Modern theories of metabolic control and their applications

Existing, qualitative notions with respect to the way in which enzyme properties control metabolism are discussed in the light of the control analysis developed by H. Kacser and J. A. Burns ((1973) in: Rate Control of Biological Processes, Davies DD, ed., Cambridge University Press, pp. 63–104) and R. Heinrich and T. A. Rapoport ((1974) Eur. 3. Biochem.42, 89–95), and recent experimental data. Points at which the existing notions should be adjusted are: (i) Metabolic control is shared by enzymes rather than confined to one rate-limiting enzyme per pathway. (if) Whether an enzyme exercises strong control on a flux cannot be deduced solely from its own properties, nor is it directly related to its distance from equilibrium. With respect to metabolic control, enzymes should be classified into four groups, rather than two (reversible versus irreversible). (iii) The distribution of control among the enzymes depends on the metabolic conditions. (iv) Control structures of metabolic pathways probably differ with the function of that pathway.     

1H NMR-based metabolite profiling of diet-induced obesity in a mouse mode

High-fat diets (HFD) and high-carbohydrate diets (HCD)- induced obesity through different pathways, but the metabolic differences between these diets are not fully understood. Therefore, we applied proton nuclear magnetic resonance ((1)H NMR)-based metabolomics to compare the metabolic patterns between C57BL/6 mice fed HCD and those fed HFD. Principal component analysis derived from (1)H NMR spectra of urine showed a clear separation between the HCD and HFD groups. Based on the changes in urinary metabolites, the slow rate of weight gain in mice fed the HCD related to activation of the tricarboxylic acid cycle (resulting in increased levels of citrate and succinate in HCD mice), while the HFD affected nicotinamide metabolism (increased levels of 1-methylnicotineamide, nicotinamide-N-oxide in HFD mice), which leads to systemic oxidative stress. In addition, perturbation of gut microflora metabolism was also related to different metabolic patterns of those two diets. These findings demonstrate that (1)H NMR-based metabolomics can identify diet-dependent perturbations in biological pathways.     

Recent developments in ruthenium anticancer drugs

Interest in Ru anticancer drugs has been growing rapidly since NAMI-A ((ImH(+))[Ru(III)Cl(4)(Im)(S-dmso)], where Im = imidazole and S-dmso = S-bound dimethylsulfoxide) or KP1019 ((IndH(+))[Ru(III)Cl(4)(Ind)(2)], where Ind = indazole) have successfully completed phase I clinical trials and an array of other Ru complexes have shown promise for future development. Herein, the recent literature is reviewed critically to ascertain likely mechanisms of action of Ru-based anticancer drugs, with the emphasis on their reactions with biological media. The most likely interactions of Ru complexes are with: (i) albumin and transferrin in blood plasma, the former serving as a Ru depot, and the latter possibly providing active transport of Ru into cells; (ii) collagens of the extracellular matrix and actins on the cell surface, which are likely to be involved in the specific anti-metastatic action of Ru complexes; (iii) regulatory enzymes within the cell membrane and/or in the cytoplasm; and (iv) DNA in the cell nucleus. Some types of Ru complexes can also promote the intracellular formation of free radical species, either through irradiation (photodynamic therapy), or through reactions with cellular reductants. The metabolic pathways involve competition among reduction, aquation, and hydrolysis in the extracellular medium; binding to transport proteins, the extracellular matrix, and cell-surface biomolecules; and diffusion into cells; with the extent to which individual drugs participate in various steps along these pathways being crucial factors in determining whether they are mainly anti-metastatic or cytotoxic. This diversity of modes of action of Ru anticancer drugs is also likely to enhance their anticancer activities and to reduce the potential for them to develop tumour resistance. New approaches to metabolic studies, such as X-ray absorption spectroscopy and X-ray fluorescence microscopy, are required to provide further mechanistic insights, which could lead to the rational design of improved Ru anticancer drugs.     

1H NMR-based metabonomics study on serum of renal interstitial fibrosis rats induced by unilateral ureteral obstruction

Renal fibrosis is the common pathway of progressive renal disease with complex pathogenesis. Investigating the metabolic changes in the evaluation process of renal fibrosis may enhance the understanding of its pathogenesis. In this study, (1)H nuclear magnetic resonance ((1)H NMR) measurements combined with multivariate statistical techniques were performed to study the metabolic changes in serum samples of renal interstitial fibrosis (RIF) rats, induced by unilateral ureteral obstruction (UUO). Partial least squares-discriminant analysis (PLS-DA) showed satisfactory clustering between UUO and sham operation (SO) rats, suggesting that the metabolic profiles of the RIF groups are markedly different from those of the controls. Alterations in the levels of some metabolites such as valine, isoleucine, lactate, 3-hydroxybutyrate, alanine, acetate, acetoacetate, pyruvate, and glutamate, with time dependence in UUO rats, were observed in PLS-DA loading plots. These changed metabolites represent potential metabolic biomarkers and provide clues that can elucidate the mechanisms underlying the generation and development of RIF. Enhanced metabolic pathways of lipid and ketone body synthesis were predominant in RIF rats. Energy metabolism seemed to be impaired at the early stage of fibrosis but enhanced at a late stage. Our results suggest that (1)H NMR-based metabonomics can provide novel insights into the pathogenesis of RIF.     

Metabolic flux analysis and visualization

One of the ultimate goals of systems biology research is to obtain a comprehensive understanding of the control mechanisms of complex cellular metabolisms. Metabolic Flux Analysis (MFA) is a important method for the quantitative estimation of intracellular metabolic flows through metabolic pathways and the elucidation of cellular physiology. The primary challenge in the use of MFA is that many biological networks are underdetermined systems; it is therefore difficult to narrow down the solution space from the stoichiometric constraints alone. In this tutorial, we present an overview of Flux Balance Analysis (FBA) and (13)C-Metabolic Flux Analysis ((13)C-MFA), both of which are frequently used to solve such underdetermined systems, and we demonstrate FBA and (13)C-MFA using the genome-scale model and the central carbon metabolism model, respectively. Furthermore, because such comprehensive study of intracellular fluxes is inherently complex, we subsequently introduce various pathway mapping and visualization tools to facilitate understanding of these data in the context of the pathways. Specific visualization of MFA results using the BioCyc Omics Viewer and Pathway Projector are shown as illustrative examples.     

Understanding in vivo benzenoid metabolism in petunia petal tissue

In vivo stable isotope labeling and computer-assisted metabolic flux analysis were used to investigate the metabolic pathways in petunia (Petunia hybrida) cv Mitchell leading from Phe to benzenoid compounds, a process that requires the shortening of the side chain by a C(2) unit. Deuterium-labeled Phe ((2)H(5)-Phe) was supplied to excised petunia petals. The intracellular pools of benzenoid/phenylpropanoid-related compounds (intermediates and end products) as well as volatile end products within the floral bouquet were analyzed for pool sizes and labeling kinetics by gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry. Modeling of the benzenoid network revealed that both the CoA-dependent, beta-oxidative and CoA-independent, non-beta-oxidative pathways contribute to the formation of benzenoid compounds in petunia flowers. The flux through the CoA-independent, non-beta-oxidative pathway with benzaldehyde as a key intermediate was estimated to be about 2 times higher than the flux through the CoA-dependent, beta-oxidative pathway. Modeling of (2)H(5)-Phe labeling data predicted that in addition to benzaldehyde, benzylbenzoate is an intermediate between l-Phe and benzoic acid. Benzylbenzoate is the result of benzoylation of benzyl alcohol, for which activity was detected in petunia petals. A cDNA encoding a benzoyl-CoA:benzyl alcohol/phenylethanol benzoyltransferase was isolated from petunia cv Mitchell using a functional genomic approach. Biochemical characterization of a purified recombinant benzoyl-CoA:benzyl alcohol/phenylethanol benzoyltransferase protein showed that it can produce benzylbenzoate and phenylethyl benzoate, both present in petunia corollas, with similar catalytic efficiencies.     

miRNApath: a database of miRNAs, target genes and metabolic pathways

MicroRNAs (miRNAs) are small non-coding RNAs that regulate target gene expression and hence play important roles in metabolic pathways. Recent studies have evidenced the interrelation of miRNAs with cell proliferation, differentiation, development, and diseases. Since they are involved in gene regulation, they are intrinsically related to metabolic pathways. This leads to questions that are particularly interesting for investigating medical and laboratorial applications. We developed an miRNApath online database that uses miRNA target genes to link miRNAs to metabolic pathways. Currently, databases about miRNA target genes (DIANA miRGen), genomic maps (miRNAMap) and sequences (miRBase) do not provide such correlations. Additionally, miRNApath offers five search services and a download area. For each search, there is a specific type of input, which can be a list of target genes, miRNAs, or metabolic pathways, which results in different views, depending upon the input data, concerning relationships between the target genes, miRNAs and metabolic pathways. There are also internal links that lead to a deeper analysis and cross-links to other databases with more detailed information. miRNApath is being continually updated and is available at http://lgmb.fmrp.usp.br/mirnapath.     

Metabolism and cold tolerance of overwintering adult mountain pine beetles (Dendroctonus ponderosae): Evidence of facultative diapause?

We sought evidence for a distinct diapause in adult overwintering mountain pine beetles (Dendroctonus ponderosae Hopkins) by measuring metabolic rate and supercooling ability of field collected beetles throughout the year. Metabolic rates measured at 0, 5, and 10°C declined significantly from October through November, then rose slowly, reaching levels as high as those recorded in October by late May. From December to February metabolic rates were not correlated with minimum weekly phloem temperatures (R(2)=0.0%, P=0.592), but were correlated with phloem temperatures as winter advanced to spring (R(2)=44.8%, P=0.010), a pattern consistent with progression through the maintenance and termination phases of diapause. Supercooling points were also significantly lower in winter compared to fall and spring (F((8,143))=32.6, P<0.001) and were closely correlated with metabolic rates (R(2)>79% for all three temperatures). Dry mass declined linearly with winter progression (F((8,150))=8.34, P<0.001), explained by catabolism of metabolic reserves, with a concomitant accumulation of metabolic water (F((8,147))=35.24, P<0.001). The strong mid-winter metabolic suppression correlated with improved supercooling ability, coupled with their lack of response to variation in environmental temperature, are evidence of possible diapause in adult overwintering mountain pine beetles.     

Network methods for describing sample relationships in genomic datasets: application to Huntington’s disease

Background

We consider the possibility of engineering metabolic pathways in a chassis organism in order to synthesize novel target compounds that are heterologous to the chassis. For this purpose, we model metabolic networks through hypergraphs where reactions are represented by hyperarcs. Each hyperarc represents an enzyme-catalyzed reaction that transforms set of substrates compounds into product compounds. We follow a retrosynthetic approach in order to search in the metabolic space (hypergraphs) for pathways (hyperpaths) linking the target compounds to a source set of compounds.

Results

To select the best pathways to engineer, we have developed an objective function that computes the cost of inserting a heterologous pathway in a given chassis organism. In order to find minimum-cost pathways, we propose in this paper two methods based on steady state analysis and network topology that are to the best of our knowledge, the first to enumerate all possible heterologous pathways linking a target compounds to a source set of compounds. In the context of metabolic engineering, the source set is composed of all naturally produced chassis compounds (endogenuous chassis metabolites) and the target set can be any compound of the chemical space. We also provide an algorithm for identifying precursors which can be supplied to the growth media in order to increase the number of ways to synthesize specific target compounds.

Conclusions

We find the topological approach to be faster by several orders of magnitude than the steady state approach. Yet both methods are generally scalable in time with the number of pathways in the metabolic network. Therefore this work provides a powerful tool for pathway enumeration with direct application to biosynthetic pathway design.     

Categorization of metabolome in bacterial systems

Analyses of biological databases such as those of genome, proteome, metabolome etc., have given insights in organization of biological systems. However, current efforts do not utilize the complete potential of available metabolome data. In this study, metabolome of bacterial systems with reliable annotations are analyzed and a simple method is developed to categorize pathways hierarchically, using rational approach. Ninety-four bacterial systems having for each ≥ 250 annotated metabolic pathways were used to identify a set of common pathways. 42 pathways were present in all bacteria which are termed as Core/Stage I pathways. This set of pathways was used along with interacting compounds to categorize pathways in the metabolome hierarchically. In each metabolome non-interacting pathways were identified including at each stage. The case study of Escherichia coli O157, having 433 annotated pathways, shows that 378 pathways interact directly or indirectly with 41 core pathways while 14 pathways are noninteracting. These 378 pathways are distributed in Stage II (289), Stage III (75), Stage IV (13) and Stage V (1) category. The approach discussed here allows understanding of the complexity of metabolic networks. It has pointed out that core pathways could be most ancient pathways and compounds that interact with maximum pathways may be compounds with high biosynthetic potential, which can be easily identified. Further, it was shown that interactions of pathways at various stages could be one to one, one to many, many to one or many to many mappings through interacting compounds. The granularity of the method discussed being high; the impact of perturbation in a pathway on the metabolome and particularly sub networks can be studied precisely. The categorizations of metabolic pathways help in identifying choke point enzymes that are useful to identify probable drug targets. The Metabolic categorizations for 94 bacteria are available at http://115.111.37.202/mpe/.     

Recovering metabolic pathways via optimization

A metabolic pathway is a coherent set of enzyme catalysed biochemical reactions by which a living organism transforms an initial (source) compound into a final (target) compound. Some of the different metabolic pathways adopted within organisms have been experimentally determined. In this paper, we show that a number of experimentally determined metabolic pathways can be recovered by a mathematical optimization model.     

Cardiac metabolism in high performance fish

In aerobic tissues, such as cardiac and skeletal muscle, short term increases in energy demand are met primarily by acute regulation of mitochondrial pathways. Chronic increases in time-average metabolic rate of an individual or tissue can lead to modest “physiological adaptations” that may result in increased metabolic capacities and more efficient energy production and utilization. These physiological adaptations differ fundamentally from those which alter metabolic rate acutely. Analysis of the metabolic strategies used by an individual to chronically elevate cardiac metabolic rates may help identify the components of cardiac metabolism which may be constrained or malleable over evolutionary time. While pronounced physiological differences in cardiac energy transduction are apparent across species, the evolutionary origins of such differences are difficult to assess. However, the functional consequences of such differences in homologous tissues across species can be discussed with more certainty. Both chronic hypermetabolic challenges and interspecies comparisons suggest highly oxidative tissues such as heart are restricted to strategies which a) elevate the functional mass b) make more efficient use of intracellular space devoted to mitochondria and c) shift toward more efficient metabolic fuels, primarily fatty acids if oxygen delivery is not a factor.     

The (15) N isotope effect as a means for correlating phenotypic alterations and affected pathways in a trait anxiety mouse model

Stable isotope labeling techniques hold great potential for accurate quantitative proteomics comparisons by MS. To investigate the effect of stable isotopes in vivo, we metabolically labeled high anxiety-related behavior (HAB) mice with the heavy nitrogen isotope (15) N. (15) N-labeled HAB mice exhibited behavioral alterations compared to unlabeled ((14) N) HAB mice in their depression-like phenotype. To correlate behavioral alterations with changes on the molecular level, we explored the (15) N isotope effect on the brain proteome by comparing protein expression levels between (15) N-labeled and (14) N HAB mouse brains using quantitative MS. By implementing two complementary in silico pathway analysis approaches, we were able to identify altered networks in (15) N-labeled HAB mice, including major metabolic pathways such as the tricarboxylic acid (TCA) cycle and oxidative phosphorylation. Here, we discuss the affected pathways with regard to their relevance for the behavioral phenotype and critically assess the utility of exploiting the (15) N isotope effect for correlating phenotypic and molecular alterations.     

Metabolic Pathfinding Using RPAIR Annotation

Metabolic databases contain information about thousands of small molecules and reactions, which can be represented as networks. In the context of metabolic reconstruction, pathways can be inferred by searching optimal paths in such networks. A recurrent problem is the presence of pool metabolites (e.g., water, energy carriers, and cofactors), which are connected to hundreds of reactions, thus establishing irrelevant shortcuts between nodes of the network. One solution to this problem relies on weighted networks to penalize highly connected compounds. A more refined solution takes the chemical structure of reactants into account in order to differentiate between side and main compounds of a reaction. Thanks to an intensive annotation effort at KEGG, decompositions of reactions into reactant pairs (RPAIR) categorized by their role (main, trans, cofac, ligase, and leave) are now available.The goal of this article is to evaluate the impact of RPAIR data on pathfinding in metabolic networks. To this end, we measure the impact of different parameters concerning the construction of the metabolic network: mapping of reactions and reactant pairs onto a graph, use of selected categories of reactant pairs, weighting schemes for compounds and reactions, removal of highly connected metabolites, and reaction directionality. In total, we tested 104 combinations of parameters and identified their optimal values for pathfinding on the basis of 55 reference pathways from three organisms.The best-performing metabolic network combines the biochemical knowledge encoded by KEGG RPAIR with a weighting scheme penalizing highly connected compounds. With this network, we could recover reference pathways from Escherichia coli with an average accuracy of 93% (32 pathways), from Saccharomyces cerevisiae with an average accuracy of 66% (11 pathways), and from humans with an average accuracy of 70% (12 pathways). Our pathfinding approach is available as part of the Network Analysis Tools.     

Coordinated activation of metabolic pathways for antioxidants and defence compounds by jasmonates and their roles in stress tolerance in Arabidopsis

Jasmonic acid (JA) and methyl jasmonate (MeJA), collectively termed jasmonates, are ubiquitous plant signalling compounds. Several types of stress conditions, such as wounding and pathogen infection, cause endogenous JA accumulation and the expression of jasmonate-responsive genes. Although jasmonates are important signalling components for the stress response in plants, the mechanism by which jasmonate signalling contributes to stress tolerance has not been clearly defined. A comprehensive analysis of jasmonate-regulated metabolic pathways in Arabidopsis was performed using cDNA macroarrays containing 13516 expressed sequence tags (ESTs) covering 8384 loci. The results showed that jasmonates activate the coordinated gene expression of factors involved in nine metabolic pathways belonging to two functionally related groups: (i) ascorbate and glutathione metabolic pathways, which are important in defence responses to oxidative stress, and (ii) biosynthesis of indole glucosinolate, which is a defence compound occurring in the Brassicaceae family. We confirmed that JA induces the accumulation of ascorbate, glutathione and cysteine and increases the activity of dehydroascorbate reductase, an enzyme in the ascorbate recycling pathway. These antioxidant metabolic pathways are known to be activated under oxidative stress conditions. Ozone (O3) exposure, a representative oxidative stress, is known to cause activation of antioxidant metabolism. We showed that O3 exposure caused the induction of several genes involved in antioxidant metabolism in the wild type. However, in jasmonate-deficient Arabidopsis 12-oxophytodienoate reductase 3 (opr3) mutants, the induction of antioxidant genes was abolished. Compared with the wild type, opr3 mutants were more sensitive to O3 exposure. These results suggest that the coordinated activation of the metabolic pathways mediated by jasmonates provides resistance to environmental stresses.     

Optimal stoichiometric designs of ATP-producing systems as determined by an evolutionary algorithm.

The design of metabolic pathways is thought to be the result of an optimization process such that the structure of contemporary metabolic routes maximizes a particular objective function. Recently, it has been shown that some essential stoichiometric properties of glycolysis can be explained on the basis of the requirement for a high ATP production rate. Because the number of stoichiometrically feasible designs increases strongly with the number of reactions involved, a systematic analysis of all the possibilities turns out to be inaccessible beyond a certain system size. We present, therefore, an alternative approach to compute in a more efficient way the optimal design of glycolysis interacting with an external ATP-consuming reaction. The algorithm is based on the laws of evolution by natural selection, and may be viewed as a particular version of evolutionary algorithms. The following conclusions are derived: (a) evolutionary algorithms are very useful search strategies in determining optimal stoichiometries of metabolic pathways. (b) Essential topological features of the glycolytic network may be explained on the basis of flux optimization. (c) There is a strong interrelation between the optimal stoichiometries and the thermodynamic and kinetic properties of the participating reactions. (d) Some subsequences of reactions in optimal pathways are strongly conserved at variation of system parameters, which may be understood by applying principles of metabolic control analysis.     

病毒辅助代谢基因的研究进展

病毒通过影响微生物的营养循环、生物多样性和遗传信息传递等,在全球海洋的生物地球化学循环中发挥关键作用。病毒还可以控制微生物的群落组成、关键代谢过程等,这些依赖于病毒基因组上的辅助代谢基因(auxiliary metabolic genes,AMGs)。AMGs在病毒感染宿主的过程中表达并参与调控宿主的代谢过程。病毒基因组中的AMGs包括中央碳代谢、氮代谢、磷和硫循环、核苷酸代谢以及与氧化应激反应相关的基因。AMGs有利于子代病毒更高效地组装和释放,对于病毒种群的繁衍具有重要意义,同时对病毒-宿主相互作用机制的研究产生重要影响。本文针对病毒辅助代谢基因的起源、类别及其重要的生态作用进行简要综述,以期为进一步阐明病毒在不同生态系统中的功能提供依据。     

Thermodynamically based profiling of drug metabolism and drug-drug metabolic interactions: a case study of acetaminophen and ethanol toxic interaction

Drug-drug metabolic interactions can result in unwanted side effects, including reduced drug efficacy and formation of toxic metabolic intermediates. In this work, thermodynamic constraints on non-equilibrium metabolite concentrations are used to reveal the biochemical interactions between the metabolic pathways of ethanol and acetaminophen (N-acetyl-p-aminophenol), two drugs known to interact unfavorably. It is known that many reactions of these pathways are coupled to the central energy metabolic reactions through a number of metabolites and the cellular redox potential. Based on these observations, a metabolic network model has been constructed and a database of thermodynamic properties for all participating metabolites and reactions has been compiled. Constraint-based computational analysis of the feasible metabolite concentrations reveals that the non-toxic pathways for APAP metabolism and the pathway for detoxifying N-acetyl-p-benzoquinoneimine (NAPQI) are inhibited by network interactions with ethanol metabolism. These results point to the potential utility of thermodynamically based profiling of metabolic network interactions in screening of drug candidates and analysis of potential toxicity.