survival. Previous work produced such biomarkers in preclinical GBM using
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Glioblastoma (GBM) is the most common aggressive primary brain tumor in adults, with a short survival time even after aggressive therapy. Non-invasive surrogate biomarkers of therapy response may be relevant for improving patient survival. Previous work produced such biomarkers in preclinical GBM using semi-supervised source extraction and single-slice Magnetic Resonance Spectroscopic Imaging (MRSI). Nevertheless, GBMs are heterogeneous and single-slice studies could prevent obtaining relevant information. The purpose of this work was to evaluate whether a multi-slice MRSI approach, acquiring consecutive grids across the tumor, is feasible for preclinical models and may produce additional insight into therapy response. Nosological images were analyzed pixel-by-pixel and a relative responding volume, the Tumor Responding Index (TRI), was defined to quantify response. Heterogeneous response levels were observed and treated animals were ascribed to three arbitrary predefined groups: high response (HR, n = 2), TRI = 68.2 ± 2.8%, intermediate response (IR, n = 6), TRI = 41.1 ± 4.2% and low response (LR, n = 2), TRI = 13.4 ± 14.3%, producing therapy response categorization which had not been fully registered in single-slice studies. Results agreed with the multi-slice approach being feasible and producing an inverse correlation between TRI and Ki67 immunostaining. Additionally, ca. 7-day oscillations of TRI were observed, suggesting that host immune system activation in response to treatment could contribute to the responding patterns detected. Full article
(This article belongs to the Special Issue Cancer Metabolism)
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Open AccessArticle A Novel Anti-Hepatitis C Virus and Antiproliferative Agent Alters Metabolic Networks in HepG2 and Hep3B Cells
Metabolites 2017, 7(2), 23; doi:10.3390/metabo7020023
Received: 12 April 2017 / Revised: 18 May 2017 / Accepted: 29 May 2017 / Published: 2 June 2017
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Abstract
A series of novel diflunisal hydrazide-hydrazones has been reported together with their anti-hepatitis C virus and antiproliferative activities in a number of human hepatoma cell lines. However, the mechanisms underlying the efficacy of these agents remain unclear. It was chosen to investigate the
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A series of novel diflunisal hydrazide-hydrazones has been reported together with their anti-hepatitis C virus and antiproliferative activities in a number of human hepatoma cell lines. However, the mechanisms underlying the efficacy of these agents remain unclear. It was chosen to investigate the lead diflunisal hydrazide-hydrazone, 2′,4′-difluoro-4-hydroxy-N′- [(pyridin-2-yl)methylidene]biphenyl-3-carbohydrazide (compound 3b), in two cultured human hepatoma cell lines—HepG2 and Hep3B—using a metabolomic protocol aimed at uncovering any effects of this agent on cellular metabolism. One sub-therapeutic concentration (2.5 μM) and one close to the IC50 for antimitotic effect (10 μM), after 72 h in cell culture, were chosen for both compound 3b and its inactive parent compound diflusinal as a control. A GCMS-based metabolomic investigation was performed on cell lysates after culture for 24 h. The intracellular levels of a total of 42 metabolites were found to be statistically significantly altered in either HepG2 or Hep3B cells, only eight of which were affected in both cell lines. It was concluded that compound 3b affected the following pathways—purine and pyrimidine catabolism, the glutathione cycle, and energy metabolism through glycolysis and the pentose phosphate pathway. Although the metabolomic findings occurred after 24 h in culture, significant cytotoxicity of compound 3b to both HepG2 and Hep3B cells at 10 μM were reported not to occur until 72 h in culture. These observations show that metabolomics can provide mechanistic insights into the efficacy of novel drug candidates prior to the appearance of their pharmacological effect. Full article
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Open AccessArticle Seasonal Variation of Triacylglycerol Profile of Bovine Milk
Metabolites 2017, 7(2), 24; doi:10.3390/metabo7020024
Received: 21 March 2017 / Revised: 26 May 2017 / Accepted: 30 May 2017 / Published: 2 June 2017
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Abstract
Milk contains 3–6% of fat, of which the dominant component is triacylglycerol (TAG). Over 100 TAG groups can be readily detected in any non-enriched milk sample by LC-MS; most TAG groups contain several isomers (TAG molecules with different fatty acid composition), which cannot
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Milk contains 3–6% of fat, of which the dominant component is triacylglycerol (TAG). Over 100 TAG groups can be readily detected in any non-enriched milk sample by LC-MS; most TAG groups contain several isomers (TAG molecules with different fatty acid composition), which cannot be fully resolved chromatographically by any single stationary phase. TAG profile of mature milk from 19 cows was surveyed in this study for eight consecutive months using RP-LC-Orbitrap MS. It was found that TAG profile of milk was not constant throughout the milking season and the seasonal pattern varied with TAG groups. The overall unsaturation level of TAG was stable from October 2013 to January 2014, decreased in February/March 2014 and then increased from April and peaked in May 2014. In addition to the seasonal fluctuation in TAG profile, the proportion of different isomeric species within a TAG group also changed substantially across seasons. However, the proportion of different positional isomers within a given TAG group does not seem to vary during the milking season. To our knowledge, this is the first report on the seasonal change of milk lipid at the TAG group and isomer level. Full article
(This article belongs to the Special Issue Selected Papers from the 3rd Australian Lipid Meeting)
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Open AccessArticle Effects of Obesity on Pro-Oxidative Conditions and DNA Damage in Liver of DMBA-Induced Mammary Carcinogenesis Models
Metabolites 2017, 7(2), 26; doi:10.3390/metabo7020026
Received: 8 May 2017 / Revised: 31 May 2017 / Accepted: 5 June 2017 / Published: 8 June 2017
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Abstract
The prevalence of the overweight and obesity is on the rise worldwide. Obesity can increase the risk of certain cancers and liver steatosis development. Previously, we reported that obesity increased liver steatosis in a mammary tumor model, but little is known about the
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The prevalence of the overweight and obesity is on the rise worldwide. Obesity can increase the risk of certain cancers and liver steatosis development. Previously, we reported that obesity increased liver steatosis in a mammary tumor model, but little is known about the effects of obesity in the liver in regard to global DNA methylation, DNA damage, and oxidative/nitrosative stress. Using a mammary tumor model, we investigated the effects of obesity on oxidative stress and DNA reaction. Five-week-old lean and obese female rats were used. At 50 days of age, all rats received 7,12-dimethylbenz(α)anthracene (DMBA) and were sacrificed 155 days later. HPLC with electrochemical and ultraviolet detection and LC-MS were used. Obesity caused higher (p < 0.0004) methionine levels, had no effect (p < 0.055) on SAM levels, caused lower (p < 0.0005) SAH levels, caused higher (p < 0.0005) SAM/SAH ratios, and increased (p < 0.02) global DNA methylation. Levels of free reduced GSH were not significantly lower (p < 0.08), but free oxidized GSSG was higher (p < 0.002) in obese rats. The GSH/GSSG ratio was lower (p < 0.0001), and oxidized guanosine was higher (p < 0.002) in DNA of obese rats compared to lean rats. Obesity caused significant oxidative/nitrosative stress, oxidative DNA damage, and change of DNA methylation pattern in the liver, and these changes may contribute to the development of liver steatosis in breast cancer models. Full article
(This article belongs to the Special Issue Metabolomic Studies in Metabolic Diseases)
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Open AccessArticle Furanoterpene Diversity and Variability in the Marine Sponge Spongia officinalis, from Untargeted LC–MS/MS Metabolomic Profiling to Furanolactam Derivatives
Metabolites 2017, 7(2), 27; doi:10.3390/metabo7020027
Received: 14 April 2017 / Revised: 23 May 2017 / Accepted: 6 June 2017 / Published: 13 June 2017
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Abstract
The Mediterranean marine sponge Spongia officinalis has been reported as a rich source of secondary metabolites and also as a bioindicator of water quality given its capacity to concentrate trace metals. In this study, we evaluated the chemical diversity within 30 S. officinalis
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The Mediterranean marine sponge Spongia officinalis has been reported as a rich source of secondary metabolites and also as a bioindicator of water quality given its capacity to concentrate trace metals. In this study, we evaluated the chemical diversity within 30 S. officinalis samples collected over three years at two sites differentially impacted by anthropogenic pollutants located near Marseille (South of France). Untargeted liquid chromatography—mass spectrometry (LC–MS) metabolomic profiling (C18 LC, ESI-Q-TOF MS) combined with XCMS Online data processing and multivariate statistical analysis revealed 297 peaks assigned to at least 86 compounds. The spatio-temporal metabolite variability was mainly attributed to variations in relative content of furanoterpene derivatives. This family was further characterized through LC–MS/MS analyses in positive and negative ion modes combined with molecular networking, together with a comprehensive NMR study of isolated representatives such as demethylfurospongin-4 and furospongin-1. The MS/MS and NMR spectroscopic data led to the identification of a new furanosesterterpene, furofficin (2), as well as two derivatives with a glycinyl lactam moiety, spongialactam A (12a) and B (12b). This study illustrates the potential of untargeted LC–MS metabolomics and molecular networking to discover new natural compounds even in an extensively studied organism such as S. officinalis. It also highlights the effect of anthropogenic pollution on the chemical profiles within the sponge. Full article
(This article belongs to the Special Issue Marine Metabolomics)
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Open AccessArticle Metabolomic Profiling of Bile Acids in Clinical and Experimental Samples of Alzheimer’s Disease
Metabolites 2017, 7(2), 28; doi:10.3390/metabo7020028
Received: 2 May 2017 / Revised: 11 June 2017 / Accepted: 14 June 2017 / Published: 17 June 2017
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Abstract
Certain endogenous bile acids have been proposed as potential therapies for ameliorating Alzheimer’s disease (AD) but their role, if any, in the pathophysiology of this disease is not currently known. Given recent evidence of bile acids having protective and anti-inflammatory effects on the
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Certain endogenous bile acids have been proposed as potential therapies for ameliorating Alzheimer’s disease (AD) but their role, if any, in the pathophysiology of this disease is not currently known. Given recent evidence of bile acids having protective and anti-inflammatory effects on the brain, it is important to establish how AD affects levels of endogenous bile acids. Using LC-MS/MS, this study profiled 22 bile acids in brain extracts and blood plasma from AD patients (n = 10) and age-matched control subjects (n = 10). In addition, we also profiled brain/plasma samples from APP/PS1 and WT mice (aged 6 and 12 months). In human plasma, we detected significantly lower cholic acid (CA, p = 0.03) in AD patients than age-matched control subjects. In APP/PS1 mouse plasma we detected higher CA (p = 0.05, 6 months) and lower hyodeoxycholic acid (p = 0.04, 12 months) than WT. In human brain with AD pathology (Braak stages V-VI) taurocholic acid (TCA) were significantly lower (p = 0.01) than age-matched control subjects. In APP/PS1 mice we detected higher brain lithocholic acid (p = 0.05) and lower tauromuricholic acid (TMCA; p = 0.05, 6 months). TMCA was also decreased (p = 0.002) in 12-month-old APP/PS1 mice along with 5 other acids: CA (p = 0.02), β-muricholic acid (p = 0.02), Ω-muricholic acid (p = 0.05), TCA (p = 0.04), and tauroursodeoxycholic acid (p = 0.02). The levels of bile acids are clearly disturbed during the development of AD pathology and, since some bile acids are being proposed as potential AD therapeutics, we demonstrate a method that can be used to support work to advance bile acid therapeutics. Full article
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Open AccessArticle Evaluation of Classifier Performance for Multiclass Phenotype Discrimination in Untargeted Metabolomics
Metabolites 2017, 7(2), 30; doi:10.3390/metabo7020030
Received: 22 May 2017 / Revised: 13 June 2017 / Accepted: 17 June 2017 / Published: 21 June 2017
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Abstract
Statistical classification is a critical component of utilizing metabolomics data for examining the molecular determinants of phenotypes. Despite this, a comprehensive and rigorous evaluation of the accuracy of classification techniques for phenotype discrimination given metabolomics data has not been conducted. We conducted such
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Statistical classification is a critical component of utilizing metabolomics data for examining the molecular determinants of phenotypes. Despite this, a comprehensive and rigorous evaluation of the accuracy of classification techniques for phenotype discrimination given metabolomics data has not been conducted. We conducted such an evaluation using both simulated and real metabolomics datasets, comparing Partial Least Squares-Discriminant Analysis (PLS-DA), Sparse PLS-DA, Random Forests, Support Vector Machines (SVM), Artificial Neural Network, k-Nearest Neighbors (k-NN), and Naïve Bayes classification techniques for discrimination. We evaluated the techniques on simulated data generated to mimic global untargeted metabolomics data by incorporating realistic block-wise correlation and partial correlation structures for mimicking the correlations and metabolite clustering generated by biological processes. Over the simulation studies, covariance structures, means, and effect sizes were stochastically varied to provide consistent estimates of classifier performance over a wide range of possible scenarios. The effects of the presence of non-normal error distributions, the introduction of biological and technical outliers, unbalanced phenotype allocation, missing values due to abundances below a limit of detection, and the effect of prior-significance filtering (dimension reduction) were evaluated via simulation. In each simulation, classifier parameters, such as the number of hidden nodes in a Neural Network, were optimized by cross-validation to minimize the probability of detecting spurious results due to poorly tuned classifiers. Classifier performance was then evaluated using real metabolomics datasets of varying sample medium, sample size, and experimental design. We report that in the most realistic simulation studies that incorporated non-normal error distributions, unbalanced phenotype allocation, outliers, missing values, and dimension reduction, classifier performance (least to greatest error) was ranked as follows: SVM, Random Forest, Naïve Bayes, sPLS-DA, Neural Networks, PLS-DA and k-NN classifiers. When non-normal error distributions were introduced, the performance of PLS-DA and k-NN classifiers deteriorated further relative to the remaining techniques. Over the real datasets, a trend of better performance of SVM and Random Forest classifier performance was observed. Full article
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Review

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Open AccessReview Mitochondrial Deficiencies in the Predisposition to Paraganglioma
Metabolites 2017, 7(2), 17; doi:10.3390/metabo7020017
Received: 30 March 2017 / Revised: 27 April 2017 / Accepted: 30 April 2017 / Published: 4 May 2017
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Abstract
Paragangliomas and pheochromocytomas are rare neuroendocrine tumours with a very strong genetic component. It is estimated that around 40% of all cases are caused by a germline mutation in one of the 13 predisposing genes identified so far. Half of these inherited cases
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Paragangliomas and pheochromocytomas are rare neuroendocrine tumours with a very strong genetic component. It is estimated that around 40% of all cases are caused by a germline mutation in one of the 13 predisposing genes identified so far. Half of these inherited cases are intriguingly caused by mutations in genes encoding tricarboxylic acid enzymes, namely SDHA, SDHB, SDHC, SDHD, and SDHAF2 genes, encoding succinate dehydrogenase and its assembly protein, FH encoding fumarate hydratase, and MDH2 encoding malate dehydrogenase. These mutations may also predispose to other type of cancers, such as renal cancer, leiomyomas, or gastro-intestinal stromal tumours. SDH, which is also the complex II of the oxidative respiratory chain, was the first mitochondrial enzyme to be identified having tumour suppressor functions, demonstrating that 80 years after his initial proposal, Otto Warburg may have actually been right when he hypothesized that low mitochondrial respiration was the origin of cancer. This review reports the current view on how such metabolic deficiencies may lead to cancer predisposition and shows that the recent data may lead to the development of innovative therapeutic strategies and establish precision medicine approaches for the management of patients affected by these rare diseases. Full article
(This article belongs to the Special Issue Cancer Metabolism)
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Open AccessReview Metabolic Portraits of Breast Cancer by HR MAS MR Spectroscopy of Intact Tissue Samples
Metabolites 2017, 7(2), 18; doi:10.3390/metabo7020018
Received: 18 March 2017 / Revised: 20 April 2017 / Accepted: 9 May 2017 / Published: 16 May 2017
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Abstract
Despite progress in early detection and therapeutic strategies, breast cancer remains the second leading cause of cancer-related death among women globally. Due to the heterogeneity and complexity of tumor biology, breast cancer patients with similar diagnosis might have different prognosis and response to
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Despite progress in early detection and therapeutic strategies, breast cancer remains the second leading cause of cancer-related death among women globally. Due to the heterogeneity and complexity of tumor biology, breast cancer patients with similar diagnosis might have different prognosis and response to treatment. Thus, deeper understanding of individual tumor properties is necessary. Cancer cells must be able to convert nutrients to biomass while maintaining energy production, which requires reprogramming of central metabolic processes in the cells. This phenomenon is increasingly recognized as a potential target for treatment, but also as a source for biomarkers that can be used for prognosis, risk stratification and therapy monitoring. Magnetic resonance (MR) metabolomics is a widely used approach in translational research, aiming to identify clinically relevant metabolic biomarkers or generate novel understanding of the molecular biology in tumors. Ex vivo proton high-resolution magic angle spinning (HR MAS) MR spectroscopy is widely used to study central metabolic processes in a non-destructive manner. Here we review the current status for HR MAS MR spectroscopy findings in breast cancer in relation to glucose, amino acid and choline metabolism. Full article
(This article belongs to the Special Issue Cancer Metabolism)
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Open AccessReview Regulation of Metabolic Activity by p53
Metabolites 2017, 7(2), 21; doi:10.3390/metabo7020021
Received: 7 April 2017 / Revised: 16 May 2017 / Accepted: 16 May 2017 / Published: 20 May 2017
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Abstract
Metabolic reprogramming in cancer cells is controlled by the activation of multiple oncogenic signalling pathways in order to promote macromolecule biosynthesis during rapid proliferation. Cancer cells also need to adapt their metabolism to survive and multiply under the metabolically compromised conditions provided by
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Metabolic reprogramming in cancer cells is controlled by the activation of multiple oncogenic signalling pathways in order to promote macromolecule biosynthesis during rapid proliferation. Cancer cells also need to adapt their metabolism to survive and multiply under the metabolically compromised conditions provided by the tumour microenvironment. The tumour suppressor p53 interacts with the metabolic network at multiple nodes, mostly to reduce anabolic metabolism and promote preservation of cellular energy under conditions of nutrient restriction. Inactivation of this tumour suppressor by deletion or mutation is a frequent event in human cancer. While loss of p53 function lifts an important barrier to cancer development by deleting cell cycle and apoptosis checkpoints, it also removes a crucial regulatory mechanism and can render cancer cells highly sensitive to metabolic perturbation. In this review, we will summarise the major concepts of metabolic regulation by p53 and explore how this knowledge can be used to selectively target p53 deficient cancer cells in the context of the tumour microenvironment. Full article
(This article belongs to the Special Issue Cancer Metabolism)
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Open AccessReview Metabolic Investigations of the Molecular Mechanisms Associated with Parkinson’s Disease
Metabolites 2017, 7(2), 22; doi:10.3390/metabo7020022
Received: 5 April 2017 / Revised: 16 May 2017 / Accepted: 16 May 2017 / Published: 24 May 2017
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Abstract
Parkinson’s disease (PD) is a neurodegenerative disorder characterized by fibrillar cytoplasmic aggregates of α-synuclein (i.e., Lewy bodies) and the associated loss of dopaminergic cells in the substantia nigra. Mutations in genes such as α-synuclein (SNCA) account for only 10% of PD
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Parkinson’s disease (PD) is a neurodegenerative disorder characterized by fibrillar cytoplasmic aggregates of α-synuclein (i.e., Lewy bodies) and the associated loss of dopaminergic cells in the substantia nigra. Mutations in genes such as α-synuclein (SNCA) account for only 10% of PD occurrences. Exposure to environmental toxicants including pesticides and metals (e.g., paraquat (PQ) and manganese (Mn)) is also recognized as an important PD risk factor. Thus, aging, genetic alterations, and environmental factors all contribute to the etiology of PD. In fact, both genetic and environmental factors are thought to interact in the promotion of idiopathic PD, but the mechanisms involved are still unclear. In this study, we summarize our findings to date regarding the toxic synergistic effect between α-synuclein and paraquat treatment. We identified an essential role for central carbon (glucose) metabolism in dopaminergic cell death induced by paraquat treatment that is enhanced by the overexpression of α-synuclein. PQ “hijacks” the pentose phosphate pathway (PPP) to increase NADPH reducing equivalents and stimulate paraquat redox cycling, oxidative stress, and cell death. PQ also stimulated an increase in glucose uptake, the translocation of glucose transporters to the plasma membrane, and AMP-activated protein kinase (AMPK) activation. The overexpression of α-synuclein further stimulated an increase in glucose uptake and AMPK activity, but impaired glucose metabolism, likely directing additional carbon to the PPP to supply paraquat redox cycling. Full article
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Open AccessReview Breast Tissue Metabolism by Magnetic Resonance Spectroscopy
Metabolites 2017, 7(2), 25; doi:10.3390/metabo7020025
Received: 13 April 2017 / Revised: 31 May 2017 / Accepted: 31 May 2017 / Published: 7 June 2017
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Abstract
Metabolic alterations are known to occur with oncogenesis and tumor progression. During malignant transformation, the metabolism of cells and tissues is altered. Cancer metabolism can be studied using advanced technologies that detect both metabolites and metabolic activities. Identification, characterization, and quantification of metabolites
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Metabolic alterations are known to occur with oncogenesis and tumor progression. During malignant transformation, the metabolism of cells and tissues is altered. Cancer metabolism can be studied using advanced technologies that detect both metabolites and metabolic activities. Identification, characterization, and quantification of metabolites (metabolomics) are important for metabolic analysis and are usually done by nuclear magnetic resonance (NMR) or by mass spectrometry. In contrast to the magnetic resonance imaging that is used to monitor the tumor morphology during progression of the disease and during therapy, in vivo NMR spectroscopy is used to study and monitor tumor metabolism of cells/tissues by detection of various biochemicals or metabolites involved in various metabolic pathways. Several in vivo, in vitro and ex vivo NMR studies using 1H and 31P magnetic resonance spectroscopy (MRS) nuclei have documented increased levels of total choline containing compounds, phosphomonoesters and phosphodiesters in human breast cancer tissues, which is indicative of altered choline and phospholipid metabolism. These levels get reversed with successful treatment. Another method that increases the sensitivity of substrate detection by using nuclear spin hyperpolarization of 13C-lableled substrates by dynamic nuclear polarization has revived a great interest in the study of cancer metabolism. This review discusses breast tissue metabolism studied by various NMR/MRS methods. Full article
(This article belongs to the Special Issue Cancer Metabolism)
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Open AccessReview Magnetic Resonance Spectroscopy for Detection of 2-Hydroxyglutarate as a Biomarker for IDH Mutation in Gliomas
Metabolites 2017, 7(2), 29; doi:10.3390/metabo7020029
Received: 12 April 2017 / Revised: 12 June 2017 / Accepted: 12 June 2017 / Published: 19 June 2017
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Abstract
Mutations in the isocitrate dehydrogenase (IDH)1/2 genes are highly prevalent in gliomas and have been suggested to play an important role in the development and progression of the disease. Tumours harbouring these mutations exhibit a significant alteration in their metabolism resulting in the
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Mutations in the isocitrate dehydrogenase (IDH)1/2 genes are highly prevalent in gliomas and have been suggested to play an important role in the development and progression of the disease. Tumours harbouring these mutations exhibit a significant alteration in their metabolism resulting in the aberrant accumulation of the oncometabolite 2-hydroxygluarate (2-HG). As well as being suggested to play an important role in tumour progression, 2-HG may serve as a surrogate indicator of IDH status through non-invasive detection using magnetic resonance spectroscopy (MRS). In this review, we describe the recent efforts in developing MRS methods for detection and quantification of 2-HG in vivo and provide an assessment of the role of the 2-HG in gliomagenesis and patient prognosis. Full article
(This article belongs to the Special Issue Cancer Metabolism)
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Open AccessMeeting Report Proceedings of 3rd Australian Lipid Meeting
Metabolites 2017, 7(2), 16; doi:10.3390/metabo7020016
Received: 15 April 2017 / Revised: 27 April 2017 / Accepted: 29 April 2017 / Published: 1 May 2017
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Abstract
More than 100 lipid researchers from across Australia participated in the 3rd Australian Lipid Meeting (ALM3), held on the 21st and 22nd of November 2016 in Melbourne, Australia.[...] Full article
(This article belongs to the Special Issue Selected Papers from the 3rd Australian Lipid Meeting)

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