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Metabolites, Volume 2, Issue 4 (December 2012), Pages 648-1138

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Research

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Open AccessArticle Structural Identification of O-Linked Oligosaccharides Using Exoglycosidases and MSn Together with UniCarb-DB Fragment Spectra Comparison
Metabolites 2012, 2(4), 648-666; doi:10.3390/metabo2040648
Received: 17 July 2012 / Revised: 18 September 2012 / Accepted: 28 September 2012 / Published: 8 October 2012
Cited by 4 | PDF Full-text (796 KB) | HTML Full-text | XML Full-text
Abstract
The availability of specific exoglycosidases alongside a spectral library of O-linked oligosaccharide collision induced dissociation (CID) MS fragments, UniCarb-DB, provides a pathway to make the elucidation of O-linked oligosaccharides more efficient. Here, we advise an approach of exoglycosidase-digestion of O [...] Read more.
The availability of specific exoglycosidases alongside a spectral library of O-linked oligosaccharide collision induced dissociation (CID) MS fragments, UniCarb-DB, provides a pathway to make the elucidation of O-linked oligosaccharides more efficient. Here, we advise an approach of exoglycosidase-digestion of O-linked oligosaccharide mixtures, for structures that do not provide confirmative spectra. The combination of specific exoglycosidase digestion and MS2 matching of the exoglycosidase products with structures from UniCarb-DB, allowed the assignment of unknown structures. This approach was illustrated by treating sialylated core 2 O-linked oligosaccharides, released from the human synovial glycoprotein (lubricin), with a α2–3 specific sialidase. This methodology demonstrated the exclusive 3 linked nature of the sialylation of core 2 oligosaccharides on lubricin. When specific exoglycosidases were not available, MS3 spectral matching using standards was used. This allowed the unusual 4-linked terminal GlcNAc epitope in a porcine stomach to be identified in the GlcNAc1-4Galb1–3(GlcNAcb1-6)GalNAcol structure, indicating the antibacterial epitope GlcNAca1–4. In total, 13 structures were identified using exoglycosidase and MSn, alongside UniCarb-DB fragment spectra comparison. UniCarb-DB could also be used to identify the specificity of unknown exoglycosidases in human saliva. Endogenous salivary exoglycosidase activity on mucin oligosaccharides could be monitored by comparing the generated tandem MS spectra with those present in UniCarb-DB, showing that oral exoglycosidases were dominated by sialidases with a higher activity towards 3-linked sialic acid rather than 6-linked sialic acid. Full article
(This article belongs to the Special Issue Glycomics and Glycoproteomics)
Open AccessArticle From Cycling Between Coupled Reactions to the Cross-Bridge Cycle: Mechanical Power Output as an Integral Part of Energy Metabolism
Metabolites 2012, 2(4), 667-700; doi:10.3390/metabo2040667
Received: 20 July 2012 / Revised: 7 September 2012 / Accepted: 24 September 2012 / Published: 8 October 2012
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Abstract
ATP delivery and its usage are achieved by cycling of respective intermediates through interconnected coupled reactions. At steady state, cycling between coupled reactions always occurs at zero resistance of the whole cycle without dissipation of free energy. The cross-bridge cycle can also [...] Read more.
ATP delivery and its usage are achieved by cycling of respective intermediates through interconnected coupled reactions. At steady state, cycling between coupled reactions always occurs at zero resistance of the whole cycle without dissipation of free energy. The cross-bridge cycle can also be described by a system of coupled reactions: one energising reaction, which energises myosin heads by coupled ATP splitting, and one de-energising reaction, which transduces free energy from myosin heads to coupled actin movement. The whole cycle of myosin heads via cross-bridge formation and dissociation proceeds at zero resistance. Dissipation of free energy from coupled reactions occurs whenever the input potential overcomes the counteracting output potential. In addition, dissipation is produced by uncoupling. This is brought about by a load dependent shortening of the cross-bridge stroke to zero, which allows isometric force generation without mechanical power output. The occurrence of maximal efficiency is caused by uncoupling. Under coupled conditions, Hill’s equation (velocity as a function of load) is fulfilled. In addition, force and shortening velocity both depend on [Ca2+]. Muscular fatigue is triggered when ATP consumption overcomes ATP delivery. As a result, the substrate of the cycle, [MgATP2−], is reduced. This leads to a switch off of cycling and ATP consumption, so that a recovery of [ATP] is possible. In this way a potentially harmful, persistent low energy state of the cell can be avoided. Full article
(This article belongs to the Special Issue Metabolic Network Models)
Open AccessArticle Differentiating Hepatocellular Carcinoma from Hepatitis C Using Metabolite Profiling
Metabolites 2012, 2(4), 701-716; doi:10.3390/metabo2040701
Received: 1 August 2012 / Revised: 12 September 2012 / Accepted: 25 September 2012 / Published: 10 October 2012
Cited by 7 | PDF Full-text (455 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Hepatocellular carcinoma (HCC) accounts for most liver cancer cases worldwide. Contraction of the hepatitis C virus (HCV) is considered a major risk factor for liver cancer. In order to identify the risk of cancer, metabolic profiling of serum samples from patients with [...] Read more.
Hepatocellular carcinoma (HCC) accounts for most liver cancer cases worldwide. Contraction of the hepatitis C virus (HCV) is considered a major risk factor for liver cancer. In order to identify the risk of cancer, metabolic profiling of serum samples from patients with HCC (n=40) and HCV (n=22) was performed by 1H nuclear magnetic resonance spectroscopy. Multivariate statistical analysis showed a distinct separation of the two patient cohorts, indicating a distinct metabolic difference between HCC and HCV patient groups based on signals from lipids and other individual metabolites. Univariate analysis showed that three metabolites (choline, valine and creatinine) were significantly altered in HCC. A PLS-DA model based on these three metabolites showed a sensitivity of 80%, specificity of 71% and an area under the receiver operating curve of 0.83, outperforming the clinical marker alpha-fetoprotein (AFP). The robustness of the model was tested using Monte-Carlo cross validation (MCCV). This study showed that metabolite profiling could provide an alternative approach for HCC screening in HCV patients, many of whom have high risk for developing liver cancer. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
Open AccessArticle Influence of the RelA Activity on E. coli Metabolism by Metabolite Profiling of Glucose-Limited Chemostat Cultures
Metabolites 2012, 2(4), 717-732; doi:10.3390/metabo2040717
Received: 3 August 2012 / Revised: 13 September 2012 / Accepted: 28 September 2012 / Published: 12 October 2012
Cited by 3 | PDF Full-text (755 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Metabolite profiling of E. coli W3110 and the isogenic DrelA mutant cells was used to characterize the RelA-dependent stringent control of metabolism under different growth conditions. Metabolic profiles were obtained by gas chromatography–mass spectrometry (GC-MS) analysis and revealed significant differences [...] Read more.
Metabolite profiling of E. coli W3110 and the isogenic DrelA mutant cells was used to characterize the RelA-dependent stringent control of metabolism under different growth conditions. Metabolic profiles were obtained by gas chromatography–mass spectrometry (GC-MS) analysis and revealed significant differences between E. coli strains grown at different conditions. Major differences between the two strains were assessed in the levels of amino acids and fatty acids and their precursor metabolites, especially when growing at the lower dilution rates, demonstrating differences in their metabolic behavior. Despite the fatty acid biosynthesis being the most affected due to the lack of the RelA activity, other metabolic pathways involving succinate, lactate and threonine were also affected. Overall, metabolite profiles indicate that under nutrient-limiting conditions the RelA-dependent stringent response may be elicited and promotes key changes in the E. coli metabolism. Full article
Open AccessArticle Characterization of the Interaction Between the Small Regulatory Peptide SgrT and the EIICBGlc of the Glucose-Phosphotransferase System of E. coli K-12
Metabolites 2012, 2(4), 756-774; doi:10.3390/metabo2040756
Received: 1 August 2012 / Revised: 29 August 2012 / Accepted: 10 October 2012 / Published: 16 October 2012
Cited by 3 | PDF Full-text (425 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Escherichia coli is a widely used microorganism in biotechnological processes. An obvious goal for current scientific and technical research in this field is the search for new tools to optimize productivity. Usually glucose is the preferred carbon source in biotechnological applications. In [...] Read more.
Escherichia coli is a widely used microorganism in biotechnological processes. An obvious goal for current scientific and technical research in this field is the search for new tools to optimize productivity. Usually glucose is the preferred carbon source in biotechnological applications. In E. coli, glucose is taken up by the phosphoenolpyruvate-dependent glucose phosphotransferase system (PTS). The regulation of the ptsG gene for the glucose transporter is very complex and involves several regulatory proteins. Recently, a novel posttranscriptional regulation system has been identified which consists of a small regulatory RNA SgrS and a small regulatory polypeptide called SgrT. During the accumulation of glucose-6-phosphate or fructose-6-phosphate, SgrS is involved in downregulation of ptsG mRNA stability, whereas SgrT inhibits glucose transport activity by a yet unknown mechanism. The function of SgrS has been studied intensively. In contrast, the knowledge about the function of SgrT is still limited. Therefore, in this paper, we focused our interest on the regulation of glucose transport activity by SgrT. We identified the SgrT target sequence within the glucose transporter and characterized the interaction in great detail. Finally, we suggest a novel experimental approach to regulate artificially carbohydrate uptake in E. coli to minimize metabolic overflow in biotechnological applications. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
Open AccessArticle Validated and Predictive Processing of Gas Chromatography-Mass Spectrometry Based Metabolomics Data for Large Scale Screening Studies, Diagnostics and Metabolite Pattern Verification
Metabolites 2012, 2(4), 796-817; doi:10.3390/metabo2040796
Received: 11 September 2012 / Revised: 15 October 2012 / Accepted: 16 October 2012 / Published: 31 October 2012
Cited by 3 | PDF Full-text (622 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The suggested approach makes it feasible to screen large metabolomics data, sample sets with retained data quality or to retrieve significant metabolic information from small sample sets that can be verified over multiple studies. Hierarchical multivariate curve resolution (H-MCR), followed by orthogonal [...] Read more.
The suggested approach makes it feasible to screen large metabolomics data, sample sets with retained data quality or to retrieve significant metabolic information from small sample sets that can be verified over multiple studies. Hierarchical multivariate curve resolution (H-MCR), followed by orthogonal partial least squares discriminant analysis (OPLS-DA) was used for processing and classification of gas chromatography/time of flight mass spectrometry (GC/TOFMS) data characterizing human serum samples collected in a study of strenuous physical exercise. The efficiency of predictive H-MCR processing of representative sample subsets, selected by chemometric approaches, for generating high quality data was proven. Extensive model validation by means of cross-validation and external predictions verified the robustness of the extracted metabolite patterns in the data. Comparisons of extracted metabolite patterns between models emphasized the reliability of the methodology in a biological information context. Furthermore, the high predictive power in longitudinal data provided proof for the potential use in clinical diagnosis. Finally, the predictive metabolite pattern was interpreted physiologically, highlighting the biological relevance of the diagnostic pattern. Full article
(This article belongs to the Special Issue Data Processing in Metabolomics)
Open AccessArticle Determining Enzyme Kinetics for Systems Biology with Nuclear Magnetic Resonance Spectroscopy
Metabolites 2012, 2(4), 818-843; doi:10.3390/metabo2040818
Received: 20 August 2012 / Revised: 12 October 2012 / Accepted: 29 October 2012 / Published: 6 November 2012
Cited by 3 | PDF Full-text (3440 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Enzyme kinetics for systems biology should ideally yield information about the enzyme’s activity under in vivo conditions, including such reaction features as substrate cooperativity, reversibility and allostery, and be applicable to enzymatic reactions with multiple substrates. A large body of enzyme-kinetic data [...] Read more.
Enzyme kinetics for systems biology should ideally yield information about the enzyme’s activity under in vivo conditions, including such reaction features as substrate cooperativity, reversibility and allostery, and be applicable to enzymatic reactions with multiple substrates. A large body of enzyme-kinetic data in the literature is based on the uni-substrate Michaelis–Menten equation, which makes unnatural assumptions about enzymatic reactions (e.g., irreversibility), and its application in systems biology models is therefore limited. To overcome this limitation, we have utilised NMR time-course data in a combined theoretical and experimental approach to parameterize the generic reversible Hill equation, which is capable of describing enzymatic reactions in terms of all the properties mentioned above and has fewer parameters than detailed mechanistic kinetic equations; these parameters are moreover defined operationally. Traditionally, enzyme kinetic data have been obtained from initial-rate studies, often using assays coupled to NAD(P)H-producing or NAD(P)H-consuming reactions. However, these assays are very labour-intensive, especially for detailed characterisation of multi-substrate reactions. We here present a cost-effective and relatively rapid method for obtaining enzyme-kinetic parameters from metabolite time-course data generated using NMR spectroscopy. The method requires fewer runs than traditional initial-rate studies and yields more information per experiment, as whole time-courses are analyzed and used for parameter fitting. Additionally, this approach allows real-time simultaneous quantification of all metabolites present in the assay system (including products and allosteric modifiers), which demonstrates the superiority of NMR over traditional spectrophotometric coupled enzyme assays. The methodology presented is applied to the elucidation of kinetic parameters for two coupled glycolytic enzymes from Escherichia coli (phosphoglucose isomerase and phosphofructokinase). 31P-NMR time-course data were collected by incubating cell extracts with substrates, products and modifiers at different initial concentrations. NMR kinetic data were subsequently processed using a custom software module written in the Python programming language, and globally fitted to appropriately modified Hill equations. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
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Open AccessArticle Analysis and Design of Stimulus Response Curves of E. coli
Metabolites 2012, 2(4), 844-871; doi:10.3390/metabo2040844
Received: 17 October 2012 / Accepted: 29 October 2012 / Published: 12 November 2012
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Abstract
Metabolism and signalling are tightly coupled in bacteria. Combining several theoretical approaches, a core model is presented that describes transcriptional and allosteric control of glycolysis in Escherichia coli. Experimental data based on microarrays, signalling components and extracellular metabolites are used to estimate [...] Read more.
Metabolism and signalling are tightly coupled in bacteria. Combining several theoretical approaches, a core model is presented that describes transcriptional and allosteric control of glycolysis in Escherichia coli. Experimental data based on microarrays, signalling components and extracellular metabolites are used to estimate kinetic parameters. A newly designed strain was used that adjusts the incoming glucose flux into the system and allows a kinetic analysis. Based on the results, prediction for intracelluar metabolite concentrations over a broad range of the growth rate could be performed and compared with data from literature. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
Open AccessArticle Flux-P: Automating Metabolic Flux Analysis
Metabolites 2012, 2(4), 872-890; doi:10.3390/metabo2040872
Received: 4 September 2012 / Revised: 29 October 2012 / Accepted: 1 November 2012 / Published: 12 November 2012
Cited by 6 | PDF Full-text (989 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Quantitative knowledge of intracellular fluxes in metabolic networks is invaluable for inferring metabolic system behavior and the design principles of biological systems. However, intracellular reaction rates can not often be calculated directly but have to be estimated; for instance, via 13C-based [...] Read more.
Quantitative knowledge of intracellular fluxes in metabolic networks is invaluable for inferring metabolic system behavior and the design principles of biological systems. However, intracellular reaction rates can not often be calculated directly but have to be estimated; for instance, via 13C-based metabolic flux analysis, a model-based interpretation of stable carbon isotope patterns in intermediates of metabolism. Existing software such as FiatFlux, OpenFLUX or 13CFLUX supports experts in this complex analysis, but requires several steps that have to be carried out manually, hence restricting the use of this software for data interpretation to a rather small number of experiments. In this paper, we present Flux-P as an approach to automate and standardize 13C-based metabolic flux analysis, using the Bio-jETI workflow framework. Exemplarily based on the FiatFlux software, it demonstrates how services can be created that carry out the different analysis steps autonomously and how these can subsequently be assembled into software workflows that perform automated, high-throughput intracellular flux analysis of high quality and reproducibility. Besides significant acceleration and standardization of the data analysis, the agile workflow-based realization supports flexible changes of the analysis workflows on the user level, making it easy to perform custom analyses. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
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Open AccessArticle Ensemble Kinetic Modeling of Metabolic Networks from Dynamic Metabolic Profiles
Metabolites 2012, 2(4), 891-912; doi:10.3390/metabo2040891
Received: 14 September 2012 / Revised: 2 November 2012 / Accepted: 5 November 2012 / Published: 12 November 2012
Cited by 8 | PDF Full-text (558 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Kinetic modeling of metabolic pathways has important applications in metabolic engineering, but significant challenges still remain. The difficulties faced vary from finding best-fit parameters in a highly multidimensional search space to incomplete parameter identifiability. To meet some of these challenges, an ensemble [...] Read more.
Kinetic modeling of metabolic pathways has important applications in metabolic engineering, but significant challenges still remain. The difficulties faced vary from finding best-fit parameters in a highly multidimensional search space to incomplete parameter identifiability. To meet some of these challenges, an ensemble modeling method is developed for characterizing a subset of kinetic parameters that give statistically equivalent goodness-of-fit to time series concentration data. The method is based on the incremental identification approach, where the parameter estimation is done in a step-wise manner. Numerical efficacy is achieved by reducing the dimensionality of parameter space and using efficient random parameter exploration algorithms. The shift toward using model ensembles, instead of the traditional “best-fit” models, is necessary to directly account for model uncertainty during the application of such models. The performance of the ensemble modeling approach has been demonstrated in the modeling of a generic branched pathway and the trehalose pathway in Saccharomyces cerevisiae using generalized mass action (GMA) kinetics. Full article
(This article belongs to the Special Issue Metabolic Network Models)
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Open AccessArticle Comparative Analysis of End Point Enzymatic Digests of Arabino-Xylan Isolated from Switchgrass (Panicum virgatum L) of Varying Maturities using LC-MSn
Metabolites 2012, 2(4), 959-982; doi:10.3390/metabo2040959
Received: 14 September 2012 / Revised: 30 October 2012 / Accepted: 9 November 2012 / Published: 19 November 2012
Cited by 3 | PDF Full-text (683 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Switchgrass (Panicum virgatum L., SG) is a perennial grass presently used for forage and being developed as a bioenergy crop for conversion of cell wall carbohydrates to biofuels. Up to 50% of the cell wall associated carbohydrates are xylan. SG was [...] Read more.
Switchgrass (Panicum virgatum L., SG) is a perennial grass presently used for forage and being developed as a bioenergy crop for conversion of cell wall carbohydrates to biofuels. Up to 50% of the cell wall associated carbohydrates are xylan. SG was analyzed for xylan structural features at variable harvest maturities. Xylan from each of three maturities was isolated using classical alkaline extraction to yield fractions (Xyl A and B) with varying compositional ratios. The Xyl B fraction was observed to decrease with plant age. Xylan samples were subsequently prepared for structure analysis by digesting with pure endo-xylanase, which preserved side-groups, or a commercial carbohydrase preparation favored for biomass conversion work. Enzymatic digestion products were successfully permethylated and analyzed by reverse-phase liquid chromatography with mass spectrometric detection (RP-HPLC-MSn). This method is advantageous compared to prior work on plant biomass because it avoids isolation of individual arabinoxylan oligomers. The use of RP-HPLC- MSn differentiated 14 structural oligosaccharides (d.p. 3–9) from the monocomponent enzyme digestion and nine oligosaccharide structures (d.p. 3–9) from hydrolysis with a cellulase enzyme cocktail. The distribution of arabinoxylan oligomers varied depending upon the enzyme(s) applied but did not vary with harvest maturity. Full article
(This article belongs to the Special Issue Glycomics and Glycoproteomics)
Open AccessArticle Metabolic Consequences of TGFb Stimulation in CulturedPrimary Mouse Hepatocytes Screened from Transcript Data with ModeScore 
Metabolites 2012, 2(4), 983-1003; doi:10.3390/metabo2040983
Received: 29 August 2012 / Revised: 18 October 2012 / Accepted: 7 November 2012 / Published: 21 November 2012
Cited by 1 | PDF Full-text (1195 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
TGFb signaling plays a major role in the reorganization of liver tissue upon injury and is an important driver of chronic liver disease. This is achieved by a deep impact on a cohort of cellular functions. To comprehensively assess the full range [...] Read more.
TGFb signaling plays a major role in the reorganization of liver tissue upon injury and is an important driver of chronic liver disease. This is achieved by a deep impact on a cohort of cellular functions. To comprehensively assess the full range of affected metabolic functions, transcript changes of cultured mouse hepatocytes were analyzed with a novel method (ModeScore), which predicts the activity of metabolic functions by scoring transcript expression changes with 987 reference flux distributions, which yielded the following hypotheses. TGFb multiplies down-regulation of most metabolic functions occurring in culture stressed controls. This is especially pronounced for tyrosine degradation, urea synthesis, glucuronization capacity, and cholesterol synthesis. Ethanol degradation and creatine synthesis are down-regulated only in TGFb treated hepatocytes, but not in the control. Among the few TGFb dependently up-regulated functions, synthesis of various collagens is most pronounced. Further interesting findings include: down-regulation of glucose export is postponed by TGFb, TGFb up-regulates the synthesis capacity of ketone bodies only as an early response, TGFb suppresses the strong up-regulation of Vanin, and TGFb induces re-formation of ceramides and sphingomyelin. Full article
(This article belongs to the Special Issue Metabolic Network Models)
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Open AccessArticle Error Propagation Analysis for Quantitative Intracellular Metabolomics
Metabolites 2012, 2(4), 1012-1030; doi:10.3390/metabo2041012
Received: 14 August 2012 / Revised: 7 November 2012 / Accepted: 14 November 2012 / Published: 21 November 2012
Cited by 4 | PDF Full-text (1547 KB) | HTML Full-text | XML Full-text
Abstract
Model-based analyses have become an integral part of modern metabolic engineering and systems biology in order to gain knowledge about complex and not directly observable cellular processes. For quantitative analyses, not only experimental data, but also measurement errors, play a crucial role. [...] Read more.
Model-based analyses have become an integral part of modern metabolic engineering and systems biology in order to gain knowledge about complex and not directly observable cellular processes. For quantitative analyses, not only experimental data, but also measurement errors, play a crucial role. The total measurement error of any analytical protocol is the result of an accumulation of single errors introduced by several processing steps. Here, we present a framework for the quantification of intracellular metabolites, including error propagation during metabolome sample processing. Focusing on one specific protocol, we comprehensively investigate all currently known and accessible factors that ultimately impact the accuracy of intracellular metabolite concentration data. All intermediate steps are modeled, and their uncertainty with respect to the final concentration data is rigorously quantified. Finally, on the basis of a comprehensive metabolome dataset of Corynebacterium glutamicum, an integrated error propagation analysis for all parts of the model is conducted, and the most critical steps for intracellular metabolite quantification are detected. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
Open AccessArticle Medicinal Plants: A Public Resource for Metabolomics and Hypothesis Development
Metabolites 2012, 2(4), 1031-1059; doi:10.3390/metabo2041031
Received: 18 September 2012 / Revised: 30 October 2012 / Accepted: 31 October 2012 / Published: 21 November 2012
Cited by 5 | PDF Full-text (1583 KB) | HTML Full-text | XML Full-text
Abstract
Specialized compounds from photosynthetic organisms serve as rich resources for drug development. From aspirin to atropine, plant-derived natural products have had a profound impact on human health. Technological advances provide new opportunities to access these natural products in a metabolic context. Here, [...] Read more.
Specialized compounds from photosynthetic organisms serve as rich resources for drug development. From aspirin to atropine, plant-derived natural products have had a profound impact on human health. Technological advances provide new opportunities to access these natural products in a metabolic context. Here, we describe a database and platform for storing, visualizing and statistically analyzing metabolomics data from fourteen medicinal plant species. The metabolomes and associated transcriptomes (RNAseq) for each plant species, gathered from up to twenty tissue/organ samples that have experienced varied growth conditions and developmental histories, were analyzed in parallel. Three case studies illustrate different ways that the data can be integrally used to generate testable hypotheses concerning the biochemistry, phylogeny and natural product diversity of medicinal plants. Deep metabolomics analysis of Camptotheca acuminata exemplifies how such data can be used to inform metabolic understanding of natural product chemical diversity and begin to formulate hypotheses about their biogenesis. Metabolomics data from Prunella vulgaris, a species that contains a wide range of antioxidant, antiviral, tumoricidal and anti-inflammatory constituents, provide a case study of obtaining biosystematic and developmental fingerprint information from metabolite accumulation data in a little studied species. Digitalis purpurea, well known as a source of cardiac glycosides, is used to illustrate how integrating metabolomics and transcriptomics data can lead to identification of candidate genes encoding biosynthetic enzymes in the cardiac glycoside pathway. Medicinal Plant Metabolomics Resource (MPM) [1] provides a framework for generating experimentally testable hypotheses about the metabolic networks that lead to the generation of specialized compounds, identifying genes that control their biosynthesis and establishing a basis for modeling metabolism in less studied species. The database is publicly available and can be used by researchers in medicine and plant biology. Full article
(This article belongs to the Special Issue Metabolic Network Models)
Open AccessArticle Changes in Primary and Secondary Metabolite Levels in Response to Gene Targeting-Mediated Site-Directed Mutagenesis of the Anthranilate Synthase Gene in Rice
Metabolites 2012, 2(4), 1123-1138; doi:10.3390/metabo2041123
Received: 16 October 2012 / Revised: 4 December 2012 / Accepted: 9 December 2012 / Published: 18 December 2012
Cited by 2 | PDF Full-text (679 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Gene targeting (GT) via homologous recombination allows precise modification of a target gene of interest. In a previous study, we successfully used GT to produce rice plants accumulating high levels of free tryptophan (Trp) in mature seeds and young leaves via targeted [...] Read more.
Gene targeting (GT) via homologous recombination allows precise modification of a target gene of interest. In a previous study, we successfully used GT to produce rice plants accumulating high levels of free tryptophan (Trp) in mature seeds and young leaves via targeted modification of a gene encoding anthranilate synthase—a key enzyme of Trp biosynthesis. Here, we performed metabolome analysis in the leaves and mature seeds of GT plants. Of 72 metabolites detected in both organs, a total of 13, including Trp, involved in amino acid metabolism, accumulated to levels >1.5-fold higher than controls in both leaves and mature seeds of GT plants. Surprisingly, the contents of certain metabolites valuable for both humans and livestock, such as γ-aminobutyric acid and vitamin B, were significantly increased in mature seeds of GT plants. Moreover, untargeted analysis using LC-MS revealed that secondary metabolites, including an indole alkaloid, 2-[2-hydroxy-3-β-d-glucopyranosyloxy-1-(1H-indol-3-yl)propyl] tryptophan, also accumulate to higher levels in GT plants. Some of these metabolite changes in plants produced via GT are similar to those observed in plants over expressing mutated genes, thus demonstrating that in vivo protein engineering via GT can be an effective approach to metabolic engineering in crops. Full article
(This article belongs to the Special Issue Metabolomics in Metabolic Engineering)

Review

Jump to: Research

Open AccessReview Computational Methods for Metabolomic Data Analysis of Ion Mobility Spectrometry Data—Reviewing the State of the Art
Metabolites 2012, 2(4), 733-755; doi:10.3390/metabo2040733
Received: 8 August 2012 / Revised: 24 September 2012 / Accepted: 25 September 2012 / Published: 16 October 2012
Cited by 8 | PDF Full-text (2822 KB) | HTML Full-text | XML Full-text
Abstract
Ion mobility spectrometry combined with multi-capillary columns (MCC/IMS) is a well known technology for detecting volatile organic compounds (VOCs). We may utilize MCC/IMS for scanning human exhaled air, bacterial colonies or cell lines, for example. Thereby we gain information about the human [...] Read more.
Ion mobility spectrometry combined with multi-capillary columns (MCC/IMS) is a well known technology for detecting volatile organic compounds (VOCs). We may utilize MCC/IMS for scanning human exhaled air, bacterial colonies or cell lines, for example. Thereby we gain information about the human health status or infection threats. We may further study the metabolic response of living cells to external perturbations. The instrument is comparably cheap, robust and easy to use in every day practice. However, the potential of the MCC/IMS methodology depends on the successful application of computational approaches for analyzing the huge amount of emerging data sets. Here, we will review the state of the art and highlight existing challenges. First, we address methods for raw data handling, data storage and visualization. Afterwards we will introduce de-noising, peak picking and other pre-processing approaches. We will discuss statistical methods for analyzing correlations between peaks and diseases or medical treatment. Finally, we study up-to-date machine learning techniques for identifying robust biomarker molecules that allow classifying patients into healthy and diseased groups. We conclude that MCC/IMS coupled with sophisticated computational methods has the potential to successfully address a broad range of biomedical questions. While we can solve most of the data pre-processing steps satisfactorily, some computational challenges with statistical learning and model validation remain. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
Open AccessReview A Guideline to Univariate Statistical Analysis for LC/MS-Based Untargeted Metabolomics-Derived Data
Metabolites 2012, 2(4), 775-795; doi:10.3390/metabo2040775
Received: 2 August 2012 / Revised: 2 October 2012 / Accepted: 10 October 2012 / Published: 18 October 2012
Cited by 25 | PDF Full-text (420 KB) | HTML Full-text | XML Full-text
Abstract
Several metabolomic software programs provide methods for peak picking, retention time alignment and quantification of metabolite features in LC/MS-based metabolomics. Statistical analysis, however, is needed in order to discover those features significantly altered between samples. By comparing the retention time and MS/MS [...] Read more.
Several metabolomic software programs provide methods for peak picking, retention time alignment and quantification of metabolite features in LC/MS-based metabolomics. Statistical analysis, however, is needed in order to discover those features significantly altered between samples. By comparing the retention time and MS/MS data of a model compound to that from the altered feature of interest in the research sample, metabolites can be then unequivocally identified. This paper reports on a comprehensive overview of a workflow for statistical analysis to rank relevant metabolite features that will be selected for further MS/MS experiments. We focus on univariate data analysis applied in parallel on all detected features. Characteristics and challenges of this analysis are discussed and illustrated using four different real LC/MS untargeted metabolomic datasets. We demonstrate the influence of considering or violating mathematical assumptions on which univariate statistical test rely, using high-dimensional LC/MS datasets. Issues in data analysis such as determination of sample size, analytical variation, assumption of normality and homocedasticity, or correction for multiple testing are discussed and illustrated in the context of our four untargeted LC/MS working examples. Full article
(This article belongs to the Special Issue Analytical Techniques in Metabolomics)
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Open AccessReview Tumor-Associated Glycans and Their Role in Gynecological Cancers: Accelerating Translational Research by Novel High-Throughput Approaches
Metabolites 2012, 2(4), 913-939; doi:10.3390/metabo2040913
Received: 31 October 2012 / Revised: 8 November 2012 / Accepted: 9 November 2012 / Published: 14 November 2012
Cited by 11 | PDF Full-text (373 KB) | HTML Full-text | XML Full-text
Abstract
Glycans are important partners in many biological processes, including carcinogenesis. The rapidly developing field of functional glycomics becomes one of the frontiers of biology and biomedicine. Aberrant glycosylation of proteins and lipids occurs commonly during malignant transformation and leads to the expression [...] Read more.
Glycans are important partners in many biological processes, including carcinogenesis. The rapidly developing field of functional glycomics becomes one of the frontiers of biology and biomedicine. Aberrant glycosylation of proteins and lipids occurs commonly during malignant transformation and leads to the expression of specific tumor-associated glycans. The appearance of aberrant glycans on carcinoma cells is typically associated with grade, invasion, metastasis and overall poor prognosis. Cancer-associated carbohydrates are mostly located on the surface of cancer cells and are therefore potential diagnostic biomarkers. Currently, there is increasing interest in cancer-associated aberrant glycosylation, with growing numbers of characteristic cancer targets being detected every day. Breast and ovarian cancer are the most common and lethal malignancies in women, respectively, and potential glycan biomarkers hold promise for early detection and targeted therapies. However, the acceleration of research and comprehensive multi-target investigation of cancer-specific glycans could only be successfully achieved with the help of a combination of novel high-throughput glycomic approaches. Full article
(This article belongs to the Special Issue Glycomics and Glycoproteomics)
Open AccessReview Metabolic Adaptation and Protein Complexes in Prokaryotes
Metabolites 2012, 2(4), 940-958; doi:10.3390/metabo2040940
Received: 20 October 2012 / Revised: 10 November 2012 / Accepted: 12 November 2012 / Published: 16 November 2012
Cited by 2 | PDF Full-text (741 KB) | HTML Full-text | XML Full-text
Abstract
Protein complexes are classified and have been charted in several large-scale screening studies in prokaryotes. These complexes are organized in a factory-like fashion to optimize protein production and metabolism. Central components are conserved between different prokaryotes; major complexes involve carbohydrate, amino acid, [...] Read more.
Protein complexes are classified and have been charted in several large-scale screening studies in prokaryotes. These complexes are organized in a factory-like fashion to optimize protein production and metabolism. Central components are conserved between different prokaryotes; major complexes involve carbohydrate, amino acid, fatty acid and nucleotide metabolism. Metabolic adaptation changes protein complexes according to environmental conditions. Protein modification depends on specific modifying enzymes. Proteins such as trigger enzymes display condition-dependent adaptation to different functions by participating in several complexes. Several bacterial pathogens adapt rapidly to intracellular survival with concomitant changes in protein complexes in central metabolism and optimize utilization of their favorite available nutrient source. Regulation optimizes protein costs. Master regulators lead to up- and downregulation in specific subnetworks and all involved complexes. Long protein half-life and low level expression detaches protein levels from gene expression levels. However, under optimal growth conditions, metabolite fluxes through central carbohydrate pathways correlate well with gene expression. In a system-wide view, major metabolic changes lead to rapid adaptation of complexes and feedback or feedforward regulation. Finally, prokaryotic enzyme complexes are involved in crowding and substrate channeling. This depends on detailed structural interactions and is verified for specific effects by experiments and simulations. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
Open AccessReview Glycomic Expression in Esophageal Disease
Metabolites 2012, 2(4), 1004-1011; doi:10.3390/metabo2041004
Received: 25 September 2012 / Revised: 5 November 2012 / Accepted: 9 November 2012 / Published: 21 November 2012
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Abstract
Glycosylation is among the most common post translation modifications of proteins in humans. Decades of research have demonstrated that aberrant glycosylation can lead to malignant degeneration. Glycoproteomic studies in the past several years have identified techniques that can successfully characterize a glycan [...] Read more.
Glycosylation is among the most common post translation modifications of proteins in humans. Decades of research have demonstrated that aberrant glycosylation can lead to malignant degeneration. Glycoproteomic studies in the past several years have identified techniques that can successfully characterize a glycan or glycan profile associated with a high-grade dysplastic or malignant state. This review summarizes the current glycomic and glycoproteomic literature with specific reference to esophageal cancer. Esophageal adenocarcinoma represents a highly morbid and mortal cancer with a defined progression from metaplasia (Barrett's esophagus) to dysplasia to neoplasia. This disease is highlighted because (1) differences in glycan profiles between the stages of disease progression have been described in the glycoproteomic literature; (2) a glycan biomarker that identifies a given stage may be used as a predictor of disease progression and thus may have significant influence over clinical management; and (3) the differences in glycan profiles between disease and disease-free states in esophageal cancer are more dramatic than in other cancers. Full article
(This article belongs to the Special Issue Glycomics and Glycoproteomics)
Open AccessReview Glycomics Approaches for the Bioassay and Structural Analysis of Heparin/Heparan Sulphates
Metabolites 2012, 2(4), 1060-1089; doi:10.3390/metabo2041060
Received: 10 October 2012 / Revised: 13 November 2012 / Accepted: 15 November 2012 / Published: 28 November 2012
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Abstract
The glycosaminoglycan heparan sulphate (HS) has a heterogeneous structure; evidence shows that specific structures may be responsible for specific functions in biological processes such as blood coagulation and regulation of growth factor signalling. This review summarises the different experimental tools and methods [...] Read more.
The glycosaminoglycan heparan sulphate (HS) has a heterogeneous structure; evidence shows that specific structures may be responsible for specific functions in biological processes such as blood coagulation and regulation of growth factor signalling. This review summarises the different experimental tools and methods developed to provide more rapid methods for studying the structure and functions of HS. Rapid and sensitive methods for the facile purification of HS, from tissue and cell sources are reviewed. Data sets for the structural analysis are often complex and include multiple sample sets, therefore different software and tools have been developed for the analysis of different HS data sets. These can be readily applied to chromatographic data sets for the simplification of data (e.g., charge separation using strong anion exchange chromatography and from size separation using gel filtration techniques. Finally, following the sequencing of the human genome, research has rapidly advanced with the introduction of high throughput technologies to carry out simultaneous analyses of many samples. Microarrays to study macromolecular interactions (including glycan arrays) have paved the way for bioassay technologies which utilize cell arrays to study the effects of multiple macromolecules on cells. Glycan bioassay technologies are described in which immobilisation techniques for saccharides are exploited to develop a platform to probe cell responses such as signalling pathway activation. This review aims at reviewing available techniques and tools for the purification, analysis and bioassay of HS saccharides in biological systems using “glycomics” approaches. Full article
(This article belongs to the Special Issue Glycomics and Glycoproteomics)
Open AccessReview Systematic Applications of Metabolomics in Metabolic Engineering
Metabolites 2012, 2(4), 1090-1122; doi:10.3390/metabo2041090
Received: 1 November 2012 / Revised: 29 November 2012 / Accepted: 10 December 2012 / Published: 14 December 2012
Cited by 4 | PDF Full-text (338 KB) | HTML Full-text | XML Full-text
Abstract
The goals of metabolic engineering are well-served by the biological information provided by metabolomics: information on how the cell is currently using its biochemical resources is perhaps one of the best ways to inform strategies to engineer a cell to produce a [...] Read more.
The goals of metabolic engineering are well-served by the biological information provided by metabolomics: information on how the cell is currently using its biochemical resources is perhaps one of the best ways to inform strategies to engineer a cell to produce a target compound. Using the analysis of extracellular or intracellular levels of the target compound (or a few closely related molecules) to drive metabolic engineering is quite common. However, there is surprisingly little systematic use of metabolomics datasets, which simultaneously measure hundreds of metabolites rather than just a few, for that same purpose. Here, we review the most common systematic approaches to integrating metabolite data with metabolic engineering, with emphasis on existing efforts to use whole-metabolome datasets. We then review some of the most common approaches for computational modeling of cell-wide metabolism, including constraint-based models, and discuss current computational approaches that explicitly use metabolomics data. We conclude with discussion of the broader potential of computational approaches that systematically use metabolomics data to drive metabolic engineering. Full article
(This article belongs to the Special Issue Metabolomics in Metabolic Engineering)
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