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Metabolites, Volume 5, Issue 2 (June 2015) , Pages 184-403

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Open AccessArticle An Efficient Single Phase Method for the Extraction of Plasma Lipids
Metabolites 2015, 5(2), 389-403; https://doi.org/10.3390/metabo5020389
Received: 17 April 2015 / Revised: 4 June 2015 / Accepted: 8 June 2015 / Published: 17 June 2015
Cited by 33 | Viewed by 2490 | PDF Full-text (341 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Lipidomic approaches are now widely used to investigate the relationship between lipid metabolism, health and disease. Large-scale lipidomics studies typically aim to quantify hundreds to thousands of lipid molecular species in a large number of samples. Consequently, high throughput methodology that can efficiently [...] Read more.
Lipidomic approaches are now widely used to investigate the relationship between lipid metabolism, health and disease. Large-scale lipidomics studies typically aim to quantify hundreds to thousands of lipid molecular species in a large number of samples. Consequently, high throughput methodology that can efficiently extract a wide range of lipids from biological samples is required. Current methods often rely on extraction in chloroform:methanol with or without two phase partitioning or other solvents, which are often incompatible with liquid chromatography electrospray ionization-tandem mass spectrometry (LC ESI-MS/MS). Here, we present a fast, simple extraction method that is suitable for high throughput LC ESI-MS/MS. Plasma (10 μL) was mixed with 100 μL 1-butanol:methanol (1:1 v/v) containing internal standards resulting in efficient extraction of all major lipid classes (including sterols, glycerolipids, glycerophospholipids and sphingolipids). Lipids were quantified using positive-ion mode LC ESI-MS/MS. The method showed high recovery (>90%) and reproducibility (%CV < 20%). It showed a strong correlation of all lipid measures with an established chloroform:methanol extraction method (R2 = 0.976). This method uses non-halogenated solvents, requires no drying or reconstitution steps and is suitable for large-scale LC ESI-MS/MS-based lipidomic analyses in research and clinical laboratories. Full article
(This article belongs to the Special Issue Metabolomic Methodology)
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Open AccessEditorial Metabolites Best Paper Awards for 2015
Metabolites 2015, 5(2), 386-388; https://doi.org/10.3390/metabo5020386
Received: 15 June 2015 / Accepted: 15 June 2015 / Published: 17 June 2015
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Abstract
Metabolites is instituting annual awards to recognize the most outstanding papers in metabolism and metabolomics published in Metabolites. [...] Full article
Open AccessArticle Fructose Alters Intermediary Metabolism of Glucose in Human Adipocytes and Diverts Glucose to Serine Oxidation in the One–Carbon Cycle Energy Producing Pathway
Metabolites 2015, 5(2), 364-385; https://doi.org/10.3390/metabo5020364
Received: 26 February 2015 / Accepted: 9 June 2015 / Published: 16 June 2015
Cited by 7 | Viewed by 3199 | PDF Full-text (2133 KB) | HTML Full-text | XML Full-text
Abstract
Increased consumption of sugar and fructose as sweeteners has resulted in the utilization of fructose as an alternative metabolic fuel that may compete with glucose and alter its metabolism. To explore this, human Simpson-Golabi-Behmel Syndrome (SGBS) preadipocytes were differentiated to adipocytes in the [...] Read more.
Increased consumption of sugar and fructose as sweeteners has resulted in the utilization of fructose as an alternative metabolic fuel that may compete with glucose and alter its metabolism. To explore this, human Simpson-Golabi-Behmel Syndrome (SGBS) preadipocytes were differentiated to adipocytes in the presence of 0, 1, 2.5, 5 or 10 mM of fructose added to a medium containing 5 mM of glucose representing the normal blood glucose concentration. Targeted tracer [1,2-13C2]-d-glucose fate association approach was employed to examine the influence of fructose on the intermediary metabolism of glucose. Increasing concentrations of fructose robustly increased the oxidation of [1,2-13C2]-d-glucose to 13CO2 (p < 0.000001). However, glucose-derived 13CO2 negatively correlated with 13C labeled glutamate, 13C palmitate, and M+1 labeled lactate. These are strong markers of limited tricarboxylic acid (TCA) cycle, fatty acid synthesis, pentose cycle fluxes, substrate turnover and NAD+/NADP+ or ATP production from glucose via complete oxidation, indicating diminished mitochondrial energy metabolism. Contrarily, a positive correlation was observed between glucose-derived 13CO2 formed and 13C oleate and doses of fructose which indicate the elongation and desaturation of palmitate to oleate for storage. Collectively, these results suggest that fructose preferentially drives glucose through serine oxidation glycine cleavage (SOGC pathway) one-carbon cycle for NAD+/NADP+ production that is utilized in fructose-induced lipogenesis and storage in adipocytes. Full article
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Open AccessArticle Carotta: Revealing Hidden Confounder Markers in Metabolic Breath Profiles
Metabolites 2015, 5(2), 344-363; https://doi.org/10.3390/metabo5020344
Received: 13 March 2015 / Revised: 20 May 2015 / Accepted: 25 May 2015 / Published: 10 June 2015
Cited by 10 | Viewed by 2358 | PDF Full-text (8257 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Computational breath analysis is a growing research area aiming at identifying volatile organic compounds (VOCs) in human breath to assist medical diagnostics of the next generation. While inexpensive and non-invasive bioanalytical technologies for metabolite detection in exhaled air and bacterial/fungal vapor exist and [...] Read more.
Computational breath analysis is a growing research area aiming at identifying volatile organic compounds (VOCs) in human breath to assist medical diagnostics of the next generation. While inexpensive and non-invasive bioanalytical technologies for metabolite detection in exhaled air and bacterial/fungal vapor exist and the first studies on the power of supervised machine learning methods for profiling of the resulting data were conducted, we lack methods to extract hidden data features emerging from confounding factors. Here, we present Carotta, a new cluster analysis framework dedicated to uncovering such hidden substructures by sophisticated unsupervised statistical learning methods. We study the power of transitivity clustering and hierarchical clustering to identify groups of VOCs with similar expression behavior over most patient breath samples and/or groups of patients with a similar VOC intensity pattern. This enables the discovery of dependencies between metabolites. On the one hand, this allows us to eliminate the effect of potential confounding factors hindering disease classification, such as smoking. On the other hand, we may also identify VOCs associated with disease subtypes or concomitant diseases. Carotta is an open source software with an intuitive graphical user interface promoting data handling, analysis and visualization. The back-end is designed to be modular, allowing for easy extensions with plugins in the future, such as new clustering methods and statistics. It does not require much prior knowledge or technical skills to operate. We demonstrate its power and applicability by means of one artificial dataset. We also apply Carotta exemplarily to a real-world example dataset on chronic obstructive pulmonary disease (COPD). While the artificial data are utilized as a proof of concept, we will demonstrate how Carotta finds candidate markers in our real dataset associated with confounders rather than the primary disease (COPD) and bronchial carcinoma (BC). Carotta is publicly available at http://carotta.compbio.sdu.dk [1]. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
Open AccessReview Metabolism at Evolutionary Optimal States
Metabolites 2015, 5(2), 311-343; https://doi.org/10.3390/metabo5020311
Received: 3 March 2015 / Revised: 20 May 2015 / Accepted: 25 May 2015 / Published: 2 June 2015
Cited by 3 | Viewed by 3077 | PDF Full-text (1707 KB) | HTML Full-text | XML Full-text
Abstract
Metabolism is generally required for cellular maintenance and for the generation of offspring under conditions that support growth. The rates, yields (efficiencies), adaptation time and robustness of metabolism are therefore key determinants of cellular fitness. For biotechnological applications and our understanding of the [...] Read more.
Metabolism is generally required for cellular maintenance and for the generation of offspring under conditions that support growth. The rates, yields (efficiencies), adaptation time and robustness of metabolism are therefore key determinants of cellular fitness. For biotechnological applications and our understanding of the evolution of metabolism, it is necessary to figure out how the functional system properties of metabolism can be optimized, via adjustments of the kinetics and expression of enzymes, and by rewiring metabolism. The trade-offs that can occur during such optimizations then indicate fundamental limits to evolutionary innovations and bioengineering. In this paper, we review several theoretical and experimental findings about mechanisms for metabolic optimization. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
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Open AccessArticle Computational Metabolomics Operations at BioCyc.org
Metabolites 2015, 5(2), 291-310; https://doi.org/10.3390/metabo5020291
Received: 20 November 2014 / Revised: 28 February 2015 / Accepted: 30 March 2015 / Published: 22 May 2015
Cited by 8 | Viewed by 2985 | PDF Full-text (1396 KB) | HTML Full-text | XML Full-text
Abstract
BioCyc.org is a genome and metabolic pathway web portal covering 5500 organisms, including Homo sapiens, Arabidopsis thaliana, Saccharomyces cerevisiae and Escherichia coli. These organism-specific databases have undergone variable degrees of curation. The EcoCyc (Escherichia coli Encyclopedia) database is the most highly [...] Read more.
BioCyc.org is a genome and metabolic pathway web portal covering 5500 organisms, including Homo sapiens, Arabidopsis thaliana, Saccharomyces cerevisiae and Escherichia coli. These organism-specific databases have undergone variable degrees of curation. The EcoCyc (Escherichia coli Encyclopedia) database is the most highly curated; its contents have been derived from 27,000 publications. The MetaCyc (Metabolic Encyclopedia) database within BioCyc is a “universal” metabolic database that describes pathways, reactions, enzymes and metabolites from all domains of life. Metabolic pathways provide an organizing framework for analyzing metabolomics data, and the BioCyc website provides computational operations for metabolomics data that include metabolite search and translation of metabolite identifiers across multiple metabolite databases. The site allows researchers to store and manipulate metabolite lists using a facility called SmartTables, which supports metabolite enrichment analysis. That analysis operation identifies metabolite sets that are statistically over-represented for the substrates of specific metabolic pathways. BioCyc also enables visualization of metabolomics data on individual pathway diagrams and on the organism-specific metabolic map diagrams that are available for every BioCyc organism. Most of these operations are available both interactively and as programmatic web services. Full article
(This article belongs to the Special Issue Bioinformatics and Data Analysis)
Open AccessReview Essences in Metabolic Engineering of Lignan Biosynthesis
Metabolites 2015, 5(2), 270-290; https://doi.org/10.3390/metabo5020270
Received: 26 February 2015 / Revised: 21 April 2015 / Accepted: 27 April 2015 / Published: 4 May 2015
Cited by 21 | Viewed by 3089 | PDF Full-text (1033 KB) | HTML Full-text | XML Full-text
Abstract
Lignans are structurally and functionally diverse phytochemicals biosynthesized in diverse plant species and have received wide attentions as leading compounds of novel drugs for tumor treatment and healthy diets to reduce of the risks of lifestyle-related non-communicable diseases. However, the lineage-specific distribution and [...] Read more.
Lignans are structurally and functionally diverse phytochemicals biosynthesized in diverse plant species and have received wide attentions as leading compounds of novel drugs for tumor treatment and healthy diets to reduce of the risks of lifestyle-related non-communicable diseases. However, the lineage-specific distribution and the low-amount of production in natural plants, some of which are endangered species, hinder the efficient and stable production of beneficial lignans. Accordingly, the development of new procedures for lignan production is of keen interest. Recent marked advances in the molecular and functional characterization of lignan biosynthetic enzymes and endogenous and exogenous factors for lignan biosynthesis have suggested new methods for the metabolic engineering of lignan biosynthesis cascades leading to the efficient, sustainable, and stable lignan production in plants, including plant cell/organ cultures. Optimization of light conditions, utilization of a wide range of elicitor treatments, and construction of transiently gene-transfected or transgenic lignan-biosynthesizing plants are mainly being attempted. This review will present the basic and latest knowledge regarding metabolic engineering of lignans based on their biosynthetic pathways and biological activities, and the perspectives in lignan production via metabolic engineering. Full article
(This article belongs to the Special Issue Metabolic Engineering and Synthetic Biology)
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Open AccessArticle Footprints of Optimal Protein Assembly Strategies in the Operonic Structure of Prokaryotes
Metabolites 2015, 5(2), 252-269; https://doi.org/10.3390/metabo5020252
Received: 11 February 2015 / Revised: 27 March 2015 / Accepted: 24 April 2015 / Published: 28 April 2015
Cited by 3 | Viewed by 2207 | PDF Full-text (828 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
In this work, we investigate optimality principles behind synthesis strategies for protein complexes using a dynamic optimization approach. We show that the cellular capacity of protein synthesis has a strong influence on optimal synthesis strategies reaching from a simultaneous to a sequential synthesis [...] Read more.
In this work, we investigate optimality principles behind synthesis strategies for protein complexes using a dynamic optimization approach. We show that the cellular capacity of protein synthesis has a strong influence on optimal synthesis strategies reaching from a simultaneous to a sequential synthesis of the subunits of a protein complex. Sequential synthesis is preferred if protein synthesis is strongly limited, whereas a simultaneous synthesis is optimal in situations with a high protein synthesis capacity. We confirm the predictions of our optimization approach through the analysis of the operonic organization of protein complexes in several hundred prokaryotes. Thereby, we are able to show that cellular protein synthesis capacity is a driving force in the dissolution of operons comprising the subunits of a protein complex. Thus, we also provide a tested hypothesis explaining why the subunits of many prokaryotic protein complexes are distributed across several operons despite the presumably less precise co-regulation. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
Open AccessReview Mathematical Modelling of Metabolic Regulation in Aging
Metabolites 2015, 5(2), 232-251; https://doi.org/10.3390/metabo5020232
Received: 3 November 2014 / Revised: 24 March 2015 / Accepted: 25 March 2015 / Published: 27 April 2015
Cited by 10 | Viewed by 2465 | PDF Full-text (695 KB) | HTML Full-text | XML Full-text
Abstract
The underlying cellular mechanisms that characterize aging are complex and multifaceted. However, it is emerging that aging could be regulated by two distinct metabolic hubs. These hubs are the pathway defined by the mammalian target of rapamycin (mTOR) and that defined by the [...] Read more.
The underlying cellular mechanisms that characterize aging are complex and multifaceted. However, it is emerging that aging could be regulated by two distinct metabolic hubs. These hubs are the pathway defined by the mammalian target of rapamycin (mTOR) and that defined by the NAD+-dependent deacetylase enzyme, SIRT1. Recent experimental evidence suggests that there is crosstalk between these two important pathways; however, the mechanisms underpinning their interaction(s) remains poorly understood. In this review, we propose using computational modelling in tandem with experimentation to delineate the mechanism(s). We briefly discuss the main modelling frameworks that could be used to disentangle this relationship and present a reduced reaction pathway that could be modelled. We conclude by outlining the limitations of computational modelling and by discussing opportunities for future progress in this area. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
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Open AccessArticle Fermentative Production of the Diamine Putrescine: System Metabolic Engineering of Corynebacterium Glutamicum
Metabolites 2015, 5(2), 211-231; https://doi.org/10.3390/metabo5020211
Received: 26 February 2015 / Revised: 8 April 2015 / Accepted: 13 April 2015 / Published: 24 April 2015
Cited by 25 | Viewed by 3762 | PDF Full-text (1210 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Corynebacterium glutamicum shows great potential for the production of the glutamate-derived diamine putrescine, a monomeric compound of polyamides. A genome-scale stoichiometric model of a C. glutamicum strain with reduced ornithine transcarbamoylase activity, derepressed arginine biosynthesis, and an anabolic plasmid-addiction system for heterologous expression [...] Read more.
Corynebacterium glutamicum shows great potential for the production of the glutamate-derived diamine putrescine, a monomeric compound of polyamides. A genome-scale stoichiometric model of a C. glutamicum strain with reduced ornithine transcarbamoylase activity, derepressed arginine biosynthesis, and an anabolic plasmid-addiction system for heterologous expression of E. coli ornithine decarboxylase gene speC was investigated by flux balance analysis with respect to its putrescine production potential. Based on these simulations, enhancing glycolysis and anaplerosis by plasmid-borne overexpression of the genes for glyceraldehyde 3-phosphate dehydrogenase and pyruvate carboxylase as well as reducing 2-oxoglutarate dehydrogenase activity were chosen as targets for metabolic engineering. Changing the translational start codon of the chromosomal gene for 2-oxoglutarate dehydrogenase subunit E1o to the less preferred TTG and changing threonine 15 of OdhI to alanine reduced 2-oxoglutarate dehydrogenase activity about five fold and improved putrescine titers by 28%. Additional engineering steps improved further putrescine production with the largest contributions from preventing the formation of the by-product N-acetylputrescine by deletion of spermi(di)ne N-acetyltransferase gene snaA and from overexpression of the gene for a feedback-resistant N-acetylglutamate kinase variant. The resulting C. glutamicum strain NA6 obtained by systems metabolic engineering accumulated two fold more putrescine than the base strain, i.e., 58.1 ± 0.2 mM, and showed a specific productivity of 0.045 g·g−1·h−1 and a yield on glucose of 0.26 g·g−1. Full article
(This article belongs to the Special Issue Metabolic Engineering and Synthetic Biology)
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Open AccessArticle Systemic Metabolomic Changes in Blood Samples of Lung Cancer Patients Identified by Gas Chromatography Time-of-Flight Mass Spectrometry
Metabolites 2015, 5(2), 192-210; https://doi.org/10.3390/metabo5020192
Received: 19 December 2014 / Revised: 24 March 2015 / Accepted: 1 April 2015 / Published: 9 April 2015
Cited by 25 | Viewed by 3853 | PDF Full-text (1651 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Lung cancer is a leading cause of cancer deaths worldwide. Metabolic alterations in tumor cells coupled with systemic indicators of the host response to tumor development have the potential to yield blood profiles with clinical utility for diagnosis and monitoring of treatment. We [...] Read more.
Lung cancer is a leading cause of cancer deaths worldwide. Metabolic alterations in tumor cells coupled with systemic indicators of the host response to tumor development have the potential to yield blood profiles with clinical utility for diagnosis and monitoring of treatment. We report results from two separate studies using gas chromatography time-of-flight mass spectrometry (GC-TOF MS) to profile metabolites in human blood samples that significantly differ from non-small cell lung cancer (NSCLC) adenocarcinoma and other lung cancer cases. Metabolomic analysis of blood samples from the two studies yielded a total of 437 metabolites, of which 148 were identified as known compounds and 289 identified as unknown compounds. Differential analysis identified 15 known metabolites in one study and 18 in a second study that were statistically different (p-values <0.05). Levels of maltose, palmitic acid, glycerol, ethanolamine, glutamic acid, and lactic acid were increased in cancer samples while amino acids tryptophan, lysine and histidine decreased. Many of the metabolites were found to be significantly different in both studies, suggesting that metabolomics appears to be robust enough to find systemic changes from lung cancer, thus showing the potential of this type of analysis for lung cancer detection. Full article
(This article belongs to the Special Issue Biomarkers in Metabolomics)
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Open AccessCommunication Factors Influencing Production of Fusaristatin A in Fusarium graminearum
Metabolites 2015, 5(2), 184-191; https://doi.org/10.3390/metabo5020184
Received: 26 January 2015 / Revised: 27 February 2015 / Accepted: 16 March 2015 / Published: 1 April 2015
Cited by 5 | Viewed by 2401 | PDF Full-text (485 KB) | HTML Full-text | XML Full-text
Abstract
Fusarium graminearum is a ubiquitous plant pathogen, which is able to produce several bioactive secondary metabolites. Recently, the cyclic lipopeptide fusaristatin A was isolated from this species and the biosynthetic gene cluster identified. Fusaristatin A consists of a C24 reduced polyketide and [...] Read more.
Fusarium graminearum is a ubiquitous plant pathogen, which is able to produce several bioactive secondary metabolites. Recently, the cyclic lipopeptide fusaristatin A was isolated from this species and the biosynthetic gene cluster identified. Fusaristatin A consists of a C24 reduced polyketide and the three amino acids dehydroalanine, β-aminoisobutyric acid and glutamine and is biosynthesized by a collaboration of a polyketide synthase and a nonribosomal peptide synthetase. To gain insight into the environmental factors, which controls the production of fusaristatin A, we cultivated F. graminearum under various conditions. We developed an LC-MS/MS method to quantify fusaristatin A in F. graminearum extracts. The results showed that yeast extract sucrose (YES) medium was the best medium for fusaristatin A production and that the optimal pH was 7.5 and temperature 25–30 °C. Furthermore, production of fusaristatin A was more than four times higher in stationary cultures than in agitated cultures when F. graminearum was grown in liquid YES medium. The results also showed that fusaristatin A was only present in the mycelium and not in the liquid, which suggests that fusaristatin A is stored intracellulally and not exported to the extracellular environment. Full article
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Metabolites EISSN 2218-1989 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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