Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Metabolic analysis”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6Linked to original sources

Molecular basis for anaerobic growth of Saccharomyces cerevisiae on xylose, investigated by global gene expression and metabolic flux analysis.

Yeast xylose metabolism is generally considered to be restricted to respirative conditions because the two-step oxidoreductase reactions from xylose to xylulose impose an anaerobic redox imbalance. We have recently developed, however, a Saccharomyces cerevisiae strain that is at present the only known yeast capable of anaerobic growth on xylose alone. Using transcriptome analysis of aerobic chemostat cultures grown on xylose-glucose mixtures and xylose alone, as well as a combination of global gene expression and metabolic flux analysis of anaerobic chemostat cultures grown on xylose-glucose mixtures, we identified the distinguishing characteristics of this unique phenotype. First, the transcript levels and metabolic fluxes throughout central carbon metabolism were significantly higher than those in the parent strain, and they were most pronounced in the xylose-specific, pentose phosphate, and glycerol pathways. Second, differential expression of many genes involved in redox metabolism indicates that increased cytosolic NADPH formation and NADH consumption enable a higher flux through the two-step oxidoreductase reaction of xylose to xylulose in the mutant. Redox balancing is apparently still a problem in this strain, since anaerobic growth on xylose could be improved further by providing acetoin as an external NADH sink. This improved growth was accompanied by an increased ATP production rate and was not accompanied by higher rates of xylose uptake or cytosolic NADPH production. We concluded that anaerobic growth of the yeast on xylose is ultimately limited by the rate of ATP production and not by the redox balance per se, although the redox imbalance, in turn, limits ATP production.

Adenosine Triphosphate↗

Deriving phylogenetic trees from the similarity analysis of metabolic pathways.

MOTIVATION: Comparative analysis of metabolic pathways in different genomes can give insights into the understanding of evolutionary and organizational relationships among species. This type of analysis allows one to measure the evolution of complete processes (with different functional roles) rather than the individual elements of a conventional analysis. We present a new technique for the phylogenetic analysis of metabolic pathways based on the topology of the underlying graphs. A distance measure between graphs is defined using the similarity between nodes of the graphs and the structural relationship between them. This distance measure is applied to the enzyme-enzyme relational graphs derived from metabolic pathways. Using this approach, pathways and group of pathways of different organisms are compared to each other and the resulting distance matrix is used to obtain a phylogenetic tree. RESULTS: We apply the method to the Citric Acid Cycle and the Glycolysis pathways of different groups of organisms, as well as to the Carbohydrate metabolic networks. Phylogenetic trees obtained from the experiments were close to existing phylogenies and revealed interesting relationships among organisms.

Algorithms↗

HPLC analysis of amino acids in inborn errors of metabolism.

Analysis of amino acids in blood or urine is a valuable diagnostic tool in cases of suspected metabolic disorders. The presence of a characteristic pattern of elevated amino acids is very useful in the diagnosis of these rare disorders. The detection of an apparently normal pattern of amino acids is also helpful to the clinician since it will eliminate many inborn errors of metabolism from the list of potential disorders. Methodologies for amino acid analysis in physiological fluids range from the very simple thin layer chromatography to automated low pressure or high pressure chromatography. Low pressure chromatography using a Beckman analyzer or similar instrument is the most common methodology for physiological amino acid analysis. Chromatography (HPLC) systems for amino acid analysis of proteins are available that can be modified for use with physiological samples. Waters makes a system called Picotag(TM) and Applied Biosystems makes an automated analyzer. These HPLC systems have some advantages over LPLC systems, including lower equipment cost, less caustic buffer systems and improved separation of certain amino acids.

Amino Acid Metabolism, Inborn Errors↗

The fusion of control analysis and temporal analysis of metabolic systems.

Metabolic control analysis and the study of the transient response of metabolic systems had coincident births in 1973. They developed along parallel lines until in 1989/90 their complete fusion occurred. It was evident that the control of the transient response of metabolism could be described in terms of general control properties, such as the flux and concentration control coefficients and elasticities. Consequently, it is possible to define temporal control coefficients which relate to the lifetimes of individual metabolite pools or to the total system temporal response. These control coefficients are readily expressed in terms of the flux and concentration control coefficients. Therefore, to analyse the control of metabolism is also to analyse its temporal response.

Animals↗

MetaModel: a program for modelling and control analysis of metabolic pathways on the IBM PC and compatibles.

MetaModel is a user-friendly program for calculating steady-state fluxes and metabolite concentrations of metabolic systems on the IBM PC and compatible computers. For any steady state that is obtained, one can then calculate a matrix of elasticity coefficients at that steady state, or a matrix of control and response coefficients. It thus offers a simple way to calculate the control structure of a pathway: it provides not only an educational tool that allows the student to verify empirically the classic summation relationships of metabolic control analysis but also a research tool for addressing 'what if?' questions about the behaviour of metabolic systems. Results can not only be printed or stored in a file, but can also be written to a special file that can be read by popular spreadsheet programs, thereby giving access to rapid, flexible and powerful methods for subsequent analysis and plotting of these results.

Algorithms↗

Thermodynamics-based metabolic flux analysis.

A new form of metabolic flux analysis (MFA) called thermodynamics-based metabolic flux analysis (TMFA) is introduced with the capability of generating thermodynamically feasible flux and metabolite activity profiles on a genome scale. TMFA involves the use of a set of linear thermodynamic constraints in addition to the mass balance constraints typically used in MFA. TMFA produces flux distributions that do not contain any thermodynamically infeasible reactions or pathways, and it provides information about the free energy change of reactions and the range of metabolite activities in addition to reaction fluxes. TMFA is applied to study the thermodynamically feasible ranges for the fluxes and the Gibbs free energy change, Delta(r)G', of the reactions and the activities of the metabolites in the genome-scale metabolic model of Escherichia coli developed by Palsson and co-workers. In the TMFA of the genome scale model, the metabolite activities and reaction Delta(r)G' are able to achieve a wide range of values at optimal growth. The reaction dihydroorotase is identified as a possible thermodynamic bottleneck in E. coli metabolism with a Delta(r)G' constrained close to zero while numerous reactions are identified throughout metabolism for which Delta(r)G' is always highly negative regardless of metabolite concentrations. As it has been proposed previously, these reactions with exclusively negative Delta(r)G' might be candidates for cell regulation, and we find that a significant number of these reactions appear to be the first steps in the linear portion of numerous biosynthesis pathways. The thermodynamically feasible ranges for the concentration ratios ATP/ADP, NAD(P)/NAD(P)H, and H(extracellular)(+)/H(intracellular)(+) are also determined and found to encompass the values observed experimentally in every case. Further, we find that the NAD/NADH and NADP/NADPH ratios maintained in the cell are close to the minimum feasible ratio and maximum feasible ratio, respectively.

Escherichia coli↗

Control-pattern analysis of metabolic pathways. Flux and concentration control in linear pathways.

Metabolic control analysis [Kacser and Burns (1973) Symp. Soc. Exp. Biol. 27, 65-104; Heinrich and Rapoport (1974) Eur. J. Biochem. 42, 89-95] leads to a description of the systemic properties of a metabolic system (expressed as control coefficients) in terms of the local kinetic properties of the individual enzyme-catalyzed reactions (expressed as elasticity coefficients). This paper describes a non-algebraic diagrammatic method which generates the mathematical expressions for flux or concentration-control coefficients in terms of elasticity coefficients. According to a set of simple rules, 'flux-control patterns' or 'concentration-control patterns' are drawn on a metabolic diagram. Each control pattern represents a product of elasticity coefficients that occurs as a term in the expression for a control coefficient. The rules also generate the correct sign that precedes each term. The control patterns are then used to build the expressions for control coefficients. The procedure was developed in such a way that each control pattern can be understood in terms of a 'chain of local effects' which shows how a perturbation in the activity of an enzyme is propagated through the metabolic pathway.

Biological Transport↗

Combined use of proteomic analysis and enzyme activity assays for metabolic pathway analysis of glycerol fermentation by Klebsiella pneumoniae.

The fed-batch fermentation of glycerol to 1,3-propanediol by Klebsiella pneumoniae displayed an unusual dynamic behavior that can be clearly divided into four distinct phases according to cell growth and CO(2) evolution rate. Metabolism changed significantly during the different phases as reflected by the varied specific rates of substrate consumption and product formation. An assay of activities of the three initial enzymes of glycerol metabolism, namely glycerol dehydratase (GDHt), glycerol dehydrogenase (GDH), and 1,3-propanediol-oxidoreductase (PDOR), showed apparently different patterns of expression. To understand the culture dynamics and patterns of enzyme formation at a more systemic level we analyzed the expression patterns of intracellular proteins of K. pneumoniae from different phases of the fed-batch fermentation using two-dimensional gel electrophoresis (2DE). Two new enzymes, namely a phosphoenolpyruvate-dependent dihydroxyacetone kinase (DHAK II) and a hypothetical oxidoreductase (HOR), which are directly related to glycerol metabolism and 1,3-propanediol formation, were identified among the highly expressed proteins. The changes in expression of these new enzymes and several other proteins identified from the 2DE analysis helped to understand not only the dynamic behavior of the fed-batch fermentation reported in this work but also some previously insufficiently understood phenomena related to this fermentation process. In particular, we demonstrated the combined use of proteomic analysis and enzyme activity assay data for metabolic pathway analysis and for a better identification of targets for bioprocess improvement.

Amino Acid Sequence↗

Control analysis of metabolic networks. 1. Homogeneous functions and the summation theorems for control coefficients.

1. The summation theorem CJ1 + ... + CJn = 1 for flux control coefficients CJi is shown to be equivalent to the assumption that flux J is a homogeneous function of degree 1 of enzyme concentrations E1,..., En, that is to the assumption J (tE1,..., tEn) = tJ (E1,..., En) for any t not equal to 0. Likewise, the summation theorem CXj1 + ... + CXjn = 0 for concentration control coefficients CXj1 is equivalent to homogeneity of degree 0 of steady-state metabolite concentrations Xj, or Xj (tE1,..., tEn) = Xj (E1,..., En). From this equivalence it is obvious that metabolic control analysis applies only to homogeneous systems. 2. The summation theorem for flux control coefficients is shown to be equivalent to that for concentration control coefficients, provided all reaction rates vi are homogeneous functions of enzyme concentrations Ei. 3. The equivalence between homogeneity of flux J and the summation theorem for flux control coefficients is used to analyse branching of fluxes in metabolic pathways in terms of flux control coefficients.

Enzymes↗

Experimental application of top-down control analysis to metabolic systems.

Metabolic control analysis (MCA) has provided the language and framework for quantitative study of control over flux, or over metabolites, by individual enzymes of a pathway. By contrast, top-down control analysis (TDCA) yields an immediate overview of the control structure of the whole system of interest, giving information about the control exercised by large sections of complex pathways. Unlike MCA, TDCA does not rely on the use of specific inhibitors or genetic manipulation to determine control coefficients. The method and an application of TDCA to ketogenesis are described.

Animals↗

Sensitivity analysis of stoichiometric networks: an extension of metabolic control analysis to non-steady state trajectories.

A sensitivity analysis of general stoichiometric networks is considered. The results are presented as a generalization of Metabolic Control Analysis, which has been concerned primarily with system sensitivities at steady state. An expression for time-varying sensitivity coefficients is given and the Summation and Connectivity Theorems are generalized. The results are compared to previous treatments. The analysis is accompanied by a discussion of the computation of the sensitivity coefficients and an application to a model of phototransduction.

Animals↗

Potential drug targets in Mycobacterium tuberculosis through metabolic pathway analysis.

The emergence of multidrug resistant varieties of Mycobacterium tuberculosis has led to a search for novel drug targets. We have performed an insilico comparative analysis of metabolic pathways of the host Homo sapiens and the pathogen M. tuberculosis. Enzymes from the biochemical pathways of M. tuberculosis from the KEGG metabolic pathway database were compared with proteins from the host H. sapiens, by performing a BLASTp search against the non-redundant database restricted to the H. sapiens subset. The e-value threshold cutoff was set to 0.005. Enzymes, which do not show similarity to any of the host proteins, below this threshold, were filtered out as potential drug targets. We have identified six pathways unique to the pathogen M. tuberculosis when compared to the host H. sapiens. Potential drug targets from these pathways could be useful for the discovery of broad spectrum drugs. Potential drug targets were also identified from pathways related to lipid metabolism, carbohydrate metabolism, amino acid metabolism, energy metabolism, vitamin and cofactor biosynthetic pathways and nucleotide metabolism. Of the 185 distinct targets identified from these pathways, many are in various stages of progress at the TB Structural Genomics Consortium. However, 67 of our targets are new and can be considered for rational drug design. As a case study, we have built a homology model of one of the potential drug targets MurD ligase using WHAT IF software. The model could be further explored for insilico docking studies with suitable inhibitors. The study was successful in listing out potential drug targets from the M. tuberculosis proteome involved in vital aspects of the pathogen's metabolism, persistence, virulence and cell wall biosynthesis. This systematic evaluation of metabolic pathways of host and pathogen through reliable and conventional bioinformatic methods can be extended to other pathogens of clinical interest.

Amino Acid Sequence↗

Metabolic pathway analysis in trypanosomes and malaria parasites.

Identification of novel drug targets is required for the development of new classes of drugs to overcome drug resistance and replace less efficacious treatments. In theory, knowledge of the entire genome of a pathogen identifies every potential drug target in any given microbe. In practice, the sheer complexity and the inadequate or inaccurate annotation of genomic information makes target identification and selection somewhat more difficult. Analysis of metabolic pathways provides a useful conceptual framework for the identification of potential drug targets and also for improving our understanding of microbial responses to nutritional, chemical and other environmental stresses. A number of metabolic databases are available as tools for such analyses. The strengths and weaknesses of this approach are discussed.

Animals↗

In silico metabolic pathway analysis and design: succinic acid production by metabolically engineered Escherichia coli as an example.

The intracellular metabolic fluxes can be calculated by metabolic flux analysis, which uses a stoichiometric model for the intracellular reactions along with mass balances around the intracellular metabolites. In this study, we have constructed in silico metabolic pathway network of Escherichia coli consisting of 301 reactions and 294 metabolites. Metabolic flux analyses were carried out to estimate flux distributions to achieve the maximum in silico yield of succinic acid in E. coli. The maximum in silico yield of succinic acid was only 83% of its theoretical yield. The lower in silico yield of succinic acid was found to be due to the insufficient reducing power, which could be increased to its theoretical yield by supplying more reducing power. Furthermore, the optimal metabolic pathways for the production of succinic acid could be proposed based on the results of metabolic flux analyses. In the case of succinic acid production, it was found that pyruvate carboxylation pathway should be used rather than phosphoenolpyruvate carboxylation pathway for its optimal production in E. coli. Then, the in silico optimal succinic acid pathway was compared with conventional succinic acid pathway through minimum set of wet experiments. The results of wet experiments indicate that the pathway predicted by in silico analysis is more efficient than conventional pathway.

Computational Biology↗

Metabolic channeling versus free diffusion: transition-time analysis.

Metabolic channeling is the term used to describe the restricted flow of substrates and products in multienzyme systems. It has been argued for some time that free diffusion is sufficiently rapid to obviate the need for channeling and, furthermore, that it is also fast enough to prevent competing side reactions from interfering with the metabolic flow. In this article we argue that a thorough consideration of the temporal behavior of metabolite pools suggests that channeling is important in many cases.

Biological Transport↗

Covalent modification and metabolic control analysis. Modification to the theorems and their application to metabolic systems containing covalently modifiable enzymes.

A study of the sensitivity properties of metabolic systems containing covalently modifiable enzymes and cascades has been carried out with the aid of metabolic control analysis. We have considered how the theorems of metabolic control analysis must be modified to take into account covalently modifiable enzymes, and have used these results to investigate the effects of increasing the total amount of modifiable enzyme. The sensitivity of system variables to an effector acting through a covalent-modification cycle has also been investigated.

Energy Metabolism↗

Nonlinear metabolic control analysis.

Mathematical description of metabolic systems allows the calculation of the expected responses of metabolism to genetic modifications and the identification of the most promising targets for metabolic engineering. Metabolic control analysis (MCA) provides such a description in the form of quantitative indices (elasticities and control coefficients). These indices are determined by perturbation experiments around a reference steady state and, therefore, the predictive power of MCA is limited to small changes in the metabolic parameters. The modeling framework introduced here allows accurate description of the metabolic responses over wide range of changes in the metabolic parameters. The framework requires information about the MCA indices at the reference state and the corresponding values of the metabolic reaction rates, and employs simplifying assumptions about the reaction mechanisms. It is shown that knowledge of the intracellular metabolite concentrations is not necessary for the application of the framework. The performance of the methodology is illustrated using three elementary metabolic systems that display highly nonlinear responses to the modification in their parameters: an unbranched pathway, an interconvertible enzyme system, and a branched pathway subject to feedback inhibition.

Metabolism↗

Modelling and analysis of metabolic pathways.

Modelling and analysis of metabolic pathways has received an increasing amount of attention over the past few years. Progress has been made in many aspects such as the identification of rate-controlling steps, applications of optimization principles, and stoichiometric analyses. In addition, the scope of modelling has also expanded. These efforts have led to an improved understanding of metabolic pathways and have facilitated their manipulation.

Bacteria↗