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Metabolites, Volume 3, Issue 3 (September 2013), Pages 517-852

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Research

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Open AccessArticle The Critical Assessment of Small Molecule Identification (CASMI): Challenges and Solutions
Metabolites 2013, 3(3), 517-538; doi:10.3390/metabo3030517
Received: 1 April 2013 / Revised: 25 May 2013 / Accepted: 7 June 2013 / Published: 25 June 2013
Cited by 3 | PDF Full-text (2206 KB) | HTML Full-text | XML Full-text
Abstract
The Critical Assessment of Small Molecule Identification, or CASMI, contest was founded in 2012 to provide scientists with a common open dataset to evaluate their identification methods. In this article, the challenges and solutions for the inaugural CASMI 2012 are presented. The [...] Read more.
The Critical Assessment of Small Molecule Identification, or CASMI, contest was founded in 2012 to provide scientists with a common open dataset to evaluate their identification methods. In this article, the challenges and solutions for the inaugural CASMI 2012 are presented. The contest was split into four categories corresponding with tasks to determine molecular formula and molecular structure, each from two measurement types, liquid chromatography-high resolution mass spectrometry (LC-HRMS), where preference was given to high mass accuracy data, and gas chromatography-electron impact-mass spectrometry (GC-MS), i.e., unit accuracy data. These challenges were obtained from plant material, environmental samples and reference standards. It was surprisingly difficult to obtain data suitable for a contest, especially for GC-MS data where existing databases are very large. The level of difficulty of the challenges is thus quite varied. In this article, the challenges and the answers are discussed, and recommendations for challenge selection in subsequent CASMI contests are given. Full article
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Open AccessArticle Metabolic Profiling of Plasma from Benign and Malignant Pulmonary Nodules Patients Using Mass Spectrometry-Based Metabolomics
Metabolites 2013, 3(3), 539-551; doi:10.3390/metabo3030539
Received: 3 May 2013 / Revised: 7 June 2013 / Accepted: 24 June 2013 / Published: 4 July 2013
Cited by 3 | PDF Full-text (637 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Solitary pulmonary nodule (SPN or coin lesion) is a mass in the lung and can be commonly found in chest X-rays or computerized tomography (CT) scans. However, despite the advancement of imaging technologies, it is still difficult to distinguish malignant cancer from [...] Read more.
Solitary pulmonary nodule (SPN or coin lesion) is a mass in the lung and can be commonly found in chest X-rays or computerized tomography (CT) scans. However, despite the advancement of imaging technologies, it is still difficult to distinguish malignant cancer from benign SPNs. Here we investigated the metabolic profiling of patients with benign and malignant pulmonary nodules. A combination of gas chromatography/mass spectrometry (GC/MS) and liquid chromatography/mass spectrometry (LC/MS) was used to profile the plasma metabolites in 17 patients with malignant SPNs, 15 patients with benign SPNs and 20 healthy controls. The metabolic profiles were assayed using OPLS-DA, and further analyzed to identify marker metabolites related to diseases. Both GC/MS- and LC/MS-derived models showed clear discriminations in metabolic profiles among three groups. It was found that 63 metabolites (12 from GC/MS, 51 from LC/MS) contributed to the differences. Of these, 48 metabolites showed same change trend in both malignant and benign SPNs as compared with healthy controls, indicating some common pathways including inflammation and oxidative injury shared by two diseases. In contrast, 14 metabolites constituted distinct profiles that differentiated malignant from benign SPNs, which might be a unique biochemical feature associated with lung cancer. Overall, our data suggested that integration of two highly sensitive and complementary metabolomics platforms could enable a comprehensive metabolic profiling and assist in discrimination malignant from benign SPNs. Full article
(This article belongs to the Special Issue Cancer Metabolomics)
Open AccessArticle Combining Hydrophilic Interaction Chromatography (HILIC) and Isotope Tagging for Off-Line LC-NMR Applications in Metabolite Analysis
Metabolites 2013, 3(3), 575-591; doi:10.3390/metabo3030575
Received: 17 May 2013 / Revised: 6 July 2013 / Accepted: 15 July 2013 / Published: 18 July 2013
Cited by 1 | PDF Full-text (1566 KB) | HTML Full-text | XML Full-text
Abstract
The complementary use of liquid chromatography (LC) and nuclear magnetic resonance (NMR) has shown high utility in a variety of fields. While the significant benefit of spectral simplification can be achieved for the analysis of complex samples, other limitations remain. For example, [...] Read more.
The complementary use of liquid chromatography (LC) and nuclear magnetic resonance (NMR) has shown high utility in a variety of fields. While the significant benefit of spectral simplification can be achieved for the analysis of complex samples, other limitations remain. For example, 1H LC-NMR suffers from pH dependent chemical shift variations, especially during urine analysis, owing to the high physiological variation of urine pH. Additionally, large solvent signals from the mobile phase in LC can obscure lower intensity signals and severely limit the number of metabolites detected. These limitations, along with sample dilution, hinder the ability to make reliable chemical shift assignments. Recently, stable isotopic labeling has been used to detect quantitatively specific classes of metabolites of interest in biofluids. Here we present a strategy that explores the combined use of two-dimensional hydrophilic interaction chromatography (HILIC) and isotope tagged NMR for the unambiguous identification of carboxyl containing metabolites present in human urine. The ability to separate structurally related compounds chromatographically, in off-line mode, followed by detection using 1H-15N 2D HSQC (two-dimensional heteronuclear single quantum coherence) spectroscopy, resulted in the assignment of low concentration carboxyl-containing metabolites from a library of isotope labeled compounds. The quantitative nature of this strategy is also demonstrated. Full article
Open AccessArticle Evaluation of Extraction Protocols for Simultaneous Polar and Non-Polar Yeast Metabolite Analysis Using Multivariate Projection Methods
Metabolites 2013, 3(3), 592-605; doi:10.3390/metabo3030592
Received: 17 June 2013 / Revised: 17 July 2013 / Accepted: 17 July 2013 / Published: 23 July 2013
Cited by 7 | PDF Full-text (397 KB) | HTML Full-text | XML Full-text
Abstract
Metabolomic and lipidomic approaches aim to measure metabolites or lipids in the cell. Metabolite extraction is a key step in obtaining useful and reliable data for successful metabolite studies. Significant efforts have been made to identify the optimal extraction protocol for various [...] Read more.
Metabolomic and lipidomic approaches aim to measure metabolites or lipids in the cell. Metabolite extraction is a key step in obtaining useful and reliable data for successful metabolite studies. Significant efforts have been made to identify the optimal extraction protocol for various platforms and biological systems, for both polar and non-polar metabolites. Here we report an approach utilizing chemoinformatics for systematic comparison of protocols to extract both from a single sample of the model yeast organism Saccharomyces cerevisiae. Three chloroform/methanol/water partitioning based extraction protocols found in literature were evaluated for their effectiveness at reproducibly extracting both polar and non-polar metabolites. Fatty acid methyl esters and methoxyamine/trimethylsilyl derivatized aqueous compounds were analyzed by gas chromatography mass spectrometry to evaluate non-polar or polar metabolite analysis. The comparative breadth and amount of recovered metabolites was evaluated using multivariate projection methods. This approach identified an optimal protocol consisting of 64 identified polar metabolites from 105 ion hits and 12 fatty acids recovered, and will potentially attenuate the error and variation associated with combining metabolite profiles from different samples for untargeted analysis with both polar and non-polar analytes. It also confirmed the value of using multivariate projection methods to compare established extraction protocols. Full article
(This article belongs to the Special Issue Sample Preparation for Metabolite Analysis)
Open AccessArticle Acylcarnitine Profiles in Acetaminophen Toxicity in the Mouse: Comparison to Toxicity, Metabolism and Hepatocyte Regeneration
Metabolites 2013, 3(3), 606-622; doi:10.3390/metabo3030606
Received: 16 April 2013 / Revised: 7 June 2013 / Accepted: 22 July 2013 / Published: 2 August 2013
Cited by 11 | PDF Full-text (644 KB) | HTML Full-text | XML Full-text
Abstract
High doses of acetaminophen (APAP) result in hepatotoxicity that involves metabolic activation of the parent compound, covalent binding of the reactive intermediate N-acetyl-p-benzoquinone imine (NAPQI) to liver proteins, and depletion of hepatic glutathione. Impaired fatty acid β-oxidation has been implicated in [...] Read more.
High doses of acetaminophen (APAP) result in hepatotoxicity that involves metabolic activation of the parent compound, covalent binding of the reactive intermediate N-acetyl-p-benzoquinone imine (NAPQI) to liver proteins, and depletion of hepatic glutathione. Impaired fatty acid β-oxidation has been implicated in previous studies of APAP-induced hepatotoxicity. To better understand relationships between toxicity and fatty acid β-oxidation in the liver in APAP toxicity, metabolomic assays for long chain acylcarnitines were examined in relationship to established markers of liver toxicity, oxidative metabolism, and liver regeneration in a time course study in mice. Male B6C3F1 mice were treated with APAP (200 mg/kg IP) or saline and sacrificed at 1, 2, 4, 8, 24 or 48 h after APAP. At 1 h, hepatic glutathione was depleted and APAP protein adducts were markedly increased. Alanine aminotransferase (ALT) levels were elevated at 4 and 8 h, while proliferating cell nuclear antigen (PCNA) expression, indicative of hepatocyte regeneration, was apparent at 24 h and 48 h. Elevations of palmitoyl, oleoyl and myristoyl carnitine were apparent by 2–4 h, concurrent with the onset of Oil Red O staining in liver sections. By 8 h, acylcarnitine levels were below baseline levels and remained low at 24 and 48 h. A partial least squares (PLS) model suggested a direct association of acylcarnitine accumulation in serum to APAP protein adduct and hepatic glutathione levels in mice. Overall, the kinetics of serum acylcarnitines in APAP toxicity in mice followed a biphasic pattern involving early elevation after the metabolism phases of toxicity and later depletion of acylcarnitines. Full article
Open AccessArticle Tackling CASMI 2012: Solutions from MetFrag and MetFusion
Metabolites 2013, 3(3), 623-636; doi:10.3390/metabo3030623
Received: 24 April 2013 / Revised: 29 July 2013 / Accepted: 30 July 2013 / Published: 5 August 2013
Cited by 3 | PDF Full-text (572 KB) | HTML Full-text | XML Full-text
Abstract
The task in the critical assessment of small molecule identification (CASMI) contest category 2 was to determine the identification of (initially) unknown compounds for which high-resolution tandem mass spectra were published. We focused on computer-assisted methods that tried to correctly identify the [...] Read more.
The task in the critical assessment of small molecule identification (CASMI) contest category 2 was to determine the identification of (initially) unknown compounds for which high-resolution tandem mass spectra were published. We focused on computer-assisted methods that tried to correctly identify the compound automatically and entered the contest with MetFrag and MetFusion to score candidate structures retrieved from the PubChem structure database. MetFrag was combined with the metabolite-likeness score, which helped to improve the performance for the natural product challenges. We present the results, discuss the performance, and give details of how to interpret the MetFrag and MetFusion output. Full article
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Open AccessArticle Quantitative Determination of Common Urinary Odorants and Their Glucuronide Conjugates in Human Urine
Metabolites 2013, 3(3), 637-657; doi:10.3390/metabo3030637
Received: 13 May 2013 / Revised: 24 July 2013 / Accepted: 26 July 2013 / Published: 7 August 2013
Cited by 3 | PDF Full-text (486 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Our previous study on the identification of common odorants and their conjugates in human urine demonstrated that this substance fraction is a little-understood but nonetheless a promising medium for analysis and diagnostics in this easily accessible physiological medium. Smell as an indicator [...] Read more.
Our previous study on the identification of common odorants and their conjugates in human urine demonstrated that this substance fraction is a little-understood but nonetheless a promising medium for analysis and diagnostics in this easily accessible physiological medium. Smell as an indicator for diseases, or volatile excretion in the course of dietary processes bares high potential for a series of physiological insights. Still, little is known today about the quantitative composition of odorous or volatile targets, as well as their non-volatile conjugates, both with regard to their common occurrence in urine of healthy subjects, as well as in that of individuals suffering from diseases or other physiological misbalancing. Accordingly, the aim of our study was to develop a highly sensitive and selective approach to determine the common quantitative composition of selected odorant markers in healthy human subjects, as well as their corresponding glucuronide conjugates. We used one- and two-dimensional high resolution gas chromatography-mass spectrometry in combination with stable isotope dilution assays to quantify commonly occurring and potent odorants in human urine. The studies were carried out on both native urine and on urine that had been treated by glucuronidase assays, with analysis of the liberated odor-active compounds using the same techniques. Analytical data are discussed with regard to their potential translation as future diagnostic tool. Full article
Open AccessArticle Global Metabolomics Reveals Urinary Biomarkers of Breast Cancer in a MCF-7 Xenograft Mouse Model
Metabolites 2013, 3(3), 658-672; doi:10.3390/metabo3030658
Received: 21 June 2013 / Revised: 2 August 2013 / Accepted: 2 August 2013 / Published: 7 August 2013
Cited by 2 | PDF Full-text (950 KB) | HTML Full-text | XML Full-text
Abstract
Global metabolomics analysis has the potential to uncover novel metabolic pathways that are differentially regulated during carcinogenesis, aiding in biomarker discovery for early diagnosis and remission monitoring. Metabolomics studies with human samples can be problematic due to high inter-individual variation; however xenografts [...] Read more.
Global metabolomics analysis has the potential to uncover novel metabolic pathways that are differentially regulated during carcinogenesis, aiding in biomarker discovery for early diagnosis and remission monitoring. Metabolomics studies with human samples can be problematic due to high inter-individual variation; however xenografts of human cancers in mice offer a well-controlled model system. Urine was collected from a xenograft mouse model of MCF-7 breast cancer and analyzed by mass spectrometry-based metabolomics to identify metabolites associated with cancer progression. Over 10 weeks, 24 h urine was collected weekly from control mice, mice dosed with estradiol cypionate (1 mg/mL), mice inoculated with MCF-7 cells (1 × 107) and estradiol cypionate (1 mg/mL), and mice dosed with MCF-7 cells (1 × 107) only (n = 10/group). Mice that received both estradiol cypionate and MCF-7 cells developed tumors from four weeks after inoculation. Five urinary metabolites were identified that were associated with breast cancer; enterolactone glucuronide, coumaric acid sulfate, capric acid glucuronide, an unknown metabolite, and a novel mammalian metabolite, “taurosebacic acid”. These metabolites revealed a correlation between tumor growth, fatty acid synthesis, and potential anti-proliferative effects of gut microbiota-metabolized food derivatives. These biomarkers may be of value for early diagnosis of cancer, monitoring of cancer therapeutics, and may also lead to future mechanistic studies. Full article
(This article belongs to the Special Issue Cancer Metabolomics)
Open AccessArticle On Functional Module Detection in Metabolic Networks
Metabolites 2013, 3(3), 673-700; doi:10.3390/metabo3030673
Received: 31 May 2013 / Revised: 30 July 2013 / Accepted: 30 July 2013 / Published: 12 August 2013
Cited by 2 | PDF Full-text (579 KB) | HTML Full-text | XML Full-text
Abstract
Functional modules of metabolic networks are essential for understanding the metabolism of an organism as a whole. With the vast amount of experimental data and the construction of complex and large-scale, often genome-wide, models, the computer-aided identification of functional modules becomes more [...] Read more.
Functional modules of metabolic networks are essential for understanding the metabolism of an organism as a whole. With the vast amount of experimental data and the construction of complex and large-scale, often genome-wide, models, the computer-aided identification of functional modules becomes more and more important. Since steady states play a key role in biology, many methods have been developed in that context, for example, elementary flux modes, extreme pathways, transition invariants and place invariants. Metabolic networks can be studied also from the point of view of graph theory, and algorithms for graph decomposition have been applied for the identification of functional modules. A prominent and currently intensively discussed field of methods in graph theory addresses the Q-modularity. In this paper, we recall known concepts of module detection based on the steady-state assumption, focusing on transition-invariants (elementary modes) and their computation as minimal solutions of systems of Diophantine equations. We present the Fourier-Motzkin algorithm in detail. Afterwards, we introduce the Q-modularity as an example for a useful non-steady-state method and its application to metabolic networks. To illustrate and discuss the concepts of invariants and Q-modularity, we apply a part of the central carbon metabolism in potato tubers (Solanum tuberosum) as running example. The intention of the paper is to give a compact presentation of known steady-state concepts from a graph-theoretical viewpoint in the context of network decomposition and reduction and to introduce the application of Q-modularity to metabolic Petri net models. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
Open AccessArticle Electrospray Quadrupole Travelling Wave Ion Mobility Time-of-Flight Mass Spectrometry for the Detection of Plasma Metabolome Changes Caused by Xanthohumol in Obese Zucker (fa/fa) Rats
Metabolites 2013, 3(3), 701-717; doi:10.3390/metabo3030701
Received: 3 June 2013 / Revised: 1 August 2013 / Accepted: 7 August 2013 / Published: 13 August 2013
Cited by 9 | PDF Full-text (1085 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This study reports on the use of traveling wave ion mobility quadrupole time-of-flight (ToF) mass spectrometry for plasma metabolomics. Plasma metabolite profiles of obese Zucker fa/fa rats were obtained after the administration of different oral doses of Xanthohumol; a hop-derived dietary supplement. [...] Read more.
This study reports on the use of traveling wave ion mobility quadrupole time-of-flight (ToF) mass spectrometry for plasma metabolomics. Plasma metabolite profiles of obese Zucker fa/fa rats were obtained after the administration of different oral doses of Xanthohumol; a hop-derived dietary supplement. Liquid chromatography coupled data independent tandem mass spectrometry (LC-MSE) and LC-ion mobility spectrometry (IMS)-MSE acquisitions were conducted in both positive and negative modes using a Synapt G2 High Definition Mass Spectrometry (HDMS) instrument. This method provides identification of metabolite classes in rat plasma using parallel alternating low energy and high energy collision spectral acquisition modes. Data sets were analyzed using pattern recognition methods. Statistically significant (p < 0.05 and fold change (FC) threshold > 1.5) features were selected to identify the up-/down-regulated metabolite classes. Ion mobility data visualized using drift scope software provided a graphical read-out of differences in metabolite classes. Full article
(This article belongs to the Special Issue Response to Environment and Stress Metabolism)
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Open AccessArticle 1H NMR-Based Metabolomic Analysis of Sub-Lethal Perfluorooctane Sulfonate Exposure to the Earthworm, Eisenia fetida, in Soil
Metabolites 2013, 3(3), 718-740; doi:10.3390/metabo3030718
Received: 1 July 2013 / Revised: 15 July 2013 / Accepted: 19 August 2013 / Published: 27 August 2013
Cited by 4 | PDF Full-text (789 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
1H NMR-based metabolomics was used to measure the response of Eisenia fetida earthworms after exposure to sub-lethal concentrations of perfluorooctane sulfonate (PFOS) in soil. Earthworms were exposed to a range of PFOS concentrations (five, 10, 25, 50, 100 or 150 mg/kg) [...] Read more.
1H NMR-based metabolomics was used to measure the response of Eisenia fetida earthworms after exposure to sub-lethal concentrations of perfluorooctane sulfonate (PFOS) in soil. Earthworms were exposed to a range of PFOS concentrations (five, 10, 25, 50, 100 or 150 mg/kg) for two, seven and fourteen days. Earthworm tissues were extracted and analyzed by 1H NMR. Multivariate statistical analysis of the metabolic response of E. fetida to PFOS exposure identified time-dependent responses that were comprised of two separate modes of action: a non-polar narcosis type mechanism after two days of exposure and increased fatty acid oxidation after seven and fourteen days of exposure. Univariate statistical analysis revealed that 2-hexyl-5-ethyl-3-furansulfonate (HEFS), betaine, leucine, arginine, glutamate, maltose and ATP are potential indicators of PFOS exposure, as the concentrations of these metabolites fluctuated significantly. Overall, NMR-based metabolomic analysis suggests elevated fatty acid oxidation, disruption in energy metabolism and biological membrane structure and a possible interruption of ATP synthesis. These conclusions obtained from analysis of the metabolic profile in response to sub-lethal PFOS exposure indicates that NMR-based metabolomics is an excellent discovery tool when the mode of action (MOA) of contaminants is not clearly defined. Full article
(This article belongs to the Special Issue Response to Environment and Stress Metabolism)
Open AccessArticle Integrative Analysis of Longitudinal Metabolomics Data from a Personal Multi-Omics Profile
Metabolites 2013, 3(3), 741-760; doi:10.3390/metabo3030741
Received: 8 June 2013 / Revised: 30 July 2013 / Accepted: 5 August 2013 / Published: 3 September 2013
Cited by 16 | PDF Full-text (1066 KB) | HTML Full-text | XML Full-text
Abstract
The integrative personal omics profile (iPOP) is a pioneering study that combines genomics, transcriptomics, proteomics, metabolomics and autoantibody profiles from a single individual over a 14-month period. The observation period includes two episodes of viral infection: a human rhinovirus and a respiratory [...] Read more.
The integrative personal omics profile (iPOP) is a pioneering study that combines genomics, transcriptomics, proteomics, metabolomics and autoantibody profiles from a single individual over a 14-month period. The observation period includes two episodes of viral infection: a human rhinovirus and a respiratory syncytial virus. The profile studies give an informative snapshot into the biological functioning of an organism. We hypothesize that pathway expression levels are associated with disease status. To test this hypothesis, we use biological pathways to integrate metabolomics and proteomics iPOP data. The approach computes the pathways’ differential expression levels at each time point, while taking into account the pathway structure and the longitudinal design. The resulting pathway levels show strong association with the disease status. Further, we identify temporal patterns in metabolite expression levels. The changes in metabolite expression levels also appear to be consistent with the disease status. The results of the integrative analysis suggest that changes in biological pathways may be used to predict and monitor the disease. The iPOP experimental design, data acquisition and analysis issues are discussed within the broader context of personal profiling. Full article
(This article belongs to the Special Issue Integrative Metabolomics)
Open AccessArticle Physiological and Molecular Timing of the Glucose to Acetate Transition in Escherichia coli
Metabolites 2013, 3(3), 820-837; doi:10.3390/metabo3030820
Received: 7 June 2013 / Revised: 28 August 2013 / Accepted: 4 September 2013 / Published: 20 September 2013
Cited by 5 | PDF Full-text (1976 KB) | HTML Full-text | XML Full-text
Abstract
The glucose-acetate transition in Escherichia coli is a classical model of metabolic adaptation. Here, we describe the dynamics of the molecular processes involved in this metabolic transition, with a particular focus on glucose exhaustion. Although changes in the metabolome were observed before [...] Read more.
The glucose-acetate transition in Escherichia coli is a classical model of metabolic adaptation. Here, we describe the dynamics of the molecular processes involved in this metabolic transition, with a particular focus on glucose exhaustion. Although changes in the metabolome were observed before glucose exhaustion, our results point to a massive reshuffling at both the transcriptome and metabolome levels in the very first min following glucose exhaustion. A new transcriptional pattern, involving a change in genome expression in one-sixth of the E. coli genome, was established within 10 min and remained stable until the acetate was completely consumed. Changes in the metabolome took longer and stabilized 40 min after glucose exhaustion. Integration of multi-omics data revealed different modifications and timescales between the transcriptome and metabolome, but both point to a rapid adaptation of less than an hour. This work provides detailed information on the order, timing and extent of the molecular and physiological events that occur during the glucose-acetate transition and that are of particular interest for the development of dynamic models of metabolism. Full article
(This article belongs to the Special Issue Integrative Metabolomics)
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Open AccessArticle A Novel Methodology to Estimate Metabolic Flux Distributions in Constraint-Based Models
Metabolites 2013, 3(3), 838-852; doi:10.3390/metabo3030838
Received: 1 August 2013 / Revised: 10 September 2013 / Accepted: 10 September 2013 / Published: 20 September 2013
Cited by 3 | PDF Full-text (434 KB) | HTML Full-text | XML Full-text
Abstract
Quite generally, constraint-based metabolic flux analysis describes the space of viable flux configurations for a metabolic network as a high-dimensional polytope defined by the linear constraints that enforce the balancing of production and consumption fluxes for each chemical species in the system. [...] Read more.
Quite generally, constraint-based metabolic flux analysis describes the space of viable flux configurations for a metabolic network as a high-dimensional polytope defined by the linear constraints that enforce the balancing of production and consumption fluxes for each chemical species in the system. In some cases, the complexity of the solution space can be reduced by performing an additional optimization, while in other cases, knowing the range of variability of fluxes over the polytope provides a sufficient characterization of the allowed configurations. There are cases, however, in which the thorough information encoded in the individual distributions of viable fluxes over the polytope is required. Obtaining such distributions is known to be a highly challenging computational task when the dimensionality of the polytope is sufficiently large, and the problem of developing cost-effective ad hoc algorithms has recently seen a major surge of interest. Here, we propose a method that allows us to perform the required computation heuristically in a time scaling linearly with the number of reactions in the network, overcoming some limitations of similar techniques employed in recent years. As a case study, we apply it to the analysis of the human red blood cell metabolic network, whose solution space can be sampled by different exact techniques, like Hit-and-Run Monte Carlo (scaling roughly like the third power of the system size). Remarkably accurate estimates for the true distributions of viable reaction fluxes are obtained, suggesting that, although further improvements are desirable, our method enhances our ability to analyze the space of allowed configurations for large biochemical reaction networks. Full article
(This article belongs to the Special Issue Data Processing in Metabolomics)

Review

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Open AccessReview A Review of Applications of Metabolomics in Cancer
Metabolites 2013, 3(3), 552-574; doi:10.3390/metabo3030552
Received: 24 April 2013 / Revised: 17 May 2013 / Accepted: 24 June 2013 / Published: 5 July 2013
Cited by 25 | PDF Full-text (262 KB) | HTML Full-text | XML Full-text
Abstract
Cancer is a devastating disease that alters the metabolism of a cell and the surrounding milieu. Metabolomics is a growing and powerful technology capable of detecting hundreds to thousands of metabolites in tissues and biofluids. The recent advances in metabolomics technologies have [...] Read more.
Cancer is a devastating disease that alters the metabolism of a cell and the surrounding milieu. Metabolomics is a growing and powerful technology capable of detecting hundreds to thousands of metabolites in tissues and biofluids. The recent advances in metabolomics technologies have enabled a deeper investigation into the metabolism of cancer and a better understanding of how cancer cells use glycolysis, known as the “Warburg effect,” advantageously to produce the amino acids, nucleotides and lipids necessary for tumor proliferation and vascularization. Currently, metabolomics research is being used to discover diagnostic cancer biomarkers in the clinic, to better understand its complex heterogeneous nature, to discover pathways involved in cancer that could be used for new targets and to monitor metabolic biomarkers during therapeutic intervention. These metabolomics approaches may also provide clues to personalized cancer treatments by providing useful information to the clinician about the cancer patient’s response to medical interventions. Full article
(This article belongs to the Special Issue Cancer Metabolomics)
Open AccessReview Coordinating Metabolite Changes with Our Perception of Plant Abiotic Stress Responses: Emerging Views Revealed by Integrative—Omic Analyses
Metabolites 2013, 3(3), 761-786; doi:10.3390/metabo3030761
Received: 18 July 2013 / Revised: 21 August 2013 / Accepted: 28 August 2013 / Published: 6 September 2013
Cited by 2 | PDF Full-text (731 KB) | HTML Full-text | XML Full-text
Abstract
Metabolic configuration and adaptation under a range of abiotic stresses, including drought, heat, salinity, cold, and nutrient deprivation, are subjected to an intricate span of molecular pathways that work in parallel in order to enhance plant fitness and increase stress tolerance. In [...] Read more.
Metabolic configuration and adaptation under a range of abiotic stresses, including drought, heat, salinity, cold, and nutrient deprivation, are subjected to an intricate span of molecular pathways that work in parallel in order to enhance plant fitness and increase stress tolerance. In recent years, unprecedented advances have been made in identifying and linking different abiotic stresses, and the current challenge in plant molecular biology is deciphering how the signaling responses are integrated and transduced throughout metabolism. Metabolomics have often played a fundamental role in elucidating the distinct and overlapping biochemical changes that occur in plants. However, a far greater understanding and appreciation of the complexity in plant metabolism under specific stress conditions have become apparent when combining metabolomics with other—omic platforms. This review focuses on recent advances made in understanding the global changes occurring in plant metabolism under abiotic stress conditions using metabolite profiling as an integrated discovery platform. Full article
(This article belongs to the Special Issue Metabolomics in Plant Metabolic Engineering)
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Other

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Open AccessTechnical Note A Comprehensive Workflow of Mass Spectrometry-Based Untargeted Metabolomics in Cancer Metabolic Biomarker Discovery Using Human Plasma and Urine
Metabolites 2013, 3(3), 787-819; doi:10.3390/metabo3030787
Received: 10 July 2013 / Revised: 30 August 2013 / Accepted: 2 September 2013 / Published: 11 September 2013
Cited by 6 | PDF Full-text (1870 KB) | HTML Full-text | XML Full-text
Abstract
Current available biomarkers lack sensitivity and/or specificity for early detection of cancer. To address this challenge, a robust and complete workflow for metabolic profiling and data mining is described in details. Three independent and complementary analytical techniques for metabolic profiling are applied: [...] Read more.
Current available biomarkers lack sensitivity and/or specificity for early detection of cancer. To address this challenge, a robust and complete workflow for metabolic profiling and data mining is described in details. Three independent and complementary analytical techniques for metabolic profiling are applied: hydrophilic interaction liquid chromatography (HILIC–LC), reversed-phase liquid chromatography (RP–LC), and gas chromatography (GC). All three techniques are coupled to a mass spectrometer (MS) in the full scan acquisition mode, and both unsupervised and supervised methods are used for data mining. The univariate and multivariate feature selection are used to determine subsets of potentially discriminative predictors. These predictors are further identified by obtaining accurate masses and isotopic ratios using selected ion monitoring (SIM) and data-dependent MS/MS and/or accurate mass MSn ion tree scans utilizing high resolution MS. A list combining all of the identified potential biomarkers generated from different platforms and algorithms is used for pathway analysis. Such a workflow combining comprehensive metabolic profiling and advanced data mining techniques may provide a powerful approach for metabolic pathway analysis and biomarker discovery in cancer research. Two case studies with previous published data are adapted and included in the context to elucidate the application of the workflow. Full article
(This article belongs to the Special Issue Analytical Techniques in Metabolomics)
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