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Metabolites, Volume 8, Issue 1 (March 2018)

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Editorial

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Open AccessEditorial Acknowledgement to Reviewers of Metabolites in 2017
Metabolites 2018, 8(1), 7; doi:10.3390/metabo8010007
Received: 16 January 2018 / Revised: 16 January 2018 / Accepted: 16 January 2018 / Published: 16 January 2018
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Abstract
Peer review is an essential part in the publication process, ensuring that Metabolites maintains high quality standards for its published papers [...] Full article

Research

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Open AccessArticle Methanol Generates Numerous Artifacts during Sample Extraction and Storage of Extracts in Metabolomics Research
Metabolites 2018, 8(1), 1; doi:10.3390/metabo8010001
Received: 31 October 2017 / Revised: 15 December 2017 / Accepted: 18 December 2017 / Published: 22 December 2017
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Abstract
Many metabolomics studies use mixtures of (acidified) methanol and water for sample extraction. In the present study, we investigated if the extraction with methanol can result in artifacts. To this end, wheat leaves were extracted with mixtures of native and deuterium-labeled methanol and
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Many metabolomics studies use mixtures of (acidified) methanol and water for sample extraction. In the present study, we investigated if the extraction with methanol can result in artifacts. To this end, wheat leaves were extracted with mixtures of native and deuterium-labeled methanol and water, with or without 0.1% formic acid. Subsequently, the extracts were analyzed immediately or after storage at 10 °C, −20 °C or −80 °C with an HPLC-HESI-QExactive HF-Orbitrap instrument. Our results showed that 88 (8%) of the >1100 detected compounds were derived from the reaction with methanol and either formed during sample extraction or short-term storage. Artifacts were found for various substance classes such as flavonoids, carotenoids, tetrapyrrols, fatty acids and other carboxylic acids that are typically investigated in metabolomics studies. 58 of 88 artifacts were common between the two tested extraction variants. Remarkably, 34 of 73 (acidified extraction solvent) and 33 of 73 (non-acidified extraction solvent) artifacts were formed de novo as none of these meth(ox)ylated metabolites were found after extraction of native leaf samples with CD3OH/H2O. Moreover, sample extracts stored at 10 °C for several days, as can typically be the case during longer measurement sequences, led to an increase in both the number and abundance of methylated artifacts. In contrast, frozen sample extracts were relatively stable during a storage period of one week. Our study shows that caution has to be exercised if methanol is used as the extraction solvent as the detected metabolites might be artifacts rather than natural constituents of the biological system. In addition, we recommend storing sample extracts in deep freezers immediately after extraction until measurement. Full article
(This article belongs to the Special Issue Isotope Guided Metabolomics and Flux Analysis)
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Open AccessArticle Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for 13C Metabolic Flux Analysis
Metabolites 2018, 8(1), 3; doi:10.3390/metabo8010003
Received: 10 November 2017 / Revised: 23 December 2017 / Accepted: 2 January 2018 / Published: 4 January 2018
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Abstract
Determination of internal metabolic fluxes is crucial for fundamental and applied biology because they map how carbon and electrons flow through metabolism to enable cell function. 13C Metabolic Flux Analysis (13C MFA) and Two-Scale 13C Metabolic Flux Analysis (2S-
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Determination of internal metabolic fluxes is crucial for fundamental and applied biology because they map how carbon and electrons flow through metabolism to enable cell function. 13 C Metabolic Flux Analysis ( 13 C MFA) and Two-Scale 13 C Metabolic Flux Analysis (2S- 13 C MFA) are two techniques used to determine such fluxes. Both operate on the simplifying approximation that metabolic flux from peripheral metabolism into central “core” carbon metabolism is minimal, and can be omitted when modeling isotopic labeling in core metabolism. The validity of this “two-scale” or “bow tie” approximation is supported both by the ability to accurately model experimental isotopic labeling data, and by experimentally verified metabolic engineering predictions using these methods. However, the boundaries of core metabolism that satisfy this approximation can vary across species, and across cell culture conditions. Here, we present a set of algorithms that (1) systematically calculate flux bounds for any specified “core” of a genome-scale model so as to satisfy the bow tie approximation and (2) automatically identify an updated set of core reactions that can satisfy this approximation more efficiently. First, we leverage linear programming to simultaneously identify the lowest fluxes from peripheral metabolism into core metabolism compatible with the observed growth rate and extracellular metabolite exchange fluxes. Second, we use Simulated Annealing to identify an updated set of core reactions that allow for a minimum of fluxes into core metabolism to satisfy these experimental constraints. Together, these methods accelerate and automate the identification of a biologically reasonable set of core reactions for use with 13 C MFA or 2S- 13 C MFA, as well as provide for a substantially lower set of flux bounds for fluxes into the core as compared with previous methods. We provide an open source Python implementation of these algorithms at https://github.com/JBEI/limitfluxtocore. Full article
(This article belongs to the Special Issue Metabolic Network Models Volume 2)
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Open AccessArticle Calcitriol Supplementation Causes Decreases in Tumorigenic Proteins and Different Proteomic and Metabolomic Signatures in Right versus Left-Sided Colon Cancer
Metabolites 2018, 8(1), 5; doi:10.3390/metabo8010005
Received: 15 December 2017 / Revised: 5 January 2018 / Accepted: 9 January 2018 / Published: 11 January 2018
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Abstract
Vitamin D deficiency is a common problem worldwide. In particular, it is an issue in the Northern Hemisphere where UVB radiation does not penetrate the atmosphere as readily. There is a correlation between vitamin D deficiency and colorectal cancer incidence and mortality. Furthermore,
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Vitamin D deficiency is a common problem worldwide. In particular, it is an issue in the Northern Hemisphere where UVB radiation does not penetrate the atmosphere as readily. There is a correlation between vitamin D deficiency and colorectal cancer incidence and mortality. Furthermore, there is strong evidence that cancer of the ascending (right side) colon is different from cancer of the descending (left side) colon in terms of prognosis, tumor differentiation, and polyp type, as well as at the molecular level. Right-side tumors have elevated Wnt signaling and are more likely to relapse, whereas left-side tumors have reduced expression of tumor suppressor genes. This study seeks to understand both the proteomic and metabolomic changes resulting from treatment of the active metabolite of vitamin D, calcitriol, in right-sided and left-sided colon cancer. Our results show that left-sided colon cancer treated with calcitriol has a substantially greater number of changes in both the proteome and the metabolome than right-sided colon cancer. We found that calcitriol treatment in both right-sided and left-sided colon cancer causes a downregulation of ribosomal protein L37 and protein S100A10. Both of these proteins are heavily involved in tumorigenesis, suggesting a possible mechanism for the correlation between low vitamin D levels and colon cancer. Full article
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Open AccessArticle Impact of Prolonged Blood Incubation and Extended Serum Storage at Room Temperature on the Human Serum Metabolome
Metabolites 2018, 8(1), 6; doi:10.3390/metabo8010006
Received: 24 November 2017 / Revised: 5 January 2018 / Accepted: 11 January 2018 / Published: 13 January 2018
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Abstract
Metabolomics is a powerful technology with broad applications in life science that, like other -omics approaches, requires high-quality samples to achieve reliable results and ensure reproducibility. Therefore, along with quality assurance, methods to assess sample quality regarding pre-analytical confounders are urgently needed. In
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Metabolomics is a powerful technology with broad applications in life science that, like other -omics approaches, requires high-quality samples to achieve reliable results and ensure reproducibility. Therefore, along with quality assurance, methods to assess sample quality regarding pre-analytical confounders are urgently needed. In this study, we analyzed the response of the human serum metabolome to pre-analytical variations comprising prolonged blood incubation and extended serum storage at room temperature by using gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) -based metabolomics. We found that the prolonged incubation of blood results in a statistically significant 20% increase and 4% decrease of 225 tested serum metabolites. Extended serum storage affected 21% of the analyzed metabolites (14% increased, 7% decreased). Amino acids and nucleobases showed the highest percentage of changed metabolites in both confounding conditions, whereas lipids were remarkably stable. Interestingly, the amounts of taurine and O-phosphoethanolamine, which have both been discussed as biomarkers for various diseases, were 1.8- and 2.9-fold increased after 6 h of blood incubation. Since we found that both are more stable in ethylenediaminetetraacetic acid (EDTA) blood, EDTA plasma should be the preferred metabolomics matrix. Full article
(This article belongs to the Special Issue Clinical Metabolomics)
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Open AccessArticle Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction
Metabolites 2018, 8(1), 8; doi:10.3390/metabo8010008
Received: 28 December 2017 / Revised: 13 January 2018 / Accepted: 13 January 2018 / Published: 17 January 2018
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Abstract
We introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS2), and NMR into a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature,
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We introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS2), and NMR into a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature, and then combines the experimental MS/MS and NMR information of the unknown to effectively filter out the false positive candidate structures based on their predicted MS/MS and NMR spectra. We demonstrate the approach on a model mixture, and then we identify an uncatalogued secondary metabolite in Arabidopsis thaliana. The NMR/MS2 approach is well suited to the discovery of new metabolites in plant extracts, microbes, soils, dissolved organic matter, food extracts, biofuels, and biomedical samples, facilitating the identification of metabolites that are not present in experimental NMR and MS metabolomics databases. Full article
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Open AccessArticle Enhanced Isotopic Ratio Outlier Analysis (IROA) Peak Detection and Identification with Ultra-High Resolution GC-Orbitrap/MS: Potential Application for Investigation of Model Organism Metabolomes
Metabolites 2018, 8(1), 9; doi:10.3390/metabo8010009 (registering DOI)
Received: 7 November 2017 / Revised: 10 January 2018 / Accepted: 10 January 2018 / Published: 18 January 2018
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Abstract
Identifying non-annotated peaks may have a significant impact on the understanding of biological systems. In silico methodologies have focused on ESI LC/MS/MS for identifying non-annotated MS peaks. In this study, we employed in silico methodology to develop an Isotopic Ratio Outlier Analysis (IROA)
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Identifying non-annotated peaks may have a significant impact on the understanding of biological systems. In silico methodologies have focused on ESI LC/MS/MS for identifying non-annotated MS peaks. In this study, we employed in silico methodology to develop an Isotopic Ratio Outlier Analysis (IROA) workflow using enhanced mass spectrometric data acquired with the ultra-high resolution GC-Orbitrap/MS to determine the identity of non-annotated metabolites. The higher resolution of the GC-Orbitrap/MS, together with its wide dynamic range, resulted in more IROA peak pairs detected, and increased reliability of chemical formulae generation (CFG). IROA uses two different 13C-enriched carbon sources (randomized 95% 12C and 95% 13C) to produce mirror image isotopologue pairs, whose mass difference reveals the carbon chain length (n), which aids in the identification of endogenous metabolites. Accurate m/z, n, and derivatization information are obtained from our GC/MS workflow for unknown metabolite identification, and aids in silico methodologies for identifying isomeric and non-annotated metabolites. We were able to mine more mass spectral information using the same Saccharomyces cerevisiae growth protocol (Qiu et al. Anal. Chem 2016) with the ultra-high resolution GC-Orbitrap/MS, using 10% ammonia in methane as the CI reagent gas. We identified 244 IROA peaks pairs, which significantly increased IROA detection capability compared with our previous report (126 IROA peak pairs using a GC-TOF/MS machine). For 55 selected metabolites identified from matched IROA CI and EI spectra, using the GC-Orbitrap/MS vs. GC-TOF/MS, the average mass deviation for GC-Orbitrap/MS was 1.48 ppm, however, the average mass deviation was 32.2 ppm for the GC-TOF/MS machine. In summary, the higher resolution and wider dynamic range of the GC-Orbitrap/MS enabled more accurate CFG, and the coupling of accurate mass GC/MS IROA methodology with in silico fragmentation has great potential in unknown metabolite identification, with applications for characterizing model organism networks. Full article
(This article belongs to the Special Issue Microbial Metabolomics Volume 2)
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Review

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Open AccessReview Rethinking the Combination of Proton Exchanger Inhibitors in Cancer Therapy
Metabolites 2018, 8(1), 2; doi:10.3390/metabo8010002
Received: 21 November 2017 / Revised: 16 December 2017 / Accepted: 21 December 2017 / Published: 23 December 2017
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Abstract
Microenvironmental acidity is becoming a key target for the new age of cancer treatment. In fact, while cancer is characterized by genetic heterogeneity, extracellular acidity is a common phenotype of almost all cancers. To survive and proliferate under acidic conditions, tumor cells up-regulate
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Microenvironmental acidity is becoming a key target for the new age of cancer treatment. In fact, while cancer is characterized by genetic heterogeneity, extracellular acidity is a common phenotype of almost all cancers. To survive and proliferate under acidic conditions, tumor cells up-regulate proton exchangers and transporters (mainly V-ATPase, Na+/H+ exchanger (NHE), monocarboxylate transporters (MCTs), and carbonic anhydrases (CAs)), that actively extrude excess protons, avoiding intracellular accumulation of toxic molecules, thus becoming a sort of survival option with many similarities compared with unicellular microorganisms. These systems are also involved in the unresponsiveness or resistance to chemotherapy, leading to the protection of cancer cells from the vast majority of drugs, that when protonated in the acidic tumor microenvironment, do not enter into cancer cells. Indeed, as usually occurs in the progression versus malignancy, resistant tumor clones emerge and proliferate, following a transient initial response to a therapy, thus giving rise to more malignant behavior and rapid tumor progression. Recent studies are supporting the use of a cocktail of proton exchanger inhibitors as a new strategy against cancer. Full article
(This article belongs to the Special Issue Carbonic Anhydrases and Metabolism)
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Open AccessReview Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling
Metabolites 2018, 8(1), 4; doi:10.3390/metabo8010004
Received: 13 December 2017 / Revised: 8 January 2018 / Accepted: 9 January 2018 / Published: 11 January 2018
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Abstract
Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge
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Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies. Full article
(This article belongs to the Special Issue Metabolomics Modelling)
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