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Metabolites, Volume 2, Issue 3 (September 2012), Pages 382-647

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Research

Jump to: Review

Open AccessArticle Construction of a Genome-Scale Kinetic Model of Mycobacterium Tuberculosis Using Generic Rate Equations
Metabolites 2012, 2(3), 382-397; doi:10.3390/metabo2030382
Received: 29 March 2012 / Revised: 7 June 2012 / Accepted: 25 June 2012 / Published: 3 July 2012
Cited by 1 | PDF Full-text (500 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The study of biological systems at the genome scale helps us understand fundamental biological processes that govern the activity of living organisms and regulate their interactions with the environment. Genome-scale metabolic models are usually analysed using constraint-based methods, since detailed rate equations [...] Read more.
The study of biological systems at the genome scale helps us understand fundamental biological processes that govern the activity of living organisms and regulate their interactions with the environment. Genome-scale metabolic models are usually analysed using constraint-based methods, since detailed rate equations and kinetic parameters are often missing. However, constraint-based analysis is limited in capturing the dynamics of cellular processes. In this paper, we present an approach to build a genome-scale kinetic model of Mycobacterium tuberculosis metabolism using generic rate equations. M. tuberculosis causes tuberculosis which remains one of the largest killer infectious diseases. Using a genetic algorithm, we estimated kinetic parameters for a genome-scale metabolic model of M. tuberculosis based on flux distributions derived from Flux Balance Analysis. Our results show that an excellent agreement with flux values is obtained under several growth conditions, although kinetic parameters may vary in different conditions. Parameter variability analysis indicates that a high degree of redundancy remains present in model parameters, which suggests that the integration of other types of high-throughput datasets will enable the development of better constrained models accounting for a variety of in vivo phenotypes. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
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Open AccessArticle An UPLC-ESI-MS/MS Assay Using 6-Aminoquinolyl-N-Hydroxysuccinimidyl Carbamate Derivatization for Targeted Amino Acid Analysis: Application to Screening of Arabidopsis thaliana Mutants
Metabolites 2012, 2(3), 398-428; doi:10.3390/metabo2030398
Received: 2 May 2012 / Revised: 29 June 2012 / Accepted: 4 July 2012 / Published: 6 July 2012
Cited by 4 | PDF Full-text (769 KB) | HTML Full-text | XML Full-text
Abstract
In spite of the large arsenal of methodologies developed for amino acid assessment in complex matrices, their implementation in metabolomics studies involving wide-ranging mutant screening is hampered by their lack of high-throughput, sensitivity, reproducibility, and/or wide dynamic range. In response to the [...] Read more.
In spite of the large arsenal of methodologies developed for amino acid assessment in complex matrices, their implementation in metabolomics studies involving wide-ranging mutant screening is hampered by their lack of high-throughput, sensitivity, reproducibility, and/or wide dynamic range. In response to the challenge of developing amino acid analysis methods that satisfy the criteria required for metabolomic studies, improved reverse-phase high-performance liquid chromatography-mass spectrometry (RPHPLC-MS) methods have been recently reported for large-scale screening of metabolic phenotypes. However, these methods focus on the direct analysis of underivatized amino acids and, therefore, problems associated with insufficient retention and resolution are observed due to the hydrophilic nature of amino acids. It is well known that derivatization methods render amino acids more amenable for reverse phase chromatographic analysis by introducing highly-hydrophobic tags in their carboxylic acid or amino functional group. Therefore, an analytical platform that combines the 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (AQC) pre-column derivatization method with ultra performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC-ESI-MS/MS) is presented in this article. For numerous reasons typical amino acid derivatization methods would be inadequate for large scale metabolic projects. However, AQC derivatization is a simple, rapid and reproducible way of obtaining stable amino acid adducts amenable for UPLC-ESI-MS/MS and the applicability of the method for high-throughput metabolomic analysis in Arabidopsis thaliana is demonstrated in this study. Overall, the major advantages offered by this amino acid analysis method include high-throughput, enhanced sensitivity and selectivity; characteristics that showcase its utility for the rapid screening of the preselected plant metabolites without compromising the quality of the metabolic data. The presented method enabled thirty-eight metabolites (proteinogenic amino acids and related compounds) to be analyzed within 10 min with detection limits down to 1.02 × 10−11 M (i.e., atomole level on column), which represents an improved sensitivity of 1 to 5 orders of magnitude compared to existing methods. Our UPLC-ESI-MS/MS method is one of the seven analytical platforms used by the Arabidopsis Metabolomics Consortium. The amino acid dataset obtained by analysis of Arabidopsis T-DNA mutant stocks with our platform is captured and open to the public in the web portal PlantMetabolomics.org. The analytical platform herein described could find important applications in other studies where the rapid, high-throughput and sensitive assessment of low abundance amino acids in complex biosamples is necessary. Full article
(This article belongs to the Special Issue Analytical Techniques in Metabolomics)
Open AccessArticle Development of Metabolic Indicators of Burn Injury: Very Low Density Lipoprotein (VLDL) and Acetoacetate Are Highly Correlated to Severity of Burn Injury in Rats
Metabolites 2012, 2(3), 458-478; doi:10.3390/metabo2030458
Received: 9 June 2012 / Revised: 3 July 2012 / Accepted: 4 July 2012 / Published: 16 July 2012
Cited by 2 | PDF Full-text (328 KB) | HTML Full-text | XML Full-text
Abstract
Hypermetabolism is a significant sequela to severe trauma such as burns, as well as critical illnesses such as cancer. It persists in parallel to, or beyond, the original pathology for many months as an often-fatal comorbidity. Currently, diagnosis is based solely on [...] Read more.
Hypermetabolism is a significant sequela to severe trauma such as burns, as well as critical illnesses such as cancer. It persists in parallel to, or beyond, the original pathology for many months as an often-fatal comorbidity. Currently, diagnosis is based solely on clinical observations of increased energy expenditure, severe muscle wasting and progressive organ dysfunction. In order to identify the minimum number of necessary variables, and to develop a rat model of burn injury-induced hypermetabolism, we utilized data mining approaches to identify the metabolic variables that strongly correlate to the severity of injury. A clustering-based algorithm was introduced into a regression model of the extent of burn injury. As a result, a neural network model which employs VLDL and acetoacetate levels was demonstrated to predict the extent of burn injury with 88% accuracy in the rat model. The physiological importance of the identified variables in the context of hypermetabolism, and necessary steps in extension of this preliminary model to a clinically utilizable index of severity of burn injury are outlined. Full article
Open AccessArticle 1H Nuclear Magnetic Resonance (NMR) Metabolomic Study of Chronic Organophosphate Exposure in Rats
Metabolites 2012, 2(3), 479-495; doi:10.3390/metabo2030479
Received: 18 April 2012 / Revised: 26 June 2012 / Accepted: 5 July 2012 / Published: 24 July 2012
Cited by 4 | PDF Full-text (769 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
1H NMR spectroscopy and chemometric analysis were used to characterize rat urine obtained after chronic exposure to either tributyl phosphate (TBP) or triphenyl phosphate (TPP). In this study, the daily dose exposure was 1.5 mg/kg body weight for TBP, or 2.0 [...] Read more.
1H NMR spectroscopy and chemometric analysis were used to characterize rat urine obtained after chronic exposure to either tributyl phosphate (TBP) or triphenyl phosphate (TPP). In this study, the daily dose exposure was 1.5 mg/kg body weight for TBP, or 2.0 mg/kg body weight for TPP, administered over a 15-week period. Orthogonal signal correction (OSC) -filtered partial least square discriminant analysis (OSC-PLSDA) was used to predict and classify exposure to these organophosphates. During the development of the model, the classification error was evaluated as a function of the number of latent variables. NMR spectral regions and corresponding metabolites important for determination of exposure type were identified using variable importance in projection (VIP) coefficients obtained from the OSC-PLSDA analysis. As expected, the model for classification of chronic (1.5–2.0 mg/kg body weight daily) TBP or TPP exposure was not as strong as the previously reported model developed for identifying acute (15–20 mg/kg body weight) exposure. The set of majorly impacted metabolites identified for chronic TBP or TPP exposure was slightly different than those metabolites previously identified for acute exposure. These metabolites were then mapped to different metabolite pathways and ranked, allowing the metabolic response to chronic organophosphate exposure to be addressed. Full article
(This article belongs to the Special Issue Feature Papers)
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Open AccessArticle Metabolic and Pharmacokinetic Differentiation of STX209 and Racemic Baclofen in Humans
Metabolites 2012, 2(3), 596-613; doi:10.3390/metabo2030596
Received: 25 July 2012 / Revised: 21 August 2012 / Accepted: 29 August 2012 / Published: 11 September 2012
Cited by 1 | PDF Full-text (331 KB) | HTML Full-text | XML Full-text
Abstract
STX209 is an exploratory drug comprising the single, active R-enantiomer of baclofen which is in later stage clinical trials for the treatment of fragile x syndrome (FXS) and autism spectrum disorders (ASD). New clinical data in this article on the metabolism and [...] Read more.
STX209 is an exploratory drug comprising the single, active R-enantiomer of baclofen which is in later stage clinical trials for the treatment of fragile x syndrome (FXS) and autism spectrum disorders (ASD). New clinical data in this article on the metabolism and pharmacokinetics of the R- and S-enantiomers of baclofen presents scientific evidence for stereoselective metabolism of only S-baclofen to an abundant oxidative deamination metabolite that is sterically resolved as the S-enantiomeric configuration. This metabolite undergoes some further metabolism by glucuronide conjugation. Consequences of this metabolic difference are a lower Cmax and lower early plasma exposure of S-baclofen compared to R-baclofen and marginally lower urinary excretion of S-baclofen after racemic baclofen administration. These differences introduce compound-related exposure variances in humans in which subjects dosed with racemic baclofen are exposed to a prominent metabolite of baclofen whilst subjects dosed with STX209 are not. For potential clinical use, our findings suggest that STX209 has the advantage of being a biologically defined and active enantiomer. Full article
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Open AccessArticle A Topological Characterization of Medium-Dependent Essential Metabolic Reactions
Metabolites 2012, 2(3), 632-647; doi:10.3390/metabo2030632
Received: 1 August 2012 / Revised: 28 August 2012 / Accepted: 12 September 2012 / Published: 24 September 2012
Cited by 6 | PDF Full-text (5037 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Metabolism has frequently been analyzed from a network perspective. A major question is how network properties correlate with biological features like growth rates, flux patterns and enzyme essentiality. Using methods from graph theory as well as established topological categories of metabolic systems, [...] Read more.
Metabolism has frequently been analyzed from a network perspective. A major question is how network properties correlate with biological features like growth rates, flux patterns and enzyme essentiality. Using methods from graph theory as well as established topological categories of metabolic systems, we analyze the essentiality of metabolic reactions depending on the growth medium and identify the topological footprint of these reactions. We find that the typical topological context of a medium-dependent essential reaction is systematically different from that of a globally essential reaction. In particular, we observe systematic differences in the distribution of medium-dependent essential reactions across three-node subgraphs (the network motif signature of medium-dependent essential reactions) compared to globally essential or globally redundant reactions. In this way, we provide evidence that the analysis of metabolic systems on the few-node subgraph scale is meaningful for explaining dynamic patterns. This topological characterization of medium-dependent essentiality provides a better understanding of the interplay between reaction deletions and environmental conditions. Full article
(This article belongs to the Special Issue Metabolic Network Models)
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Review

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Open AccessReview Current Understanding of the Formation and Adaptation of Metabolic Systems Based on Network Theory
Metabolites 2012, 2(3), 429-457; doi:10.3390/metabo2030429
Received: 24 May 2012 / Revised: 26 June 2012 / Accepted: 9 July 2012 / Published: 12 July 2012
Cited by 8 | PDF Full-text (428 KB) | HTML Full-text | XML Full-text
Abstract
Formation and adaptation of metabolic networks has been a long-standing question in biology. With recent developments in biotechnology and bioinformatics, the understanding of metabolism is progressively becoming clearer from a network perspective. This review introduces the comprehensive metabolic world that has been [...] Read more.
Formation and adaptation of metabolic networks has been a long-standing question in biology. With recent developments in biotechnology and bioinformatics, the understanding of metabolism is progressively becoming clearer from a network perspective. This review introduces the comprehensive metabolic world that has been revealed by a wide range of data analyses and theoretical studies; in particular, it illustrates the role of evolutionary events, such as gene duplication and horizontal gene transfer, and environmental factors, such as nutrient availability and growth conditions, in evolution of the metabolic network. Furthermore, the mathematical models for the formation and adaptation of metabolic networks have also been described, according to the current understanding from a perspective of metabolic networks. These recent findings are helpful in not only understanding the formation of metabolic networks and their adaptation, but also metabolic engineering. Full article
(This article belongs to the Special Issue Metabolic Network Models)
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Open AccessReview Separation Technique for the Determination of Highly Polar Metabolites in Biological Samples
Metabolites 2012, 2(3), 496-515; doi:10.3390/metabo2030496
Received: 8 June 2012 / Revised: 31 July 2012 / Accepted: 6 August 2012 / Published: 16 August 2012
Cited by 2 | PDF Full-text (300 KB) | HTML Full-text | XML Full-text
Abstract
Metabolomics is a new approach that is based on the systematic study of the full complement of metabolites in a biological sample. Metabolomics has the potential to fundamentally change clinical chemistry and, by extension, the fields of nutrition, toxicology, and medicine. However, [...] Read more.
Metabolomics is a new approach that is based on the systematic study of the full complement of metabolites in a biological sample. Metabolomics has the potential to fundamentally change clinical chemistry and, by extension, the fields of nutrition, toxicology, and medicine. However, it can be difficult to separate highly polar compounds. Mass spectrometry (MS), in combination with capillary electrophoresis (CE), gas chromatography (GC), or high performance liquid chromatography (HPLC) is the key analytical technique on which emerging "omics" technologies, namely, proteomics, metabolomics, and lipidomics, are based. In this review, we introduce various methods for the separation of highly polar metabolites. Full article
(This article belongs to the Special Issue Analytical Techniques in Metabolomics)
Open AccessReview Polyamines under Abiotic Stress: Metabolic Crossroads and Hormonal Crosstalks in Plants
Metabolites 2012, 2(3), 516-528; doi:10.3390/metabo2030516
Received: 22 June 2012 / Revised: 6 August 2012 / Accepted: 10 August 2012 / Published: 20 August 2012
Cited by 29 | PDF Full-text (312 KB) | HTML Full-text | XML Full-text
Abstract
Polyamines are essential compounds for cell survival and have key roles in plant stress protection. Current evidence points to the occurrence of intricate cross-talks between polyamines, stress hormones and other metabolic pathways required for their function. In this review we integrate the [...] Read more.
Polyamines are essential compounds for cell survival and have key roles in plant stress protection. Current evidence points to the occurrence of intricate cross-talks between polyamines, stress hormones and other metabolic pathways required for their function. In this review we integrate the polyamine metabolic pathway in the context of its immediate metabolic network which is required to understand the multiple ways by which polyamines can maintain their homeostasis and participate in plant stress responses. Full article
(This article belongs to the Special Issue Feature Papers)
Open AccessReview Optimality Principles in the Regulation of Metabolic Networks
Metabolites 2012, 2(3), 529-552; doi:10.3390/metabo2030529
Received: 20 July 2012 / Revised: 15 August 2012 / Accepted: 17 August 2012 / Published: 29 August 2012
Cited by 5 | PDF Full-text (2889 KB) | HTML Full-text | XML Full-text
Abstract
One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks [...] Read more.
One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks need to be identified in addition to the characterisation of the molecular components and interactions. Then, the cellular “task” of the network—its function—should be identified. A network contributes to organismal fitness through its function. The premise is that the same functions are often implemented in different organisms by the same type of network; hence, the concept of design principles. In biology, due to the strong forces of selective pressure and natural selection, network functions can often be understood as the outcome of fitness optimisation. The hypothesis of fitness optimisation to understand the design of a network has proven to be a powerful strategy. Here, we outline the use of several optimisation principles applied to biological networks, with an emphasis on metabolic regulatory networks. We discuss the different objective functions and constraints that are considered and the kind of understanding that they provide. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
Open AccessReview Mathematical Modeling of Plant Metabolism―From Reconstruction to Prediction
Metabolites 2012, 2(3), 553-566; doi:10.3390/metabo2030553
Received: 7 July 2012 / Revised: 22 August 2012 / Accepted: 28 August 2012 / Published: 6 September 2012
Cited by 6 | PDF Full-text (231 KB) | HTML Full-text | XML Full-text
Abstract
Due to their sessile lifestyle, plants are exposed to a large set of environmental cues. In order to cope with changes in environmental conditions a multitude of complex strategies to regulate metabolism has evolved. The complexity is mainly attributed to interlaced regulatory [...] Read more.
Due to their sessile lifestyle, plants are exposed to a large set of environmental cues. In order to cope with changes in environmental conditions a multitude of complex strategies to regulate metabolism has evolved. The complexity is mainly attributed to interlaced regulatory circuits between genes, proteins and metabolites and a high degree of cellular compartmentalization. The genetic model plant Arabidopsis thaliana was intensely studied to characterize adaptive traits to a changing environment. The availability of genetically distinct natural populations has made it an attractive system to study plant-environment interactions. The impact on metabolism caused by changing environmental conditions can be estimated by mathematical approaches and deepens the understanding of complex biological systems. In combination with experimental high-throughput technologies this provides a promising platform to develop in silico models which are not only able to reproduce but also to predict metabolic phenotypes and to allow for the interpretation of plant physiological mechanisms leading to successful adaptation to a changing environment. Here, we provide an overview of mathematical approaches to analyze plant metabolism, with experimental procedures being used to validate their output, and we discuss them in the context of establishing a comprehensive understanding of plant-environment interactions. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
Open AccessReview Minimal Cut Sets and the Use of Failure Modes in Metabolic Networks
Metabolites 2012, 2(3), 567-595; doi:10.3390/metabo2030567
Received: 12 July 2012 / Revised: 25 August 2012 / Accepted: 29 August 2012 / Published: 11 September 2012
Cited by 1 | PDF Full-text (417 KB) | HTML Full-text | XML Full-text
Abstract
A minimal cut set is a minimal set of reactions whose inactivation would guarantee a failure in a certain network function or functions. Minimal cut sets (MCSs) were initially developed from the metabolic pathway analysis method (MPA) of elementary modes (EMs); they [...] Read more.
A minimal cut set is a minimal set of reactions whose inactivation would guarantee a failure in a certain network function or functions. Minimal cut sets (MCSs) were initially developed from the metabolic pathway analysis method (MPA) of elementary modes (EMs); they provide a way of identifying target genes for eliminating a certain objective function from a holistic perspective that takes into account the structure of the whole metabolic network. The concept of MCSs is fairly new and still being explored and developed; the initial concept has developed into a generalized form and its similarity to other network characterizations are discussed. MCSs can be used in conjunction with other constraints-based methods to get a better understanding of the capability of metabolic networks and the interrelationship between metabolites and enzymes/genes. The concept could play an important role in systems biology by contributing to fields such as metabolic and genetic engineering where it could assist in finding ways of producing industrially relevant compounds from renewable resources, not only for economical, but also for sustainability, reasons. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
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Open AccessReview What mRNA Abundances Can Tell us about Metabolism
Metabolites 2012, 2(3), 614-631; doi:10.3390/metabo2030614
Received: 1 August 2012 / Revised: 24 August 2012 / Accepted: 4 September 2012 / Published: 12 September 2012
Cited by 9 | PDF Full-text (165 KB) | HTML Full-text | XML Full-text
Abstract
Inferring decreased or increased metabolic functions from transcript profiles is at first sight a bold and speculative attempt because of the functional layers in between: proteins, enzymatic activities, and reaction fluxes. However, the growing interest in this field can easily be explained [...] Read more.
Inferring decreased or increased metabolic functions from transcript profiles is at first sight a bold and speculative attempt because of the functional layers in between: proteins, enzymatic activities, and reaction fluxes. However, the growing interest in this field can easily be explained by two facts: the high quality of genome-scale metabolic network reconstructions and the highly developed technology to obtain genome-covering RNA profiles. Here, an overview of important algorithmic approaches is given by means of criteria by which published procedures can be classified. The frontiers of the methods are sketched and critical voices are being heard. Finally, an outlook for the prospects of the field is given. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)

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