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Metabolites, Volume 6, Issue 4 (December 2016)

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Research

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Open AccessFeature PaperArticle Optimized Method for Untargeted Metabolomics Analysis of MDA-MB-231 Breast Cancer Cells
Metabolites 2016, 6(4), 30; doi:10.3390/metabo6040030
Received: 31 August 2016 / Revised: 15 September 2016 / Accepted: 19 September 2016 / Published: 22 September 2016
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Abstract
Cancer cells often have dysregulated metabolism, which is largely characterized by the Warburg effect—an increase in glycolytic activity at the expense of oxidative phosphorylation—and increased glutamine utilization. Modern metabolomics tools offer an efficient means to investigate metabolism in cancer cells. Currently, a number
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Cancer cells often have dysregulated metabolism, which is largely characterized by the Warburg effect—an increase in glycolytic activity at the expense of oxidative phosphorylation—and increased glutamine utilization. Modern metabolomics tools offer an efficient means to investigate metabolism in cancer cells. Currently, a number of protocols have been described for harvesting adherent cells for metabolomics analysis, but the techniques vary greatly and they lack specificity to particular cancer cell lines with diverse metabolic and structural features. Here we present an optimized method for untargeted metabolomics characterization of MDA-MB-231 triple negative breast cancer cells, which are commonly used to study metastatic breast cancer. We found that an approach that extracted all metabolites in a single step within the culture dish optimally detected both polar and non-polar metabolite classes with higher relative abundance than methods that involved removal of cells from the dish. We show that this method is highly suited to diverse applications, including the characterization of central metabolic flux by stable isotope labelling and differential analysis of cells subjected to specific pharmacological interventions. Full article
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Open AccessArticle A Conversation on Data Mining Strategies in LC-MS Untargeted Metabolomics: Pre-Processing and Pre-Treatment Steps
Metabolites 2016, 6(4), 40; doi:10.3390/metabo6040040
Received: 15 September 2016 / Revised: 27 October 2016 / Accepted: 27 October 2016 / Published: 3 November 2016
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Abstract
Untargeted metabolomic studies generate information-rich, high-dimensional, and complex datasets that remain challenging to handle and fully exploit. Despite the remarkable progress in the development of tools and algorithms, the “exhaustive” extraction of information from these metabolomic datasets is still a non-trivial undertaking. A
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Untargeted metabolomic studies generate information-rich, high-dimensional, and complex datasets that remain challenging to handle and fully exploit. Despite the remarkable progress in the development of tools and algorithms, the “exhaustive” extraction of information from these metabolomic datasets is still a non-trivial undertaking. A conversation on data mining strategies for a maximal information extraction from metabolomic data is needed. Using a liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomic dataset, this study explored the influence of collection parameters in the data pre-processing step, scaling and data transformation on the statistical models generated, and feature selection, thereafter. Data obtained in positive mode generated from a LC-MS-based untargeted metabolomic study (sorghum plants responding dynamically to infection by a fungal pathogen) were used. Raw data were pre-processed with MarkerLynxTM software (Waters Corporation, Manchester, UK). Here, two parameters were varied: the intensity threshold (50–100 counts) and the mass tolerance (0.005–0.01 Da). After the pre-processing, the datasets were imported into SIMCA (Umetrics, Umea, Sweden) for more data cleaning and statistical modeling. In addition, different scaling (unit variance, Pareto, etc.) and data transformation (log and power) methods were explored. The results showed that the pre-processing parameters (or algorithms) influence the output dataset with regard to the number of defined features. Furthermore, the study demonstrates that the pre-treatment of data prior to statistical modeling affects the subspace approximation outcome: e.g., the amount of variation in X-data that the model can explain and predict. The pre-processing and pre-treatment steps subsequently influence the number of statistically significant extracted/selected features (variables). Thus, as informed by the results, to maximize the value of untargeted metabolomic data, understanding of the data structures and exploration of different algorithms and methods (at different steps of the data analysis pipeline) might be the best trade-off, currently, and possibly an epistemological imperative. Full article
(This article belongs to the Special Issue Bioinformatics and Data Analysis)
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Open AccessFeature PaperArticle Staphylococcus aureus Infection Reduces Nutrition Uptake and Nucleotide Biosynthesis in a Human Airway Epithelial Cell Line
Metabolites 2016, 6(4), 41; doi:10.3390/metabo6040041
Received: 6 October 2016 / Revised: 28 October 2016 / Accepted: 2 November 2016 / Published: 9 November 2016
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Abstract
The Gram positive opportunistic human pathogen Staphylococcus aureus induces a variety of diseases including pneumonia. S. aureus is the second most isolated pathogen in cystic fibrosis patients and accounts for a large proportion of nosocomial pneumonia. Inside the lung, the human airway epithelium
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The Gram positive opportunistic human pathogen Staphylococcus aureus induces a variety of diseases including pneumonia. S. aureus is the second most isolated pathogen in cystic fibrosis patients and accounts for a large proportion of nosocomial pneumonia. Inside the lung, the human airway epithelium is the first line in defence with regard to microbial recognition and clearance as well as regulation of the immune response. The metabolic host response is, however, yet unknown. To address the question of whether the infection alters the metabolome and metabolic activity of airway epithelial cells, we used a metabolomics approach. The nutrition uptake by the human airway epithelial cell line A549 was monitored over time by proton magnetic resonance spectroscopy (1H-NMR) and the intracellular metabolic fingerprints were investigated by gas chromatography and high performance liquid chromatography (GC-MS) and (HPLC-MS). To test the metabolic activity of the host cells, glutamine analogues and labelled precursors were applied after the infection. We found that A549 cells restrict uptake of essential nutrients from the medium after S. aureus infection. Moreover, the infection led to a shutdown of the purine and pyrimidine synthesis in the A549 host cell, whereas other metabolic routes such as the hexosamine biosynthesis pathway remained active. In summary, our data show that the infection with S. aureus negatively affects growth, alters the metabolic composition and specifically impacts the de novo nucleotide biosynthesis in this human airway epithelial cell model. Full article
(This article belongs to the Special Issue Carbon Metabolism)
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Open AccessArticle Metabolomics and Cheminformatics Analysis of Antifungal Function of Plant Metabolites
Metabolites 2016, 6(4), 31; doi:10.3390/metabo6040031
Received: 22 August 2016 / Revised: 26 September 2016 / Accepted: 27 September 2016 / Published: 30 September 2016
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Abstract
Fusarium head blight (FHB), primarily caused by Fusarium graminearum, is a devastating disease of wheat. Partial resistance to FHB of several wheat cultivars includes specific metabolic responses to inoculation. Previously published studies have determined major metabolic changes induced by pathogens in resistant
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Fusarium head blight (FHB), primarily caused by Fusarium graminearum, is a devastating disease of wheat. Partial resistance to FHB of several wheat cultivars includes specific metabolic responses to inoculation. Previously published studies have determined major metabolic changes induced by pathogens in resistant and susceptible plants. Functionality of the majority of these metabolites in resistance remains unknown. In this work we have made a compilation of all metabolites determined as selectively accumulated following FHB inoculation in resistant plants. Characteristics, as well as possible functions and targets of these metabolites, are investigated using cheminformatics approaches with focus on the likelihood of these metabolites acting as drug-like molecules against fungal pathogens. Results of computational analyses of binding properties of several representative metabolites to homology models of fungal proteins are presented. Theoretical analysis highlights the possibility for strong inhibitory activity of several metabolites against some major proteins in Fusarium graminearum, such as carbonic anhydrases and cytochrome P450s. Activity of several of these compounds has been experimentally confirmed in fungal growth inhibition assays. Analysis of anti-fungal properties of plant metabolites can lead to the development of more resistant wheat varieties while showing novel application of cheminformatics approaches in the analysis of plant/pathogen interactions. Full article
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Open AccessArticle A Simple Method for Measuring Carbon-13 Fatty Acid Enrichment in the Major Lipid Classes of Microalgae Using GC-MS
Metabolites 2016, 6(4), 42; doi:10.3390/metabo6040042
Received: 5 September 2016 / Revised: 29 October 2016 / Accepted: 7 November 2016 / Published: 11 November 2016
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Abstract
A simple method for tracing carbon fixation and lipid synthesis in microalgae was developed using a combination of solid-phase extraction (SPE) and negative ion chemical ionisation gas chromatography mass spectrometry (NCI-GC-MS). NCI-GC-MS is an extremely sensitive technique that can produce an unfragmented molecular
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A simple method for tracing carbon fixation and lipid synthesis in microalgae was developed using a combination of solid-phase extraction (SPE) and negative ion chemical ionisation gas chromatography mass spectrometry (NCI-GC-MS). NCI-GC-MS is an extremely sensitive technique that can produce an unfragmented molecular ion making this technique particularly useful for stable isotope enrichment studies. Derivatisation of fatty acids using pentafluorobenzyl bromide (PFBBr) allows the coupling of the high separation efficiency of GC and the measurement of unfragmented molecular ions for each of the fatty acids by single quadrupole MS. The key is that isotope spectra can be measured without interference from co-eluting fatty acids or other molecules. Pre-fractionation of lipid extracts by SPE allows the measurement of 13C isotope incorporation into the three main lipid classes (phospholipids, glycolipids, neutral lipids) in microalgae thus allowing the study of complex lipid biochemistry using relatively straightforward analytical technology. The high selectivity of GC is necessary as it allows the collection of mass spectra for individual fatty acids, including cis/trans isomers, of the PFB-derivatised fatty acids. The combination of solid-phase extraction and GC-MS enables the accurate determination of 13C incorporation into each lipid pool. Three solvent extraction protocols that are commonly used in lipidomics were also evaluated and are described here with regard to extraction efficiencies for lipid analysis in microalgae. Full article
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Open AccessArticle The Redox Status of Cancer Cells Supports Mechanisms behind the Warburg Effect
Metabolites 2016, 6(4), 33; doi:10.3390/metabo6040033
Received: 12 August 2016 / Accepted: 27 September 2016 / Published: 3 October 2016
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Abstract
To better understand the energetic status of proliferating cells, we have measured the intracellular pH (pHi) and concentrations of key metabolites, such as adenosine triphosphate (ATP), nicotinamide adenine dinucleotide (NAD), and nicotinamide adenine dinucleotide phosphate (NADP) in normal and cancer cells, extracted from
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To better understand the energetic status of proliferating cells, we have measured the intracellular pH (pHi) and concentrations of key metabolites, such as adenosine triphosphate (ATP), nicotinamide adenine dinucleotide (NAD), and nicotinamide adenine dinucleotide phosphate (NADP) in normal and cancer cells, extracted from fresh human colon tissues. Cells were sorted by elutriation and segregated in different phases of the cell cycle (G0/G1/S/G2/M) in order to study their redox (NAD, NADP) and bioenergetic (ATP, pHi) status. Our results show that the average ATP concentration over the cell cycle is higher and the pHi is globally more acidic in normal proliferating cells. The NAD+/NADH and NADP+/NADPH redox ratios are, respectively, five times and ten times higher in cancer cells compared to the normal cell population. These energetic differences in normal and cancer cells may explain the well-described mechanisms behind the Warburg effect. Oscillations in ATP concentration, pHi, NAD+/NADH, and NADP+/NADPH ratios over one cell cycle are reported and the hypothesis addressed. We also investigated the mitochondrial membrane potential (MMP) of human and mice normal and cancer cell lines. A drastic decrease of the MMP is reported in cancer cell lines compared to their normal counterparts. Altogether, these results strongly support the high throughput aerobic glycolysis, or Warburg effect, observed in cancer cells. Full article
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Open AccessArticle Detection of Volatile Metabolites Derived from Garlic (Allium sativum) in Human Urine
Metabolites 2016, 6(4), 43; doi:10.3390/metabo6040043
Received: 13 October 2016 / Revised: 23 November 2016 / Accepted: 28 November 2016 / Published: 1 December 2016
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Abstract
The metabolism and excretion of flavor constituents of garlic, a common plant used in flavoring foods and attributed with several health benefits, in humans is not fully understood. Likewise, the physiologically active principles of garlic have not been fully clarified to date. It
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The metabolism and excretion of flavor constituents of garlic, a common plant used in flavoring foods and attributed with several health benefits, in humans is not fully understood. Likewise, the physiologically active principles of garlic have not been fully clarified to date. It is possible that not only the parent compounds present in garlic but also its metabolites are responsible for the specific physiological properties of garlic, including its influence on the characteristic body odor signature of humans after garlic consumption. Accordingly, the aim of this study was to investigate potential garlic-derived metabolites in human urine. To this aim, 14 sets of urine samples were obtained from 12 volunteers, whereby each set comprised one sample that was collected prior to consumption of food-relevant concentrations of garlic, followed by five to eight subsequent samples after garlic consumption that covered a time interval of up to 26 h. The samples were analyzed chemo-analytically using gas chromatography-mass spectrometry/olfactometry (GC-MS/O), as well as sensorially by a trained human panel. The analyses revealed three different garlic-derived metabolites in urine, namely allyl methyl sulfide (AMS), allyl methyl sulfoxide (AMSO) and allyl methyl sulfone (AMSO2), confirming our previous findings on human milk metabolite composition. The excretion rates of these metabolites into urine were strongly time-dependent with distinct inter-individual differences. These findings indicate that the volatile odorant fraction of garlic is heavily biotransformed in humans, opening up a window into substance circulation within the human body with potential wider ramifications in view of physiological effects of this aromatic plant that is appreciated by humans in their daily diet. Full article
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Open AccessFeature PaperArticle Analysis of Mammalian Cell Proliferation and Macromolecule Synthesis Using Deuterated Water and Gas Chromatography-Mass Spectrometry
Metabolites 2016, 6(4), 34; doi:10.3390/metabo6040034
Received: 2 September 2016 / Revised: 10 October 2016 / Accepted: 10 October 2016 / Published: 13 October 2016
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Abstract
Deuterated water (2H2O), a stable isotopic tracer, provides a convenient and reliable way to label multiple cellular biomass components (macromolecules), thus permitting the calculation of their synthesis rates. Here, we have combined 2H2O labelling, GC-MS analysis
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Deuterated water (2H2O), a stable isotopic tracer, provides a convenient and reliable way to label multiple cellular biomass components (macromolecules), thus permitting the calculation of their synthesis rates. Here, we have combined 2H2O labelling, GC-MS analysis and a novel cell fractionation method to extract multiple biomass components (DNA, protein and lipids) from the one biological sample, thus permitting the simultaneous measurement of DNA (cell proliferation), protein and lipid synthesis rates. We have used this approach to characterize the turnover rates and metabolism of a panel of mammalian cells in vitro (muscle C2C12 and colon cancer cell lines). Our data show that in actively-proliferating cells, biomass synthesis rates are strongly linked to the rate of cell division. Furthermore, in both proliferating and non-proliferating cells, it is the lipid pool that undergoes the most rapid turnover when compared to DNA and protein. Finally, our data in human colon cancer cell lines reveal a marked heterogeneity in the reliance on the de novo lipogenic pathway, with the cells being dependent on both ‘self-made’ and exogenously-derived fatty acid. Full article
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Open AccessFeature PaperArticle Metabolomic Profiling of the Effects of Melittin on Cisplatin Resistant and Cisplatin Sensitive Ovarian Cancer Cells Using Mass Spectrometry and Biolog Microarray Technology
Metabolites 2016, 6(4), 35; doi:10.3390/metabo6040035
Received: 7 September 2016 / Revised: 10 October 2016 / Accepted: 11 October 2016 / Published: 13 October 2016
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Abstract
In the present study, liquid chromatography-mass spectrometry (LC-MS) was employed to characterise the metabolic profiles of two human ovarian cancer cell lines A2780 (cisplatin-sensitive) and A2780CR (cisplatin-resistant) in response to their exposure to melittin, a cytotoxic peptide from bee venom. In addition, the
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In the present study, liquid chromatography-mass spectrometry (LC-MS) was employed to characterise the metabolic profiles of two human ovarian cancer cell lines A2780 (cisplatin-sensitive) and A2780CR (cisplatin-resistant) in response to their exposure to melittin, a cytotoxic peptide from bee venom. In addition, the metabolomics data were supported by application of Biolog microarray technology to examine the utilisation of carbon sources by the two cell lines. Data extraction with MZmine 2.14 and database searching were applied to provide metabolite lists. Principal component analysis (PCA) gave clear separation between the cisplatin-sensitive and resistant strains and their respective controls. The cisplatin-resistant cells were slightly more sensitive to melittin than the sensitive cells with IC50 values of 4.5 and 6.8 μg/mL respectively, although the latter cell line exhibited the greatest metabolic perturbation upon treatment. The changes induced by melittin in the cisplatin-sensitive cells led mostly to reduced levels of amino acids in the proline/glutamine/arginine pathway, as well as to decreased levels of carnitines, polyamines, adenosine triphosphate (ATP) and nicotinamide adenine dinucleotide (NAD+). The effects on energy metabolism were supported by the data from the Biolog assays. The lipid compositions of the two cell lines were quite different with the A2780 cells having higher levels of several ether lipids than the A2780CR cells. Melittin also had some effect on the lipid composition of the cells. Overall, this study suggests that melittin might have some potential as an adjuvant therapy in cancer treatment. Full article
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Open AccessArticle FoodPro: A Web-Based Tool for Evaluating Covariance and Correlation NMR Spectra Associated with Food Processes
Metabolites 2016, 6(4), 36; doi:10.3390/metabo6040036
Received: 25 August 2016 / Revised: 7 October 2016 / Accepted: 17 October 2016 / Published: 19 October 2016
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Abstract
Foods from agriculture and fishery products are processed using various technologies. Molecular mixture analysis during food processing has the potential to help us understand the molecular mechanisms involved, thus enabling better cooking of the analyzed foods. To date, there has been no web-based
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Foods from agriculture and fishery products are processed using various technologies. Molecular mixture analysis during food processing has the potential to help us understand the molecular mechanisms involved, thus enabling better cooking of the analyzed foods. To date, there has been no web-based tool focusing on accumulating Nuclear Magnetic Resonance (NMR) spectra from various types of food processing. Therefore, we have developed a novel web-based tool, FoodPro, that includes a food NMR spectrum database and computes covariance and correlation spectra to tasting and hardness. As a result, FoodPro has accumulated 236 aqueous (extracted in D2O) and 131 hydrophobic (extracted in CDCl3) experimental bench-top 60-MHz NMR spectra, 1753 tastings scored by volunteers, and 139 hardness measurements recorded by a penetrometer, all placed into a core database. The database content was roughly classified into fish and vegetable groups from the viewpoint of different spectrum patterns. FoodPro can query a user food NMR spectrum, search similar NMR spectra with a specified similarity threshold, and then compute estimated tasting and hardness, covariance, and correlation spectra to tasting and hardness. Querying fish spectra exemplified specific covariance spectra to tasting and hardness, giving positive covariance for tasting at 1.31 ppm for lactate and 3.47 ppm for glucose and a positive covariance for hardness at 3.26 ppm for trimethylamine N-oxide. Full article
(This article belongs to the Special Issue Challenging Biochemical Complexities by NMR)
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Open AccessArticle Metabolomics with Nuclear Magnetic Resonance Spectroscopy in a Drosophila melanogaster Model of Surviving Sepsis
Metabolites 2016, 6(4), 47; doi:10.3390/metabo6040047
Received: 28 October 2016 / Revised: 3 December 2016 / Accepted: 13 December 2016 / Published: 21 December 2016
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Abstract
Patients surviving sepsis demonstrate sustained inflammation, which has been associated with long-term complications. One of the main mechanisms behind sustained inflammation is a metabolic switch in parenchymal and immune cells, thus understanding metabolic alterations after sepsis may provide important insights to the pathophysiology
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Patients surviving sepsis demonstrate sustained inflammation, which has been associated with long-term complications. One of the main mechanisms behind sustained inflammation is a metabolic switch in parenchymal and immune cells, thus understanding metabolic alterations after sepsis may provide important insights to the pathophysiology of sepsis recovery. In this study, we explored metabolomics in a novel Drosophila melanogaster model of surviving sepsis using Nuclear Magnetic Resonance (NMR), to determine metabolite profiles. We used a model of percutaneous infection in Drosophila melanogaster to mimic sepsis. We had three experimental groups: sepsis survivors (infected with Staphylococcus aureus and treated with oral linezolid), sham (pricked with an aseptic needle), and unmanipulated (positive control). We performed metabolic measurements seven days after sepsis. We then implemented metabolites detected in NMR spectra into the MetExplore web server in order to identify the metabolic pathway alterations in sepsis surviving Drosophila. Our NMR metabolomic approach in a Drosophila model of recovery from sepsis clearly distinguished between all three groups and showed two different metabolomic signatures of inflammation. Sham flies had decreased levels of maltose, alanine, and glutamine, while their level of choline was increased. Sepsis survivors had a metabolic signature characterized by decreased glucose, maltose, tyrosine, beta-alanine, acetate, glutamine, and succinate. Full article
(This article belongs to the Special Issue Metabolomics and Its Application in Human Diseases)
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Open AccessFeature PaperArticle Prediction, Detection, and Validation of Isotope Clusters in Mass Spectrometry Data
Metabolites 2016, 6(4), 37; doi:10.3390/metabo6040037
Received: 31 August 2016 / Revised: 29 September 2016 / Accepted: 14 October 2016 / Published: 20 October 2016
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Abstract
Mass spectrometry is a key analytical platform for metabolomics. The precise quantification and identification of small molecules is a prerequisite for elucidating the metabolism and the detection, validation, and evaluation of isotope clusters in LC-MS data is important for this task. Here, we
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Mass spectrometry is a key analytical platform for metabolomics. The precise quantification and identification of small molecules is a prerequisite for elucidating the metabolism and the detection, validation, and evaluation of isotope clusters in LC-MS data is important for this task. Here, we present an approach for the improved detection of isotope clusters using chemical prior knowledge and the validation of detected isotope clusters depending on the substance mass using database statistics. We find remarkable improvements regarding the number of detected isotope clusters and are able to predict the correct molecular formula in the top three ranks in 92 % of the cases. We make our methodology freely available as part of the Bioconductor packages xcms version 1.50.0 and CAMERA version 1.30.0. Full article
(This article belongs to the Special Issue Bioinformatics and Data Analysis)
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Open AccessArticle Partial Least Squares with Structured Output for Modelling the Metabolomics Data Obtained from Complex Experimental Designs: A Study into the Y-Block Coding
Metabolites 2016, 6(4), 38; doi:10.3390/metabo6040038
Received: 31 August 2016 / Revised: 20 October 2016 / Accepted: 24 October 2016 / Published: 28 October 2016
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Abstract
Partial least squares (PLS) is one of the most commonly used supervised modelling approaches for analysing multivariate metabolomics data. PLS is typically employed as either a regression model (PLS-R) or a classification model (PLS-DA). However, in metabolomics studies it is common to investigate
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Partial least squares (PLS) is one of the most commonly used supervised modelling approaches for analysing multivariate metabolomics data. PLS is typically employed as either a regression model (PLS-R) or a classification model (PLS-DA). However, in metabolomics studies it is common to investigate multiple, potentially interacting, factors simultaneously following a specific experimental design. Such data often cannot be considered as a “pure” regression or a classification problem. Nevertheless, these data have often still been treated as a regression or classification problem and this could lead to ambiguous results. In this study, we investigated the feasibility of designing a hybrid target matrix Y that better reflects the experimental design than simple regression or binary class membership coding commonly used in PLS modelling. The new design of Y coding was based on the same principle used by structural modelling in machine learning techniques. Two real metabolomics datasets were used as examples to illustrate how the new Y coding can improve the interpretability of the PLS model compared to classic regression/classification coding. Full article
(This article belongs to the Special Issue Bioinformatics and Data Analysis)
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Open AccessArticle MetMatch: A Semi-Automated Software Tool for the Comparison and Alignment of LC-HRMS Data from Different Metabolomics Experiments
Metabolites 2016, 6(4), 39; doi:10.3390/metabo6040039
Received: 30 August 2016 / Revised: 27 October 2016 / Accepted: 28 October 2016 / Published: 2 November 2016
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Abstract
Due to its unsurpassed sensitivity and selectivity, LC-HRMS is one of the major analytical techniques in metabolomics research. However, limited stability of experimental and instrument parameters may cause shifts and drifts of retention time and mass accuracy or the formation of different ion
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Due to its unsurpassed sensitivity and selectivity, LC-HRMS is one of the major analytical techniques in metabolomics research. However, limited stability of experimental and instrument parameters may cause shifts and drifts of retention time and mass accuracy or the formation of different ion species, thus complicating conclusive interpretation of the raw data, especially when generated in different analytical batches. Here, a novel software tool for the semi-automated alignment of different measurement sequences is presented. The tool is implemented in the Java programming language, it features an intuitive user interface and its main goal is to facilitate the comparison of data obtained from different metabolomics experiments. Based on a feature list (i.e., processed LC-HRMS chromatograms with mass-to-charge ratio (m/z) values and retention times) that serves as a reference, the tool recognizes both m/z and retention time shifts of single or multiple analytical datafiles/batches of interest. MetMatch is also designed to account for differently formed ion species of detected metabolites. Corresponding ions and metabolites are matched and chromatographic peak areas, m/z values and retention times are combined into a single data matrix. The convenient user interface allows for easy manipulation of processing results and graphical illustration of the raw data as well as the automatically matched ions and metabolites. The software tool is exemplified with LC-HRMS data from untargeted metabolomics experiments investigating phenylalanine-derived metabolites in wheat and T-2 toxin/HT-2 toxin detoxification products in barley. Full article
(This article belongs to the Special Issue Bioinformatics and Data Analysis)
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Review

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Open AccessReview The Metabolic Implications of Glucocorticoids in a High-Fat Diet Setting and the Counter-Effects of Exercise
Metabolites 2016, 6(4), 44; doi:10.3390/metabo6040044
Received: 2 November 2016 / Revised: 25 November 2016 / Accepted: 30 November 2016 / Published: 5 December 2016
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Abstract
Glucocorticoids (GCs) are steroid hormones, naturally produced by activation of the hypothalamic-pituitary-adrenal (HPA) axis, that mediate the immune and metabolic systems. Synthetic GCs are used to treat a number of inflammatory conditions and diseases including lupus and rheumatoid arthritis. Generally, chronic or high
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Glucocorticoids (GCs) are steroid hormones, naturally produced by activation of the hypothalamic-pituitary-adrenal (HPA) axis, that mediate the immune and metabolic systems. Synthetic GCs are used to treat a number of inflammatory conditions and diseases including lupus and rheumatoid arthritis. Generally, chronic or high dose GC administration is associated with side effects such as steroid-induced skeletal muscle loss, visceral adiposity, and diabetes development. Patients who are taking exogenous GCs could also be more susceptible to poor food choices, but the effect that increasing fat consumption in combination with elevated exogenous GCs has only recently been investigated. Overall, these studies show that the damaging metabolic effects initiated through exogenous GC treatment are significantly amplified when combined with a high fat diet (HFD). Rodent studies of a HFD and elevated GCs demonstrate more glucose intolerance, hyperinsulinemia, visceral adiposity, and skeletal muscle lipid deposition when compared to rodents subjected to either treatment on its own. Exercise has recently been shown to be a viable therapeutic option for GC-treated, high-fat fed rodents, with the potential mechanisms still being examined. Clinically, these mechanistic studies underscore the importance of a low fat diet and increased physical activity levels when individuals are given a course of GC treatment. Full article
(This article belongs to the Special Issue Glucocorticoids and Energy Metabolism)
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Open AccessReview Quantification of Microbial Phenotypes
Metabolites 2016, 6(4), 45; doi:10.3390/metabo6040045
Received: 31 October 2016 / Revised: 5 December 2016 / Accepted: 6 December 2016 / Published: 9 December 2016
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Abstract
Metabolite profiling technologies have improved to generate close to quantitative metabolomics data, which can be employed to quantitatively describe the metabolic phenotype of an organism. Here, we review the current technologies available for quantitative metabolomics, present their advantages and drawbacks, and the current
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Metabolite profiling technologies have improved to generate close to quantitative metabolomics data, which can be employed to quantitatively describe the metabolic phenotype of an organism. Here, we review the current technologies available for quantitative metabolomics, present their advantages and drawbacks, and the current challenges to generate fully quantitative metabolomics data. Metabolomics data can be integrated into metabolic networks using thermodynamic principles to constrain the directionality of reactions. Here we explain how to estimate Gibbs energy under physiological conditions, including examples of the estimations, and the different methods for thermodynamics-based network analysis. The fundamentals of the methods and how to perform the analyses are described. Finally, an example applying quantitative metabolomics to a yeast model by 13C fluxomics and thermodynamics-based network analysis is presented. The example shows that (1) these two methods are complementary to each other; and (2) there is a need to take into account Gibbs energy errors. Better estimations of metabolic phenotypes will be obtained when further constraints are included in the analysis. Full article
(This article belongs to the Special Issue Microbial Metabolomics Volume 2)
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Open AccessReview Current and Future Perspectives on the Structural Identification of Small Molecules in Biological Systems
Metabolites 2016, 6(4), 46; doi:10.3390/metabo6040046
Received: 6 November 2016 / Revised: 4 December 2016 / Accepted: 6 December 2016 / Published: 15 December 2016
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Abstract
Although significant advances have been made in recent years, the structural elucidation of small molecules continues to remain a challenging issue for metabolite profiling. Many metabolomic studies feature unknown compounds; sometimes even in the list of features identified as “statistically significant” in the
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Although significant advances have been made in recent years, the structural elucidation of small molecules continues to remain a challenging issue for metabolite profiling. Many metabolomic studies feature unknown compounds; sometimes even in the list of features identified as “statistically significant” in the study. Such metabolic “dark matter” means that much of the potential information collected by metabolomics studies is lost. Accurate structure elucidation allows researchers to identify these compounds. This in turn, facilitates downstream metabolite pathway analysis, and a better understanding of the underlying biology of the system under investigation. This review covers a range of methods for the structural elucidation of individual compounds, including those based on gas and liquid chromatography hyphenated to mass spectrometry, single and multi-dimensional nuclear magnetic resonance spectroscopy, and high-resolution mass spectrometry and includes discussion of data standardization. Future perspectives in structure elucidation are also discussed; with a focus on the potential development of instruments and techniques, in both nuclear magnetic resonance spectroscopy and mass spectrometry that, may help solve some of the current issues that are hampering the complete identification of metabolite structure and function. Full article
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Other

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Open AccessFeature PaperBrief Report Strategies for Extending Metabolomics Studies with Stable Isotope Labelling and Fluxomics
Metabolites 2016, 6(4), 32; doi:10.3390/metabo6040032
Received: 31 August 2016 / Revised: 21 September 2016 / Accepted: 28 September 2016 / Published: 1 October 2016
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Abstract
This is a perspective from the peer session on stable isotope labelling and fluxomics at the Australian & New Zealand Metabolomics Conference (ANZMET) held from 30 March to 1 April 2016 at La Trobe University, Melbourne, Australia. This report summarizes the key points
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This is a perspective from the peer session on stable isotope labelling and fluxomics at the Australian & New Zealand Metabolomics Conference (ANZMET) held from 30 March to 1 April 2016 at La Trobe University, Melbourne, Australia. This report summarizes the key points raised in the peer session which focused on the advantages of using stable isotopes in modern metabolomics and the challenges in conducting flux analyses. The session highlighted the utility of stable isotope labelling in generating reference standards for metabolite identification, absolute quantification, and in the measurement of the dynamic activity of metabolic pathways. The advantages and disadvantages of different approaches of fluxomics analyses including flux balance analysis, metabolic flux analysis and kinetic flux profiling were also discussed along with the use of stable isotope labelling in in vivo dynamic metabolomics. A number of crucial technical considerations for designing experiments and analyzing data with stable isotope labelling were discussed which included replication, instrumentation, methods of labelling, tracer dilution and data analysis. This report reflects the current viewpoint on the use of stable isotope labelling in metabolomics experiments, identifying it as a great tool with the potential to improve biological interpretation of metabolomics data in a number of ways. Full article

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