Attractor Metabolic Networks

PubMed Central

De la Fuente, Ildefonso M.; Cortes, Jesus M.; Pelta, David A.; Veguillas, Juan

2013-01-01

Background The experimental observations and numerical studies with dissipative metabolic networks have shown that cellular enzymatic activity self-organizes spontaneously leading to the emergence of a Systemic Metabolic Structure in the cell, characterized by a set of different enzymatic reactions always locked into active states (metabolic core) while the rest of the catalytic processes are only intermittently active. This global metabolic structure was verified for Escherichia coli, Helicobacter pylori and Saccharomyces cerevisiae, and it seems to be a common key feature to all cellular organisms. In concordance with these observations, the cell can be considered a complex metabolic network which mainly integrates a large ensemble of self-organized multienzymatic complexes interconnected by substrate fluxes and regulatory signals, where multiple autonomous oscillatory and quasi-stationary catalytic patterns simultaneously emerge. The network adjusts the internal metabolic activities to the external change by means of flux plasticity and structural plasticity. Methodology/Principal Findings In order to research the systemic mechanisms involved in the regulation of the cellular enzymatic activity we have studied different catalytic activities of a dissipative metabolic network under different external stimuli. The emergent biochemical data have been analysed using statistical mechanic tools, studying some macroscopic properties such as the global information and the energy of the system. We have also obtained an equivalent Hopfield network using a Boltzmann machine. Our main result shows that the dissipative metabolic network can behave as an attractor metabolic network. Conclusions/Significance We have found that the systemic enzymatic activities are governed by attractors with capacity to store functional metabolic patterns which can be correctly recovered from specific input stimuli. The network attractors regulate the catalytic patterns, modify the efficiency in the connection between the multienzymatic complexes, and stably retain these modifications. Here for the first time, we have introduced the general concept of attractor metabolic network, in which this dynamic behavior is observed. PMID:23554883

Reconstruction of biological pathways and metabolic networks from in silico labeled metabolites.

PubMed

Hadadi, Noushin; Hafner, Jasmin; Soh, Keng Cher; Hatzimanikatis, Vassily

2017-01-01

Reaction atom mappings track the positional changes of all of the atoms between the substrates and the products as they undergo the biochemical transformation. However, information on atom transitions in the context of metabolic pathways is not widely available in the literature. The understanding of metabolic pathways at the atomic level is of great importance as it can deconvolute the overlapping catabolic/anabolic pathways resulting in the observed metabolic phenotype. The automated identification of atom transitions within a metabolic network is a very challenging task since the degree of complexity of metabolic networks dramatically increases when we transit from metabolite-level studies to atom-level studies. Despite being studied extensively in various approaches, the field of atom mapping of metabolic networks is lacking an automated approach, which (i) accounts for the information of reaction mechanism for atom mapping and (ii) is extendable from individual atom-mapped reactions to atom-mapped reaction networks. Hereby, we introduce a computational framework, iAM.NICE (in silico Atom Mapped Network Integrated Computational Explorer), for the systematic atom-level reconstruction of metabolic networks from in silico labelled substrates. iAM.NICE is to our knowledge the first automated atom-mapping algorithm that is based on the underlying enzymatic biotransformation mechanisms, and its application goes beyond individual reactions and it can be used for the reconstruction of atom-mapped metabolic networks. We illustrate the applicability of our method through the reconstruction of atom-mapped reactions of the KEGG database and we provide an example of an atom-level representation of the core metabolic network of E. coli. Copyright © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Small-worldness and gender differences of large scale brain metabolic covariance networks in young adults: a FDG PET study of 400 subjects.

PubMed

Hu, Yuxiao; Xu, Qiang; Shen, Junkang; Li, Kai; Zhu, Hong; Zhang, Zhiqiang; Lu, Guangming

2015-02-01

Many studies have demonstrated the small-worldness of the human brain, and have revealed a sexual dimorphism in brain network properties. However, little is known about the gender effects on the topological organization of the brain metabolic covariance networks. To investigate the small-worldness and the gender differences in the topological architectures of human brain metabolic networks. FDG-PET data of 400 healthy right-handed subjects (200 women and 200 age-matched men) were involved in the present study. Metabolic networks of each gender were constructed by calculating the covariance of regional cerebral glucose metabolism (rCMglc) across subjects on the basis of AAL parcellation. Gender differences of network and nodal properties were investigated by using the graph theoretical approaches. Moreover, the gender-related difference of rCMglc in each brain region was tested for investigating the relationships between the hub regions and the brain regions showing significant gender-related differences in rCMglc. We found prominent small-world properties in the domain of metabolic networks in each gender. No significant gender difference in the global characteristics was found. Gender differences of nodal characteristic were observed in a few brain regions. We also found bilateral and lateralized distributions of network hubs in the females and males. Furthermore, we first reported that some hubs of a gender located in the brain regions showing weaker rCMglc in this gender than the other gender. The present study demonstrated that small-worldness was existed in metabolic networks, and revealed gender differences of organizational patterns in metabolic network. These results maybe provided insights into the understanding of the metabolic substrates underlying individual differences in cognition and behaviors. © The Foundation Acta Radiologica 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Topological and kinetic determinants of the modal matrices of dynamic models of metabolism

PubMed Central

2017-01-01

Large-scale kinetic models of metabolism are becoming increasingly comprehensive and accurate. A key challenge is to understand the biochemical basis of the dynamic properties of these models. Linear analysis methods are well-established as useful tools for characterizing the dynamic response of metabolic networks. Central to linear analysis methods are two key matrices: the Jacobian matrix (J) and the modal matrix (M-1) arising from its eigendecomposition. The modal matrix M-1 contains dynamically independent motions of the kinetic model near a reference state, and it is sparse in practice for metabolic networks. However, connecting the structure of M-1 to the kinetic properties of the underlying reactions is non-trivial. In this study, we analyze the relationship between J, M-1, and the kinetic properties of the underlying network for kinetic models of metabolism. Specifically, we describe the origin of mode sparsity structure based on features of the network stoichiometric matrix S and the reaction kinetic gradient matrix G. First, we show that due to the scaling of kinetic parameters in real networks, diagonal dominance occurs in a substantial fraction of the rows of J, resulting in simple modal structures with clear biological interpretations. Then, we show that more complicated modes originate from topologically-connected reactions that have similar reaction elasticities in G. These elasticities represent dynamic equilibrium balances within reactions and are key determinants of modal structure. The work presented should prove useful towards obtaining an understanding of the dynamics of kinetic models of metabolism, which are rooted in the network structure and the kinetic properties of reactions. PMID:29267329

A genome scale metabolic network for rice and accompanying analysis of tryptophan, auxin and serotonin biosynthesis regulation under biotic stress

USDA-ARS?s Scientific Manuscript database

Functional annotations of large plant genome projects mostly provide information on gene function and gene families based on the presence of protein domains and gene homology, but not necessarily in association with gene expression or metabolic and regulatory networks. These additional annotations a...

Control of fluxes in metabolic networks

PubMed Central

Basler, Georg; Nikoloski, Zoran; Larhlimi, Abdelhalim; Barabási, Albert-László; Liu, Yang-Yu

2016-01-01

Understanding the control of large-scale metabolic networks is central to biology and medicine. However, existing approaches either require specifying a cellular objective or can only be used for small networks. We introduce new coupling types describing the relations between reaction activities, and develop an efficient computational framework, which does not require any cellular objective for systematic studies of large-scale metabolism. We identify the driver reactions facilitating control of 23 metabolic networks from all kingdoms of life. We find that unicellular organisms require a smaller degree of control than multicellular organisms. Driver reactions are under complex cellular regulation in Escherichia coli, indicating their preeminent role in facilitating cellular control. In human cancer cells, driver reactions play pivotal roles in malignancy and represent potential therapeutic targets. The developed framework helps us gain insights into regulatory principles of diseases and facilitates design of engineering strategies at the interface of gene regulation, signaling, and metabolism. PMID:27197218

Identification of functional differences in metabolic networks using comparative genomics and constraint-based models.

PubMed

Hamilton, Joshua J; Reed, Jennifer L

2012-01-01

Genome-scale network reconstructions are useful tools for understanding cellular metabolism, and comparisons of such reconstructions can provide insight into metabolic differences between organisms. Recent efforts toward comparing genome-scale models have focused primarily on aligning metabolic networks at the reaction level and then looking at differences and similarities in reaction and gene content. However, these reaction comparison approaches are time-consuming and do not identify the effect network differences have on the functional states of the network. We have developed a bilevel mixed-integer programming approach, CONGA, to identify functional differences between metabolic networks by comparing network reconstructions aligned at the gene level. We first identify orthologous genes across two reconstructions and then use CONGA to identify conditions under which differences in gene content give rise to differences in metabolic capabilities. By seeking genes whose deletion in one or both models disproportionately changes flux through a selected reaction (e.g., growth or by-product secretion) in one model over another, we are able to identify structural metabolic network differences enabling unique metabolic capabilities. Using CONGA, we explore functional differences between two metabolic reconstructions of Escherichia coli and identify a set of reactions responsible for chemical production differences between the two models. We also use this approach to aid in the development of a genome-scale model of Synechococcus sp. PCC 7002. Finally, we propose potential antimicrobial targets in Mycobacterium tuberculosis and Staphylococcus aureus based on differences in their metabolic capabilities. Through these examples, we demonstrate that a gene-centric approach to comparing metabolic networks allows for a rapid comparison of metabolic models at a functional level. Using CONGA, we can identify differences in reaction and gene content which give rise to different functional predictions. Because CONGA provides a general framework, it can be applied to find functional differences across models and biological systems beyond those presented here.

Identification of Functional Differences in Metabolic Networks Using Comparative Genomics and Constraint-Based Models

PubMed Central

Hamilton, Joshua J.; Reed, Jennifer L.

2012-01-01

Genome-scale network reconstructions are useful tools for understanding cellular metabolism, and comparisons of such reconstructions can provide insight into metabolic differences between organisms. Recent efforts toward comparing genome-scale models have focused primarily on aligning metabolic networks at the reaction level and then looking at differences and similarities in reaction and gene content. However, these reaction comparison approaches are time-consuming and do not identify the effect network differences have on the functional states of the network. We have developed a bilevel mixed-integer programming approach, CONGA, to identify functional differences between metabolic networks by comparing network reconstructions aligned at the gene level. We first identify orthologous genes across two reconstructions and then use CONGA to identify conditions under which differences in gene content give rise to differences in metabolic capabilities. By seeking genes whose deletion in one or both models disproportionately changes flux through a selected reaction (e.g., growth or by-product secretion) in one model over another, we are able to identify structural metabolic network differences enabling unique metabolic capabilities. Using CONGA, we explore functional differences between two metabolic reconstructions of Escherichia coli and identify a set of reactions responsible for chemical production differences between the two models. We also use this approach to aid in the development of a genome-scale model of Synechococcus sp. PCC 7002. Finally, we propose potential antimicrobial targets in Mycobacterium tuberculosis and Staphylococcus aureus based on differences in their metabolic capabilities. Through these examples, we demonstrate that a gene-centric approach to comparing metabolic networks allows for a rapid comparison of metabolic models at a functional level. Using CONGA, we can identify differences in reaction and gene content which give rise to different functional predictions. Because CONGA provides a general framework, it can be applied to find functional differences across models and biological systems beyond those presented here. PMID:22666308

An integrated network visualization framework towards metabolic engineering applications.

PubMed

Noronha, Alberto; Vilaça, Paulo; Rocha, Miguel

2014-12-30

Over the last years, several methods for the phenotype simulation of microorganisms, under specified genetic and environmental conditions have been proposed, in the context of Metabolic Engineering (ME). These methods provided insight on the functioning of microbial metabolism and played a key role in the design of genetic modifications that can lead to strains of industrial interest. On the other hand, in the context of Systems Biology research, biological network visualization has reinforced its role as a core tool in understanding biological processes. However, it has been scarcely used to foster ME related methods, in spite of the acknowledged potential. In this work, an open-source software that aims to fill the gap between ME and metabolic network visualization is proposed, in the form of a plugin to the OptFlux ME platform. The framework is based on an abstract layer, where the network is represented as a bipartite graph containing minimal information about the underlying entities and their desired relative placement. The framework provides input/output support for networks specified in standard formats, such as XGMML, SBGN or SBML, providing a connection to genome-scale metabolic models. An user-interface makes it possible to edit, manipulate and query nodes in the network, providing tools to visualize diverse effects, including visual filters and aspect changing (e.g. colors, shapes and sizes). These tools are particularly interesting for ME, since they allow overlaying phenotype simulation results or elementary flux modes over the networks. The framework and its source code are freely available, together with documentation and other resources, being illustrated with well documented case studies.

Metabolic Profiling of a Mapping Population Exposes New Insights in the Regulation of Seed Metabolism and Seed, Fruit, and Plant Relations

PubMed Central

Toubiana, David; Semel, Yaniv; Tohge, Takayuki; Beleggia, Romina; Cattivelli, Luigi; Rosental, Leah; Nikoloski, Zoran; Zamir, Dani; Fernie, Alisdair R.; Fait, Aaron

2012-01-01

To investigate the regulation of seed metabolism and to estimate the degree of metabolic natural variability, metabolite profiling and network analysis were applied to a collection of 76 different homozygous tomato introgression lines (ILs) grown in the field in two consecutive harvest seasons. Factorial ANOVA confirmed the presence of 30 metabolite quantitative trait loci (mQTL). Amino acid contents displayed a high degree of variability across the population, with similar patterns across the two seasons, while sugars exhibited significant seasonal fluctuations. Upon integration of data for tomato pericarp metabolite profiling, factorial ANOVA identified the main factor for metabolic polymorphism to be the genotypic background rather than the environment or the tissue. Analysis of the coefficient of variance indicated greater phenotypic plasticity in the ILs than in the M82 tomato cultivar. Broad-sense estimate of heritability suggested that the mode of inheritance of metabolite traits in the seed differed from that in the fruit. Correlation-based metabolic network analysis comparing metabolite data for the seed with that for the pericarp showed that the seed network displayed tighter interdependence of metabolic processes than the fruit. Amino acids in the seed metabolic network were shown to play a central hub-like role in the topology of the network, maintaining high interactions with other metabolite categories, i.e., sugars and organic acids. Network analysis identified six exceptionally highly co-regulated amino acids, Gly, Ser, Thr, Ile, Val, and Pro. The strong interdependence of this group was confirmed by the mQTL mapping. Taken together these results (i) reflect the extensive redundancy of the regulation underlying seed metabolism, (ii) demonstrate the tight co-ordination of seed metabolism with respect to fruit metabolism, and (iii) emphasize the centrality of the amino acid module in the seed metabolic network. Finally, the study highlights the added value of integrating metabolic network analysis with mQTL mapping. PMID:22479206

Integrating data from biological experiments into metabolic networks with the DBE information system.

PubMed

Borisjuk, Ljudmilla; Hajirezaei, Mohammad-Reza; Klukas, Christian; Rolletschek, Hardy; Schreiber, Falk

2005-01-01

Modern 'omics'-technologies result in huge amounts of data about life processes. For analysis and data mining purposes this data has to be considered in the context of the underlying biological networks. This work presents an approach for integrating data from biological experiments into metabolic networks by mapping the data onto network elements and visualising the data enriched networks automatically. This methodology is implemented in DBE, an information system that supports the analysis and visualisation of experimental data in the context of metabolic networks. It consists of five parts: (1) the DBE-Database for consistent data storage, (2) the Excel-Importer application for the data import, (3) the DBE-Website as the interface for the system, (4) the DBE-Pictures application for the up- and download of binary (e. g. image) files, and (5) DBE-Gravisto, a network analysis and graph visualisation system. The usability of this approach is demonstrated in two examples.

A toolbox model of evolution of metabolic pathways on networks of arbitrary topology.

PubMed

Pang, Tin Yau; Maslov, Sergei

2011-05-01

In prokaryotic genomes the number of transcriptional regulators is known to be proportional to the square of the total number of protein-coding genes. A toolbox model of evolution was recently proposed to explain this empirical scaling for metabolic enzymes and their regulators. According to its rules, the metabolic network of an organism evolves by horizontal transfer of pathways from other species. These pathways are part of a larger "universal" network formed by the union of all species-specific networks. It remained to be understood, however, how the topological properties of this universal network influence the scaling law of functional content of genomes in the toolbox model. Here we answer this question by first analyzing the scaling properties of the toolbox model on arbitrary tree-like universal networks. We prove that critical branching topology, in which the average number of upstream neighbors of a node is equal to one, is both necessary and sufficient for quadratic scaling. We further generalize the rules of the model to incorporate reactions with multiple substrates/products as well as branched and cyclic metabolic pathways. To achieve its metabolic tasks, the new model employs evolutionary optimized pathways with minimal number of reactions. Numerical simulations of this realistic model on the universal network of all reactions in the KEGG database produced approximately quadratic scaling between the number of regulated pathways and the size of the metabolic network. To quantify the geometrical structure of individual pathways, we investigated the relationship between their number of reactions, byproducts, intermediate, and feedback metabolites. Our results validate and explain the ubiquitous appearance of the quadratic scaling for a broad spectrum of topologies of underlying universal metabolic networks. They also demonstrate why, in spite of "small-world" topology, real-life metabolic networks are characterized by a broad distribution of pathway lengths and sizes of metabolic regulons in regulatory networks.

Identification of lethal reactions in the Esherichia coli metabolic network: Graph theory approach

NASA Astrophysics Data System (ADS)

Ghim, C.-M.; Goh, K.-I.; Kahng, B.; Kim, D.

2004-03-01

As a first step toward holistic modeling of cells, we analyze the biochemical reactions occurring in the genome-scale metabolism of Esherichia coli. To this end, we construct a directed bipartite graph by assigning metabolite or reaction to each node. We apply various measures of centrality, a well-known concept in the graph theory, and their modifications to the metabolic network, finding that there exist lethal reactions involved in the central metabolism. Such lethal reactions or associated enzymes under diverse environments in silico are identified and compared with earlier results obtained from flux balance analysis.

A genome-scale metabolic network reconstruction of tomato (Solanum lycopersicum L.) and its application to photorespiratory metabolism.

PubMed

Yuan, Huili; Cheung, C Y Maurice; Poolman, Mark G; Hilbers, Peter A J; van Riel, Natal A W

2016-01-01

Tomato (Solanum lycopersicum L.) has been studied extensively due to its high economic value in the market, and high content in health-promoting antioxidant compounds. Tomato is also considered as an excellent model organism for studying the development and metabolism of fleshy fruits. However, the growth, yield and fruit quality of tomatoes can be affected by drought stress, a common abiotic stress for tomato. To investigate the potential metabolic response of tomato plants to drought, we reconstructed iHY3410, a genome-scale metabolic model of tomato leaf, and used this metabolic network to simulate tomato leaf metabolism. The resulting model includes 3410 genes and 2143 biochemical and transport reactions distributed across five intracellular organelles including cytosol, plastid, mitochondrion, peroxisome and vacuole. The model successfully described the known metabolic behaviour of tomato leaf under heterotrophic and phototrophic conditions. The in silico investigation of the metabolic characteristics for photorespiration and other relevant metabolic processes under drought stress suggested that: (i) the flux distributions through the mevalonate (MVA) pathway under drought were distinct from that under normal conditions; and (ii) the changes in fluxes through core metabolic pathways with varying flux ratio of RubisCO carboxylase to oxygenase may contribute to the adaptive stress response of plants. In addition, we improved on previous studies of reaction essentiality analysis for leaf metabolism by including potential alternative routes for compensating reaction knockouts. Altogether, the genome-scale model provides a sound framework for investigating tomato metabolism and gives valuable insights into the functional consequences of abiotic stresses. © 2015 The Authors.The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.

Control of fluxes in metabolic networks.

PubMed

Basler, Georg; Nikoloski, Zoran; Larhlimi, Abdelhalim; Barabási, Albert-László; Liu, Yang-Yu

2016-07-01

Understanding the control of large-scale metabolic networks is central to biology and medicine. However, existing approaches either require specifying a cellular objective or can only be used for small networks. We introduce new coupling types describing the relations between reaction activities, and develop an efficient computational framework, which does not require any cellular objective for systematic studies of large-scale metabolism. We identify the driver reactions facilitating control of 23 metabolic networks from all kingdoms of life. We find that unicellular organisms require a smaller degree of control than multicellular organisms. Driver reactions are under complex cellular regulation in Escherichia coli, indicating their preeminent role in facilitating cellular control. In human cancer cells, driver reactions play pivotal roles in malignancy and represent potential therapeutic targets. The developed framework helps us gain insights into regulatory principles of diseases and facilitates design of engineering strategies at the interface of gene regulation, signaling, and metabolism. © 2016 Basler et al.; Published by Cold Spring Harbor Laboratory Press.

Integration of Genome-Scale Modeling and Transcript Profiling Reveals Metabolic Pathways Underlying Light and Temperature Acclimation in Arabidopsis[C][W

PubMed Central

Töpfer, Nadine; Caldana, Camila; Grimbs, Sergio; Willmitzer, Lothar; Fernie, Alisdair R.; Nikoloski, Zoran

2013-01-01

Understanding metabolic acclimation of plants to challenging environmental conditions is essential for dissecting the role of metabolic pathways in growth and survival. As stresses involve simultaneous physiological alterations across all levels of cellular organization, a comprehensive characterization of the role of metabolic pathways in acclimation necessitates integration of genome-scale models with high-throughput data. Here, we present an integrative optimization-based approach, which, by coupling a plant metabolic network model and transcriptomics data, can predict the metabolic pathways affected in a single, carefully controlled experiment. Moreover, we propose three optimization-based indices that characterize different aspects of metabolic pathway behavior in the context of the entire metabolic network. We demonstrate that the proposed approach and indices facilitate quantitative comparisons and characterization of the plant metabolic response under eight different light and/or temperature conditions. The predictions of the metabolic functions involved in metabolic acclimation of Arabidopsis thaliana to the changing conditions are in line with experimental evidence and result in a hypothesis about the role of homocysteine-to-Cys interconversion and Asn biosynthesis. The approach can also be used to reveal the role of particular metabolic pathways in other scenarios, while taking into consideration the entirety of characterized plant metabolism. PMID:23613196

An objective function exploiting suboptimal solutions in metabolic networks

PubMed Central

2013-01-01

Background Flux Balance Analysis is a theoretically elegant, computationally efficient, genome-scale approach to predicting biochemical reaction fluxes. Yet FBA models exhibit persistent mathematical degeneracy that generally limits their predictive power. Results We propose a novel objective function for cellular metabolism that accounts for and exploits degeneracy in the metabolic network to improve flux predictions. In our model, regulation drives metabolism toward a region of flux space that allows nearly optimal growth. Metabolic mutants deviate minimally from this region, a function represented mathematically as a convex cone. Near-optimal flux configurations within this region are considered equally plausible and not subject to further optimizing regulation. Consistent with relaxed regulation near optimality, we find that the size of the near-optimal region predicts flux variability under experimental perturbation. Conclusion Accounting for suboptimal solutions can improve the predictive power of metabolic FBA models. Because fluctuations of enzyme and metabolite levels are inevitable, tolerance for suboptimality may support a functionally robust metabolic network. PMID:24088221

BiNA: A Visual Analytics Tool for Biological Network Data

PubMed Central

Gerasch, Andreas; Faber, Daniel; Küntzer, Jan; Niermann, Peter; Kohlbacher, Oliver; Lenhof, Hans-Peter; Kaufmann, Michael

2014-01-01

Interactive visual analysis of biological high-throughput data in the context of the underlying networks is an essential task in modern biomedicine with applications ranging from metabolic engineering to personalized medicine. The complexity and heterogeneity of data sets require flexible software architectures for data analysis. Concise and easily readable graphical representation of data and interactive navigation of large data sets are essential in this context. We present BiNA - the Biological Network Analyzer - a flexible open-source software for analyzing and visualizing biological networks. Highly configurable visualization styles for regulatory and metabolic network data offer sophisticated drawings and intuitive navigation and exploration techniques using hierarchical graph concepts. The generic projection and analysis framework provides powerful functionalities for visual analyses of high-throughput omics data in the context of networks, in particular for the differential analysis and the analysis of time series data. A direct interface to an underlying data warehouse provides fast access to a wide range of semantically integrated biological network databases. A plugin system allows simple customization and integration of new analysis algorithms or visual representations. BiNA is available under the 3-clause BSD license at http://bina.unipax.info/. PMID:24551056

CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Noise effect in metabolic networks

NASA Astrophysics Data System (ADS)

Li, Zheng-Yan; Xie, Zheng-Wei; Chen, Tong; Ouyang, Qi

2009-12-01

Constraint-based models such as flux balance analysis (FBA) are a powerful tool to study biological metabolic networks. Under the hypothesis that cells operate at an optimal growth rate as the result of evolution and natural selection, this model successfully predicts most cellular behaviours in growth rate. However, the model ignores the fact that cells can change their cellular metabolic states during evolution, leaving optimal metabolic states unstable. Here, we consider all the cellular processes that change metabolic states into a single term 'noise', and assume that cells change metabolic states by randomly walking in feasible solution space. By simulating a state of a cell randomly walking in the constrained solution space of metabolic networks, we found that in a noisy environment cells in optimal states tend to travel away from these points. On considering the competition between the noise effect and the growth effect in cell evolution, we found that there exists a trade-off between these two effects. As a result, the population of the cells contains different cellular metabolic states, and the population growth rate is at suboptimal states.

Advanced Stoichiometric Analysis of Metabolic Networks of Mammalian Systems

PubMed Central

Orman, Mehmet A.; Berthiaume, Francois; Androulakis, Ioannis P.; Ierapetritou, Marianthi G.

2013-01-01

Metabolic engineering tools have been widely applied to living organisms to gain a comprehensive understanding about cellular networks and to improve cellular properties. Metabolic flux analysis (MFA), flux balance analysis (FBA), and metabolic pathway analysis (MPA) are among the most popular tools in stoichiometric network analysis. Although application of these tools into well-known microbial systems is extensive in the literature, various barriers prevent them from being utilized in mammalian cells. Limited experimental data, complex regulatory mechanisms, and the requirement of more complex nutrient media are some major obstacles in mammalian cell systems. However, mammalian cells have been used to produce therapeutic proteins, to characterize disease states or related abnormal metabolic conditions, and to analyze the toxicological effects of some medicinally important drugs. Therefore, there is a growing need for extending metabolic engineering principles to mammalian cells in order to understand their underlying metabolic functions. In this review article, advanced metabolic engineering tools developed for stoichiometric analysis including MFA, FBA, and MPA are described. Applications of these tools in mammalian cells are discussed in detail, and the challenges and opportunities are highlighted. PMID:22196224

Metabolic pathway analysis of Scheffersomyces (Pichia) stipitis: effect of oxygen availability on ethanol synthesis and flux distributions.

PubMed

Unrean, Pornkamol; Nguyen, Nhung H A

2012-06-01

Elementary mode analysis (EMA) identifies all possible metabolic states of the cell metabolic network. Investigation of these states can provide a detailed insight into the underlying metabolism in the cell. In this study, the flux states of Scheffersomyces (Pichia) stipitis metabolism were examined. It was shown that increasing oxygen levels led to a decrease of ethanol synthesis. This trend was confirmed by experimental evaluation of S. stipitis in glucose-xylose fermentation. The oxygen transfer rate for an optimal ethanol production was 1.8 mmol/l/h, which gave the ethanol yield of 0.40 g/g and the ethanol productivity of 0.25 g/l/h. For a better understanding of the cell's regulatory mechanism in response to oxygenation levels, EMA was used to examine metabolic flux patterns under different oxygen levels. Up- and downregulation of enzymes in the network during the change of culturing condition from oxygen limitation to oxygen sufficiency were identified. The results indicated the flexibility of S. stipitis metabolism to cope with oxygen availability. In addition, relevant genetic targets towards improved ethanol-producing strains under all oxygenation levels were identified. These targeted genes limited the metabolic functionality of the cell to function according to the most efficient ethanol synthesis pathways. The presented approach is promising and can contribute to the development of culture optimization and strain engineers for improved lignocellulosic ethanol production by S. stipitis.

iAK692: A genome-scale metabolic model of Spirulina platensis C1

PubMed Central

2012-01-01

Background Spirulina (Arthrospira) platensis is a well-known filamentous cyanobacterium used in the production of many industrial products, including high value compounds, healthy food supplements, animal feeds, pharmaceuticals and cosmetics, for example. It has been increasingly studied around the world for scientific purposes, especially for its genome, biology, physiology, and also for the analysis of its small-scale metabolic network. However, the overall description of the metabolic and biotechnological capabilities of S. platensis requires the development of a whole cellular metabolism model. Recently, the S. platensis C1 (Arthrospira sp. PCC9438) genome sequence has become available, allowing systems-level studies of this commercial cyanobacterium. Results In this work, we present the genome-scale metabolic network analysis of S. platensis C1, iAK692, its topological properties, and its metabolic capabilities and functions. The network was reconstructed from the S. platensis C1 annotated genomic sequence using Pathway Tools software to generate a preliminary network. Then, manual curation was performed based on a collective knowledge base and a combination of genomic, biochemical, and physiological information. The genome-scale metabolic model consists of 692 genes, 837 metabolites, and 875 reactions. We validated iAK692 by conducting fermentation experiments and simulating the model under autotrophic, heterotrophic, and mixotrophic growth conditions using COBRA toolbox. The model predictions under these growth conditions were consistent with the experimental results. The iAK692 model was further used to predict the unique active reactions and essential genes for each growth condition. Additionally, the metabolic states of iAK692 during autotrophic and mixotrophic growths were described by phenotypic phase plane (PhPP) analysis. Conclusions This study proposes the first genome-scale model of S. platensis C1, iAK692, which is a predictive metabolic platform for a global understanding of physiological behaviors and metabolic engineering. This platform could accelerate the integrative analysis of various “-omics” data, leading to strain improvement towards a diverse range of desired industrial products from Spirulina. PMID:22703714

iAK692: a genome-scale metabolic model of Spirulina platensis C1.

PubMed

Klanchui, Amornpan; Khannapho, Chiraphan; Phodee, Atchara; Cheevadhanarak, Supapon; Meechai, Asawin

2012-06-15

Spirulina (Arthrospira) platensis is a well-known filamentous cyanobacterium used in the production of many industrial products, including high value compounds, healthy food supplements, animal feeds, pharmaceuticals and cosmetics, for example. It has been increasingly studied around the world for scientific purposes, especially for its genome, biology, physiology, and also for the analysis of its small-scale metabolic network. However, the overall description of the metabolic and biotechnological capabilities of S. platensis requires the development of a whole cellular metabolism model. Recently, the S. platensis C1 (Arthrospira sp. PCC9438) genome sequence has become available, allowing systems-level studies of this commercial cyanobacterium. In this work, we present the genome-scale metabolic network analysis of S. platensis C1, iAK692, its topological properties, and its metabolic capabilities and functions. The network was reconstructed from the S. platensis C1 annotated genomic sequence using Pathway Tools software to generate a preliminary network. Then, manual curation was performed based on a collective knowledge base and a combination of genomic, biochemical, and physiological information. The genome-scale metabolic model consists of 692 genes, 837 metabolites, and 875 reactions. We validated iAK692 by conducting fermentation experiments and simulating the model under autotrophic, heterotrophic, and mixotrophic growth conditions using COBRA toolbox. The model predictions under these growth conditions were consistent with the experimental results. The iAK692 model was further used to predict the unique active reactions and essential genes for each growth condition. Additionally, the metabolic states of iAK692 during autotrophic and mixotrophic growths were described by phenotypic phase plane (PhPP) analysis. This study proposes the first genome-scale model of S. platensis C1, iAK692, which is a predictive metabolic platform for a global understanding of physiological behaviors and metabolic engineering. This platform could accelerate the integrative analysis of various "-omics" data, leading to strain improvement towards a diverse range of desired industrial products from Spirulina.

Systems Nutrigenomics Reveals Brain Gene Networks Linking Metabolic and Brain Disorders.

PubMed

Meng, Qingying; Ying, Zhe; Noble, Emily; Zhao, Yuqi; Agrawal, Rahul; Mikhail, Andrew; Zhuang, Yumei; Tyagi, Ethika; Zhang, Qing; Lee, Jae-Hyung; Morselli, Marco; Orozco, Luz; Guo, Weilong; Kilts, Tina M; Zhu, Jun; Zhang, Bin; Pellegrini, Matteo; Xiao, Xinshu; Young, Marian F; Gomez-Pinilla, Fernando; Yang, Xia

2016-05-01

Nutrition plays a significant role in the increasing prevalence of metabolic and brain disorders. Here we employ systems nutrigenomics to scrutinize the genomic bases of nutrient-host interaction underlying disease predisposition or therapeutic potential. We conducted transcriptome and epigenome sequencing of hypothalamus (metabolic control) and hippocampus (cognitive processing) from a rodent model of fructose consumption, and identified significant reprogramming of DNA methylation, transcript abundance, alternative splicing, and gene networks governing cell metabolism, cell communication, inflammation, and neuronal signaling. These signals converged with genetic causal risks of metabolic, neurological, and psychiatric disorders revealed in humans. Gene network modeling uncovered the extracellular matrix genes Bgn and Fmod as main orchestrators of the effects of fructose, as validated using two knockout mouse models. We further demonstrate that an omega-3 fatty acid, DHA, reverses the genomic and network perturbations elicited by fructose, providing molecular support for nutritional interventions to counteract diet-induced metabolic and brain disorders. Our integrative approach complementing rodent and human studies supports the applicability of nutrigenomics principles to predict disease susceptibility and to guide personalized medicine. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Distinct Functional Networks Associated with Improvement of Affective Symptoms and Cognitive Function During Citalopram Treatment in Geriatric Depression

PubMed Central

Diaconescu, Andreea Oliviana; Kramer, Elisse; Hermann, Carol; Ma, Yilong; Dhawan, Vijay; Chaly, Thomas; Eidelberg, David; McIntosh, Anthony Randal; Smith, Gwenn S.

2010-01-01

Variability in the affective and cognitive symptom response to antidepressant treatment has been observed in geriatric depression. The underlying neural circuitry is poorly understood. The current study evaluated the cerebral glucose metabolic effects of citalopram treatment and applied multivariate, functional connectivity analyses to identify brain networks associated with improvements in affective symptoms and cognitive function. Sixteen geriatric depressed patients underwent resting Positron Emission Tomography (PET) studies of cerebral glucose metabolism and assessment of affective symptoms and cognitive function before and after eight weeks of selective serotonin reuptake inhibitor treatment (citalopram). Voxel-wise analyses of the normalized glucose metabolic data showed decreased cerebral metabolism during citalopram treatment in the anterior cingulate gyrus, middle temporal gyrus, precuneus, amygdala, and parahippocampal gyrus. Increased metabolism was observed in the putamen, occipital cortex and cerebellum. Functional connectivity analyses revealed two networks which were uniquely associated with improvement of affective symptoms and cognitive function during treatment. A subcortical-limbic-frontal network was associated with improvement in affect (depression and anxiety), while a medial temporal-parietal-frontal network was associated with improvement in cognition (immediate verbal learning/memory and verbal fluency). The regions that comprise the cognitive network overlap with the regions that are affected in Alzheimer’s dementia. Thus, alterations in specific brain networks associated with improvement of affective symptoms and cognitive function are observed during citalopram treatment in geriatric depression. PMID:20886575

Genetic Interaction Is Associated with Lower Metabolic Connectivity and Memory Impairment in Clinically Mild Alzheimer's Disease.

PubMed

Chang, Ya-Ting; Huang, Chi-Wei; Huang, Shu-Hua; Hsu, Shih-Wei; Chang, Wen-Neng; Lee, Jun-Jun; Chang, Chiung-Chih

2018-06-08

Metabolic connectivity as revealed by [18F] fluorodeoxyglucose positron emission tomography reflects neuronal connectivity. The aim of this study was to investigate the genetic impact on metabolic connectivity in default mode subnetworks and its clinical-pathological relationships in patients with Alzheimer's disease. We separately investigated the modulation of two default mode subnetworks, as identified with independent component analysis, by comparing APOE-ε4 carriers to non-carriers with Alzheimer's disease. We further analyzed the interaction effects of APOE (APOE-ε4 carriers versus non-carriers) with PICALM (rs3851179-GG versus rs3851179-A-allele carriers) on episodic memory deficits, reduction in cerebral metabolic rate for glucose, and decreased metabolic connectivity in default mode subnetworks. The metabolic connectivity in the ventral default mode network was positively correlated with episodic memory scores (β= 0.441, p< 0.001). The APOE-ε4 carriers had significantly lower metabolic connectivity in the ventral default mode network than the APOE-ε4 carriers (t(96)= -2.233, P= 0.028). There was an effect of the APOE-PICALM (rs3851179) interactions on reduced cerebral metabolic rate for glucose in regions of ventral default mode network (p< 0.001), and on memory deficits (F3,93= 5.568, p= 0.020). This study identified that PICALM may modulates memory deficits, reduced cerebral metabolic rate for glucose, and decreased metabolic connectivity in the ventral default mode network in APOE-ε4 carriers. [18F] fluorodeoxyglucose positron emission tomography-based metabolic connectivity may serve a useful tool to elucidate the neural networks underlying clinical-pathological relationships in Alzheimer's disease. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Impact of the Carbon and Nitrogen Supply on Relationships and Connectivity between Metabolism and Biomass in a Broad Panel of Arabidopsis Accessions1[W][OA

PubMed Central

Sulpice, Ronan; Nikoloski, Zoran; Tschoep, Hendrik; Antonio, Carla; Kleessen, Sabrina; Larhlimi, Abdelhalim; Selbig, Joachim; Ishihara, Hirofumi; Gibon, Yves; Fernie, Alisdair R.; Stitt, Mark

2013-01-01

Natural genetic diversity provides a powerful tool to study the complex interrelationship between metabolism and growth. Profiling of metabolic traits combined with network-based and statistical analyses allow the comparison of conditions and identification of sets of traits that predict biomass. However, it often remains unclear why a particular set of metabolites is linked with biomass and to what extent the predictive model is applicable beyond a particular growth condition. A panel of 97 genetically diverse Arabidopsis (Arabidopsis thaliana) accessions was grown in near-optimal carbon and nitrogen supply, restricted carbon supply, and restricted nitrogen supply and analyzed for biomass and 54 metabolic traits. Correlation-based metabolic networks were generated from the genotype-dependent variation in each condition to reveal sets of metabolites that show coordinated changes across accessions. The networks were largely specific for a single growth condition. Partial least squares regression from metabolic traits allowed prediction of biomass within and, slightly more weakly, across conditions (cross-validated Pearson correlations in the range of 0.27–0.58 and 0.21–0.51 and P values in the range of <0.001–<0.13 and <0.001–<0.023, respectively). Metabolic traits that correlate with growth or have a high weighting in the partial least squares regression were mainly condition specific and often related to the resource that restricts growth under that condition. Linear mixed-model analysis using the combined metabolic traits from all growth conditions as an input indicated that inclusion of random effects for the conditions improves predictions of biomass. Thus, robust prediction of biomass across a range of conditions requires condition-specific measurement of metabolic traits to take account of environment-dependent changes of the underlying networks. PMID:23515278

Find_tfSBP: find thermodynamics-feasible and smallest balanced pathways with high yield from large-scale metabolic networks.

PubMed

Xu, Zixiang; Sun, Jibin; Wu, Qiaqing; Zhu, Dunming

2017-12-11

Biologically meaningful metabolic pathways are important references in the design of industrial bacterium. At present, constraint-based method is the only way to model and simulate a genome-scale metabolic network under steady-state criteria. Due to the inadequate assumption of the relationship in gene-enzyme-reaction as one-to-one unique association, computational difficulty or ignoring the yield from substrate to product, previous pathway finding approaches can't be effectively applied to find out the high yield pathways that are mass balanced in stoichiometry. In addition, the shortest pathways may not be the pathways with high yield. At the same time, a pathway, which exists in stoichiometry, may not be feasible in thermodynamics. By using mixed integer programming strategy, we put forward an algorithm to identify all the smallest balanced pathways which convert the source compound to the target compound in large-scale metabolic networks. The resulting pathways by our method can finely satisfy the stoichiometric constraints and non-decomposability condition. Especially, the functions of high yield and thermodynamics feasibility have been considered in our approach. This tool is tailored to direct the metabolic engineering practice to enlarge the metabolic potentials of industrial strains by integrating the extensive metabolic network information built from systems biology dataset.

A general model for metabolic scaling in self-similar asymmetric networks

PubMed Central

Savage, Van M.; Enquist, Brian J.

2017-01-01

How a particular attribute of an organism changes or scales with its body size is known as an allometry. Biological allometries, such as metabolic scaling, have been hypothesized to result from selection to maximize how vascular networks fill space yet minimize internal transport distances and resistances. The West, Brown, Enquist (WBE) model argues that these two principles (space-filling and energy minimization) are (i) general principles underlying the evolution of the diversity of biological networks across plants and animals and (ii) can be used to predict how the resulting geometry of biological networks then governs their allometric scaling. Perhaps the most central biological allometry is how metabolic rate scales with body size. A core assumption of the WBE model is that networks are symmetric with respect to their geometric properties. That is, any two given branches within the same generation in the network are assumed to have identical lengths and radii. However, biological networks are rarely if ever symmetric. An open question is: Does incorporating asymmetric branching change or influence the predictions of the WBE model? We derive a general network model that relaxes the symmetric assumption and define two classes of asymmetrically bifurcating networks. We show that asymmetric branching can be incorporated into the WBE model. This asymmetric version of the WBE model results in several theoretical predictions for the structure, physiology, and metabolism of organisms, specifically in the case for the cardiovascular system. We show how network asymmetry can now be incorporated in the many allometric scaling relationships via total network volume. Most importantly, we show that the 3/4 metabolic scaling exponent from Kleiber’s Law can still be attained within many asymmetric networks. PMID:28319153

A general model for metabolic scaling in self-similar asymmetric networks.

PubMed

Brummer, Alexander Byers; Savage, Van M; Enquist, Brian J

2017-03-01

How a particular attribute of an organism changes or scales with its body size is known as an allometry. Biological allometries, such as metabolic scaling, have been hypothesized to result from selection to maximize how vascular networks fill space yet minimize internal transport distances and resistances. The West, Brown, Enquist (WBE) model argues that these two principles (space-filling and energy minimization) are (i) general principles underlying the evolution of the diversity of biological networks across plants and animals and (ii) can be used to predict how the resulting geometry of biological networks then governs their allometric scaling. Perhaps the most central biological allometry is how metabolic rate scales with body size. A core assumption of the WBE model is that networks are symmetric with respect to their geometric properties. That is, any two given branches within the same generation in the network are assumed to have identical lengths and radii. However, biological networks are rarely if ever symmetric. An open question is: Does incorporating asymmetric branching change or influence the predictions of the WBE model? We derive a general network model that relaxes the symmetric assumption and define two classes of asymmetrically bifurcating networks. We show that asymmetric branching can be incorporated into the WBE model. This asymmetric version of the WBE model results in several theoretical predictions for the structure, physiology, and metabolism of organisms, specifically in the case for the cardiovascular system. We show how network asymmetry can now be incorporated in the many allometric scaling relationships via total network volume. Most importantly, we show that the 3/4 metabolic scaling exponent from Kleiber's Law can still be attained within many asymmetric networks.

Dynamic brain glucose metabolism identifies anti-correlated cortical-cerebellar networks at rest.

PubMed

Tomasi, Dardo G; Shokri-Kojori, Ehsan; Wiers, Corinde E; Kim, Sunny W; Demiral, Şukru B; Cabrera, Elizabeth A; Lindgren, Elsa; Miller, Gregg; Wang, Gene-Jack; Volkow, Nora D

2017-12-01

It remains unclear whether resting state functional magnetic resonance imaging (rfMRI) networks are associated with underlying synchrony in energy demand, as measured by dynamic 2-deoxy-2-[ 18 F]fluoroglucose (FDG) positron emission tomography (PET). We measured absolute glucose metabolism, temporal metabolic connectivity (t-MC) and rfMRI patterns in 53 healthy participants at rest. Twenty-two rfMRI networks emerged from group independent component analysis (gICA). In contrast, only two anti-correlated t-MC emerged from FDG-PET time series using gICA or seed-voxel correlations; one included frontal, parietal and temporal cortices, the other included the cerebellum and medial temporal regions. Whereas cerebellum, thalamus, globus pallidus and calcarine cortex arose as the strongest t-MC hubs, the precuneus and visual cortex arose as the strongest rfMRI hubs. The strength of the t-MC linearly increased with the metabolic rate of glucose suggesting that t-MC measures are strongly associated with the energy demand of the brain tissue, and could reflect regional differences in glucose metabolism, counterbalanced metabolic network demand, and/or differential time-varying delivery of FDG. The mismatch between metabolic and functional connectivity patterns computed as a function of time could reflect differences in the temporal characteristics of glucose metabolism as measured with PET-FDG and brain activation as measured with rfMRI.

Atmospheric reaction systems as null-models to identify structural traces of evolution in metabolism.

PubMed

Holme, Petter; Huss, Mikael; Lee, Sang Hoon

2011-05-06

The metabolism is the motor behind the biological complexity of an organism. One problem of characterizing its large-scale structure is that it is hard to know what to compare it to. All chemical reaction systems are shaped by the same physics that gives molecules their stability and affinity to react. These fundamental factors cannot be captured by standard null-models based on randomization. The unique property of organismal metabolism is that it is controlled, to some extent, by an enzymatic machinery that is subject to evolution. In this paper, we explore the possibility that reaction systems of planetary atmospheres can serve as a null-model against which we can define metabolic structure and trace the influence of evolution. We find that the two types of data can be distinguished by their respective degree distributions. This is especially clear when looking at the degree distribution of the reaction network (of reaction connected to each other if they involve the same molecular species). For the Earth's atmospheric network and the human metabolic network, we look into more detail for an underlying explanation of this deviation. However, we cannot pinpoint a single cause of the difference, rather there are several concurrent factors. By examining quantities relating to the modular-functional organization of the metabolism, we confirm that metabolic networks have a more complex modular organization than the atmospheric networks, but not much more. We interpret the more variegated modular arrangement of metabolism as a trace of evolved functionality. On the other hand, it is quite remarkable how similar the structures of these two types of networks are, which emphasizes that the constraints from the chemical properties of the molecules has a larger influence in shaping the reaction system than does natural selection.

Theoretical Basis for Dynamic Label Propagation in Stationary Metabolic Networks under Step and Periodic Inputs

PubMed Central

Sokol, Serguei; Portais, Jean-Charles

2015-01-01

The dynamics of label propagation in a stationary metabolic network during an isotope labeling experiment can provide highly valuable information on the network topology, metabolic fluxes, and on the size of metabolite pools. However, major issues, both in the experimental set-up and in the accompanying numerical methods currently limit the application of this approach. Here, we propose a method to apply novel types of label inputs, sinusoidal or more generally periodic label inputs, to address both the practical and numerical challenges of dynamic labeling experiments. By considering a simple metabolic system, i.e. a linear, non-reversible pathway of arbitrary length, we develop mathematical descriptions of label propagation for both classical and novel label inputs. Theoretical developments and computer simulations show that the application of rectangular periodic pulses has both numerical and practical advantages over other approaches. We applied the strategy to estimate fluxes in a simulated experiment performed on a complex metabolic network (the central carbon metabolism of Escherichia coli), to further demonstrate its value in conditions which are close to those in real experiments. This study provides a theoretical basis for the rational interpretation of label propagation curves in real experiments, and will help identify the strengths, pitfalls and limitations of such experiments. The cases described here can also be used as test cases for more general numerical methods aimed at identifying network topology, analyzing metabolic fluxes or measuring concentrations of metabolites. PMID:26641860

Glycogen debranching enzyme 6 (AGL), enolase 1 (ENOSF1), ectonucleotide pyrophosphatase 2 (ENPP2_1), glutathione S-transferase 3 (GSTM3_3) and mannosidase (MAN2B2) metabolism computational network analysis between chimpanzee and human left cerebrum.

PubMed

Sun, Lingjun; Wang, Lin; Jiang, Minghu; Huang, Juxiang; Lin, Hong

2011-12-01

We identified significantly higher expression of the genes glycogen debranching enzyme 6 (AGL), enolase 1 (ENOSF1), ectonucleotide pyrophosphatase 2 (ENPP2_1), glutathione S-transferase 3 (GSTM3_3) and mannosidase (MAN2B2) from human left cerebrums versus chimpanzees. Yet the distinct low- and high-expression AGL, ENOSF1, ENPP2_1, GSTM3_3 and MAN2B2 metabolism networks between chimpanzee and human left cerebrum remain to be elucidated. Here, we constructed low- and high-expression activated and inhibited upstream and downstream AGL, ENOSF1, ENPP2_1, GSTM3_3 and MAN2B2 metabolism network between chimpanzee and human left cerebrum in GEO data set by gene regulatory network inference method based on linear programming and decomposition procedure, under covering AGL, ENOSF1, ENPP2_1, GSTM3_3 and MAN2B2 pathway and matching metabolism enrichment analysis by CapitalBio MAS 3.0 integration of public databases, including Gene Ontology, KEGG, BioCarta, GenMapp, Intact, UniGene, OMIM, etc. Our results show that the AGL, ENOSF1, ENPP2_1, GSTM3_3 and MAN2B2 metabolism network has more activated and less inhibited molecules in chimpanzee, but less activated and more inhibited in the human left cerebrum. We inferred stronger carbohydrate, glutathione and proteoglycan metabolism, ATPase activity, but weaker base excision repair, arachidonic acid and drug metabolism as a result of inducing cell growth in low-expression AGL, ENOSF1, ENPP2_1, GSTM3_3 and MAN2B2 metabolism network of chimpanzee left cerebrum; whereas stronger lipid metabolism, amino acid catabolism, DNA repair but weaker inflammatory response, cell proliferation, glutathione and carbohydrate metabolism as a result of inducing cell differentiation in high-expression AGL, ENOSF1, ENPP2_1, GSTM3_3 and MAN2B2 metabolism network of human left cerebrum. Our inferences are consistent with recent reports and computational activation and inhibition gene number patterns, respectively.

Signaling in large-scale neural networks.

PubMed

Berg, Rune W; Hounsgaard, Jørn

2009-02-01

We examine the recent finding that neurons in spinal motor circuits enter a high conductance state during functional network activity. The underlying concomitant increase in random inhibitory and excitatory synaptic activity leads to stochastic signal processing. The possible advantages of this metabolically costly organization are analyzed by comparing with synaptically less intense networks driven by the intrinsic response properties of the network neurons.

Quantitative Analysis of Cellular Metabolic Dissipative, Self-Organized Structures

PubMed Central

de la Fuente, Ildefonso Martínez

2010-01-01

One of the most important goals of the postgenomic era is understanding the metabolic dynamic processes and the functional structures generated by them. Extensive studies during the last three decades have shown that the dissipative self-organization of the functional enzymatic associations, the catalytic reactions produced during the metabolite channeling, the microcompartmentalization of these metabolic processes and the emergence of dissipative networks are the fundamental elements of the dynamical organization of cell metabolism. Here we present an overview of how mathematical models can be used to address the properties of dissipative metabolic structures at different organizational levels, both for individual enzymatic associations and for enzymatic networks. Recent analyses performed with dissipative metabolic networks have shown that unicellular organisms display a singular global enzymatic structure common to all living cellular organisms, which seems to be an intrinsic property of the functional metabolism as a whole. Mathematical models firmly based on experiments and their corresponding computational approaches are needed to fully grasp the molecular mechanisms of metabolic dynamical processes. They are necessary to enable the quantitative and qualitative analysis of the cellular catalytic reactions and also to help comprehend the conditions under which the structural dynamical phenomena and biological rhythms arise. Understanding the molecular mechanisms responsible for the metabolic dissipative structures is crucial for unraveling the dynamics of cellular life. PMID:20957111

A network biology approach to denitrification in Pseudomonas aeruginosa

DOE PAGES

Arat, Seda; Bullerjahn, George S.; Laubenbacher, Reinhard

2015-02-23

Pseudomonas aeruginosa is a metabolically flexible member of the Gammaproteobacteria. Under anaerobic conditions and the presence of nitrate, P. aeruginosa can perform (complete) denitrification, a respiratory process of dissimilatory nitrate reduction to nitrogen gas via nitrite (NO₂), nitric oxide (NO) and nitrous oxide (N₂O). This study focuses on understanding the influence of environmental conditions on bacterial denitrification performance, using a mathematical model of a metabolic network in P. aeruginosa. To our knowledge, this is the first mathematical model of denitrification for this bacterium. Analysis of the long-term behavior of the network under changing concentration levels of oxygen (O₂), nitrate (NO₃),more » and phosphate (PO₄) suggests that PO₄ concentration strongly affects denitrification performance. The model provides three predictions on denitrification activity of P. aeruginosa under various environmental conditions, and these predictions are either experimentally validated or supported by pertinent biological literature. One motivation for this study is to capture the effect of PO₄ on a denitrification metabolic network of P. aeruginosa in order to shed light on mechanisms for greenhouse gas N₂O accumulation during seasonal oxygen depletion in aquatic environments such as Lake Erie (Laurentian Great Lakes, USA). Simulating the microbial production of greenhouse gases in anaerobic aquatic systems such as Lake Erie allows a deeper understanding of the contributing environmental effects that will inform studies on, and remediation strategies for, other hypoxic sites worldwide.« less

Biogeochemical metabolic modeling of methanogenesis by Methanosarcina barkeri

NASA Astrophysics Data System (ADS)

Jensvold, Z. D.; Jin, Q.

2015-12-01

Methanogenesis, the biological process of methane production, is the final step of natural organic matter degradation. In studying natural methanogenesis, important questions include how fast methanogenesis proceeds and how methanogens adapt to the environment. To address these questions, we propose a new approach - biogeochemical reaction modeling - by simulating the metabolic networks of methanogens. Biogeochemical reaction modeling combines geochemical reaction modeling and genome-scale metabolic modeling. Geochemical reaction modeling focuses on the speciation of electron donors and acceptors in the environment, and therefore the energy available to methanogens. Genome-scale metabolic modeling predicts microbial rates and metabolic strategies. Specifically, this approach describes methanogenesis using an enzyme network model, and computes enzyme rates by accounting for both the kinetics and thermodynamics. The network model is simulated numerically to predict enzyme abundances and rates of methanogen metabolism. We applied this new approach to Methanosarcina barkeri strain fusaro, a model methanogen that makes methane by reducing carbon dioxide and oxidizing dihydrogen. The simulation results match well with the results of previous laboratory experiments, including the magnitude of proton motive force and the kinetic parameters of Methanosarcina barkeri. The results also predict that in natural environments, the configuration of methanogenesis network, including the concentrations of enzymes and metabolites, differs significantly from that under laboratory settings.

Bacterial Unculturability and the Formation of Intercellular Metabolic Networks.

PubMed

Pande, Samay; Kost, Christian

2017-05-01

The majority of known bacterial species cannot be cultivated under laboratory conditions. Here we argue that the adaptive emergence of obligate metabolic interactions in natural bacterial communities can explain this pattern. Bacteria commonly release metabolites into the external environment. Accumulating pools of extracellular metabolites create an ecological niche that benefits auxotrophic mutants, which have lost the ability to autonomously produce the corresponding metabolites. In addition to a diffusion-based metabolite transfer, auxotrophic cells can use contact-dependent means to obtain nutrients from other co-occurring cells. Spatial colocalisation and a continuous coevolution further increase the nutritional dependency and optimise fluxes through combined metabolic networks. Thus, bacteria likely function as networks of interacting cells that reciprocally exchange nutrients and biochemical functions rather than as physiologically autonomous units. Copyright © 2017 Elsevier Ltd. All rights reserved.

Genome scale metabolic reconstruction of Chlorella variabilis for exploring its metabolic potential for biofuels.

PubMed

Juneja, Ankita; Chaplen, Frank W R; Murthy, Ganti S

2016-08-01

A compartmentalized genome scale metabolic network was reconstructed for Chlorella variabilis to offer insight into various metabolic potentials from this alga. The model, iAJ526, was reconstructed with 1455 reactions, 1236 metabolites and 526 genes. 21% of the reactions were transport reactions and about 81% of the total reactions were associated with enzymes. Along with gap filling reactions, 2 major sub-pathways were added to the model, chitosan synthesis and rhamnose metabolism. The reconstructed model had reaction participation of 4.3 metabolites per reaction and average lethality fraction of 0.21. The model was effective in capturing the growth of C. variabilis under three light conditions (white, red and red+blue light) with fair agreement. This reconstructed metabolic network will serve an important role in systems biology for further exploration of metabolism for specific target metabolites and enable improved characteristics in the strain through metabolic engineering. Copyright © 2016 Elsevier Ltd. All rights reserved.

Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks.

PubMed

Oh, S June; Joung, Je-Gun; Chang, Jeong-Ho; Zhang, Byoung-Tak

2006-06-06

To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees. To compare the structures of metabolic networks in organisms, we adopted the exponential graph kernel, which is a kernel-based approach with a labeled graph that includes a label matrix and an adjacency matrix. To construct the phylogenetic trees, we used an unweighted pair-group method with arithmetic mean, i.e., a hierarchical clustering algorithm. We applied the kernel-based network profiling method in a comparative analysis of nine carbohydrate metabolic networks from 81 biological species encompassing Archaea, Eukaryota, and Eubacteria. The resulting phylogenetic hierarchies generally support the tripartite scheme of three domains rather than the two domains of prokaryotes and eukaryotes. By combining the kernel machines with metabolic information, the method infers the context of biosphere development that covers physiological events required for adaptation by genetic reconstruction. The results show that one may obtain a global view of the tree of life by comparing the metabolic pathway structures using meta-level information rather than sequence information. This method may yield further information about biological evolution, such as the history of horizontal transfer of each gene, by studying the detailed structure of the phylogenetic tree constructed by the kernel-based method.

A mixed-integer linear programming approach to the reduction of genome-scale metabolic networks.

PubMed

Röhl, Annika; Bockmayr, Alexander

2017-01-03

Constraint-based analysis has become a widely used method to study metabolic networks. While some of the associated algorithms can be applied to genome-scale network reconstructions with several thousands of reactions, others are limited to small or medium-sized models. In 2015, Erdrich et al. introduced a method called NetworkReducer, which reduces large metabolic networks to smaller subnetworks, while preserving a set of biological requirements that can be specified by the user. Already in 2001, Burgard et al. developed a mixed-integer linear programming (MILP) approach for computing minimal reaction sets under a given growth requirement. Here we present an MILP approach for computing minimum subnetworks with the given properties. The minimality (with respect to the number of active reactions) is not guaranteed by NetworkReducer, while the method by Burgard et al. does not allow specifying the different biological requirements. Our procedure is about 5-10 times faster than NetworkReducer and can enumerate all minimum subnetworks in case there exist several ones. This allows identifying common reactions that are present in all subnetworks, and reactions appearing in alternative pathways. Applying complex analysis methods to genome-scale metabolic networks is often not possible in practice. Thus it may become necessary to reduce the size of the network while keeping important functionalities. We propose a MILP solution to this problem. Compared to previous work, our approach is more efficient and allows computing not only one, but even all minimum subnetworks satisfying the required properties.

Identification of genetic elements in metabolism by high-throughput mouse phenotyping.

PubMed

Rozman, Jan; Rathkolb, Birgit; Oestereicher, Manuela A; Schütt, Christine; Ravindranath, Aakash Chavan; Leuchtenberger, Stefanie; Sharma, Sapna; Kistler, Martin; Willershäuser, Monja; Brommage, Robert; Meehan, Terrence F; Mason, Jeremy; Haselimashhadi, Hamed; Hough, Tertius; Mallon, Ann-Marie; Wells, Sara; Santos, Luis; Lelliott, Christopher J; White, Jacqueline K; Sorg, Tania; Champy, Marie-France; Bower, Lynette R; Reynolds, Corey L; Flenniken, Ann M; Murray, Stephen A; Nutter, Lauryl M J; Svenson, Karen L; West, David; Tocchini-Valentini, Glauco P; Beaudet, Arthur L; Bosch, Fatima; Braun, Robert B; Dobbie, Michael S; Gao, Xiang; Herault, Yann; Moshiri, Ala; Moore, Bret A; Kent Lloyd, K C; McKerlie, Colin; Masuya, Hiroshi; Tanaka, Nobuhiko; Flicek, Paul; Parkinson, Helen E; Sedlacek, Radislav; Seong, Je Kyung; Wang, Chi-Kuang Leo; Moore, Mark; Brown, Steve D; Tschöp, Matthias H; Wurst, Wolfgang; Klingenspor, Martin; Wolf, Eckhard; Beckers, Johannes; Machicao, Fausto; Peter, Andreas; Staiger, Harald; Häring, Hans-Ulrich; Grallert, Harald; Campillos, Monica; Maier, Holger; Fuchs, Helmut; Gailus-Durner, Valerie; Werner, Thomas; Hrabe de Angelis, Martin

2018-01-18

Metabolic diseases are a worldwide problem but the underlying genetic factors and their relevance to metabolic disease remain incompletely understood. Genome-wide research is needed to characterize so-far unannotated mammalian metabolic genes. Here, we generate and analyze metabolic phenotypic data of 2016 knockout mouse strains under the aegis of the International Mouse Phenotyping Consortium (IMPC) and find 974 gene knockouts with strong metabolic phenotypes. 429 of those had no previous link to metabolism and 51 genes remain functionally completely unannotated. We compared human orthologues of these uncharacterized genes in five GWAS consortia and indeed 23 candidate genes are associated with metabolic disease. We further identify common regulatory elements in promoters of candidate genes. As each regulatory element is composed of several transcription factor binding sites, our data reveal an extensive metabolic phenotype-associated network of co-regulated genes. Our systematic mouse phenotype analysis thus paves the way for full functional annotation of the genome.

Abnormal metabolic brain networks in Parkinson's disease from blackboard to bedside.

PubMed

Tang, Chris C; Eidelberg, David

2010-01-01

Metabolic imaging in the rest state has provided valuable information concerning the abnormalities of regional brain function that underlie idiopathic Parkinson's disease (PD). Moreover, network modeling procedures, such as spatial covariance analysis, have further allowed for the quantification of these changes at the systems level. In recent years, we have utilized this strategy to identify and validate three discrete metabolic networks in PD associated with the motor and cognitive manifestations of the disease. In this chapter, we will review and compare the specific functional topographies underlying parkinsonian akinesia/rigidity, tremor, and cognitive disturbance. While network activity progressed over time, the rate of change for each pattern was distinctive and paralleled the development of the corresponding clinical symptoms in early-stage patients. This approach is already showing great promise in identifying individuals with prodromal manifestations of PD and in assessing the rate of progression before clinical onset. Network modulation was found to correlate with the clinical effects of dopaminergic treatment and surgical interventions, such as subthalamic nucleus (STN) deep brain stimulation (DBS) and gene therapy. Abnormal metabolic networks have also been identified for atypical parkinsonian syndromes, such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). Using multiple disease-related networks for PD, MSA, and PSP, we have developed a novel, fully automated algorithm for accurate classification at the single-patient level, even at early disease stages. Copyright © 2010 Elsevier B.V. All rights reserved.

A multiscale, model-based analysis of the multi-tissue interplay underlying blood glucose regulation in type I diabetes.

PubMed

Wadehn, Federico; Schaller, Stephan; Eissing, Thomas; Krauss, Markus; Kupfer, Lars

2016-08-01

A multiscale model for blood glucose regulation in diabetes type I patients is constructed by integrating detailed metabolic network models for fat, liver and muscle cells into a whole body physiologically-based pharmacokinetic/pharmacodynamic (pBPK/PD) model. The blood glucose regulation PBPK/PD model simulates the distribution and metabolization of glucose, insulin and glucagon on an organ and whole body level. The genome-scale metabolic networks in contrast describe intracellular reactions. The developed multiscale model is fitted to insulin, glucagon and glucose measurements of a 48h clinical trial featuring 6 subjects and is subsequently used to simulate (in silico) the influence of geneknockouts and drug-induced enzyme inhibitions on whole body blood glucose levels. Simulations of diabetes associated gene knockouts and impaired cellular glucose metabolism, resulted in elevated whole body blood-glucose levels, but also in a metabolic shift within the cell's reaction network. Such multiscale models have the potential to be employed in the exploration of novel drug-targets or to be integrated into control algorithms for artificial pancreas systems.

The Metabolic Core and Catalytic Switches Are Fundamental Elements in the Self-Regulation of the Systemic Metabolic Structure of Cells

PubMed Central

De la Fuente, Ildefonso M.; Cortes, Jesus M.; Perez-Pinilla, Martin B.; Ruiz-Rodriguez, Vicente; Veguillas, Juan

2011-01-01

Background Experimental observations and numerical studies with dissipative metabolic networks have shown that cellular enzymatic activity self-organizes spontaneously leading to the emergence of a metabolic core formed by a set of enzymatic reactions which are always active under all environmental conditions, while the rest of catalytic processes are only intermittently active. The reactions of the metabolic core are essential for biomass formation and to assure optimal metabolic performance. The on-off catalytic reactions and the metabolic core are essential elements of a Systemic Metabolic Structure which seems to be a key feature common to all cellular organisms. Methodology/Principal Findings In order to investigate the functional importance of the metabolic core we have studied different catalytic patterns of a dissipative metabolic network under different external conditions. The emerging biochemical data have been analysed using information-based dynamic tools, such as Pearson's correlation and Transfer Entropy (which measures effective functionality). Our results show that a functional structure of effective connectivity emerges which is dynamical and characterized by significant variations of bio-molecular information flows. Conclusions/Significance We have quantified essential aspects of the metabolic core functionality. The always active enzymatic reactions form a hub –with a high degree of effective connectivity- exhibiting a wide range of functional information values being able to act either as a source or as a sink of bio-molecular causal interactions. Likewise, we have found that the metabolic core is an essential part of an emergent functional structure characterized by catalytic modules and metabolic switches which allow critical transitions in enzymatic activity. Both, the metabolic core and the catalytic switches in which also intermittently-active enzymes are involved seem to be fundamental elements in the self-regulation of the Systemic Metabolic Structure. PMID:22125607

Pharmacogenomic and clinical data link non-pharmacokinetic metabolic dysregulation to drug side effect pathogenesis

PubMed Central

Zielinski, Daniel C.; Filipp, Fabian V.; Bordbar, Aarash; Jensen, Kasper; Smith, Jeffrey W.; Herrgard, Markus J.; Mo, Monica L.; Palsson, Bernhard O.

2015-01-01

Drug side effects cause a significant clinical and economic burden. However, mechanisms of drug action underlying side effect pathogenesis remain largely unknown. Here, we integrate pharmacogenomic and clinical data with a human metabolic network and find that non-pharmacokinetic metabolic pathways dysregulated by drugs are linked to the development of side effects. We show such dysregulated metabolic pathways contain genes with sequence variants affecting side effect incidence, play established roles in pathophysiology, have significantly altered activity in corresponding diseases, are susceptible to metabolic inhibitors and are effective targets for therapeutic nutrient supplementation. Our results indicate that metabolic dysregulation represents a common mechanism underlying side effect pathogenesis that is distinct from the role of metabolism in drug clearance. We suggest that elucidating the relationships between the cellular response to drugs, genetic variation of patients and cell metabolism may help managing side effects by personalizing drug prescriptions and nutritional intervention strategies. PMID:26055627

Proteome- and transcriptome-driven reconstruction of the human myocyte metabolic network and its use for identification of markers for diabetes.

PubMed

Väremo, Leif; Scheele, Camilla; Broholm, Christa; Mardinoglu, Adil; Kampf, Caroline; Asplund, Anna; Nookaew, Intawat; Uhlén, Mathias; Pedersen, Bente Klarlund; Nielsen, Jens

2015-05-12

Skeletal myocytes are metabolically active and susceptible to insulin resistance and are thus implicated in type 2 diabetes (T2D). This complex disease involves systemic metabolic changes, and their elucidation at the systems level requires genome-wide data and biological networks. Genome-scale metabolic models (GEMs) provide a network context for the integration of high-throughput data. We generated myocyte-specific RNA-sequencing data and investigated their correlation with proteome data. These data were then used to reconstruct a comprehensive myocyte GEM. Next, we performed a meta-analysis of six studies comparing muscle transcription in T2D versus healthy subjects. Transcriptional changes were mapped on the myocyte GEM, revealing extensive transcriptional regulation in T2D, particularly around pyruvate oxidation, branched-chain amino acid catabolism, and tetrahydrofolate metabolism, connected through the downregulated dihydrolipoamide dehydrogenase. Strikingly, the gene signature underlying this metabolic regulation successfully classifies the disease state of individual samples, suggesting that regulation of these pathways is a ubiquitous feature of myocytes in response to T2D. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Foundations of metabolic organization: coherence as a basis of computational properties in metabolic networks.

PubMed

Igamberdiev, A U

1999-04-01

Biological organization is based on the coherent energy transfer allowing for macromolecules to operate with high efficiency and realize computation. Computation is executed with virtually 100% efficiency via the coherent operation of molecular machines in which low-energy recognitions trigger energy-driven non-equilibrium dynamic processes. The recognition process is of quantum mechanical nature being a non-demolition measurement. It underlies the enzymatic conversion of a substrate into the product (an elementary metabolic phenomenon); the switching via separation of the direct and reverse routes in futile cycles provides the generation and complication of metabolic networks (coherence within cycles is maintained by the supramolecular organization of enzymes); the genetic level corresponding to the appearance of digital information is based on reflective arrows (catalysts realize their own self-reproduction) and operation of hypercycles. Every metabolic cycle via reciprocal regulation of both its halves can generate rhythms and spatial structures (resulting from the temporally organized depositions from the cycles). Via coherent events which percolate from the elementary submolecular level to organismic entities, self-assembly based on the molecular complementarity is realized and the dynamic informational field operating within the metabolic network is generated.

Cross‐talk between circadian clocks, sleep‐wake cycles, and metabolic networks: Dispelling the darkness

PubMed Central

Ray, Sandipan

2016-01-01

Integration of knowledge concerning circadian rhythms, metabolic networks, and sleep‐wake cycles is imperative for unraveling the mysteries of biological cycles and their underlying mechanisms. During the last decade, enormous progress in circadian biology research has provided a plethora of new insights into the molecular architecture of circadian clocks. However, the recent identification of autonomous redox oscillations in cells has expanded our view of the clockwork beyond conventional transcription/translation feedback loop models, which have been dominant since the first circadian period mutants were identified in fruit fly. Consequently, non‐transcriptional timekeeping mechanisms have been proposed, and the antioxidant peroxiredoxin proteins have been identified as conserved markers for 24‐hour rhythms. Here, we review recent advances in our understanding of interdependencies amongst circadian rhythms, sleep homeostasis, redox cycles, and other cellular metabolic networks. We speculate that systems‐level investigations implementing integrated multi‐omics approaches could provide novel mechanistic insights into the connectivity between daily cycles and metabolic systems. PMID:26866932

Development of an internet based system for modeling biotin metabolism using Bayesian networks.

PubMed

Zhou, Jinglei; Wang, Dong; Schlegel, Vicki; Zempleni, Janos

2011-11-01

Biotin is an essential water-soluble vitamin crucial for maintaining normal body functions. The importance of biotin for human health has been under-appreciated but there is plenty of opportunity for future research with great importance for human health. Currently, carrying out predictions of biotin metabolism involves tedious manual manipulations. In this paper, we report the development of BiotinNet, an internet based program that uses Bayesian networks to integrate published data on various aspects of biotin metabolism. Users can provide a combination of values on the levels of biotin related metabolites to obtain the predictions on other metabolites that are not specified. As an inherent feature of Bayesian networks, the uncertainty of the prediction is also quantified and reported to the user. This program enables convenient in silico experiments regarding biotin metabolism, which can help researchers design future experiments while new data can be continuously incorporated. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Cross-talk between circadian clocks, sleep-wake cycles, and metabolic networks: Dispelling the darkness.

PubMed

Ray, Sandipan; Reddy, Akhilesh B

2016-04-01

Integration of knowledge concerning circadian rhythms, metabolic networks, and sleep-wake cycles is imperative for unraveling the mysteries of biological cycles and their underlying mechanisms. During the last decade, enormous progress in circadian biology research has provided a plethora of new insights into the molecular architecture of circadian clocks. However, the recent identification of autonomous redox oscillations in cells has expanded our view of the clockwork beyond conventional transcription/translation feedback loop models, which have been dominant since the first circadian period mutants were identified in fruit fly. Consequently, non-transcriptional timekeeping mechanisms have been proposed, and the antioxidant peroxiredoxin proteins have been identified as conserved markers for 24-hour rhythms. Here, we review recent advances in our understanding of interdependencies amongst circadian rhythms, sleep homeostasis, redox cycles, and other cellular metabolic networks. We speculate that systems-level investigations implementing integrated multi-omics approaches could provide novel mechanistic insights into the connectivity between daily cycles and metabolic systems. © 2016 The Authors. Bioessays published by WILEY Periodicals, Inc.

Flux coupling and transcriptional regulation within the metabolic network of the photosynthetic bacterium Synechocystis sp. PCC6803.

PubMed

Montagud, Arnau; Zelezniak, Aleksej; Navarro, Emilio; de Córdoba, Pedro Fernández; Urchueguía, Javier F; Patil, Kiran Raosaheb

2011-03-01

Synechocystis sp. PCC6803 is a model cyanobacterium capable of producing biofuels with CO(2) as carbon source and with its metabolism fueled by light, for which it stands as a potential production platform of socio-economic importance. Compilation and characterization of Synechocystis genome-scale metabolic model is a pre-requisite toward achieving a proficient photosynthetic cell factory. To this end, we report iSyn811, an upgraded genome-scale metabolic model of Synechocystis sp. PCC6803 consisting of 956 reactions and accounting for 811 genes. To gain insights into the interplay between flux activities and metabolic physiology, flux coupling analysis was performed for iSyn811 under four different growth conditions, viz., autotrophy, mixotrophy, heterotrophy, and light-activated heterotrophy (LH). Initial steps of carbon acquisition and catabolism formed the versatile center of the flux coupling networks, surrounded by a stable core of pathways leading to biomass building blocks. This analysis identified potential bottlenecks for hydrogen and ethanol production. Integration of transcriptomic data with the Synechocystis flux coupling networks lead to identification of reporter flux coupling pairs and reporter flux coupling groups - regulatory hot spots during metabolic shifts triggered by the availability of light. Overall, flux coupling analysis provided insight into the structural organization of Synechocystis sp. PCC6803 metabolic network toward designing of a photosynthesis-based production platform. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Metabolic principles of river basin organization.

PubMed

Rodriguez-Iturbe, Ignacio; Caylor, Kelly K; Rinaldo, Andrea

2011-07-19

The metabolism of a river basin is defined as the set of processes through which the basin maintains its structure and responds to its environment. Green (or biotic) metabolism is measured via transpiration and blue (or abiotic) metabolism through runoff. A principle of equal metabolic rate per unit area throughout the basin structure is developed and tested in a river basin characterized by large heterogeneities in precipitation, vegetation, soil, and geomorphology. This principle is suggested to have profound implications for the spatial organization of river basin hydrologic dynamics, including the minimization of energy expenditure known to control the scale-invariant characteristics of river networks over several orders of magnitude. Empirically derived, remarkably constant rates of average transpiration per unit area through the basin structure lead to a power law for the probability distribution of transpiration from a randomly chosen subbasin. The average runoff per unit area, evaluated for subbasins of a wide range of topological magnitudes, is also shown to be remarkably constant independently of size. A similar result is found for the rainfall after accounting for canopy interception. Allometric scaling of metabolic rates with size, variously addressed in the biological literature and network theory under the label of Kleiber's law, is similarly derived. The empirical evidence suggests that river basin metabolic activity is linked with the spatial organization that takes place around the drainage network and therefore with the mechanisms responsible for the fractal geometry of the network, suggesting a new coevolutionary framework for biological, geomorphological, and hydrologic dynamics.

Mathematical methods to analysis of topology, functional variability and evolution of metabolic systems based on different decomposition concepts.

PubMed

Mrabet, Yassine; Semmar, Nabil

2010-05-01

Complexity of metabolic systems can be undertaken at different scales (metabolites, metabolic pathways, metabolic network map, biological population) and under different aspects (structural, functional, evolutive). To analyse such a complexity, metabolic systems need to be decomposed into different components according to different concepts. Four concepts are presented here consisting in considering metabolic systems as sets of metabolites, chemical reactions, metabolic pathways or successive processes. From a metabolomic dataset, such decompositions are performed using different mathematical methods including correlation, stiochiometric, ordination, classification, combinatorial and kinetic analyses. Correlation analysis detects and quantifies affinities/oppositions between metabolites. Stoichiometric analysis aims to identify the organisation of a metabolic network into different metabolic pathways on the hand, and to quantify/optimize the metabolic flux distribution through the different chemical reactions of the system. Ordination and classification analyses help to identify different metabolic trends and their associated metabolites in order to highlight chemical polymorphism representing different variability poles of the metabolic system. Then, metabolic processes/correlations responsible for such a polymorphism can be extracted in silico by combining metabolic profiles representative of different metabolic trends according to a weighting bootstrap approach. Finally evolution of metabolic processes in time can be analysed by different kinetic/dynamic modelling approaches.

Seed-specific elevation of non-symbiotic hemoglobin AtHb1: beneficial effects and underlying molecular networks in Arabidopsis thaliana

PubMed Central

2011-01-01

Background Seed metabolism is dynamically adjusted to oxygen availability. Processes underlying this auto-regulatory mechanism control the metabolic efficiency under changing environmental conditions/stress and thus, are of relevance for biotechnology. Non-symbiotic hemoglobins have been shown to be involved in scavenging of nitric oxide (NO) molecules, which play a key role in oxygen sensing/balancing in plants and animals. Steady state levels of NO are suggested to act as an integrator of energy and carbon metabolism and subsequently, influence energy-demanding growth processes in plants. Results We aimed to manipulate oxygen stress perception in Arabidopsis seeds by overexpression of the non-symbiotic hemoglobin AtHb1 under the control of the seed-specific LeB4 promoter. Seeds of transgenic AtHb1 plants did not accumulate NO under transient hypoxic stress treatment, showed higher respiratory activity and energy status compared to the wild type. Global transcript profiling of seeds/siliques from wild type and transgenic plants under transient hypoxic and standard conditions using Affymetrix ATH1 chips revealed a rearrangement of transcriptional networks by AtHb1 overexpression under non-stress conditions, which included the induction of transcripts related to ABA synthesis and signaling, receptor-like kinase- and MAP kinase-mediated signaling pathways, WRKY transcription factors and ROS metabolism. Overexpression of AtHb1 shifted seed metabolism to an energy-saving mode with the most prominent alterations occurring in cell wall metabolism. In combination with metabolite and physiological measurements, these data demonstrate that AtHb1 overexpression improves oxidative stress tolerance compared to the wild type where a strong transcriptional and metabolic reconfiguration was observed in the hypoxic response. Conclusions AtHb1 overexpression mediates a pre-adaptation to hypoxic stress. Under transient stress conditions transgenic seeds were able to keep low levels of endogenous NO and to maintain a high energy status, in contrast to wild type. Higher weight of mature transgenic seeds demonstrated the beneficial effects of seed-specific overexpression of AtHb1. PMID:21406103

Network-level architecture and the evolutionary potential of underground metabolism.

PubMed

Notebaart, Richard A; Szappanos, Balázs; Kintses, Bálint; Pál, Ferenc; Györkei, Ádám; Bogos, Balázs; Lázár, Viktória; Spohn, Réka; Csörgő, Bálint; Wagner, Allon; Ruppin, Eytan; Pál, Csaba; Papp, Balázs

2014-08-12

A central unresolved issue in evolutionary biology is how metabolic innovations emerge. Low-level enzymatic side activities are frequent and can potentially be recruited for new biochemical functions. However, the role of such underground reactions in adaptation toward novel environments has remained largely unknown and out of reach of computational predictions, not least because these issues demand analyses at the level of the entire metabolic network. Here, we provide a comprehensive computational model of the underground metabolism in Escherichia coli. Most underground reactions are not isolated and 45% of them can be fully wired into the existing network and form novel pathways that produce key precursors for cell growth. This observation allowed us to conduct an integrated genome-wide in silico and experimental survey to characterize the evolutionary potential of E. coli to adapt to hundreds of nutrient conditions. We revealed that underground reactions allow growth in new environments when their activity is increased. We estimate that at least ∼20% of the underground reactions that can be connected to the existing network confer a fitness advantage under specific environments. Moreover, our results demonstrate that the genetic basis of evolutionary adaptations via underground metabolism is computationally predictable. The approach used here has potential for various application areas from bioengineering to medical genetics.

What Can Causal Networks Tell Us about Metabolic Pathways?

PubMed Central

Blair, Rachael Hageman; Kliebenstein, Daniel J.; Churchill, Gary A.

2012-01-01

Graphical models describe the linear correlation structure of data and have been used to establish causal relationships among phenotypes in genetic mapping populations. Data are typically collected at a single point in time. Biological processes on the other hand are often non-linear and display time varying dynamics. The extent to which graphical models can recapitulate the architecture of an underlying biological processes is not well understood. We consider metabolic networks with known stoichiometry to address the fundamental question: “What can causal networks tell us about metabolic pathways?”. Using data from an Arabidopsis BaySha population and simulated data from dynamic models of pathway motifs, we assess our ability to reconstruct metabolic pathways using graphical models. Our results highlight the necessity of non-genetic residual biological variation for reliable inference. Recovery of the ordering within a pathway is possible, but should not be expected. Causal inference is sensitive to subtle patterns in the correlation structure that may be driven by a variety of factors, which may not emphasize the substrate-product relationship. We illustrate the effects of metabolic pathway architecture, epistasis and stochastic variation on correlation structure and graphical model-derived networks. We conclude that graphical models should be interpreted cautiously, especially if the implied causal relationships are to be used in the design of intervention strategies. PMID:22496633

Cold adaptation shapes the robustness of metabolic networks in Drosophila melanogaster

PubMed Central

Williams, CM; Watanabe, M; Guarracino, MR; Ferraro, MB; Edison, AS; Morgan, TJ; Boroujerdi, AFB; Hahn, DA

2015-01-01

When ectotherms are exposed to low temperatures, they enter a cold-induced coma (chill coma) that prevents resource acquisition, mating, oviposition, and escape from predation. There is substantial variation in time taken to recover from chill coma both within and among species, and this variation is correlated with habitat temperatures such that insects from cold environments recover more quickly. This suggests an adaptive response, but the mechanisms underlying variation in recovery times are unknown, making it difficult to decisively test adaptive hypotheses. We use replicated lines of Drosophila melanogaster selected in the laboratory for fast (hardy) or slow (susceptible) chill-coma recovery times to investigate modifications to metabolic profiles associated with cold adaptation. We measured metabolite concentrations of flies before, during, and after cold exposure using NMR spectroscopy to test the hypotheses that hardy flies maintain metabolic homeostasis better during cold exposure and recovery, and that their metabolic networks are more robust to cold-induced perturbations. The metabolites of cold-hardy flies were less cold responsive and their metabolic networks during cold exposure were more robust, supporting our hypotheses. Metabolites involved in membrane lipid synthesis, tryptophan metabolism, oxidative stress, energy balance, and proline metabolism were altered by selection on cold tolerance. We discuss the potential significance of these alterations. PMID:25308124

Metabolic networks are almost nonfractal: a comprehensive evaluation.

PubMed

Takemoto, Kazuhiro

2014-08-01

Network self-similarity or fractality are widely accepted as an important topological property of metabolic networks; however, recent studies cast doubt on the reality of self-similarity in the networks. Therefore, we perform a comprehensive evaluation of metabolic network fractality using a box-covering method with an earlier version and the latest version of metabolic networks and demonstrate that the latest metabolic networks are almost self-dissimilar, while the earlier ones are fractal, as reported in a number of previous studies. This result may be because the networks were randomized because of an increase in network density due to database updates, suggesting that the previously observed network fractality was due to a lack of available data on metabolic reactions. This finding may not entirely discount the importance of self-similarity of metabolic networks. Rather, it highlights the need for a more suitable definition of network fractality and a more careful examination of self-similarity of metabolic networks.

MetaNET--a web-accessible interactive platform for biological metabolic network analysis.

PubMed

Narang, Pankaj; Khan, Shawez; Hemrom, Anmol Jaywant; Lynn, Andrew Michael

2014-01-01

Metabolic reactions have been extensively studied and compiled over the last century. These have provided a theoretical base to implement models, simulations of which are used to identify drug targets and optimize metabolic throughput at a systemic level. While tools for the perturbation of metabolic networks are available, their applications are limited and restricted as they require varied dependencies and often a commercial platform for full functionality. We have developed MetaNET, an open source user-friendly platform-independent and web-accessible resource consisting of several pre-defined workflows for metabolic network analysis. MetaNET is a web-accessible platform that incorporates a range of functions which can be combined to produce different simulations related to metabolic networks. These include (i) optimization of an objective function for wild type strain, gene/catalyst/reaction knock-out/knock-down analysis using flux balance analysis. (ii) flux variability analysis (iii) chemical species participation (iv) cycles and extreme paths identification and (v) choke point reaction analysis to facilitate identification of potential drug targets. The platform is built using custom scripts along with the open-source Galaxy workflow and Systems Biology Research Tool as components. Pre-defined workflows are available for common processes, and an exhaustive list of over 50 functions are provided for user defined workflows. MetaNET, available at http://metanet.osdd.net , provides a user-friendly rich interface allowing the analysis of genome-scale metabolic networks under various genetic and environmental conditions. The framework permits the storage of previous results, the ability to repeat analysis and share results with other users over the internet as well as run different tools simultaneously using pre-defined workflows, and user-created custom workflows.

Time-resolved metabolomics reveals metabolic modulation in rice foliage

PubMed Central

Sato, Shigeru; Arita, Masanori; Soga, Tomoyoshi; Nishioka, Takaaki; Tomita, Masaru

2008-01-01

Background To elucidate the interaction of dynamics among modules that constitute biological systems, comprehensive datasets obtained from "omics" technologies have been used. In recent plant metabolomics approaches, the reconstruction of metabolic correlation networks has been attempted using statistical techniques. However, the results were unsatisfactory and effective data-mining techniques that apply appropriate comprehensive datasets are needed. Results Using capillary electrophoresis mass spectrometry (CE-MS) and capillary electrophoresis diode-array detection (CE-DAD), we analyzed the dynamic changes in the level of 56 basic metabolites in plant foliage (Oryza sativa L. ssp. japonica) at hourly intervals over a 24-hr period. Unsupervised clustering of comprehensive metabolic profiles using Kohonen's self-organizing map (SOM) allowed classification of the biochemical pathways activated by the light and dark cycle. The carbon and nitrogen (C/N) metabolism in both periods was also visualized as a phenotypic linkage map that connects network modules on the basis of traditional metabolic pathways rather than pairwise correlations among metabolites. The regulatory networks of C/N assimilation/dissimilation at each time point were consistent with previous works on plant metabolism. In response to environmental stress, glutathione and spermidine fluctuated synchronously with their regulatory targets. Adenine nucleosides and nicotinamide coenzymes were regulated by phosphorylation and dephosphorylation. We also demonstrated that SOM analysis was applicable to the estimation of unidentifiable metabolites in metabolome analysis. Hierarchical clustering of a correlation coefficient matrix could help identify the bottleneck enzymes that regulate metabolic networks. Conclusion Our results showed that our SOM analysis with appropriate metabolic time-courses effectively revealed the synchronous dynamics among metabolic modules and elucidated the underlying biochemical functions. The application of discrimination of unidentified metabolites and the identification of bottleneck enzymatic steps even to non-targeted comprehensive analysis promise to facilitate an understanding of large-scale interactions among components in biological systems. PMID:18564421

The Nuclear Receptor DAF-12 Regulates Nutrient Metabolism and Reproductive Growth in Nematodes

PubMed Central

Wang, Zhu; Stoltzfus, Jonathan; You, Young-jai; Ranjit, Najju; Tang, Hao; Xie, Yang; Lok, James B.; Mangelsdorf, David J.; Kliewer, Steven A.

2015-01-01

Appropriate nutrient response is essential for growth and reproduction. Under favorable nutrient conditions, the C. elegans nuclear receptor DAF-12 is activated by dafachronic acids, hormones that commit larvae to reproductive growth. Here, we report that in addition to its well-studied role in controlling developmental gene expression, the DAF-12 endocrine system governs expression of a gene network that stimulates the aerobic catabolism of fatty acids. Thus, activation of the DAF-12 transcriptome coordinately mobilizes energy stores to permit reproductive growth. DAF-12 regulation of this metabolic gene network is conserved in the human parasite, Strongyloides stercoralis, and inhibition of specific steps in this network blocks reproductive growth in both of the nematodes. Our study provides a molecular understanding for metabolic adaptation of nematodes to their environment, and suggests a new therapeutic strategy for treating parasitic diseases. PMID:25774872

The Role of Target of Rapamycin Signaling Networks in Plant Growth and Metabolism1

PubMed Central

Sheen, Jen

2014-01-01

The target of rapamycin (TOR) kinase, a master regulator that is evolutionarily conserved among yeasts (Saccharomyces cerevisiae), plants, animals, and humans, integrates nutrient and energy signaling to promote cell proliferation and growth. Recent breakthroughs made possible by integrating chemical, genetic, and genomic analyses have greatly increased our understanding of the molecular functions and dynamic regulation of the TOR kinase in photosynthetic plants. TOR signaling plays fundamental roles in embryogenesis, meristem activation, root and leaf growth, flowering, senescence, and life span determination. The molecular mechanisms underlying TOR-mediated ribosomal biogenesis, translation promotion, readjustment of metabolism, and autophagy inhibition are now being uncovered. Moreover, monitoring photosynthesis-derived Glc and bioenergetics relays has revealed that TOR orchestrates unprecedented transcriptional networks that wire central metabolism and biosynthesis for energy and biomass production. In addition, these networks integrate localized stem/progenitor cell proliferation through interorgan nutrient coordination to control developmental transitions and growth. PMID:24385567

Including metabolite concentrations into flux balance analysis: thermodynamic realizability as a constraint on flux distributions in metabolic networks

PubMed Central

Hoppe, Andreas; Hoffmann, Sabrina; Holzhütter, Hermann-Georg

2007-01-01

Background In recent years, constrained optimization – usually referred to as flux balance analysis (FBA) – has become a widely applied method for the computation of stationary fluxes in large-scale metabolic networks. The striking advantage of FBA as compared to kinetic modeling is that it basically requires only knowledge of the stoichiometry of the network. On the other hand, results of FBA are to a large degree hypothetical because the method relies on plausible but hardly provable optimality principles that are thought to govern metabolic flux distributions. Results To augment the reliability of FBA-based flux calculations we propose an additional side constraint which assures thermodynamic realizability, i.e. that the flux directions are consistent with the corresponding changes of Gibb's free energies. The latter depend on metabolite levels for which plausible ranges can be inferred from experimental data. Computationally, our method results in the solution of a mixed integer linear optimization problem with quadratic scoring function. An optimal flux distribution together with a metabolite profile is determined which assures thermodynamic realizability with minimal deviations of metabolite levels from their expected values. We applied our novel approach to two exemplary metabolic networks of different complexity, the metabolic core network of erythrocytes (30 reactions) and the metabolic network iJR904 of Escherichia coli (931 reactions). Our calculations show that increasing network complexity entails increasing sensitivity of predicted flux distributions to variations of standard Gibb's free energy changes and metabolite concentration ranges. We demonstrate the usefulness of our method for assessing critical concentrations of external metabolites preventing attainment of a metabolic steady state. Conclusion Our method incorporates the thermodynamic link between flux directions and metabolite concentrations into a practical computational algorithm. The weakness of conventional FBA to rely on intuitive assumptions about the reversibility of biochemical reactions is overcome. This enables the computation of reliable flux distributions even under extreme conditions of the network (e.g. enzyme inhibition, depletion of substrates or accumulation of end products) where metabolite concentrations may be drastically altered. PMID:17543097

Topological analysis of metabolic networks integrating co-segregating transcriptomes and metabolomes in type 2 diabetic rat congenic series.

PubMed

Dumas, Marc-Emmanuel; Domange, Céline; Calderari, Sophie; Martínez, Andrea Rodríguez; Ayala, Rafael; Wilder, Steven P; Suárez-Zamorano, Nicolas; Collins, Stephan C; Wallis, Robert H; Gu, Quan; Wang, Yulan; Hue, Christophe; Otto, Georg W; Argoud, Karène; Navratil, Vincent; Mitchell, Steve C; Lindon, John C; Holmes, Elaine; Cazier, Jean-Baptiste; Nicholson, Jeremy K; Gauguier, Dominique

2016-09-30

The genetic regulation of metabolic phenotypes (i.e., metabotypes) in type 2 diabetes mellitus occurs through complex organ-specific cellular mechanisms and networks contributing to impaired insulin secretion and insulin resistance. Genome-wide gene expression profiling systems can dissect the genetic contributions to metabolome and transcriptome regulations. The integrative analysis of multiple gene expression traits and metabolic phenotypes (i.e., metabotypes) together with their underlying genetic regulation remains a challenge. Here, we introduce a systems genetics approach based on the topological analysis of a combined molecular network made of genes and metabolites identified through expression and metabotype quantitative trait locus mapping (i.e., eQTL and mQTL) to prioritise biological characterisation of candidate genes and traits. We used systematic metabotyping by 1 H NMR spectroscopy and genome-wide gene expression in white adipose tissue to map molecular phenotypes to genomic blocks associated with obesity and insulin secretion in a series of rat congenic strains derived from spontaneously diabetic Goto-Kakizaki (GK) and normoglycemic Brown-Norway (BN) rats. We implemented a network biology strategy approach to visualize the shortest paths between metabolites and genes significantly associated with each genomic block. Despite strong genomic similarities (95-99 %) among congenics, each strain exhibited specific patterns of gene expression and metabotypes, reflecting the metabolic consequences of series of linked genetic polymorphisms in the congenic intervals. We subsequently used the congenic panel to map quantitative trait loci underlying specific mQTLs and genome-wide eQTLs. Variation in key metabolites like glucose, succinate, lactate, or 3-hydroxybutyrate and second messenger precursors like inositol was associated with several independent genomic intervals, indicating functional redundancy in these regions. To navigate through the complexity of these association networks we mapped candidate genes and metabolites onto metabolic pathways and implemented a shortest path strategy to highlight potential mechanistic links between metabolites and transcripts at colocalized mQTLs and eQTLs. Minimizing the shortest path length drove prioritization of biological validations by gene silencing. These results underline the importance of network-based integration of multilevel systems genetics datasets to improve understanding of the genetic architecture of metabotype and transcriptomic regulation and to characterize novel functional roles for genes determining tissue-specific metabolism.

BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions

PubMed Central

2010-01-01

Background Genome-scale metabolic reconstructions under the Constraint Based Reconstruction and Analysis (COBRA) framework are valuable tools for analyzing the metabolic capabilities of organisms and interpreting experimental data. As the number of such reconstructions and analysis methods increases, there is a greater need for data uniformity and ease of distribution and use. Description We describe BiGG, a knowledgebase of Biochemically, Genetically and Genomically structured genome-scale metabolic network reconstructions. BiGG integrates several published genome-scale metabolic networks into one resource with standard nomenclature which allows components to be compared across different organisms. BiGG can be used to browse model content, visualize metabolic pathway maps, and export SBML files of the models for further analysis by external software packages. Users may follow links from BiGG to several external databases to obtain additional information on genes, proteins, reactions, metabolites and citations of interest. Conclusions BiGG addresses a need in the systems biology community to have access to high quality curated metabolic models and reconstructions. It is freely available for academic use at http://bigg.ucsd.edu. PMID:20426874

Global metabolite profiling analysis of lipotoxicity in HER2/neu-positive breast cancer cells.

PubMed

Baumann, Jan; Kokabee, Mostafa; Wong, Jason; Balasubramaniyam, Rakshika; Sun, Yan; Conklin, Douglas S

2018-06-05

Recent work has shown that HER2/neu-positive breast cancer cells rely on a unique Warburg-like metabolism for survival and aggressive behavior. These cells are dependent on fatty acid (FA) synthesis, show markedly increased levels of stored fats and disruption of the synthetic process results in apoptosis. In this study, we used global metabolite profiling and a multi-omics network analysis approach to model the metabolic changes in this physiology under palmitate-supplemented growth conditions to gain insights into the molecular mechanism and its relevance to disease prevention and treatment. Computational analyses were used to define pathway enrichment based on the dataset of significantly altered metabolites and to integrate metabolomics and transcriptomics data in a multi-omics network analysis. Network-predicted changes and functional relationships were tested with cell assays in vitro . Palmitate-supplemented growth conditions induce distinct metabolic alterations. Growth of HER2-normal MCF7 cells is unaffected under these conditions whereas HER2/neu-positive cells display unchanged neutral lipid content, AMPK activation, inhibition of fatty acid synthesis and significantly altered glutamine, glucose and serine/glycine metabolism. The predominant upregulated lipid species is the novel bioactive lipid N-palmitoylglycine, which is non-toxic to these cells. Limiting the availability of glutamine significantly ameliorates the lipotoxic effects of palmitate, reduces CHOP and XBP1(s) induction and restores the expression levels of HER2 and HER3. The study shows that HER2/neu-positive breast cancer cells change their metabolic phenotype in the presence of palmitate. Palmitate induces AMPK activation and inhibition of fatty acid synthesis that feeds back into glycolysis as well as anaplerotic glutamine metabolism.

A Combined Proteomic and Transcriptomic Analysis on Sulfur Metabolism Pathways of Arabidopsis thaliana under Simulated Acid Rain

PubMed Central

Wang, Wenhua; Simon, Martin; Wu, Feihua; Hu, Wenjun; Chen, Juan B.; Zheng, Hailei

2014-01-01

With rapid economic development, most regions in southern China have suffered acid rain (AR) pollution. In our study, we analyzed the changes in sulfur metabolism in Arabidopsis under simulated AR stress which provide one of the first case studies, in which the systematic responses in sulfur metabolism were characterized by high-throughput methods at different levels including proteomic, genomic and physiological approaches. Generally, we found that all of the processes related to sulfur metabolism responded to AR stress, including sulfur uptake, activation and also synthesis of sulfur-containing amino acid and other secondary metabolites. Finally, we provided a catalogue of the detected sulfur metabolic changes and reconstructed the coordinating network of their mutual influences. This study can help us to understand the mechanisms of plants to adapt to AR stress. PMID:24595051

Differential Network Analysis Reveals Evolutionary Complexity in Secondary Metabolism of Rauvolfia serpentina over Catharanthus roseus

PubMed Central

Pathania, Shivalika; Bagler, Ganesh; Ahuja, Paramvir S.

2016-01-01

Comparative co-expression analysis of multiple species using high-throughput data is an integrative approach to determine the uniformity as well as diversification in biological processes. Rauvolfia serpentina and Catharanthus roseus, both members of Apocyanacae family, are reported to have remedial properties against multiple diseases. Despite of sharing upstream of terpenoid indole alkaloid pathway, there is significant diversity in tissue-specific synthesis and accumulation of specialized metabolites in these plants. This led us to implement comparative co-expression network analysis to investigate the modules and genes responsible for differential tissue-specific expression as well as species-specific synthesis of metabolites. Toward these goals differential network analysis was implemented to identify candidate genes responsible for diversification of metabolites profile. Three genes were identified with significant difference in connectivity leading to differential regulatory behavior between these plants. These genes may be responsible for diversification of secondary metabolism, and thereby for species-specific metabolite synthesis. The network robustness of R. serpentina, determined based on topological properties, was also complemented by comparison of gene-metabolite networks of both plants, and may have evolved to have complex metabolic mechanisms as compared to C. roseus under the influence of various stimuli. This study reveals evolution of complexity in secondary metabolism of R. serpentina, and key genes that contribute toward diversification of specific metabolites. PMID:27588023

Differential Network Analysis Reveals Evolutionary Complexity in Secondary Metabolism of Rauvolfia serpentina over Catharanthus roseus.

PubMed

Pathania, Shivalika; Bagler, Ganesh; Ahuja, Paramvir S

2016-01-01

Comparative co-expression analysis of multiple species using high-throughput data is an integrative approach to determine the uniformity as well as diversification in biological processes. Rauvolfia serpentina and Catharanthus roseus, both members of Apocyanacae family, are reported to have remedial properties against multiple diseases. Despite of sharing upstream of terpenoid indole alkaloid pathway, there is significant diversity in tissue-specific synthesis and accumulation of specialized metabolites in these plants. This led us to implement comparative co-expression network analysis to investigate the modules and genes responsible for differential tissue-specific expression as well as species-specific synthesis of metabolites. Toward these goals differential network analysis was implemented to identify candidate genes responsible for diversification of metabolites profile. Three genes were identified with significant difference in connectivity leading to differential regulatory behavior between these plants. These genes may be responsible for diversification of secondary metabolism, and thereby for species-specific metabolite synthesis. The network robustness of R. serpentina, determined based on topological properties, was also complemented by comparison of gene-metabolite networks of both plants, and may have evolved to have complex metabolic mechanisms as compared to C. roseus under the influence of various stimuli. This study reveals evolution of complexity in secondary metabolism of R. serpentina, and key genes that contribute toward diversification of specific metabolites.

Estimating the size of the solution space of metabolic networks

PubMed Central

Braunstein, Alfredo; Mulet, Roberto; Pagnani, Andrea

2008-01-01

Background Cellular metabolism is one of the most investigated system of biological interactions. While the topological nature of individual reactions and pathways in the network is quite well understood there is still a lack of comprehension regarding the global functional behavior of the system. In the last few years flux-balance analysis (FBA) has been the most successful and widely used technique for studying metabolism at system level. This method strongly relies on the hypothesis that the organism maximizes an objective function. However only under very specific biological conditions (e.g. maximization of biomass for E. coli in reach nutrient medium) the cell seems to obey such optimization law. A more refined analysis not assuming extremization remains an elusive task for large metabolic systems due to algorithmic limitations. Results In this work we propose a novel algorithmic strategy that provides an efficient characterization of the whole set of stable fluxes compatible with the metabolic constraints. Using a technique derived from the fields of statistical physics and information theory we designed a message-passing algorithm to estimate the size of the affine space containing all possible steady-state flux distributions of metabolic networks. The algorithm, based on the well known Bethe approximation, can be used to approximately compute the volume of a non full-dimensional convex polytope in high dimensions. We first compare the accuracy of the predictions with an exact algorithm on small random metabolic networks. We also verify that the predictions of the algorithm match closely those of Monte Carlo based methods in the case of the Red Blood Cell metabolic network. Then we test the effect of gene knock-outs on the size of the solution space in the case of E. coli central metabolism. Finally we analyze the statistical properties of the average fluxes of the reactions in the E. coli metabolic network. Conclusion We propose a novel efficient distributed algorithmic strategy to estimate the size and shape of the affine space of a non full-dimensional convex polytope in high dimensions. The method is shown to obtain, quantitatively and qualitatively compatible results with the ones of standard algorithms (where this comparison is possible) being still efficient on the analysis of large biological systems, where exact deterministic methods experience an explosion in algorithmic time. The algorithm we propose can be considered as an alternative to Monte Carlo sampling methods. PMID:18489757

Metabolomic Analysis in Brain Research: Opportunities and Challenges

PubMed Central

Vasilopoulou, Catherine G.; Margarity, Marigoula; Klapa, Maria I.

2016-01-01

Metabolism being a fundamental part of molecular physiology, elucidating the structure and regulation of metabolic pathways is crucial for obtaining a comprehensive perspective of cellular function and understanding the underlying mechanisms of its dysfunction(s). Therefore, quantifying an accurate metabolic network activity map under various physiological conditions is among the major objectives of systems biology in the context of many biological applications. Especially for CNS, metabolic network activity analysis can substantially enhance our knowledge about the complex structure of the mammalian brain and the mechanisms of neurological disorders, leading to the design of effective therapeutic treatments. Metabolomics has emerged as the high-throughput quantitative analysis of the concentration profile of small molecular weight metabolites, which act as reactants and products in metabolic reactions and as regulatory molecules of proteins participating in many biological processes. Thus, the metabolic profile provides a metabolic activity fingerprint, through the simultaneous analysis of tens to hundreds of molecules of pathophysiological and pharmacological interest. The application of metabolomics is at its standardization phase in general, and the challenges for paving a standardized procedure are even more pronounced in brain studies. In this review, we support the value of metabolomics in brain research. Moreover, we demonstrate the challenges of designing and setting up a reliable brain metabolomic study, which, among other parameters, has to take into consideration the sex differentiation and the complexity of brain physiology manifested in its regional variation. We finally propose ways to overcome these challenges and design a study that produces reproducible and consistent results. PMID:27252656

An engineered non-oxidative glycolysis pathway for acetone production in Escherichia coli.

PubMed

Yang, Xiaoyan; Yuan, Qianqian; Zheng, Yangyang; Ma, Hongwu; Chen, Tao; Zhao, Xueming

2016-08-01

To find new metabolic engineering strategies to improve the yield of acetone in Escherichia coli. Results of flux balance analysis from a modified Escherichia coli genome-scale metabolic network suggested that the introduction of a non-oxidative glycolysis (NOG) pathway would improve the theoretical acetone yield from 1 to 1.5 mol acetone/mol glucose. By inserting the fxpk gene encoding phosphoketolase from Bifidobacterium adolescentis into the genome, we constructed a NOG pathway in E.coli. The resulting strain produced 47 mM acetone from glucose under aerobic conditions in shake-flasks. The yield of acetone was improved from 0.38 to 0.47 mol acetone/mol glucose which is a significant over the parent strain. Guided by computational analysis of metabolic networks, we introduced a NOG pathway into E. coli and increased the yield of acetone, which demonstrates the importance of modeling analysis for the novel metabolic engineering strategies.

A proof for loop-law constraints in stoichiometric metabolic networks

PubMed Central

2012-01-01

Background Constraint-based modeling is increasingly employed for metabolic network analysis. Its underlying assumption is that natural metabolic phenotypes can be predicted by adding physicochemical constraints to remove unrealistic metabolic flux solutions. The loopless-COBRA approach provides an additional constraint that eliminates thermodynamically infeasible internal cycles (or loops) from the space of solutions. This allows the prediction of flux solutions that are more consistent with experimental data. However, it is not clear if this approach over-constrains the models by removing non-loop solutions as well. Results Here we apply Gordan’s theorem from linear algebra to prove for the first time that the constraints added in loopless-COBRA do not over-constrain the problem beyond the elimination of the loops themselves. Conclusions The loopless-COBRA constraints can be reliably applied. Furthermore, this proof may be adapted to evaluate the theoretical soundness for other methods in constraint-based modeling. PMID:23146116

Temperature dependence of the multistability of lactose utilization network of Escherichia coli

NASA Astrophysics Data System (ADS)

Nepal, Sudip; Kumar, Pradeep

Biological systems are capable of producing multiple states out of a single set of inputs. Multistability acts like a biological switch that allows organisms to respond differently to different environmental conditions and hence plays an important role in adaptation to changing environment. One of the widely studied gene regulatory networks underlying the metabolism of bacteria is the lactose utilization network, which exhibits a multistable behavior as a function of lactose concentration. We have studied the effect of temperature on multistability of the lactose utilization network at various concentrations of thio-methylgalactoside (TMG), a synthetic lactose. We find that while the lactose utilization network exhibits a bistable behavior for temperature T >20° C , a graded response arises for temperature T <=20° C. Furthermore, we construct a phase diagram of the graded and bistable response of lactose utilization network as a function of temperature and TMG concentration. Our results suggest that environmental conditions, in this case temperature, can alter the nature of cellular regulation of metabolism.

Metabolic Profile of the Cellulolytic Industrial Actinomycete Thermobifida fusca

PubMed Central

Vanee, Niti

2017-01-01

Actinomycetes have a long history of being the source of numerous valuable natural products and medicinals. To expedite product discovery and optimization of biochemical production, high-throughput technologies can now be used to screen the library of compounds present (or produced) at a given time in an organism. This not only facilitates chemical product screening, but also provides a comprehensive methodology to the study cellular metabolic networks to inform cellular engineering. Here, we present some of the first metabolomic data of the industrial cellulolytic actinomycete Thermobifida fusca generated using LC-MS/MS. The underlying objective of conducting global metabolite profiling was to gain better insight on the innate capabilities of T. fusca, with a long-term goal of facilitating T. fusca-based bioprocesses. The T. fusca metabolome was characterized for growth on two cellulose-relevant carbon sources, cellobiose and Avicel. Furthermore, the comprehensive list of measured metabolites was computationally integrated into a metabolic model of T. fusca, to study metabolic shifts in the network flux associated with carbohydrate and amino acid metabolism. PMID:29137138

Effects of Creatine Monohydrate Augmentation on Brain Metabolic and Network Outcome Measures in Women With Major Depressive Disorder.

PubMed

Yoon, Sujung; Kim, Jieun E; Hwang, Jaeuk; Kim, Tae-Suk; Kang, Hee Jin; Namgung, Eun; Ban, Soonhyun; Oh, Subin; Yang, Jeongwon; Renshaw, Perry F; Lyoo, In Kyoon

2016-09-15

Creatine monohydrate (creatine) augmentation has the potential to accelerate the clinical responses to and enhance the overall efficacy of selective serotonin reuptake inhibitor treatment in women with major depressive disorder (MDD). Although it has been suggested that creatine augmentation may involve the restoration of brain energy metabolism, the mechanisms underlying its antidepressant efficacy are unknown. In a randomized, double-blind, placebo-controlled trial, 52 women with MDD were assigned to receive either creatine augmentation or placebo augmentation of escitalopram; 34 subjects participated in multimodal neuroimaging assessments at baseline and week 8. Age-matched healthy women (n = 39) were also assessed twice at the same intervals. Metabolic and network outcomes were measured for changes in prefrontal N-acetylaspartate and changes in rich club hub connections of the structural brain network using proton magnetic resonance spectroscopy and diffusion tensor imaging, respectively. We found MDD-related metabolic and network dysfunction at baseline. Improvement in depressive symptoms was greater in patients receiving creatine augmentation relative to placebo augmentation. After 8 weeks of treatment, prefrontal N-acetylaspartate levels increased significantly in the creatine augmentation group compared with the placebo augmentation group. Increment in rich club hub connections was also greater in the creatine augmentation group than in the placebo augmentation group. N-acetylaspartate levels and rich club connections increased after creatine augmentation of selective serotonin reuptake inhibitor treatment. Effects of creatine administration on brain energy metabolism and network organization may partly underlie its efficacy in treating women with MDD. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Environmental versatility promotes modularity in genome-scale metabolic networks.

PubMed

Samal, Areejit; Wagner, Andreas; Martin, Olivier C

2011-08-24

The ubiquity of modules in biological networks may result from an evolutionary benefit of a modular organization. For instance, modularity may increase the rate of adaptive evolution, because modules can be easily combined into new arrangements that may benefit their carrier. Conversely, modularity may emerge as a by-product of some trait. We here ask whether this last scenario may play a role in genome-scale metabolic networks that need to sustain life in one or more chemical environments. For such networks, we define a network module as a maximal set of reactions that are fully coupled, i.e., whose fluxes can only vary in fixed proportions. This definition overcomes limitations of purely graph based analyses of metabolism by exploiting the functional links between reactions. We call a metabolic network viable in a given chemical environment if it can synthesize all of an organism's biomass compounds from nutrients in this environment. An organism's metabolism is highly versatile if it can sustain life in many different chemical environments. We here ask whether versatility affects the modularity of metabolic networks. Using recently developed techniques to randomly sample large numbers of viable metabolic networks from a vast space of metabolic networks, we use flux balance analysis to study in silico metabolic networks that differ in their versatility. We find that highly versatile networks are also highly modular. They contain more modules and more reactions that are organized into modules. Most or all reactions in a module are associated with the same biochemical pathways. Modules that arise in highly versatile networks generally involve reactions that process nutrients or closely related chemicals. We also observe that the metabolism of E. coli is significantly more modular than even our most versatile networks. Our work shows that modularity in metabolic networks can be a by-product of functional constraints, e.g., the need to sustain life in multiple environments. This organizational principle is insensitive to the environments we consider and to the number of reactions in a metabolic network. Because we observe this principle not just in one or few biological networks, but in large random samples of networks, we propose that it may be a generic principle of metabolic network organization.

The role of short-chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism

PubMed Central

den Besten, Gijs; van Eunen, Karen; Groen, Albert K.; Venema, Koen; Reijngoud, Dirk-Jan; Bakker, Barbara M.

2013-01-01

Short-chain fatty acids (SCFAs), the end products of fermentation of dietary fibers by the anaerobic intestinal microbiota, have been shown to exert multiple beneficial effects on mammalian energy metabolism. The mechanisms underlying these effects are the subject of intensive research and encompass the complex interplay between diet, gut microbiota, and host energy metabolism. This review summarizes the role of SCFAs in host energy metabolism, starting from the production by the gut microbiota to the uptake by the host and ending with the effects on host metabolism. There are interesting leads on the underlying molecular mechanisms, but there are also many apparently contradictory results. A coherent understanding of the multilevel network in which SCFAs exert their effects is hampered by the lack of quantitative data on actual fluxes of SCFAs and metabolic processes regulated by SCFAs. In this review we address questions that, when answered, will bring us a great step forward in elucidating the role of SCFAs in mammalian energy metabolism. PMID:23821742

Exploring mitochondrial evolution and metabolism organization principles by comparative analysis of metabolic networks.

PubMed

Chang, Xiao; Wang, Zhuo; Hao, Pei; Li, Yuan-Yuan; Li, Yi-Xue

2010-06-01

The endosymbiotic theory proposed that mitochondrial genomes are derived from an alpha-proteobacterium-like endosymbiont, which was concluded from sequence analysis. We rebuilt the metabolic networks of mitochondria and 22 relative species, and studied the evolution of mitochondrial metabolism at the level of enzyme content and network topology. Our phylogenetic results based on network alignment and motif identification supported the endosymbiotic theory from the point of view of systems biology for the first time. It was found that the mitochondrial metabolic network were much more compact than the relative species, probably related to the higher efficiency of oxidative phosphorylation of the specialized organelle, and the network is highly clustered around the TCA cycle. Moreover, the mitochondrial metabolic network exhibited high functional specificity to the modules. This work provided insight to the understanding of mitochondria evolution, and the organization principle of mitochondrial metabolic network at the network level. Copyright 2010 Elsevier Inc. All rights reserved.

MetaFluxNet: the management of metabolic reaction information and quantitative metabolic flux analysis.

PubMed

Lee, Dong-Yup; Yun, Hongsoek; Park, Sunwon; Lee, Sang Yup

2003-11-01

MetaFluxNet is a program package for managing information on the metabolic reaction network and for quantitatively analyzing metabolic fluxes in an interactive and customized way. It allows users to interpret and examine metabolic behavior in response to genetic and/or environmental modifications. As a result, quantitative in silico simulations of metabolic pathways can be carried out to understand the metabolic status and to design the metabolic engineering strategies. The main features of the program include a well-developed model construction environment, user-friendly interface for metabolic flux analysis (MFA), comparative MFA of strains having different genotypes under various environmental conditions, and automated pathway layout creation. http://mbel.kaist.ac.kr/ A manual for MetaFluxNet is available as PDF file.

The Human Blood Metabolome-Transcriptome Interface

PubMed Central

Schramm, Katharina; Adamski, Jerzy; Gieger, Christian; Herder, Christian; Carstensen, Maren; Peters, Annette; Rathmann, Wolfgang; Roden, Michael; Strauch, Konstantin; Suhre, Karsten; Kastenmüller, Gabi; Prokisch, Holger; Theis, Fabian J.

2015-01-01

Biological systems consist of multiple organizational levels all densely interacting with each other to ensure function and flexibility of the system. Simultaneous analysis of cross-sectional multi-omics data from large population studies is a powerful tool to comprehensively characterize the underlying molecular mechanisms on a physiological scale. In this study, we systematically analyzed the relationship between fasting serum metabolomics and whole blood transcriptomics data from 712 individuals of the German KORA F4 cohort. Correlation-based analysis identified 1,109 significant associations between 522 transcripts and 114 metabolites summarized in an integrated network, the ‘human blood metabolome-transcriptome interface’ (BMTI). Bidirectional causality analysis using Mendelian randomization did not yield any statistically significant causal associations between transcripts and metabolites. A knowledge-based interpretation and integration with a genome-scale human metabolic reconstruction revealed systematic signatures of signaling, transport and metabolic processes, i.e. metabolic reactions mainly belonging to lipid, energy and amino acid metabolism. Moreover, the construction of a network based on functional categories illustrated the cross-talk between the biological layers at a pathway level. Using a transcription factor binding site enrichment analysis, this pathway cross-talk was further confirmed at a regulatory level. Finally, we demonstrated how the constructed networks can be used to gain novel insights into molecular mechanisms associated to intermediate clinical traits. Overall, our results demonstrate the utility of a multi-omics integrative approach to understand the molecular mechanisms underlying both normal physiology and disease. PMID:26086077

Critical assessment of human metabolic pathway databases: a stepping stone for future integration

PubMed Central

2011-01-01

Background Multiple pathway databases are available that describe the human metabolic network and have proven their usefulness in many applications, ranging from the analysis and interpretation of high-throughput data to their use as a reference repository. However, so far the various human metabolic networks described by these databases have not been systematically compared and contrasted, nor has the extent to which they differ been quantified. For a researcher using these databases for particular analyses of human metabolism, it is crucial to know the extent of the differences in content and their underlying causes. Moreover, the outcomes of such a comparison are important for ongoing integration efforts. Results We compared the genes, EC numbers and reactions of five frequently used human metabolic pathway databases. The overlap is surprisingly low, especially on reaction level, where the databases agree on 3% of the 6968 reactions they have combined. Even for the well-established tricarboxylic acid cycle the databases agree on only 5 out of the 30 reactions in total. We identified the main causes for the lack of overlap. Importantly, the databases are partly complementary. Other explanations include the number of steps a conversion is described in and the number of possible alternative substrates listed. Missing metabolite identifiers and ambiguous names for metabolites also affect the comparison. Conclusions Our results show that each of the five networks compared provides us with a valuable piece of the puzzle of the complete reconstruction of the human metabolic network. To enable integration of the networks, next to a need for standardizing the metabolite names and identifiers, the conceptual differences between the databases should be resolved. Considerable manual intervention is required to reach the ultimate goal of a unified and biologically accurate model for studying the systems biology of human metabolism. Our comparison provides a stepping stone for such an endeavor. PMID:21999653

A hierarchical model of metabolic machinery based on the kcore decomposition of plant metabolic networks.

PubMed

Filho, Humberto A; Machicao, Jeaneth; Bruno, Odemir M

2018-01-01

Modeling the basic structure of metabolic machinery is a challenge for modern biology. Some models based on complex networks have provided important information regarding this machinery. In this paper, we constructed metabolic networks of 17 plants covering unicellular organisms to more complex dicotyledonous plants. The metabolic networks were built based on the substrate-product model and a topological percolation was performed using the kcore decomposition. The distribution of metabolites across the percolation layers showed correlations between the metabolic integration hierarchy and the network topology. We show that metabolites concentrated in the internal network (maximum kcore) only comprise molecules of the primary basal metabolism. Moreover, we found a high proportion of a set of common metabolites, among the 17 plants, centered at the inner kcore layers. Meanwhile, the metabolites recognized as participants in the secondary metabolism of plants are concentrated in the outermost layers of the network. This data suggests that the metabolites in the central layer form a basic molecular module in which the whole plant metabolism is anchored. The elements from this central core participate in almost all plant metabolic reactions, which suggests that plant metabolic networks follows a centralized topology.

A hierarchical model of metabolic machinery based on the kcore decomposition of plant metabolic networks

PubMed Central

Filho, Humberto A.; Machicao, Jeaneth

2018-01-01

Modeling the basic structure of metabolic machinery is a challenge for modern biology. Some models based on complex networks have provided important information regarding this machinery. In this paper, we constructed metabolic networks of 17 plants covering unicellular organisms to more complex dicotyledonous plants. The metabolic networks were built based on the substrate-product model and a topological percolation was performed using the kcore decomposition. The distribution of metabolites across the percolation layers showed correlations between the metabolic integration hierarchy and the network topology. We show that metabolites concentrated in the internal network (maximum kcore) only comprise molecules of the primary basal metabolism. Moreover, we found a high proportion of a set of common metabolites, among the 17 plants, centered at the inner kcore layers. Meanwhile, the metabolites recognized as participants in the secondary metabolism of plants are concentrated in the outermost layers of the network. This data suggests that the metabolites in the central layer form a basic molecular module in which the whole plant metabolism is anchored. The elements from this central core participate in almost all plant metabolic reactions, which suggests that plant metabolic networks follows a centralized topology. PMID:29734359

Quantification of Microbial Phenotypes

PubMed Central

Martínez, Verónica S.; Krömer, Jens O.

2016-01-01

Metabolite profiling technologies have improved to generate close to quantitative metabolomics data, which can be employed to quantitatively describe the metabolic phenotype of an organism. Here, we review the current technologies available for quantitative metabolomics, present their advantages and drawbacks, and the current challenges to generate fully quantitative metabolomics data. Metabolomics data can be integrated into metabolic networks using thermodynamic principles to constrain the directionality of reactions. Here we explain how to estimate Gibbs energy under physiological conditions, including examples of the estimations, and the different methods for thermodynamics-based network analysis. The fundamentals of the methods and how to perform the analyses are described. Finally, an example applying quantitative metabolomics to a yeast model by 13C fluxomics and thermodynamics-based network analysis is presented. The example shows that (1) these two methods are complementary to each other; and (2) there is a need to take into account Gibbs energy errors. Better estimations of metabolic phenotypes will be obtained when further constraints are included in the analysis. PMID:27941694

Integrated metabolic flux and omics analysis of Synechocystis sp. PCC 6803 under mixotrophic and photoheterotrophic conditions.

PubMed

Nakajima, Tsubasa; Kajihata, Shuichi; Yoshikawa, Katsunori; Matsuda, Fumio; Furusawa, Chikara; Hirasawa, Takashi; Shimizu, Hiroshi

2014-09-01

Cyanobacteria have flexible metabolic capability that enables them to adapt to various environments. To investigate their underlying metabolic regulation mechanisms, we performed an integrated analysis of metabolic flux using transcriptomic and metabolomic data of a cyanobacterium Synechocystis sp. PCC 6803, under mixotrophic and photoheterotrophic conditions. The integrated analysis indicated drastic metabolic flux changes, with much smaller changes in gene expression levels and metabolite concentrations between the conditions, suggesting that the flux change was not caused mainly by the expression levels of the corresponding genes. Under photoheterotrophic conditions, created by the addition of the photosynthesis inhibitor atrazine in mixotrophic conditions, the result of metabolic flux analysis indicated the significant repression of carbon fixation and the activation of the oxidative pentose phosphate pathway (PPP). Moreover, we observed gluconeogenic activity of upstream of glycolysis, which enhanced the flux of the oxidative PPP to compensate for NADPH depletion due to the inhibition of the light reaction of photosynthesis. 'Omics' data suggested that these changes were probably caused by the repression of the gap1 gene, which functions as a control valve in the metabolic network. Since metabolic flux is the outcome of a complicated interplay of cellular components, integrating metabolic flux with other 'omics' layers can identify metabolic changes and narrow down these regulatory mechanisms more effectively. © The Author 2014. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: journals.permissions@oup.com.

System-level modeling of acetone-butanol-ethanol fermentation.

PubMed

Liao, Chen; Seo, Seung-Oh; Lu, Ting

2016-05-01

Acetone-butanol-ethanol (ABE) fermentation is a metabolic process of clostridia that produces bio-based solvents including butanol. It is enabled by an underlying metabolic reaction network and modulated by cellular gene regulation and environmental cues. Mathematical modeling has served as a valuable strategy to facilitate the understanding, characterization and optimization of this process. In this review, we highlight recent advances in system-level, quantitative modeling of ABE fermentation. We begin with an overview of integrative processes underlying the fermentation. Next we survey modeling efforts including early simple models, models with a systematic metabolic description, and those incorporating metabolism through simple gene regulation. Particular focus is given to a recent system-level model that integrates the metabolic reactions, gene regulation and environmental cues. We conclude by discussing the remaining challenges and future directions towards predictive understanding of ABE fermentation. © FEMS 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Physiologically Shrinking the Solution Space of a Saccharomyces cerevisiae Genome-Scale Model Suggests the Role of the Metabolic Network in Shaping Gene Expression Noise.

PubMed

Chi, Baofang; Tao, Shiheng; Liu, Yanlin

2015-01-01

Sampling the solution space of genome-scale models is generally conducted to determine the feasible region for metabolic flux distribution. Because the region for actual metabolic states resides only in a small fraction of the entire space, it is necessary to shrink the solution space to improve the predictive power of a model. A common strategy is to constrain models by integrating extra datasets such as high-throughput datasets and C13-labeled flux datasets. However, studies refining these approaches by performing a meta-analysis of massive experimental metabolic flux measurements, which are closely linked to cellular phenotypes, are limited. In the present study, experimentally identified metabolic flux data from 96 published reports were systematically reviewed. Several strong associations among metabolic flux phenotypes were observed. These phenotype-phenotype associations at the flux level were quantified and integrated into a Saccharomyces cerevisiae genome-scale model as extra physiological constraints. By sampling the shrunken solution space of the model, the metabolic flux fluctuation level, which is an intrinsic trait of metabolic reactions determined by the network, was estimated and utilized to explore its relationship to gene expression noise. Although no correlation was observed in all enzyme-coding genes, a relationship between metabolic flux fluctuation and expression noise of genes associated with enzyme-dosage sensitive reactions was detected, suggesting that the metabolic network plays a role in shaping gene expression noise. Such correlation was mainly attributed to the genes corresponding to non-essential reactions, rather than essential ones. This was at least partially, due to regulations underlying the flux phenotype-phenotype associations. Altogether, this study proposes a new approach in shrinking the solution space of a genome-scale model, of which sampling provides new insights into gene expression noise.

Sequence- and Structure-Based Functional Annotation and Assessment of Metabolic Transporters in Aspergillus oryzae: A Representative Case Study

PubMed Central

Raethong, Nachon; Wong-ekkabut, Jirasak; Laoteng, Kobkul; Vongsangnak, Wanwipa

2016-01-01

Aspergillus oryzae is widely used for the industrial production of enzymes. In A. oryzae metabolism, transporters appear to play crucial roles in controlling the flux of molecules for energy generation, nutrients delivery, and waste elimination in the cell. While the A. oryzae genome sequence is available, transporter annotation remains limited and thus the connectivity of metabolic networks is incomplete. In this study, we developed a metabolic annotation strategy to understand the relationship between the sequence, structure, and function for annotation of A. oryzae metabolic transporters. Sequence-based analysis with manual curation showed that 58 genes of 12,096 total genes in the A. oryzae genome encoded metabolic transporters. Under consensus integrative databases, 55 unambiguous metabolic transporter genes were distributed into channels and pores (7 genes), electrochemical potential-driven transporters (33 genes), and primary active transporters (15 genes). To reveal the transporter functional role, a combination of homology modeling and molecular dynamics simulation was implemented to assess the relationship between sequence to structure and structure to function. As in the energy metabolism of A. oryzae, the H+-ATPase encoded by the AO090005000842 gene was selected as a representative case study of multilevel linkage annotation. Our developed strategy can be used for enhancing metabolic network reconstruction. PMID:27274991

Sequence- and Structure-Based Functional Annotation and Assessment of Metabolic Transporters in Aspergillus oryzae: A Representative Case Study.

PubMed

Raethong, Nachon; Wong-Ekkabut, Jirasak; Laoteng, Kobkul; Vongsangnak, Wanwipa

2016-01-01

Aspergillus oryzae is widely used for the industrial production of enzymes. In A. oryzae metabolism, transporters appear to play crucial roles in controlling the flux of molecules for energy generation, nutrients delivery, and waste elimination in the cell. While the A. oryzae genome sequence is available, transporter annotation remains limited and thus the connectivity of metabolic networks is incomplete. In this study, we developed a metabolic annotation strategy to understand the relationship between the sequence, structure, and function for annotation of A. oryzae metabolic transporters. Sequence-based analysis with manual curation showed that 58 genes of 12,096 total genes in the A. oryzae genome encoded metabolic transporters. Under consensus integrative databases, 55 unambiguous metabolic transporter genes were distributed into channels and pores (7 genes), electrochemical potential-driven transporters (33 genes), and primary active transporters (15 genes). To reveal the transporter functional role, a combination of homology modeling and molecular dynamics simulation was implemented to assess the relationship between sequence to structure and structure to function. As in the energy metabolism of A. oryzae, the H(+)-ATPase encoded by the AO090005000842 gene was selected as a representative case study of multilevel linkage annotation. Our developed strategy can be used for enhancing metabolic network reconstruction.

Imbalanced network biomarkers for traditional Chinese medicine Syndrome in gastritis patients.

PubMed

Li, Rui; Ma, Tao; Gu, Jin; Liang, Xujun; Li, Shao

2013-01-01

Cold Syndrome and Hot Syndrome are thousand-year-old key therapeutic concepts in traditional Chinese medicine (TCM), which depict the loss of body homeostasis. However, the scientific basis of TCM Syndrome remains unclear due to limitations of current reductionist approaches. Here, we established a network balance model to evaluate the imbalanced network underlying TCM Syndrome and find potential biomarkers. By implementing this approach and investigating a group of chronic superficial gastritis (CSG) and chronic atrophic gastritis (CAG) patients, we found that with leptin as a biomarker, Cold Syndrome patients experience low levels of energy metabolism, while the CCL2/MCP1 biomarker indicated that immune regulation is intensified in Hot Syndrome patients. Such a metabolism-immune imbalanced network is consistent during the course from CSG to CAG. This work provides a new way to understand TCM Syndrome scientifically, which in turn benefits the personalized medicine in terms of the ancient medicine and complex biological systems.

Integrating tracer-based metabolomics data and metabolic fluxes in a linear fashion via Elementary Carbon Modes.

PubMed

Pey, Jon; Rubio, Angel; Theodoropoulos, Constantinos; Cascante, Marta; Planes, Francisco J

2012-07-01

Constraints-based modeling is an emergent area in Systems Biology that includes an increasing set of methods for the analysis of metabolic networks. In order to refine its predictions, the development of novel methods integrating high-throughput experimental data is currently a key challenge in the field. In this paper, we present a novel set of constraints that integrate tracer-based metabolomics data from Isotope Labeling Experiments and metabolic fluxes in a linear fashion. These constraints are based on Elementary Carbon Modes (ECMs), a recently developed concept that generalizes Elementary Flux Modes at the carbon level. To illustrate the effect of our ECMs-based constraints, a Flux Variability Analysis approach was applied to a previously published metabolic network involving the main pathways in the metabolism of glucose. The addition of our ECMs-based constraints substantially reduced the under-determination resulting from a standard application of Flux Variability Analysis, which shows a clear progress over the state of the art. In addition, our approach is adjusted to deal with combinatorial explosion of ECMs in genome-scale metabolic networks. This extension was applied to infer the maximum biosynthetic capacity of non-essential amino acids in human metabolism. Finally, as linearity is the hallmark of our approach, its importance is discussed at a methodological, computational and theoretical level and illustrated with a practical application in the field of Isotope Labeling Experiments. Copyright © 2012 Elsevier Inc. All rights reserved.

Beyond topology: coevolution of structure and flux in metabolic networks.

PubMed

Morrison, E S; Badyaev, A V

2017-10-01

Interactions between the structure of a metabolic network and its functional properties underlie its evolutionary diversification, but the mechanism by which such interactions arise remains elusive. Particularly unclear is whether metabolic fluxes that determine the concentrations of compounds produced by a metabolic network, are causally linked to a network's structure or emerge independently of it. A direct empirical study of populations where both structural and functional properties vary among individuals' metabolic networks is required to establish whether changes in structure affect the distribution of metabolic flux. In a population of house finches (Haemorhous mexicanus), we reconstructed full carotenoid metabolic networks for 442 individuals and uncovered 11 structural variants of this network with different compounds and reactions. We examined the consequences of this structural diversity for the concentrations of plumage-bound carotenoids produced by flux in these networks. We found that concentrations of metabolically derived, but not dietary carotenoids, depended on network structure. Flux was partitioned similarly among compounds in individuals of the same network structure: within each network, compound concentrations were closely correlated. The highest among-individual variation in flux occurred in networks with the strongest among-compound correlations, suggesting that changes in the magnitude, but not the distribution of flux, underlie individual differences in compound concentrations on a static network structure. These findings indicate that the distribution of flux in carotenoid metabolism closely follows network structure. Thus, evolutionary diversification and local adaptations in carotenoid metabolism may depend more on the gain or loss of enzymatic reactions than on changes in flux within a network structure. © 2017 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2017 European Society For Evolutionary Biology.

The connectivity structure, giant strong component and centrality of metabolic networks.

PubMed

Ma, Hong-Wu; Zeng, An-Ping

2003-07-22

Structural and functional analysis of genome-based large-scale metabolic networks is important for understanding the design principles and regulation of the metabolism at a system level. The metabolic network is conventionally considered to be highly integrated and very complex. A rational reduction of the metabolic network to its core structure and a deeper understanding of its functional modules are important. In this work, we show that the metabolites in a metabolic network are far from fully connected. A connectivity structure consisting of four major subsets of metabolites and reactions, i.e. a fully connected sub-network, a substrate subset, a product subset and an isolated subset is found to exist in metabolic networks of 65 fully sequenced organisms. The largest fully connected part of a metabolic network, called 'the giant strong component (GSC)', represents the most complicated part and the core of the network and has the feature of scale-free networks. The average path length of the whole network is primarily determined by that of the GSC. For most of the organisms, GSC normally contains less than one-third of the nodes of the network. This connectivity structure is very similar to the 'bow-tie' structure of World Wide Web. Our results indicate that the bow-tie structure may be common for large-scale directed networks. More importantly, the uncovered structure feature makes a structural and functional analysis of large-scale metabolic network more amenable. As shown in this work, comparing the closeness centrality of the nodes in the GSC can identify the most central metabolites of a metabolic network. To quantitatively characterize the overall connection structure of the GSC we introduced the term 'overall closeness centralization index (OCCI)'. OCCI correlates well with the average path length of the GSC and is a useful parameter for a system-level comparison of metabolic networks of different organisms. http://genome.gbf.de/bioinformatics/

Linking metabolic network features to phenotypes using sparse group lasso.

PubMed

Samal, Satya Swarup; Radulescu, Ovidiu; Weber, Andreas; Fröhlich, Holger

2017-11-01

Integration of metabolic networks with '-omics' data has been a subject of recent research in order to better understand the behaviour of such networks with respect to differences between biological and clinical phenotypes. Under the conditions of steady state of the reaction network and the non-negativity of fluxes, metabolic networks can be algebraically decomposed into a set of sub-pathways often referred to as extreme currents (ECs). Our objective is to find the statistical association of such sub-pathways with given clinical outcomes, resulting in a particular instance of a self-contained gene set analysis method. In this direction, we propose a method based on sparse group lasso (SGL) to identify phenotype associated ECs based on gene expression data. SGL selects a sparse set of feature groups and also introduces sparsity within each group. Features in our model are clusters of ECs, and feature groups are defined based on correlations among these features. We apply our method to metabolic networks from KEGG database and study the association of network features to prostate cancer (where the outcome is tumor and normal, respectively) as well as glioblastoma multiforme (where the outcome is survival time). In addition, simulations show the superior performance of our method compared to global test, which is an existing self-contained gene set analysis method. R code (compatible with version 3.2.5) is available from http://www.abi.bit.uni-bonn.de/index.php?id=17. samal@combine.rwth-aachen.de or frohlich@bit.uni-bonn.de. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

FCDECOMP: decomposition of metabolic networks based on flux coupling relations.

PubMed

Rezvan, Abolfazl; Marashi, Sayed-Amir; Eslahchi, Changiz

2014-10-01

A metabolic network model provides a computational framework to study the metabolism of a cell at the system level. Due to their large sizes and complexity, rational decomposition of these networks into subsystems is a strategy to obtain better insight into the metabolic functions. Additionally, decomposing metabolic networks paves the way to use computational methods that will be otherwise very slow when run on the original genome-scale network. In the present study, we propose FCDECOMP decomposition method based on flux coupling relations (FCRs) between pairs of reaction fluxes. This approach utilizes a genetic algorithm (GA) to obtain subsystems that can be analyzed in isolation, i.e. without considering the reactions of the original network in the analysis. Therefore, we propose that our method is useful for discovering biologically meaningful modules in metabolic networks. As a case study, we show that when this method is applied to the metabolic networks of barley seeds and yeast, the modules are in good agreement with the biological compartments of these networks.

Metabolic Networks and Metabolites Underlie Associations Between Maternal Glucose During Pregnancy and Newborn Size at Birth

PubMed Central

Bain, James R.; Reisetter, Anna C.; Muehlbauer, Michael J.; Nodzenski, Michael; Stevens, Robert D.; Ilkayeva, Olga; Lowe, Lynn P.; Metzger, Boyd E.; Newgard, Christopher B.; Lowe, William L.

2016-01-01

Maternal metabolites and metabolic networks underlying associations between maternal glucose during pregnancy and newborn birth weight and adiposity demand fuller characterization. We performed targeted and nontargeted gas chromatography/mass spectrometry metabolomics on maternal serum collected at fasting and 1 h following glucose beverage consumption during an oral glucose tolerance test (OGTT) for 400 northern European mothers at ∼28 weeks' gestation in the Hyperglycemia and Adverse Pregnancy Outcome Study. Amino acids, fatty acids, acylcarnitines, and products of lipid metabolism decreased and triglycerides increased during the OGTT. Analyses of individual metabolites indicated limited maternal glucose associations at fasting, but broader associations, including amino acids, fatty acids, carbohydrates, and lipids, were found at 1 h. Network analyses modeling metabolite correlations provided context for individual metabolite associations and elucidated collective associations of multiple classes of metabolic fuels with newborn size and adiposity, including acylcarnitines, fatty acids, carbohydrates, and organic acids. Random forest analyses indicated an improved ability to predict newborn size outcomes by using maternal metabolomics data beyond traditional risk factors, including maternal glucose. Broad-scale association of fuel metabolites with maternal glucose is evident during pregnancy, with unique maternal metabolites potentially contributing specifically to newborn birth weight and adiposity. PMID:27207545

Adaptive bi-level programming for optimal gene knockouts for targeted overproduction under phenotypic constraints

PubMed Central

2013-01-01

Background Optimization procedures to identify gene knockouts for targeted biochemical overproduction have been widely in use in modern metabolic engineering. Flux balance analysis (FBA) framework has provided conceptual simplifications for genome-scale dynamic analysis at steady states. Based on FBA, many current optimization methods for targeted bio-productions have been developed under the maximum cell growth assumption. The optimization problem to derive gene knockout strategies recently has been formulated as a bi-level programming problem in OptKnock for maximum targeted bio-productions with maximum growth rates. However, it has been shown that knockout mutants in fact reach the steady states with the minimization of metabolic adjustment (MOMA) from the corresponding wild-type strains instead of having maximal growth rates after genetic or metabolic intervention. In this work, we propose a new bi-level computational framework--MOMAKnock--which can derive robust knockout strategies under the MOMA flux distribution approximation. Methods In this new bi-level optimization framework, we aim to maximize the production of targeted chemicals by identifying candidate knockout genes or reactions under phenotypic constraints approximated by the MOMA assumption. Hence, the targeted chemical production is the primary objective of MOMAKnock while the MOMA assumption is formulated as the inner problem of constraining the knockout metabolic flux to be as close as possible to the steady-state phenotypes of wide-type strains. As this new inner problem becomes a quadratic programming problem, a novel adaptive piecewise linearization algorithm is developed in this paper to obtain the exact optimal solution to this new bi-level integer quadratic programming problem for MOMAKnock. Results Our new MOMAKnock model and the adaptive piecewise linearization solution algorithm are tested with a small E. coli core metabolic network and a large-scale iAF1260 E. coli metabolic network. The derived knockout strategies are compared with those from OptKnock. Our preliminary experimental results show that MOMAKnock can provide improved targeted productions with more robust knockout strategies. PMID:23368729

Adaptive bi-level programming for optimal gene knockouts for targeted overproduction under phenotypic constraints.

PubMed

Ren, Shaogang; Zeng, Bo; Qian, Xiaoning

2013-01-01

Optimization procedures to identify gene knockouts for targeted biochemical overproduction have been widely in use in modern metabolic engineering. Flux balance analysis (FBA) framework has provided conceptual simplifications for genome-scale dynamic analysis at steady states. Based on FBA, many current optimization methods for targeted bio-productions have been developed under the maximum cell growth assumption. The optimization problem to derive gene knockout strategies recently has been formulated as a bi-level programming problem in OptKnock for maximum targeted bio-productions with maximum growth rates. However, it has been shown that knockout mutants in fact reach the steady states with the minimization of metabolic adjustment (MOMA) from the corresponding wild-type strains instead of having maximal growth rates after genetic or metabolic intervention. In this work, we propose a new bi-level computational framework--MOMAKnock--which can derive robust knockout strategies under the MOMA flux distribution approximation. In this new bi-level optimization framework, we aim to maximize the production of targeted chemicals by identifying candidate knockout genes or reactions under phenotypic constraints approximated by the MOMA assumption. Hence, the targeted chemical production is the primary objective of MOMAKnock while the MOMA assumption is formulated as the inner problem of constraining the knockout metabolic flux to be as close as possible to the steady-state phenotypes of wide-type strains. As this new inner problem becomes a quadratic programming problem, a novel adaptive piecewise linearization algorithm is developed in this paper to obtain the exact optimal solution to this new bi-level integer quadratic programming problem for MOMAKnock. Our new MOMAKnock model and the adaptive piecewise linearization solution algorithm are tested with a small E. coli core metabolic network and a large-scale iAF1260 E. coli metabolic network. The derived knockout strategies are compared with those from OptKnock. Our preliminary experimental results show that MOMAKnock can provide improved targeted productions with more robust knockout strategies.

Exploring complex networks.

PubMed

Strogatz, S H

2001-03-08

The study of networks pervades all of science, from neurobiology to statistical physics. The most basic issues are structural: how does one characterize the wiring diagram of a food web or the Internet or the metabolic network of the bacterium Escherichia coli? Are there any unifying principles underlying their topology? From the perspective of nonlinear dynamics, we would also like to understand how an enormous network of interacting dynamical systems-be they neurons, power stations or lasers-will behave collectively, given their individual dynamics and coupling architecture. Researchers are only now beginning to unravel the structure and dynamics of complex networks.

Metabolomic Profiling of Soybeans (Glycine max L.) Reveals the Importance of Sugar and Nitrogen Metabolism under Drought and Heat Stress

PubMed Central

Das, Aayudh; Rushton, Paul J.; Rohila, Jai S.

2017-01-01

Soybean is an important crop that is continually threatened by abiotic stresses, especially drought and heat stress. At molecular levels, reduced yields due to drought and heat stress can be seen as a result of alterations in metabolic homeostasis of vegetative tissues. At present an incomplete understanding of abiotic stress-associated metabolism and identification of associated metabolites remains a major gap in soybean stress research. A study with a goal to profile leaf metabolites under control conditions (28/24 °C), drought [28/24 °C, 10% volumetric water content (VWC)], and heat stress (43/35 °C) was conducted in a controlled environment. Analyses of non-targeted metabolomic data showed that in response to drought and heat stress, key metabolites (carbohydrates, amino acids, lipids, cofactors, nucleotides, peptides and secondary metabolites) were differentially accumulated in soybean leaves. The metabolites for various cellular processes, such as glycolysis, the tricarboxylic acid (TCA) cycle, the pentose phosphate pathway, and starch biosynthesis, that regulate carbohydrate metabolism, amino acid metabolism, peptide metabolism, and purine and pyrimidine biosynthesis, were found to be affected by drought as well as heat stress. Computationally based regulatory networks predicted additional compounds that address the possibility of other metabolites and metabolic pathways that could also be important for soybean under drought and heat stress conditions. Metabolomic profiling demonstrated that in soybeans, keeping up with sugar and nitrogen metabolism is of prime significance, along with phytochemical metabolism under drought and heat stress conditions. PMID:28587097

Randomizing Genome-Scale Metabolic Networks

PubMed Central

Samal, Areejit; Martin, Olivier C.

2011-01-01

Networks coming from protein-protein interactions, transcriptional regulation, signaling, or metabolism may appear to have “unusual” properties. To quantify this, it is appropriate to randomize the network and test the hypothesis that the network is not statistically different from expected in a motivated ensemble. However, when dealing with metabolic networks, the randomization of the network using edge exchange generates fictitious reactions that are biochemically meaningless. Here we provide several natural ensembles of randomized metabolic networks. A first constraint is to use valid biochemical reactions. Further constraints correspond to imposing appropriate functional constraints. We explain how to perform these randomizations with the help of Markov Chain Monte Carlo (MCMC) and show that they allow one to approach the properties of biological metabolic networks. The implication of the present work is that the observed global structural properties of real metabolic networks are likely to be the consequence of simple biochemical and functional constraints. PMID:21779409

Cerebral energy metabolism and the brain's functional network architecture: an integrative review.

PubMed

Lord, Louis-David; Expert, Paul; Huckins, Jeremy F; Turkheimer, Federico E

2013-09-01

Recent functional magnetic resonance imaging (fMRI) studies have emphasized the contributions of synchronized activity in distributed brain networks to cognitive processes in both health and disease. The brain's 'functional connectivity' is typically estimated from correlations in the activity time series of anatomically remote areas, and postulated to reflect information flow between neuronal populations. Although the topological properties of functional brain networks have been studied extensively, considerably less is known regarding the neurophysiological and biochemical factors underlying the temporal coordination of large neuronal ensembles. In this review, we highlight the critical contributions of high-frequency electrical oscillations in the γ-band (30 to 100 Hz) to the emergence of functional brain networks. After describing the neurobiological substrates of γ-band dynamics, we specifically discuss the elevated energy requirements of high-frequency neural oscillations, which represent a mechanistic link between the functional connectivity of brain regions and their respective metabolic demands. Experimental evidence is presented for the high oxygen and glucose consumption, and strong mitochondrial performance required to support rhythmic cortical activity in the γ-band. Finally, the implications of mitochondrial impairments and deficits in glucose metabolism for cognition and behavior are discussed in the context of neuropsychiatric and neurodegenerative syndromes characterized by large-scale changes in the organization of functional brain networks.

Reconstruction of metabolic networks from high-throughput metabolite profiling data: in silico analysis of red blood cell metabolism.

PubMed

Nemenman, Ilya; Escola, G Sean; Hlavacek, William S; Unkefer, Pat J; Unkefer, Clifford J; Wall, Michael E

2007-12-01

We investigate the ability of algorithms developed for reverse engineering of transcriptional regulatory networks to reconstruct metabolic networks from high-throughput metabolite profiling data. For benchmarking purposes, we generate synthetic metabolic profiles based on a well-established model for red blood cell metabolism. A variety of data sets are generated, accounting for different properties of real metabolic networks, such as experimental noise, metabolite correlations, and temporal dynamics. These data sets are made available online. We use ARACNE, a mainstream algorithm for reverse engineering of transcriptional regulatory networks from gene expression data, to predict metabolic interactions from these data sets. We find that the performance of ARACNE on metabolic data is comparable to that on gene expression data.

Inborn errors of metabolism and the human interactome: a systems medicine approach.

PubMed

Woidy, Mathias; Muntau, Ania C; Gersting, Søren W

2018-02-05

The group of inborn errors of metabolism (IEM) displays a marked heterogeneity and IEM can affect virtually all functions and organs of the human organism; however, IEM share that their associated proteins function in metabolism. Most proteins carry out cellular functions by interacting with other proteins, and thus are organized in biological networks. Therefore, diseases are rarely the consequence of single gene mutations but of the perturbations caused in the related cellular network. Systematic approaches that integrate multi-omics and database information into biological networks have successfully expanded our knowledge of complex disorders but network-based strategies have been rarely applied to study IEM. We analyzed IEM on a proteome scale and found that IEM-associated proteins are organized as a network of linked modules within the human interactome of protein interactions, the IEM interactome. Certain IEM disease groups formed self-contained disease modules, which were highly interlinked. On the other hand, we observed disease modules consisting of proteins from many different disease groups in the IEM interactome. Moreover, we explored the overlap between IEM and non-IEM disease genes and applied network medicine approaches to investigate shared biological pathways, clinical signs and symptoms, and links to drug targets. The provided resources may help to elucidate the molecular mechanisms underlying new IEM, to uncover the significance of disease-associated mutations, to identify new biomarkers, and to develop novel therapeutic strategies.

Computational modelling of genome-scale metabolic networks and its application to CHO cell cultures.

PubMed

Rejc, Živa; Magdevska, Lidija; Tršelič, Tilen; Osolin, Timotej; Vodopivec, Rok; Mraz, Jakob; Pavliha, Eva; Zimic, Nikolaj; Cvitanović, Tanja; Rozman, Damjana; Moškon, Miha; Mraz, Miha

2017-09-01

Genome-scale metabolic models (GEMs) have become increasingly important in recent years. Currently, GEMs are the most accurate in silico representation of the genotype-phenotype link. They allow us to study complex networks from the systems perspective. Their application may drastically reduce the amount of experimental and clinical work, improve diagnostic tools and increase our understanding of complex biological phenomena. GEMs have also demonstrated high potential for the optimisation of bio-based production of recombinant proteins. Herein, we review the basic concepts, methods, resources and software tools used for the reconstruction and application of GEMs. We overview the evolution of the modelling efforts devoted to the metabolism of Chinese Hamster Ovary (CHO) cells. We present a case study on CHO cell metabolism under different amino acid depletions. This leads us to the identification of the most influential as well as essential amino acids in selected CHO cell lines. Copyright © 2017 Elsevier Ltd. All rights reserved.

The players may change but the game remains: network analyses of ruminal microbiomes suggest taxonomic differences mask functional similarity

PubMed Central

Taxis, Tasia M.; Wolff, Sara; Gregg, Sarah J.; Minton, Nicholas O.; Zhang, Chiqian; Dai, Jingjing; Schnabel, Robert D.; Taylor, Jeremy F.; Kerley, Monty S.; Pires, J. Chris; Lamberson, William R.; Conant, Gavin C.

2015-01-01

By mapping translated metagenomic reads to a microbial metabolic network, we show that ruminal ecosystems that are rather dissimilar in their taxonomy can be considerably more similar at the metabolic network level. Using a new network bi-partition approach for linking the microbial network to a bovine metabolic network, we observe that these ruminal metabolic networks exhibit properties consistent with distinct metabolic communities producing similar outputs from common inputs. For instance, the closer in network space that a microbial reaction is to a reaction found in the host, the lower will be the variability of its enzyme copy number across hosts. Similarly, these microbial enzymes that are nearby to host nodes are also higher in copy number than are more distant enzymes. Collectively, these results demonstrate a widely expected pattern that, to our knowledge, has not been explicitly demonstrated in microbial communities: namely that there can exist different community metabolic networks that have the same metabolic inputs and outputs but differ in their internal structure. PMID:26420832

Exploring candidate biomarkers for lung and prostate cancers using gene expression and flux variability analysis.

PubMed

Asgari, Yazdan; Khosravi, Pegah; Zabihinpour, Zahra; Habibi, Mahnaz

2018-02-19

Genome-scale metabolic models have provided valuable resources for exploring changes in metabolism under normal and cancer conditions. However, metabolism itself is strongly linked to gene expression, so integration of gene expression data into metabolic models might improve the detection of genes involved in the control of tumor progression. Herein, we considered gene expression data as extra constraints to enhance the predictive powers of metabolic models. We reconstructed genome-scale metabolic models for lung and prostate, under normal and cancer conditions to detect the major genes associated with critical subsystems during tumor development. Furthermore, we utilized gene expression data in combination with an information theory-based approach to reconstruct co-expression networks of the human lung and prostate in both cohorts. Our results revealed 19 genes as candidate biomarkers for lung and prostate cancer cells. This study also revealed that the development of a complementary approach (integration of gene expression and metabolic profiles) could lead to proposing novel biomarkers and suggesting renovated cancer treatment strategies which have not been possible to detect using either of the methods alone.

Revealing the cerebral regions and networks mediating vulnerability to depression: oxidative metabolism mapping of rat brain.

PubMed

Harro, Jaanus; Kanarik, Margus; Kaart, Tanel; Matrov, Denis; Kõiv, Kadri; Mällo, Tanel; Del Río, Joaquin; Tordera, Rosa M; Ramirez, Maria J

2014-07-01

The large variety of available animal models has revealed much on the neurobiology of depression, but each model appears as specific to a significant extent, and distinction between stress response, pathogenesis of depression and underlying vulnerability is difficult to make. Evidence from epidemiological studies suggests that depression occurs in biologically predisposed subjects under impact of adverse life events. We applied the diathesis-stress concept to reveal brain regions and functional networks that mediate vulnerability to depression and response to chronic stress by collapsing data on cerebral long term neuronal activity as measured by cytochrome c oxidase histochemistry in distinct animal models. Rats were rendered vulnerable to depression either by partial serotonergic lesion or by maternal deprivation, or selected for a vulnerable phenotype (low positive affect, low novelty-related activity or high hedonic response). Environmental adversity was brought about by applying chronic variable stress or chronic social defeat. Several brain regions, most significantly median raphe, habenula, retrosplenial cortex and reticular thalamus, were universally implicated in long-term metabolic stress response, vulnerability to depression, or both. Vulnerability was associated with higher oxidative metabolism levels as compared to resilience to chronic stress. Chronic stress, in contrast, had three distinct patterns of effect on oxidative metabolism in vulnerable vs. resilient animals. In general, associations between regional activities in several brain circuits were strongest in vulnerable animals, and chronic stress disrupted this interrelatedness. These findings highlight networks that underlie resilience to stress, and the distinct response to stress that occurs in vulnerable subjects. Copyright © 2014 Elsevier B.V. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hay, J.; Schwender, J.

Computational simulation of large-scale biochemical networks can be used to analyze and predict the metabolic behavior of an organism, such as a developing seed. Based on the biochemical literature, pathways databases and decision rules defining reaction directionality we reconstructed bna572, a stoichiometric metabolic network model representing Brassica napus seed storage metabolism. In the highly compartmentalized network about 25% of the 572 reactions are transport reactions interconnecting nine subcellular compartments and the environment. According to known physiological capabilities of developing B. napus embryos, four nutritional conditions were defined to simulate heterotrophy or photoheterotrophy, each in combination with the availability of inorganicmore » nitrogen (ammonia, nitrate) or amino acids as nitrogen sources. Based on mathematical linear optimization the optimal solution space was comprehensively explored by flux variability analysis, thereby identifying for each reaction the range of flux values allowable under optimality. The range and variability of flux values was then categorized into flux variability types. Across the four nutritional conditions, approximately 13% of the reactions have variable flux values and 10-11% are substitutable (can be inactive), both indicating metabolic redundancy given, for example, by isoenzymes, subcellular compartmentalization or the presence of alternative pathways. About one-third of the reactions are never used and are associated with pathways that are suboptimal for storage synthesis. Fifty-seven reactions change flux variability type among the different nutritional conditions, indicating their function in metabolic adjustments. This predictive modeling framework allows analysis and quantitative exploration of storage metabolism of a developing B. napus oilseed.« less

Phenotypic constraints promote latent versatility and carbon efficiency in metabolic networks.

PubMed

Bardoscia, Marco; Marsili, Matteo; Samal, Areejit

2015-07-01

System-level properties of metabolic networks may be the direct product of natural selection or arise as a by-product of selection on other properties. Here we study the effect of direct selective pressure for growth or viability in particular environments on two properties of metabolic networks: latent versatility to function in additional environments and carbon usage efficiency. Using a Markov chain Monte Carlo (MCMC) sampling based on flux balance analysis (FBA), we sample from a known biochemical universe random viable metabolic networks that differ in the number of directly constrained environments. We find that the latent versatility of sampled metabolic networks increases with the number of directly constrained environments and with the size of the networks. We then show that the average carbon wastage of sampled metabolic networks across the constrained environments decreases with the number of directly constrained environments and with the size of the networks. Our work expands the growing body of evidence about nonadaptive origins of key functional properties of biological networks.

Mathematical Modeling and Dynamic Simulation of Metabolic Reaction Systems Using Metabolome Time Series Data.

PubMed

Sriyudthsak, Kansuporn; Shiraishi, Fumihide; Hirai, Masami Yokota

2016-01-01

The high-throughput acquisition of metabolome data is greatly anticipated for the complete understanding of cellular metabolism in living organisms. A variety of analytical technologies have been developed to acquire large-scale metabolic profiles under different biological or environmental conditions. Time series data are useful for predicting the most likely metabolic pathways because they provide important information regarding the accumulation of metabolites, which implies causal relationships in the metabolic reaction network. Considerable effort has been undertaken to utilize these data for constructing a mathematical model merging system properties and quantitatively characterizing a whole metabolic system in toto. However, there are technical difficulties between benchmarking the provision and utilization of data. Although, hundreds of metabolites can be measured, which provide information on the metabolic reaction system, simultaneous measurement of thousands of metabolites is still challenging. In addition, it is nontrivial to logically predict the dynamic behaviors of unmeasurable metabolite concentrations without sufficient information on the metabolic reaction network. Yet, consolidating the advantages of advancements in both metabolomics and mathematical modeling remain to be accomplished. This review outlines the conceptual basis of and recent advances in technologies in both the research fields. It also highlights the potential for constructing a large-scale mathematical model by estimating model parameters from time series metabolome data in order to comprehensively understand metabolism at the systems level.

Synthetic gene circuits for metabolic control: design trade-offs and constraints

PubMed Central

Oyarzún, Diego A.; Stan, Guy-Bart V.

2013-01-01

A grand challenge in synthetic biology is to push the design of biomolecular circuits from purely genetic constructs towards systems that interface different levels of the cellular machinery, including signalling networks and metabolic pathways. In this paper, we focus on a genetic circuit for feedback regulation of unbranched metabolic pathways. The objective of this feedback system is to dampen the effect of flux perturbations caused by changes in cellular demands or by engineered pathways consuming metabolic intermediates. We consider a mathematical model for a control circuit with an operon architecture, whereby the expression of all pathway enzymes is transcriptionally repressed by the metabolic product. We address the existence and stability of the steady state, the dynamic response of the network under perturbations, and their dependence on common tuneable knobs such as the promoter characteristic and ribosome binding site (RBS) strengths. Our analysis reveals trade-offs between the steady state of the enzymes and the intermediates, together with a separation principle between promoter and RBS design. We show that enzymatic saturation imposes limits on the parameter design space, which must be satisfied to prevent metabolite accumulation and guarantee the stability of the network. The use of promoters with a broad dynamic range and a small leaky expression enlarges the design space. Simulation results with realistic parameter values also suggest that the control circuit can effectively upregulate enzyme production to compensate flux perturbations. PMID:23054953

Gender Differences of Brain Glucose Metabolic Networks Revealed by FDG-PET: Evidence from a Large Cohort of 400 Young Adults

PubMed Central

Li, Kai; Zhu, Hong; Qi, Rongfeng; Zhang, Zhiqiang; Lu, Guangming

2013-01-01

Background Gender differences of the human brain are an important issue in neuroscience research. In recent years, an increasing amount of evidence has been gathered from noninvasive neuroimaging studies supporting a sexual dimorphism of the human brain. However, there is a lack of imaging studies on gender differences of brain metabolic networks based on a large population sample. Materials and Methods FDG PET data of 400 right-handed, healthy subjects, including 200 females (age: 25∼45 years, mean age±SD: 40.9±3.9 years) and 200 age-matched males were obtained and analyzed in the present study. We first investigated the regional differences of brain glucose metabolism between genders using a voxel-based two-sample t-test analysis. Subsequently, we investigated the gender differences of the metabolic networks. Sixteen metabolic covariance networks using seed-based correlation were analyzed. Seven regions showing significant regional metabolic differences between genders, and nine regions conventionally used in the resting-state network studies were selected as regions-of-interest. Permutation tests were used for comparing within- and between-network connectivity between genders. Results Compared with the males, females showed higher metabolism in the posterior part and lower metabolism in the anterior part of the brain. Moreover, there were widely distributed patterns of the metabolic networks in the human brain. In addition, significant gender differences within and between brain glucose metabolic networks were revealed in the present study. Conclusion This study provides solid data that reveal gender differences in regional brain glucose metabolism and brain glucose metabolic networks. These observations might contribute to the better understanding of the gender differences in human brain functions, and suggest that gender should be included as a covariate when designing experiments and explaining results of brain glucose metabolic networks in the control and experimental individuals or patients. PMID:24358312

Gender differences of brain glucose metabolic networks revealed by FDG-PET: evidence from a large cohort of 400 young adults.

PubMed

Hu, Yuxiao; Xu, Qiang; Li, Kai; Zhu, Hong; Qi, Rongfeng; Zhang, Zhiqiang; Lu, Guangming

2013-01-01

Gender differences of the human brain are an important issue in neuroscience research. In recent years, an increasing amount of evidence has been gathered from noninvasive neuroimaging studies supporting a sexual dimorphism of the human brain. However, there is a lack of imaging studies on gender differences of brain metabolic networks based on a large population sample. FDG PET data of 400 right-handed, healthy subjects, including 200 females (age: 25:45 years, mean age ± SD: 40.9 ± 3.9 years) and 200 age-matched males were obtained and analyzed in the present study. We first investigated the regional differences of brain glucose metabolism between genders using a voxel-based two-sample t-test analysis. Subsequently, we investigated the gender differences of the metabolic networks. Sixteen metabolic covariance networks using seed-based correlation were analyzed. Seven regions showing significant regional metabolic differences between genders, and nine regions conventionally used in the resting-state network studies were selected as regions-of-interest. Permutation tests were used for comparing within- and between-network connectivity between genders. Compared with the males, females showed higher metabolism in the posterior part and lower metabolism in the anterior part of the brain. Moreover, there were widely distributed patterns of the metabolic networks in the human brain. In addition, significant gender differences within and between brain glucose metabolic networks were revealed in the present study. This study provides solid data that reveal gender differences in regional brain glucose metabolism and brain glucose metabolic networks. These observations might contribute to the better understanding of the gender differences in human brain functions, and suggest that gender should be included as a covariate when designing experiments and explaining results of brain glucose metabolic networks in the control and experimental individuals or patients.

Metabolomic analysis reveals altered metabolic pathways in a rat model of gastric carcinogenesis.

PubMed

Gu, Jinping; Hu, Xiaomin; Shao, Wei; Ji, Tianhai; Yang, Wensheng; Zhuo, Huiqin; Jin, Zeyu; Huang, Huiying; Chen, Jiacheng; Huang, Caihua; Lin, Donghai

2016-09-13

Gastric cancer (GC) is one of the most malignant tumors with a poor prognosis. Alterations in metabolic pathways are inextricably linked to GC progression. However, the underlying molecular mechanisms remain elusive. We performed NMR-based metabolomic analysis of sera derived from a rat model of gastric carcinogenesis, revealed significantly altered metabolic pathways correlated with the progression of gastric carcinogenesis. Rats were histologically classified into four pathological groups (gastritis, GS; low-grade gastric dysplasia, LGD; high-grade gastric dysplasia, HGD; GC) and the normal control group (CON). The metabolic profiles of the five groups were clearly distinguished from each other. Furthermore, significant inter-metabolite correlations were extracted and used to reconstruct perturbed metabolic networks associated with the four pathological stages compared with the normal stage. Then, significantly altered metabolic pathways were identified by pathway analysis. Our results showed that oxidative stress-related metabolic pathways, choline phosphorylation and fatty acid degradation were continually disturbed during gastric carcinogenesis. Moreover, amino acid metabolism was perturbed dramatically in gastric dysplasia and GC. The GC stage showed more changed metabolite levels and more altered metabolic pathways. Two activated pathways (glycolysis; glycine, serine and threonine metabolism) substantially contributed to the metabolic alterations in GC. These results lay the basis for addressing the molecular mechanisms underlying gastric carcinogenesis and extend our understanding of GC progression.

Context-specific metabolic networks are consistent with experiments.

PubMed

Becker, Scott A; Palsson, Bernhard O

2008-05-16

Reconstructions of cellular metabolism are publicly available for a variety of different microorganisms and some mammalian genomes. To date, these reconstructions are "genome-scale" and strive to include all reactions implied by the genome annotation, as well as those with direct experimental evidence. Clearly, many of the reactions in a genome-scale reconstruction will not be active under particular conditions or in a particular cell type. Methods to tailor these comprehensive genome-scale reconstructions into context-specific networks will aid predictive in silico modeling for a particular situation. We present a method called Gene Inactivity Moderated by Metabolism and Expression (GIMME) to achieve this goal. The GIMME algorithm uses quantitative gene expression data and one or more presupposed metabolic objectives to produce the context-specific reconstruction that is most consistent with the available data. Furthermore, the algorithm provides a quantitative inconsistency score indicating how consistent a set of gene expression data is with a particular metabolic objective. We show that this algorithm produces results consistent with biological experiments and intuition for adaptive evolution of bacteria, rational design of metabolic engineering strains, and human skeletal muscle cells. This work represents progress towards producing constraint-based models of metabolism that are specific to the conditions where the expression profiling data is available.

Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism

PubMed Central

Chang, Roger L; Ghamsari, Lila; Manichaikul, Ani; Hom, Erik F Y; Balaji, Santhanam; Fu, Weiqi; Shen, Yun; Hao, Tong; Palsson, Bernhard Ø; Salehi-Ashtiani, Kourosh; Papin, Jason A

2011-01-01

Metabolic network reconstruction encompasses existing knowledge about an organism's metabolism and genome annotation, providing a platform for omics data analysis and phenotype prediction. The model alga Chlamydomonas reinhardtii is employed to study diverse biological processes from photosynthesis to phototaxis. Recent heightened interest in this species results from an international movement to develop algal biofuels. Integrating biological and optical data, we reconstructed a genome-scale metabolic network for this alga and devised a novel light-modeling approach that enables quantitative growth prediction for a given light source, resolving wavelength and photon flux. We experimentally verified transcripts accounted for in the network and physiologically validated model function through simulation and generation of new experimental growth data, providing high confidence in network contents and predictive applications. The network offers insight into algal metabolism and potential for genetic engineering and efficient light source design, a pioneering resource for studying light-driven metabolism and quantitative systems biology. PMID:21811229

Metabolic reconstruction, constraint-based analysis and game theory to probe genome-scale metabolic networks.

PubMed

Ruppin, Eytan; Papin, Jason A; de Figueiredo, Luis F; Schuster, Stefan

2010-08-01

With the advent of modern omics technologies, it has become feasible to reconstruct (quasi-) whole-cell metabolic networks and characterize them in more and more detail. Computer simulations of the dynamic behavior of such networks are difficult due to a lack of kinetic data and to computational limitations. In contrast, network analysis based on appropriate constraints such as the steady-state condition (constraint-based analysis) is feasible and allows one to derive conclusions about the system's metabolic capabilities. Here, we review methods for the reconstruction of metabolic networks, modeling techniques such as flux balance analysis and elementary flux modes and current progress in their development and applications. Game-theoretical methods for studying metabolic networks are discussed as well. Copyright © 2010 Elsevier Ltd. All rights reserved.

Thermodynamic Constraints Improve Metabolic Networks.

PubMed

Krumholz, Elias W; Libourel, Igor G L

2017-08-08

In pursuit of establishing a realistic metabolic phenotypic space, the reversibility of reactions is thermodynamically constrained in modern metabolic networks. The reversibility constraints follow from heuristic thermodynamic poise approximations that take anticipated cellular metabolite concentration ranges into account. Because constraints reduce the feasible space, draft metabolic network reconstructions may need more extensive reconciliation, and a larger number of genes may become essential. Notwithstanding ubiquitous application, the effect of reversibility constraints on the predictive capabilities of metabolic networks has not been investigated in detail. Instead, work has focused on the implementation and validation of the thermodynamic poise calculation itself. With the advance of fast linear programming-based network reconciliation, the effects of reversibility constraints on network reconciliation and gene essentiality predictions have become feasible and are the subject of this study. Networks with thermodynamically informed reversibility constraints outperformed gene essentiality predictions compared to networks that were constrained with randomly shuffled constraints. Unconstrained networks predicted gene essentiality as accurately as thermodynamically constrained networks, but predicted substantially fewer essential genes. Networks that were reconciled with sequence similarity data and strongly enforced reversibility constraints outperformed all other networks. We conclude that metabolic network analysis confirmed the validity of the thermodynamic constraints, and that thermodynamic poise information is actionable during network reconciliation. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

The players may change but the game remains: network analyses of ruminal microbiomes suggest taxonomic differences mask functional similarity.

PubMed

Taxis, Tasia M; Wolff, Sara; Gregg, Sarah J; Minton, Nicholas O; Zhang, Chiqian; Dai, Jingjing; Schnabel, Robert D; Taylor, Jeremy F; Kerley, Monty S; Pires, J Chris; Lamberson, William R; Conant, Gavin C

2015-11-16

By mapping translated metagenomic reads to a microbial metabolic network, we show that ruminal ecosystems that are rather dissimilar in their taxonomy can be considerably more similar at the metabolic network level. Using a new network bi-partition approach for linking the microbial network to a bovine metabolic network, we observe that these ruminal metabolic networks exhibit properties consistent with distinct metabolic communities producing similar outputs from common inputs. For instance, the closer in network space that a microbial reaction is to a reaction found in the host, the lower will be the variability of its enzyme copy number across hosts. Similarly, these microbial enzymes that are nearby to host nodes are also higher in copy number than are more distant enzymes. Collectively, these results demonstrate a widely expected pattern that, to our knowledge, has not been explicitly demonstrated in microbial communities: namely that there can exist different community metabolic networks that have the same metabolic inputs and outputs but differ in their internal structure. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

A new network representation of the metabolism to detect chemical transformation modules.

PubMed

Sorokina, Maria; Medigue, Claudine; Vallenet, David

2015-11-14

Metabolism is generally modeled by directed networks where nodes represent reactions and/or metabolites. In order to explore metabolic pathway conservation and divergence among organisms, previous studies were based on graph alignment to find similar pathways. Few years ago, the concept of chemical transformation modules, also called reaction modules, was introduced and correspond to sequences of chemical transformations which are conserved in metabolism. We propose here a novel graph representation of the metabolic network where reactions sharing a same chemical transformation type are grouped in Reaction Molecular Signatures (RMS). RMS were automatically computed for all reactions and encode changes in atoms and bonds. A reaction network containing all available metabolic knowledge was then reduced by an aggregation of reaction nodes and edges to obtain a RMS network. Paths in this network were explored and a substantial number of conserved chemical transformation modules was detected. Furthermore, this graph-based formalism allows us to define several path scores reflecting different biological conservation meanings. These scores are significantly higher for paths corresponding to known metabolic pathways and were used conjointly to build association rules that should predict metabolic pathway types like biosynthesis or degradation. This representation of metabolism in a RMS network offers new insights to capture relevant metabolic contexts. Furthermore, along with genomic context methods, it should improve the detection of gene clusters corresponding to new metabolic pathways.

Cytoskeleton in motion: the dynamics of keratin intermediate filaments in epithelia.

PubMed

Windoffer, Reinhard; Beil, Michael; Magin, Thomas M; Leube, Rudolf E

2011-09-05

Epithelia are exposed to multiple forms of stress. Keratin intermediate filaments are abundant in epithelia and form cytoskeletal networks that contribute to cell type-specific functions, such as adhesion, migration, and metabolism. A perpetual keratin filament turnover cycle supports these functions. This multistep process keeps the cytoskeleton in motion, facilitating rapid and protein biosynthesis-independent network remodeling while maintaining an intact network. The current challenge is to unravel the molecular mechanisms underlying the regulation of the keratin cycle in relation to actin and microtubule networks and in the context of epithelial tissue function.

Cytoskeleton in motion: the dynamics of keratin intermediate filaments in epithelia

PubMed Central

Windoffer, Reinhard; Beil, Michael; Magin, Thomas M.

2011-01-01

Epithelia are exposed to multiple forms of stress. Keratin intermediate filaments are abundant in epithelia and form cytoskeletal networks that contribute to cell type–specific functions, such as adhesion, migration, and metabolism. A perpetual keratin filament turnover cycle supports these functions. This multistep process keeps the cytoskeleton in motion, facilitating rapid and protein biosynthesis–independent network remodeling while maintaining an intact network. The current challenge is to unravel the molecular mechanisms underlying the regulation of the keratin cycle in relation to actin and microtubule networks and in the context of epithelial tissue function. PMID:21893596

Controllability and observability analysis for vertex domination centrality in directed networks

NASA Astrophysics Data System (ADS)

Wang, Bingbo; Gao, Lin; Gao, Yong; Deng, Yue; Wang, Yu

2014-06-01

Topological centrality is a significant measure for characterising the relative importance of a node in a complex network. For directed networks that model dynamic processes, however, it is of more practical importance to quantify a vertex's ability to dominate (control or observe) the state of other vertices. In this paper, based on the determination of controllable and observable subspaces under the global minimum-cost condition, we introduce a novel direction-specific index, domination centrality, to assess the intervention capabilities of vertices in a directed network. Statistical studies demonstrate that the domination centrality is, to a great extent, encoded by the underlying network's degree distribution and that most network positions through which one can intervene in a system are vertices with high domination centrality rather than network hubs. To analyse the interaction and functional dependence between vertices when they are used to dominate a network, we define the domination similarity and detect significant functional modules in glossary and metabolic networks through clustering analysis. The experimental results provide strong evidence that our indices are effective and practical in accurately depicting the structure of directed networks.

Controllability and observability analysis for vertex domination centrality in directed networks

PubMed Central

Wang, Bingbo; Gao, Lin; Gao, Yong; Deng, Yue; Wang, Yu

2014-01-01

Topological centrality is a significant measure for characterising the relative importance of a node in a complex network. For directed networks that model dynamic processes, however, it is of more practical importance to quantify a vertex's ability to dominate (control or observe) the state of other vertices. In this paper, based on the determination of controllable and observable subspaces under the global minimum-cost condition, we introduce a novel direction-specific index, domination centrality, to assess the intervention capabilities of vertices in a directed network. Statistical studies demonstrate that the domination centrality is, to a great extent, encoded by the underlying network's degree distribution and that most network positions through which one can intervene in a system are vertices with high domination centrality rather than network hubs. To analyse the interaction and functional dependence between vertices when they are used to dominate a network, we define the domination similarity and detect significant functional modules in glossary and metabolic networks through clustering analysis. The experimental results provide strong evidence that our indices are effective and practical in accurately depicting the structure of directed networks. PMID:24954137

Imbalanced network biomarkers for traditional Chinese medicine Syndrome in gastritis patients

PubMed Central

Li, Rui; Ma, Tao; Gu, Jin; Liang, Xujun; Li, Shao

2013-01-01

Cold Syndrome and Hot Syndrome are thousand-year-old key therapeutic concepts in traditional Chinese medicine (TCM), which depict the loss of body homeostasis. However, the scientific basis of TCM Syndrome remains unclear due to limitations of current reductionist approaches. Here, we established a network balance model to evaluate the imbalanced network underlying TCM Syndrome and find potential biomarkers. By implementing this approach and investigating a group of chronic superficial gastritis (CSG) and chronic atrophic gastritis (CAG) patients, we found that with leptin as a biomarker, Cold Syndrome patients experience low levels of energy metabolism, while the CCL2/MCP1 biomarker indicated that immune regulation is intensified in Hot Syndrome patients. Such a metabolism-immune imbalanced network is consistent during the course from CSG to CAG. This work provides a new way to understand TCM Syndrome scientifically, which in turn benefits the personalized medicine in terms of the ancient medicine and complex biological systems. PMID:23529020

Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks

PubMed Central

Prigent, Sylvain; Frioux, Clémence; Dittami, Simon M.; Larhlimi, Abdelhalim; Collet, Guillaume; Gutknecht, Fabien; Got, Jeanne; Eveillard, Damien; Bourdon, Jérémie; Plewniak, Frédéric; Tonon, Thierry; Siegel, Anne

2017-01-01

Increasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system. PMID:28129330

Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks.

PubMed

Prigent, Sylvain; Frioux, Clémence; Dittami, Simon M; Thiele, Sven; Larhlimi, Abdelhalim; Collet, Guillaume; Gutknecht, Fabien; Got, Jeanne; Eveillard, Damien; Bourdon, Jérémie; Plewniak, Frédéric; Tonon, Thierry; Siegel, Anne

2017-01-01

Increasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system.

Functional Alignment of Metabolic Networks.

PubMed

Mazza, Arnon; Wagner, Allon; Ruppin, Eytan; Sharan, Roded

2016-05-01

Network alignment has become a standard tool in comparative biology, allowing the inference of protein function, interaction, and orthology. However, current alignment techniques are based on topological properties of networks and do not take into account their functional implications. Here we propose, for the first time, an algorithm to align two metabolic networks by taking advantage of their coupled metabolic models. These models allow us to assess the functional implications of genes or reactions, captured by the metabolic fluxes that are altered following their deletion from the network. Such implications may spread far beyond the region of the network where the gene or reaction lies. We apply our algorithm to align metabolic networks from various organisms, ranging from bacteria to humans, showing that our alignment can reveal functional orthology relations that are missed by conventional topological alignments.

Metabolic pathway resources at MaizeGDB

USDA-ARS?s Scientific Manuscript database

Two maize metabolic networks are available at MaizeGDB: MaizeCyc (http://maizecyc.maizegdb.org, also at Gramene) and CornCyc (http://corncyc.maizegdb.org, also at the Plant Metabolic Network). MaizeCyc was developed by Gramene, and CornCyc by the Plant Metabolic Network, both in collaboration with M...

Compartmentalized metabolic network reconstruction of microbial communities to determine the effect of agricultural intervention on soils

PubMed Central

Álvarez-Yela, Astrid Catalina; Gómez-Cano, Fabio; Zambrano, María Mercedes; Husserl, Johana; Danies, Giovanna; Restrepo, Silvia; González-Barrios, Andrés Fernando

2017-01-01

Soil microbial communities are responsible for a wide range of ecological processes and have an important economic impact in agriculture. Determining the metabolic processes performed by microbial communities is crucial for understanding and managing ecosystem properties. Metagenomic approaches allow the elucidation of the main metabolic processes that determine the performance of microbial communities under different environmental conditions and perturbations. Here we present the first compartmentalized metabolic reconstruction at a metagenomics scale of a microbial ecosystem. This systematic approach conceives a meta-organism without boundaries between individual organisms and allows the in silico evaluation of the effect of agricultural intervention on soils at a metagenomics level. To characterize the microbial ecosystems, topological properties, taxonomic and metabolic profiles, as well as a Flux Balance Analysis (FBA) were considered. Furthermore, topological and optimization algorithms were implemented to carry out the curation of the models, to ensure the continuity of the fluxes between the metabolic pathways, and to confirm the metabolite exchange between subcellular compartments. The proposed models provide specific information about ecosystems that are generally overlooked in non-compartmentalized or non-curated networks, like the influence of transport reactions in the metabolic processes, especially the important effect on mitochondrial processes, as well as provide more accurate results of the fluxes used to optimize the metabolic processes within the microbial community. PMID:28767679

Clinical, Dopaminergic, and Metabolic Correlations in Parkinson Disease: A Dual-Tracer PET Study.

PubMed

Liu, Feng-Tao; Ge, Jing-Jie; Wu, Jian-Jun; Wu, Ping; Ma, Yilong; Zuo, Chuan-Tao; Wang, Jian

2018-05-31

Neuroimaging indicators of Parkinson disease have been developed and applied in clinical practices. Dopaminergic imaging reflects nigrostriatal dopaminergic dysfunction, and metabolic network imaging offers disease-related metabolic changes at a system level. We aimed to elucidate the association between Parkinsonian symptoms and neuroimaging, and interactions between different imaging techniques. We conducted a dual-tracer PET study for the combined assessments of dopaminergic binding (C-CFT) and glucose metabolism (F-FDG) in 103 participants with Parkinson disease (65 male and 38 female subjects). The detailed clinical rating scores were systematically collected in all members. The interactions among dopaminergic bindings, metabolic changes, and clinical manifestations were evaluated at voxel, regional, and network levels. Striatal DAT binding correlated with akinesia-rigidity (P < 0.001) but not with tremor; the metabolic PET imaging, nonspecific to the dopaminergic dysfunction, disclosed a set of brain regions correlating with the cardinal symptoms, including tremor. In addition, the unilateral symptom correlated with the contralateral nigrostriatal dopamine loss, but with bilateral metabolic changes, suggesting their differences in the application of disease-related mechanistic studies. Further imaging-imaging correlation study revealed that dopaminergic dysfunction correlated with widely distributed metabolic changes in Parkinson disease, and the modest correlations supported the findings on the clinical-imaging correlation. In this dual-tracer PET study, we demonstrated the robust interactions among dopaminergic dysfunction, metabolic brain changes and clinical manifestations at voxel, regional, and network levels. Our findings might promote the understanding in the proper application of dopaminergic and metabolic PET imaging in Parkinson disease and offer more evidence in support of Parkinsonian pathophysiological mechanisms.This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

Dissecting Leishmania infantum Energy Metabolism - A Systems Perspective

PubMed Central

Subramanian, Abhishek; Jhawar, Jitesh; Sarkar, Ram Rup

2015-01-01

Leishmania infantum, causative agent of visceral leishmaniasis in humans, illustrates a complex lifecycle pertaining to two extreme environments, namely, the gut of the sandfly vector and human macrophages. Leishmania is capable of dynamically adapting and tactically switching between these critically hostile situations. The possible metabolic routes ventured by the parasite to achieve this exceptional adaptation to its varying environments are still poorly understood. In this study, we present an extensively reconstructed energy metabolism network of Leishmania infantum as an attempt to identify certain strategic metabolic routes preferred by the parasite to optimize its survival in such dynamic environments. The reconstructed network consists of 142 genes encoding for enzymes performing 237 reactions distributed across five distinct model compartments. We annotated the subcellular locations of different enzymes and their reactions on the basis of strong literature evidence and sequence-based detection of cellular localization signal within a protein sequence. To explore the diverse features of parasite metabolism the metabolic network was implemented and analyzed as a constraint-based model. Using a systems-based approach, we also put forth an extensive set of lethal reaction knockouts; some of which were validated using published data on Leishmania species. Performing a robustness analysis, the model was rigorously validated and tested for the secretion of overflow metabolites specific to Leishmania under varying extracellular oxygen uptake rate. Further, the fate of important non-essential amino acids in L. infantum metabolism was investigated. Stage-specific scenarios of L. infantum energy metabolism were incorporated in the model and key metabolic differences were outlined. Analysis of the model revealed the essentiality of glucose uptake, succinate fermentation, glutamate biosynthesis and an active TCA cycle as driving forces for parasite energy metabolism and its optimal growth. Finally, through our in silico knockout analysis, we could identify possible therapeutic targets that provide experimentally testable hypotheses. PMID:26367006

Comprehensive quantitative analysis of central carbon and amino-acid metabolism in Saccharomyces cerevisiae under multiple conditions by targeted proteomics

PubMed Central

Costenoble, Roeland; Picotti, Paola; Reiter, Lukas; Stallmach, Robert; Heinemann, Matthias; Sauer, Uwe; Aebersold, Ruedi

2011-01-01

Decades of biochemical research have identified most of the enzymes that catalyze metabolic reactions in the yeast Saccharomyces cerevisiae. The adaptation of metabolism to changing nutritional conditions, in contrast, is much less well understood. As an important stepping stone toward such understanding, we exploit the power of proteomics assays based on selected reaction monitoring (SRM) mass spectrometry to quantify abundance changes of the 228 proteins that constitute the central carbon and amino-acid metabolic network in the yeast Saccharomyces cerevisiae, at five different metabolic steady states. Overall, 90% of the targeted proteins, including families of isoenzymes, were consistently detected and quantified in each sample, generating a proteomic data set that represents a nutritionally perturbed biological system at high reproducibility. The data set is near comprehensive because we detect 95–99% of all proteins that are required under a given condition. Interpreted through flux balance modeling, the data indicate that S. cerevisiae retains proteins not necessarily used in a particular environment. Further, the data suggest differential functionality for several metabolic isoenzymes. PMID:21283140

Analysis of optimal phenotypic space using elementary modes as applied to Corynebacterium glutamicum

PubMed Central

Gayen, Kalyan; Venkatesh, KV

2006-01-01

Background Quantification of the metabolic network of an organism offers insights into possible ways of developing mutant strain for better productivity of an extracellular metabolite. The first step in this quantification is the enumeration of stoichiometries of all reactions occurring in a metabolic network. The structural details of the network in combination with experimentally observed accumulation rates of external metabolites can yield flux distribution at steady state. One such methodology for quantification is the use of elementary modes, which are minimal set of enzymes connecting external metabolites. Here, we have used a linear objective function subject to elementary modes as constraint to determine the fluxes in the metabolic network of Corynebacterium glutamicum. The feasible phenotypic space was evaluated at various combinations of oxygen and ammonia uptake rates. Results Quantification of the fluxes of the elementary modes in the metabolism of C. glutamicum was formulated as linear programming. The analysis demonstrated that the solution was dependent on the criteria of objective function when less than four accumulation rates of the external metabolites were considered. The analysis yielded feasible ranges of fluxes of elementary modes that satisfy the experimental accumulation rates. In C. glutamicum, the elementary modes relating to biomass synthesis through glycolysis and TCA cycle were predominantly operational in the initial growth phase. At a later time, the elementary modes contributing to lysine synthesis became active. The oxygen and ammonia uptake rates were shown to be bounded in the phenotypic space due to the stoichiometric constraint of the elementary modes. Conclusion We have demonstrated the use of elementary modes and the linear programming to quantify a metabolic network. We have used the methodology to quantify the network of C. glutamicum, which evaluates the set of operational elementary modes at different phases of fermentation. The methodology was also used to determine the feasible solution space for a given set of substrate uptake rates under specific optimization criteria. Such an approach can be used to determine the optimality of the accumulation rates of any metabolite in a given network. PMID:17038164

Inferring transcriptional gene regulation network of starch metabolism in Arabidopsis thaliana leaves using graphical Gaussian model

PubMed Central

2012-01-01

Background Starch serves as a temporal storage of carbohydrates in plant leaves during day/night cycles. To study transcriptional regulatory modules of this dynamic metabolic process, we conducted gene regulation network analysis based on small-sample inference of graphical Gaussian model (GGM). Results Time-series significant analysis was applied for Arabidopsis leaf transcriptome data to obtain a set of genes that are highly regulated under a diurnal cycle. A total of 1,480 diurnally regulated genes included 21 starch metabolic enzymes, 6 clock-associated genes, and 106 transcription factors (TF). A starch-clock-TF gene regulation network comprising 117 nodes and 266 edges was constructed by GGM from these 133 significant genes that are potentially related to the diurnal control of starch metabolism. From this network, we found that β-amylase 3 (b-amy3: At4g17090), which participates in starch degradation in chloroplast, is the most frequently connected gene (a hub gene). The robustness of gene-to-gene regulatory network was further analyzed by TF binding site prediction and by evaluating global co-expression of TFs and target starch metabolic enzymes. As a result, two TFs, indeterminate domain 5 (AtIDD5: At2g02070) and constans-like (COL: At2g21320), were identified as positive regulators of starch synthase 4 (SS4: At4g18240). The inference model of AtIDD5-dependent positive regulation of SS4 gene expression was experimentally supported by decreased SS4 mRNA accumulation in Atidd5 mutant plants during the light period of both short and long day conditions. COL was also shown to positively control SS4 mRNA accumulation. Furthermore, the knockout of AtIDD5 and COL led to deformation of chloroplast and its contained starch granules. This deformity also affected the number of starch granules per chloroplast, which increased significantly in both knockout mutant lines. Conclusions In this study, we utilized a systematic approach of microarray analysis to discover the transcriptional regulatory network of starch metabolism in Arabidopsis leaves. With this inference method, the starch regulatory network of Arabidopsis was found to be strongly associated with clock genes and TFs, of which AtIDD5 and COL were evidenced to control SS4 gene expression and starch granule formation in chloroplasts. PMID:22898356

Toward the automated generation of genome-scale metabolic networks in the SEED.

PubMed

DeJongh, Matthew; Formsma, Kevin; Boillot, Paul; Gould, John; Rycenga, Matthew; Best, Aaron

2007-04-26

Current methods for the automated generation of genome-scale metabolic networks focus on genome annotation and preliminary biochemical reaction network assembly, but do not adequately address the process of identifying and filling gaps in the reaction network, and verifying that the network is suitable for systems level analysis. Thus, current methods are only sufficient for generating draft-quality networks, and refinement of the reaction network is still largely a manual, labor-intensive process. We have developed a method for generating genome-scale metabolic networks that produces substantially complete reaction networks, suitable for systems level analysis. Our method partitions the reaction space of central and intermediary metabolism into discrete, interconnected components that can be assembled and verified in isolation from each other, and then integrated and verified at the level of their interconnectivity. We have developed a database of components that are common across organisms, and have created tools for automatically assembling appropriate components for a particular organism based on the metabolic pathways encoded in the organism's genome. This focuses manual efforts on that portion of an organism's metabolism that is not yet represented in the database. We have demonstrated the efficacy of our method by reverse-engineering and automatically regenerating the reaction network from a published genome-scale metabolic model for Staphylococcus aureus. Additionally, we have verified that our method capitalizes on the database of common reaction network components created for S. aureus, by using these components to generate substantially complete reconstructions of the reaction networks from three other published metabolic models (Escherichia coli, Helicobacter pylori, and Lactococcus lactis). We have implemented our tools and database within the SEED, an open-source software environment for comparative genome annotation and analysis. Our method sets the stage for the automated generation of substantially complete metabolic networks for over 400 complete genome sequences currently in the SEED. With each genome that is processed using our tools, the database of common components grows to cover more of the diversity of metabolic pathways. This increases the likelihood that components of reaction networks for subsequently processed genomes can be retrieved from the database, rather than assembled and verified manually.

Salmonella Modulates Metabolism during Growth under Conditions that Induce Expression of Virulence Genes

PubMed Central

Kim, Young-Mo; Schmidt, Brian J.; Kidwai, Afshan S.; Jones, Marcus B.; Deatherage Kaiser, Brooke L.; Brewer, Heather M.; Mitchell, Hugh D.; Palsson, Bernhard O.; McDermott, Jason E.; Heffron, Fred; Smith, Richard D.; Peterson, Scott N.; Ansong, Charles; Hyduke, Daniel R.; Metz, Thomas O.; Adkins, Joshua N.

2013-01-01

Salmonella enterica serovar Typhimurium (S. Typhimurium) is a facultative pathogen that uses complex mechanisms to invade and proliferate within mammalian host cells. To investigate possible contributions of metabolic processes to virulence in S. Typhimurium grown under conditions known to induce expression of virulence genes, we used a metabolomics-driven systems biology approach coupled with genome scale modeling. First, we identified distinct metabolite profiles associated with bacteria grown in either rich or virulence-inducing media and report the most comprehensive coverage of the S. Typhimurium metabolome to date. Second, we applied an omics-informed genome scale modeling analysis of the functional consequences of adaptive alterations in S. Typhimurium metabolism during growth under our conditions. Modeling efforts highlighted a decreased cellular capability to both produce and utilize intracellular amino acids during stationary phase culture in virulence conditions, despite significant abundance increases for these molecules as observed by our metabolomics measurements. Furthermore, analyses of omics data in the context of the metabolic model indicated rewiring of the metabolic network to support pathways associated with virulence. For example, cellular concentrations of polyamines were perturbed, as well as the predicted capacity for secretion and uptake. PMID:23559334

Metabolic capability and in situ activity of microorganisms in an oil reservoir.

PubMed

Liu, Yi-Fan; Galzerani, Daniela Domingos; Mbadinga, Serge Maurice; Zaramela, Livia S; Gu, Ji-Dong; Mu, Bo-Zhong; Zengler, Karsten

2018-01-05

Microorganisms have long been associated with oxic and anoxic degradation of hydrocarbons in oil reservoirs and oil production facilities. While we can readily determine the abundance of microorganisms in the reservoir and study their activity in the laboratory, it has been challenging to resolve what microbes are actively participating in crude oil degradation in situ and to gain insight into what metabolic pathways they deploy. Here, we describe the metabolic potential and in situ activity of microbial communities obtained from the Jiangsu Oil Reservoir (China) by an integrated metagenomics and metatranscriptomics approach. Almost complete genome sequences obtained by differential binning highlight the distinct capability of different community members to degrade hydrocarbons under oxic or anoxic condition. Transcriptomic data delineate active members of the community and give insights that Acinetobacter species completely oxidize alkanes into carbon dioxide with the involvement of oxygen, and Archaeoglobus species mainly ferment alkanes to generate acetate which could be consumed by Methanosaeta species. Furthermore, nutritional requirements based on amino acid and vitamin auxotrophies suggest a complex network of interactions and dependencies among active community members that go beyond classical syntrophic exchanges; this network defines community composition and microbial ecology in oil reservoirs undergoing secondary recovery. Our data expand current knowledge of the metabolic potential and role in hydrocarbon metabolism of individual members of thermophilic microbial communities from an oil reservoir. The study also reveals potential metabolic exchanges based on vitamin and amino acid auxotrophies indicating the presence of complex network of interactions between microbial taxa within the community.

Application of Petri net theory for modelling and validation of the sucrose breakdown pathway in the potato tuber.

PubMed

Koch, Ina; Junker, Björn H; Heiner, Monika

2005-04-01

Because of the complexity of metabolic networks and their regulation, formal modelling is a useful method to improve the understanding of these systems. An essential step in network modelling is to validate the network model. Petri net theory provides algorithms and methods, which can be applied directly to metabolic network modelling and analysis in order to validate the model. The metabolism between sucrose and starch in the potato tuber is of great research interest. Even if the metabolism is one of the best studied in sink organs, it is not yet fully understood. We provide an approach for model validation of metabolic networks using Petri net theory, which we demonstrate for the sucrose breakdown pathway in the potato tuber. We start with hierarchical modelling of the metabolic network as a Petri net and continue with the analysis of qualitative properties of the network. The results characterize the net structure and give insights into the complex net behaviour.

Deciphering transcriptional and metabolic networks associated with lysine metabolism during Arabidopsis seed development.

PubMed

Angelovici, Ruthie; Fait, Aaron; Zhu, Xiaohong; Szymanski, Jedrzej; Feldmesser, Ester; Fernie, Alisdair R; Galili, Gad

2009-12-01

In order to elucidate transcriptional and metabolic networks associated with lysine (Lys) metabolism, we utilized developing Arabidopsis (Arabidopsis thaliana) seeds as a system in which Lys synthesis could be stimulated developmentally without application of chemicals and coupled this to a T-DNA insertion knockout mutation impaired in Lys catabolism. This seed-specific metabolic perturbation stimulated Lys accumulation starting from the initiation of storage reserve accumulation. Our results revealed that the response of seed metabolism to the inducible alteration of Lys metabolism was relatively minor; however, that which was observable operated in a modular manner. They also demonstrated that Lys metabolism is strongly associated with the operation of the tricarboxylic acid cycle while largely disconnected from other metabolic networks. In contrast, the inducible alteration of Lys metabolism was strongly associated with gene networks, stimulating the expression of hundreds of genes controlling anabolic processes that are associated with plant performance and vigor while suppressing a small number of genes associated with plant stress interactions. The most pronounced effect of the developmentally inducible alteration of Lys metabolism was an induction of expression of a large set of genes encoding ribosomal proteins as well as genes encoding translation initiation and elongation factors, all of which are associated with protein synthesis. With respect to metabolic regulation, the inducible alteration of Lys metabolism was primarily associated with altered expression of genes belonging to networks of amino acids and sugar metabolism. The combined data are discussed within the context of network interactions both between and within metabolic and transcriptional control systems.

Multi-omic network-based interrogation of rat liver metabolism following gastric bypass surgery featuring SWATH proteomics.

PubMed

Sridharan, Gautham Vivek; D'Alessandro, Matthew; Bale, Shyam Sundhar; Bhagat, Vicky; Gagnon, Hugo; Asara, John M; Uygun, Korkut; Yarmush, Martin L; Saeidi, Nima

2017-09-01

Morbidly obese patients often elect for Roux-en-Y gastric bypass (RYGB), a form of bariatric surgery that triggers a remarkable 30% reduction in excess body weight and reversal of insulin resistance for those who are type II diabetic. A more complete understanding of the underlying molecular mechanisms that drive the complex metabolic reprogramming post-RYGB could lead to innovative non-invasive therapeutics that mimic the beneficial effects of the surgery, namely weight loss, achievement of glycemic control, or reversal of non-alcoholic steatohepatitis (NASH). To facilitate these discoveries, we hereby demonstrate the first multi-omic interrogation of a rodent RYGB model to reveal tissue-specific pathway modules implicated in the control of body weight regulation and energy homeostasis. In this study, we focus on and evaluate liver metabolism three months following RYGB in rats using both SWATH proteomics, a burgeoning label free approach using high resolution mass spectrometry to quantify protein levels in biological samples, as well as MRM metabolomics. The SWATH analysis enabled the quantification of 1378 proteins in liver tissue extracts, of which we report the significant down-regulation of Thrsp and Acot13 in RYGB as putative targets of lipid metabolism for weight loss. Furthermore, we develop a computational graph-based metabolic network module detection algorithm for the discovery of non-canonical pathways, or sub-networks, enriched with significantly elevated or depleted metabolites and proteins in RYGB-treated rat livers. The analysis revealed a network connection between the depleted protein Baat and the depleted metabolite taurine, corroborating the clinical observation that taurine-conjugated bile acid levels are perturbed post-RYGB.

Investigating host-pathogen behavior and their interaction using genome-scale metabolic network models.

PubMed

Sadhukhan, Priyanka P; Raghunathan, Anu

2014-01-01

Genome Scale Metabolic Modeling methods represent one way to compute whole cell function starting from the genome sequence of an organism and contribute towards understanding and predicting the genotype-phenotype relationship. About 80 models spanning all the kingdoms of life from archaea to eukaryotes have been built till date and used to interrogate cell phenotype under varying conditions. These models have been used to not only understand the flux distribution in evolutionary conserved pathways like glycolysis and the Krebs cycle but also in applications ranging from value added product formation in Escherichia coli to predicting inborn errors of Homo sapiens metabolism. This chapter describes a protocol that delineates the process of genome scale metabolic modeling for analysing host-pathogen behavior and interaction using flux balance analysis (FBA). The steps discussed in the process include (1) reconstruction of a metabolic network from the genome sequence, (2) its representation in a precise mathematical framework, (3) its translation to a model, and (4) the analysis using linear algebra and optimization. The methods for biological interpretations of computed cell phenotypes in the context of individual host and pathogen models and their integration are also discussed.

Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation.

PubMed

Chaiboonchoe, Amphun; Ghamsari, Lila; Dohai, Bushra; Ng, Patrick; Khraiwesh, Basel; Jaiswal, Ashish; Jijakli, Kenan; Koussa, Joseph; Nelson, David R; Cai, Hong; Yang, Xinping; Chang, Roger L; Papin, Jason; Yu, Haiyuan; Balaji, Santhanam; Salehi-Ashtiani, Kourosh

2016-07-19

Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii, a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolic network that we have reconstructed for C. reinhardtii, we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii's network in dealing with varied environmental challenges that the species may face. The defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.

On the feasibility of growth-coupled product synthesis in microbial strains.

PubMed

Klamt, Steffen; Mahadevan, Radhakrishnan

2015-07-01

Enforcing obligate coupling of growth with synthesis of a desired product has become a key principle for metabolic engineering of microbial production strains. Various methods from stoichiometric and constraint-based modeling have been developed to calculate intervention strategies by which a given microorganism can only grow if it synthesizes a desired compound as a mandatory by-product. However, growth-coupled synthesis is not necessarily feasible for every compound of a metabolic network and no rigorous criterion is currently known to test feasibility of coupled product and biomass formation (before searching for suitable intervention strategies). In this work, we show which properties a network must fulfill such that strain designs guaranteeing coupled biomass and product synthesis can exist at all. In networks without flux bounds, coupling is feasible if and only if an elementary mode exists that leads to formation of both biomass and product. Setting flux boundaries leads to more complicated inhomogeneous problems. Making use of the concept of elementary (flux) vectors, a generalization of elementary modes, a criterion for feasibility can also be derived for this situation. We applied our criteria to a metabolic model of Escherichia coli and determined for each metabolite, whether its net production can be coupled with biomass synthesis and calculated the maximal (guaranteed) coupling yield. The somewhat surprising result is that, under aerobic conditions, coupling is indeed possible for each carbon metabolite of the central metabolism. This also holds true for most metabolites under anaerobic conditions but consideration of ATP maintenance requirements implies infeasibility of coupling for certain compounds. On the other hand, ATP maintenance may also increase the maximal coupling yield for some metabolites. Overall, our work provides important insights and novel tools for a central problem of computational strain design. Copyright © 2015 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Fluxes through plant metabolic networks: measurements, predictions, insights and challenges.

PubMed

Kruger, Nicholas J; Ratcliffe, R George

2015-01-01

Although the flows of material through metabolic networks are central to cell function, they are not easy to measure other than at the level of inputs and outputs. This is particularly true in plant cells, where the network spans multiple subcellular compartments and where the network may function either heterotrophically or photoautotrophically. For many years, kinetic modelling of pathways provided the only method for describing the operation of fragments of the network. However, more recently, it has become possible to map the fluxes in central carbon metabolism using the stable isotope labelling techniques of metabolic flux analysis (MFA), and to predict intracellular fluxes using constraints-based modelling procedures such as flux balance analysis (FBA). These approaches were originally developed for the analysis of microbial metabolism, but over the last decade, they have been adapted for the more demanding analysis of plant metabolic networks. Here, the principal features of MFA and FBA as applied to plants are outlined, followed by a discussion of the insights that have been gained into plant metabolic networks through the application of these time-consuming and non-trivial methods. The discussion focuses on how a system-wide view of plant metabolism has increased our understanding of network structure, metabolic perturbations and the provision of reducing power and energy for cell function. Current methodological challenges that limit the scope of plant MFA are discussed and particular emphasis is placed on the importance of developing methods for cell-specific MFA.

Meta-review of protein network regulating obesity between validated obesity candidate genes in the white adipose tissue of high-fat diet-induced obese C57BL/6J mice.

PubMed

Kim, Eunjung; Kim, Eun Jung; Seo, Seung-Won; Hur, Cheol-Goo; McGregor, Robin A; Choi, Myung-Sook

2014-01-01

Worldwide obesity and related comorbidities are increasing, but identifying new therapeutic targets remains a challenge. A plethora of microarray studies in diet-induced obesity models has provided large datasets of obesity associated genes. In this review, we describe an approach to examine the underlying molecular network regulating obesity, and we discuss interactions between obesity candidate genes. We conducted network analysis on functional protein-protein interactions associated with 25 obesity candidate genes identified in a literature-driven approach based on published microarray studies of diet-induced obesity. The obesity candidate genes were closely associated with lipid metabolism and inflammation. Peroxisome proliferator activated receptor gamma (Pparg) appeared to be a core obesity gene, and obesity candidate genes were highly interconnected, suggesting a coordinately regulated molecular network in adipose tissue. In conclusion, the current network analysis approach may help elucidate the underlying molecular network regulating obesity and identify anti-obesity targets for therapeutic intervention.

Metabolic connectomics targeting brain pathology in dementia with Lewy bodies

PubMed Central

Caminiti, Silvia P; Tettamanti, Marco; Sala, Arianna; Presotto, Luca; Iannaccone, Sandro; Cappa, Stefano F; Magnani, Giuseppe

2016-01-01

Dementia with Lewy bodies is characterized by α-synuclein accumulation and degeneration of dopaminergic and cholinergic pathways. To gain an overview of brain systems affected by neurodegeneration, we characterized the [18F]FDG-PET metabolic connectivity in 42 dementia with Lewy bodies patients, as compared to 42 healthy controls, using sparse inverse covariance estimation method and graph theory. We performed whole-brain and anatomically driven analyses, targeting cholinergic and dopaminergic pathways, and the α-synuclein spreading. The first revealed substantial alterations in connectivity indexes, brain modularity, and hubs configuration. Namely, decreases in local metabolic connectivity within occipital cortex, thalamus, and cerebellum, and increases within frontal, temporal, parietal, and basal ganglia regions. There were also long-range disconnections among these brain regions, all supporting a disruption of the functional hierarchy characterizing the normal brain. The anatomically driven analysis revealed alterations within brain structures early affected by α-synuclein pathology, supporting Braak’s early pathological staging in dementia with Lewy bodies. The dopaminergic striato-cortical pathway was severely affected, as well as the cholinergic networks, with an extensive decrease in connectivity in Ch1-Ch2, Ch5-Ch6 networks, and the lateral Ch4 capsular network significantly towards the occipital cortex. These altered patterns of metabolic connectivity unveil a new in vivo scenario for dementia with Lewy bodies underlying pathology in terms of changes in whole-brain metabolic connectivity, spreading of α-synuclein, and neurotransmission impairment. PMID:27306756

MicroRNA Regulators of Anxiety and Metabolic Disorders.

PubMed

Meydan, Chanan; Shenhar-Tsarfaty, Shani; Soreq, Hermona

2016-09-01

Anxiety-related and metabolic disorders are under intense research focus. Anxiety-induced microRNAs (miRNAs) are emerging as regulators that are not only capable of suppressing inflammation but can also induce metabolic syndrome-related processes. We summarize here evidence linking miRNA pathways which share regulatory networks in metabolic and anxiety-related conditions. In particular, miRNAs involved in these disorders include regulators of acetylcholine signaling in the nervous system and their accompanying molecular machinery. These have been associated with anxiety-prone states in individuals, while also acting as inflammatory suppressors. In peripheral tissues, altered miRNA pathways can lead to dysregulated metabolism. Common pathways in metabolic and anxiety-related phenomena might offer an opportunity to reclassify 'healthy' and 'unhealthy', as well as metabolic and anxiety-prone biological states, and inform putative strategies to treat these disorders. Copyright © 2016 Elsevier Ltd. All rights reserved.

Genetic tools for the investigation of Roseobacter clade bacteria

PubMed Central

2009-01-01

Background The Roseobacter clade represents one of the most abundant, metabolically versatile and ecologically important bacterial groups found in marine habitats. A detailed molecular investigation of the regulatory and metabolic networks of these organisms is currently limited for many strains by missing suitable genetic tools. Results Conjugation and electroporation methods for the efficient and stable genetic transformation of selected Roseobacter clade bacteria including Dinoroseobacter shibae, Oceanibulbus indolifex, Phaeobacter gallaeciensis, Phaeobacter inhibens, Roseobacter denitrificans and Roseobacter litoralis were tested. For this purpose an antibiotic resistance screening was performed and suitable genetic markers were selected. Based on these transformation protocols stably maintained plasmids were identified. A plasmid encoded oxygen-independent fluorescent system was established using the flavin mononucleotide-based fluorescent protein FbFP. Finally, a chromosomal gene knockout strategy was successfully employed for the inactivation of the anaerobic metabolism regulatory gene dnr from D. shibae DFL12T. Conclusion A genetic toolbox for members of the Roseobacter clade was established. This provides a solid methodical basis for the detailed elucidation of gene regulatory and metabolic networks underlying the ecological success of this group of marine bacteria. PMID:20021642

An integrated approach to uncover quality marker underlying the effects of Alisma orientale on lipid metabolism, using chemical analysis and network pharmacology.

PubMed

Liao, Maoliang; Shang, Haihua; Li, Yazhuo; Li, Tian; Wang, Miao; Zheng, Yanan; Hou, Wenbin; Liu, Changxiao

2018-06-01

Quality control of traditional Chinese medicines is currently a great concern, due to the correlation between the quality control indicators and clinic effect is often questionable. According to the "multi-components and multi-targets" property of TCMs, a new special quality and bioactivity evaluation system is urgently needed. Present study adopted an integrated approach to provide new insights relating to uncover quality marker underlying the effects of Alisma orientale (AO) on lipid metabolism. In this paper, guided by the concept of the quality marker (Q-marker), an integrated strategies "effect-compound-target-fingerprint" was established to discovery and screen the potential quality marker of AO based on network pharmacology and chemical analysis. Firstly, a bioactivity evaluation was performed to screen the main active fractions. Then the chemical compositions were rapidly identified by chemical analysis. Next, networks were constructed to illuminate the interactions between these component and their targets for lipid metabolism, and the potential Q-marker of AO was initially screened. Finally, the activity of the Q-markers was validated in vitro. 50% ethanol extract fraction was found to have the strongest lipid-lowering activity. Then, the network pharmacology was used to clarify the unique relationship between the Q-markers and their integral pharmacological action. Combined with the results obtained, five active ingredients in the 50% ethanol extract fraction were given special considerations to be representative Q-markers: Alisol A, Alisol B, Alisol A 23-acetate, Alisol B 23-acetate and Alisol A 24-acetate, respectively. The chromatographic fingerprints based Q-marker was establishment. The integrated Q-marker screen may offer an alternative quality assessment of herbal medicines. Copyright © 2018. Published by Elsevier GmbH.

Patterns of Metabolite Changes Identified from Large-Scale Gene Perturbations in Arabidopsis Using a Genome-Scale Metabolic Network1[OPEN

PubMed Central

Kim, Taehyong; Dreher, Kate; Nilo-Poyanco, Ricardo; Lee, Insuk; Fiehn, Oliver; Lange, Bernd Markus; Nikolau, Basil J.; Sumner, Lloyd; Welti, Ruth; Wurtele, Eve S.; Rhee, Seung Y.

2015-01-01

Metabolomics enables quantitative evaluation of metabolic changes caused by genetic or environmental perturbations. However, little is known about how perturbing a single gene changes the metabolic system as a whole and which network and functional properties are involved in this response. To answer this question, we investigated the metabolite profiles from 136 mutants with single gene perturbations of functionally diverse Arabidopsis (Arabidopsis thaliana) genes. Fewer than 10 metabolites were changed significantly relative to the wild type in most of the mutants, indicating that the metabolic network was robust to perturbations of single metabolic genes. These changed metabolites were closer to each other in a genome-scale metabolic network than expected by chance, supporting the notion that the genetic perturbations changed the network more locally than globally. Surprisingly, the changed metabolites were close to the perturbed reactions in only 30% of the mutants of the well-characterized genes. To determine the factors that contributed to the distance between the observed metabolic changes and the perturbation site in the network, we examined nine network and functional properties of the perturbed genes. Only the isozyme number affected the distance between the perturbed reactions and changed metabolites. This study revealed patterns of metabolic changes from large-scale gene perturbations and relationships between characteristics of the perturbed genes and metabolic changes. PMID:25670818

The investigation of anti-inflammatory activity of Yi Guanjian decoction by serum metabonomics approach.

PubMed

Shui, Sufang; Cai, Xiaorong; Huang, Rongqing; Xiao, Bingkun; Yang, Jianyun

2017-01-30

Yi Guanjian (YGJ), one of the Chinese herbal medicines most commonly used in western countries, reported to possess significant anti-inflammatary effects that inhibit the process of inflammation. However, the mechanisms underlying its anti-inflammation effects remain largely unresolved. This study was aimed to investigate the anti-inflammatory activity of YGJ and to explore its potential anti-inflammatory mechanisms by serum metabonomics approach. An xylene-induced mouse right-ear-edema model was used as an inflammatory response in vivo model. Ear edema, prostaglandin E2 (PGE 2 ) and Tumor-Necrosis-Factor-alpha (TNF-α) were detected. Then, serum metabolic profiling was analyzed and pathway analysis performed on the biomarkers reversed after YGJ administration and further integration of metabolic networks. The results showed that YGJ alleviated ear edema and decreased serum PGE 2 and TNF-α levels. Fourteen biomarkers were screened, and the levels were all reversed to different degrees after YGJ administration. These biomarkers were mainly related to linoleic acid metabolism, taurine and hypotaurine metabolism, glyoxylate and dicarboxylate metabolism, glycine, serine and threonine metabolism and citrate cycle (TCA cycle). In metabolic networks, glycine and pyruvate were node molecules. This indicated that YGJ could significantly inhibit inflammatory response triggered by acute local stimulation and exerted anti-inflammatory activity mainly by regulating node molecules. Copyright © 2016 Elsevier B.V. All rights reserved.

Metabolism as a Target for Modulation in Autoimmune Diseases.

PubMed

Huang, Nick; Perl, Andras

2018-05-05

Metabolic pathways are now well recognized as important regulators of immune differentiation and activation, and thus influence the development of autoimmune diseases such as systemic lupus erythematosus (SLE). The mechanistic target of rapamycin (mTOR) has emerged as a key sensor of metabolic stress and an important mediator of proinflammatory lineage specification. Metabolic pathways control the production of mitochondrial reactive oxygen species (ROS), which promote mTOR activation and also modulate the antigenicity of proteins, lipids, and DNA, thus placing ROS at the heart of metabolic disturbances during pathogenesis of SLE. Therefore, we review here the pathways that control ROS production and mTOR activation and identify targets for safe therapeutic modulation of the signaling network that underlies autoimmune diseases, focusing on SLE. Copyright © 2018. Published by Elsevier Ltd.

Decomposition of metabolic network into functional modules based on the global connectivity structure of reaction graph.

PubMed

Ma, Hong-Wu; Zhao, Xue-Ming; Yuan, Ying-Jin; Zeng, An-Ping

2004-08-12

Metabolic networks are organized in a modular, hierarchical manner. Methods for a rational decomposition of the metabolic network into relatively independent functional subsets are essential to better understand the modularity and organization principle of a large-scale, genome-wide network. Network decomposition is also necessary for functional analysis of metabolism by pathway analysis methods that are often hampered by the problem of combinatorial explosion due to the complexity of metabolic network. Decomposition methods proposed in literature are mainly based on the connection degree of metabolites. To obtain a more reasonable decomposition, the global connectivity structure of metabolic networks should be taken into account. In this work, we use a reaction graph representation of a metabolic network for the identification of its global connectivity structure and for decomposition. A bow-tie connectivity structure similar to that previously discovered for metabolite graph is found also to exist in the reaction graph. Based on this bow-tie structure, a new decomposition method is proposed, which uses a distance definition derived from the path length between two reactions. An hierarchical classification tree is first constructed from the distance matrix among the reactions in the giant strong component of the bow-tie structure. These reactions are then grouped into different subsets based on the hierarchical tree. Reactions in the IN and OUT subsets of the bow-tie structure are subsequently placed in the corresponding subsets according to a 'majority rule'. Compared with the decomposition methods proposed in literature, ours is based on combined properties of the global network structure and local reaction connectivity rather than, primarily, on the connection degree of metabolites. The method is applied to decompose the metabolic network of Escherichia coli. Eleven subsets are obtained. More detailed investigations of the subsets show that reactions in the same subset are really functionally related. The rational decomposition of metabolic networks, and subsequent studies of the subsets, make it more amenable to understand the inherent organization and functionality of metabolic networks at the modular level. http://genome.gbf.de/bioinformatics/

Transcriptome analysis reveals regulatory networks underlying differential susceptibility to Botrytis cinerea in response to nitrogen availability in Solanum lycopersicum

PubMed Central

Vega, Andrea; Canessa, Paulo; Hoppe, Gustavo; Retamal, Ignacio; Moyano, Tomas C.; Canales, Javier; Gutiérrez, Rodrigo A.; Rubilar, Joselyn

2015-01-01

Nitrogen (N) is one of the main limiting nutrients for plant growth and crop yield. It is well documented that changes in nitrate availability, the main N source found in agricultural soils, influences a myriad of developmental programs and processes including the plant defense response. Indeed, many agronomical reports indicate that the plant N nutritional status influences their ability to respond effectively when challenged by different pathogens. However, the molecular mechanisms involved in N-modulation of plant susceptibility to pathogens are poorly characterized. In this work, we show that Solanum lycopersicum defense response to the necrotrophic fungus Botrytis cinerea is affected by plant N availability, with higher susceptibility in nitrate-limiting conditions. Global gene expression responses of tomato against B. cinerea under contrasting nitrate conditions reveals that plant primary metabolism is affected by the fungal infection regardless of N regimes. This result suggests that differential susceptibility to pathogen attack under contrasting N conditions is not only explained by a metabolic alteration. We used a systems biology approach to identify the transcriptional regulatory network implicated in plant response to the fungus infection under contrasting nitrate conditions. Interestingly, hub genes in this network are known key transcription factors involved in ethylene and jasmonic acid signaling. This result positions these hormones as key integrators of nitrate and defense against B. cinerea in tomato plants. Our results provide insights into potential crosstalk mechanisms between necrotrophic defense response and N status in plants. PMID:26583019

A Systematic Analysis of Candidate Genes Associated with Nicotine Addiction

PubMed Central

Liu, Meng; Li, Xia; Fan, Rui; Liu, Xinhua; Wang, Ju

2015-01-01

Nicotine, as the major psychoactive component of tobacco, has broad physiological effects within the central nervous system, but our understanding of the molecular mechanism underlying its neuronal effects remains incomplete. In this study, we performed a systematic analysis on a set of nicotine addiction-related genes to explore their characteristics at network levels. We found that NAGenes tended to have a more moderate degree and weaker clustering coefficient and to be less central in the network compared to alcohol addiction-related genes or cancer genes. Further, clustering of these genes resulted in six clusters with themes in synaptic transmission, signal transduction, metabolic process, and apoptosis, which provided an intuitional view on the major molecular functions of the genes. Moreover, functional enrichment analysis revealed that neurodevelopment, neurotransmission activity, and metabolism related biological processes were involved in nicotine addiction. In summary, by analyzing the overall characteristics of the nicotine addiction related genes, this study provided valuable information for understanding the molecular mechanisms underlying nicotine addiction. PMID:26097843

Elucidating the role of copper in CHO cell energy metabolism using (13)C metabolic flux analysis.

PubMed

Nargund, Shilpa; Qiu, Jinshu; Goudar, Chetan T

2015-01-01

(13)C-metabolic flux analysis was used to understand copper deficiency-related restructuring of energy metabolism, which leads to excessive lactate production in recombinant protein-producing CHO cells. Stationary-phase labeling experiments with U-(13)C glucose were conducted on CHO cells grown under high and limiting copper in 3 L fed-batch bioreactors. The resultant labeling patterns of soluble metabolites were measured by GC-MS and used to estimate metabolic fluxes in the central carbon metabolism pathways using OpenFlux. Fluxes were evaluated 300 times from stoichiometrically feasible random guess values and their confidence intervals calculated by Monte Carlo simulations. Results from metabolic flux analysis exhibited significant carbon redistribution throughout the metabolic network in cells under Cu deficiency. Specifically, glycolytic fluxes increased (25%-79% relative to glucose uptake) whereas fluxes through the TCA and pentose phosphate pathway (PPP) were lower (15%-23% and 74%, respectively) compared with the Cu-containing condition. Furthermore, under Cu deficiency, 33% of the flux entering TCA via the pyruvate node was redirected to lactate and malate production. Based on these results, we hypothesize that Cu deficiency disrupts the electron transport chain causing ATP deficiency, redox imbalance, and oxidative stress, which in turn drive copper-deficient CHO cells to produce energy via aerobic glycolysis, which is associated with excessive lactate production, rather than the more efficient route of oxidative phosphorylation. © 2015 American Institute of Chemical Engineers.

A Computational Solution to Automatically Map Metabolite Libraries in the Context of Genome Scale Metabolic Networks.

PubMed

Merlet, Benjamin; Paulhe, Nils; Vinson, Florence; Frainay, Clément; Chazalviel, Maxime; Poupin, Nathalie; Gloaguen, Yoann; Giacomoni, Franck; Jourdan, Fabien

2016-01-01

This article describes a generic programmatic method for mapping chemical compound libraries on organism-specific metabolic networks from various databases (KEGG, BioCyc) and flat file formats (SBML and Matlab files). We show how this pipeline was successfully applied to decipher the coverage of chemical libraries set up by two metabolomics facilities MetaboHub (French National infrastructure for metabolomics and fluxomics) and Glasgow Polyomics (GP) on the metabolic networks available in the MetExplore web server. The present generic protocol is designed to formalize and reduce the volume of information transfer between the library and the network database. Matching of metabolites between libraries and metabolic networks is based on InChIs or InChIKeys and therefore requires that these identifiers are specified in both libraries and networks. In addition to providing covering statistics, this pipeline also allows the visualization of mapping results in the context of metabolic networks. In order to achieve this goal, we tackled issues on programmatic interaction between two servers, improvement of metabolite annotation in metabolic networks and automatic loading of a mapping in genome scale metabolic network analysis tool MetExplore. It is important to note that this mapping can also be performed on a single or a selection of organisms of interest and is thus not limited to large facilities.

Artificial intelligence techniques for colorectal cancer drug metabolism: ontology and complex network.

PubMed

Martínez-Romero, Marcos; Vázquez-Naya, José M; Rabuñal, Juan R; Pita-Fernández, Salvador; Macenlle, Ramiro; Castro-Alvariño, Javier; López-Roses, Leopoldo; Ulla, José L; Martínez-Calvo, Antonio V; Vázquez, Santiago; Pereira, Javier; Porto-Pazos, Ana B; Dorado, Julián; Pazos, Alejandro; Munteanu, Cristian R

2010-05-01

Colorectal cancer is one of the most frequent types of cancer in the world and generates important social impact. The understanding of the specific metabolism of this disease and the transformations of the specific drugs will allow finding effective prevention, diagnosis and treatment of the colorectal cancer. All the terms that describe the drug metabolism contribute to the construction of ontology in order to help scientists to link the correlated information and to find the most useful data about this topic. The molecular components involved in this metabolism are included in complex network such as metabolic pathways in order to describe all the molecular interactions in the colorectal cancer. The graphical method of processing biological information such as graphs and complex networks leads to the numerical characterization of the colorectal cancer drug metabolic network by using invariant values named topological indices. Thus, this method can help scientists to study the most important elements in the metabolic pathways and the dynamics of the networks during mutations, denaturation or evolution for any type of disease. This review presents the last studies regarding ontology and complex networks of the colorectal cancer drug metabolism and a basic topology characterization of the drug metabolic process sub-ontology from the Gene Ontology.

Microbial Community Metabolic Modeling: A Community Data-Driven Network Reconstruction: COMMUNITY DATA-DRIVEN METABOLIC NETWORK MODELING

DOE Office of Scientific and Technical Information (OSTI.GOV)

Henry, Christopher S.; Bernstein, Hans C.; Weisenhorn, Pamela

Metabolic network modeling of microbial communities provides an in-depth understanding of community-wide metabolic and regulatory processes. Compared to single organism analyses, community metabolic network modeling is more complex because it needs to account for interspecies interactions. To date, most approaches focus on reconstruction of high-quality individual networks so that, when combined, they can predict community behaviors as a result of interspecies interactions. However, this conventional method becomes ineffective for communities whose members are not well characterized and cannot be experimentally interrogated in isolation. Here, we tested a new approach that uses community-level data as a critical input for the networkmore » reconstruction process. This method focuses on directly predicting interspecies metabolic interactions in a community, when axenic information is insufficient. We validated our method through the case study of a bacterial photoautotroph-heterotroph consortium that was used to provide data needed for a community-level metabolic network reconstruction. Resulting simulations provided experimentally validated predictions of how a photoautotrophic cyanobacterium supports the growth of an obligate heterotrophic species by providing organic carbon and nitrogen sources.« less

FindPrimaryPairs: An efficient algorithm for predicting element-transferring reactant/product pairs in metabolic networks.

PubMed

Steffensen, Jon Lund; Dufault-Thompson, Keith; Zhang, Ying

2018-01-01

The metabolism of individual organisms and biological communities can be viewed as a network of metabolites connected to each other through chemical reactions. In metabolic networks, chemical reactions transform reactants into products, thereby transferring elements between these metabolites. Knowledge of how elements are transferred through reactant/product pairs allows for the identification of primary compound connections through a metabolic network. However, such information is not readily available and is often challenging to obtain for large reaction databases or genome-scale metabolic models. In this study, a new algorithm was developed for automatically predicting the element-transferring reactant/product pairs using the limited information available in the standard representation of metabolic networks. The algorithm demonstrated high efficiency in analyzing large datasets and provided accurate predictions when benchmarked with manually curated data. Applying the algorithm to the visualization of metabolic networks highlighted pathways of primary reactant/product connections and provided an organized view of element-transferring biochemical transformations. The algorithm was implemented as a new function in the open source software package PSAMM in the release v0.30 (https://zhanglab.github.io/psamm/).

Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast.

PubMed

Wang, Zhuo; Danziger, Samuel A; Heavner, Benjamin D; Ma, Shuyi; Smith, Jennifer J; Li, Song; Herricks, Thurston; Simeonidis, Evangelos; Baliga, Nitin S; Aitchison, John D; Price, Nathan D

2017-05-01

Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAM's enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.

Reactivity landscape of pyruvate under simulated hydrothermal vent conditions

PubMed Central

Novikov, Yehor; Copley, Shelley D.

2013-01-01

Pyruvate is an important “hub” metabolite that is a precursor for amino acids, sugars, cofactors, and lipids in extant metabolic networks. Pyruvate has been produced under simulated hydrothermal vent conditions from alkyl thiols and carbon monoxide in the presence of transition metal sulfides at 250 °C [Cody GD et al. (2000) Science 289(5483):1337–1340], so it is plausible that pyruvate was formed in hydrothermal systems on the early earth. We report here that pyruvate reacts readily in the presence of transition metal sulfide minerals under simulated hydrothermal vent fluids at more moderate temperatures (25–110 °C) that are more conducive to survival of biogenic molecules. We found that pyruvate partitions among five reaction pathways at rates that depend upon the nature of the mineral present; the concentrations of H2S, H2, and NH4Cl; and the temperature. In most cases, high yields of one or two primary products are found due to preferential acceleration of certain pathways. Reactions observed include reduction of ketones to alcohols and aldol condensation, both reactions that are common in extant metabolic networks. We also observed reductive amination to form alanine and reduction to form propionic acid. Amino acids and fatty acids formed by analogous processes may have been important components of a protometabolic network that allowed the emergence of life. PMID:23872841

Mitochondrial biogenesis and energy production in differentiating murine stem cells: a functional metabolic study.

PubMed

Han, Sungwon; Auger, Christopher; Thomas, Sean C; Beites, Crestina L; Appanna, Vasu D

2014-02-01

The significance of metabolic networks in guiding the fate of the stem cell differentiation is only beginning to emerge. Oxidative metabolism has been suggested to play a major role during this process. Therefore, it is critical to understand the underlying mechanisms of metabolic alterations occurring in stem cells to manipulate the ultimate outcome of these pluripotent cells. Here, using P19 murine embryonal carcinoma cells as a model system, the role of mitochondrial biogenesis and the modulation of metabolic networks during dimethyl sulfoxide (DMSO)-induced differentiation are revealed. Blue native polyacrylamide gel electrophoresis (BN-PAGE) technology aided in profiling key enzymes, such as hexokinase (HK) [EC 2.7.1.1], glucose-6-phosphate isomerase (GPI) [EC 5.3.1.9], pyruvate kinase (PK) [EC 2.7.1.40], Complex I [EC 1.6.5.3], and Complex IV [EC 1.9.3.1], that are involved in the energy budget of the differentiated cells. Mitochondrial adenosine triphosphate (ATP) production was shown to be increased in DMSO-treated cells upon exposure to the tricarboxylic acid (TCA) cycle substrates, such as succinate and malate. The increased mitochondrial activity and biogenesis were further confirmed by immunofluorescence microscopy. Collectively, the results indicate that oxidative energy metabolism and mitochondrial biogenesis were sharply upregulated in DMSO-differentiated P19 cells. This functional metabolic and proteomic study provides further evidence that modulation of mitochondrial energy metabolism is a pivotal component of the cellular differentiation process and may dictate the final destiny of stem cells.

Natural selection drove metabolic specialization of the chromatophore in Paulinella chromatophora.

PubMed

Valadez-Cano, Cecilio; Olivares-Hernández, Roberto; Resendis-Antonio, Osbaldo; DeLuna, Alexander; Delaye, Luis

2017-04-14

Genome degradation of host-restricted mutualistic endosymbionts has been attributed to inactivating mutations and genetic drift while genes coding for host-relevant functions are conserved by purifying selection. Unlike their free-living relatives, the metabolism of mutualistic endosymbionts and endosymbiont-originated organelles is specialized in the production of metabolites which are released to the host. This specialization suggests that natural selection crafted these metabolic adaptations. In this work, we analyzed the evolution of the metabolism of the chromatophore of Paulinella chromatophora by in silico modeling. We asked whether genome reduction is driven by metabolic engineering strategies resulted from the interaction with the host. As its widely known, the loss of enzyme coding genes leads to metabolic network restructuring sometimes improving the production rates. In this case, the production rate of reduced-carbon in the metabolism of the chromatophore. We reconstructed the metabolic networks of the chromatophore of P. chromatophora CCAC 0185 and a close free-living relative, the cyanobacterium Synechococcus sp. WH 5701. We found that the evolution of free-living to host-restricted lifestyle rendered a fragile metabolic network where >80% of genes in the chromatophore are essential for metabolic functionality. Despite the lack of experimental information, the metabolic reconstruction of the chromatophore suggests that the host provides several metabolites to the endosymbiont. By using these metabolites as intracellular conditions, in silico simulations of genome evolution by gene lose recover with 77% accuracy the actual metabolic gene content of the chromatophore. Also, the metabolic model of the chromatophore allowed us to predict by flux balance analysis a maximum rate of reduced-carbon released by the endosymbiont to the host. By inspecting the central metabolism of the chromatophore and the free-living cyanobacteria we found that by improvements in the gluconeogenic pathway the metabolism of the endosymbiont uses more efficiently the carbon source for reduced-carbon production. In addition, our in silico simulations of the evolutionary process leading to the reduced metabolic network of the chromatophore showed that the predicted rate of released reduced-carbon is obtained in less than 5% of the times under a process guided by random gene deletion and genetic drift. We interpret previous findings as evidence that natural selection at holobiont level shaped the rate at which reduced-carbon is exported to the host. Finally, our model also predicts that the ABC phosphate transporter (pstSACB) which is conserved in the genome of the chromatophore of P. chromatophora strain CCAC 0185 is a necessary component to release reduced-carbon molecules to the host. Our evolutionary analysis suggests that in the case of Paulinella chromatophora natural selection at the holobiont level played a prominent role in shaping the metabolic specialization of the chromatophore. We propose that natural selection acted as a "metabolic engineer" by favoring metabolic restructurings that led to an increased release of reduced-carbon to the host.

Flux balance analysis of primary metabolism in Chlamydomonas reinhardtii.

PubMed

Boyle, Nanette R; Morgan, John A

2009-01-07

Photosynthetic organisms convert atmospheric carbon dioxide into numerous metabolites along the pathways to make new biomass. Aquatic photosynthetic organisms, which fix almost half of global inorganic carbon, have great potential: as a carbon dioxide fixation method, for the economical production of chemicals, or as a source for lipids and starch which can then be converted to biofuels. To harness this potential through metabolic engineering and to maximize production, a more thorough understanding of photosynthetic metabolism must first be achieved. A model algal species, C. reinhardtii, was chosen and the metabolic network reconstructed. Intracellular fluxes were then calculated using flux balance analysis (FBA). The metabolic network of primary metabolism for a green alga, C. reinhardtii, was reconstructed using genomic and biochemical information. The reconstructed network accounts for the intracellular localization of enzymes to three compartments and includes 484 metabolic reactions and 458 intracellular metabolites. Based on BLAST searches, one newly annotated enzyme (fructose-1,6-bisphosphatase) was added to the Chlamydomonas reinhardtii database. FBA was used to predict metabolic fluxes under three growth conditions, autotrophic, heterotrophic and mixotrophic growth. Biomass yields ranged from 28.9 g per mole C for autotrophic growth to 15 g per mole C for heterotrophic growth. The flux balance analysis model of central and intermediary metabolism in C. reinhardtii is the first such model for algae and the first model to include three metabolically active compartments. In addition to providing estimates of intracellular fluxes, metabolic reconstruction and modelling efforts also provide a comprehensive method for annotation of genome databases. As a result of our reconstruction, one new enzyme was annotated in the database and several others were found to be missing; implying new pathways or non-conserved enzymes. The use of FBA to estimate intracellular fluxes also provides flux values that can be used as a starting point for rational engineering of C. reinhardtii. From these initial estimates, it is clear that aerobic heterotrophic growth on acetate has a low yield on carbon, while mixotrophically and autotrophically grown cells are significantly more carbon efficient.

Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chaiboonchoe, Amphun; Ghamsari, Lila; Dohai, Bushra

Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii, a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolicmore » network that we have reconstructed for C. reinhardtii, we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii's network in dealing with varied environmental challenges that the species may face. As a result, the defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.« less

Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation

DOE PAGES

Chaiboonchoe, Amphun; Ghamsari, Lila; Dohai, Bushra; ...

2016-06-14

Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii, a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolicmore » network that we have reconstructed for C. reinhardtii, we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii's network in dealing with varied environmental challenges that the species may face. As a result, the defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.« less

PathCase-SB architecture and database design

PubMed Central

2011-01-01

Background Integration of metabolic pathways resources and regulatory metabolic network models, and deploying new tools on the integrated platform can help perform more effective and more efficient systems biology research on understanding the regulation in metabolic networks. Therefore, the tasks of (a) integrating under a single database environment regulatory metabolic networks and existing models, and (b) building tools to help with modeling and analysis are desirable and intellectually challenging computational tasks. Description PathCase Systems Biology (PathCase-SB) is built and released. The PathCase-SB database provides data and API for multiple user interfaces and software tools. The current PathCase-SB system provides a database-enabled framework and web-based computational tools towards facilitating the development of kinetic models for biological systems. PathCase-SB aims to integrate data of selected biological data sources on the web (currently, BioModels database and KEGG), and to provide more powerful and/or new capabilities via the new web-based integrative framework. This paper describes architecture and database design issues encountered in PathCase-SB's design and implementation, and presents the current design of PathCase-SB's architecture and database. Conclusions PathCase-SB architecture and database provide a highly extensible and scalable environment with easy and fast (real-time) access to the data in the database. PathCase-SB itself is already being used by researchers across the world. PMID:22070889

Effect of alternate energy substrates on mammalian brain metabolism during ischemic events.

PubMed

Koppaka, S S; Puchowicz; LaManna, J C; Gatica, J E

2008-01-01

Regulation of brain metabolism and cerebral blood flow involves complex control systems with several interacting variables at both cellular and organ levels. Quantitative understanding of the spatially and temporally heterogeneous brain control mechanisms during internal and external stimuli requires the development and validation of a computational (mathematical) model of metabolic processes in brain. This paper describes a computational model of cellular metabolism in blood-perfused brain tissue, which considers the astrocyte-neuron lactate-shuttle (ANLS) hypothesis. The model structure consists of neurons, astrocytes, extra-cellular space, and a surrounding capillary network. Each cell is further compartmentalized into cytosol and mitochondria. Inter-compartment interaction is accounted in the form of passive and carrier-mediated transport. Our model was validated against experimental data reported by Crumrine and LaManna, who studied the effect of ischemia and its recovery on various intra-cellular tissue substrates under standard diet conditions. The effect of ketone bodies on brain metabolism was also examined under ischemic conditions following cardiac resuscitation through our model simulations. The influence of ketone bodies on lactate dynamics on mammalian brain following ischemia is studied incorporating experimental data.

Inference and Prediction of Metabolic Network Fluxes

PubMed Central

Nikoloski, Zoran; Perez-Storey, Richard; Sweetlove, Lee J.

2015-01-01

In this Update, we cover the basic principles of the estimation and prediction of the rates of the many interconnected biochemical reactions that constitute plant metabolic networks. This includes metabolic flux analysis approaches that utilize the rates or patterns of redistribution of stable isotopes of carbon and other atoms to estimate fluxes, as well as constraints-based optimization approaches such as flux balance analysis. Some of the major insights that have been gained from analysis of fluxes in plants are discussed, including the functioning of metabolic pathways in a network context, the robustness of the metabolic phenotype, the importance of cell maintenance costs, and the mechanisms that enable energy and redox balancing at steady state. We also discuss methodologies to exploit 'omic data sets for the construction of tissue-specific metabolic network models and to constrain the range of permissible fluxes in such models. Finally, we consider the future directions and challenges faced by the field of metabolic network flux phenotyping. PMID:26392262

Conditional iron and pH-dependent activity of a non-enzymatic glycolysis and pentose phosphate pathway.

PubMed

Keller, Markus A; Zylstra, Andre; Castro, Cecilia; Turchyn, Alexandra V; Griffin, Julian L; Ralser, Markus

2016-01-01

Little is known about the evolutionary origins of metabolism. However, key biochemical reactions of glycolysis and the pentose phosphate pathway (PPP), ancient metabolic pathways central to the metabolic network, have non-enzymatic pendants that occur in a prebiotically plausible reaction milieu reconstituted to contain Archean sediment metal components. These non-enzymatic reactions could have given rise to the origin of glycolysis and the PPP during early evolution. Using nuclear magnetic resonance spectroscopy and high-content metabolomics that allowed us to measure several thousand reaction mixtures, we experimentally address the chemical logic of a metabolism-like network constituted from these non-enzymatic reactions. Fe(II), the dominant transition metal component of Archean oceanic sediments, has binding affinity toward metabolic sugar phosphates and drives metabolism-like reactivity acting as both catalyst and cosubstrate. Iron and pH dependencies determine a metabolism-like network topology and comediate reaction rates over several orders of magnitude so that the network adopts conditional activity. Alkaline pH triggered the activity of the non-enzymatic PPP pendant, whereas gentle acidic or neutral conditions favored non-enzymatic glycolytic reactions. Fe(II)-sensitive glycolytic and PPP-like reactions thus form a chemical network mimicking structural features of extant carbon metabolism, including topology, pH dependency, and conditional reactivity. Chemical networks that obtain structure and catalysis on the basis of transition metals found in Archean sediments are hence plausible direct precursors of cellular metabolic networks.

Conditional iron and pH-dependent activity of a non-enzymatic glycolysis and pentose phosphate pathway

PubMed Central

Keller, Markus A.; Zylstra, Andre; Castro, Cecilia; Turchyn, Alexandra V.; Griffin, Julian L.; Ralser, Markus

2016-01-01

Little is known about the evolutionary origins of metabolism. However, key biochemical reactions of glycolysis and the pentose phosphate pathway (PPP), ancient metabolic pathways central to the metabolic network, have non-enzymatic pendants that occur in a prebiotically plausible reaction milieu reconstituted to contain Archean sediment metal components. These non-enzymatic reactions could have given rise to the origin of glycolysis and the PPP during early evolution. Using nuclear magnetic resonance spectroscopy and high-content metabolomics that allowed us to measure several thousand reaction mixtures, we experimentally address the chemical logic of a metabolism-like network constituted from these non-enzymatic reactions. Fe(II), the dominant transition metal component of Archean oceanic sediments, has binding affinity toward metabolic sugar phosphates and drives metabolism-like reactivity acting as both catalyst and cosubstrate. Iron and pH dependencies determine a metabolism-like network topology and comediate reaction rates over several orders of magnitude so that the network adopts conditional activity. Alkaline pH triggered the activity of the non-enzymatic PPP pendant, whereas gentle acidic or neutral conditions favored non-enzymatic glycolytic reactions. Fe(II)-sensitive glycolytic and PPP-like reactions thus form a chemical network mimicking structural features of extant carbon metabolism, including topology, pH dependency, and conditional reactivity. Chemical networks that obtain structure and catalysis on the basis of transition metals found in Archean sediments are hence plausible direct precursors of cellular metabolic networks. PMID:26824074

A discrete mathematical model applied to genetic regulation and metabolic networks.

PubMed

Asenjo, A J; Ramirez, P; Rapaport, I; Aracena, J; Goles, E; Andrews, B A

2007-03-01

This paper describes the use of a discrete mathematical model to represent the basic mechanisms of regulation of the bacteria E. coli in batch fermentation. The specific phenomena studied were the changes in metabolism and genetic regulation when the bacteria use three different carbon substrates (glucose, glycerol, and acetate). The model correctly predicts the behavior of E. coli vis-à-vis substrate mixtures. In a mixture of glucose, glycerol, and acetate, it prefers glucose, then glycerol, and finally acetate. The model included 67 nodes; 28 were genes, 20 enzymes, and 19 regulators/biochemical compounds. The model represents both the genetic regulation and metabolic networks in an inrtegrated form, which is how they function biologically. This is one of the first attempts to include both of these networks in one model. Previously, discrete mathematical models were used only to describe genetic regulation networks. The study of the network dynamics generated 8 (2(3)) fixed points, one for each nutrient configuration (substrate mixture) in the medium. The fixed points of the discrete model reflect the phenotypes described. Gene expression and the patterns of the metabolic fluxes generated are described accurately. The activation of the gene regulation network depends basically on the presence of glucose and glycerol. The model predicts the behavior when mixed carbon sources are utilized as well as when there is no carbon source present. Fictitious jokers (Joker1, Joker2, and Repressor SdhC) had to be created to control 12 genes whose regulation mechanism is unknown, since glycerol and glucose do not act directly on the genes. The approach presented in this paper is particularly useful to investigate potential unknown gene regulation mechanisms; such a novel approach can also be used to describe other gene regulation situations such as the comparison between non-recombinant and recombinant yeast strain, producing recombinant proteins, presently under investigation in our group.

Computational Tools for Metabolic Engineering

PubMed Central

Copeland, Wilbert B.; Bartley, Bryan A.; Chandran, Deepak; Galdzicki, Michal; Kim, Kyung H.; Sleight, Sean C.; Maranas, Costas D.; Sauro, Herbert M.

2012-01-01

A great variety of software applications are now employed in the metabolic engineering field. These applications have been created to support a wide range of experimental and analysis techniques. Computational tools are utilized throughout the metabolic engineering workflow to extract and interpret relevant information from large data sets, to present complex models in a more manageable form, and to propose efficient network design strategies. In this review, we present a number of tools that can assist in modifying and understanding cellular metabolic networks. The review covers seven areas of relevance to metabolic engineers. These include metabolic reconstruction efforts, network visualization, nucleic acid and protein engineering, metabolic flux analysis, pathway prospecting, post-structural network analysis and culture optimization. The list of available tools is extensive and we can only highlight a small, representative portion of the tools from each area. PMID:22629572

Elementary Mode Analysis: A Useful Metabolic Pathway Analysis Tool for Characterizing Cellular Metabolism

PubMed Central

Trinh, Cong T.; Wlaschin, Aaron; Srienc, Friedrich

2010-01-01

Elementary Mode Analysis is a useful Metabolic Pathway Analysis tool to identify the structure of a metabolic network that links the cellular phenotype to the corresponding genotype. The analysis can decompose the intricate metabolic network comprised of highly interconnected reactions into uniquely organized pathways. These pathways consisting of a minimal set of enzymes that can support steady state operation of cellular metabolism represent independent cellular physiological states. Such pathway definition provides a rigorous basis to systematically characterize cellular phenotypes, metabolic network regulation, robustness, and fragility that facilitate understanding of cell physiology and implementation of metabolic engineering strategies. This mini-review aims to overview the development and application of elementary mode analysis as a metabolic pathway analysis tool in studying cell physiology and as a basis of metabolic engineering. PMID:19015845

Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D

DOE PAGES

Preciat Gonzalez, German A.; El Assal, Lemmer R. P.; Noronha, Alberto; ...

2017-06-14

The mechanism of each chemical reaction in a metabolic network can be represented as a set of atom mappings, each of which relates an atom in a substrate metabolite to an atom of the same element in a product metabolite. Genome-scale metabolic network reconstructions typically represent biochemistry at the level of reaction stoichiometry. However, a more detailed representation at the underlying level of atom mappings opens the possibility for a broader range of biological, biomedical and biotechnological applications than with stoichiometry alone. Complete manual acquisition of atom mapping data for a genome-scale metabolic network is a laborious process. However, manymore » algorithms exist to predict atom mappings. How do their predictions compare to each other and to manually curated atom mappings? For more than four thousand metabolic reactions in the latest human metabolic reconstruction, Recon 3D, we compared the atom mappings predicted by six atom mapping algorithms. We also compared these predictions to those obtained by manual curation of atom mappings for over five hundred reactions distributed among all top level Enzyme Commission number classes. Five of the evaluated algorithms had similarly high prediction accuracy of over 91% when compared to manually curated atom mapped reactions. On average, the accuracy of the prediction was highest for reactions catalysed by oxidoreductases and lowest for reactions catalysed by ligases. In addition to prediction accuracy, the algorithms were evaluated on their accessibility, their advanced features, such as the ability to identify equivalent atoms, and their ability to map hydrogen atoms. In addition to prediction accuracy, we found that software accessibility and advanced features were fundamental to the selection of an atom mapping algorithm in practice.« less

Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D

DOE Office of Scientific and Technical Information (OSTI.GOV)

Preciat Gonzalez, German A.; El Assal, Lemmer R. P.; Noronha, Alberto

The mechanism of each chemical reaction in a metabolic network can be represented as a set of atom mappings, each of which relates an atom in a substrate metabolite to an atom of the same element in a product metabolite. Genome-scale metabolic network reconstructions typically represent biochemistry at the level of reaction stoichiometry. However, a more detailed representation at the underlying level of atom mappings opens the possibility for a broader range of biological, biomedical and biotechnological applications than with stoichiometry alone. Complete manual acquisition of atom mapping data for a genome-scale metabolic network is a laborious process. However, manymore » algorithms exist to predict atom mappings. How do their predictions compare to each other and to manually curated atom mappings? For more than four thousand metabolic reactions in the latest human metabolic reconstruction, Recon 3D, we compared the atom mappings predicted by six atom mapping algorithms. We also compared these predictions to those obtained by manual curation of atom mappings for over five hundred reactions distributed among all top level Enzyme Commission number classes. Five of the evaluated algorithms had similarly high prediction accuracy of over 91% when compared to manually curated atom mapped reactions. On average, the accuracy of the prediction was highest for reactions catalysed by oxidoreductases and lowest for reactions catalysed by ligases. In addition to prediction accuracy, the algorithms were evaluated on their accessibility, their advanced features, such as the ability to identify equivalent atoms, and their ability to map hydrogen atoms. In addition to prediction accuracy, we found that software accessibility and advanced features were fundamental to the selection of an atom mapping algorithm in practice.« less

Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D.

PubMed

Preciat Gonzalez, German A; El Assal, Lemmer R P; Noronha, Alberto; Thiele, Ines; Haraldsdóttir, Hulda S; Fleming, Ronan M T

2017-06-14

The mechanism of each chemical reaction in a metabolic network can be represented as a set of atom mappings, each of which relates an atom in a substrate metabolite to an atom of the same element in a product metabolite. Genome-scale metabolic network reconstructions typically represent biochemistry at the level of reaction stoichiometry. However, a more detailed representation at the underlying level of atom mappings opens the possibility for a broader range of biological, biomedical and biotechnological applications than with stoichiometry alone. Complete manual acquisition of atom mapping data for a genome-scale metabolic network is a laborious process. However, many algorithms exist to predict atom mappings. How do their predictions compare to each other and to manually curated atom mappings? For more than four thousand metabolic reactions in the latest human metabolic reconstruction, Recon 3D, we compared the atom mappings predicted by six atom mapping algorithms. We also compared these predictions to those obtained by manual curation of atom mappings for over five hundred reactions distributed among all top level Enzyme Commission number classes. Five of the evaluated algorithms had similarly high prediction accuracy of over 91% when compared to manually curated atom mapped reactions. On average, the accuracy of the prediction was highest for reactions catalysed by oxidoreductases and lowest for reactions catalysed by ligases. In addition to prediction accuracy, the algorithms were evaluated on their accessibility, their advanced features, such as the ability to identify equivalent atoms, and their ability to map hydrogen atoms. In addition to prediction accuracy, we found that software accessibility and advanced features were fundamental to the selection of an atom mapping algorithm in practice.

Prioritizing chronic obstructive pulmonary disease (COPD) candidate genes in COPD-related networks

PubMed Central

Zhang, Yihua; Li, Wan; Feng, Yuyan; Guo, Shanshan; Zhao, Xilei; Wang, Yahui; He, Yuehan; He, Weiming; Chen, Lina

2017-01-01

Chronic obstructive pulmonary disease (COPD) is a multi-factor disease, which could be caused by many factors, including disturbances of metabolism and protein-protein interactions (PPIs). In this paper, a weighted COPD-related metabolic network and a weighted COPD-related PPI network were constructed base on COPD disease genes and functional information. Candidate genes in these weighted COPD-related networks were prioritized by making use of a gene prioritization method, respectively. Literature review and functional enrichment analysis of the top 100 genes in these two networks suggested the correlation of COPD and these genes. The performance of our gene prioritization method was superior to that of ToppGene and ToppNet for genes from the COPD-related metabolic network or the COPD-related PPI network after assessing using leave-one-out cross-validation, literature validation and functional enrichment analysis. The top-ranked genes prioritized from COPD-related metabolic and PPI networks could promote the better understanding about the molecular mechanism of this disease from different perspectives. The top 100 genes in COPD-related metabolic network or COPD-related PPI network might be potential markers for the diagnosis and treatment of COPD. PMID:29262568

Prioritizing chronic obstructive pulmonary disease (COPD) candidate genes in COPD-related networks.

PubMed

Zhang, Yihua; Li, Wan; Feng, Yuyan; Guo, Shanshan; Zhao, Xilei; Wang, Yahui; He, Yuehan; He, Weiming; Chen, Lina

2017-11-28

Chronic obstructive pulmonary disease (COPD) is a multi-factor disease, which could be caused by many factors, including disturbances of metabolism and protein-protein interactions (PPIs). In this paper, a weighted COPD-related metabolic network and a weighted COPD-related PPI network were constructed base on COPD disease genes and functional information. Candidate genes in these weighted COPD-related networks were prioritized by making use of a gene prioritization method, respectively. Literature review and functional enrichment analysis of the top 100 genes in these two networks suggested the correlation of COPD and these genes. The performance of our gene prioritization method was superior to that of ToppGene and ToppNet for genes from the COPD-related metabolic network or the COPD-related PPI network after assessing using leave-one-out cross-validation, literature validation and functional enrichment analysis. The top-ranked genes prioritized from COPD-related metabolic and PPI networks could promote the better understanding about the molecular mechanism of this disease from different perspectives. The top 100 genes in COPD-related metabolic network or COPD-related PPI network might be potential markers for the diagnosis and treatment of COPD.

Effect of resistant and digestible rice starches on human cytokine and lactate metabolic networks in serum.

PubMed

Wang, Huisong; Pang, Guangchang

2017-05-01

Resistant starch generated after treating ordinary starch is of great significance to human health in the countries with overnutrition. However, its functional evaluation in the human body has been rarely reported. By determining the lactate metabolic flux, 12 serum enzymes expression level and 38 serum cytokines in healthy volunteers, the variation in cytokine network and lactate metabolic network in serum were investigated to compare the mechanism of the physiological effects between the two starches. The results indicated that compared with digestible starch, resistant starch had anti-inflammatory effects, increased anabolism, and decreased catabolism. Further, the intercellular communication networks including cytokine and lactate metabolic networks were mapped out. The relationship suggested that resistant starch might affect and control the secretion of cytokines to regulate lactate metabolic network in the body, promoting the development of immunometabolism. Copyright © 2017 Elsevier Ltd. All rights reserved.

The microbial nitrogen-cycling network.

PubMed

Kuypers, Marcel M M; Marchant, Hannah K; Kartal, Boran

2018-05-01

Nitrogen is an essential component of all living organisms and the main nutrient limiting life on our planet. By far, the largest inventory of freely accessible nitrogen is atmospheric dinitrogen, but most organisms rely on more bioavailable forms of nitrogen, such as ammonium and nitrate, for growth. The availability of these substrates depends on diverse nitrogen-transforming reactions that are carried out by complex networks of metabolically versatile microorganisms. In this Review, we summarize our current understanding of the microbial nitrogen-cycling network, including novel processes, their underlying biochemical pathways, the involved microorganisms, their environmental importance and industrial applications.

VRML metabolic network visualizer.

PubMed

Rojdestvenski, Igor

2003-03-01

A successful date collection visualization should satisfy a set of many requirements: unification of diverse data formats, support for serendipity research, support of hierarchical structures, algorithmizability, vast information density, Internet-readiness, and other. Recently, virtual reality has made significant progress in engineering, architectural design, entertainment and communication. We experiment with the possibility of using the immersive abstract three-dimensional visualizations of the metabolic networks. We present the trial Metabolic Network Visualizer software, which produces graphical representation of a metabolic network as a VRML world from a formal description written in a simple SGML-type scripting language.

Systems metabolic engineering: the creation of microbial cell factories by rational metabolic design and evolution.

PubMed

Furusawa, Chikara; Horinouchi, Takaaki; Hirasawa, Takashi; Shimizu, Hiroshi

2013-01-01

It is widely acknowledged that in order to establish sustainable societies, production processes should shift from petrochemical-based processes to bioprocesses. Because bioconversion technologies, in which biomass resources are converted to valuable materials, are preferable to processes dependent on fossil resources, the former should be further developed. The following two approaches can be adopted to improve cellular properties and obtain high productivity and production yield of target products: (1) optimization of cellular metabolic pathways involved in various bioprocesses and (2) creation of stress-tolerant cells that can be active even under severe stress conditions in the bioprocesses. Recent progress in omics analyses has facilitated the analysis of microorganisms based on bioinformatics data for molecular breeding and bioprocess development. Systems metabolic engineering is a new area of study, and it has been defined as a methodology in which metabolic engineering and systems biology are integrated to upgrade the designability of industrially useful microorganisms. This chapter discusses multi-omics analyses and rational design methods for molecular breeding. The first is an example of the rational design of metabolic networks for target production by flux balance analysis using genome-scale metabolic models. Recent progress in the development of genome-scale metabolic models and the application of these models to the design of desirable metabolic networks is also described in this example. The second is an example of evolution engineering with omics analyses for the creation of stress-tolerant microorganisms. Long-term culture experiments to obtain the desired phenotypes and omics analyses to identify the phenotypic changes are described here.

A Comprehensively Curated Genome-Scale Two-Cell Model for the Heterocystous Cyanobacterium Anabaena sp. PCC 71201[CC-BY

PubMed Central

Steuer, Ralf

2017-01-01

Anabaena sp. PCC 7120 is a nitrogen-fixing filamentous cyanobacterium. Under nitrogen-limiting conditions, a fraction of the vegetative cells in each filament terminally differentiate to nongrowing heterocysts. Heterocysts are metabolically and structurally specialized to enable O2-sensitive nitrogen fixation. The functionality of the filament, as an association of vegetative cells and heterocysts, is postulated to depend on metabolic exchange of electrons, carbon, and fixed nitrogen. In this study, we compile and evaluate a comprehensive curated stoichiometric model of this two-cell system, with the objective function based on the growth of the filament under diazotrophic conditions. The predicted growth rate under nitrogen-replete and -deplete conditions, as well as the effect of external carbon and nitrogen sources, was thereafter verified. Furthermore, the model was utilized to comprehensively evaluate the optimality of putative metabolic exchange reactions between heterocysts and vegetative cells. The model suggested that optimal growth requires at least four exchange metabolites. Several combinations of exchange metabolites resulted in predicted growth rates that are higher than growth rates achieved by only considering exchange of metabolites previously suggested in the literature. The curated model of the metabolic network of Anabaena sp. PCC 7120 enhances our ability to understand the metabolic organization of multicellular cyanobacteria and provides a platform for further study and engineering of their metabolism. PMID:27899536

Metabolomics and transcriptomics profiles reveal the dysregulation of the tricarboxylic acid cycle and related mechanisms in prostate cancer.

PubMed

Shao, Yaping; Ye, Guozhu; Ren, Shancheng; Piao, Hai-Long; Zhao, Xinjie; Lu, Xin; Wang, Fubo; Ma, Wang; Li, Jia; Yin, Peiyuan; Xia, Tian; Xu, Chuanliang; Yu, Jane J; Sun, Yinghao; Xu, Guowang

2018-07-15

Genetic alterations drive metabolic reprograming to meet increased biosynthetic precursor and energy demands for cancer cell proliferation and survival in unfavorable environments. A systematic study of gene-metabolite regulatory networks and metabolic dysregulation should reveal the molecular mechanisms underlying prostate cancer (PCa) pathogenesis. Herein, we performed gas chromatography-mass spectrometry (GC-MS)-based metabolomics and RNA-seq analyses in prostate tumors and matched adjacent normal tissues (ANTs) to elucidate the molecular alterations and potential underlying regulatory mechanisms in PCa. Significant accumulation of metabolic intermediates and enrichment of genes in the tricarboxylic acid (TCA) cycle were observed in tumor tissues, indicating TCA cycle hyperactivation in PCa tissues. In addition, the levels of fumarate and malate were highly correlated with the Gleason score, tumor stage and expression of genes encoding related enzymes and were significantly related to the expression of genes involved in branched chain amino acid degradation. Using an integrated omics approach, we further revealed the potential anaplerotic routes from pyruvate, glutamine catabolism and branched chain amino acid (BCAA) degradation contributing to replenishing metabolites for TCA cycle. Integrated omics techniques enable the performance of network-based analyses to gain a comprehensive and in-depth understanding of PCa pathophysiology and may facilitate the development of new and effective therapeutic strategies. © 2018 UICC.

Mathematical modelling of clostridial acetone-butanol-ethanol fermentation.

PubMed

Millat, Thomas; Winzer, Klaus

2017-03-01

Clostridial acetone-butanol-ethanol (ABE) fermentation features a remarkable shift in the cellular metabolic activity from acid formation, acidogenesis, to the production of industrial-relevant solvents, solventogensis. In recent decades, mathematical models have been employed to elucidate the complex interlinked regulation and conditions that determine these two distinct metabolic states and govern the transition between them. In this review, we discuss these models with a focus on the mechanisms controlling intra- and extracellular changes between acidogenesis and solventogenesis. In particular, we critically evaluate underlying model assumptions and predictions in the light of current experimental knowledge. Towards this end, we briefly introduce key ideas and assumptions applied in the discussed modelling approaches, but waive a comprehensive mathematical presentation. We distinguish between structural and dynamical models, which will be discussed in their chronological order to illustrate how new biological information facilitates the 'evolution' of mathematical models. Mathematical models and their analysis have significantly contributed to our knowledge of ABE fermentation and the underlying regulatory network which spans all levels of biological organization. However, the ties between the different levels of cellular regulation are not well understood. Furthermore, contradictory experimental and theoretical results challenge our current notion of ABE metabolic network structure. Thus, clostridial ABE fermentation still poses theoretical as well as experimental challenges which are best approached in close collaboration between modellers and experimentalists.

Reconstruction of metabolic pathways by combining probabilistic graphical model-based and knowledge-based methods

PubMed Central

2014-01-01

Automatic reconstruction of metabolic pathways for an organism from genomics and transcriptomics data has been a challenging and important problem in bioinformatics. Traditionally, known reference pathways can be mapped into an organism-specific ones based on its genome annotation and protein homology. However, this simple knowledge-based mapping method might produce incomplete pathways and generally cannot predict unknown new relations and reactions. In contrast, ab initio metabolic network construction methods can predict novel reactions and interactions, but its accuracy tends to be low leading to a lot of false positives. Here we combine existing pathway knowledge and a new ab initio Bayesian probabilistic graphical model together in a novel fashion to improve automatic reconstruction of metabolic networks. Specifically, we built a knowledge database containing known, individual gene / protein interactions and metabolic reactions extracted from existing reference pathways. Known reactions and interactions were then used as constraints for Bayesian network learning methods to predict metabolic pathways. Using individual reactions and interactions extracted from different pathways of many organisms to guide pathway construction is new and improves both the coverage and accuracy of metabolic pathway construction. We applied this probabilistic knowledge-based approach to construct the metabolic networks from yeast gene expression data and compared its results with 62 known metabolic networks in the KEGG database. The experiment showed that the method improved the coverage of metabolic network construction over the traditional reference pathway mapping method and was more accurate than pure ab initio methods. PMID:25374614

Correlated mRNAs and miRNAs from co-expression and regulatory networks affect porcine muscle and finally meat properties.

PubMed

Ponsuksili, Siriluck; Du, Yang; Hadlich, Frieder; Siengdee, Puntita; Murani, Eduard; Schwerin, Manfred; Wimmers, Klaus

2013-08-05

Physiological processes aiding the conversion of muscle to meat involve many genes associated with muscle structure and metabolic processes. MicroRNAs regulate networks of genes to orchestrate cellular functions, in turn regulating phenotypes. We applied weighted gene co-expression network analysis to identify co-expression modules that correlated to meat quality phenotypes and were highly enriched for genes involved in glucose metabolism, response to wounding, mitochondrial ribosome, mitochondrion, and extracellular matrix. Negative correlation of miRNA with mRNA and target prediction were used to select transcripts out of the modules of trait-associated mRNAs to further identify those genes that are correlated with post mortem traits. Porcine muscle co-expression transcript networks that correlated to post mortem traits were identified. The integration of miRNA and mRNA expression analyses, as well as network analysis, enabled us to interpret the differentially-regulated genes from a systems perspective. Linking co-expression networks of transcripts and hierarchically organized pairs of miRNAs and mRNAs to meat properties yields new insight into several biological pathways underlying phenotype differences. These pathways may also be diagnostic for many myopathies, which are accompanied by deficient nutrient and oxygen supply of muscle fibers.

Fruit metabolite networks in engineered and non-engineered tomato genotypes reveal fluidity in a hormone and agroecosystem specific manner.

PubMed

Fatima, Tahira; Sobolev, Anatoly P; Teasdale, John R; Kramer, Matthew; Bunce, Jim; Handa, Avtar K; Mattoo, Autar K

Metabolomics provides a view of endogenous metabolic patterns not only during plant growth, development and senescence but also in response to genetic events, environment and disease. The effects of the field environment on plant hormone-specific metabolite profiles are largely unknown. Few studies have analyzed useful phenotypes generated by introducing single or multiple gene events alongside the non-engineered wild type control at field scale to determine the robustness of the genetic trait and its modulation in the metabolome as a function of specific agroecosystem environments. We evaluated the influence of genetic background (high polyamine lines; low methyl jasmonate line; low ethylene line; and isogenic genotypes carrying double transgenic events) and environments (hairy vetch, rye, plastic black mulch and bare soil mulching systems) on the metabolomic profile of isogenic reverse genetic mutations and selected mulch based cropping systems in tomato fruit. Net photosynthesis and fruit yield were also determined. NMR spectroscopy was used for quantifying metabolites that are central to primary metabolism. We analyzed both the first moment (means) of metabolic response to genotypes and agroecosystems by traditional univariate/multivariate methods, and the second moment (covariances) of responses by creating networks that depicted changes in correlations of paired metabolites. This particular approach is novel and was necessary because our experimental material yielded highly variable metabolic responses that could not be easily understood using the traditional analytical approaches for first moment statistics. High endogenous spermidine and spermine content exhibited strong effects on amino acids, Krebs cycle intermediates and energy molecules (ADP + ATP) in ripening fruits of plants grown under different agroecosystem environments. The metabolic response to high polyamine genotypes was similar to the response to hairy vetch cover crop mulch; supported by the pattern of changes in correlation between metabolites. Changes in primary metabolites of genotypes mutated for the deficiency of ethylene or methyl jasmonate were unique under all growth conditions and opposite of high polyamine genotype results. The high polyamine trait was found to dominate the low ethylene and low jasmonate mutations under field conditions. For several metabolites low ethylene and low methyl jasmonate genotypes had an inverse relationship. Collectively, these results affirm that interactions between metabolite pathways and growth environments are affected by genotype, and influence the metabolite quality of a crop. This study portrays how metabolite relationships change, both in mean and in correlation, under different genotypic and environmental conditions. Although these networks are surprisingly dynamic, we also find examples of selectively conserved associations.

Effects of Combined Low Glutathione with Mild Oxidative and Low Phosphorus Stress on the Metabolism of Arabidopsis thaliana

PubMed Central

Fukushima, Atsushi; Iwasa, Mami; Nakabayashi, Ryo; Kobayashi, Makoto; Nishizawa, Tomoko; Okazaki, Yozo; Saito, Kazuki; Kusano, Miyako

2017-01-01

Plants possess highly sensitive mechanisms that monitor environmental stress levels for a dose-dependent fine-tuning of their growth and development. Differences in plant responses to severe and mild abiotic stresses have been recognized. Although many studies have revealed that glutathione can contribute to plant tolerance to various environmental stresses, little is known about the relationship between glutathione and mild abiotic stress, especially the effect of stress-induced altered glutathione levels on the metabolism. Here, we applied a systems biology approach to identify key pathways involved in the gene-to-metabolite networks perturbed by low glutathione content under mild abiotic stress in Arabidopsis thaliana. We used glutathione synthesis mutants (cad2-1 and pad2-1) and plants overexpressing the gene encoding γ-glutamylcysteine synthetase, the first enzyme of the glutathione biosynthetic pathway. The plants were exposed to two mild stress conditions—oxidative stress elicited by methyl viologen and stress induced by the limited availability of phosphate. We observed that the mutants and transgenic plants showed similar shoot growth as that of the wild-type plants under mild abiotic stress. We then selected the synthesis mutants and performed multi-platform metabolomics and microarray experiments to evaluate the possible effects on the overall metabolome and the transcriptome. As a common oxidative stress response, several flavonoids that we assessed showed overaccumulation, whereas the mild phosphate stress resulted in increased levels of specific kaempferol- and quercetin-glycosides. Remarkably, in addition to a significant increased level of sugar, osmolytes, and lipids as mild oxidative stress-responsive metabolites, short-chain aliphatic glucosinolates over-accumulated in the mutants, whereas the level of long-chain aliphatic glucosinolates and specific lipids decreased. Coordinated gene expressions related to glucosinolate and flavonoid biosynthesis also supported the metabolite responses in the pad2-1 mutant. Our results suggest that glutathione synthesis mutants accelerate transcriptional regulatory networks to control the biosynthetic pathways involved in glutathione-independent scavenging metabolites, and that they might reconfigure the metabolic networks in primary and secondary metabolism, including lipids, glucosinolates, and flavonoids. This work provides a basis for the elucidation of the molecular mechanisms involved in the metabolic and transcriptional regulatory networks in response to combined low glutathione content with mild oxidative and nutrient stress in A. thaliana. PMID:28894456

Amino Acid Flux from Metabolic Network Benefits Protein Translation: the Role of Resource Availability.

PubMed

Hu, Xiao-Pan; Yang, Yi; Ma, Bin-Guang

2015-06-09

Protein translation is a central step in gene expression and affected by many factors such as codon usage bias, mRNA folding energy and tRNA abundance. Despite intensive previous studies, how metabolic amino acid supply correlates with protein translation efficiency remains unknown. In this work, we estimated the amino acid flux from metabolic network for each protein in Escherichia coli and Saccharomyces cerevisiae by using Flux Balance Analysis. Integrated with the mRNA expression level, protein abundance and ribosome profiling data, we provided a detailed description of the role of amino acid supply in protein translation. Our results showed that amino acid supply positively correlates with translation efficiency and ribosome density. Moreover, with the rank-based regression model, we found that metabolic amino acid supply facilitates ribosome utilization. Based on the fact that the ribosome density change of well-amino-acid-supplied genes is smaller than poorly-amino-acid-supply genes under amino acid starvation, we reached the conclusion that amino acid supply may buffer ribosome density change against amino acid starvation and benefit maintaining a relatively stable translation environment. Our work provided new insights into the connection between metabolic amino acid supply and protein translation process by revealing a new regulation strategy that is dependent on resource availability.

Web-based metabolic network visualization with a zooming user interface

PubMed Central

2011-01-01

Background Displaying complex metabolic-map diagrams, for Web browsers, and allowing users to interact with them for querying and overlaying expression data over them is challenging. Description We present a Web-based metabolic-map diagram, which can be interactively explored by the user, called the Cellular Overview. The main characteristic of this application is the zooming user interface enabling the user to focus on appropriate granularities of the network at will. Various searching commands are available to visually highlight sets of reactions, pathways, enzymes, metabolites, and so on. Expression data from single or multiple experiments can be overlaid on the diagram, which we call the Omics Viewer capability. The application provides Web services to highlight the diagram and to invoke the Omics Viewer. This application is entirely written in JavaScript for the client browsers and connect to a Pathway Tools Web server to retrieve data and diagrams. It uses the OpenLayers library to display tiled diagrams. Conclusions This new online tool is capable of displaying large and complex metabolic-map diagrams in a very interactive manner. This application is available as part of the Pathway Tools software that powers multiple metabolic databases including Biocyc.org: The Cellular Overview is accessible under the Tools menu. PMID:21595965

Decreased Metabolism in the Posterior Medial Network with Concomitantly Increased Metabolism in the Anterior Temporal Network During Transient Global Amnesia.

PubMed

Yi, SangHak; Park, Young Ho; Jang, Jae-Won; Lim, Jae-Sung; Chun, In Kook; Kim, SangYun

2018-05-01

Perturbation of corticohippocampal circuits is a key step in the pathogenesis of transient global amnesia. We evaluated the spatial distribution of altered cerebral metabolism to determine the location of the corticohippocampal circuits perturbed during the acute stage of transient global amnesia. A consecutive series of 12 patients with transient global amnesia who underwent 18 F-fluorodeoxyglucose positron emission tomography within 3 days after symptom onset was identified. We used statistical parametric mapping with two contrasts to identify regions of decreased and increased brain metabolism in transient global amnesia patients compared with 25 age-matched controls. Transient global amnesia patients showed hypometabolic clusters in the left temporal and bilateral parieto-occipital regions that belong to the posterior medial network as well as, hypermetabolic clusters in the bilateral inferior frontal regions that belong to the anterior temporal network. The posterior medial and anterior temporal networks are the two main corticohippocampal circuits involved in memory-guided behavior. Decreased metabolism in the posterior medial network might explain the impairment of episodic memory observed during the acute stage of transient global amnesia. Concomitant increased metabolism within the anterior temporal network might occur as a compensatory mechanism.

Integrated analysis of gene expression and metabolic fluxes in PHA-producing Pseudomonas putida grown on glycerol.

PubMed

Beckers, Veronique; Poblete-Castro, Ignacio; Tomasch, Jürgen; Wittmann, Christoph

2016-05-03

Given its high surplus and low cost, glycerol has emerged as interesting carbon substrate for the synthesis of value-added chemicals. The soil bacterium Pseudomonas putida KT2440 can use glycerol to synthesize medium-chain-length poly(3-hydroxyalkanoates) (mcl-PHA), a class of biopolymers of industrial interest. Here, glycerol metabolism in P. putida KT2440 was studied on the level of gene expression (transcriptome) and metabolic fluxes (fluxome), using precisely adjusted chemostat cultures, growth kinetics and stoichiometry, to gain a systematic understanding of the underlying metabolic and regulatory network. Glycerol-grown P. putida KT2440 has a maintenance energy requirement [0.039 (mmolglycerol (gCDW h)(-1))] that is about sixteen times lower than that of other bacteria, such as Escherichia coli, which provides a great advantage to use this substrate commercially. The shift from carbon (glycerol) to nitrogen (ammonium) limitation drives the modulation of specific genes involved in glycerol metabolism, transport electron chain, sensors to assess the energy level of the cell, and PHA synthesis, as well as changes in flux distribution to increase the precursor availability for PHA synthesis (Entner-Doudoroff pathway and pyruvate metabolism) and to reduce respiration (glyoxylate shunt). Under PHA-producing conditions (N-limitation), a higher PHA yield was achieved at low dilution rate (29.7 wt% of CDW) as compared to a high rate (12.8 wt% of CDW). By-product formation (succinate, malate) was specifically modulated under these regimes. On top of experimental data, elementary flux mode analysis revealed the metabolic potential of P. putida KT2440 to synthesize PHA and identified metabolic engineering targets towards improved production performance on glycerol. This study revealed the complex interplay of gene expression levels and metabolic fluxes under PHA- and non-PHA producing conditions using the attractive raw material glycerol as carbon substrate. This knowledge will form the basis for the development of future metabolically engineered hyper-PHA-producing strains derived from the versatile bacterium P. putida KT2440.

Nutrition-induced ketosis alters metabolic and signaling gene networks in liver of periparturient dairy cows.

PubMed

Loor, Juan J; Everts, Robin E; Bionaz, Massimo; Dann, Heather M; Morin, Dawn E; Oliveira, Rosane; Rodriguez-Zas, Sandra L; Drackley, James K; Lewin, Harris A

2007-12-19

Dairy cows are highly susceptible after parturition to developing liver lipidosis and ketosis, which are costly diseases to farmers. A bovine microarray platform consisting of 13,257-annotated oligonucleotides was used to study hepatic gene networks underlying nutrition-induced ketosis. On day 5 postpartum, 14 Holstein cows were randomly assigned to ketosis-induction (n = 7) or control (n = 7) groups. Cows in the ketosis-induction group were fed at 50% of day 4 intake until they developed signs of clinical ketosis, and cows in the control group were fed ad libitum throughout the treatment period. Liver was biopsied at 10-14 (ketosis) or 14 days postpartum (controls). Feed restriction increased blood concentrations of nonesterified fatty acids and beta-hydroxybutyrate, but decreased glucose. Liver triacylglycerol concentration also increased. A total of 2,415 genes were altered by ketosis (false discovery rate = 0.05). Ingenuity Pathway Analysis revealed downregulation of genes associated with oxidative phosphorylation, protein ubiquitination, and ubiquinone biosynthesis with ketosis. Other molecular adaptations included upregulation of genes and nuclear receptors associated with cytokine signaling, fatty acid uptake/transport, and fatty acid oxidation. Genes downregulated during ketosis included several associated with cholesterol metabolism, growth hormone signaling, proton transport, and fatty acid desaturation. Feed restriction and ketosis resulted in previously unrecognized alterations in gene network expression underlying key cellular functions and discrete metabolic events. These responses might help explain well-documented physiological adaptations to reduced feed intake in early postpartum cows and, thus, provide molecular targets that might be useful in prevention and treatment of liver lipidosis and ketosis.

Genome-scale metabolic model for Lactococcus lactis MG1363 and its application to the analysis of flavor formation.

PubMed

Flahaut, Nicolas A L; Wiersma, Anne; van de Bunt, Bert; Martens, Dirk E; Schaap, Peter J; Sijtsma, Lolke; Dos Santos, Vitor A Martins; de Vos, Willem M

2013-10-01

Lactococcus lactis subsp. cremoris MG1363 is a paradigm strain for lactococci used in industrial dairy fermentations. However, despite of its importance for process development, no genome-scale metabolic model has been reported thus far. Moreover, current models for other lactococci only focus on growth and sugar degradation. A metabolic model that includes nitrogen metabolism and flavor-forming pathways is instrumental for the understanding and designing new industrial applications of these lactic acid bacteria. A genome-scale, constraint-based model of the metabolism and transport in L. lactis MG1363, accounting for 518 genes, 754 reactions, and 650 metabolites, was developed and experimentally validated. Fifty-nine reactions are directly or indirectly involved in flavor formation. Flux Balance Analysis and Flux Variability Analysis were used to investigate flux distributions within the whole metabolic network. Anaerobic carbon-limited continuous cultures were used for estimating the energetic parameters. A thorough model-driven analysis showing a highly flexible nitrogen metabolism, e.g., branched-chain amino acid catabolism which coupled with the redox balance, is pivotal for the prediction of the formation of different flavor compounds. Furthermore, the model predicted the formation of volatile sulfur compounds as a result of the fermentation. These products were subsequently identified in the experimental fermentations carried out. Thus, the genome-scale metabolic model couples the carbon and nitrogen metabolism in L. lactis MG1363 with complete known catabolic pathways leading to flavor formation. The model provided valuable insights into the metabolic networks underlying flavor formation and has the potential to contribute to new developments in dairy industries and cheese-flavor research.

Inference of scale-free networks from gene expression time series.

PubMed

Daisuke, Tominaga; Horton, Paul

2006-04-01

Quantitative time-series observation of gene expression is becoming possible, for example by cell array technology. However, there are no practical methods with which to infer network structures using only observed time-series data. As most computational models of biological networks for continuous time-series data have a high degree of freedom, it is almost impossible to infer the correct structures. On the other hand, it has been reported that some kinds of biological networks, such as gene networks and metabolic pathways, may have scale-free properties. We hypothesize that the architecture of inferred biological network models can be restricted to scale-free networks. We developed an inference algorithm for biological networks using only time-series data by introducing such a restriction. We adopt the S-system as the network model, and a distributed genetic algorithm to optimize models to fit its simulated results to observed time series data. We have tested our algorithm on a case study (simulated data). We compared optimization under no restriction, which allows for a fully connected network, and under the restriction that the total number of links must equal that expected from a scale free network. The restriction reduced both false positive and false negative estimation of the links and also the differences between model simulation and the given time-series data.

Do all roads lead to Rome? A comparison of brain networks derived from inter-subject volumetric and metabolic covariance and moment-to-moment hemodynamic correlations in old individuals.

PubMed

Di, Xin; Gohel, Suril; Thielcke, Andre; Wehrl, Hans F; Biswal, Bharat B

2017-11-01

Relationships between spatially remote brain regions in human have typically been estimated by moment-to-moment correlations of blood-oxygen-level dependent signals in resting-state using functional MRI (fMRI). Recently, studies using subject-to-subject covariance of anatomical volumes, cortical thickness, and metabolic activity are becoming increasingly popular. However, question remains on whether these measures reflect the same inter-region connectivity and brain network organizations. In the current study, we systematically analyzed inter-subject volumetric covariance from anatomical MRI images, metabolic covariance from fluorodeoxyglucose positron emission tomography images from 193 healthy subjects, and resting-state moment-to-moment correlations from fMRI images of a subset of 44 subjects. The correlation matrices calculated from the three methods were found to be minimally correlated, with higher correlation in the range of 0.31, as well as limited proportion of overlapping connections. The volumetric network showed the highest global efficiency and lowest mean clustering coefficient, leaning toward random-like network, while the metabolic and resting-state networks conveyed properties more resembling small-world networks. Community structures of the volumetric and metabolic networks did not reflect known functional organizations, which could be observed in resting-state network. The current results suggested that inter-subject volumetric and metabolic covariance do not necessarily reflect the inter-regional relationships and network organizations as resting-state correlations, thus calling for cautions on interpreting results of inter-subject covariance networks.

Cardiac metabolism and its interactions with contraction, growth, and survival of cardiomyocytes.

PubMed

Kolwicz, Stephen C; Purohit, Suneet; Tian, Rong

2013-08-16

The network for cardiac fuel metabolism contains intricate sets of interacting pathways that result in both ATP-producing and non-ATP-producing end points for each class of energy substrates. The most salient feature of the network is the metabolic flexibility demonstrated in response to various stimuli, including developmental changes and nutritional status. The heart is also capable of remodeling the metabolic pathways in chronic pathophysiological conditions, which results in modulations of myocardial energetics and contractile function. In a quest to understand the complexity of the cardiac metabolic network, pharmacological and genetic tools have been engaged to manipulate cardiac metabolism in a variety of research models. In concert, a host of therapeutic interventions have been tested clinically to target substrate preference, insulin sensitivity, and mitochondrial function. In addition, the contribution of cellular metabolism to growth, survival, and other signaling pathways through the production of metabolic intermediates has been increasingly noted. In this review, we provide an overview of the cardiac metabolic network and highlight alterations observed in cardiac pathologies as well as strategies used as metabolic therapies in heart failure. Lastly, the ability of metabolic derivatives to intersect growth and survival are also discussed.

Cardiac Metabolism and Its Interactions with Contraction, Growth, and Survival of the Cardiomyocte

PubMed Central

Kolwicz, Stephen C.; Purohit, Suneet; Tian, Rong

2013-01-01

The network for cardiac fuel metabolism contains intricate sets of interacting pathways that result in both ATP producing and non-ATP producing end-points for each class of energy substrates. The most salient feature of the network is the metabolic flexibility demonstrated in response to various stimuli, including developmental changes and nutritional status. The heart is also capable of remodeling the metabolic pathways in chronic pathophysiological conditions, which results in modulations of myocardial energetics and contractile function. In a quest to understand the complexity of the cardiac metabolic network, pharmacological and genetic tools have been engaged to manipulate cardiac metabolism in a variety of research models. In concert, a host of therapeutic interventions have been tested clinically to target substrate preference, insulin sensitivity, and mitochondrial function. In addition, the contribution of cellular metabolism to growth, survival, and other signaling pathways through the production of metabolic intermediates has been increasingly noted. In this review, we provide an overview of the cardiac metabolic network and highlight alterations observed in cardiac pathologies as well as strategies employed as metabolic therapies in heart failure. Lastly, the ability of metabolic derivatives to intersect growth and survival are also discussed. PMID:23948585

Metabolism and evolution: A comparative study of reconstructed genome-level metabolic networks

NASA Astrophysics Data System (ADS)

Almaas, Eivind

2008-03-01

The availability of high-quality annotations of sequenced genomes has made it possible to generate organism-specific comprehensive maps of cellular metabolism. Currently, more than twenty such metabolic reconstructions are publicly available, with the majority focused on bacteria. A typical metabolic reconstruction for a bacterium results in a complex network containing hundreds of metabolites (nodes) and reactions (links), while some even contain more than a thousand. The constrain-based optimization approach of flux-balance analysis (FBA) is used to investigate the functional characteristics of such large-scale metabolic networks, making it possible to estimate an organism's growth behavior in a wide variety of nutrient environments, as well as its robustness to gene loss. We have recently completed the genome-level metabolic reconstruction of Yersinia pseudotuberculosis, as well as the three Yersinia pestis biovars Antiqua, Mediaevalis, and Orientalis. While Y. pseudotuberculosis typically only causes fever and abdominal pain that can mimic appendicitis, the evolutionary closely related Y. pestis strains are the aetiological agents of the bubonic plague. In this presentation, I will discuss our results and conclusions from a comparative study on the evolution of metabolic function in the four Yersiniae networks using FBA and related techniques, and I will give particular focus to the interplay between metabolic network topology and evolutionary flexibility.

Metabolic network modeling with model organisms.

PubMed

Yilmaz, L Safak; Walhout, Albertha Jm

2017-02-01

Flux balance analysis (FBA) with genome-scale metabolic network models (GSMNM) allows systems level predictions of metabolism in a variety of organisms. Different types of predictions with different accuracy levels can be made depending on the applied experimental constraints ranging from measurement of exchange fluxes to the integration of gene expression data. Metabolic network modeling with model organisms has pioneered method development in this field. In addition, model organism GSMNMs are useful for basic understanding of metabolism, and in the case of animal models, for the study of metabolic human diseases. Here, we discuss GSMNMs of most highly used model organisms with the emphasis on recent reconstructions. Published by Elsevier Ltd.

Metabolic network modeling with model organisms

PubMed Central

Yilmaz, L. Safak; Walhout, Albertha J.M.

2017-01-01

Flux balance analysis (FBA) with genome-scale metabolic network models (GSMNM) allows systems level predictions of metabolism in a variety of organisms. Different types of predictions with different accuracy levels can be made depending on the applied experimental constraints ranging from measurement of exchange fluxes to the integration of gene expression data. Metabolic network modeling with model organisms has pioneered method development in this field. In addition, model organism GSMNMs are useful for basic understanding of metabolism, and in the case of animal models, for the study of metabolic human diseases. Here, we discuss GSMNMs of most highly used model organisms with the emphasis on recent reconstructions. PMID:28088694

Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery☆

PubMed Central

Kell, Douglas B.; Goodacre, Royston

2014-01-01

Metabolism represents the ‘sharp end’ of systems biology, because changes in metabolite concentrations are necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs. To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of the human metabolic network that include the important transporters. Small molecule ‘drug’ transporters are in fact metabolite transporters, because drugs bear structural similarities to metabolites known from the network reconstructions and from measurements of the metabolome. Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia: (i) the effects of inborn errors of metabolism; (ii) which metabolites are exometabolites, and (iii) how metabolism varies between tissues and cellular compartments. However, even these qualitative network models are not yet complete. As our understanding improves so do we recognise more clearly the need for a systems (poly)pharmacology. PMID:23892182

Involvement of Fumarase C and NADH Oxidase in Metabolic Adaptation of Pseudomonas fluorescens Cells Evoked by Aluminum and Gallium Toxicity▿

PubMed Central

Chenier, Daniel; Beriault, Robin; Mailloux, Ryan; Baquie, Mathurin; Abramia, Gia; Lemire, Joseph; Appanna, Vasu

2008-01-01

Iron (Fe) is a critical element in all aerobic organisms as it participates in a variety of metabolic networks. In this study, aluminum (Al) and gallium (Ga), two Fe mimetics, severely impeded the ability of the soil microbe Pseudomonas fluorescens to perform oxidative phosphorylation. This was achieved by disrupting the activity and expression of complexes I, II, and IV. These toxic metals also inactivated aconitase (ACN) and fumarase A (FUM A), two tricarboxylic acid cycle enzymes dependent on Fe for their catalytic activity, while FUM C, an Fe-independent enzyme, displayed an increase in activity and expression under these stressed situations. Furthermore, in the Al- and Ga-exposed cells, the activity and expression of an H2O-forming NADH oxidase were markedly increased. The incubation of the Al- and Ga-challenged cells in an Fe-containing medium led to the recovery of the affected enzymatic activities. Taken together, these data provide novel insights into how environmental pollutants such as Al and Ga interfere with cellular Fe metabolism and also illustrate the ability of Pseudomonas fluorescens to modulate metabolic networks to combat this situation. PMID:18469122

Regulation of yeast central metabolism by enzyme phosphorylation

PubMed Central

Oliveira, Ana Paula; Ludwig, Christina; Picotti, Paola; Kogadeeva, Maria; Aebersold, Ruedi; Sauer, Uwe

2012-01-01

As a frequent post-translational modification, protein phosphorylation regulates many cellular processes. Although several hundred phosphorylation sites have been mapped to metabolic enzymes in Saccharomyces cerevisiae, functionality was demonstrated for few of them. Here, we describe a novel approach to identify in vivo functionality of enzyme phosphorylation by combining flux analysis with proteomics and phosphoproteomics. Focusing on the network of 204 enzymes that constitute the yeast central carbon and amino-acid metabolism, we combined protein and phosphoprotein levels to identify 35 enzymes that change their degree of phosphorylation during growth under five conditions. Correlations between previously determined intracellular fluxes and phosphoprotein abundances provided first functional evidence for five novel phosphoregulated enzymes in this network, adding to nine known phosphoenzymes. For the pyruvate dehydrogenase complex E1 α subunit Pda1 and the newly identified phosphoregulated glycerol-3-phosphate dehydrogenase Gpd1 and phosphofructose-1-kinase complex β subunit Pfk2, we then validated functionality of specific phosphosites through absolute peptide quantification by targeted mass spectrometry, metabolomics and physiological flux analysis in mutants with genetically removed phosphosites. These results demonstrate the role of phosphorylation in controlling the metabolic flux realised by these three enzymes. PMID:23149688

Redesigning metabolism based on orthogonality principles

PubMed Central

Pandit, Aditya Vikram; Srinivasan, Shyam; Mahadevan, Radhakrishnan

2017-01-01

Modifications made during metabolic engineering for overproduction of chemicals have network-wide effects on cellular function due to ubiquitous metabolic interactions. These interactions, that make metabolic network structures robust and optimized for cell growth, act to constrain the capability of the cell factory. To overcome these challenges, we explore the idea of an orthogonal network structure that is designed to operate with minimal interaction between chemical production pathways and the components of the network that produce biomass. We show that this orthogonal pathway design approach has significant advantages over contemporary growth-coupled approaches using a case study on succinate production. We find that natural pathways, fundamentally linked to biomass synthesis, are less orthogonal in comparison to synthetic pathways. We suggest that the use of such orthogonal pathways can be highly amenable for dynamic control of metabolism and have other implications for metabolic engineering. PMID:28555623

A systems biology approach toward understanding seed composition in soybean.

PubMed

Li, Ling; Hur, Manhoi; Lee, Joon-Yong; Zhou, Wenxu; Song, Zhihong; Ransom, Nick; Demirkale, Cumhur Yusuf; Nettleton, Dan; Westgate, Mark; Arendsee, Zebulun; Iyer, Vidya; Shanks, Jackie; Nikolau, Basil; Wurtele, Eve Syrkin

2015-01-01

The molecular, biochemical, and genetic mechanisms that regulate the complex metabolic network of soybean seed development determine the ultimate balance of protein, lipid, and carbohydrate stored in the mature seed. Many of the genes and metabolites that participate in seed metabolism are unknown or poorly defined; even more remains to be understood about the regulation of their metabolic networks. A global omics analysis can provide insights into the regulation of seed metabolism, even without a priori assumptions about the structure of these networks. With the future goal of predictive biology in mind, we have combined metabolomics, transcriptomics, and metabolic flux technologies to reveal the global developmental and metabolic networks that determine the structure and composition of the mature soybean seed. We have coupled this global approach with interactive bioinformatics and statistical analyses to gain insights into the biochemical programs that determine soybean seed composition. For this purpose, we used Plant/Eukaryotic and Microbial Metabolomics Systems Resource (PMR, http://www.metnetdb.org/pmr, a platform that incorporates metabolomics data to develop hypotheses concerning the organization and regulation of metabolic networks, and MetNet systems biology tools http://www.metnetdb.org for plant omics data, a framework to enable interactive visualization of metabolic and regulatory networks. This combination of high-throughput experimental data and bioinformatics analyses has revealed sets of specific genes, genetic perturbations and mechanisms, and metabolic changes that are associated with the developmental variation in soybean seed composition. Researchers can explore these metabolomics and transcriptomics data interactively at PMR.

Systems Rebalancing of Metabolism in Response to Sulfur Deprivation, as Revealed by Metabolome Analysis of Arabidopsis Plants1[w

PubMed Central

Nikiforova, Victoria J.; Kopka, Joachim; Tolstikov, Vladimir; Fiehn, Oliver; Hopkins, Laura; Hawkesford, Malcolm J.; Hesse, Holger; Hoefgen, Rainer

2005-01-01

Sulfur is an essential macroelement in plant and animal nutrition. Plants assimilate inorganic sulfate into two sulfur-containing amino acids, cysteine and methionine. Low supply of sulfate leads to decreased sulfur pools within plant tissues. As sulfur-related metabolites represent an integral part of plant metabolism with multiple interactions, sulfur deficiency stress induces a number of adaptive responses, which must be coordinated. To reveal the coordinating network of adaptations to sulfur deficiency, metabolite profiling of Arabidopsis has been undertaken. Gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry techniques revealed the response patterns of 6,023 peaks of nonredundant ion traces and relative concentration levels of 134 nonredundant compounds of known chemical structure. Here, we provide a catalogue of the detected metabolic changes and reconstruct the coordinating network of their mutual influences. The observed decrease in biomass, as well as in levels of proteins, chlorophylls, and total RNA, gives evidence for a general reduction of metabolic activity under conditions of depleted sulfur supply. This is achieved by a systemic adjustment of metabolism involving the major metabolic pathways. Sulfur/carbon/nitrogen are partitioned by accumulation of metabolites along the pathway O-acetylserine to serine to glycine, and are further channeled together with the nitrogen-rich compound glutamine into allantoin. Mutual influences between sulfur assimilation, nitrogen imbalance, lipid breakdown, purine metabolism, and enhanced photorespiration associated with sulfur-deficiency stress are revealed in this study. These responses may be assembled into a global scheme of metabolic regulation induced by sulfur nutritional stress, which optimizes resources for seed production. PMID:15834012

Integrated Regulatory and Metabolic Networks of the Marine Diatom Phaeodactylum tricornutum Predict the Response to Rising CO 2 Levels

DOE PAGES

Levering, Jennifer; Dupont, Christopher L.; Allen, Andrew E.; ...

2017-02-14

Diatoms are eukaryotic microalgae that are responsible for up to 40% of the ocean’s primary productivity. How diatoms respond to environmental perturbations such as elevated carbon concentrations in the atmosphere is currently poorly understood. We developed a transcriptional regulatory network based on various transcriptome sequencing expression libraries for different environmental responses to gain insight into the marine diatom’s metabolic and regulatory interactions and provide a comprehensive framework of responses to increasing atmospheric carbon levels. This transcriptional regulatory network was integrated with a recently published genome-scale metabolic model of Phaeodactylum tricornutum to explore the connectivity of the regulatory network and sharedmore » metabolites. The integrated regulatory and metabolic model revealed highly connected modules within carbon and nitrogen metabolism. P. tricornutum’s response to rising carbon levels was analyzed by using the recent genome-scale metabolic model with cross comparison to experimental manipulations of carbon dioxide. Using a systems biology approach, we studied the response of the marine diatom Phaeodactylum tricornutum to changing atmospheric carbon concentrations on an ocean-wide scale. By integrating an available genome-scale metabolic model and a newly developed transcriptional regulatory network inferred from transcriptome sequencing expression data, we demonstrate that carbon metabolism and nitrogen metabolism are strongly connected and the genes involved are coregulated in this model diatom. These tight regulatory constraints could play a major role during the adaptation of P. tricornutum to increasing carbon levels. The transcriptional regulatory network developed can be further used to study the effects of different environmental perturbations on P. tricornutum’s metabolism.« less

Integrated Regulatory and Metabolic Networks of the Marine Diatom Phaeodactylum tricornutum Predict the Response to Rising CO 2 Levels

DOE Office of Scientific and Technical Information (OSTI.GOV)

Levering, Jennifer; Dupont, Christopher L.; Allen, Andrew E.

Diatoms are eukaryotic microalgae that are responsible for up to 40% of the ocean’s primary productivity. How diatoms respond to environmental perturbations such as elevated carbon concentrations in the atmosphere is currently poorly understood. We developed a transcriptional regulatory network based on various transcriptome sequencing expression libraries for different environmental responses to gain insight into the marine diatom’s metabolic and regulatory interactions and provide a comprehensive framework of responses to increasing atmospheric carbon levels. This transcriptional regulatory network was integrated with a recently published genome-scale metabolic model of Phaeodactylum tricornutum to explore the connectivity of the regulatory network and sharedmore » metabolites. The integrated regulatory and metabolic model revealed highly connected modules within carbon and nitrogen metabolism. P. tricornutum’s response to rising carbon levels was analyzed by using the recent genome-scale metabolic model with cross comparison to experimental manipulations of carbon dioxide. Using a systems biology approach, we studied the response of the marine diatom Phaeodactylum tricornutum to changing atmospheric carbon concentrations on an ocean-wide scale. By integrating an available genome-scale metabolic model and a newly developed transcriptional regulatory network inferred from transcriptome sequencing expression data, we demonstrate that carbon metabolism and nitrogen metabolism are strongly connected and the genes involved are coregulated in this model diatom. These tight regulatory constraints could play a major role during the adaptation of P. tricornutum to increasing carbon levels. The transcriptional regulatory network developed can be further used to study the effects of different environmental perturbations on P. tricornutum’s metabolism.« less

Underlying Principles of Natural Selection in Network Evolution: Systems Biology Approach

PubMed Central

Chen, Bor-Sen; Wu, Wei-Sheng

2007-01-01

Systems biology is a rapidly expanding field that integrates diverse areas of science such as physics, engineering, computer science, mathematics, and biology toward the goal of elucidating the underlying principles of hierarchical metabolic and regulatory systems in the cell, and ultimately leading to predictive understanding of cellular response to perturbations. Because post-genomics research is taking place throughout the tree of life, comparative approaches offer a way for combining data from many organisms to shed light on the evolution and function of biological networks from the gene to the organismal level. Therefore, systems biology can build on decades of theoretical work in evolutionary biology, and at the same time evolutionary biology can use the systems biology approach to go in new uncharted directions. In this study, we present a review of how the post-genomics era is adopting comparative approaches and dynamic system methods to understand the underlying design principles of network evolution and to shape the nascent field of evolutionary systems biology. Finally, the application of evolutionary systems biology to robust biological network designs is also discussed from the synthetic biology perspective. PMID:19468310

A Systems Biology Analysis Unfolds the Molecular Pathways and Networks of Two Proteobacteria in Spaceflight and Simulated Microgravity Conditions.

PubMed

Roy, Raktim; Shilpa, P Phani; Bagh, Sangram

2016-09-01

Bacteria are important organisms for space missions due to their increased pathogenesis in microgravity that poses risks to the health of astronauts and for projected synthetic biology applications at the space station. We understand little about the effect, at the molecular systems level, of microgravity on bacteria, despite their significant incidence. In this study, we proposed a systems biology pipeline and performed an analysis on published gene expression data sets from multiple seminal studies on Pseudomonas aeruginosa and Salmonella enterica serovar Typhimurium under spaceflight and simulated microgravity conditions. By applying gene set enrichment analysis on the global gene expression data, we directly identified a large number of new, statistically significant cellular and metabolic pathways involved in response to microgravity. Alteration of metabolic pathways in microgravity has rarely been reported before, whereas in this analysis metabolic pathways are prevalent. Several of those pathways were found to be common across studies and species, indicating a common cellular response in microgravity. We clustered genes based on their expression patterns using consensus non-negative matrix factorization. The genes from different mathematically stable clusters showed protein-protein association networks with distinct biological functions, suggesting the plausible functional or regulatory network motifs in response to microgravity. The newly identified pathways and networks showed connection with increased survival of pathogens within macrophages, virulence, and antibiotic resistance in microgravity. Our work establishes a systems biology pipeline and provides an integrated insight into the effect of microgravity at the molecular systems level. Systems biology-Microgravity-Pathways and networks-Bacteria. Astrobiology 16, 677-689.

Topological Organization of Metabolic Brain Networks in Pre-Chemotherapy Cancer with Depression: A Resting-State PET Study

PubMed Central

An, Jianping; Chen, Xuejiao; Xie, Yuanwei; Zhao, Hui; Mao, Junfeng; Liang, Wangsheng; Ma, Xiangxing

2016-01-01

This study aimed to investigate the metabolic brain network and its relationship with depression symptoms using 18F-fluorodeoxyglucose positron emission tomography data in 78 pre-chemotherapy cancer patients with depression and 80 matched healthy subjects. Functional and structural imbalance or disruption of brain networks frequently occur following chemotherapy in cancer patients. However, few studies have focused on the topological organization of the metabolic brain network in cancer with depression, especially those without chemotherapy. The nodal and global parameters of the metabolic brain network were computed for cancer patients and healthy subjects. Significant decreases in metabolism were found in the frontal and temporal gyri in cancer patients compared with healthy subjects. Negative correlations between depression and metabolism were found predominantly in the inferior frontal and cuneus regions, whereas positive correlations were observed in several regions, primarily including the insula, hippocampus, amygdala, and middle temporal gyri. Furthermore, a higher clustering efficiency, longer path length, and fewer hubs were found in cancer patients compared with healthy subjects. The topological organization of the whole-brain metabolic networks may be disrupted in cancer. Finally, the present findings may provide a new avenue for exploring the neurobiological mechanism, which plays a key role in lessening the depression effects in pre-chemotherapy cancer patients. PMID:27832148

Topological Organization of Metabolic Brain Networks in Pre-Chemotherapy Cancer with Depression: A Resting-State PET Study.

PubMed

Fang, Lei; Yao, Zhijun; An, Jianping; Chen, Xuejiao; Xie, Yuanwei; Zhao, Hui; Mao, Junfeng; Liang, Wangsheng; Ma, Xiangxing

2016-01-01

This study aimed to investigate the metabolic brain network and its relationship with depression symptoms using 18F-fluorodeoxyglucose positron emission tomography data in 78 pre-chemotherapy cancer patients with depression and 80 matched healthy subjects. Functional and structural imbalance or disruption of brain networks frequently occur following chemotherapy in cancer patients. However, few studies have focused on the topological organization of the metabolic brain network in cancer with depression, especially those without chemotherapy. The nodal and global parameters of the metabolic brain network were computed for cancer patients and healthy subjects. Significant decreases in metabolism were found in the frontal and temporal gyri in cancer patients compared with healthy subjects. Negative correlations between depression and metabolism were found predominantly in the inferior frontal and cuneus regions, whereas positive correlations were observed in several regions, primarily including the insula, hippocampus, amygdala, and middle temporal gyri. Furthermore, a higher clustering efficiency, longer path length, and fewer hubs were found in cancer patients compared with healthy subjects. The topological organization of the whole-brain metabolic networks may be disrupted in cancer. Finally, the present findings may provide a new avenue for exploring the neurobiological mechanism, which plays a key role in lessening the depression effects in pre-chemotherapy cancer patients.

Apathy and impaired emotional facial recognition networks overlap in Parkinson's disease: a PET study with conjunction analyses.

PubMed

Robert, Gabriel; Le Jeune, Florence; Dondaine, Thibault; Drapier, Sophie; Péron, Julie; Lozachmeur, Clément; Sauleau, Paul; Houvenaghel, Jean-François; Travers, David; Millet, Bruno; Vérin, Marc; Drapier, Dominique

2014-10-01

Apathy is a disabling non-motor symptom that is frequently observed in Parkinson's disease (PD). Its description and physiopathology suggest that it is partially mediated by emotional impairment, but this research issue has never been addressed at a clinical and metabolic level. We therefore conducted a metabolic study using (18)fluorodeoxyglucose positron emission tomography ((18)FDG PET) in 36 PD patients without depression and dementia. Apathy was assessed on the Apathy Evaluation Scale (AES), and emotional facial recognition (EFR) performances (ie, percentage of correct responses) were calculated for each patient. Confounding factors such as age, antiparkinsonian and antidepressant medication, global cognitive functions and depressive symptoms were controlled for. We found a significant negative correlation between AES scores and performances on the EFR task. The apathy network was characterised by increased metabolism within the left posterior cingulate (PC) cortex (Brodmann area (BA) 31). The impaired EFR network was characterised by decreased metabolism within the bilateral PC gyrus (BA 31), right superior frontal gyrus (BAs 10, 9 and 6) and left superior frontal gyrus (BA 10 and 11). By applying conjunction analyses to both networks, we identified the right premotor cortex (BA 6), right orbitofrontal cortex (BA 10), left middle frontal gyrus (BA 8) and left posterior cingulate gyrus (BA 31) as the structures supporting the association between apathy and impaired EFR. These results confirm that apathy in PD is partially mediated by impaired EFR, opening up new prospects for alleviating apathy in PD, such as emotional rehabilitation. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

GSA-PCA: gene set generation by principal component analysis of the Laplacian matrix of a metabolic network

PubMed Central

2012-01-01

Background Gene Set Analysis (GSA) has proven to be a useful approach to microarray analysis. However, most of the method development for GSA has focused on the statistical tests to be used rather than on the generation of sets that will be tested. Existing methods of set generation are often overly simplistic. The creation of sets from individual pathways (in isolation) is a poor reflection of the complexity of the underlying metabolic network. We have developed a novel approach to set generation via the use of Principal Component Analysis of the Laplacian matrix of a metabolic network. We have analysed a relatively simple data set to show the difference in results between our method and the current state-of-the-art pathway-based sets. Results The sets generated with this method are semi-exhaustive and capture much of the topological complexity of the metabolic network. The semi-exhaustive nature of this method has also allowed us to design a hypergeometric enrichment test to determine which genes are likely responsible for set significance. We show that our method finds significant aspects of biology that would be missed (i.e. false negatives) and addresses the false positive rates found with the use of simple pathway-based sets. Conclusions The set generation step for GSA is often neglected but is a crucial part of the analysis as it defines the full context for the analysis. As such, set generation methods should be robust and yield as complete a representation of the extant biological knowledge as possible. The method reported here achieves this goal and is demonstrably superior to previous set analysis methods. PMID:22876834

Unraveling novel broad-spectrum antibacterial targets in food and waterborne pathogens using comparative genomics and protein interaction network analysis.

PubMed

Jadhav, Ankush; Shanmugham, Buvaneswari; Rajendiran, Anjana; Pan, Archana

2014-10-01

Food and waterborne diseases are a growing concern in terms of human morbidity and mortality worldwide, even in the 21st century, emphasizing the need for new therapeutic interventions for these diseases. The current study aims at prioritizing broad-spectrum antibacterial targets, present in multiple food and waterborne bacterial pathogens, through a comparative genomics strategy coupled with a protein interaction network analysis. The pathways unique and common to all the pathogens under study (viz., methane metabolism, d-alanine metabolism, peptidoglycan biosynthesis, bacterial secretion system, two-component system, C5-branched dibasic acid metabolism), identified by comparative metabolic pathway analysis, were considered for the analysis. The proteins/enzymes involved in these pathways were prioritized following host non-homology analysis, essentiality analysis, gut flora non-homology analysis and protein interaction network analysis. The analyses revealed a set of promising broad-spectrum antibacterial targets, present in multiple food and waterborne pathogens, which are essential for bacterial survival, non-homologous to host and gut flora, and functionally important in the metabolic network. The identified broad-spectrum candidates, namely, integral membrane protein/virulence factor (MviN), preprotein translocase subunits SecB and SecG, carbon storage regulator (CsrA), and nitrogen regulatory protein P-II 1 (GlnB), contributed by the peptidoglycan pathway, bacterial secretion systems and two-component systems, were also found to be present in a wide range of other disease-causing bacteria. Cytoplasmic proteins SecG, CsrA and GlnB were considered as drug targets, while membrane proteins MviN and SecB were classified as vaccine targets. The identified broad-spectrum targets can aid in the design and development of antibacterial agents not only against food and waterborne pathogens but also against other pathogens. Copyright © 2014 Elsevier B.V. All rights reserved.

Structure versus time in the evolutionary diversification of avian carotenoid metabolic networks.

PubMed

Morrison, Erin S; Badyaev, Alexander V

2018-05-01

Historical associations of genes and proteins are thought to delineate pathways available to subsequent evolution; however, the effects of past functional involvements on contemporary evolution are rarely quantified. Here, we examined the extent to which the structure of a carotenoid enzymatic network persists in avian evolution. Specifically, we tested whether the evolution of carotenoid networks was most concordant with phylogenetically structured expansion from core reactions of common ancestors or with subsampling of biochemical pathway modules from an ancestral network. We compared structural and historical associations in 467 carotenoid networks of extant and ancestral species and uncovered the overwhelming effect of pre-existing metabolic network structure on carotenoid diversification over the last 50 million years of avian evolution. Over evolutionary time, birds repeatedly subsampled and recombined conserved biochemical modules, which likely maintained the overall structure of the carotenoid metabolic network during avian evolution. These findings explain the recurrent convergence of evolutionary distant species in carotenoid metabolism and weak phylogenetic signal in avian carotenoid evolution. Remarkable retention of an ancient metabolic structure throughout extensive and prolonged ecological diversification in avian carotenoid metabolism illustrates a fundamental requirement of organismal evolution - historical continuity of a deterministic network that links past and present functional associations of its components. © 2018 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2018 European Society For Evolutionary Biology.

Gene expression networks underlying ovarian development in wild largemouth bass (Micropterus salmoides).

PubMed

Martyniuk, Christopher J; Prucha, Melinda S; Doperalski, Nicholas J; Antczak, Philipp; Kroll, Kevin J; Falciani, Francesco; Barber, David S; Denslow, Nancy D

2013-01-01

Oocyte maturation in fish involves numerous cell signaling cascades that are activated or inhibited during specific stages of oocyte development. The objectives of this study were to characterize molecular pathways and temporal gene expression patterns throughout a complete breeding cycle in wild female largemouth bass to improve understanding of the molecular sequence of events underlying oocyte maturation. Transcriptomic analysis was performed on eight morphologically diverse stages of the ovary, including primary and secondary stages of oocyte growth, ovulation, and atresia. Ovary histology, plasma vitellogenin, 17β-estradiol, and testosterone were also measured to correlate with gene networks. Global expression patterns revealed dramatic differences across ovarian development, with 552 and 2070 genes being differentially expressed during both ovulation and atresia respectively. Gene set enrichment analysis (GSEA) revealed that early primary stages of oocyte growth involved increases in expression of genes involved in pathways of B-cell and T-cell receptor-mediated signaling cascades and fibronectin regulation. These pathways as well as pathways that included adrenergic receptor signaling, sphingolipid metabolism and natural killer cell activation were down-regulated at ovulation. At atresia, down-regulated pathways included gap junction and actin cytoskeleton regulation, gonadotrope and mast cell activation, and vasopressin receptor signaling and up-regulated pathways included oxidative phosphorylation and reactive oxygen species metabolism. Expression targets for luteinizing hormone signaling were low during vitellogenesis but increased 150% at ovulation. Other networks found to play a significant role in oocyte maturation included those with genes regulated by members of the TGF-beta superfamily (activins, inhibins, bone morphogenic protein 7 and growth differentiation factor 9), neuregulin 1, retinoid X receptor, and nerve growth factor family. This study offers novel insight into the gene networks underlying vitellogenesis, ovulation and atresia and generates new hypotheses about the cellular pathways regulating oocyte maturation.

Interconnectivity of human cellular metabolism and disease prevalence

NASA Astrophysics Data System (ADS)

Lee, Deok-Sun

2010-12-01

Fluctuations of metabolic reaction fluxes may cause abnormal concentrations of toxic or essential metabolites, possibly leading to metabolic diseases. The mutual binding of enzymatic proteins and ones involving common metabolites enforces distinct coupled reactions, by which local perturbations may spread through the cellular network. Such network effects at the molecular interaction level in human cellular metabolism can reappear in the patterns of disease occurrence. Here we construct the enzyme-reaction network and the metabolite-reaction network, capturing the flux coupling of metabolic reactions caused by the interacting enzymes and the shared metabolites, respectively. Diseases potentially caused by the failure of individual metabolic reactions can be identified by using the known disease-gene association, which allows us to derive the probability of an inactivated reaction causing diseases from the disease records at the population level. We find that the greater the number of proteins that catalyze a reaction, the higher the mean prevalence of its associated diseases. Moreover, the number of connected reactions and the mean size of the avalanches in the networks constructed are also shown to be positively correlated with the disease prevalence. These findings illuminate the impact of the cellular network topology on disease development, suggesting that the global organization of the molecular interaction network should be understood to assist in disease diagnosis, treatment, and drug discovery.

Reconstruction of the yeast Snf1 kinase regulatory network reveals its role as a global energy regulator

PubMed Central

Usaite, Renata; Jewett, Michael C; Oliveira, Ana Paula; Yates, John R; Olsson, Lisbeth; Nielsen, Jens

2009-01-01

Highly conserved among eukaryotic cells, the AMP-activated kinase (AMPK) is a central regulator of carbon metabolism. To map the complete network of interactions around AMPK in yeast (Snf1) and to evaluate the role of its regulatory subunit Snf4, we measured global mRNA, protein and metabolite levels in wild type, Δsnf1, Δsnf4, and Δsnf1Δsnf4 knockout strains. Using four newly developed computational tools, including novel DOGMA sub-network analysis, we showed the benefits of three-level ome-data integration to uncover the global Snf1 kinase role in yeast. We for the first time identified Snf1's global regulation on gene and protein expression levels, and showed that yeast Snf1 has a far more extensive function in controlling energy metabolism than reported earlier. Additionally, we identified complementary roles of Snf1 and Snf4. Similar to the function of AMPK in humans, our findings showed that Snf1 is a low-energy checkpoint and that yeast can be used more extensively as a model system for studying the molecular mechanisms underlying the global regulation of AMPK in mammals, failure of which leads to metabolic diseases. PMID:19888214

Microalgal Metabolic Network Model Refinement through High-Throughput Functional Metabolic Profiling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chaiboonchoe, Amphun; Dohai, Bushra Saeed; Cai, Hong

2014-12-10

Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology (Biolog, Hayward, CA, USA) provides an efficient, high-throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverifiedmore » reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi, but it has not been reported for the phenotyping of microalgae. Here, we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of d-amino acids, l-dipeptides, and l-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates.« less

Microalgal Metabolic Network Model Refinement through High-Throughput Functional Metabolic Profiling

PubMed Central

Chaiboonchoe, Amphun; Dohai, Bushra Saeed; Cai, Hong; Nelson, David R.; Jijakli, Kenan; Salehi-Ashtiani, Kourosh

2014-01-01

Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology (Biolog, Hayward, CA, USA) provides an efficient, high-throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverified reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi, but it has not been reported for the phenotyping of microalgae. Here, we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of d-amino acids, l-dipeptides, and l-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates. PMID:25540776

Microalgal Metabolic Network Model Refinement through High-Throughput Functional Metabolic Profiling.

PubMed

Chaiboonchoe, Amphun; Dohai, Bushra Saeed; Cai, Hong; Nelson, David R; Jijakli, Kenan; Salehi-Ashtiani, Kourosh

2014-01-01

Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology (Biolog, Hayward, CA, USA) provides an efficient, high-throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverified reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi, but it has not been reported for the phenotyping of microalgae. Here, we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of d-amino acids, l-dipeptides, and l-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates.

Modeling cellular compartmentation in one-carbon metabolism

PubMed Central

Scotti, Marco; Stella, Lorenzo; Shearer, Emily J.; Stover, Patrick J.

2015-01-01

Folate-mediated one-carbon metabolism (FOCM) is associated with risk for numerous pathological states including birth defects, cancers, and chronic diseases. Although the enzymes that constitute the biological pathways have been well described and their interdependency through the shared use of folate cofactors appreciated, the biological mechanisms underlying disease etiologies remain elusive. The FOCM network is highly sensitive to nutritional status of several B-vitamins and numerous penetrant gene variants that alter network outputs, but current computational approaches do not fully capture the dynamics and stochastic noise of the system. Combining the stochastic approach with a rule-based representation will help model the intrinsic noise displayed by FOCM, address the limited flexibility of standard simulation methods for coarse-graining the FOCM-associated biochemical processes, and manage the combinatorial complexity emerging from reactions within FOCM that would otherwise be intractable. PMID:23408533

Synthetic biology: exploring and exploiting genetic modularity through the design of novel biological networks.

PubMed

Agapakis, Christina M; Silver, Pamela A

2009-07-01

Synthetic biology has been used to describe many biological endeavors over the past thirty years--from designing enzymes and in vitro systems, to manipulating existing metabolisms and gene expression, to creating entirely synthetic replicating life forms. What separates the current incarnation of synthetic biology from the recombinant DNA technology or metabolic engineering of the past is an emphasis on principles from engineering such as modularity, standardization, and rigorously predictive models. As such, synthetic biology represents a new paradigm for learning about and using biological molecules and data, with applications in basic science, biotechnology, and medicine. This review covers the canonical examples as well as some recent advances in synthetic biology in terms of what we know and what we can learn about the networks underlying biology, and how this endeavor may shape our understanding of living systems.

Use of historic metabolic biotransformation data as a means of anticipating metabolic sites using MetaPrint2D and Bioclipse.

PubMed

Carlsson, Lars; Spjuth, Ola; Adams, Samuel; Glen, Robert C; Boyer, Scott

2010-07-01

Predicting metabolic sites is important in the drug discovery process to aid in rapid compound optimisation. No interactive tool exists and most of the useful tools are quite expensive. Here a fast and reliable method to analyse ligands and visualise potential metabolic sites is presented which is based on annotated metabolic data, described by circular fingerprints. The method is available via the graphical workbench Bioclipse, which is equipped with advanced features in cheminformatics. Due to the speed of predictions (less than 50 ms per molecule), scientists can get real time decision support when editing chemical structures. Bioclipse is a rich client, which means that all calculations are performed on the local computer and do not require network connection. Bioclipse and MetaPrint2D are free for all users, released under open source licenses, and available from http://www.bioclipse.net.

Gene regulation network behind drought escape, avoidance and tolerance strategies in black poplar (Populus nigra L.).

PubMed

Yıldırım, Kubilay; Kaya, Zeki

2017-06-01

Drought is the major environmental problem limiting the productivity and survival of plant species. Here, previously identified three black poplar genotypes having contrasting response to drought were subjected to gradual soil water depletion in a pot trial to identify their physiological, morphological and antioxidation related adaptations. We also performed a microarray based transcriptome analyses on the leaves of genotypes by using Affymetrix poplar Genome Array containing 56,000 transcripts. Phenotypic analyses of each genotype confirmed their differential adaptations to drought that could be classified as drought escape, avoidance and tolerance. Comparative transcriptomic analysis indicated highly divergent gene expression patterns among the genotypes in response to drought and post drought re-watering (PDR). We identified 10641, 3824 and 9411 transcripts exclusively regulated in drought escape, avoidance and tolerant genotypes, respectively. The key genes involved in metabolic pathways, such as carbohydrate metabolism, photosynthesis, lipid metabolism, generation of precursor metabolites/energy, protein folding, redox homeostasis, secondary metabolic process and cell wall component biogenesis, were affected by drought stresses in the leaves of these genotypes. Transcript isoforms showed increased expression specificity in the genes coding for bark storage proteins and small heat shock proteins in drought tolerant genotype. On the other hand, drought-avoiding genotype specifically induced the transcripts annotated to the genes functional in secondary metabolite production that linked to enhanced leaf water content and growth performance under drought stress. Transcriptome profiling of drought escape genotype indicated specific regulation of the genes functional in programmed cell death and leaf senescence. Specific upregulation of GTP cyclohydrolase II and transcription factors (WRKY and ERFs) in only this genotype were associated to ROS dependent signalling pathways and gene regulation network responsible in induction of many degrading enzymes acting on cell wall carbohydrates, fatty acids and proteins under drought stress. Our findings provide new insights into the transcriptome dynamics and components of regulatory network associated with drought adaptation strategies. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Analyzing the genes related to Alzheimer's disease via a network and pathway-based approach.

PubMed

Hu, Yan-Shi; Xin, Juncai; Hu, Ying; Zhang, Lei; Wang, Ju

2017-04-27

Our understanding of the molecular mechanisms underlying Alzheimer's disease (AD) remains incomplete. Previous studies have revealed that genetic factors provide a significant contribution to the pathogenesis and development of AD. In the past years, numerous genes implicated in this disease have been identified via genetic association studies on candidate genes or at the genome-wide level. However, in many cases, the roles of these genes and their interactions in AD are still unclear. A comprehensive and systematic analysis focusing on the biological function and interactions of these genes in the context of AD will therefore provide valuable insights to understand the molecular features of the disease. In this study, we collected genes potentially associated with AD by screening publications on genetic association studies deposited in PubMed. The major biological themes linked with these genes were then revealed by function and biochemical pathway enrichment analysis, and the relation between the pathways was explored by pathway crosstalk analysis. Furthermore, the network features of these AD-related genes were analyzed in the context of human interactome and an AD-specific network was inferred using the Steiner minimal tree algorithm. We compiled 430 human genes reported to be associated with AD from 823 publications. Biological theme analysis indicated that the biological processes and biochemical pathways related to neurodevelopment, metabolism, cell growth and/or survival, and immunology were enriched in these genes. Pathway crosstalk analysis then revealed that the significantly enriched pathways could be grouped into three interlinked modules-neuronal and metabolic module, cell growth/survival and neuroendocrine pathway module, and immune response-related module-indicating an AD-specific immune-endocrine-neuronal regulatory network. Furthermore, an AD-specific protein network was inferred and novel genes potentially associated with AD were identified. By means of network and pathway-based methodology, we explored the pathogenetic mechanism underlying AD at a systems biology level. Results from our work could provide valuable clues for understanding the molecular mechanism underlying AD. In addition, the framework proposed in this study could be used to investigate the pathological molecular network and genes relevant to other complex diseases or phenotypes.

Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network

PubMed Central

Martín-Jiménez, Cynthia A.; Salazar-Barreto, Diego; Barreto, George E.; González, Janneth

2017-01-01

Astrocytes are the most abundant cells of the central nervous system; they have a predominant role in maintaining brain metabolism. In this sense, abnormal metabolic states have been found in different neuropathological diseases. Determination of metabolic states of astrocytes is difficult to model using current experimental approaches given the high number of reactions and metabolites present. Thus, genome-scale metabolic networks derived from transcriptomic data can be used as a framework to elucidate how astrocytes modulate human brain metabolic states during normal conditions and in neurodegenerative diseases. We performed a Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network with the purpose of elucidating a significant portion of the metabolic map of the astrocyte. This is the first global high-quality, manually curated metabolic reconstruction network of a human astrocyte. It includes 5,007 metabolites and 5,659 reactions distributed among 8 cell compartments, (extracellular, cytoplasm, mitochondria, endoplasmic reticle, Golgi apparatus, lysosome, peroxisome and nucleus). Using the reconstructed network, the metabolic capabilities of human astrocytes were calculated and compared both in normal and ischemic conditions. We identified reactions activated in these two states, which can be useful for understanding the astrocytic pathways that are affected during brain disease. Additionally, we also showed that the obtained flux distributions in the model, are in accordance with literature-based findings. Up to date, this is the most complete representation of the human astrocyte in terms of inclusion of genes, proteins, reactions and metabolic pathways, being a useful guide for in-silico analysis of several metabolic behaviors of the astrocyte during normal and pathologic states. PMID:28243200

Metabolic GARD: Replicating Catalytic Network of Lipid-Anchored Metabolites

NASA Astrophysics Data System (ADS)

Lancet, D.; Zidovetzki, R.; Shenhav, B.; Markovitch, O.

2017-07-01

We propose a computer-simulated M-GARD model, with mutually catalytic metabolic network of amphiphiles. It can show compositional reproduction of both bilayer and lumen content of lipid vesicles, thus joining metabolism, compartment and replication.

Metabolic Pathways and Networks Associated with Tobacco Use in Military Personnel

PubMed Central

Jones, Dean P.; Walker, Douglas I.; Uppal, Karan; Rohrbeck, Patricia; Mallon, Timothy M.; Go, Young-Mi

2016-01-01

Objective Use high-resolution metabolomics (HRM) to identify metabolic pathways and networks associated with tobacco use in military personnel. Methods Four hundred de-identified samples obtained from the Department of Defense Serum Repository were classified as tobacco users or non-users according to cotinine content. HRM and bioinformatic methods were used to determine pathways and networks associated with classification. Results Eighty individuals were classified as tobacco users compared to 320 non-users based on cotinine levels ≥10 ng/mL. Alterations in lipid and xenobiotic metabolism, and diverse effects on amino acid, sialic acid and purine and pyrimidine metabolism were observed. Importantly, network analysis showed broad effects on metabolic associations not simply linked to well-defined pathways. Conclusions Tobacco use has complex metabolic effects which must be considered in evaluation of deployment-associated environmental exposures in military personnel. PMID:27501098

Metabolic Pathways and Networks Associated With Tobacco Use in Military Personnel.

PubMed

Jones, Dean P; Walker, Douglas I; Uppal, Karan; Rohrbeck, Patricia; Mallon, Col Timothy M; Go, Young-Mi

2016-08-01

The aim of this study is to use high-resolution metabolomics (HRM) to identify metabolic pathways and networks associated with tobacco use in military personnel. Four hundred deidentified samples obtained from the Department of Defense Serum Repository were classified as tobacco users or nonusers according to cotinine content. HRM and bioinformatic methods were used to determine pathways and networks associated with classification. Eighty individuals were classified as tobacco users compared with 320 nonusers on the basis of cotinine levels at least 10 ng/mL. Alterations in lipid and xenobiotic metabolism, and diverse effects on amino acid, sialic acid, and purine and pyrimidine metabolism were observed. Importantly, network analysis showed broad effects on metabolic associations not simply linked to well-defined pathways. Tobacco use has complex metabolic effects that must be considered in evaluation of deployment-associated environmental exposures in military personnel.

Finding elementary flux modes in metabolic networks based on flux balance analysis and flux coupling analysis: application to the analysis of Escherichia coli metabolism.

PubMed

Tabe-Bordbar, Shayan; Marashi, Sayed-Amir

2013-12-01

Elementary modes (EMs) are steady-state metabolic flux vectors with minimal set of active reactions. Each EM corresponds to a metabolic pathway. Therefore, studying EMs is helpful for analyzing the production of biotechnologically important metabolites. However, memory requirements for computing EMs may hamper their applicability as, in most genome-scale metabolic models, no EM can be computed due to running out of memory. In this study, we present a method for computing randomly sampled EMs. In this approach, a network reduction algorithm is used for EM computation, which is based on flux balance-based methods. We show that this approach can be used to recover the EMs in the medium- and genome-scale metabolic network models, while the EMs are sampled in an unbiased way. The applicability of such results is shown by computing “estimated” control-effective flux values in Escherichia coli metabolic network.

Effects of MeJA on Arabidopsis metabolome under endogenous JA deficiency

NASA Astrophysics Data System (ADS)

Cao, Jingjing; Li, Mengya; Chen, Jian; Liu, Pei; Li, Zhen

2016-11-01

Jasmonates (JAs) play important roles in plant growth, development and defense. Comprehensive metabolomics profiling of plants under JA treatment provides insights into the interaction and regulation network of plant hormones. Here we applied high resolution mass spectrometry based metabolomics approach on Arabidopsis wild type and JA synthesis deficiency mutant opr3. The effects of exogenous MeJA treatment on the metabolites of opr3 were investigated. More than 10000 ion signals were detected and more than 2000 signals showed significant variation in different genotypes and treatment groups. Multivariate statistic analyses (PCA and PLS-DA) were performed and a differential compound library containing 174 metabolites with high resolution precursor ion-product ions pairs was obtained. Classification and pathway analysis of 109 identified compounds in this library showed that glucosinolates and tryptophan metabolism, amino acids and small peptides metabolism, lipid metabolism, especially fatty acyls metabolism, were impacted by endogenous JA deficiency and exogenous MeJA treatment. These results were further verified by quantitative reverse transcription PCR (RT-qPCR) analysis of 21 related genes involved in the metabolism of glucosinolates, tryptophan and α-linolenic acid pathways. The results would greatly enhance our understanding of the biological functions of JA.

Path finding methods accounting for stoichiometry in metabolic networks

PubMed Central

2011-01-01

Graph-based methods have been widely used for the analysis of biological networks. Their application to metabolic networks has been much discussed, in particular noting that an important weakness in such methods is that reaction stoichiometry is neglected. In this study, we show that reaction stoichiometry can be incorporated into path-finding approaches via mixed-integer linear programming. This major advance at the modeling level results in improved prediction of topological and functional properties in metabolic networks. PMID:21619601

Cellular metabolic network analysis: discovering important reactions in Treponema pallidum.

PubMed

Chen, Xueying; Zhao, Min; Qu, Hong

2015-01-01

T. pallidum, the syphilis-causing pathogen, performs very differently in metabolism compared with other bacterial pathogens. The desire for safe and effective vaccine of syphilis requests identification of important steps in T. pallidum's metabolism. Here, we apply Flux Balance Analysis to represent the reactions quantitatively. Thus, it is possible to cluster all reactions in T. pallidum. By calculating minimal cut sets and analyzing topological structure for the metabolic network of T. pallidum, critical reactions are identified. As a comparison, we also apply the analytical approaches to the metabolic network of H. pylori to find coregulated drug targets and unique drug targets for different microorganisms. Based on the clustering results, all reactions are further classified into various roles. Therefore, the general picture of their metabolic network is obtained and two types of reactions, both of which are involved in nucleic acid metabolism, are found to be essential for T. pallidum. It is also discovered that both hubs of reactions and the isolated reactions in purine and pyrimidine metabolisms play important roles in T. pallidum. These reactions could be potential drug targets for treating syphilis.

A Systems Biology Analysis Unfolds the Molecular Pathways and Networks of Two Proteobacteria in Spaceflight and Simulated Microgravity Conditions

NASA Astrophysics Data System (ADS)

Roy, Raktim; Phani Shilpa, P.; Bagh, Sangram

2016-09-01

Bacteria are important organisms for space missions due to their increased pathogenesis in microgravity that poses risks to the health of astronauts and for projected synthetic biology applications at the space station. We understand little about the effect, at the molecular systems level, of microgravity on bacteria, despite their significant incidence. In this study, we proposed a systems biology pipeline and performed an analysis on published gene expression data sets from multiple seminal studies on Pseudomonas aeruginosa and Salmonella enterica serovar Typhimurium under spaceflight and simulated microgravity conditions. By applying gene set enrichment analysis on the global gene expression data, we directly identified a large number of new, statistically significant cellular and metabolic pathways involved in response to microgravity. Alteration of metabolic pathways in microgravity has rarely been reported before, whereas in this analysis metabolic pathways are prevalent. Several of those pathways were found to be common across studies and species, indicating a common cellular response in microgravity. We clustered genes based on their expression patterns using consensus non-negative matrix factorization. The genes from different mathematically stable clusters showed protein-protein association networks with distinct biological functions, suggesting the plausible functional or regulatory network motifs in response to microgravity. The newly identified pathways and networks showed connection with increased survival of pathogens within macrophages, virulence, and antibiotic resistance in microgravity. Our work establishes a systems biology pipeline and provides an integrated insight into the effect of microgravity at the molecular systems level.

NIBBS-search for fast and accurate prediction of phenotype-biased metabolic systems.

PubMed

Schmidt, Matthew C; Rocha, Andrea M; Padmanabhan, Kanchana; Shpanskaya, Yekaterina; Banfield, Jill; Scott, Kathleen; Mihelcic, James R; Samatova, Nagiza F

2012-01-01

Understanding of genotype-phenotype associations is important not only for furthering our knowledge on internal cellular processes, but also essential for providing the foundation necessary for genetic engineering of microorganisms for industrial use (e.g., production of bioenergy or biofuels). However, genotype-phenotype associations alone do not provide enough information to alter an organism's genome to either suppress or exhibit a phenotype. It is important to look at the phenotype-related genes in the context of the genome-scale network to understand how the genes interact with other genes in the organism. Identification of metabolic subsystems involved in the expression of the phenotype is one way of placing the phenotype-related genes in the context of the entire network. A metabolic system refers to a metabolic network subgraph; nodes are compounds and edges labels are the enzymes that catalyze the reaction. The metabolic subsystem could be part of a single metabolic pathway or span parts of multiple pathways. Arguably, comparative genome-scale metabolic network analysis is a promising strategy to identify these phenotype-related metabolic subsystems. Network Instance-Based Biased Subgraph Search (NIBBS) is a graph-theoretic method for genome-scale metabolic network comparative analysis that can identify metabolic systems that are statistically biased toward phenotype-expressing organismal networks. We set up experiments with target phenotypes like hydrogen production, TCA expression, and acid-tolerance. We show via extensive literature search that some of the resulting metabolic subsystems are indeed phenotype-related and formulate hypotheses for other systems in terms of their role in phenotype expression. NIBBS is also orders of magnitude faster than MULE, one of the most efficient maximal frequent subgraph mining algorithms that could be adjusted for this problem. Also, the set of phenotype-biased metabolic systems output by NIBBS comes very close to the set of phenotype-biased subgraphs output by an exact maximally-biased subgraph enumeration algorithm ( MBS-Enum ). The code (NIBBS and the module to visualize the identified subsystems) is available at http://freescience.org/cs/NIBBS.

NIBBS-Search for Fast and Accurate Prediction of Phenotype-Biased Metabolic Systems

PubMed Central

Padmanabhan, Kanchana; Shpanskaya, Yekaterina; Banfield, Jill; Scott, Kathleen; Mihelcic, James R.; Samatova, Nagiza F.

2012-01-01

Understanding of genotype-phenotype associations is important not only for furthering our knowledge on internal cellular processes, but also essential for providing the foundation necessary for genetic engineering of microorganisms for industrial use (e.g., production of bioenergy or biofuels). However, genotype-phenotype associations alone do not provide enough information to alter an organism's genome to either suppress or exhibit a phenotype. It is important to look at the phenotype-related genes in the context of the genome-scale network to understand how the genes interact with other genes in the organism. Identification of metabolic subsystems involved in the expression of the phenotype is one way of placing the phenotype-related genes in the context of the entire network. A metabolic system refers to a metabolic network subgraph; nodes are compounds and edges labels are the enzymes that catalyze the reaction. The metabolic subsystem could be part of a single metabolic pathway or span parts of multiple pathways. Arguably, comparative genome-scale metabolic network analysis is a promising strategy to identify these phenotype-related metabolic subsystems. Network Instance-Based Biased Subgraph Search (NIBBS) is a graph-theoretic method for genome-scale metabolic network comparative analysis that can identify metabolic systems that are statistically biased toward phenotype-expressing organismal networks. We set up experiments with target phenotypes like hydrogen production, TCA expression, and acid-tolerance. We show via extensive literature search that some of the resulting metabolic subsystems are indeed phenotype-related and formulate hypotheses for other systems in terms of their role in phenotype expression. NIBBS is also orders of magnitude faster than MULE, one of the most efficient maximal frequent subgraph mining algorithms that could be adjusted for this problem. Also, the set of phenotype-biased metabolic systems output by NIBBS comes very close to the set of phenotype-biased subgraphs output by an exact maximally-biased subgraph enumeration algorithm ( MBS-Enum ). The code (NIBBS and the module to visualize the identified subsystems) is available at http://freescience.org/cs/NIBBS. PMID:22589706

Reconstructing metabolic flux vectors from extreme pathways: defining the alpha-spectrum.

PubMed

Wiback, Sharon J; Mahadevan, Radhakrishnan; Palsson, Bernhard Ø

2003-10-07

The move towards genome-scale analysis of cellular functions has necessitated the development of analytical (in silico) methods to understand such large and complex biochemical reaction networks. One such method is extreme pathway analysis that uses stoichiometry and thermodynamic irreversibly to define mathematically unique, systemic metabolic pathways. These extreme pathways form the edges of a high-dimensional convex cone in the flux space that contains all the attainable steady state solutions, or flux distributions, for the metabolic network. By definition, any steady state flux distribution can be described as a nonnegative linear combination of the extreme pathways. To date, much effort has been focused on calculating, defining, and understanding these extreme pathways. However, little work has been performed to determine how these extreme pathways contribute to a given steady state flux distribution. This study represents an initial effort aimed at defining how physiological steady state solutions can be reconstructed from a network's extreme pathways. In general, there is not a unique set of nonnegative weightings on the extreme pathways that produce a given steady state flux distribution but rather a range of possible values. This range can be determined using linear optimization to maximize and minimize the weightings of a particular extreme pathway in the reconstruction, resulting in what we have termed the alpha-spectrum. The alpha-spectrum defines which extreme pathways can and cannot be included in the reconstruction of a given steady state flux distribution and to what extent they individually contribute to the reconstruction. It is shown that accounting for transcriptional regulatory constraints can considerably shrink the alpha-spectrum. The alpha-spectrum is computed and interpreted for two cases; first, optimal states of a skeleton representation of core metabolism that include transcriptional regulation, and second for human red blood cell metabolism under various physiological, non-optimal conditions.

Osmoregulation in the Halophilic Bacterium Halomonas elongata: A Case Study for Integrative Systems Biology.

PubMed

Kindzierski, Viktoria; Raschke, Silvia; Knabe, Nicole; Siedler, Frank; Scheffer, Beatrix; Pflüger-Grau, Katharina; Pfeiffer, Friedhelm; Oesterhelt, Dieter; Marin-Sanguino, Alberto; Kunte, Hans-Jörg

2017-01-01

Halophilic bacteria use a variety of osmoregulatory methods, such as the accumulation of one or more compatible solutes. The wide diversity of compounds that can act as compatible solute complicates the task of understanding the different strategies that halophilic bacteria use to cope with salt. This is specially challenging when attempting to go beyond the pathway that produces a certain compatible solute towards an understanding of how the metabolic network as a whole addresses the problem. Metabolic reconstruction based on genomic data together with Flux Balance Analysis (FBA) is a promising tool to gain insight into this problem. However, as more of these reconstructions become available, it becomes clear that processes predicted by genome annotation may not reflect the processes that are active in vivo. As a case in point, E. coli is unable to grow aerobically on citrate in spite of having all the necessary genes to do it. It has also been shown that the realization of this genetic potential into an actual capability to metabolize citrate is an extremely unlikely event under normal evolutionary conditions. Moreover, many marine bacteria seem to have the same pathways to metabolize glucose but each species uses a different one. In this work, a metabolic network inferred from genomic annotation of the halophilic bacterium Halomonas elongata and proteomic profiling experiments are used as a starting point to motivate targeted experiments in order to find out some of the defining features of the osmoregulatory strategies of this bacterium. This new information is then used to refine the network in order to describe the actual capabilities of H. elongata, rather than its genetic potential.

Exploring metabolic pathways in genome-scale networks via generating flux modes.

PubMed

Rezola, A; de Figueiredo, L F; Brock, M; Pey, J; Podhorski, A; Wittmann, C; Schuster, S; Bockmayr, A; Planes, F J

2011-02-15

The reconstruction of metabolic networks at the genome scale has allowed the analysis of metabolic pathways at an unprecedented level of complexity. Elementary flux modes (EFMs) are an appropriate concept for such analysis. However, their number grows in a combinatorial fashion as the size of the metabolic network increases, which renders the application of EFMs approach to large metabolic networks difficult. Novel methods are expected to deal with such complexity. In this article, we present a novel optimization-based method for determining a minimal generating set of EFMs, i.e. a convex basis. We show that a subset of elements of this convex basis can be effectively computed even in large metabolic networks. Our method was applied to examine the structure of pathways producing lysine in Escherichia coli. We obtained a more varied and informative set of pathways in comparison with existing methods. In addition, an alternative pathway to produce lysine was identified using a detour via propionyl-CoA, which shows the predictive power of our novel approach. The source code in C++ is available upon request.

Robustness in Regulatory Interaction Networks. A Generic Approach with Applications at Different Levels: Physiologic, Metabolic and Genetic

PubMed Central

Demongeot, Jacques; Ben Amor, Hedi; Elena, Adrien; Gillois, Pierre; Noual, Mathilde; Sené, Sylvain

2009-01-01

Regulatory interaction networks are often studied on their dynamical side (existence of attractors, study of their stability). We focus here also on their robustness, that is their ability to offer the same spatiotemporal patterns and to resist to external perturbations such as losses of nodes or edges in the networks interactions architecture, changes in their environmental boundary conditions as well as changes in the update schedule (or updating mode) of the states of their elements (e.g., if these elements are genes, their synchronous coexpression mode versus their sequential expression). We define the generic notions of boundary, core, and critical vertex or edge of the underlying interaction graph of the regulatory network, whose disappearance causes dramatic changes in the number and nature of attractors (e.g., passage from a bistable behaviour to a unique periodic regime) or in the range of their basins of stability. The dynamic transition of states will be presented in the framework of threshold Boolean automata rules. A panorama of applications at different levels will be given: brain and plant morphogenesis, bulbar cardio-respiratory regulation, glycolytic/oxidative metabolic coupling, and eventually cell cycle and feather morphogenesis genetic control. PMID:20057955

CardioNet: a human metabolic network suited for the study of cardiomyocyte metabolism.

PubMed

Karlstädt, Anja; Fliegner, Daniela; Kararigas, Georgios; Ruderisch, Hugo Sanchez; Regitz-Zagrosek, Vera; Holzhütter, Hermann-Georg

2012-08-29

Availability of oxygen and nutrients in the coronary circulation is a crucial determinant of cardiac performance. Nutrient composition of coronary blood may significantly vary in specific physiological and pathological conditions, for example, administration of special diets, long-term starvation, physical exercise or diabetes. Quantitative analysis of cardiac metabolism from a systems biology perspective may help to a better understanding of the relationship between nutrient supply and efficiency of metabolic processes required for an adequate cardiac output. Here we present CardioNet, the first large-scale reconstruction of the metabolic network of the human cardiomyocyte comprising 1793 metabolic reactions, including 560 transport processes in six compartments. We use flux-balance analysis to demonstrate the capability of the network to accomplish a set of 368 metabolic functions required for maintaining the structural and functional integrity of the cell. Taking the maintenance of ATP, biosynthesis of ceramide, cardiolipin and further important phospholipids as examples, we analyse how a changed supply of glucose, lactate, fatty acids and ketone bodies may influence the efficiency of these essential processes. CardioNet is a functionally validated metabolic network of the human cardiomyocyte that enables theorectical studies of cellular metabolic processes crucial for the accomplishment of an adequate cardiac output.

Advances in the integration of transcriptional regulatory information into genome-scale metabolic models.

PubMed

Vivek-Ananth, R P; Samal, Areejit

2016-09-01

A major goal of systems biology is to build predictive computational models of cellular metabolism. Availability of complete genome sequences and wealth of legacy biochemical information has led to the reconstruction of genome-scale metabolic networks in the last 15 years for several organisms across the three domains of life. Due to paucity of information on kinetic parameters associated with metabolic reactions, the constraint-based modelling approach, flux balance analysis (FBA), has proved to be a vital alternative to investigate the capabilities of reconstructed metabolic networks. In parallel, advent of high-throughput technologies has led to the generation of massive amounts of omics data on transcriptional regulation comprising mRNA transcript levels and genome-wide binding profile of transcriptional regulators. A frontier area in metabolic systems biology has been the development of methods to integrate the available transcriptional regulatory information into constraint-based models of reconstructed metabolic networks in order to increase the predictive capabilities of computational models and understand the regulation of cellular metabolism. Here, we review the existing methods to integrate transcriptional regulatory information into constraint-based models of metabolic networks. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

The large-scale organization of metabolic networks

NASA Astrophysics Data System (ADS)

Jeong, H.; Tombor, B.; Albert, R.; Oltvai, Z. N.; Barabási, A.-L.

2000-10-01

In a cell or microorganism, the processes that generate mass, energy, information transfer and cell-fate specification are seamlessly integrated through a complex network of cellular constituents and reactions. However, despite the key role of these networks in sustaining cellular functions, their large-scale structure is essentially unknown. Here we present a systematic comparative mathematical analysis of the metabolic networks of 43 organisms representing all three domains of life. We show that, despite significant variation in their individual constituents and pathways, these metabolic networks have the same topological scaling properties and show striking similarities to the inherent organization of complex non-biological systems. This may indicate that metabolic organization is not only identical for all living organisms, but also complies with the design principles of robust and error-tolerant scale-free networks, and may represent a common blueprint for the large-scale organization of interactions among all cellular constituents.

Protein Network Signatures Associated with Exogenous Biofuels Treatments in Cyanobacterium Synechocystis sp. PCC 6803

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pei, Guangsheng; Chen, Lei; Wang, Jiangxin

2014-11-03

Although recognized as a promising microbial cell factory for producing biofuels, current productivity in cyanobacterial systems is low. To make the processes economically feasible, one of the hurdles, which need to be overcome is the low tolerance of hosts to toxic biofuels. Meanwhile, little information is available regarding the cellular responses to biofuels stress in cyanobacteria, which makes it challenging for tolerance engineering. Using large proteomic datasets of Synechocystis under various biofuels stress and environmental perturbation, a protein co-expression network was first constructed and then combined with the experimentally determined protein–protein interaction network. Proteins with statistically higher topological overlap inmore » the integrated network were identified as common responsive proteins to both biofuels stress and environmental perturbations. In addition, a weighted gene co-expression network analysis was performed to distinguish unique responses to biofuels from those to environmental perturbations and to uncover metabolic modules and proteins uniquely associated with biofuels stress. The results showed that biofuel-specific proteins and modules were enriched in several functional categories, including photosynthesis, carbon fixation, and amino acid metabolism, which may represent potential key signatures for biofuels stress responses in Synechocystis. Network-based analysis allowed determination of the responses specifically related to biofuels stress, and the results constituted an important knowledge foundation for tolerance engineering against biofuels in Synechocystis.« less

Inferring general relations between network characteristics from specific network ensembles.

PubMed

Cardanobile, Stefano; Pernice, Volker; Deger, Moritz; Rotter, Stefan

2012-01-01

Different network models have been suggested for the topology underlying complex interactions in natural systems. These models are aimed at replicating specific statistical features encountered in real-world networks. However, it is rarely considered to which degree the results obtained for one particular network class can be extrapolated to real-world networks. We address this issue by comparing different classical and more recently developed network models with respect to their ability to generate networks with large structural variability. In particular, we consider the statistical constraints which the respective construction scheme imposes on the generated networks. After having identified the most variable networks, we address the issue of which constraints are common to all network classes and are thus suitable candidates for being generic statistical laws of complex networks. In fact, we find that generic, not model-related dependencies between different network characteristics do exist. This makes it possible to infer global features from local ones using regression models trained on networks with high generalization power. Our results confirm and extend previous findings regarding the synchronization properties of neural networks. Our method seems especially relevant for large networks, which are difficult to map completely, like the neural networks in the brain. The structure of such large networks cannot be fully sampled with the present technology. Our approach provides a method to estimate global properties of under-sampled networks in good approximation. Finally, we demonstrate on three different data sets (C. elegans neuronal network, R. prowazekii metabolic network, and a network of synonyms extracted from Roget's Thesaurus) that real-world networks have statistical relations compatible with those obtained using regression models.

A General Map of Iron Metabolism and Tissue-specific Subnetworks

PubMed Central

Hower, Valerie; Mendes, Pedro; Torti, Frank M.; Laubenbacher, Reinhard; Akman, Steven; Shulaev, Vladmir; Torti, Suzy V.

2009-01-01

Iron is required for survival of mammalian cells. Recently, understanding of iron metabolism and trafficking has increased dramatically, revealing a complex, interacting network largely unknown just a few years ago. This provides an excellent model for systems biology development and analysis. The first step in such an analysis is the construction of a structural network of iron metabolism, which we present here. This network was created using CellDesigner version 3.5.2 and includes reactions occurring in mammalian cells of numerous tissue types. The iron metabolic network contains 151 chemical species and 107 reactions and transport steps. Starting from this general model, we construct iron networks for specific tissues and cells that are fundamental to maintaining body iron homeostasis. We include subnetworks for cells of the intestine and liver, tissues important in iron uptake and storage, respectively; as well as the reticulocyte and macrophage, key cells in iron utilization and recycling. The addition of kinetic information to our structural network will permit the simulation of iron metabolism in different tissues as well as in health and disease. PMID:19381358

Polymorphisms for ghrelin with consequences on satiety and metabolic alterations.

PubMed

Perret, Jason; De Vriese, Carine; Delporte, Christine

2014-07-01

To understand the current trend of ghrelin genetic variations on the control of satiety, eating behaviours, obesity, and metabolic alterations, and its development over the last 18 months. Several polymorphisms of the ghrelin gene, its receptor gene and ghrelin's acylating enzyme, ghrelin O-acyl transferase, have been identified and studied over the last decade in relation to control of satiety, obesity, eating behaviours, metabolic syndrome, glucose homeostasis, and type 2 diabetes. However, the effects described are either small or nonsignificant and often subjected to contradictory conclusions between studies. In the last 18 months, several of these areas of investigations have been revisited under more controlled conditions or have been subjected to meta-analysis. The effects of ghrelin gene polymorphism, is a complex area of investigation, due to ghrelin's interplay with a host of various factors part of an integrative network. However, taken together, results suggest that there are no or nonsignificant effects of the common genetic variants. A better understanding of the network, probably by a systems biology type approach, will be necessary to assign the exact role played by gene polymorphism of the component of the ghrelin axis.

Diet shifts provoke complex and variable changes in the metabolic networks of the ruminal microbiome.

PubMed

Wolff, Sara M; Ellison, Melinda J; Hao, Yue; Cockrum, Rebecca R; Austin, Kathy J; Baraboo, Michael; Burch, Katherine; Lee, Hyuk Jin; Maurer, Taylor; Patil, Rocky; Ravelo, Andrea; Taxis, Tasia M; Truong, Huan; Lamberson, William R; Cammack, Kristi M; Conant, Gavin C

2017-06-08

Grazing mammals rely on their ruminal microbial symbionts to convert plant structural biomass into metabolites they can assimilate. To explore how this complex metabolic system adapts to the host animal's diet, we inferred a microbiome-level metabolic network from shotgun metagenomic data. Using comparative genomics, we then linked this microbial network to that of the host animal using a set of interface metabolites likely to be transferred to the host. When the host sheep were fed a grain-based diet, the induced microbial metabolic network showed several critical differences from those seen on the evolved forage-based diet. Grain-based (e.g., concentrate) diets tend to be dominated by a smaller set of reactions that employ metabolites that are nearer in network space to the host's metabolism. In addition, these reactions are more central in the network and employ substrates with shorter carbon backbones. Despite this apparent lower complexity, the concentrate-associated metabolic networks are actually more dissimilar from each other than are those of forage-fed animals. Because both groups of animals were initially fed on a forage diet, we propose that the diet switch drove the appearance of a number of different microbial networks, including a degenerate network characterized by an inefficient use of dietary nutrients. We used network simulations to show that such disparate networks are not an unexpected result of a diet shift. We argue that network approaches, particularly those that link the microbial network with that of the host, illuminate aspects of the structure of the microbiome not seen from a strictly taxonomic perspective. In particular, different diets induce predictable and significant differences in the enzymes used by the microbiome. Nonetheless, there are clearly a number of microbiomes of differing structure that show similar functional properties. Changes such as a diet shift uncover more of this type of diversity.

Multi-equilibrium property of metabolic networks: SSI module.

PubMed

Lei, Hong-Bo; Zhang, Ji-Feng; Chen, Luonan

2011-06-20

Revealing the multi-equilibrium property of a metabolic network is a fundamental and important topic in systems biology. Due to the complexity of the metabolic network, it is generally a difficult task to study the problem as a whole from both analytical and numerical viewpoint. On the other hand, the structure-oriented modularization idea is a good choice to overcome such a difficulty, i.e. decomposing the network into several basic building blocks and then studying the whole network through investigating the dynamical characteristics of the basic building blocks and their interactions. Single substrate and single product with inhibition (SSI) metabolic module is one type of the basic building blocks of metabolic networks, and its multi-equilibrium property has important influence on that of the whole metabolic networks. In this paper, we describe what the SSI metabolic module is, characterize the rates of the metabolic reactions by Hill kinetics and give a unified model for SSI modules by using a set of nonlinear ordinary differential equations with multi-variables. Specifically, a sufficient and necessary condition is first given to describe the injectivity of a class of nonlinear systems, and then, the sufficient condition is used to study the multi-equilibrium property of SSI modules. As a main theoretical result, for the SSI modules in which each reaction has no more than one inhibitor, a sufficient condition is derived to rule out multiple equilibria, i.e. the Jacobian matrix of its rate function is nonsingular everywhere. In summary, we describe SSI modules and give a general modeling framework based on Hill kinetics, and provide a sufficient condition for ruling out multiple equilibria of a key type of SSI module.

Multi-equilibrium property of metabolic networks: SSI module

PubMed Central

2011-01-01

Background Revealing the multi-equilibrium property of a metabolic network is a fundamental and important topic in systems biology. Due to the complexity of the metabolic network, it is generally a difficult task to study the problem as a whole from both analytical and numerical viewpoint. On the other hand, the structure-oriented modularization idea is a good choice to overcome such a difficulty, i.e. decomposing the network into several basic building blocks and then studying the whole network through investigating the dynamical characteristics of the basic building blocks and their interactions. Single substrate and single product with inhibition (SSI) metabolic module is one type of the basic building blocks of metabolic networks, and its multi-equilibrium property has important influence on that of the whole metabolic networks. Results In this paper, we describe what the SSI metabolic module is, characterize the rates of the metabolic reactions by Hill kinetics and give a unified model for SSI modules by using a set of nonlinear ordinary differential equations with multi-variables. Specifically, a sufficient and necessary condition is first given to describe the injectivity of a class of nonlinear systems, and then, the sufficient condition is used to study the multi-equilibrium property of SSI modules. As a main theoretical result, for the SSI modules in which each reaction has no more than one inhibitor, a sufficient condition is derived to rule out multiple equilibria, i.e. the Jacobian matrix of its rate function is nonsingular everywhere. Conclusions In summary, we describe SSI modules and give a general modeling framework based on Hill kinetics, and provide a sufficient condition for ruling out multiple equilibria of a key type of SSI module. PMID:21689474

The Importance of Transition Metals in the Expanding Network of Microbial Metabolism in the Archean Eon

NASA Astrophysics Data System (ADS)

Moore, E. K.; Jelen, B. I.; Giovannelli, D.; Prabhu, A.; Raanan, H.; Falkowski, P. G.

2017-12-01

Deep time changes in Earth surface redox conditions, particularly due to global oxygenation, has impacted the availability of different metals and substrates that are central in biology. Oxidoreductase proteins are molecular nanomachines responsible for all biological electron transfer processes across the tree of life. These enzymes largely contain transition metals in their active sites. Microbial metabolic pathways form a global network of electron transfer, which expanded throughout the Archean eon. Older metabolisms (sulfur reduction, methanogenesis, anoxygenic photosynthesis) accessed negative redox potentials, while later evolving metabolisms (oxygenic photosynthesis, nitrification/denitrification, aerobic respiration) accessed positive redox potentials. The incorporation of different transition metals facilitated biological innovation and the expansion of the network of microbial metabolism. Network analysis was used to examine the connections between microbial taxa, metabolic pathways, crucial metallocofactors, and substrates in deep time by incorporating biosignatures preserved in the geologic record. Nitrogen fixation and aerobic respiration have the highest level of betweenness among metabolisms in the network, indicating that the oldest metabolisms are not the most central. Fe has by far the highest betweenness among metals. Clustering analysis largely separates High Metal Bacteria (HMB), Low Metal Bacteria (LMB), and Archaea showing that simple un-weighted links between taxa, metabolism, and metals have phylogenetic relevance. On average HMB have the highest betweenness among taxa, followed by Archaea and LMB. There is a correlation between the number of metallocofactors and metabolic pathways in representative bacterial taxa, but Archaea do not follow this trend. In many cases older and more recently evolved metabolisms were clustered together supporting previous findings that proliferation of metabolic pathways is not necessarily chronological.

Dynamic optimization of metabolic networks coupled with gene expression.

PubMed

Waldherr, Steffen; Oyarzún, Diego A; Bockmayr, Alexander

2015-01-21

The regulation of metabolic activity by tuning enzyme expression levels is crucial to sustain cellular growth in changing environments. Metabolic networks are often studied at steady state using constraint-based models and optimization techniques. However, metabolic adaptations driven by changes in gene expression cannot be analyzed by steady state models, as these do not account for temporal changes in biomass composition. Here we present a dynamic optimization framework that integrates the metabolic network with the dynamics of biomass production and composition. An approximation by a timescale separation leads to a coupled model of quasi-steady state constraints on the metabolic reactions, and differential equations for the substrate concentrations and biomass composition. We propose a dynamic optimization approach to determine reaction fluxes for this model, explicitly taking into account enzyme production costs and enzymatic capacity. In contrast to the established dynamic flux balance analysis, our approach allows predicting dynamic changes in both the metabolic fluxes and the biomass composition during metabolic adaptations. Discretization of the optimization problems leads to a linear program that can be efficiently solved. We applied our algorithm in two case studies: a minimal nutrient uptake network, and an abstraction of core metabolic processes in bacteria. In the minimal model, we show that the optimized uptake rates reproduce the empirical Monod growth for bacterial cultures. For the network of core metabolic processes, the dynamic optimization algorithm predicted commonly observed metabolic adaptations, such as a diauxic switch with a preference ranking for different nutrients, re-utilization of waste products after depletion of the original substrate, and metabolic adaptation to an impending nutrient depletion. These examples illustrate how dynamic adaptations of enzyme expression can be predicted solely from an optimization principle. Copyright © 2014 Elsevier Ltd. All rights reserved.

Sequential Metabolic Phases as a Means to Optimize Cellular Output in a Constant Environment

PubMed Central

Bockmayr, Alexander; Holzhütter, Hermann-Georg

2015-01-01

Temporal changes of gene expression are a well-known regulatory feature of all cells, which is commonly perceived as a strategy to adapt the proteome to varying external conditions. However, temporal (rhythmic and non-rhythmic) changes of gene expression are also observed under virtually constant external conditions. Here we hypothesize that such changes are a means to render the synthesis of the metabolic output more efficient than under conditions of constant gene activities. In order to substantiate this hypothesis, we used a flux-balance model of the cellular metabolism. The total time span spent on the production of a given set of target metabolites was split into a series of shorter time intervals (metabolic phases) during which only selected groups of metabolic genes are active. The related flux distributions were calculated under the constraint that genes can be either active or inactive whereby the amount of protein related to an active gene is only controlled by the number of active genes: the lower the number of active genes the more protein can be allocated to the enzymes carrying non-zero fluxes. This concept of a predominantly protein-limited efficiency of gene expression clearly differs from other concepts resting on the assumption of an optimal gene regulation capable of allocating to all enzymes and transporters just that fraction of protein necessary to prevent rate limitation. Applying this concept to a simplified metabolic network of the central carbon metabolism with glucose or lactate as alternative substrates, we demonstrate that switching between optimally chosen stationary flux modes comprising different sets of active genes allows producing a demanded amount of target metabolites in a significantly shorter time than by a single optimal flux mode at fixed gene activities. Our model-based findings suggest that temporal expression of metabolic genes can be advantageous even under conditions of constant external substrate supply. PMID:25786979

Metabolic networks in motion: 13C-based flux analysis

PubMed Central

Sauer, Uwe

2006-01-01

Many properties of complex networks cannot be understood from monitoring the components—not even when comprehensively monitoring all protein or metabolite concentrations—unless such information is connected and integrated through mathematical models. The reason is that static component concentrations, albeit extremely informative, do not contain functional information per se. The functional behavior of a network emerges only through the nonlinear gene, protein, and metabolite interactions across multiple metabolic and regulatory layers. I argue here that intracellular reaction rates are the functional end points of these interactions in metabolic networks, hence are highly relevant for systems biology. Methods for experimental determination of metabolic fluxes differ fundamentally from component concentration measurements; that is, intracellular reaction rates cannot be detected directly, but must be estimated through computer model-based interpretation of stable isotope patterns in products of metabolism. PMID:17102807

Association of Resting Metabolism in the Fear Neural Network With Extinction Recall Activations and Clinical Measures in Trauma-Exposed Individuals.

PubMed

Marin, Marie-France; Song, Huijin; VanElzakker, Michael B; Staples-Bradley, Lindsay K; Linnman, Clas; Pace-Schott, Edward F; Lasko, Natasha B; Shin, Lisa M; Milad, Mohammed R

2016-09-01

Exposure-based therapy, an effective treatment for posttraumatic stress disorder (PTSD), relies on extinction learning principles. In PTSD patients, dysfunctional patterns in the neural circuitry underlying fear extinction have been observed using resting-state or functional activation measures. It remains undetermined whether resting activity predicts activations during extinction recall or PTSD symptom severity. Moreover, it remains unclear whether trauma exposure per se affects resting activity in this circuitry. The authors employed a multimodal approach to examine the relationships among resting metabolism, clinical symptoms, and activations during extinction recall. Three cohorts were recruited: PTSD patients (N=24), trauma-exposed individuals with no PTSD (TENP) (N=20), and trauma-unexposed healthy comparison subjects (N=21). Participants underwent a resting positron emission tomography scan 4 days before a functional MRI fear conditioning and extinction paradigm. Amygdala resting metabolism negatively correlated with clinical functioning (as measured by the Global Assessment of Functioning Scale) in the TENP group, and hippocampal resting metabolism negatively correlated with clinical functioning in the PTSD group. In the PTSD group, dorsal anterior cingulate cortex (dACC) resting metabolism positively correlated with PTSD symptom severity, and it predicted increased dACC activations but decreased hippocampal and ventromedial prefrontal cortex activations during extinction recall. The TENP group had lower amygdala resting metabolism compared with the PTSD and healthy comparison groups, and it exhibited lower hippocampus resting metabolism relative to the healthy comparison group. Resting metabolism in the fear circuitry correlated with functioning, PTSD symptoms, and extinction recall activations, further supporting the relevance of this network to the pathophysiology of PTSD. The study findings also highlight the fact that chronic dysfunction in the amygdala and hippocampus is demonstrable in PTSD and other trauma-exposed individuals, even without exposure to an evocative stimulus.

Bonded Cumomer Analysis of Human Melanoma Metabolism Monitored by 13C NMR Spectroscopy of Perfused Tumor Cells*

PubMed Central

Shestov, Alexander A.; Mancuso, Anthony; Lee, Seung-Cheol; Guo, Lili; Nelson, David S.; Roman, Jeffrey C.; Henry, Pierre-Gilles; Leeper, Dennis B.; Blair, Ian A.; Glickson, Jerry D.

2016-01-01

A network model for the determination of tumor metabolic fluxes from 13C NMR kinetic isotopomer data has been developed and validated with perfused human DB-1 melanoma cells carrying the BRAF V600E mutation, which promotes oxidative metabolism. The model generated in the bonded cumomer formalism describes key pathways of tumor intermediary metabolism and yields dynamic curves for positional isotopic enrichment and spin-spin multiplets. Cells attached to microcarrier beads were perfused with 26 mm [1,6-13C2]glucose under normoxic conditions at 37 °C and monitored by 13C NMR spectroscopy. Excellent agreement between model-predicted and experimentally measured values of the rates of oxygen and glucose consumption, lactate production, and glutamate pool size validated the model. ATP production by glycolytic and oxidative metabolism were compared under hyperglycemic normoxic conditions; 51% of the energy came from oxidative phosphorylation and 49% came from glycolysis. Even though the rate of glutamine uptake was ∼50% of the tricarboxylic acid cycle flux, the rate of ATP production from glutamine was essentially zero (no glutaminolysis). De novo fatty acid production was ∼6% of the tricarboxylic acid cycle flux. The oxidative pentose phosphate pathway flux was 3.6% of glycolysis, and three non-oxidative pentose phosphate pathway exchange fluxes were calculated. Mass spectrometry was then used to compare fluxes through various pathways under hyperglycemic (26 mm) and euglycemic (5 mm) conditions. Under euglycemic conditions glutamine uptake doubled, but ATP production from glutamine did not significantly change. A new parameter measuring the Warburg effect (the ratio of lactate production flux to pyruvate influx through the mitochondrial pyruvate carrier) was calculated to be 21, close to upper limit of oxidative metabolism. PMID:26703469

Metabolic Networks Integrative Cardiac Health Project (ICHP) - Center of Excellence

DTIC Science & Technology

2016-08-01

Award Number: TITLE: Metabolic Networks Integrative Cardiac Health Project (ICHP) - Center of Excellence PRINCIPAL INVESTIGATOR: COL (Ret) Marina N...2016 2. REPORT TYPE FINAL 3. DATES COVERED (From - To) 29 Sep 2011 – 31 May 2016 4. TITLE AND SUBTITLE "Metabolic Networks Integrative Cardiac Health...ABSTRACT The Integrative Cardiac Health Project (ICHP) aims to lead the way in Cardiovascular Disease (CVD) Prevention by conducting novel research

Experimental Definition and Validation of Protein Coding Transcripts in Chlamydomonas reinhardtii

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kourosh Salehi-Ashtiani; Jason A. Papin

Algal fuel sources promise unsurpassed yields in a carbon neutral manner that minimizes resource competition between agriculture and fuel crops. Many challenges must be addressed before algal biofuels can be accepted as a component of the fossil fuel replacement strategy. One significant challenge is that the cost of algal fuel production must become competitive with existing fuel alternatives. Algal biofuel production presents the opportunity to fine-tune microbial metabolic machinery for an optimal blend of biomass constituents and desired fuel molecules. Genome-scale model-driven algal metabolic design promises to facilitate both goals by directing the utilization of metabolites in the complex, interconnectedmore » metabolic networks to optimize production of the compounds of interest. Using Chlamydomonas reinhardtii as a model, we developed a systems-level methodology bridging metabolic network reconstruction with annotation and experimental verification of enzyme encoding open reading frames. We reconstructed a genome-scale metabolic network for this alga and devised a novel light-modeling approach that enables quantitative growth prediction for a given light source, resolving wavelength and photon flux. We experimentally verified transcripts accounted for in the network and physiologically validated model function through simulation and generation of new experimental growth data, providing high confidence in network contents and predictive applications. The network offers insight into algal metabolism and potential for genetic engineering and efficient light source design, a pioneering resource for studying light-driven metabolism and quantitative systems biology. Our approach to generate a predictive metabolic model integrated with cloned open reading frames, provides a cost-effective platform to generate metabolic engineering resources. While the generated resources are specific to algal systems, the approach that we have developed is not specific to algae and can be readily expanded to other microbial systems as well as higher plants and animals.« less

Metabolomic profiling from leaves and roots of tomato (Solanum lycopersicum L.) plants grown under nitrogen, phosphorus or potassium-deficient condition

USDA-ARS?s Scientific Manuscript database

Nitrogen (N), phosphorus (P) and potassium (K) are essential macronutrients that are required in large quantities by growing plants. Deficiency of N, P or K can strongly affect metabolites in plant tissues. However, specific metabolic network responses to nutrient deficiencies are not well-defined. ...

Metabolic immune restraints: implications for anticancer vaccines.

PubMed

Mocellin, Simone

2010-01-01

Metabolic immune restraints belong to a highly complex network of molecular mechanisms underlying the failure of naturally occurring and therapeutically induced immune responses against cancer. In the light of the disappointing results yielded so far with anticancer vaccines in the clinical setting, the dissection of the cascade of molecular events leading to tumor immune escape appears the most promising way to develop more effective immunotherapeutic strategies. Here we review the significant advances recently made in the understanding of the tumor-specific metabolic features that contribute to keep malignant cells from being recognized and destroyed by immune effectors. These mechanistic insights are fostering the development of rationally designed therapeutics aimed to revert the immunosuppressive circuits and thus to enhance the effectiveness of anticancer vaccines.

Brain Energetics During the Sleep-Wake Cycle

PubMed Central

DiNuzzo, Mauro; Nedergaard, Maiken

2017-01-01

Brain activity during wakefulness is associated with high metabolic rates that are believed to support information processing and memory encoding. In spite of loss of consciousness, sleep still carries a substantial energy cost. Experimental evidence supports a cerebral metabolic shift taking place during sleep that suppresses aerobic glycolysis, a hallmark of environment-oriented waking behavior and synaptic plasticity. Recent studies reveal that glial astrocytes respond to the reduction of wake-promoting neuromodulators by regulating volume, composition and glymphatic drainage of interstitial fluid. These events are accompanied by changes in neuronal discharge patterns, astrocyte-neuron interactions, synaptic transactions and underlying metabolic features. Internally-generated neuronal activity and network homeostasis are proposed to account for the high sleep-related energy demand. PMID:29024871

Integrated in silico analyses of regulatory and metabolic networks of Synechococcus sp. PCC 7002 reveal relationships between gene centrality and essentiality

DOE PAGES

Song, Hyun-Seob; McClure, Ryan S.; Bernstein, Hans C.; ...

2015-03-27

Cyanobacteria dynamically relay environmental inputs to intracellular adaptations through a coordinated adjustment of photosynthetic efficiency and carbon processing rates. The output of such adaptations is reflected through changes in transcriptional patterns and metabolic flux distributions that ultimately define growth strategy. To address interrelationships between metabolism and regulation, we performed integrative analyses of metabolic and gene co-expression networks in a model cyanobacterium, Synechococcus sp. PCC 7002. Centrality analyses using the gene co-expression network identified a set of key genes, which were defined here as ‘topologically important.’ Parallel in silico gene knock-out simulations, using the genome-scale metabolic network, classified what we termedmore » as ‘functionally important’ genes, deletion of which affected growth or metabolism. A strong positive correlation was observed between topologically and functionally important genes. Functionally important genes exhibited variable levels of topological centrality; however, the majority of topologically central genes were found to be functionally essential for growth. Subsequent functional enrichment analysis revealed that both functionally and topologically important genes in Synechococcus sp. PCC 7002 are predominantly associated with translation and energy metabolism, two cellular processes critical for growth. This research demonstrates how synergistic network-level analyses can be used for reconciliation of metabolic and gene expression data to uncover fundamental biological principles.« less

Integrated in silico analyses of regulatory and metabolic networks of Synechococcus sp. PCC 7002 reveal relationships between gene centrality and essentiality

DOE Office of Scientific and Technical Information (OSTI.GOV)

Song, Hyun-Seob; McClure, Ryan S.; Bernstein, Hans C.

Cyanobacteria dynamically relay environmental inputs to intracellular adaptations through a coordinated adjustment of photosynthetic efficiency and carbon processing rates. The output of such adaptations is reflected through changes in transcriptional patterns and metabolic flux distributions that ultimately define growth strategy. To address interrelationships between metabolism and regulation, we performed integrative analyses of metabolic and gene co-expression networks in a model cyanobacterium, Synechococcus sp. PCC 7002. Centrality analyses using the gene co-expression network identified a set of key genes, which were defined here as ‘topologically important.’ Parallel in silico gene knock-out simulations, using the genome-scale metabolic network, classified what we termedmore » as ‘functionally important’ genes, deletion of which affected growth or metabolism. A strong positive correlation was observed between topologically and functionally important genes. Functionally important genes exhibited variable levels of topological centrality; however, the majority of topologically central genes were found to be functionally essential for growth. Subsequent functional enrichment analysis revealed that both functionally and topologically important genes in Synechococcus sp. PCC 7002 are predominantly associated with translation and energy metabolism, two cellular processes critical for growth. This research demonstrates how synergistic network-level analyses can be used for reconciliation of metabolic and gene expression data to uncover fundamental biological principles.« less

Integrative Transcriptomic Analysis Uncovers Novel Gene Modules That Underlie the Sulfate Response in Arabidopsis thaliana

PubMed Central

Henríquez-Valencia, Carlos; Arenas-M, Anita; Medina, Joaquín; Canales, Javier

2018-01-01

Sulfur is an essential nutrient for plant growth and development. Sulfur is a constituent of proteins, the plasma membrane and cell walls, among other important cellular components. To obtain new insights into the gene regulatory networks underlying the sulfate response, we performed an integrative meta-analysis of transcriptomic data from five different sulfate experiments available in public databases. This bioinformatic approach allowed us to identify a robust set of genes whose expression depends only on sulfate availability, indicating that those genes play an important role in the sulfate response. In relation to sulfate metabolism, the biological function of approximately 45% of these genes is currently unknown. Moreover, we found several consistent Gene Ontology terms related to biological processes that have not been extensively studied in the context of the sulfate response; these processes include cell wall organization, carbohydrate metabolism, nitrogen compound transport, and the regulation of proteolysis. Gene co-expression network analyses revealed relationships between the sulfate-responsive genes that were distributed among seven function-specific co-expression modules. The most connected genes in the sulfate co-expression network belong to a module related to the carbon response, suggesting that this biological function plays an important role in the control of the sulfate response. Temporal analyses of the network suggest that sulfate starvation generates a biphasic response, which involves that major changes in gene expression occur during both the early and late responses. Network analyses predicted that the sulfate response is regulated by a limited number of transcription factors, including MYBs, bZIPs, and NF-YAs. In conclusion, our analysis identified new candidate genes and provided new hypotheses to advance our understanding of the transcriptional regulation of sulfate metabolism in plants. PMID:29692794

Integrative Transcriptomic Analysis Uncovers Novel Gene Modules That Underlie the Sulfate Response in Arabidopsis thaliana.

PubMed

Henríquez-Valencia, Carlos; Arenas-M, Anita; Medina, Joaquín; Canales, Javier

2018-01-01

Sulfur is an essential nutrient for plant growth and development. Sulfur is a constituent of proteins, the plasma membrane and cell walls, among other important cellular components. To obtain new insights into the gene regulatory networks underlying the sulfate response, we performed an integrative meta-analysis of transcriptomic data from five different sulfate experiments available in public databases. This bioinformatic approach allowed us to identify a robust set of genes whose expression depends only on sulfate availability, indicating that those genes play an important role in the sulfate response. In relation to sulfate metabolism, the biological function of approximately 45% of these genes is currently unknown. Moreover, we found several consistent Gene Ontology terms related to biological processes that have not been extensively studied in the context of the sulfate response; these processes include cell wall organization, carbohydrate metabolism, nitrogen compound transport, and the regulation of proteolysis. Gene co-expression network analyses revealed relationships between the sulfate-responsive genes that were distributed among seven function-specific co-expression modules. The most connected genes in the sulfate co-expression network belong to a module related to the carbon response, suggesting that this biological function plays an important role in the control of the sulfate response. Temporal analyses of the network suggest that sulfate starvation generates a biphasic response, which involves that major changes in gene expression occur during both the early and late responses. Network analyses predicted that the sulfate response is regulated by a limited number of transcription factors, including MYBs, bZIPs, and NF-YAs. In conclusion, our analysis identified new candidate genes and provided new hypotheses to advance our understanding of the transcriptional regulation of sulfate metabolism in plants.

In silico identification of miRNAs and their target genes and analysis of gene co-expression network in saffron (Crocus sativus L.) stigma

PubMed Central

Zinati, Zahra; Shamloo-Dashtpagerdi, Roohollah; Behpouri, Ali

2016-01-01

As an aromatic and colorful plant of substantive taste, saffron (Crocus sativus L.) owes such properties of matter to growing class of the secondary metabolites derived from the carotenoids, apocarotenoids. Regarding the critical role of microRNAs in secondary metabolic synthesis and the limited number of identified miRNAs in C. sativus, on the other hand, one may see the point how the characterization of miRNAs along with the corresponding target genes in C. sativus might expand our perspectives on the roles of miRNAs in carotenoid/apocarotenoid biosynthetic pathway. A computational analysis was used to identify miRNAs and their targets using EST (Expressed Sequence Tag) library from mature saffron stigmas. Then, a gene co- expression network was constructed to identify genes which are potentially involved in carotenoid/apocarotenoid biosynthetic pathways. EST analysis led to the identification of two putative miRNAs (miR414 and miR837-5p) along with the corresponding stem- looped precursors. To our knowledge, this is the first report on miR414 and miR837-5p in C. sativus. Co-expression network analysis indicated that miR414 and miR837-5p may play roles in C. sativus metabolic pathways and led to identification of candidate genes including six transcription factors and one protein kinase probably involved in carotenoid/apocarotenoid biosynthetic pathway. Presence of transcription factors, miRNAs and protein kinase in the network indicated multiple layers of regulation in saffron stigma. The candidate genes from this study may help unraveling regulatory networks underlying the carotenoid/apocarotenoid biosynthesis in saffron and designing metabolic engineering for enhanced secondary metabolites. PMID:28261627

Final Technical Report for Award # ER64999

DOE Office of Scientific and Technical Information (OSTI.GOV)

Metcalf, William W.

2014-10-08

This report provides a summary of activities for Award # ER64999, a Genomes to Life Project funded by the Office of Science, Basic Energy Research. The project was entitled "Methanogenic archaea and the global carbon cycle: a systems biology approach to the study of Methanosarcina species". The long-term goal of this multi-investigator project was the creation of integrated, multiscale models that accurately and quantitatively predict the role of Methanosarcina species in the global carbon cycle under dynamic environmental conditions. To achieve these goals we pursed four specific aims: (1) genome sequencing of numerous members of the Order Methanosarcinales, (2) identificationmore » of genomic sources of phenotypic variation through in silico comparative genomics, (3) elucidation of the transcriptional networks of two Methanosarcina species, and (4) development of comprehensive metabolic network models for characterized strains to address the question of how metabolic models scale with genetic distance.« less

Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis

PubMed Central

Sung, Jaeyun; Kim, Seunghyeon; Cabatbat, Josephine Jill T.; Jang, Sungho; Jin, Yong-Su; Jung, Gyoo Yeol; Chia, Nicholas; Kim, Pan-Jun

2017-01-01

A system-level framework of complex microbe–microbe and host–microbe chemical cross-talk would help elucidate the role of our gut microbiota in health and disease. Here we report a literature-curated interspecies network of the human gut microbiota, called NJS16. This is an extensive data resource composed of ∼570 microbial species and 3 human cell types metabolically interacting through >4,400 small-molecule transport and macromolecule degradation events. Based on the contents of our network, we develop a mathematical approach to elucidate representative microbial and metabolic features of the gut microbial community in a given population, such as a disease cohort. Applying this strategy to microbiome data from type 2 diabetes patients reveals a context-specific infrastructure of the gut microbial ecosystem, core microbial entities with large metabolic influence, and frequently produced metabolic compounds that might indicate relevant community metabolic processes. Our network presents a foundation towards integrative investigations of community-scale microbial activities within the human gut. PMID:28585563

Ongoing resolution of duplicate gene functions shapes the diversification of a metabolic network

PubMed Central

Kuang, Meihua Christina; Hutchins, Paul D; Russell, Jason D; Coon, Joshua J; Hittinger, Chris Todd

2016-01-01

The evolutionary mechanisms leading to duplicate gene retention are well understood, but the long-term impacts of paralog differentiation on the regulation of metabolism remain underappreciated. Here we experimentally dissect the functions of two pairs of ancient paralogs of the GALactose sugar utilization network in two yeast species. We show that the Saccharomyces uvarum network is more active, even as over-induction is prevented by a second co-repressor that the model yeast Saccharomyces cerevisiae lacks. Surprisingly, removal of this repression system leads to a strong growth arrest, likely due to overly rapid galactose catabolism and metabolic overload. Alternative sugars, such as fructose, circumvent metabolic control systems and exacerbate this phenotype. We further show that S. cerevisiae experiences homologous metabolic constraints that are subtler due to how the paralogs have diversified. These results show how the functional differentiation of paralogs continues to shape regulatory network architectures and metabolic strategies long after initial preservation. DOI: http://dx.doi.org/10.7554/eLife.19027.001 PMID:27690225

Cyanobacterial Biofuels: Strategies and Developments on Network and Modeling.

PubMed

Klanchui, Amornpan; Raethong, Nachon; Prommeenate, Peerada; Vongsangnak, Wanwipa; Meechai, Asawin

Cyanobacteria, the phototrophic microorganisms, have attracted much attention recently as a promising source for environmentally sustainable biofuels production. However, barriers for commercial markets of cyanobacteria-based biofuels concern the economic feasibility. Miscellaneous strategies for improving the production performance of cyanobacteria have thus been developed. Among these, the simple ad hoc strategies resulting in failure to optimize fully cell growth coupled with desired product yield are explored. With the advancement of genomics and systems biology, a new paradigm toward systems metabolic engineering has been recognized. In particular, a genome-scale metabolic network reconstruction and modeling is a crucial systems-based tool for whole-cell-wide investigation and prediction. In this review, the cyanobacterial genome-scale metabolic models, which offer a system-level understanding of cyanobacterial metabolism, are described. The main process of metabolic network reconstruction and modeling of cyanobacteria are summarized. Strategies and developments on genome-scale network and modeling through the systems metabolic engineering approach are advanced and employed for efficient cyanobacterial-based biofuels production.

Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis.

PubMed

Sung, Jaeyun; Kim, Seunghyeon; Cabatbat, Josephine Jill T; Jang, Sungho; Jin, Yong-Su; Jung, Gyoo Yeol; Chia, Nicholas; Kim, Pan-Jun

2017-06-06

A system-level framework of complex microbe-microbe and host-microbe chemical cross-talk would help elucidate the role of our gut microbiota in health and disease. Here we report a literature-curated interspecies network of the human gut microbiota, called NJS16. This is an extensive data resource composed of ∼570 microbial species and 3 human cell types metabolically interacting through >4,400 small-molecule transport and macromolecule degradation events. Based on the contents of our network, we develop a mathematical approach to elucidate representative microbial and metabolic features of the gut microbial community in a given population, such as a disease cohort. Applying this strategy to microbiome data from type 2 diabetes patients reveals a context-specific infrastructure of the gut microbial ecosystem, core microbial entities with large metabolic influence, and frequently produced metabolic compounds that might indicate relevant community metabolic processes. Our network presents a foundation towards integrative investigations of community-scale microbial activities within the human gut.

Maturation of metabolic connectivity of the adolescent rat brain

PubMed Central

Choi, Hongyoon; Choi, Yoori; Kim, Kyu Wan; Kang, Hyejin; Hwang, Do Won; Kim, E Edmund; Chung, June-Key; Lee, Dong Soo

2015-01-01

Neuroimaging has been used to examine developmental changes of the brain. While PET studies revealed maturation-related changes, maturation of metabolic connectivity of the brain is not yet understood. Here, we show that rat brain metabolism is reconfigured to achieve long-distance connections with higher energy efficiency during maturation. Metabolism increased in anterior cerebrum and decreased in thalamus and cerebellum during maturation. When functional covariance patterns of PET images were examined, metabolic networks including default mode network (DMN) were extracted. Connectivity increased between the anterior and posterior parts of DMN and sensory-motor cortices during maturation. Energy efficiency, a ratio of connectivity strength to metabolism of a region, increased in medial prefrontal and retrosplenial cortices. Our data revealed that metabolic networks mature to increase metabolic connections and establish its efficiency between large-scale spatial components from childhood to early adulthood. Neurodevelopmental diseases might be understood by abnormal reconfiguration of metabolic connectivity and efficiency. DOI: http://dx.doi.org/10.7554/eLife.11571.001 PMID:26613413

Integration of systems biology with bioprocess engineering: L: -threonine production by systems metabolic engineering of Escherichia coli.

PubMed

Lee, Sang Yup; Park, Jin Hwan

2010-01-01

Random mutation and selection or targeted metabolic engineering without consideration of its impact on the entire metabolic and regulatory networks can unintentionally cause genetic alterations in the region, which is not directly related to the target metabolite. This is one of the reasons why strategies for developing industrial strains are now shifted towards targeted metabolic engineering based on systems biology, which is termed systems metabolic engineering. Using systems metabolic engineering strategies, all the metabolic engineering works are conducted in systems biology framework, whereby entire metabolic and regulatory networks are thoroughly considered in an integrated manner. The targets for purposeful engineering are selected after all possible effects on the entire metabolic and regulatory networks are thoroughly considered. Finally, the strain, which is capable of producing the target metabolite to a high level close to the theoretical maximum value, can be constructed. Here we review strategies and applications of systems biology successfully implemented on bioprocess engineering, with particular focus on developing L: -threonine production strains of Escherichia coli.

Metabolic networks evolve towards states of maximum entropy production.

PubMed

Unrean, Pornkamol; Srienc, Friedrich

2011-11-01

A metabolic network can be described by a set of elementary modes or pathways representing discrete metabolic states that support cell function. We have recently shown that in the most likely metabolic state the usage probability of individual elementary modes is distributed according to the Boltzmann distribution law while complying with the principle of maximum entropy production. To demonstrate that a metabolic network evolves towards such state we have carried out adaptive evolution experiments with Thermoanaerobacterium saccharolyticum operating with a reduced metabolic functionality based on a reduced set of elementary modes. In such reduced metabolic network metabolic fluxes can be conveniently computed from the measured metabolite secretion pattern. Over a time span of 300 generations the specific growth rate of the strain continuously increased together with a continuous increase in the rate of entropy production. We show that the rate of entropy production asymptotically approaches the maximum entropy production rate predicted from the state when the usage probability of individual elementary modes is distributed according to the Boltzmann distribution. Therefore, the outcome of evolution of a complex biological system can be predicted in highly quantitative terms using basic statistical mechanical principles. Copyright © 2011 Elsevier Inc. All rights reserved.

Popularity versus similarity in growing networks.

PubMed

Papadopoulos, Fragkiskos; Kitsak, Maksim; Serrano, M Ángeles; Boguñá, Marián; Krioukov, Dmitri

2012-09-27

The principle that 'popularity is attractive' underlies preferential attachment, which is a common explanation for the emergence of scaling in growing networks. If new connections are made preferentially to more popular nodes, then the resulting distribution of the number of connections possessed by nodes follows power laws, as observed in many real networks. Preferential attachment has been directly validated for some real networks (including the Internet), and can be a consequence of different underlying processes based on node fitness, ranking, optimization, random walks or duplication. Here we show that popularity is just one dimension of attractiveness; another dimension is similarity. We develop a framework in which new connections optimize certain trade-offs between popularity and similarity, instead of simply preferring popular nodes. The framework has a geometric interpretation in which popularity preference emerges from local optimization. As opposed to preferential attachment, our optimization framework accurately describes the large-scale evolution of technological (the Internet), social (trust relationships between people) and biological (Escherichia coli metabolic) networks, predicting the probability of new links with high precision. The framework that we have developed can thus be used for predicting new links in evolving networks, and provides a different perspective on preferential attachment as an emergent phenomenon.

Time-Series Analyses of Transcriptomes and Proteomes Reveal Molecular Networks Underlying Oil Accumulation in Canola.

PubMed

Wan, Huafang; Cui, Yixin; Ding, Yijuan; Mei, Jiaqin; Dong, Hongli; Zhang, Wenxin; Wu, Shiqi; Liang, Ying; Zhang, Chunyu; Li, Jiana; Xiong, Qing; Qian, Wei

2016-01-01

Understanding the regulation of lipid metabolism is vital for genetic engineering of canola ( Brassica napus L.) to increase oil yield or modify oil composition. We conducted time-series analyses of transcriptomes and proteomes to uncover the molecular networks associated with oil accumulation and dynamic changes in these networks in canola. The expression levels of genes and proteins were measured at 2, 4, 6, and 8 weeks after pollination (WAP). Our results show that the biosynthesis of fatty acids is a dominant cellular process from 2 to 6 WAP, while the degradation mainly happens after 6 WAP. We found that genes in almost every node of fatty acid synthesis pathway were significantly up-regulated during oil accumulation. Moreover, significant expression changes of two genes, acetyl-CoA carboxylase and acyl-ACP desaturase, were detected on both transcriptomic and proteomic levels. We confirmed the temporal expression patterns revealed by the transcriptomic analyses using quantitative real-time PCR experiments. The gene set association analysis show that the biosynthesis of fatty acids and unsaturated fatty acids are the most significant biological processes from 2-4 WAP and 4-6 WAP, respectively, which is consistent with the results of time-series analyses. These results not only provide insight into the mechanisms underlying lipid metabolism, but also reveal novel candidate genes that are worth further investigation for their values in the genetic engineering of canola.

Quantitative petri net model of gene regulated metabolic networks in the cell.

PubMed

Chen, Ming; Hofestädt, Ralf

2011-01-01

A method to exploit hybrid Petri nets (HPN) for quantitatively modeling and simulating gene regulated metabolic networks is demonstrated. A global kinetic modeling strategy and Petri net modeling algorithm are applied to perform the bioprocess functioning and model analysis. With the model, the interrelations between pathway analysis and metabolic control mechanism are outlined. Diagrammatical results of the dynamics of metabolites are simulated and observed by implementing a HPN tool, Visual Object Net ++. An explanation of the observed behavior of the urea cycle is proposed to indicate possibilities for metabolic engineering and medical care. Finally, the perspective of Petri nets on modeling and simulation of metabolic networks is discussed.

LeishCyc: a guide to building a metabolic pathway database and visualization of metabolomic data.

PubMed

Saunders, Eleanor C; MacRae, James I; Naderer, Thomas; Ng, Milica; McConville, Malcolm J; Likić, Vladimir A

2012-01-01

The complexity of the metabolic networks in even the simplest organisms has raised new challenges in organizing metabolic information. To address this, specialized computer frameworks have been developed to capture, manage, and visualize metabolic knowledge. The leading databases of metabolic information are those organized under the umbrella of the BioCyc project, which consists of the reference database MetaCyc, and a number of pathway/genome databases (PGDBs) each focussed on a specific organism. A number of PGDBs have been developed for bacterial, fungal, and protozoan pathogens, greatly facilitating dissection of the metabolic potential of these organisms and the identification of new drug targets. Leishmania are protozoan parasites belonging to the family Trypanosomatidae that cause a broad spectrum of diseases in humans. In this work we use the LeishCyc database, the BioCyc database for Leishmania major, to describe how to build a BioCyc database from genomic sequences and associated annotations. By using metabolomic data generated in our group, we show how such databases can be utilized to elucidate specific changes in parasite metabolism.

Differential vulnerability to adverse nutritional conditions in male and female rats: Modulatory role of estradiol during development.

PubMed

Pinos, Helena; Carrillo, Beatriz; Díaz, Francisca; Chowen, Julie A; Collado, Paloma

2018-01-01

Many studies have shown the importance of an adequate nutritional environment during development to optimally establish the neurohormonal circuits that regulate feeding behavior. Under- or over-nutrition during early stages of life can lead to alterations in the physiology and brain networks that control food intake, resulting in a greater vulnerability to suffer maladjustments in energy metabolism in adulthood. These alterations produced by under- or over-nourishment during development differ between males and females, as does the modulatory action that estradiol exerts on the alterations produced by malnutrition. Estradiol regulates metabolism and brain metabolic circuits through the same transcription factor pathway, STAT3, that leptin and ghrelin use to program feeding circuits. Although more research is needed to disentangle the actual role of estradiol during development on the programming of feeding circuits, a synergistic role together with leptin and/or ghrelin might be hypothesized. Copyright © 2017 Elsevier Inc. All rights reserved.

A spectroscopic approach toward depression diagnosis: local metabolism meets functional connectivity.

PubMed

Demenescu, Liliana Ramona; Colic, Lejla; Li, Meng; Safron, Adam; Biswal, B; Metzger, Coraline Danielle; Li, Shijia; Walter, Martin

2017-03-01

Abnormal anterior insula (AI) response and functional connectivity (FC) is associated with depression. In addition to clinical features, such as severity, AI FC and its metabolism further predicted therapeutic response. Abnormal FC between anterior cingulate and AI covaried with reduced glutamate level within cingulate cortex. Recently, deficient glial glutamate conversion was found in AI in major depression disorder (MDD). We therefore postulate a local glutamatergic mechanism in insula cortex of depressive patients, which is correlated with symptoms severity and itself influences AI's network connectivity in MDD. Twenty-five MDD patients and 25 healthy controls (HC) matched on age and sex underwent resting state functional magnetic resonance imaging and magnetic resonance spectroscopy scans. To determine the role of local glutamate-glutamine complex (Glx) ratio on whole brain AI FC, we conducted regression analysis with Glx relative to creatine (Cr) ratio as factor of interest and age, sex, and voxel tissue composition as nuisance factors. We found that in MDD, but not in HC, AI Glx/Cr ratio correlated positively with AI FC to right supramarginal gyrus and negatively with AI FC toward left occipital cortex (p < 0.05 family wise error). AI Glx/Cr level was negatively correlated with HAMD score (p < 0.05) in MDD patients. We showed that the local AI ratio of glutamatergic-creatine metabolism is an underlying candidate subserving functional network disintegration of insula toward low level and supramodal integration areas, in MDD. While causality cannot directly be inferred from such correlation, our finding helps to define a multilevel network of response-predicting regions based on local metabolism and connectivity strength.

Combining graph and flux-based structures to decipher phenotypic essential metabolites within metabolic networks.

PubMed

Laniau, Julie; Frioux, Clémence; Nicolas, Jacques; Baroukh, Caroline; Cortes, Maria-Paz; Got, Jeanne; Trottier, Camille; Eveillard, Damien; Siegel, Anne

2017-01-01

The emergence of functions in biological systems is a long-standing issue that can now be addressed at the cell level with the emergence of high throughput technologies for genome sequencing and phenotyping. The reconstruction of complete metabolic networks for various organisms is a key outcome of the analysis of these data, giving access to a global view of cell functioning. The analysis of metabolic networks may be carried out by simply considering the architecture of the reaction network or by taking into account the stoichiometry of reactions. In both approaches, this analysis is generally centered on the outcome of the network and considers all metabolic compounds to be equivalent in this respect. As in the case of genes and reactions, about which the concept of essentiality has been developed, it seems, however, that some metabolites play crucial roles in system responses, due to the cell structure or the internal wiring of the metabolic network. We propose a classification of metabolic compounds according to their capacity to influence the activation of targeted functions (generally the growth phenotype) in a cell. We generalize the concept of essentiality to metabolites and introduce the concept of the phenotypic essential metabolite (PEM) which influences the growth phenotype according to sustainability, producibility or optimal-efficiency criteria. We have developed and made available a tool, Conquests , which implements a method combining graph-based and flux-based analysis, two approaches that are usually considered separately. The identification of PEMs is made effective by using a logical programming approach. The exhaustive study of phenotypic essential metabolites in six genome-scale metabolic models suggests that the combination and the comparison of graph, stoichiometry and optimal flux-based criteria allows some features of the metabolic network functionality to be deciphered by focusing on a small number of compounds. By considering the best combination of both graph-based and flux-based techniques, the Conquests python package advocates for a broader use of these compounds both to facilitate network curation and to promote a precise understanding of metabolic phenotype.

Combining graph and flux-based structures to decipher phenotypic essential metabolites within metabolic networks

PubMed Central

Frioux, Clémence; Nicolas, Jacques; Baroukh, Caroline; Cortes, Maria-Paz; Got, Jeanne; Trottier, Camille; Eveillard, Damien

2017-01-01

Background The emergence of functions in biological systems is a long-standing issue that can now be addressed at the cell level with the emergence of high throughput technologies for genome sequencing and phenotyping. The reconstruction of complete metabolic networks for various organisms is a key outcome of the analysis of these data, giving access to a global view of cell functioning. The analysis of metabolic networks may be carried out by simply considering the architecture of the reaction network or by taking into account the stoichiometry of reactions. In both approaches, this analysis is generally centered on the outcome of the network and considers all metabolic compounds to be equivalent in this respect. As in the case of genes and reactions, about which the concept of essentiality has been developed, it seems, however, that some metabolites play crucial roles in system responses, due to the cell structure or the internal wiring of the metabolic network. Results We propose a classification of metabolic compounds according to their capacity to influence the activation of targeted functions (generally the growth phenotype) in a cell. We generalize the concept of essentiality to metabolites and introduce the concept of the phenotypic essential metabolite (PEM) which influences the growth phenotype according to sustainability, producibility or optimal-efficiency criteria. We have developed and made available a tool, Conquests, which implements a method combining graph-based and flux-based analysis, two approaches that are usually considered separately. The identification of PEMs is made effective by using a logical programming approach. Conclusion The exhaustive study of phenotypic essential metabolites in six genome-scale metabolic models suggests that the combination and the comparison of graph, stoichiometry and optimal flux-based criteria allows some features of the metabolic network functionality to be deciphered by focusing on a small number of compounds. By considering the best combination of both graph-based and flux-based techniques, the Conquests python package advocates for a broader use of these compounds both to facilitate network curation and to promote a precise understanding of metabolic phenotype. PMID:29038751

Metabolic Networks Integrative Cardiac Health Project (ICHP) - Center of Excellence

DTIC Science & Technology

2016-04-01

2.6; P = 0.001) among all variables, as the most significant predictor of abnormal CIMT, thus increasing risk for CVD. Conclusions: The Integrative ...1 Award Number: W81XWH-11-2-0227 TITLE: "Metabolic Networks Integrative Cardiac Health Project (ICHP) - Center of Excellence." PRINCIPAL...April 2016 2. REPORT TYPE ANNUAL 3. DATES COVERED (From - To) 4. TITLE AND SUBTITLE "Metabolic Networks Integrative Cardiac Health Project (ICHP

Visualization of Metabolic Interaction Networks in Microbial Communities Using VisANT 5.0

PubMed Central

Wang, Yan; DeLisi, Charles; Segrè, Daniel; Hu, Zhenjun

2016-01-01

The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT’s unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the “symbiotic layout” of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues. VisANT is freely available at: http://visant.bu.edu and COMETS at http://comets.bu.edu. PMID:27081850

Visualization of Metabolic Interaction Networks in Microbial Communities Using VisANT 5.0.

PubMed

Granger, Brian R; Chang, Yi-Chien; Wang, Yan; DeLisi, Charles; Segrè, Daniel; Hu, Zhenjun

2016-04-01

The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT's unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues. VisANT is freely available at: http://visant.bu.edu and COMETS at http://comets.bu.edu.

Combinatorial complexity of pathway analysis in metabolic networks.

PubMed

Klamt, Steffen; Stelling, Jörg

2002-01-01

Elementary flux mode analysis is a promising approach for a pathway-oriented perspective of metabolic networks. However, in larger networks it is hampered by the combinatorial explosion of possible routes. In this work we give some estimations on the combinatorial complexity including theoretical upper bounds for the number of elementary flux modes in a network of a given size. In a case study, we computed the elementary modes in the central metabolism of Escherichia coli while utilizing four different substrates. Interestingly, although the number of modes occurring in this complex network can exceed half a million, it is still far below the upper bound. Hence, to a certain extent, pathway analysis of central catabolism is feasible to assess network properties such as flexibility and functionality.

Resource constrained flux balance analysis predicts selective pressure on the global structure of metabolic networks.

PubMed

Abedpour, Nima; Kollmann, Markus

2015-11-23

A universal feature of metabolic networks is their hourglass or bow-tie structure on cellular level. This architecture reflects the conversion of multiple input nutrients into multiple biomass components via a small set of precursor metabolites. However, it is yet unclear to what extent this structural feature is the result of natural selection. We extend flux balance analysis to account for limited cellular resources. Using this model, optimal structure of metabolic networks can be calculated for different environmental conditions. We observe a significant structural reshaping of metabolic networks for a toy-network and E. coli core metabolism if we increase the share of invested resources for switching between different nutrient conditions. Here, hub nodes emerge and the optimal network structure becomes bow-tie-like as a consequence of limited cellular resource constraint. We confirm this theoretical finding by comparing the reconstructed metabolic networks of bacterial species with respect to their lifestyle. We show that bow-tie structure can give a system-level fitness advantage to organisms that live in highly competitive and fluctuating environments. Here, limitation of cellular resources can lead to an efficiency-flexibility tradeoff where it pays off for the organism to shorten catabolic pathways if they are frequently activated and deactivated. As a consequence, generalists that shuttle between diverse environmental conditions should have a more predominant bow-tie structure than specialists that visit just a few isomorphic habitats during their life cycle.

Cellular plasticity enables adaptation to unforeseen cell-cycle rewiring challenges.

PubMed

Katzir, Yair; Stolovicki, Elad; Stern, Shay; Braun, Erez

2012-01-01

The fundamental dynamics of the cell cycle, underlying cell growth and reproduction, were previously found to be robust under a wide range of environmental and internal perturbations. This property was commonly attributed to its network structure, which enables the coordinated interactions among hundreds of proteins. Despite significant advances in deciphering the components and autonomous interactions of this network, understanding the interfaces of the cell cycle with other major cellular processes is still lacking. To gain insight into these interfaces, we used the process of genome-rewiring in yeast by placing an essential metabolic gene HIS3 from the histidine biosynthesis pathway, under the exclusive regulation of different cell-cycle promoters. In a medium lacking histidine and under partial inhibition of the HIS3p, the rewired cells encountered an unforeseen multitasking challenge; the cell-cycle regulatory genes were required to regulate the essential histidine-pathway gene in concert with the other metabolic demands, while simultaneously driving the cell cycle through its proper temporal phases. We show here that chemostat cell populations with rewired cell-cycle promoters adapted within a short time to accommodate the inhibition of HIS3p and stabilized a new phenotypic state. Furthermore, a significant fraction of the population was able to adapt and grow into mature colonies on plates under such inhibiting conditions. The adapted state was shown to be stably inherited across generations. These adaptation dynamics were accompanied by a non-specific and irreproducible genome-wide transcriptional response. Adaptation of the cell-cycle attests to its multitasking capabilities and flexible interface with cellular metabolic processes and requirements. Similar adaptation features were found in our previous work when rewiring HIS3 to the GAL system and switching cells from galactose to glucose. Thus, at the basis of cellular plasticity is the emergence of a yet-unknown general, non-specific mechanism allowing fast inherited adaptation to unforeseen challenges.

BiomeNet: A Bayesian Model for Inference of Metabolic Divergence among Microbial Communities

PubMed Central

Chipman, Hugh; Gu, Hong; Bielawski, Joseph P.

2014-01-01

Metagenomics yields enormous numbers of microbial sequences that can be assigned a metabolic function. Using such data to infer community-level metabolic divergence is hindered by the lack of a suitable statistical framework. Here, we describe a novel hierarchical Bayesian model, called BiomeNet (Bayesian inference of metabolic networks), for inferring differential prevalence of metabolic subnetworks among microbial communities. To infer the structure of community-level metabolic interactions, BiomeNet applies a mixed-membership modelling framework to enzyme abundance information. The basic idea is that the mixture components of the model (metabolic reactions, subnetworks, and networks) are shared across all groups (microbiome samples), but the mixture proportions vary from group to group. Through this framework, the model can capture nested structures within the data. BiomeNet is unique in modeling each metagenome sample as a mixture of complex metabolic systems (metabosystems). The metabosystems are composed of mixtures of tightly connected metabolic subnetworks. BiomeNet differs from other unsupervised methods by allowing researchers to discriminate groups of samples through the metabolic patterns it discovers in the data, and by providing a framework for interpreting them. We describe a collapsed Gibbs sampler for inference of the mixture weights under BiomeNet, and we use simulation to validate the inference algorithm. Application of BiomeNet to human gut metagenomes revealed a metabosystem with greater prevalence among inflammatory bowel disease (IBD) patients. Based on the discriminatory subnetworks for this metabosystem, we inferred that the community is likely to be closely associated with the human gut epithelium, resistant to dietary interventions, and interfere with human uptake of an antioxidant connected to IBD. Because this metabosystem has a greater capacity to exploit host-associated glycans, we speculate that IBD-associated communities might arise from opportunist growth of bacteria that can circumvent the host's nutrient-based mechanism for bacterial partner selection. PMID:25412107

A novel untargeted metabolomics correlation-based network analysis incorporating human metabolic reconstructions

PubMed Central

2013-01-01

Background Metabolomics has become increasingly popular in the study of disease phenotypes and molecular pathophysiology. One branch of metabolomics that encompasses the high-throughput screening of cellular metabolism is metabolic profiling. In the present study, the metabolic profiles of different tumour cells from colorectal carcinoma and breast adenocarcinoma were exposed to hypoxic and normoxic conditions and these have been compared to reveal the potential metabolic effects of hypoxia on the biochemistry of the tumour cells; this may contribute to their survival in oxygen compromised environments. In an attempt to analyse the complex interactions between metabolites beyond routine univariate and multivariate data analysis methods, correlation analysis has been integrated with a human metabolic reconstruction to reveal connections between pathways that are associated with normoxic or hypoxic oxygen environments. Results Correlation analysis has revealed statistically significant connections between metabolites, where differences in correlations between cells exposed to different oxygen levels have been highlighted as markers of hypoxic metabolism in cancer. Network mapping onto reconstructed human metabolic models is a novel addition to correlation analysis. Correlated metabolites have been mapped onto the Edinburgh human metabolic network (EHMN) with the aim of interlinking metabolites found to be regulated in a similar fashion in response to oxygen. This revealed novel pathways within the metabolic network that may be key to tumour cell survival at low oxygen. Results show that the metabolic responses to lowering oxygen availability can be conserved or specific to a particular cell line. Network-based correlation analysis identified conserved metabolites including malate, pyruvate, 2-oxoglutarate, glutamate and fructose-6-phosphate. In this way, this method has revealed metabolites not previously linked, or less well recognised, with respect to hypoxia before. Lactate fermentation is one of the key themes discussed in the field of hypoxia; however, malate, pyruvate, 2-oxoglutarate, glutamate and fructose-6-phosphate, which are connected by a single pathway, may provide a more significant marker of hypoxia in cancer. Conclusions Metabolic networks generated for each cell line were compared to identify conserved metabolite pathway responses to low oxygen environments. Furthermore, we believe this methodology will have general application within metabolomics. PMID:24153255

Metabolic control of oocyte development: linking maternal nutrition and reproductive outcomes

PubMed Central

Liu, Honglin; Gu, Xi; Boots, Christina; Moley, Kelle H.

2015-01-01

Obesity, diabetes, and related metabolic disorders are major health issues worldwide. As the epidemic of metabolic disorders continues, the associated medical comorbidities, including the detrimental impact on reproduction, increase as well. Emerging evidence suggests that the effects of maternal nutrition on reproductive outcomes are likely to be mediated, at least in part, by oocyte metabolism. Well-balanced and timed energy metabolism is critical for optimal development of oocytes. To date, much of our understanding of oocyte metabolism comes from the effects of extrinsic nutrients on oocyte maturation. In contrast, intrinsic regulation of oocyte development by metabolic enzymes, intracellular mediators, and transport systems is less characterized. Specifically, decreased acid transport proteins levels, increased glucose/lipid content and elevated reactive oxygen species in oocytes have been implicated in meiotic defects, organelle dysfunction and epigenetic alteration. Therefore, metabolic disturbances in oocytes may contribute to the diminished reproductive potential experienced by women with metabolic disorders. In-depth research is needed to further explore the underlying mechanisms. This review also discusses several approaches for metabolic analysis. Metabolomic profiling of oocytes, the surrounding granulosa cells, and follicular fluid will uncover the metabolic networks regulating oocyte development, potentially leading to the identification of oocyte quality markers and prevention of reproductive disease and poor outcomes in offspring. PMID:25280482

Software LS-MIDA for efficient mass isotopomer distribution analysis in metabolic modelling.

PubMed

Ahmed, Zeeshan; Zeeshan, Saman; Huber, Claudia; Hensel, Michael; Schomburg, Dietmar; Münch, Richard; Eisenreich, Wolfgang; Dandekar, Thomas

2013-07-09

The knowledge of metabolic pathways and fluxes is important to understand the adaptation of organisms to their biotic and abiotic environment. The specific distribution of stable isotope labelled precursors into metabolic products can be taken as fingerprints of the metabolic events and dynamics through the metabolic networks. An open-source software is required that easily and rapidly calculates from mass spectra of labelled metabolites, derivatives and their fragments global isotope excess and isotopomer distribution. The open-source software "Least Square Mass Isotopomer Analyzer" (LS-MIDA) is presented that processes experimental mass spectrometry (MS) data on the basis of metabolite information such as the number of atoms in the compound, mass to charge ratio (m/e or m/z) values of the compounds and fragments under study, and the experimental relative MS intensities reflecting the enrichments of isotopomers in 13C- or 15 N-labelled compounds, in comparison to the natural abundances in the unlabelled molecules. The software uses Brauman's least square method of linear regression. As a result, global isotope enrichments of the metabolite or fragment under study and the molar abundances of each isotopomer are obtained and displayed. The new software provides an open-source platform that easily and rapidly converts experimental MS patterns of labelled metabolites into isotopomer enrichments that are the basis for subsequent observation-driven analysis of pathways and fluxes, as well as for model-driven metabolic flux calculations.

Optimal knockout strategies in genome-scale metabolic networks using particle swarm optimization.

PubMed

Nair, Govind; Jungreuthmayer, Christian; Zanghellini, Jürgen

2017-02-01

Knockout strategies, particularly the concept of constrained minimal cut sets (cMCSs), are an important part of the arsenal of tools used in manipulating metabolic networks. Given a specific design, cMCSs can be calculated even in genome-scale networks. We would however like to find not only the optimal intervention strategy for a given design but the best possible design too. Our solution (PSOMCS) is to use particle swarm optimization (PSO) along with the direct calculation of cMCSs from the stoichiometric matrix to obtain optimal designs satisfying multiple objectives. To illustrate the working of PSOMCS, we apply it to a toy network. Next we show its superiority by comparing its performance against other comparable methods on a medium sized E. coli core metabolic network. PSOMCS not only finds solutions comparable to previously published results but also it is orders of magnitude faster. Finally, we use PSOMCS to predict knockouts satisfying multiple objectives in a genome-scale metabolic model of E. coli and compare it with OptKnock and RobustKnock. PSOMCS finds competitive knockout strategies and designs compared to other current methods and is in some cases significantly faster. It can be used in identifying knockouts which will force optimal desired behaviors in large and genome scale metabolic networks. It will be even more useful as larger metabolic models of industrially relevant organisms become available.

Aerobic metabolism underlies complexity and capacity

PubMed Central

Koch, Lauren G; Britton, Steven L

2008-01-01

The evolution of biological complexity beyond single-celled organisms was linked temporally with the development of an oxygen atmosphere. Functionally, this linkage can be attributed to oxygen ranking high in both abundance and electronegativity amongst the stable elements of the universe. That is, reduction of oxygen provides for close to the largest possible transfer of energy for each electron transfer reaction. This suggests the general hypothesis that the steep thermodynamic gradient of an oxygen environment was permissive for the development of multicellular complexity. A corollary of this hypothesis is that aerobic metabolism underwrites complex biological function mechanistically at all levels of organization. The strong contemporary functional association of aerobic metabolism with both physical capacity and health is presumably a product of the integral role of oxygen in our evolutionary history. Here we provide arguments from thermodynamics, evolution, metabolic network analysis, clinical observations and animal models that are in accord with the centrality of oxygen in biology. PMID:17947307

Growth-coupled overproduction is feasible for almost all metabolites in five major production organisms

NASA Astrophysics Data System (ADS)

von Kamp, Axel; Klamt, Steffen

2017-06-01

Computational modelling of metabolic networks has become an established procedure in the metabolic engineering of production strains. One key principle that is frequently used to guide the rational design of microbial cell factories is the stoichiometric coupling of growth and product synthesis, which makes production of the desired compound obligatory for growth. Here we show that the coupling of growth and production is feasible under appropriate conditions for almost all metabolites in genome-scale metabolic models of five major production organisms. These organisms comprise eukaryotes and prokaryotes as well as heterotrophic and photoautotrophic organisms, which shows that growth coupling as a strain design principle has a wide applicability. The feasibility of coupling is proven by calculating appropriate reaction knockouts, which enforce the coupling behaviour. The study presented here is the most comprehensive computational investigation of growth-coupled production so far and its results are of fundamental importance for rational metabolic engineering.

Complement-Mediated Regulation of Metabolism and Basic Cellular Processes.

PubMed

Hess, Christoph; Kemper, Claudia

2016-08-16

Complement is well appreciated as a critical arm of innate immunity. It is required for the removal of invading pathogens and works by directly destroying them through the activation of innate and adaptive immune cells. However, complement activation and function is not confined to the extracellular space but also occurs within cells. Recent work indicates that complement activation regulates key metabolic pathways and thus can impact fundamental cellular processes, such as survival, proliferation, and autophagy. Newly identified functions of complement include a key role in shaping metabolic reprogramming, which underlies T cell effector differentiation, and a role as a nexus for interactions with other effector systems, in particular the inflammasome and Notch transcription-factor networks. This review focuses on the contributions of complement to basic processes of the cell, in particular the integration of complement with cellular metabolism and the potential implications in infection and other disease settings. Copyright © 2016 Elsevier Inc. All rights reserved.

Minimal metabolic pathway structure is consistent with associated biomolecular interactions

PubMed Central

Bordbar, Aarash; Nagarajan, Harish; Lewis, Nathan E; Latif, Haythem; Ebrahim, Ali; Federowicz, Stephen; Schellenberger, Jan; Palsson, Bernhard O

2014-01-01

Pathways are a universal paradigm for functionally describing cellular processes. Even though advances in high-throughput data generation have transformed biology, the core of our biological understanding, and hence data interpretation, is still predicated on human-defined pathways. Here, we introduce an unbiased, pathway structure for genome-scale metabolic networks defined based on principles of parsimony that do not mimic canonical human-defined textbook pathways. Instead, these minimal pathways better describe multiple independent pathway-associated biomolecular interaction datasets suggesting a functional organization for metabolism based on parsimonious use of cellular components. We use the inherent predictive capability of these pathways to experimentally discover novel transcriptional regulatory interactions in Escherichia coli metabolism for three transcription factors, effectively doubling the known regulatory roles for Nac and MntR. This study suggests an underlying and fundamental principle in the evolutionary selection of pathway structures; namely, that pathways may be minimal, independent, and segregated. PMID:24987116

Limitations of a metabolic network-based reverse ecology method for inferring host-pathogen interactions.

PubMed

Takemoto, Kazuhiro; Aie, Kazuki

2017-05-25

Host-pathogen interactions are important in a wide range of research fields. Given the importance of metabolic crosstalk between hosts and pathogens, a metabolic network-based reverse ecology method was proposed to infer these interactions. However, the validity of this method remains unclear because of the various explanations presented and the influence of potentially confounding factors that have thus far been neglected. We re-evaluated the importance of the reverse ecology method for evaluating host-pathogen interactions while statistically controlling for confounding effects using oxygen requirement, genome, metabolic network, and phylogeny data. Our data analyses showed that host-pathogen interactions were more strongly influenced by genome size, primary network parameters (e.g., number of edges), oxygen requirement, and phylogeny than the reserve ecology-based measures. These results indicate the limitations of the reverse ecology method; however, they do not discount the importance of adopting reverse ecology approaches altogether. Rather, we highlight the need for developing more suitable methods for inferring host-pathogen interactions and conducting more careful examinations of the relationships between metabolic networks and host-pathogen interactions.

Illuminating a plant’s tissue-specific metabolic diversity using computational metabolomics and information theory

PubMed Central

Li, Dapeng; Heiling, Sven; Baldwin, Ian T.

2016-01-01

Secondary metabolite diversity is considered an important fitness determinant for plants’ biotic and abiotic interactions in nature. This diversity can be examined in two dimensions. The first one considers metabolite diversity across plant species. A second way of looking at this diversity is by considering the tissue-specific localization of pathways underlying secondary metabolism within a plant. Although these cross-tissue metabolite variations are increasingly regarded as important readouts of tissue-level gene function and regulatory processes, they have rarely been comprehensively explored by nontargeted metabolomics. As such, important questions have remained superficially addressed. For instance, which tissues exhibit prevalent signatures of metabolic specialization? Reciprocally, which metabolites contribute most to this tissue specialization in contrast to those metabolites exhibiting housekeeping characteristics? Here, we explore tissue-level metabolic specialization in Nicotiana attenuata, an ecological model with rich secondary metabolism, by combining tissue-wide nontargeted mass spectral data acquisition, information theory analysis, and tandem MS (MS/MS) molecular networks. This analysis was conducted for two different methanolic extracts of 14 tissues and deconvoluted 895 nonredundant MS/MS spectra. Using information theory analysis, anthers were found to harbor the most specialized metabolome, and most unique metabolites of anthers and other tissues were annotated through MS/MS molecular networks. Tissue–metabolite association maps were used to predict tissue-specific gene functions. Predictions for the function of two UDP-glycosyltransferases in flavonoid metabolism were confirmed by virus-induced gene silencing. The present workflow allows biologists to amortize the vast amount of data produced by modern MS instrumentation in their quest to understand gene function. PMID:27821729

Genome-scale metabolic analysis of Clostridium thermocellum for bioethanol production

PubMed Central

2010-01-01

Background Microorganisms possess diverse metabolic capabilities that can potentially be leveraged for efficient production of biofuels. Clostridium thermocellum (ATCC 27405) is a thermophilic anaerobe that is both cellulolytic and ethanologenic, meaning that it can directly use the plant sugar, cellulose, and biochemically convert it to ethanol. A major challenge in using microorganisms for chemical production is the need to modify the organism to increase production efficiency. The process of properly engineering an organism is typically arduous. Results Here we present a genome-scale model of C. thermocellum metabolism, iSR432, for the purpose of establishing a computational tool to study the metabolic network of C. thermocellum and facilitate efforts to engineer C. thermocellum for biofuel production. The model consists of 577 reactions involving 525 intracellular metabolites, 432 genes, and a proteomic-based representation of a cellulosome. The process of constructing this metabolic model led to suggested annotation refinements for 27 genes and identification of areas of metabolism requiring further study. The accuracy of the iSR432 model was tested using experimental growth and by-product secretion data for growth on cellobiose and fructose. Analysis using this model captures the relationship between the reduction-oxidation state of the cell and ethanol secretion and allowed for prediction of gene deletions and environmental conditions that would increase ethanol production. Conclusions By incorporating genomic sequence data, network topology, and experimental measurements of enzyme activities and metabolite fluxes, we have generated a model that is reasonably accurate at predicting the cellular phenotype of C. thermocellum and establish a strong foundation for rational strain design. In addition, we are able to draw some important conclusions regarding the underlying metabolic mechanisms for observed behaviors of C. thermocellum and highlight remaining gaps in the existing genome annotations. PMID:20307315

Metabolic reconstruction and flux analysis of industrial Pichia yeasts.

PubMed

Chung, Bevan Kai-Sheng; Lakshmanan, Meiyappan; Klement, Maximilian; Ching, Chi Bun; Lee, Dong-Yup

2013-03-01

Pichia yeasts have been recognized as important microbial cell factories in the biotechnological industry. Notably, the Pichia pastoris and Pichia stipitis species have attracted much research interest due to their unique cellular physiology and metabolic capability: P. pastoris has the ability to utilize methanol for cell growth and recombinant protein production, while P. stipitis is capable of assimilating xylose to produce ethanol under oxygen-limited conditions. To harness these characteristics for biotechnological applications, it is highly required to characterize their metabolic behavior. Recently, following the genome sequencing of these two Pichia species, genome-scale metabolic networks have been reconstructed to model the yeasts' metabolism from a systems perspective. To date, there are three genome-scale models available for each of P. pastoris and P. stipitis. In this mini-review, we provide an overview of the models, discuss certain limitations of previous studies, and propose potential future works that can be conducted to better understand and engineer Pichia yeasts for industrial applications.

Biomarkers are used to predict quantitative metabolite concentration profiles in human red blood cells

DOE PAGES

Yurkovich, James T.; Yang, Laurence; Palsson, Bernhard O.; ...

2017-03-06

Deep-coverage metabolomic profiling has revealed a well-defined development of metabolic decay in human red blood cells (RBCs) under cold storage conditions. A set of extracellular biomarkers has been recently identified that reliably defines the qualitative state of the metabolic network throughout this metabolic decay process. Here, we extend the utility of these biomarkers by using them to quantitatively predict the concentrations of other metabolites in the red blood cell. We are able to accurately predict the concentration profile of 84 of the 91 (92%) measured metabolites ( p < 0.05) in RBC metabolism using only measurements of these five biomarkers.more » The median of prediction errors (symmetric mean absolute percent error) across all metabolites was 13%. Furthermore, the ability to predict numerous metabolite concentrations from a simple set of biomarkers offers the potential for the development of a powerful workflow that could be used to evaluate the metabolic state of a biological system using a minimal set of measurements.« less

Biomarkers are used to predict quantitative metabolite concentration profiles in human red blood cells

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yurkovich, James T.; Yang, Laurence; Palsson, Bernhard O.

Deep-coverage metabolomic profiling has revealed a well-defined development of metabolic decay in human red blood cells (RBCs) under cold storage conditions. A set of extracellular biomarkers has been recently identified that reliably defines the qualitative state of the metabolic network throughout this metabolic decay process. Here, we extend the utility of these biomarkers by using them to quantitatively predict the concentrations of other metabolites in the red blood cell. We are able to accurately predict the concentration profile of 84 of the 91 (92%) measured metabolites ( p < 0.05) in RBC metabolism using only measurements of these five biomarkers.more » The median of prediction errors (symmetric mean absolute percent error) across all metabolites was 13%. Furthermore, the ability to predict numerous metabolite concentrations from a simple set of biomarkers offers the potential for the development of a powerful workflow that could be used to evaluate the metabolic state of a biological system using a minimal set of measurements.« less

Untargeted Metabolic Quantitative Trait Loci Analyses Reveal a Relationship between Primary Metabolism and Potato Tuber Quality1[W][OA

PubMed Central

Carreno-Quintero, Natalia; Acharjee, Animesh; Maliepaard, Chris; Bachem, Christian W.B.; Mumm, Roland; Bouwmeester, Harro; Visser, Richard G.F.; Keurentjes, Joost J.B.

2012-01-01

Recent advances in -omics technologies such as transcriptomics, metabolomics, and proteomics along with genotypic profiling have permitted dissection of the genetics of complex traits represented by molecular phenotypes in nonmodel species. To identify the genetic factors underlying variation in primary metabolism in potato (Solanum tuberosum), we have profiled primary metabolite content in a diploid potato mapping population, derived from crosses between S. tuberosum and wild relatives, using gas chromatography-time of flight-mass spectrometry. In total, 139 polar metabolites were detected, of which we identified metabolite quantitative trait loci for approximately 72% of the detected compounds. In order to obtain an insight into the relationships between metabolic traits and classical phenotypic traits, we also analyzed statistical associations between them. The combined analysis of genetic information through quantitative trait locus coincidence and the application of statistical learning methods provide information on putative indicators associated with the alterations in metabolic networks that affect complex phenotypic traits. PMID:22223596

Network pharmacology-based prediction of active compounds and molecular targets in Yijin-Tang acting on hyperlipidaemia and atherosclerosis.

PubMed

Lee, A Yeong; Park, Won; Kang, Tae-Wook; Cha, Min Ho; Chun, Jin Mi

2018-07-15

Yijin-Tang (YJT) is a traditional prescription for the treatment of hyperlipidaemia, atherosclerosis and other ailments related to dampness phlegm, a typical pathological symptom of abnormal body fluid metabolism in Traditional Korean Medicine. However, a holistic network pharmacology approach to understanding the therapeutic mechanisms underlying hyperlipidaemia and atherosclerosis has not been pursued. To examine the network pharmacological potential effects of YJT on hyperlipidaemia and atherosclerosis, we analysed components, performed target prediction and network analysis, and investigated interacting pathways using a network pharmacology approach. Information on compounds in herbal medicines was obtained from public databases, and oral bioavailability and drug-likeness was screened using absorption, distribution, metabolism, and excretion (ADME) criteria. Correlations between compounds and genes were linked using the STITCH database, and genes related to hyperlipidaemia and atherosclerosis were gathered using the GeneCards database. Human genes were identified and subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Network analysis identified 447 compounds in five herbal medicines that were subjected to ADME screening, and 21 compounds and 57 genes formed the main pathways linked to hyperlipidaemia and atherosclerosis. Among them, 10 compounds (naringenin, nobiletin, hesperidin, galangin, glycyrrhizin, homogentisic acid, stigmasterol, 6-gingerol, quercetin and glabridin) were linked to more than four genes, and are bioactive compounds and key chemicals. Core genes in this network were CASP3, CYP1A1, CYP1A2, MMP2 and MMP9. The compound-target gene network revealed close interactions between multiple components and multiple targets, and facilitates a better understanding of the potential therapeutic effects of YJT. Pharmacological network analysis can help to explain the potential effects of YJT for treating dampness phlegm-related diseases such as hyperlipidaemia and atherosclerosis. Copyright © 2018 Elsevier B.V. All rights reserved.

Metabolic Shift of Escherichia coli under Salt Stress in the Presence of Glycine Betaine

PubMed Central

Metris, A.; George, S. M.; Mulholland, F.; Carter, A. T.

2014-01-01

An important area of food safety focuses on bacterial survival and growth in unfavorable environments. In order to understand how bacteria adapt to stresses other than nutrient limitation in batch cultures, we need to develop mechanistic models of intracellular regulation and metabolism under stress. We studied the growth of Escherichia coli in minimal medium with added salt and different osmoprotectants. To characterize the metabolic efficiency with a robust parameter, we identified the optical density (OD) values at the inflection points of measured “OD versus time” growth curves and described them as a function of glucose concentration. We found that the metabolic efficiency parameter did not necessarily follow the trend of decreasing specific growth rate as the salt concentration increased. In the absence of osmoprotectant, or in the presence of proline, the metabolic efficiency decreased with increasing NaCl concentration. However, in the presence of choline or glycine betaine, it increased between 2 and 4.5% NaCl before declining at 5% NaCl and above. Microarray analysis of the transcriptional network and proteomics analysis with glycine betaine in the medium indicated that between 4.5 and 5% NaCl, the metabolism switched from aerobic to fermentative pathways and that the response to osmotic stress is similar to that for oxidative stress. We conclude that, although the growth rate appeared to decrease smoothly with increasing NaCl, the metabolic strategy of cells changed abruptly at a threshold concentration of NaCl. PMID:24858086

Recon2Neo4j: applying graph database technologies for managing comprehensive genome-scale networks.

PubMed

Balaur, Irina; Mazein, Alexander; Saqi, Mansoor; Lysenko, Artem; Rawlings, Christopher J; Auffray, Charles

2017-04-01

The goal of this work is to offer a computational framework for exploring data from the Recon2 human metabolic reconstruction model. Advanced user access features have been developed using the Neo4j graph database technology and this paper describes key features such as efficient management of the network data, examples of the network querying for addressing particular tasks, and how query results are converted back to the Systems Biology Markup Language (SBML) standard format. The Neo4j-based metabolic framework facilitates exploration of highly connected and comprehensive human metabolic data and identification of metabolic subnetworks of interest. A Java-based parser component has been developed to convert query results (available in the JSON format) into SBML and SIF formats in order to facilitate further results exploration, enhancement or network sharing. The Neo4j-based metabolic framework is freely available from: https://diseaseknowledgebase.etriks.org/metabolic/browser/ . The java code files developed for this work are available from the following url: https://github.com/ibalaur/MetabolicFramework . ibalaur@eisbm.org. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Recon2Neo4j: applying graph database technologies for managing comprehensive genome-scale networks

PubMed Central

Mazein, Alexander; Saqi, Mansoor; Lysenko, Artem; Rawlings, Christopher J.; Auffray, Charles

2017-01-01

Abstract Summary: The goal of this work is to offer a computational framework for exploring data from the Recon2 human metabolic reconstruction model. Advanced user access features have been developed using the Neo4j graph database technology and this paper describes key features such as efficient management of the network data, examples of the network querying for addressing particular tasks, and how query results are converted back to the Systems Biology Markup Language (SBML) standard format. The Neo4j-based metabolic framework facilitates exploration of highly connected and comprehensive human metabolic data and identification of metabolic subnetworks of interest. A Java-based parser component has been developed to convert query results (available in the JSON format) into SBML and SIF formats in order to facilitate further results exploration, enhancement or network sharing. Availability and Implementation: The Neo4j-based metabolic framework is freely available from: https://diseaseknowledgebase.etriks.org/metabolic/browser/. The java code files developed for this work are available from the following url: https://github.com/ibalaur/MetabolicFramework. Contact: ibalaur@eisbm.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27993779

Computational identification of obligatorily autocatalytic replicators embedded in metabolic networks

PubMed Central

Kun, Ádám; Papp, Balázs; Szathmáry, Eörs

2008-01-01

Background If chemical A is necessary for the synthesis of more chemical A, then A has the power of replication (such systems are known as autocatalytic systems). We provide the first systems-level analysis searching for small-molecular autocatalytic components in the metabolisms of diverse organisms, including an inferred minimal metabolism. Results We find that intermediary metabolism is invariably autocatalytic for ATP. Furthermore, we provide evidence for the existence of additional, organism-specific autocatalytic metabolites in the forms of coenzymes (NAD+, coenzyme A, tetrahydrofolate, quinones) and sugars. Although the enzymatic reactions of a number of autocatalytic cycles are present in most of the studied organisms, they display obligatorily autocatalytic behavior in a few networks only, hence demonstrating the need for a systems-level approach to identify metabolic replicators embedded in large networks. Conclusion Metabolic replicators are apparently common and potentially both universal and ancestral: without their presence, kick-starting metabolic networks is impossible, even if all enzymes and genes are present in the same cell. Identification of metabolic replicators is also important for attempts to create synthetic cells, as some of these autocatalytic molecules will presumably be needed to be added to the system as, by definition, the system cannot synthesize them without their initial presence. PMID:18331628

Scheffersomyces stipitis: a comparative systems biology study with the Crabtree positive yeast Saccharomyces cerevisiae

PubMed Central

2012-01-01

Background Scheffersomyces stipitis is a Crabtree negative yeast, commonly known for its capacity to ferment pentose sugars. Differently from Crabtree positive yeasts such as Saccharomyces cerevisiae, the onset of fermentation in S. stipitis is not dependent on the sugar concentration, but is regulated by a decrease in oxygen levels. Even though S. stipitis has been extensively studied due to its potential application in pentoses fermentation, a limited amount of information is available about its metabolism during aerobic growth on glucose. Here, we provide a systems biology based comparison between the two yeasts, uncovering the metabolism of S. stipitis during aerobic growth on glucose under batch and chemostat cultivations. Results Starting from the analysis of physiological data, we confirmed through 13C-based flux analysis the fully respiratory metabolism of S. stipitis when growing both under glucose limited or glucose excess conditions. The patterns observed showed similarity to the fully respiratory metabolism observed for S. cerevisiae under chemostat cultivations however, intracellular metabolome analysis uncovered the presence of several differences in metabolite patterns. To describe gene expression levels under the two conditions, we performed RNA sequencing and the results were used to quantify transcript abundances of genes from the central carbon metabolism and compared with those obtained with S. cerevisiae. Interestingly, genes involved in central pathways showed different patterns of expression, suggesting different regulatory networks between the two yeasts. Efforts were focused on identifying shared and unique families of transcription factors between the two yeasts through in silico transcription factors analysis, suggesting a different regulation of glycolytic and glucoenogenic pathways. Conclusions The work presented addresses the impact of high-throughput methods in describing and comparing the physiology of Crabtree positive and Crabtree negative yeasts. Based on physiological data and flux analysis we identified the presence of one metabolic condition for S. stipitis under aerobic batch and chemostat cultivations, which shows similarities to the oxidative metabolism observed for S. cerevisiae under chemostat cultivations. Through metabolome analysis and genome-wide transcriptomic analysis several differences were identified. Interestingly, in silico analysis of transciption factors was useful to address a different regulation of mRNAs of genes involved in the central carbon metabolism. To our knowledge, this is the first time that the metabolism of S. stiptis is investigated in details and is compared to S. cerevisiae. Our study provides useful results and allows for the possibility to incorporate these data into recently developed genome-scaled metabolic, thus contributing to improve future industrial applications of S. stipitis as cell factory. PMID:23043429

Reconstruction and Analysis of Human Kidney-Specific Metabolic Network Based on Omics Data

PubMed Central

Zhang, Ai-Di; Dai, Shao-Xing; Huang, Jing-Fei

2013-01-01

With the advent of the high-throughput data production, recent studies of tissue-specific metabolic networks have largely advanced our understanding of the metabolic basis of various physiological and pathological processes. However, for kidney, which plays an essential role in the body, the available kidney-specific model remains incomplete. This paper reports the reconstruction and characterization of the human kidney metabolic network based on transcriptome and proteome data. In silico simulations revealed that house-keeping genes were more essential than kidney-specific genes in maintaining kidney metabolism. Importantly, a total of 267 potential metabolic biomarkers for kidney-related diseases were successfully explored using this model. Furthermore, we found that the discrepancies in metabolic processes of different tissues are directly corresponding to tissue's functions. Finally, the phenotypes of the differentially expressed genes in diabetic kidney disease were characterized, suggesting that these genes may affect disease development through altering kidney metabolism. Thus, the human kidney-specific model constructed in this study may provide valuable information for the metabolism of kidney and offer excellent insights into complex kidney diseases. PMID:24222897

A network property necessary for concentration robustness

NASA Astrophysics Data System (ADS)

Eloundou-Mbebi, Jeanne M. O.; Küken, Anika; Omranian, Nooshin; Kleessen, Sabrina; Neigenfind, Jost; Basler, Georg; Nikoloski, Zoran

2016-10-01

Maintenance of functionality of complex cellular networks and entire organisms exposed to environmental perturbations often depends on concentration robustness of the underlying components. Yet, the reasons and consequences of concentration robustness in large-scale cellular networks remain largely unknown. Here, we derive a necessary condition for concentration robustness based only on the structure of networks endowed with mass action kinetics. The structural condition can be used to design targeted experiments to study concentration robustness. We show that metabolites satisfying the necessary condition are present in metabolic networks from diverse species, suggesting prevalence of this property across kingdoms of life. We also demonstrate that our predictions about concentration robustness of energy-related metabolites are in line with experimental evidence from Escherichia coli. The necessary condition is applicable to mass action biological systems of arbitrary size, and will enable understanding the implications of concentration robustness in genetic engineering strategies and medical applications.

A network property necessary for concentration robustness.

PubMed

Eloundou-Mbebi, Jeanne M O; Küken, Anika; Omranian, Nooshin; Kleessen, Sabrina; Neigenfind, Jost; Basler, Georg; Nikoloski, Zoran

2016-10-19

Maintenance of functionality of complex cellular networks and entire organisms exposed to environmental perturbations often depends on concentration robustness of the underlying components. Yet, the reasons and consequences of concentration robustness in large-scale cellular networks remain largely unknown. Here, we derive a necessary condition for concentration robustness based only on the structure of networks endowed with mass action kinetics. The structural condition can be used to design targeted experiments to study concentration robustness. We show that metabolites satisfying the necessary condition are present in metabolic networks from diverse species, suggesting prevalence of this property across kingdoms of life. We also demonstrate that our predictions about concentration robustness of energy-related metabolites are in line with experimental evidence from Escherichia coli. The necessary condition is applicable to mass action biological systems of arbitrary size, and will enable understanding the implications of concentration robustness in genetic engineering strategies and medical applications.

A network property necessary for concentration robustness

PubMed Central

Eloundou-Mbebi, Jeanne M. O.; Küken, Anika; Omranian, Nooshin; Kleessen, Sabrina; Neigenfind, Jost; Basler, Georg; Nikoloski, Zoran

2016-01-01

Maintenance of functionality of complex cellular networks and entire organisms exposed to environmental perturbations often depends on concentration robustness of the underlying components. Yet, the reasons and consequences of concentration robustness in large-scale cellular networks remain largely unknown. Here, we derive a necessary condition for concentration robustness based only on the structure of networks endowed with mass action kinetics. The structural condition can be used to design targeted experiments to study concentration robustness. We show that metabolites satisfying the necessary condition are present in metabolic networks from diverse species, suggesting prevalence of this property across kingdoms of life. We also demonstrate that our predictions about concentration robustness of energy-related metabolites are in line with experimental evidence from Escherichia coli. The necessary condition is applicable to mass action biological systems of arbitrary size, and will enable understanding the implications of concentration robustness in genetic engineering strategies and medical applications. PMID:27759015

The study on serum and urine of renal interstitial fibrosis rats induced by unilateral ureteral obstruction based on metabonomics and network analysis methods.

PubMed

Xiang, Zheng; Sun, Hao; Cai, Xiaojun; Chen, Dahui

2016-04-01

Transmission of biological information is a biochemical process of multistep cascade from genes/proteins to metabolites. However, because most metabolites reflect the terminal information of the biochemical process, it is difficult to describe the transmission process of disease information in terms of the metabolomics strategy. In this paper, by incorporating network and metabolomics methods, an integrated approach was proposed to systematically investigate and explain the molecular mechanism of renal interstitial fibrosis. Through analysis of the network, the cascade transmission process of disease information starting from genes/proteins to metabolites was putatively identified and uncovered. The results indicated that renal fibrosis was involved in metabolic pathways of glycerophospholipid metabolism, biosynthesis of unsaturated fatty acids and arachidonic acid metabolism, riboflavin metabolism, tyrosine metabolism, and sphingolipid metabolism. These pathways involve kidney disease genes such as TGF-β1 and P2RX7. Our results showed that combining metabolomics and network analysis can provide new strategies and ideas for the interpretation of pathogenesis of disease with full consideration of "gene-protein-metabolite."

Visualization of metabolic interaction networks in microbial communities using VisANT 5.0

DOE PAGES

Granger, Brian R.; Chang, Yi -Chien; Wang, Yan; ...

2016-04-15

Here, the complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT's unique meta-graph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction networkmore » between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues.« less

Modelling microbial metabolic rewiring during growth in a complex medium.

PubMed

Fondi, Marco; Bosi, Emanuele; Presta, Luana; Natoli, Diletta; Fani, Renato

2016-11-24

In their natural environment, bacteria face a wide range of environmental conditions that change over time and that impose continuous rearrangements at all the cellular levels (e.g. gene expression, metabolism). When facing a nutritionally rich environment, for example, microbes first use the preferred compound(s) and only later start metabolizing the other one(s). A systemic re-organization of the overall microbial metabolic network in response to a variation in the composition/concentration of the surrounding nutrients has been suggested, although the range and the entity of such modifications in organisms other than a few model microbes has been scarcely described up to now. We used multi-step constraint-based metabolic modelling to simulate the growth in a complex medium over several time steps of the Antarctic model organism Pseudoalteromonas haloplanktis TAC125. As each of these phases is characterized by a specific set of amino acids to be used as carbon and energy source our modelling framework describes the major consequences of nutrients switching at the system level. The model predicts that a deep metabolic reprogramming might be required to achieve optimal biomass production in different stages of growth (different medium composition), with at least half of the cellular metabolic network involved (more than 50% of the metabolic genes). Additionally, we show that our modelling framework is able to capture metabolic functional association and/or common regulatory features of the genes embedded in our reconstruction (e.g. the presence of common regulatory motifs). Finally, to explore the possibility of a sub-optimal biomass objective function (i.e. that cells use resources in alternative metabolic processes at the expense of optimal growth) we have implemented a MOMA-based approach (called nutritional-MOMA) and compared the outcomes with those obtained with Flux Balance Analysis (FBA). Growth simulations under this scenario revealed the deep impact of choosing among alternative objective functions on the resulting predictions of fluxes distribution. Here we provide a time-resolved, systems-level scheme of PhTAC125 metabolic re-wiring as a consequence of carbon source switching in a nutritionally complex medium. Our analyses suggest the presence of a potential efficient metabolic reprogramming machinery to continuously and promptly adapt to this nutritionally changing environment, consistent with adaptation to fast growth in a fairly, but probably inconstant and highly competitive, environment. Also, we show i) how functional partnership and co-regulation features can be predicted by integrating multi-step constraint-based metabolic modelling with fed-batch growth data and ii) that performing simulations under a sub-optimal objective function may lead to different flux distributions in respect to canonical FBA.

Construction of a Genome-Scale Metabolic Model of Arthrospira platensis NIES-39 and Metabolic Design for Cyanobacterial Bioproduction

PubMed Central

Yoshikawa, Katsunori; Aikawa, Shimpei; Kojima, Yuta; Toya, Yoshihiro; Furusawa, Chikara; Kondo, Akihiko; Shimizu, Hiroshi

2015-01-01

Arthrospira (Spirulina) platensis is a promising feedstock and host strain for bioproduction because of its high accumulation of glycogen and superior characteristics for industrial production. Metabolic simulation using a genome-scale metabolic model and flux balance analysis is a powerful method that can be used to design metabolic engineering strategies for the improvement of target molecule production. In this study, we constructed a genome-scale metabolic model of A. platensis NIES-39 including 746 metabolic reactions and 673 metabolites, and developed novel strategies to improve the production of valuable metabolites, such as glycogen and ethanol. The simulation results obtained using the metabolic model showed high consistency with experimental results for growth rates under several trophic conditions and growth capabilities on various organic substrates. The metabolic model was further applied to design a metabolic network to improve the autotrophic production of glycogen and ethanol. Decreased flux of reactions related to the TCA cycle and phosphoenolpyruvate reaction were found to improve glycogen production. Furthermore, in silico knockout simulation indicated that deletion of genes related to the respiratory chain, such as NAD(P)H dehydrogenase and cytochrome-c oxidase, could enhance ethanol production by using ammonium as a nitrogen source. PMID:26640947

Identification and Analyses of AUX-IAA target genes controlling multiple pathways in developing fiber cells of Gossypium hirsutum L

PubMed Central

Nigam, Deepti; Sawant, Samir V

2013-01-01

Technological development led to an increased interest in systems biological approaches in plants to characterize developmental mechanism and candidate genes relevant to specific tissue or cell morphology. AUX-IAA proteins are important plant-specific putative transcription factors. There are several reports on physiological response of this family in Arabidopsis but in cotton fiber the transcriptional network through which AUX-IAA regulated its target genes is still unknown. in-silico modelling of cotton fiber development specific gene expression data (108 microarrays and 22,737 genes) using Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) reveals 3690 putative AUX-IAA target genes of which 139 genes were known to be AUX-IAA co-regulated within Arabidopsis. Further AUX-IAA targeted gene regulatory network (GRN) had substantial impact on the transcriptional dynamics of cotton fiber, as showed by, altered TF networks, and Gene Ontology (GO) biological processes and metabolic pathway associated with its target genes. Analysis of the AUX-IAA-correlated gene network reveals multiple functions for AUX-IAA target genes such as unidimensional cell growth, cellular nitrogen compound metabolic process, nucleosome organization, DNA-protein complex and process related to cell wall. These candidate networks/pathways have a variety of profound impacts on such cellular functions as stress response, cell proliferation, and cell differentiation. While these functions are fairly broad, their underlying TF networks may provide a global view of AUX-IAA regulated gene expression and a GRN that guides future studies in understanding role of AUX-IAA box protein and its targets regulating fiber development. PMID:24497725

The evolution of metabolic networks of E. coli

PubMed Central

2011-01-01

Background Despite the availability of numerous complete genome sequences from E. coli strains, published genome-scale metabolic models exist only for two commensal E. coli strains. These models have proven useful for many applications, such as engineering strains for desired product formation, and we sought to explore how constructing and evaluating additional metabolic models for E. coli strains could enhance these efforts. Results We used the genomic information from 16 E. coli strains to generate an E. coli pangenome metabolic network by evaluating their collective 76,990 ORFs. Each of these ORFs was assigned to one of 17,647 ortholog groups including ORFs associated with reactions in the most recent metabolic model for E. coli K-12. For orthologous groups that contain an ORF already represented in the MG1655 model, the gene to protein to reaction associations represented in this model could then be easily propagated to other E. coli strain models. All remaining orthologous groups were evaluated to see if new metabolic reactions could be added to generate a pangenome-scale metabolic model (iEco1712_pan). The pangenome model included reactions from a metabolic model update for E. coli K-12 MG1655 (iEco1339_MG1655) and enabled development of five additional strain-specific genome-scale metabolic models. These additional models include a second K-12 strain (iEco1335_W3110) and four pathogenic strains (two enterohemorrhagic E. coli O157:H7 and two uropathogens). When compared to the E. coli K-12 models, the metabolic models for the enterohemorrhagic (iEco1344_EDL933 and iEco1345_Sakai) and uropathogenic strains (iEco1288_CFT073 and iEco1301_UTI89) contained numerous lineage-specific gene and reaction differences. All six E. coli models were evaluated by comparing model predictions to carbon source utilization measurements under aerobic and anaerobic conditions, and to batch growth profiles in minimal media with 0.2% (w/v) glucose. An ancestral genome-scale metabolic model based on conserved ortholog groups in all 16 E. coli genomes was also constructed, reflecting the conserved ancestral core of E. coli metabolism (iEco1053_core). Comparative analysis of all six strain-specific E. coli models revealed that some of the pathogenic E. coli strains possess reactions in their metabolic networks enabling higher biomass yields on glucose. Finally the lineage-specific metabolic traits were compared to the ancestral core model predictions to derive new insight into the evolution of metabolism within this species. Conclusion Our findings demonstrate that a pangenome-scale metabolic model can be used to rapidly construct additional E. coli strain-specific models, and that quantitative models of different strains of E. coli can accurately predict strain-specific phenotypes. Such pangenome and strain-specific models can be further used to engineer metabolic phenotypes of interest, such as designing new industrial E. coli strains. PMID:22044664

A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology

PubMed Central

2011-01-01

Background Genome-scale metabolic reconstructions provide a biologically meaningful mechanistic basis for the genotype-phenotype relationship. The global human metabolic network, termed Recon 1, has recently been reconstructed allowing the systems analysis of human metabolic physiology and pathology. Utilizing high-throughput data, Recon 1 has recently been tailored to different cells and tissues, including the liver, kidney, brain, and alveolar macrophage. These models have shown utility in the study of systems medicine. However, no integrated analysis between human tissues has been done. Results To describe tissue-specific functions, Recon 1 was tailored to describe metabolism in three human cells: adipocytes, hepatocytes, and myocytes. These cell-specific networks were manually curated and validated based on known cellular metabolic functions. To study intercellular interactions, a novel multi-tissue type modeling approach was developed to integrate the metabolic functions for the three cell types, and subsequently used to simulate known integrated metabolic cycles. In addition, the multi-tissue model was used to study diabetes: a pathology with systemic properties. High-throughput data was integrated with the network to determine differential metabolic activity between obese and type II obese gastric bypass patients in a whole-body context. Conclusion The multi-tissue type modeling approach presented provides a platform to study integrated metabolic states. As more cell and tissue-specific models are released, it is critical to develop a framework in which to study their interdependencies. PMID:22041191

Mass Conservation and Inference of Metabolic Networks from High-Throughput Mass Spectrometry Data

PubMed Central

Bandaru, Pradeep; Bansal, Mukesh

2011-01-01

Abstract We present a step towards the metabolome-wide computational inference of cellular metabolic reaction networks from metabolic profiling data, such as mass spectrometry. The reconstruction is based on identification of irreducible statistical interactions among the metabolite activities using the ARACNE reverse-engineering algorithm and on constraining possible metabolic transformations to satisfy the conservation of mass. The resulting algorithms are validated on synthetic data from an abridged computational model of Escherichia coli metabolism. Precision rates upwards of 50% are routinely observed for identification of full metabolic reactions, and recalls upwards of 20% are also seen. PMID:21314454

Combining Flux Balance and Energy Balance Analysis for Large-Scale Metabolic Network: Biochemical Circuit Theory for Analysis of Large-Scale Metabolic Networks

NASA Technical Reports Server (NTRS)

Beard, Daniel A.; Liang, Shou-Dan; Qian, Hong; Biegel, Bryan (Technical Monitor)

2001-01-01

Predicting behavior of large-scale biochemical metabolic networks represents one of the greatest challenges of bioinformatics and computational biology. Approaches, such as flux balance analysis (FBA), that account for the known stoichiometry of the reaction network while avoiding implementation of detailed reaction kinetics are perhaps the most promising tools for the analysis of large complex networks. As a step towards building a complete theory of biochemical circuit analysis, we introduce energy balance analysis (EBA), which compliments the FBA approach by introducing fundamental constraints based on the first and second laws of thermodynamics. Fluxes obtained with EBA are thermodynamically feasible and provide valuable insight into the activation and suppression of biochemical pathways.

ocsESTdb: a database of oil crop seed EST sequences for comparative analysis and investigation of a global metabolic network and oil accumulation metabolism.

PubMed

Ke, Tao; Yu, Jingyin; Dong, Caihua; Mao, Han; Hua, Wei; Liu, Shengyi

2015-01-21

Oil crop seeds are important sources of fatty acids (FAs) for human and animal nutrition. Despite their importance, there is a lack of an essential bioinformatics resource on gene transcription of oil crops from a comparative perspective. In this study, we developed ocsESTdb, the first database of expressed sequence tag (EST) information on seeds of four large-scale oil crops with an emphasis on global metabolic networks and oil accumulation metabolism that target the involved unigenes. A total of 248,522 ESTs and 106,835 unigenes were collected from the cDNA libraries of rapeseed (Brassica napus), soybean (Glycine max), sesame (Sesamum indicum) and peanut (Arachis hypogaea). These unigenes were annotated by a sequence similarity search against databases including TAIR, NR protein database, Gene Ontology, COG, Swiss-Prot, TrEMBL and Kyoto Encyclopedia of Genes and Genomes (KEGG). Five genome-scale metabolic networks that contain different numbers of metabolites and gene-enzyme reaction-association entries were analysed and constructed using Cytoscape and yEd programs. Details of unigene entries, deduced amino acid sequences and putative annotation are available from our database to browse, search and download. Intuitive and graphical representations of EST/unigene sequences, functional annotations, metabolic pathways and metabolic networks are also available. ocsESTdb will be updated regularly and can be freely accessed at http://ocri-genomics.org/ocsESTdb/ . ocsESTdb may serve as a valuable and unique resource for comparative analysis of acyl lipid synthesis and metabolism in oilseed plants. It also may provide vital insights into improving oil content in seeds of oil crop species by transcriptional reconstruction of the metabolic network.

Detection of driver metabolites in the human liver metabolic network using structural controllability analysis

PubMed Central

2014-01-01

Background Abnormal states in human liver metabolism are major causes of human liver diseases ranging from hepatitis to hepatic tumor. The accumulation in relevant data makes it feasible to derive a large-scale human liver metabolic network (HLMN) and to discover important biological principles or drug-targets based on network analysis. Some studies have shown that interesting biological phenomenon and drug-targets could be discovered by applying structural controllability analysis (which is a newly prevailed concept in networks) to biological networks. The exploration on the connections between structural controllability theory and the HLMN could be used to uncover valuable information on the human liver metabolism from a fresh perspective. Results We applied structural controllability analysis to the HLMN and detected driver metabolites. The driver metabolites tend to have strong ability to influence the states of other metabolites and weak susceptibility to be influenced by the states of others. In addition, the metabolites were classified into three classes: critical, high-frequency and low-frequency driver metabolites. Among the identified 36 critical driver metabolites, 27 metabolites were found to be essential; the high-frequency driver metabolites tend to participate in different metabolic pathways, which are important in regulating the whole metabolic systems. Moreover, we explored some other possible connections between the structural controllability theory and the HLMN, and find that transport reactions and the environment play important roles in the human liver metabolism. Conclusion There are interesting connections between the structural controllability theory and the human liver metabolism: driver metabolites have essential biological functions; the crucial role of extracellular metabolites and transport reactions in controlling the HLMN highlights the importance of the environment in the health of human liver metabolism. PMID:24885538

Metabolic pathway analysis and kinetic studies for production of nattokinase in Bacillus subtilis.

PubMed

Unrean, Pornkamol; Nguyen, Nhung H A

2013-01-01

We have constructed a reaction network model of Bacillus subtilis. The model was analyzed using a pathway analysis tool called elementary mode analysis (EMA). The analysis tool was used to study the network capabilities and the possible effects of altered culturing conditions on the production of a fibrinolytic enzyme, nattokinase (NK) by B. subtilis. Based on all existing metabolic pathways, the maximum theoretical yield for NK synthesis in B. subtilis under different substrates and oxygen availability was predicted and the optimal culturing condition for NK production was identified. To confirm model predictions, experiments were conducted by testing these culture conditions for their influence on NK activity. The optimal culturing conditions were then applied to batch fermentation, resulting in high NK activity. The EMA approach was also applied for engineering B. subtilis metabolism towards the most efficient pathway for NK synthesis by identifying target genes for deletion and overexpression that enable the cell to produce NK at the maximum theoretical yield. The consistency between experiments and model predictions proves the feasibility of EMA being used to rationally design culture conditions and genetic manipulations for the efficient production of desired products.

Topological analysis of metabolic networks based on petri net theory.

PubMed

Zevedei-Oancea, Ionela; Schuster, Stefan

2011-01-01

Petri net concepts provide additional tools for the modelling of metabolic networks. Here, the similarities between the counterparts in traditional biochemical modelling and Petri net theory are discussed. For example the stoichiometry matrix of a metabolic network corresponds to the incidence matrix of the Petri net. The flux modes and conservation relations have the T-invariants, respectively, P-invariants as counterparts. We reveal the biological meaning of some notions specific to the Petri net framework (traps, siphons, deadlocks, liveness). We focus on the topological analysis rather than on the analysis of the dynamic behaviour. The treatment of external metabolites is discussed. Some simple theoretical examples are presented for illustration. Also the Petri nets corresponding to some biochemical networks are built to support our results. For example, the role of triose phosphate isomerase (TPI) in Trypanosoma brucei metabolism is evaluated by detecting siphons and traps. All Petri net properties treated in this contribution are exemplified on a system extracted from nucleotide metabolism.

Topological analysis of metabolic networks based on Petri net theory.

PubMed

Zevedei-Oancea, Ionela; Schuster, Stefan

2003-01-01

Petri net concepts provide additional tools for the modelling of metabolic networks. Here, the similarities between the counterparts in traditional biochemical modelling and Petri net theory are discussed. For example the stoichiometry matrix of a metabolic network corresponds to the incidence matrix of the Petri net. The flux modes and conservation relations have the T-invariants, respectively, P-invariants as counterparts. We reveal the biological meaning of some notions specific to the Petri net framework (traps, siphons, deadlocks, liveness). We focus on the topological analysis rather than on the analysis of the dynamic behaviour. The treatment of external metabolites is discussed. Some simple theoretical examples are presented for illustration. Also the Petri nets corresponding to some biochemical networks are built to support our results. For example, the role of triose phosphate isomerase (TPI) in Trypanosoma brucei metabolism is evaluated by detecting siphons and traps. All Petri net properties treated in this contribution are exemplified on a system extracted from nucleotide metabolism.

Dissecting metabolic behavior of lipid over-producing strain of Mucor circinelloides through genome-scale metabolic network and multi-level data integration.

PubMed

Vongsangnak, Wanwipa; Kingkaw, Amornthep; Yang, Junhuan; Song, Yuanda; Laoteng, Kobkul

2018-09-05

Lipid accumulation is an important cellular process of oleaginous microorganisms. To dissect metabolic behavior of oleaginous Zygomycetes, the lipid over-producing strain, Mucor circinelloides WJ11, was subjected for omics-scale analysis. The genome annotation was improved and used for construction of genome-scale metabolic network of WJ11 strain. Then, the quality of the metabolic network was enhanced by incorporating gene and protein expression data. In addition to the known oleaginous genes, our results showed a number of newly identified unique genes of WJ11 strain, which involved in central carbon metabolism, lipid, amino acid and nitrogen metabolisms. The systematic compilations indicated the additional metabolic routes with the involvement in supplying precursors (acetyl-CoA, NADPH and fatty acyl substrate) for fatty acid and lipid biosynthesis. Interestingly, amino acid metabolism played a substantial role in responsive mechanism of the fungal cells to nutrient imbalance circumstance through lipogenesis as the finding of reporter metabolites (l-methionine, l-glutamate, l-aspartate, l-asparagine and l-glutamine) at lipid-accumulating stage. The cooperative function of certain lipid-degrading enzymes at the particular growth stage was elucidated by integrating the metabolic networks with gene expression data. The unique feature of carotenoid biosynthetic route in WJ11 strain was also identified by protein domain analysis. Taken together, there were cross-functional metabolisms in regulating lipid biosynthesis and retaining high level of cellular lipids in the representative of lipid over-producing strains. Copyright © 2018 Elsevier B.V. All rights reserved.

Integration of Plant Metabolomics Data with Metabolic Networks: Progresses and Challenges.

PubMed

Töpfer, Nadine; Seaver, Samuel M D; Aharoni, Asaph

2018-01-01

In the last decade, plant genome-scale modeling has developed rapidly and modeling efforts have advanced from representing metabolic behavior of plant heterotrophic cell suspensions to studying the complex interplay of cell types, tissues, and organs. A crucial driving force for such developments is the availability and integration of "omics" data (e.g., transcriptomics, proteomics, and metabolomics) which enable the reconstruction, extraction, and application of context-specific metabolic networks. In this chapter, we demonstrate a workflow to integrate gas chromatography coupled to mass spectrometry (GC-MS)-based metabolomics data of tomato fruit pericarp (flesh) tissue, at five developmental stages, with a genome-scale reconstruction of tomato metabolism. This method allows for the extraction of context-specific networks reflecting changing activities of metabolic pathways throughout fruit development and maturation.

ReMatch: a web-based tool to construct, store and share stoichiometric metabolic models with carbon maps for metabolic flux analysis.

PubMed

Pitkänen, Esa; Akerlund, Arto; Rantanen, Ari; Jouhten, Paula; Ukkonen, Esko

2008-08-25

ReMatch is a web-based, user-friendly tool that constructs stoichiometric network models for metabolic flux analysis, integrating user-developed models into a database collected from several comprehensive metabolic data resources, including KEGG, MetaCyc and CheBI. Particularly, ReMatch augments the metabolic reactions of the model with carbon mappings to facilitate (13)C metabolic flux analysis. The construction of a network model consisting of biochemical reactions is the first step in most metabolic modelling tasks. This model construction can be a tedious task as the required information is usually scattered to many separate databases whose interoperability is suboptimal, due to the heterogeneous naming conventions of metabolites in different databases. Another, particularly severe data integration problem is faced in (13)C metabolic flux analysis, where the mappings of carbon atoms from substrates into products in the model are required. ReMatch has been developed to solve the above data integration problems. First, ReMatch matches the imported user-developed model against the internal ReMatch database while considering a comprehensive metabolite name thesaurus. This, together with wild card support, allows the user to specify the model quickly without having to look the names up manually. Second, ReMatch is able to augment reactions of the model with carbon mappings, obtained either from the internal database or given by the user with an easy-touse tool. The constructed models can be exported into 13C-FLUX and SBML file formats. Further, a stoichiometric matrix and visualizations of the network model can be generated. The constructed models of metabolic networks can be optionally made available to the other users of ReMatch. Thus, ReMatch provides a common repository for metabolic network models with carbon mappings for the needs of metabolic flux analysis community. ReMatch is freely available for academic use at http://www.cs.helsinki.fi/group/sysfys/software/rematch/.

Data-driven integration of genome-scale regulatory and metabolic network models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Imam, Saheed; Schauble, Sascha; Brooks, Aaron N.

Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription, and signaling) have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or moremore » network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. Lastly, in this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system.« less

Data-driven integration of genome-scale regulatory and metabolic network models

DOE PAGES

Imam, Saheed; Schauble, Sascha; Brooks, Aaron N.; ...

2015-05-05

Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription, and signaling) have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or moremore » network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. Lastly, in this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system.« less

Network reconstruction of platelet metabolism identifies metabolic signature for aspirin resistance

NASA Astrophysics Data System (ADS)

Thomas, Alex; Rahmanian, Sorena; Bordbar, Aarash; Palsson, Bernhard Ø.; Jamshidi, Neema

2014-01-01

Recently there has not been a systematic, objective assessment of the metabolic capabilities of the human platelet. A manually curated, functionally tested, and validated biochemical reaction network of platelet metabolism, iAT-PLT-636, was reconstructed using 33 proteomic datasets and 354 literature references. The network contains enzymes mapping to 403 diseases and 231 FDA approved drugs, alluding to an expansive scope of biochemical transformations that may affect or be affected by disease processes in multiple organ systems. The effect of aspirin (ASA) resistance on platelet metabolism was evaluated using constraint-based modeling, which revealed a redirection of glycolytic, fatty acid, and nucleotide metabolism reaction fluxes in order to accommodate eicosanoid synthesis and reactive oxygen species stress. These results were confirmed with independent proteomic data. The construction and availability of iAT-PLT-636 should stimulate further data-driven, systems analysis of platelet metabolism towards the understanding of pathophysiological conditions including, but not strictly limited to, coagulopathies.

Design Constraints on a Synthetic Metabolism

PubMed Central

Bilgin, Tugce; Wagner, Andreas

2012-01-01

A metabolism is a complex network of chemical reactions that converts sources of energy and chemical elements into biomass and other molecules. To design a metabolism from scratch and to implement it in a synthetic genome is almost within technological reach. Ideally, a synthetic metabolism should be able to synthesize a desired spectrum of molecules at a high rate, from multiple different nutrients, while using few chemical reactions, and producing little or no waste. Not all of these properties are achievable simultaneously. We here use a recently developed technique to create random metabolic networks with pre-specified properties to quantify trade-offs between these and other properties. We find that for every additional molecule to be synthesized a network needs on average three additional reactions. For every additional carbon source to be utilized, it needs on average two additional reactions. Networks able to synthesize 20 biomass molecules from each of 20 alternative sole carbon sources need to have at least 260 reactions. This number increases to 518 reactions for networks that can synthesize more than 60 molecules from each of 80 carbon sources. The maximally achievable rate of biosynthesis decreases by approximately 5 percent for every additional molecule to be synthesized. Biochemically related molecules can be synthesized at higher rates, because their synthesis produces less waste. Overall, the variables we study can explain 87 percent of variation in network size and 84 percent of the variation in synthesis rate. The constraints we identify prescribe broad boundary conditions that can help to guide synthetic metabolism design. PMID:22768162

Validation of a metabolic network for Saccharomyces cerevisiae using mixed substrate studies.

PubMed

Vanrolleghem, P A; de Jong-Gubbels, P; van Gulik, W M; Pronk, J T; van Dijken, J P; Heijnen, S

1996-01-01

Setting up a metabolic network model for respiratory growth of Saccharomyces cerevisiae requires the estimation of only two (energetic) stoichiometric parameters: (1) the operational PO ratio and (2) a growth-related maintenance factor k. It is shown, both theoretically and practically, how chemostat cultivations with different mixtures of two substrates allow unique values to be given to these unknowns of the proposed metabolic model. For the yeast and model considered, an effective PO ratio of 1.09 mol of ATP/mol of O (95% confidence interval 1.07-1.11) and a k factor of 0.415 mol of ATP/C-mol of biomass (0.385-0.445) were obtained from biomass substrate yield data on glucose/ethanol mixtures. Symbolic manipulation software proved very valuable in this study as it supported the proof of theoretical identifiability and significantly reduced the necessary computations for parameter estimation. In the transition from 100% glucose to 100% ethanol in the feed, four metabolic regimes occur. Switching between these regimes is determined by cessation of an irreversible reaction and initiation of an alternative reaction. Metabolic network predictions of these metabolic switches compared well with activity measurements of key enzymes. As a second validation of the network, the biomass yield of S. cerevisiae on acetate was also compared to the network prediction. An excellent agreement was found for a network in which acetate transport was modeled with a proton symport, while passive diffusion of acetate gave significantly higher yield predictions.

Gene Expression Networks Underlying Ovarian Development in Wild Largemouth Bass (Micropterus salmoides)

PubMed Central

Martyniuk, Christopher J.; Prucha, Melinda S.; Doperalski, Nicholas J.; Antczak, Philipp; Kroll, Kevin J.; Falciani, Francesco; Barber, David S.; Denslow, Nancy D.

2013-01-01

Background Oocyte maturation in fish involves numerous cell signaling cascades that are activated or inhibited during specific stages of oocyte development. The objectives of this study were to characterize molecular pathways and temporal gene expression patterns throughout a complete breeding cycle in wild female largemouth bass to improve understanding of the molecular sequence of events underlying oocyte maturation. Methods Transcriptomic analysis was performed on eight morphologically diverse stages of the ovary, including primary and secondary stages of oocyte growth, ovulation, and atresia. Ovary histology, plasma vitellogenin, 17β-estradiol, and testosterone were also measured to correlate with gene networks. Results Global expression patterns revealed dramatic differences across ovarian development, with 552 and 2070 genes being differentially expressed during both ovulation and atresia respectively. Gene set enrichment analysis (GSEA) revealed that early primary stages of oocyte growth involved increases in expression of genes involved in pathways of B-cell and T-cell receptor-mediated signaling cascades and fibronectin regulation. These pathways as well as pathways that included adrenergic receptor signaling, sphingolipid metabolism and natural killer cell activation were down-regulated at ovulation. At atresia, down-regulated pathways included gap junction and actin cytoskeleton regulation, gonadotrope and mast cell activation, and vasopressin receptor signaling and up-regulated pathways included oxidative phosphorylation and reactive oxygen species metabolism. Expression targets for luteinizing hormone signaling were low during vitellogenesis but increased 150% at ovulation. Other networks found to play a significant role in oocyte maturation included those with genes regulated by members of the TGF-beta superfamily (activins, inhibins, bone morphogenic protein 7 and growth differentiation factor 9), neuregulin 1, retinoid X receptor, and nerve growth factor family. Conclusions This study offers novel insight into the gene networks underlying vitellogenesis, ovulation and atresia and generates new hypotheses about the cellular pathways regulating oocyte maturation. PMID:23527095

Water deficit mechanisms in perennial shrubs Cerasus humilis leaves revealed by physiological and proteomic analyses.

PubMed

Yin, Zepeng; Ren, Jing; Zhou, Lijuan; Sun, Lina; Wang, Jiewan; Liu, Yulong; Song, Xingshun

2016-01-01

Drought (Water deficit, WD) poses a serious threat to extensively economic losses of trees throughout the world. Chinese dwarf cherry ( Cerasus humilis ) is a good perennial plant for studying the physiological and sophisticated molecular network under WD. The aim of this study is to identify the effect of WD on C. humilis through physiological and global proteomics analysis and improve understanding of the WD resistance of plants. Currently, physiological parameters were applied to investigate C. humilis response to WD. Moreover, we used two-dimensional gel electrophoresis (2DE) to identify differentially expressed proteins in C. humilis leaves subjected to WD (24 d). Furthermore, we also examined the correlation between protein and transcript levels. Several physiological parameters, including relative water content and Pn were reduced by WD. In addition, the malondialdehyde (MDA), relative electrolyte leakage (REL), total soluble sugar, and proline were increased in WD-treated C. humilis . Comparative proteomic analysis revealed 46 protein spots (representing 43 unique proteins) differentially expressed in C. humilis leaves under WD. These proteins were mainly involved in photosynthesis, ROS scavenging, carbohydrate metabolism, transcription, protein synthesis, protein processing, and nitrogen and amino acid metabolisms, respectively. WD promoted the CO 2 assimilation by increase light reaction and Calvin cycle, leading to the reprogramming of carbon metabolism. Moreover, the accumulation of osmolytes (i.e., proline and total soluble sugar) and enhancement of ascorbate-glutathione cycle and glutathione peroxidase/glutathione s-transferase pathway in leaves could minimize oxidative damage of membrane and other molecules under WD. Importantly, the regulation role of carbohydrate metabolisms (e. g. glycolysis, pentose phosphate pathways, and TCA) was enhanced. These findings provide key candidate proteins for genetic improvement of perennial plants metabolism under WD.

Connexin 43-Mediated Astroglial Metabolic Networks Contribute to the Regulation of the Sleep-Wake Cycle.

PubMed

Clasadonte, Jerome; Scemes, Eliana; Wang, Zhongya; Boison, Detlev; Haydon, Philip G

2017-09-13

Astrocytes produce and supply metabolic substrates to neurons through gap junction-mediated astroglial networks. However, the role of astroglial metabolic networks in behavior is unclear. Here, we demonstrate that perturbation of astroglial networks impairs the sleep-wake cycle. Using a conditional Cre-Lox system in mice, we show that knockout of the gap junction subunit connexin 43 in astrocytes throughout the brain causes excessive sleepiness and fragmented wakefulness during the nocturnal active phase. This astrocyte-specific genetic manipulation silenced the wake-promoting orexin neurons located in the lateral hypothalamic area (LHA) by impairing glucose and lactate trafficking through astrocytic networks. This global wakefulness instability was mimicked with viral delivery of Cre recombinase to astrocytes in the LHA and rescued by in vivo injections of lactate. Our findings propose a novel regulatory mechanism critical for maintaining normal daily cycle of wakefulness and involving astrocyte-neuron metabolic interactions. Copyright © 2017 Elsevier Inc. All rights reserved.

On Functional Module Detection in Metabolic Networks

PubMed Central

Koch, Ina; Ackermann, Jörg

2013-01-01

Functional modules of metabolic networks are essential for understanding the metabolism of an organism as a whole. With the vast amount of experimental data and the construction of complex and large-scale, often genome-wide, models, the computer-aided identification of functional modules becomes more and more important. Since steady states play a key role in biology, many methods have been developed in that context, for example, elementary flux modes, extreme pathways, transition invariants and place invariants. Metabolic networks can be studied also from the point of view of graph theory, and algorithms for graph decomposition have been applied for the identification of functional modules. A prominent and currently intensively discussed field of methods in graph theory addresses the Q-modularity. In this paper, we recall known concepts of module detection based on the steady-state assumption, focusing on transition-invariants (elementary modes) and their computation as minimal solutions of systems of Diophantine equations. We present the Fourier-Motzkin algorithm in detail. Afterwards, we introduce the Q-modularity as an example for a useful non-steady-state method and its application to metabolic networks. To illustrate and discuss the concepts of invariants and Q-modularity, we apply a part of the central carbon metabolism in potato tubers (Solanum tuberosum) as running example. The intention of the paper is to give a compact presentation of known steady-state concepts from a graph-theoretical viewpoint in the context of network decomposition and reduction and to introduce the application of Q-modularity to metabolic Petri net models. PMID:24958145

Stochastic noncooperative and cooperative evolutionary game strategies of a population of biological networks under natural selection.

PubMed

Chen, Bor-Sen; Yeh, Chin-Hsun

2017-12-01

We review current static and dynamic evolutionary game strategies of biological networks and discuss the lack of random genetic variations and stochastic environmental disturbances in these models. To include these factors, a population of evolving biological networks is modeled as a nonlinear stochastic biological system with Poisson-driven genetic variations and random environmental fluctuations (stimuli). To gain insight into the evolutionary game theory of stochastic biological networks under natural selection, the phenotypic robustness and network evolvability of noncooperative and cooperative evolutionary game strategies are discussed from a stochastic Nash game perspective. The noncooperative strategy can be transformed into an equivalent multi-objective optimization problem and is shown to display significantly improved network robustness to tolerate genetic variations and buffer environmental disturbances, maintaining phenotypic traits for longer than the cooperative strategy. However, the noncooperative case requires greater effort and more compromises between partly conflicting players. Global linearization is used to simplify the problem of solving nonlinear stochastic evolutionary games. Finally, a simple stochastic evolutionary model of a metabolic pathway is simulated to illustrate the procedure of solving for two evolutionary game strategies and to confirm and compare their respective characteristics in the evolutionary process. Copyright © 2017 Elsevier B.V. All rights reserved.

Modeling cancer metabolism on a genome scale

PubMed Central

Yizhak, Keren; Chaneton, Barbara; Gottlieb, Eyal; Ruppin, Eytan

2015-01-01

Cancer cells have fundamentally altered cellular metabolism that is associated with their tumorigenicity and malignancy. In addition to the widely studied Warburg effect, several new key metabolic alterations in cancer have been established over the last decade, leading to the recognition that altered tumor metabolism is one of the hallmarks of cancer. Deciphering the full scope and functional implications of the dysregulated metabolism in cancer requires both the advancement of a variety of omics measurements and the advancement of computational approaches for the analysis and contextualization of the accumulated data. Encouragingly, while the metabolic network is highly interconnected and complex, it is at the same time probably the best characterized cellular network. Following, this review discusses the challenges that genome-scale modeling of cancer metabolism has been facing. We survey several recent studies demonstrating the first strides that have been done, testifying to the value of this approach in portraying a network-level view of the cancer metabolism and in identifying novel drug targets and biomarkers. Finally, we outline a few new steps that may further advance this field. PMID:26130389

Metabolic networks in epilepsy by MR spectroscopic imaging.

PubMed

Pan, J W; Spencer, D D; Kuzniecky, R; Duckrow, R B; Hetherington, H; Spencer, S S

2012-12-01

The concept of an epileptic network has long been suggested from both animal and human studies of epilepsy. Based on the common observation that the MR spectroscopic imaging measure of NAA/Cr is sensitive to neuronal function and injury, we use this parameter to assess for the presence of a metabolic network in mesial temporal lobe epilepsy (MTLE) patients. A multivariate factor analysis is performed with controls and MTLE patients, using NAA/Cr measures from 12 loci: the bilateral hippocampi, thalami, basal ganglia, and insula. The factor analysis determines which and to what extent these loci are metabolically covarying. We extract two independent factors that explain the data's variability in control and MTLE patients. In controls, these factors characterize a 'thalamic' and 'dominant subcortical' function. The MTLE patients also exhibit a 'thalamic' factor, in addition to a second factor involving the ipsilateral insula and bilateral basal ganglia. These data suggest that MTLE patients demonstrate a metabolic network that involves the thalami, also seen in controls. The MTLE patients also display a second set of metabolically covarying regions that may be a manifestation of the epileptic network that characterizes limbic seizure propagation. © 2012 John Wiley & Sons A/S.

Ecological Network Analysis for a Low-Carbon and High-Tech Industrial Park

PubMed Central

Lu, Yi; Su, Meirong; Liu, Gengyuan; Chen, Bin; Zhou, Shiyi; Jiang, Meiming

2012-01-01

Industrial sector is one of the indispensable contributors in global warming. Even if the occurrence of ecoindustrial parks (EIPs) seems to be a good improvement in saving ecological crises, there is still a lack of definitional clarity and in-depth researches on low-carbon industrial parks. In order to reveal the processes of carbon metabolism in a low-carbon high-tech industrial park, we selected Beijing Development Area (BDA) International Business Park in Beijing, China as case study, establishing a seven-compartment- model low-carbon metabolic network based on the methodology of Ecological Network Analysis (ENA). Integrating the Network Utility Analysis (NUA), Network Control Analysis (NCA), and system-wide indicators, we compartmentalized system sectors into ecological structure and analyzed dependence and control degree based on carbon metabolism. The results suggest that indirect flows reveal more mutuality and exploitation relation between system compartments and they are prone to positive sides for the stability of the whole system. The ecological structure develops well as an approximate pyramidal structure, and the carbon metabolism of BDA proves self-mutualistic and sustainable. Construction and waste management were found to be two active sectors impacting carbon metabolism, which was mainly regulated by internal and external environment. PMID:23365516

Metabolic Network Modeling of Microbial Interactions in Natural and Engineered Environmental Systems

PubMed Central

Perez-Garcia, Octavio; Lear, Gavin; Singhal, Naresh

2016-01-01

We review approaches to characterize metabolic interactions within microbial communities using Stoichiometric Metabolic Network (SMN) models for applications in environmental and industrial biotechnology. SMN models are computational tools used to evaluate the metabolic engineering potential of various organisms. They have successfully been applied to design and optimize the microbial production of antibiotics, alcohols and amino acids by single strains. To date however, such models have been rarely applied to analyze and control the metabolism of more complex microbial communities. This is largely attributed to the diversity of microbial community functions, metabolisms, and interactions. Here, we firstly review different types of microbial interaction and describe their relevance for natural and engineered environmental processes. Next, we provide a general description of the essential methods of the SMN modeling workflow including the steps of network reconstruction, simulation through Flux Balance Analysis (FBA), experimental data gathering, and model calibration. Then we broadly describe and compare four approaches to model microbial interactions using metabolic networks, i.e., (i) lumped networks, (ii) compartment per guild networks, (iii) bi-level optimization simulations, and (iv) dynamic-SMN methods. These approaches can be used to integrate and analyze diverse microbial physiology, ecology and molecular community data. All of them (except the lumped approach) are suitable for incorporating species abundance data but so far they have been used only to model simple communities of two to eight different species. Interactions based on substrate exchange and competition can be directly modeled using the above approaches. However, interactions based on metabolic feedbacks, such as product inhibition and synthropy require extensions to current models, incorporating gene regulation and compounding accumulation mechanisms. SMN models of microbial interactions can be used to analyze complex “omics” data and to infer and optimize metabolic processes. Thereby, SMN models are suitable to capitalize on advances in high-throughput molecular and metabolic data generation. SMN models are starting to be applied to describe microbial interactions during wastewater treatment, in-situ bioremediation, microalgae blooms methanogenic fermentation, and bioplastic production. Despite their current challenges, we envisage that SMN models have future potential for the design and development of novel growth media, biochemical pathways and synthetic microbial associations. PMID:27242701

YANA – a software tool for analyzing flux modes, gene-expression and enzyme activities

PubMed Central

Schwarz, Roland; Musch, Patrick; von Kamp, Axel; Engels, Bernd; Schirmer, Heiner; Schuster, Stefan; Dandekar, Thomas

2005-01-01

Background A number of algorithms for steady state analysis of metabolic networks have been developed over the years. Of these, Elementary Mode Analysis (EMA) has proven especially useful. Despite its low user-friendliness, METATOOL as a reliable high-performance implementation of the algorithm has been the instrument of choice up to now. As reported here, the analysis of metabolic networks has been improved by an editor and analyzer of metabolic flux modes. Analysis routines for expression levels and the most central, well connected metabolites and their metabolic connections are of particular interest. Results YANA features a platform-independent, dedicated toolbox for metabolic networks with a graphical user interface to calculate (integrating METATOOL), edit (including support for the SBML format), visualize, centralize, and compare elementary flux modes. Further, YANA calculates expected flux distributions for a given Elementary Mode (EM) activity pattern and vice versa. Moreover, a dissection algorithm, a centralization algorithm, and an average diameter routine can be used to simplify and analyze complex networks. Proteomics or gene expression data give a rough indication of some individual enzyme activities, whereas the complete flux distribution in the network is often not known. As such data are noisy, YANA features a fast evolutionary algorithm (EA) for the prediction of EM activities with minimum error, including alerts for inconsistent experimental data. We offer the possibility to include further known constraints (e.g. growth constraints) in the EA calculation process. The redox metabolism around glutathione reductase serves as an illustration example. All software and documentation are available for download at . Conclusion A graphical toolbox and an editor for METATOOL as well as a series of additional routines for metabolic network analyses constitute a new user-friendly software for such efforts. PMID:15929789

Simulating an Infection Growth Model in Certain Healthy Metabolic Pathways of Homo sapiens for Highlighting Their Role in Type I Diabetes mellitus Using Fire-Spread Strategy, Feedbacks and Sensitivities

PubMed Central

Tagore, Somnath; De, Rajat K.

2013-01-01

Disease Systems Biology is an area of life sciences, which is not very well understood to date. Analyzing infections and their spread in healthy metabolite networks can be one of the focussed areas in this regard. We have proposed a theory based on the classical forest fire model for analyzing the path of infection spread in healthy metabolic pathways. The theory suggests that when fire erupts in a forest, it spreads, and the surrounding trees also catch fire. Similarly, when we consider a metabolic network, the infection caused in the metabolites of the network spreads like a fire. We have constructed a simulation model which is used to study the infection caused in the metabolic networks from the start of infection, to spread and ultimately combating it. For implementation, we have used two approaches, first, based on quantitative strategies using ordinary differential equations and second, using graph-theory based properties. Furthermore, we are using certain probabilistic scores to complete this task and for interpreting the harm caused in the network, given by a ‘critical value’ to check whether the infection can be cured or not. We have tested our simulation model on metabolic pathways involved in Type I Diabetes mellitus in Homo sapiens. For validating our results biologically, we have used sensitivity analysis, both local and global, as well as for identifying the role of feedbacks in spreading infection in metabolic pathways. Moreover, information in literature has also been used to validate the results. The metabolic network datasets have been collected from the Kyoto Encyclopedia of Genes and Genomes (KEGG). PMID:24039701

Metabolic design of macroscopic bioreaction models: application to Chinese hamster ovary cells.

PubMed

Provost, A; Bastin, G; Agathos, S N; Schneider, Y-J

2006-12-01

The aim of this paper is to present a systematic methodology to design macroscopic bioreaction models for cell cultures based upon metabolic networks. The cell culture is seen as a succession of phases. During each phase, a metabolic network represents the set of reactions occurring in the cell. Then, through the use of the elementary flux modes, these metabolic networks are used to derive macroscopic bioreactions linking the extracellular substrates and products. On this basis, as many separate models are obtained as there are phases. Then, a complete model is obtained by smoothly switching from model to model. This is illustrated with batch cultures of Chinese hamster ovary cells.

Polyamines in the Context of Metabolic Networks.

PubMed

Wuddineh, Wegi; Minocha, Rakesh; Minocha, Subhash C

2018-01-01

Polyamines (PAs) are essential biomolecules that are known to be involved in the regulation of many plant developmental and growth processes as well as their response to different environmental stimuli. Maintaining the cellular pools of PAs or their metabolic precursors and by-products is critical to accomplish their normal functions. Therefore, the titre of PAs in the cells must be under tight regulation to enable cellular PA homeostasis. Polyamine homeostasis is hence achieved by the regulation of their input into the cellular PA pool, their conversion into secondary metabolites, their transport to other issues/organs, and their catabolism or turnover. The major contributors of input to the PA pools are their in vivo biosynthesis, interconversion between different PAs, and transport from other tissues/organs; while the output or turnover of PAs is facilitated by transport, conjugation and catabolism. Polyamine metabolic pathways including the biosynthesis, catabolism/turnover and conjugation with various organic molecules have been widely studied in all kingdoms. Discoveries on the molecular transporters facilitating the intracellular and intercellular translocation of PAs have also been reported. Numerous recent studies using transgenic approaches and mutagenesis have shown that plants can tolerate quite large concentrations of PAs in the cells; even though, at times, high cellular accumulation of PAs is quite detrimental, and so is high rate of catabolism. The mechanism by which plants tolerate such large quantities of PAs is still unclear. Interestingly, enhanced PA biosynthesis via manipulation of the PA metabolic networks has been suggested to contribute directly to increased growth and improvements in plant abiotic and biotic stress responses; hence greater biomass and productivity. Genetic manipulation of the PA metabolic networks has also been shown to improve plant nitrogen assimilation capacity, which may in turn lead to enhanced carbon assimilation. These potential benefits on top of the widely accepted role of PAs in improving plants' tolerance to biotic and abiotic stressors are invaluable tools for future plant improvement strategies.

Preliminary results of Physiological plant growth modelling for human life support in space

NASA Astrophysics Data System (ADS)

Sasidharan L, Swathy; Dussap, Claude-Gilles; Hezard, Pauline

2012-07-01

Human life support is fundamental and crucial in any kind of space explorations. MELiSSA project of European Space Agency aims at developing a closed, artificial ecological life support system involving human, plants and micro organisms. Consuming carbon dioxide and water from the life support system, plants grow in one of the chambers and convert it into food and oxygen along with potable water. The environmental conditions, nutrient availability and its consumption of plants should be studied and necessarily modeled to predict the amount of food, oxygen and water with respect to the environmental changes and limitations. The reliability of a completely closed system mainly depends on the control laws and strategies used. An efficient control can occur, only if the system to control is itself well known, described and ideally if the responses of the system to environmental changes are predictable. In this aspect, the general structure of plant growth model has been designed together with physiological modelling.The physiological model consists of metabolic models of leaves, stem and roots, of which concern specific metabolisms of the associated plant parts. On the basis of the carbon source transport (eg. sucrose) through stem, the metabolic models (leaf and root) can be interconnected to each other and finally coupled to obtain the entire plant model. For the first step, leaf metabolic model network was built using stoichiometric, mass and energy balanced metabolic equations under steady state approach considering all necessary plant pathways for growth and maintenance of leaves. As the experimental data for lettuce plants grown in closed and controlled environmental chambers were available, the leaf metabolic model has been established for lettuce leaves. The constructed metabolic network is analyzed using known stoichiometric metabolic technique called metabolic flux analysis (MFA). Though, the leaf metabolic model alone is not sufficient to achieve the physiological plant model, in the case of lettuce (since the leaf metabolic model predominates), the developed model was verified with the carbon consumption of plant, as input. The model predicts the biomass production (as output) with respect to the quantum of light absorbed by the plant. The obtained result was found satisfying for the first initiation in the physiological plant modelling.

Convergent evolution of modularity in metabolic networks through different community structures.

PubMed

Zhou, Wanding; Nakhleh, Luay

2012-09-14

It has been reported that the modularity of metabolic networks of bacteria is closely related to the variability of their living habitats. However, given the dependency of the modularity score on the community structure, it remains unknown whether organisms achieve certain modularity via similar or different community structures. In this work, we studied the relationship between similarities in modularity scores and similarities in community structures of the metabolic networks of 1021 species. Both similarities are then compared against the genetic distances. We revisited the association between modularity and variability of the microbial living environments and extended the analysis to other aspects of their life style such as temperature and oxygen requirements. We also tested both topological and biological intuition of the community structures identified and investigated the extent of their conservation with respect to the taxonomy. We find that similar modularities are realized by different community structures. We find that such convergent evolution of modularity is closely associated with the number of (distinct) enzymes in the organism's metabolome, a consequence of different life styles of the species. We find that the order of modularity is the same as the order of the number of the enzymes under the classification based on the temperature preference but not on the oxygen requirement. Besides, inspection of modularity-based communities reveals that these communities are graph-theoretically meaningful yet not reflective of specific biological functions. From an evolutionary perspective, we find that the community structures are conserved only at the level of kingdoms. Our results call for more investigation into the interplay between evolution and modularity: how evolution shapes modularity, and how modularity affects evolution (mainly in terms of fitness and evolvability). Further, our results call for exploring new measures of modularity and network communities that better correspond to functional categorizations.

Systems Biology as an Integrated Platform for Bioinformatics, Systems Synthetic Biology, and Systems Metabolic Engineering

PubMed Central

Chen, Bor-Sen; Wu, Chia-Chou

2013-01-01

Systems biology aims at achieving a system-level understanding of living organisms and applying this knowledge to various fields such as synthetic biology, metabolic engineering, and medicine. System-level understanding of living organisms can be derived from insight into: (i) system structure and the mechanism of biological networks such as gene regulation, protein interactions, signaling, and metabolic pathways; (ii) system dynamics of biological networks, which provides an understanding of stability, robustness, and transduction ability through system identification, and through system analysis methods; (iii) system control methods at different levels of biological networks, which provide an understanding of systematic mechanisms to robustly control system states, minimize malfunctions, and provide potential therapeutic targets in disease treatment; (iv) systematic design methods for the modification and construction of biological networks with desired behaviors, which provide system design principles and system simulations for synthetic biology designs and systems metabolic engineering. This review describes current developments in systems biology, systems synthetic biology, and systems metabolic engineering for engineering and biology researchers. We also discuss challenges and future prospects for systems biology and the concept of systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering. PMID:24709875

Systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering.

PubMed

Chen, Bor-Sen; Wu, Chia-Chou

2013-10-11

Systems biology aims at achieving a system-level understanding of living organisms and applying this knowledge to various fields such as synthetic biology, metabolic engineering, and medicine. System-level understanding of living organisms can be derived from insight into: (i) system structure and the mechanism of biological networks such as gene regulation, protein interactions, signaling, and metabolic pathways; (ii) system dynamics of biological networks, which provides an understanding of stability, robustness, and transduction ability through system identification, and through system analysis methods; (iii) system control methods at different levels of biological networks, which provide an understanding of systematic mechanisms to robustly control system states, minimize malfunctions, and provide potential therapeutic targets in disease treatment; (iv) systematic design methods for the modification and construction of biological networks with desired behaviors, which provide system design principles and system simulations for synthetic biology designs and systems metabolic engineering. This review describes current developments in systems biology, systems synthetic biology, and systems metabolic engineering for engineering and biology researchers. We also discuss challenges and future prospects for systems biology and the concept of systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering.

A method for accounting for maintenance costs in flux balance analysis improves the prediction of plant cell metabolic phenotypes under stress conditions.

PubMed

Cheung, C Y Maurice; Williams, Thomas C R; Poolman, Mark G; Fell, David A; Ratcliffe, R George; Sweetlove, Lee J

2013-09-01

Flux balance models of metabolism generally utilize synthesis of biomass as the main determinant of intracellular fluxes. However, the biomass constraint alone is not sufficient to predict realistic fluxes in central heterotrophic metabolism of plant cells because of the major demand on the energy budget due to transport costs and cell maintenance. This major limitation can be addressed by incorporating transport steps into the metabolic model and by implementing a procedure that uses Pareto optimality analysis to explore the trade-off between ATP and NADPH production for maintenance. This leads to a method for predicting cell maintenance costs on the basis of the measured flux ratio between the oxidative steps of the oxidative pentose phosphate pathway and glycolysis. We show that accounting for transport and maintenance costs substantially improves the accuracy of fluxes predicted from a flux balance model of heterotrophic Arabidopsis cells in culture, irrespective of the objective function used in the analysis. Moreover, when the new method was applied to cells under control, elevated temperature and hyper-osmotic conditions, only elevated temperature led to a substantial increase in cell maintenance costs. It is concluded that the hyper-osmotic conditions tested did not impose a metabolic stress, in as much as the metabolic network is not forced to devote more resources to cell maintenance. © 2013 The Authors The Plant Journal © 2013 John Wiley & Sons Ltd.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burger, Brian T.; Imam, Saheed; Scarborough, Matthew J.

Rhodobacter sphaeroides is one of the best-studied alphaproteobacteria from biochemical, genetic, and genomic perspectives. To gain a better systems-level understanding of this organism, we generated a large transposon mutant library and used transposon sequencing (Tn-seq) to identify genes that are essential under several growth conditions. Using newly developed Tn-seq analysis software (TSAS), we identified 493 genes as essential for aerobic growth on a rich medium. We then used the mutant library to identify conditionally essential genes under two laboratory growth conditions, identifying 85 additional genes required for aerobic growth in a minimal medium and 31 additional genes required for photosyntheticmore » growth. In all instances, our analyses confirmed essentiality for many known genes and identified genes not previously considered to be essential. We used the resulting Tn-seq data to refine and improve a genome-scale metabolic network model (GEM) for R. sphaeroides. Together, we demonstrate how genetic, genomic, and computational approaches can be combined to obtain a systems-level understanding of the genetic framework underlying metabolic diversity in bacterial species.« less

An efficient graph theory based method to identify every minimal reaction set in a metabolic network

PubMed Central

2014-01-01

Background Development of cells with minimal metabolic functionality is gaining importance due to their efficiency in producing chemicals and fuels. Existing computational methods to identify minimal reaction sets in metabolic networks are computationally expensive. Further, they identify only one of the several possible minimal reaction sets. Results In this paper, we propose an efficient graph theory based recursive optimization approach to identify all minimal reaction sets. Graph theoretical insights offer systematic methods to not only reduce the number of variables in math programming and increase its computational efficiency, but also provide efficient ways to find multiple optimal solutions. The efficacy of the proposed approach is demonstrated using case studies from Escherichia coli and Saccharomyces cerevisiae. In case study 1, the proposed method identified three minimal reaction sets each containing 38 reactions in Escherichia coli central metabolic network with 77 reactions. Analysis of these three minimal reaction sets revealed that one of them is more suitable for developing minimal metabolism cell compared to other two due to practically achievable internal flux distribution. In case study 2, the proposed method identified 256 minimal reaction sets from the Saccharomyces cerevisiae genome scale metabolic network with 620 reactions. The proposed method required only 4.5 hours to identify all the 256 minimal reaction sets and has shown a significant reduction (approximately 80%) in the solution time when compared to the existing methods for finding minimal reaction set. Conclusions Identification of all minimal reactions sets in metabolic networks is essential since different minimal reaction sets have different properties that effect the bioprocess development. The proposed method correctly identified all minimal reaction sets in a both the case studies. The proposed method is computationally efficient compared to other methods for finding minimal reaction sets and useful to employ with genome-scale metabolic networks. PMID:24594118

Error and attack tolerance of complex networks

NASA Astrophysics Data System (ADS)

Albert, Réka; Jeong, Hawoong; Barabási, Albert-László

2000-07-01

Many complex systems display a surprising degree of tolerance against errors. For example, relatively simple organisms grow, persist and reproduce despite drastic pharmaceutical or environmental interventions, an error tolerance attributed to the robustness of the underlying metabolic network. Complex communication networks display a surprising degree of robustness: although key components regularly malfunction, local failures rarely lead to the loss of the global information-carrying ability of the network. The stability of these and other complex systems is often attributed to the redundant wiring of the functional web defined by the systems' components. Here we demonstrate that error tolerance is not shared by all redundant systems: it is displayed only by a class of inhomogeneously wired networks, called scale-free networks, which include the World-Wide Web, the Internet, social networks and cells. We find that such networks display an unexpected degree of robustness, the ability of their nodes to communicate being unaffected even by unrealistically high failure rates. However, error tolerance comes at a high price in that these networks are extremely vulnerable to attacks (that is, to the selection and removal of a few nodes that play a vital role in maintaining the network's connectivity). Such error tolerance and attack vulnerability are generic properties of communication networks.

Rhinal hypometabolism on FDG PET in healthy APO-E4 carriers: impact on memory function and metabolic networks.

PubMed

Didic, Mira; Felician, Olivier; Gour, Natalina; Bernard, Rafaelle; Pécheux, Christophe; Mundler, Olivier; Ceccaldi, Mathieu; Guedj, Eric

2015-09-01

The ε4 allele of the apolipoprotein E (APO-E4) gene, a genetic risk factor for Alzheimer's disease (AD), also modulates brain metabolism and function in healthy subjects. The aim of the present study was to explore cerebral metabolism using FDG PET in healthy APO-E4 carriers by comparing cognitively normal APO-E4 carriers to noncarriers and to assess if patterns of metabolism are correlated with performance on cognitive tasks. Moreover, metabolic connectivity patterns were established in order to assess if the organization of neural networks is influenced by genetic factors. Whole-brain PET statistical analysis was performed at voxel-level using SPM8 with a threshold of p < 0.005, corrected for volume, with age, gender and level of education as nuisance variables. Significant hypometabolism between APO-E4 carriers (n = 11) and noncarriers (n = 30) was first determined. Mean metabolic values with clinical/neuropsychological data were extracted at the individual level, and correlations were searched using Spearman's rank test in the whole group. To evaluate metabolic connectivity from metabolic cluster(s) previously identified in the intergroup comparison, voxel-wise interregional correlation analysis (IRCA) was performed between groups of subjects. APO-E4 carriers had reduced metabolism within the left anterior medial temporal lobe (MTL), where neuropathological changes first appear in AD, including the entorhinal and perirhinal cortices. A correlation between metabolism in this area and performance on the DMS48 (delayed matching to sample-48 items) was found, in line with converging evidence involving the perirhinal cortex in object-based memory. Finally, a voxel-wise IRCA revealed stronger metabolic connectivity of the MTL cluster with neocortical frontoparietal regions in carriers than in noncarriers, suggesting compensatory metabolic networks. Exploring cerebral metabolism using FDG PET can contribute to a better understanding of the influence of genetic factors on cerebral metabolism at both the local and network levels leading to phenotypical variations of the healthy brain and selective vulnerability.

Modeling the Metabolism of Arabidopsis thaliana: Application of Network Decomposition and Network Reduction in the Context of Petri Nets.

PubMed

Koch, Ina; Nöthen, Joachim; Schleiff, Enrico

2017-01-01

Motivation: Arabidopsis thaliana is a well-established model system for the analysis of the basic physiological and metabolic pathways of plants. Nevertheless, the system is not yet fully understood, although many mechanisms are described, and information for many processes exists. However, the combination and interpretation of the large amount of biological data remain a big challenge, not only because data sets for metabolic paths are still incomplete. Moreover, they are often inconsistent, because they are coming from different experiments of various scales, regarding, for example, accuracy and/or significance. Here, theoretical modeling is powerful to formulate hypotheses for pathways and the dynamics of the metabolism, even if the biological data are incomplete. To develop reliable mathematical models they have to be proven for consistency. This is still a challenging task because many verification techniques fail already for middle-sized models. Consequently, new methods, like decomposition methods or reduction approaches, are developed to circumvent this problem. Methods: We present a new semi-quantitative mathematical model of the metabolism of Arabidopsis thaliana . We used the Petri net formalism to express the complex reaction system in a mathematically unique manner. To verify the model for correctness and consistency we applied concepts of network decomposition and network reduction such as transition invariants, common transition pairs, and invariant transition pairs. Results: We formulated the core metabolism of Arabidopsis thaliana based on recent knowledge from literature, including the Calvin cycle, glycolysis and citric acid cycle, glyoxylate cycle, urea cycle, sucrose synthesis, and the starch metabolism. By applying network decomposition and reduction techniques at steady-state conditions, we suggest a straightforward mathematical modeling process. We demonstrate that potential steady-state pathways exist, which provide the fixed carbon to nearly all parts of the network, especially to the citric acid cycle. There is a close cooperation of important metabolic pathways, e.g., the de novo synthesis of uridine-5-monophosphate, the γ-aminobutyric acid shunt, and the urea cycle. The presented approach extends the established methods for a feasible interpretation of biological network models, in particular of large and complex models.

Modeling the Metabolism of Arabidopsis thaliana: Application of Network Decomposition and Network Reduction in the Context of Petri Nets

PubMed Central

Koch, Ina; Nöthen, Joachim; Schleiff, Enrico

2017-01-01

Motivation: Arabidopsis thaliana is a well-established model system for the analysis of the basic physiological and metabolic pathways of plants. Nevertheless, the system is not yet fully understood, although many mechanisms are described, and information for many processes exists. However, the combination and interpretation of the large amount of biological data remain a big challenge, not only because data sets for metabolic paths are still incomplete. Moreover, they are often inconsistent, because they are coming from different experiments of various scales, regarding, for example, accuracy and/or significance. Here, theoretical modeling is powerful to formulate hypotheses for pathways and the dynamics of the metabolism, even if the biological data are incomplete. To develop reliable mathematical models they have to be proven for consistency. This is still a challenging task because many verification techniques fail already for middle-sized models. Consequently, new methods, like decomposition methods or reduction approaches, are developed to circumvent this problem. Methods: We present a new semi-quantitative mathematical model of the metabolism of Arabidopsis thaliana. We used the Petri net formalism to express the complex reaction system in a mathematically unique manner. To verify the model for correctness and consistency we applied concepts of network decomposition and network reduction such as transition invariants, common transition pairs, and invariant transition pairs. Results: We formulated the core metabolism of Arabidopsis thaliana based on recent knowledge from literature, including the Calvin cycle, glycolysis and citric acid cycle, glyoxylate cycle, urea cycle, sucrose synthesis, and the starch metabolism. By applying network decomposition and reduction techniques at steady-state conditions, we suggest a straightforward mathematical modeling process. We demonstrate that potential steady-state pathways exist, which provide the fixed carbon to nearly all parts of the network, especially to the citric acid cycle. There is a close cooperation of important metabolic pathways, e.g., the de novo synthesis of uridine-5-monophosphate, the γ-aminobutyric acid shunt, and the urea cycle. The presented approach extends the established methods for a feasible interpretation of biological network models, in particular of large and complex models. PMID:28713420

A probabilistic approach to identify putative drug targets in biochemical networks.

PubMed

Murabito, Ettore; Smallbone, Kieran; Swinton, Jonathan; Westerhoff, Hans V; Steuer, Ralf

2011-06-06

Network-based drug design holds great promise in clinical research as a way to overcome the limitations of traditional approaches in the development of drugs with high efficacy and low toxicity. This novel strategy aims to study how a biochemical network as a whole, rather than its individual components, responds to specific perturbations in different physiological conditions. Proteins exerting little control over normal cells and larger control over altered cells may be considered as good candidates for drug targets. The application of network-based drug design would greatly benefit from using an explicit computational model describing the dynamics of the system under investigation. However, creating a fully characterized kinetic model is not an easy task, even for relatively small networks, as it is still significantly hampered by the lack of data about kinetic mechanisms and parameters values. Here, we propose a Monte Carlo approach to identify the differences between flux control profiles of a metabolic network in different physiological states, when information about the kinetics of the system is partially or totally missing. Based on experimentally accessible information on metabolic phenotypes, we develop a novel method to determine probabilistic differences in the flux control coefficients between the two observable phenotypes. Knowledge of how differences in flux control are distributed among the different enzymatic steps is exploited to identify points of fragility in one of the phenotypes. Using a prototypical cancerous phenotype as an example, we demonstrate how our approach can assist researchers in developing compounds with high efficacy and low toxicity. © 2010 The Royal Society

Apoptosis, energy metabolism, and fraction of radiobiologically hypoxic cells: a study of human melanoma multicellular spheroids.

PubMed

Rofstad, E K; Eide, K; Skøyum, R; Hystad, M E; Lyng, H

1996-09-01

The magnitude of the fraction of radiobiologically hypoxic cells in tumours is generally believed to reflect the efficiency of the vascular network. Theoretical studies have suggested that the hypoxic fraction might also be influenced by biological properties of the tumour cells. Quantitative experimental results of cell energy metabolism, hypoxia- induced apoptosis, and radiobiological hypoxia are reported here. Human melanoma multicellular spheroids (BEX-c and WIX-c) were used as tumour models to avoid confounding effects of the vascular network. Radiobiological studies showed that the fractions of hypoxic cells in 1000-microM spheroids were 32 +/- 12% (BEX-c) and 2.5 +/- 1.1% (WIX-c). The spheroid hypoxic volume fractions (28 +/- 6% (BEX-c) and 1.4 +/- 7% (WIX-c)), calculated from the rate of oxygen consumption per cell, the cell packing density, and the thickness of the viable rim, were similar to the fractions of radiobiologically hypoxic cells. Large differences between tumours in fraction of hypoxic cells are therefore not necessarily a result of differences in the efficiency of the vascular network. Studies of monolayer cell cultures, performed to identify the biological properties of the BEX-c and WIX-c cells leading to this large difference in fraction of hypoxic cells, gave the following results: (1) WIX-c showed lower cell surviving fractions after exposure to hypoxia than BEX-c, (2) WIX-c showed higher glucose uptake and lactate release rates than BEX-c both under aerobic and hypoxic conditions, and (3) hypoxia induced apoptosis in WIX-c but not in BEX-c. These observations suggested that the difference between BEX-c and WIX-c spheroids in fraction of hypoxic cells resulted partly from differences in cell energy metabolism and partly from a difference in capacity to retain viability under hypoxic stress. The induction of apoptosis by hypoxia was identified as a phenomenon which has an important influence on the magnitude of the fraction of radiobiologically hypoxic cells in multicellular spheroids.

Novel insights into embryonic stem cell self-renewal revealed through comparative human and mouse systems biology networks.

PubMed

Dowell, Karen G; Simons, Allen K; Bai, Hao; Kell, Braden; Wang, Zack Z; Yun, Kyuson; Hibbs, Matthew A

2014-05-01

Embryonic stem cells (ESCs), characterized by their ability to both self-renew and differentiate into multiple cell lineages, are a powerful model for biomedical research and developmental biology. Human and mouse ESCs share many features, yet have distinctive aspects, including fundamental differences in the signaling pathways and cell cycle controls that support self-renewal. Here, we explore the molecular basis of human ESC self-renewal using Bayesian network machine learning to integrate cell-type-specific, high-throughput data for gene function discovery. We integrated high-throughput ESC data from 83 human studies (~1.8 million data points collected under 1,100 conditions) and 62 mouse studies (~2.4 million data points collected under 1,085 conditions) into separate human and mouse predictive networks focused on ESC self-renewal to analyze shared and distinct functional relationships among protein-coding gene orthologs. Computational evaluations show that these networks are highly accurate, literature validation confirms their biological relevance, and reverse transcriptase polymerase chain reaction (RT-PCR) validation supports our predictions. Our results reflect the importance of key regulatory genes known to be strongly associated with self-renewal and pluripotency in both species (e.g., POU5F1, SOX2, and NANOG), identify metabolic differences between species (e.g., threonine metabolism), clarify differences between human and mouse ESC developmental signaling pathways (e.g., leukemia inhibitory factor (LIF)-activated JAK/STAT in mouse; NODAL/ACTIVIN-A-activated fibroblast growth factor in human), and reveal many novel genes and pathways predicted to be functionally associated with self-renewal in each species. These interactive networks are available online at www.StemSight.org for stem cell researchers to develop new hypotheses, discover potential mechanisms involving sparsely annotated genes, and prioritize genes of interest for experimental validation. © 2013 AlphaMed Press.

Modafinil Reverses Phencyclidine-Induced Deficits in Cognitive Flexibility, Cerebral Metabolism, and Functional Brain Connectivity

PubMed Central

Dawson, Neil; Thompson, Rhiannon J.; McVie, Allan; Thomson, David M.; Morris, Brian J.; Pratt, Judith A.

2012-01-01

Objective: In the present study, we employ mathematical modeling (partial least squares regression, PLSR) to elucidate the functional connectivity signatures of discrete brain regions in order to identify the functional networks subserving PCP-induced disruption of distinct cognitive functions and their restoration by the procognitive drug modafinil. Methods: We examine the functional connectivity signatures of discrete brain regions that show overt alterations in metabolism, as measured by semiquantitative 2-deoxyglucose autoradiography, in an animal model (subchronic phencyclidine [PCP] treatment), which shows cognitive inflexibility with relevance to the cognitive deficits seen in schizophrenia. Results: We identify the specific components of functional connectivity that contribute to the rescue of this cognitive inflexibility and to the restoration of overt cerebral metabolism by modafinil. We demonstrate that modafinil reversed both the PCP-induced deficit in the ability to switch attentional set and the PCP-induced hypometabolism in the prefrontal (anterior prelimbic) and retrosplenial cortices. Furthermore, modafinil selectively enhanced metabolism in the medial prelimbic cortex. The functional connectivity signatures of these regions identified a unifying functional subsystem underlying the influence of modafinil on cerebral metabolism and cognitive flexibility that included the nucleus accumbens core and locus coeruleus. In addition, these functional connectivity signatures identified coupling events specific to each brain region, which relate to known anatomical connectivity. Conclusions: These data support clinical evidence that modafinil may alleviate cognitive deficits in schizophrenia and also demonstrate the benefit of applying PLSR modeling to characterize functional brain networks in translational models relevant to central nervous system dysfunction. PMID:20810469

Metabolic Network Modeling of Microbial Communities

PubMed Central

Biggs, Matthew B.; Medlock, Gregory L.; Kolling, Glynis L.

2015-01-01

Genome-scale metabolic network reconstructions and constraint-based analysis are powerful methods that have the potential to make functional predictions about microbial communities. Current use of genome-scale metabolic networks to characterize the metabolic functions of microbial communities includes species compartmentalization, separating species-level and community-level objectives, dynamic analysis, the “enzyme-soup” approach, multi-scale modeling, and others. There are many challenges inherent to the field, including a need for tools that accurately assign high-level omics signals to individual community members, new automated reconstruction methods that rival manual curation, and novel algorithms for integrating omics data and engineering communities. As technologies and modeling frameworks improve, we expect that there will be proportional advances in the fields of ecology, health science, and microbial community engineering. PMID:26109480

Blueprint for antimicrobial hit discovery targeting metabolic networks.

PubMed

Shen, Y; Liu, J; Estiu, G; Isin, B; Ahn, Y-Y; Lee, D-S; Barabási, A-L; Kapatral, V; Wiest, O; Oltvai, Z N

2010-01-19

Advances in genome analysis, network biology, and computational chemistry have the potential to revolutionize drug discovery by combining system-level identification of drug targets with the atomistic modeling of small molecules capable of modulating their activity. To demonstrate the effectiveness of such a discovery pipeline, we deduced common antibiotic targets in Escherichia coli and Staphylococcus aureus by identifying shared tissue-specific or uniformly essential metabolic reactions in their metabolic networks. We then predicted through virtual screening dozens of potential inhibitors for several enzymes of these reactions and showed experimentally that a subset of these inhibited both enzyme activities in vitro and bacterial cell viability. This blueprint is applicable for any sequenced organism with high-quality metabolic reconstruction and suggests a general strategy for strain-specific antiinfective therapy.

Metabolic network flux analysis for engineering plant systems.

PubMed

Shachar-Hill, Yair

2013-04-01

Metabolic network flux analysis (NFA) tools have proven themselves to be powerful aids to metabolic engineering of microbes by providing quantitative insights into the flows of material and energy through cellular systems. The development and application of NFA tools to plant systems has advanced in recent years and are yielding significant insights and testable predictions. Plants present substantial opportunities for the practical application of NFA but they also pose serious challenges related to the complexity of plant metabolic networks and to deficiencies in our knowledge of their structure and regulation. By considering the tools available and selected examples, this article attempts to assess where and how NFA is most likely to have a real impact on plant biotechnology. Copyright © 2013 Elsevier Ltd. All rights reserved.

Experimental determination of group flux control coefficients in metabolic networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Simpson, T.W.; Shimizu, Hiroshi; Stephanopoulos, G.

1998-04-20

Grouping of reactions around key metabolite branch points can facilitate the study of metabolic control of complex metabolic networks. This top-down Metabolic Control Analysis is exemplified through the introduction of group control coefficients whose magnitudes provide a measure of the relative impact of each reaction group on the overall network flux, as well as on the overall network stability, following enzymatic amplification. In this article, the authors demonstrate the application of previously developed theory to the determination of group flux control coefficients. Experimental data for the changes in metabolic fluxes obtained in response to the introduction of six different environmentalmore » perturbations are used to determine the group flux control coefficients for three reaction groups formed around the phosphoenolpyruvate/pyruvate branch point. The consistency of the obtained group flux control coefficient estimates is systematically analyzed to ensure that all necessary conditions are satisfied. The magnitudes of the determined control coefficients suggest that the control of lysine production flux in Corynebacterium glutamicum cells at a growth base state resides within the lysine biosynthetic pathway that begins with the PEP/PYR carboxylation anaplorotic pathway.« less

Avoiding the Enumeration of Infeasible Elementary Flux Modes by Including Transcriptional Regulatory Rules in the Enumeration Process Saves Computational Costs

PubMed Central

Jungreuthmayer, Christian; Ruckerbauer, David E.; Gerstl, Matthias P.; Hanscho, Michael; Zanghellini, Jürgen

2015-01-01

Despite the significant progress made in recent years, the computation of the complete set of elementary flux modes of large or even genome-scale metabolic networks is still impossible. We introduce a novel approach to speed up the calculation of elementary flux modes by including transcriptional regulatory information into the analysis of metabolic networks. Taking into account gene regulation dramatically reduces the solution space and allows the presented algorithm to constantly eliminate biologically infeasible modes at an early stage of the computation procedure. Thereby, computational costs, such as runtime, memory usage, and disk space, are extremely reduced. Moreover, we show that the application of transcriptional rules identifies non-trivial system-wide effects on metabolism. Using the presented algorithm pushes the size of metabolic networks that can be studied by elementary flux modes to new and much higher limits without the loss of predictive quality. This makes unbiased, system-wide predictions in large scale metabolic networks possible without resorting to any optimization principle. PMID:26091045

Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology

PubMed Central

Paley, Suzanne M.; Krummenacker, Markus; Latendresse, Mario; Dale, Joseph M.; Lee, Thomas J.; Kaipa, Pallavi; Gilham, Fred; Spaulding, Aaron; Popescu, Liviu; Altman, Tomer; Paulsen, Ian; Keseler, Ingrid M.; Caspi, Ron

2010-01-01

Pathway Tools is a production-quality software environment for creating a type of model-organism database called a Pathway/Genome Database (PGDB). A PGDB such as EcoCyc integrates the evolving understanding of the genes, proteins, metabolic network and regulatory network of an organism. This article provides an overview of Pathway Tools capabilities. The software performs multiple computational inferences including prediction of metabolic pathways, prediction of metabolic pathway hole fillers and prediction of operons. It enables interactive editing of PGDBs by DB curators. It supports web publishing of PGDBs, and provides a large number of query and visualization tools. The software also supports comparative analyses of PGDBs, and provides several systems biology analyses of PGDBs including reachability analysis of metabolic networks, and interactive tracing of metabolites through a metabolic network. More than 800 PGDBs have been created using Pathway Tools by scientists around the world, many of which are curated DBs for important model organisms. Those PGDBs can be exchanged using a peer-to-peer DB sharing system called the PGDB Registry. PMID:19955237

Combined metabolomic and correlation networks analyses reveal fumarase insufficiency altered amino acid metabolism.

PubMed

Hou, Entai; Li, Xian; Liu, Zerong; Zhang, Fuchang; Tian, Zhongmin

2018-04-01

Fumarase catalyzes the interconversion of fumarate and l-malate in the tricarboxylic acid cycle. Fumarase insufficiencies were associated with increased levels of fumarate, decreased levels of malate and exacerbated salt-induced hypertension. To gain insights into the metabolism profiles induced by fumarase insufficiency and identify key regulatory metabolites, we applied a GC-MS based metabolomics platform coupled with a network approach to analyze fumarase insufficient human umbilical vein endothelial cells (HUVEC) and negative controls. A total of 24 altered metabolites involved in seven metabolic pathways were identified as significantly altered, and enriched for the biological module of amino acids metabolism. In addition, Pearson correlation network analysis revealed that fumaric acid, l-malic acid, l-aspartic acid, glycine and l-glutamic acid were hub metabolites according to Pagerank based on their three centrality indices. Alanine aminotransferase and glutamate dehydrogenase activities increased significantly in fumarase deficiency HUVEC. These results confirmed that fumarase insufficiency altered amino acid metabolism. The combination of metabolomics and network methods would provide another perspective on expounding the molecular mechanism at metabolomics level. Copyright © 2017 John Wiley & Sons, Ltd.

SS-mPMG and SS-GA: tools for finding pathways and dynamic simulation of metabolic networks.

PubMed

Katsuragi, Tetsuo; Ono, Naoaki; Yasumoto, Keiichi; Altaf-Ul-Amin, Md; Hirai, Masami Y; Sriyudthsak, Kansuporn; Sawada, Yuji; Yamashita, Yui; Chiba, Yukako; Onouchi, Hitoshi; Fujiwara, Toru; Naito, Satoshi; Shiraishi, Fumihide; Kanaya, Shigehiko

2013-05-01

Metabolomics analysis tools can provide quantitative information on the concentration of metabolites in an organism. In this paper, we propose the minimum pathway model generator tool for simulating the dynamics of metabolite concentrations (SS-mPMG) and a tool for parameter estimation by genetic algorithm (SS-GA). SS-mPMG can extract a subsystem of the metabolic network from the genome-scale pathway maps to reduce the complexity of the simulation model and automatically construct a dynamic simulator to evaluate the experimentally observed behavior of metabolites. Using this tool, we show that stochastic simulation can reproduce experimentally observed dynamics of amino acid biosynthesis in Arabidopsis thaliana. In this simulation, SS-mPMG extracts the metabolic network subsystem from published databases. The parameters needed for the simulation are determined using a genetic algorithm to fit the simulation results to the experimental data. We expect that SS-mPMG and SS-GA will help researchers to create relevant metabolic networks and carry out simulations of metabolic reactions derived from metabolomics data.

Supra-optimal expression of the cold-regulated OsMyb4 transcription factor in transgenic rice changes the complexity of transcriptional network with major effects on stress tolerance and panicle development.

PubMed

Park, Myoung-Ryoul; Yun, Kil-Young; Mohanty, Bijayalaxmi; Herath, Venura; Xu, Fuyu; Wijaya, Edward; Bajic, Vladimir B; Yun, Song-Joong; De Los Reyes, Benildo G

2010-12-01

The R2R3-type OsMyb4 transcription factor of rice has been shown to play a role in the regulation of osmotic adjustment in heterologous overexpression studies. However, the exact composition and organization of its underlying transcriptional network has not been established to be a robust tool for stress tolerance enhancement by regulon engineering. OsMyb4 network was dissected based on commonalities between the global chilling stress transcriptome and the transcriptome configured by OsMyb4 overexpression. OsMyb4 controls a hierarchical network comprised of several regulatory sub-clusters associated with cellular defense and rescue, metabolism and development. It regulates target genes either directly or indirectly through intermediary MYB, ERF, bZIP, NAC, ARF and CCAAT-HAP transcription factors. Regulatory sub-clusters have different combinations of MYB-like, GCC-box-like, ERD1-box-like, ABRE-like, G-box-like, as1/ocs/TGA-like, AuxRE-like, gibberellic acid response element (GARE)-like and JAre-like cis-elements. Cold-dependent network activity enhanced cellular antioxidant capacity through radical scavenging mechanisms and increased activities of phenylpropanoid and isoprenoid metabolic processes involving various abscisic acid (ABA), jasmonic acid (JA), salicylic acid (SA), ethylene and reactive oxygen species (ROS) responsive genes. OsMyb4 network is independent of drought response element binding protein/C-repeat binding factor (DREB/CBF) and its sub-regulons operate with possible co-regulators including nuclear factor-Y. Because of its upstream position in the network hierarchy, OsMyb4 functions quantitatively and pleiotrophically. Supra-optimal expression causes misexpression of alternative targets with costly trade-offs to panicle development. © 2010 Blackwell Publishing Ltd.

Bidirectionality and compartmentation of metabolic fluxes are revealed in the dynamics of isotopomer networks.

PubMed

Schryer, David W; Peterson, Pearu; Paalme, Toomas; Vendelin, Marko

2009-04-17

Isotope labeling is one of the few methods of revealing the in vivo bidirectionality and compartmentalization of metabolic fluxes within metabolic networks. We argue that a shift from steady state to dynamic isotopomer analysis is required to deal with these cellular complexities and provide a review of dynamic studies of compartmentalized energy fluxes in eukaryotic cells including cardiac muscle, plants, and astrocytes. Knowledge of complex metabolic behaviour on a molecular level is prerequisite for the intelligent design of genetically modified organisms able to realize their potential of revolutionizing food, energy, and pharmaceutical production. We describe techniques to explore the bidirectionality and compartmentalization of metabolic fluxes using information contained in the isotopic transient, and discuss the integration of kinetic models with MFA. The flux parameters of an example metabolic network were optimized to examine the compartmentalization of metabolites and and the bidirectionality of fluxes in the TCA cycle of Saccharomyces uvarum for steady-state respiratory growth.

Ongoing resolution of duplicate gene functions shapes the diversification of a metabolic network

DOE PAGES

Kuang, Meihua Christina; Hutchins, Paul D.; Russell, Jason D.; ...

2016-09-30

The evolutionary mechanisms leading to duplicate gene retention are well understood, but the long-term impacts of paralog differentiation on the regulation of metabolism remain underappreciated. Here we experimentally dissect the functions of two pairs of ancient paralogs of theGALactose sugar utilization network in two yeast species. Here, we show that theSaccharomyces uvarumnetwork is more active, even as over-induction is prevented by a second co-repressor that the model yeastSaccharomyces cerevisiaelacks. Surprisingly, removal of this repression system leads to a strong growth arrest, likely due to overly rapid galactose catabolism and metabolic overload. Alternative sugars, such as fructose, circumvent metabolic control systemsmore » and exacerbate this phenotype. Furthermore, we show thatS. cerevisiaeexperiences homologous metabolic constraints that are subtler due to how the paralogs have diversified. Our results show how the functional differentiation of paralogs continues to shape regulatory network architectures and metabolic strategies long after initial preservation.« less

METscout: a pathfinder exploring the landscape of metabolites, enzymes and transporters.

PubMed

Geffers, Lars; Tetzlaff, Benjamin; Cui, Xiao; Yan, Jun; Eichele, Gregor

2013-01-01

METscout (http://metscout.mpg.de) brings together metabolism and gene expression landscapes. It is a MySQL relational database linking biochemical pathway information with 3D patterns of gene expression determined by robotic in situ hybridization in the E14.5 mouse embryo. The sites of expression of ∼1500 metabolic enzymes and of ∼350 solute carriers (SLCs) were included and are accessible as single cell resolution images and in the form of semi-quantitative image abstractions. METscout provides several graphical web-interfaces allowing navigation through complex anatomical and metabolic information. Specifically, the database shows where in the organism each of the many metabolic reactions take place and where SLCs transport metabolites. To link enzymatic reactions and transport, the KEGG metabolic reaction network was extended to include metabolite transport. This network in conjunction with spatial expression pattern of the network genes allows for a tracing of metabolic reactions and transport processes across the entire body of the embryo.

Gene Discovery of Characteristic Metabolic Pathways in the Tea Plant (Camellia sinensis) Using ‘Omics’-Based Network Approaches: A Future Perspective

PubMed Central

Zhang, Shihua; Zhang, Liang; Tai, Yuling; Wang, Xuewen; Ho, Chi-Tang; Wan, Xiaochun

2018-01-01

Characteristic secondary metabolites, including flavonoids, theanine and caffeine, in the tea plant (Camellia sinensis) are the primary sources of the rich flavors, fresh taste, and health benefits of tea. The decoding of genes involved in these characteristic components is still significantly lagging, which lays an obstacle for applied genetic improvement and metabolic engineering. With the popularity of high-throughout transcriptomics and metabolomics, ‘omics’-based network approaches, such as gene co-expression network and gene-to-metabolite network, have emerged as powerful tools for gene discovery of plant-specialized (secondary) metabolism. Thus, it is pivotal to summarize and introduce such system-based strategies in facilitating gene identification of characteristic metabolic pathways in the tea plant (or other plants). In this review, we describe recent advances in transcriptomics and metabolomics for transcript and metabolite profiling, and highlight ‘omics’-based network strategies using successful examples in model and non-model plants. Further, we summarize recent progress in ‘omics’ analysis for gene identification of characteristic metabolites in the tea plant. Limitations of the current strategies are discussed by comparison with ‘omics’-based network approaches. Finally, we demonstrate the potential of introducing such network strategies in the tea plant, with a prospects ending for a promising network discovery of characteristic metabolite genes in the tea plant. PMID:29915604

Using a Genome-Scale Metabolic Network Model to Elucidate the Mechanism of Chloroquine Action in Plasmodium falciparum

DTIC Science & Technology

2017-03-22

Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, US...2017 Available online 22 March 2017 Keywords: Plasmodium Chloroquine Metabolic network modeling Redox metabolism Carbon fixation* Corresponding... available (Antony and Parija, 2016), their efficacy has declined appreciably in the last few decades owing to widespread drug resistance developed by the

Tagging and tracking individual networks within a complex mitochondrial web with photoactivatable GFP.

PubMed

Twig, Gilad; Graf, Solomon A; Wikstrom, Jakob D; Mohamed, Hibo; Haigh, Sarah E; Elorza, Alvaro; Deutsch, Motti; Zurgil, Naomi; Reynolds, Nicole; Shirihai, Orian S

2006-07-01

Assembly of mitochondria into networks supports fuel metabolism and calcium transport and is involved in the cellular response to apoptotic stimuli. A mitochondrial network is defined as a continuous matrix lumen whose boundaries limit molecular diffusion. Observation of individual networks has proven challenging in live cells that possess dense populations of mitochondria. Investigation into the electrical and morphological properties of mitochondrial networks has therefore not yielded consistent conclusions. In this study we used matrix-targeted, photoactivatable green fluorescent protein to tag single mitochondrial networks. This approach, coupled with real-time monitoring of mitochondrial membrane potential, permitted the examination of matrix lumen continuity and fusion and fission events over time. We found that adjacent and intertwined mitochondrial structures often represent a collection of distinct networks. We additionally found that all areas of a single network are invariably equipotential, suggesting that a heterogeneous pattern of membrane potential within a cell's mitochondria represents differences between discrete networks. Interestingly, fission events frequently occurred without any gross morphological changes and particularly without fragmentation. These events, which are invisible under standard confocal microscopy, redefine the mitochondrial network boundaries and result in electrically disconnected daughter units.

Determinism and Contingency Shape Metabolic Complementation in an Endosymbiotic Consortium.

PubMed

Ponce-de-Leon, Miguel; Tamarit, Daniel; Calle-Espinosa, Jorge; Mori, Matteo; Latorre, Amparo; Montero, Francisco; Pereto, Juli

2017-01-01

Bacterial endosymbionts and their insect hosts establish an intimate metabolic relationship. Bacteria offer a variety of essential nutrients to their hosts, whereas insect cells provide the necessary sources of matter and energy to their tiny metabolic allies. These nutritional complementations sustain themselves on a diversity of metabolite exchanges between the cell host and the reduced yet highly specialized bacterial metabolism-which, for instance, overproduces a small set of essential amino acids and vitamins. A well-known case of metabolic complementation is provided by the cedar aphid Cinara cedri that harbors two co-primary endosymbionts, Buchnera aphidicola BCc and Ca . Serratia symbiotica SCc, and in which some metabolic pathways are partitioned between different partners. Here we present a genome-scale metabolic network (GEM) for the bacterial consortium from the cedar aphid i BSCc. The analysis of this GEM allows us the confirmation of cases of metabolic complementation previously described by genome analysis (i.e., tryptophan and biotin biosynthesis) and the redefinition of an event of metabolic pathway sharing between the two endosymbionts, namely the biosynthesis of tetrahydrofolate. In silico knock-out experiments with i BSCc showed that the consortium metabolism is a highly integrated yet fragile network. We also have explored the evolutionary pathways leading to the emergence of metabolic complementation between reduced metabolisms starting from individual, complete networks. Our results suggest that, during the establishment of metabolic complementation in endosymbionts, adaptive evolution is significant in the case of tryptophan biosynthesis, whereas vitamin production pathways seem to adopt suboptimal solutions.

A protocol for generating a high-quality genome-scale metabolic reconstruction.

PubMed

Thiele, Ines; Palsson, Bernhard Ø

2010-01-01

Network reconstructions are a common denominator in systems biology. Bottom-up metabolic network reconstructions have been developed over the last 10 years. These reconstructions represent structured knowledge bases that abstract pertinent information on the biochemical transformations taking place within specific target organisms. The conversion of a reconstruction into a mathematical format facilitates a myriad of computational biological studies, including evaluation of network content, hypothesis testing and generation, analysis of phenotypic characteristics and metabolic engineering. To date, genome-scale metabolic reconstructions for more than 30 organisms have been published and this number is expected to increase rapidly. However, these reconstructions differ in quality and coverage that may minimize their predictive potential and use as knowledge bases. Here we present a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction, as well as the common trials and tribulations. Therefore, this protocol provides a helpful manual for all stages of the reconstruction process.

A protocol for generating a high-quality genome-scale metabolic reconstruction

PubMed Central

Thiele, Ines; Palsson, Bernhard Ø.

2011-01-01

Network reconstructions are a common denominator in systems biology. Bottom-up metabolic network reconstructions have developed over the past 10 years. These reconstructions represent structured knowledge-bases that abstract pertinent information on the biochemical transformations taking place within specific target organisms. The conversion of a reconstruction into a mathematical format facilitates myriad computational biological studies including evaluation of network content, hypothesis testing and generation, analysis of phenotypic characteristics, and metabolic engineering. To date, genome-scale metabolic reconstructions for more than 30 organisms have been published and this number is expected to increase rapidly. However, these reconstructions differ in quality and coverage that may minimize their predictive potential and use as knowledge-bases. Here, we present a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction as well as common trials and tribulations. Therefore, this protocol provides a helpful manual for all stages of the reconstruction process. PMID:20057383

A transcription factor links growth rate and metabolism in the hypersaline adapted archaeon Halobacterium salinarum.

PubMed

Todor, Horia; Dulmage, Keely; Gillum, Nicholas; Bain, James R; Muehlbauer, Michael J; Schmid, Amy K

2014-09-01

Co-ordinating metabolism and growth is a key challenge for all organisms. Despite fluctuating environments, cells must produce the same metabolic outputs to thrive. The mechanisms underlying this 'growth homeostasis' are known in bacteria and eukaryotes, but remain unexplored in archaea. In the model archaeon Halobacterium salinarum, the transcription factor TrmB regulates enzyme-coding genes in diverse metabolic pathways in response to glucose. However, H. salinarum is thought not to catabolize glucose. To resolve this discrepancy, we demonstrate that TrmB regulates the gluconeogenic production of sugars incorporated into the cell surface S-layer glycoprotein. Additionally, we show that TrmB-DNA binding correlates with instantaneous growth rate, likely because S-layer glycosylation is proportional to growth. This suggests that TrmB transduces a growth rate signal to co-regulated metabolic pathways including amino acid, purine, and cobalamin biosynthesis. Remarkably, the topology and function of this growth homeostatic network appear conserved across domains despite extensive alterations in protein components. © 2014 The Authors. Molecular Microbiology published by John Wiley & Sons Ltd.

The transcription factor Cabut coordinates energy metabolism and the circadian clock in response to sugar sensing

PubMed Central

Bartok, Osnat; Teesalu, Mari; Ashwall-Fluss, Reut; Pandey, Varun; Hanan, Mor; Rovenko, Bohdana M; Poukkula, Minna; Havula, Essi; Moussaieff, Arieh; Vodala, Sadanand; Nahmias, Yaakov; Kadener, Sebastian; Hietakangas, Ville

2015-01-01

Nutrient sensing pathways adjust metabolism and physiological functions in response to food intake. For example, sugar feeding promotes lipogenesis by activating glycolytic and lipogenic genes through the Mondo/ChREBP-Mlx transcription factor complex. Concomitantly, other metabolic routes are inhibited, but the mechanisms of transcriptional repression upon sugar sensing have remained elusive. Here, we characterize cabut (cbt), a transcription factor responsible for the repressive branch of the sugar sensing transcriptional network in Drosophila. We demonstrate that cbt is rapidly induced upon sugar feeding through direct regulation by Mondo-Mlx. We found that CBT represses several metabolic targets in response to sugar feeding, including both isoforms of phosphoenolpyruvate carboxykinase (pepck). Deregulation of pepck1 (CG17725) in mlx mutants underlies imbalance of glycerol and glucose metabolism as well as developmental lethality. Furthermore, we demonstrate that cbt provides a regulatory link between nutrient sensing and the circadian clock. Specifically, we show that a subset of genes regulated by the circadian clock are also targets of CBT. Moreover, perturbation of CBT levels leads to deregulation of the circadian transcriptome and circadian behavioral patterns. PMID:25916830

A Metabolic Profiling Strategy for the Dissection of Plant Defense against Fungal Pathogens

PubMed Central

Aliferis, Konstantinos A.; Faubert, Denis; Jabaji, Suha

2014-01-01

Here we present a metabolic profiling strategy employing direct infusion Orbitrap mass spectrometry (MS) and gas chromatography-mass spectrometry (GC/MS) for the monitoring of soybean's (Glycine max L.) global metabolism regulation in response to Rhizoctonia solani infection in a time-course. Key elements in the approach are the construction of a comprehensive metabolite library for soybean, which accelerates the steps of metabolite identification and biological interpretation of results, and bioinformatics tools for the visualization and analysis of its metabolome. The study of metabolic networks revealed that infection results in the mobilization of carbohydrates, disturbance of the amino acid pool, and activation of isoflavonoid, α-linolenate, and phenylpropanoid biosynthetic pathways of the plant. Components of these pathways include phytoalexins, coumarins, flavonoids, signaling molecules, and hormones, many of which exhibit antioxidant properties and bioactivity helping the plant to counterattack the pathogen's invasion. Unraveling the biochemical mechanism operating during soybean-Rhizoctonia interaction, in addition to its significance towards the understanding of the plant's metabolism regulation under biotic stress, provides valuable insights with potential for applications in biotechnology, crop breeding, and agrochemical and food industries. PMID:25369450

Metabolic network visualization eliminating node redundance and preserving metabolic pathways

PubMed Central

Bourqui, Romain; Cottret, Ludovic; Lacroix, Vincent; Auber, David; Mary, Patrick; Sagot, Marie-France; Jourdan, Fabien

2007-01-01

Background The tools that are available to draw and to manipulate the representations of metabolism are usually restricted to metabolic pathways. This limitation becomes problematic when studying processes that span several pathways. The various attempts that have been made to draw genome-scale metabolic networks are confronted with two shortcomings: 1- they do not use contextual information which leads to dense, hard to interpret drawings, 2- they impose to fit to very constrained standards, which implies, in particular, duplicating nodes making topological analysis considerably more difficult. Results We propose a method, called MetaViz, which enables to draw a genome-scale metabolic network and that also takes into account its structuration into pathways. This method consists in two steps: a clustering step which addresses the pathway overlapping problem and a drawing step which consists in drawing the clustered graph and each cluster. Conclusion The method we propose is original and addresses new drawing issues arising from the no-duplication constraint. We do not propose a single drawing but rather several alternative ways of presenting metabolism depending on the pathway on which one wishes to focus. We believe that this provides a valuable tool to explore the pathway structure of metabolism. PMID:17608928

optGpSampler: an improved tool for uniformly sampling the solution-space of genome-scale metabolic networks.

PubMed

Megchelenbrink, Wout; Huynen, Martijn; Marchiori, Elena

2014-01-01

Constraint-based models of metabolic networks are typically underdetermined, because they contain more reactions than metabolites. Therefore the solutions to this system do not consist of unique flux rates for each reaction, but rather a space of possible flux rates. By uniformly sampling this space, an estimated probability distribution for each reaction's flux in the network can be obtained. However, sampling a high dimensional network is time-consuming. Furthermore, the constraints imposed on the network give rise to an irregularly shaped solution space. Therefore more tailored, efficient sampling methods are needed. We propose an efficient sampling algorithm (called optGpSampler), which implements the Artificial Centering Hit-and-Run algorithm in a different manner than the sampling algorithm implemented in the COBRA Toolbox for metabolic network analysis, here called gpSampler. Results of extensive experiments on different genome-scale metabolic networks show that optGpSampler is up to 40 times faster than gpSampler. Application of existing convergence diagnostics on small network reconstructions indicate that optGpSampler converges roughly ten times faster than gpSampler towards similar sampling distributions. For networks of higher dimension (i.e. containing more than 500 reactions), we observed significantly better convergence of optGpSampler and a large deviation between the samples generated by the two algorithms. optGpSampler for Matlab and Python is available for non-commercial use at: http://cs.ru.nl/~wmegchel/optGpSampler/.

Gene network analysis identifies rumen epithelial cell proliferation, differentiation and metabolic pathways perturbed by diet and correlated with methane production

PubMed Central

Xiang, Ruidong; McNally, Jody; Rowe, Suzanne; Jonker, Arjan; Pinares-Patino, Cesar S.; Oddy, V. Hutton; Vercoe, Phil E.; McEwan, John C.; Dalrymple, Brian P.

2016-01-01

Ruminants obtain nutrients from microbial fermentation of plant material, primarily in their rumen, a multilayered forestomach. How the different layers of the rumen wall respond to diet and influence microbial fermentation, and how these process are regulated, is not well understood. Gene expression correlation networks were constructed from full thickness rumen wall transcriptomes of 24 sheep fed two different amounts and qualities of a forage and measured for methane production. The network contained two major negatively correlated gene sub-networks predominantly representing the epithelial and muscle layers of the rumen wall. Within the epithelium sub-network gene clusters representing lipid/oxo-acid metabolism, general metabolism and proliferating and differentiating cells were identified. The expression of cell cycle and metabolic genes was positively correlated with dry matter intake, ruminal short chain fatty acid concentrations and methane production. A weak correlation between lipid/oxo-acid metabolism genes and methane yield was observed. Feed consumption level explained the majority of gene expression variation, particularly for the cell cycle genes. Many known stratified epithelium transcription factors had significantly enriched targets in the epithelial gene clusters. The expression patterns of the transcription factors and their targets in proliferating and differentiating skin is mirrored in the rumen, suggesting conservation of regulatory systems. PMID:27966600

The Human Burst Suppression Electroencephalogram of Deep Hypothermia

PubMed Central

Kumaraswamy, Vishakhadatta M.; Akeju, Seun Oluwaseun; Pierce, Eric; Cash, Sydney S.; Kilbride, Ronan; Brown, Emery N.; Purdon, Patrick L.

2015-01-01

Objective Deep hypothermia induces ‘burst suppression’ (BS), an electroencephalogram pattern with low-voltage ‘suppressions’ alternating with high-voltage ‘bursts’. Current understanding of BS comes mainly from anesthesia studies, while hypothermia-induced BS has received little study. We set out to investigate the electroencephalogram changes induced by cooling the human brain through increasing depths of BS through isoelectricity. Methods We recorded scalp electroencephalograms from eleven patients undergoing deep hypothermia during cardiac surgery with complete circulatory arrest, and analyzed these using methods of spectral analysis. Results Within patients, the depth of BS systematically depends on the depth of hypothermia, though responses vary between patients except at temperature extremes. With decreasing temperature, burst lengths increase, and burst amplitudes and lengths decrease, while the spectral content of bursts remains constant. Conclusions These findings support an existing theoretical model in which the common mechanism of burst suppression across diverse etiologies is the cyclical diffuse depletion of metabolic resources, and suggest the new hypothesis of local micro-network dropout to explain decreasing burst amplitudes at lower temperatures. Significance These results pave the way for accurate noninvasive tracking of brain metabolic state during surgical procedures under deep hypothermia, and suggest new testable predictions about the network mechanisms underlying burst suppression. PMID:25649968

The human burst suppression electroencephalogram of deep hypothermia.

PubMed

Westover, M Brandon; Ching, Shinung; Kumaraswamy, Vishakhadatta M; Akeju, Seun Oluwaseun; Pierce, Eric; Cash, Sydney S; Kilbride, Ronan; Brown, Emery N; Purdon, Patrick L

2015-10-01

Deep hypothermia induces 'burst suppression' (BS), an electroencephalogram pattern with low-voltage 'suppressions' alternating with high-voltage 'bursts'. Current understanding of BS comes mainly from anesthesia studies, while hypothermia-induced BS has received little study. We set out to investigate the electroencephalogram changes induced by cooling the human brain through increasing depths of BS through isoelectricity. We recorded scalp electroencephalograms from eleven patients undergoing deep hypothermia during cardiac surgery with complete circulatory arrest, and analyzed these using methods of spectral analysis. Within patients, the depth of BS systematically depends on the depth of hypothermia, though responses vary between patients except at temperature extremes. With decreasing temperature, burst lengths increase, and burst amplitudes and lengths decrease, while the spectral content of bursts remains constant. These findings support an existing theoretical model in which the common mechanism of burst suppression across diverse etiologies is the cyclical diffuse depletion of metabolic resources, and suggest the new hypothesis of local micro-network dropout to explain decreasing burst amplitudes at lower temperatures. These results pave the way for accurate noninvasive tracking of brain metabolic state during surgical procedures under deep hypothermia, and suggest new testable predictions about the network mechanisms underlying burst suppression. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Branched Chain Amino Acids: Beyond Nutrition Metabolism.

PubMed

Nie, Cunxi; He, Ting; Zhang, Wenju; Zhang, Guolong; Ma, Xi

2018-03-23

Branched chain amino acids (BCAAs), including leucine (Leu), isoleucine (Ile), and valine (Val), play critical roles in the regulation of energy homeostasis, nutrition metabolism, gut health, immunity and disease in humans and animals. As the most abundant of essential amino acids (EAAs), BCAAs are not only the substrates for synthesis of nitrogenous compounds, they also serve as signaling molecules regulating metabolism of glucose, lipid, and protein synthesis, intestinal health, and immunity via special signaling network, especially phosphoinositide 3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/AKT/mTOR) signal pathway. Current evidence supports BCAAs and their derivatives as the potential biomarkers of diseases such as insulin resistance (IR), type 2 diabetes mellitus (T2DM), cancer, and cardiovascular diseases (CVDs). These diseases are closely associated with catabolism and balance of BCAAs. Hence, optimizing dietary BCAA levels should have a positive effect on the parameters associated with health and diseases. This review focuses on recent findings of BCAAs in metabolic pathways and regulation, and underlying the relationship of BCAAs to related disease processes.

Gut microbiota dictates the metabolic response of Drosophila to diet

PubMed Central

Wong, Adam C.-N.; Dobson, Adam J.; Douglas, Angela E.

2014-01-01

Animal nutrition is profoundly influenced by the gut microbiota, but knowledge of the scope and core mechanisms of the underlying animal–microbiota interactions is fragmentary. To investigate the nutritional traits shaped by the gut microbiota of Drosophila, we determined the microbiota-dependent response of multiple metabolic and performance indices to systematically varied diet composition. Diet-dependent differences between Drosophila bearing its unmanipulated microbiota (conventional flies) and experimentally deprived of its microbiota (axenic flies) revealed evidence for: microbial sparing of dietary B vitamins, especially riboflavin, on low-yeast diets; microbial promotion of protein nutrition, particularly in females; and microbiota-mediated suppression of lipid/carbohydrate storage, especially on high sugar diets. The microbiota also sets the relationship between energy storage and body mass, indicative of microbial modulation of the host signaling networks that coordinate metabolism with body size. This analysis identifies the multiple impacts of the microbiota on the metabolism of Drosophila, and demonstrates that the significance of these different interactions varies with diet composition and host sex. PMID:24577449

Global Metabolic Regulation of the Snow Alga Chlamydomonas nivalis in Response to Nitrate or Phosphate Deprivation by a Metabolome Profile Analysis.

PubMed

Lu, Na; Chen, Jun-Hui; Wei, Dong; Chen, Feng; Chen, Gu

2016-05-10

In the present work, Chlamydomonas nivalis, a model species of snow algae, was used to illustrate the metabolic regulation mechanism of microalgae under nutrient deprivation stress. The seed culture was inoculated into the medium without nitrate or phosphate to reveal the cell responses by a metabolome profile analysis using gas chromatography time-of-flight mass spectrometry (GC/TOF-MS). One hundred and seventy-one of the identified metabolites clustered into five groups by the orthogonal partial least squares discriminant analysis (OPLS-DA) model. Among them, thirty of the metabolites in the nitrate-deprived group and thirty-nine of the metabolites in the phosphate-deprived group were selected and identified as "responding biomarkers" by this metabolomic approach. A significant change in the abundance of biomarkers indicated that the enhanced biosynthesis of carbohydrates and fatty acids coupled with the decreased biosynthesis of amino acids, N-compounds and organic acids in all the stress groups. The up- or down-regulation of these biomarkers in the metabolic network provides new insights into the global metabolic regulation and internal relationships within amino acid and fatty acid synthesis, glycolysis, the tricarboxylic acid cycle (TCA) and the Calvin cycle in the snow alga under nitrate or phosphate deprivation stress.

Structural Control of Metabolic Flux

PubMed Central

Sajitz-Hermstein, Max; Nikoloski, Zoran

2013-01-01

Organisms have to continuously adapt to changing environmental conditions or undergo developmental transitions. To meet the accompanying change in metabolic demands, the molecular mechanisms of adaptation involve concerted interactions which ultimately induce a modification of the metabolic state, which is characterized by reaction fluxes and metabolite concentrations. These state transitions are the effect of simultaneously manipulating fluxes through several reactions. While metabolic control analysis has provided a powerful framework for elucidating the principles governing this orchestrated action to understand metabolic control, its applications are restricted by the limited availability of kinetic information. Here, we introduce structural metabolic control as a framework to examine individual reactions' potential to control metabolic functions, such as biomass production, based on structural modeling. The capability to carry out a metabolic function is determined using flux balance analysis (FBA). We examine structural metabolic control on the example of the central carbon metabolism of Escherichia coli by the recently introduced framework of functional centrality (FC). This framework is based on the Shapley value from cooperative game theory and FBA, and we demonstrate its superior ability to assign “share of control” to individual reactions with respect to metabolic functions and environmental conditions. A comparative analysis of various scenarios illustrates the usefulness of FC and its relations to other structural approaches pertaining to metabolic control. We propose a Monte Carlo algorithm to estimate FCs for large networks, based on the enumeration of elementary flux modes. We further give detailed biological interpretation of FCs for production of lactate and ATP under various respiratory conditions. PMID:24367246

Genome and Transcriptome Analyses Provide Insight into the Euryhaline Adaptation Mechanism of Crassostrea gigas

PubMed Central

Zhang, Linlin; Li, Chunyan; Li, Li; She, Zhicai; Huang, Baoyu; Zhang, Guofan

2013-01-01

Background The Pacific oyster, Crassostrea gigas, has developed special mechanisms to regulate its osmotic balance to adapt to fluctuations of salinities in coastal zones. To understand the oyster’s euryhaline adaptation, we analyzed salt stress effectors metabolism pathways under different salinities (salt 5, 10, 15, 20, 25, 30 and 40 for 7 days) using transcriptome data, physiology experiment and quantitative real-time PCR. Results Transcriptome data uncovered 189, 480, 207 and 80 marker genes for monitoring physiology status of oysters and the environment conditions. Three known salt stress effectors (involving ion channels, aquaporins and free amino acids) were examined. The analysis of ion channels and aquaporins indicated that 7 days long-term salt stress inhibited voltage-gated Na+/K+ channel and aquaporin but increased calcium-activated K+ channel and Ca2+ channel. As the most important category of osmotic stress effector, we analyzed the oyster FAAs metabolism pathways (including taurine, glycine, alanine, beta-alanine, proline and arginine) and explained FAAs functional mechanism for oyster low salinity adaptation. FAAs metabolism key enzyme genes displayed expression differentiation in low salinity adapted individuals comparing with control which further indicated that FAAs played important roles for oyster salinity adaptation. A global metabolic pathway analysis (iPath) of oyster expanded genes displayed a co-expansion of FAAs metabolism in C. gigas compared with seven other species, suggesting oyster’s powerful ability regarding FAAs metabolism, allowing it to adapt to fluctuating salinities, which may be one important mechanism underlying euryhaline adaption in oyster. Additionally, using transcriptome data analysis, we uncovered salt stress transduction networks in C. gigas. Conclusions Our results represented oyster salt stress effectors functional mechanisms under salt stress conditions and explained the expansion of FAAs metabolism pathways as the most important effectors for oyster euryhaline adaptation. This study was the first to explain oyster euryhaline adaptation at a genome-wide scale in C. gigas. PMID:23554902

Consistent abnormalities in metabolic network activity in idiopathic rapid eye movement sleep behaviour disorder.

PubMed

Wu, Ping; Yu, Huan; Peng, Shichun; Dauvilliers, Yves; Wang, Jian; Ge, Jingjie; Zhang, Huiwei; Eidelberg, David; Ma, Yilong; Zuo, Chuantao

2014-12-01

Rapid eye movement sleep behaviour disorder has been evaluated using Parkinson's disease-related metabolic network. It is unknown whether this disorder is itself associated with a unique metabolic network. 18F-fluorodeoxyglucose positron emission tomography was performed in 21 patients (age 65.0±5.6 years) with idiopathic rapid eye movement sleep behaviour disorder and 21 age/gender-matched healthy control subjects (age 62.5±7.5 years) to identify a disease-related pattern and examine its evolution in 21 hemi-parkinsonian patients (age 62.6±5.0 years) and 16 moderate parkinsonian patients (age 56.9±12.2 years). We identified a rapid eye movement sleep behaviour disorder-related metabolic network characterized by increased activity in pons, thalamus, medial frontal and sensorimotor areas, hippocampus, supramarginal and inferior temporal gyri, and posterior cerebellum, with decreased activity in occipital and superior temporal regions. Compared to the healthy control subjects, network expressions were elevated (P<0.0001) in the patients with this disorder and in the parkinsonian cohorts but decreased with disease progression. Parkinson's disease-related network activity was also elevated (P<0.0001) in the patients with rapid eye movement sleep behaviour disorder but lower than in the hemi-parkinsonian cohort. Abnormal metabolic networks may provide markers of idiopathic rapid eye movement sleep behaviour disorder to identify those at higher risk to develop neurodegenerative parkinsonism. © The Author (2014). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Efficient searching and annotation of metabolic networks using chemical similarity

PubMed Central

Pertusi, Dante A.; Stine, Andrew E.; Broadbelt, Linda J.; Tyo, Keith E.J.

2015-01-01

Motivation: The urgent need for efficient and sustainable biological production of fuels and high-value chemicals has elicited a wave of in silico techniques for identifying promising novel pathways to these compounds in large putative metabolic networks. To date, these approaches have primarily used general graph search algorithms, which are prohibitively slow as putative metabolic networks may exceed 1 million compounds. To alleviate this limitation, we report two methods—SimIndex (SI) and SimZyme—which use chemical similarity of 2D chemical fingerprints to efficiently navigate large metabolic networks and propose enzymatic connections between the constituent nodes. We also report a Byers–Waterman type pathway search algorithm for further paring down pertinent networks. Results: Benchmarking tests run with SI show it can reduce the number of nodes visited in searching a putative network by 100-fold with a computational time improvement of up to 105-fold. Subsequent Byers–Waterman search application further reduces the number of nodes searched by up to 100-fold, while SimZyme demonstrates ∼90% accuracy in matching query substrates with enzymes. Using these modules, we have designed and annotated an alternative to the methylerythritol phosphate pathway to produce isopentenyl pyrophosphate with more favorable thermodynamics than the native pathway. These algorithms will have a significant impact on our ability to use large metabolic networks that lack annotation of promiscuous reactions. Availability and implementation: Python files will be available for download at http://tyolab.northwestern.edu/tools/. Contact: k-tyo@northwestern.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25417203

Efficient searching and annotation of metabolic networks using chemical similarity.

PubMed

Pertusi, Dante A; Stine, Andrew E; Broadbelt, Linda J; Tyo, Keith E J

2015-04-01

The urgent need for efficient and sustainable biological production of fuels and high-value chemicals has elicited a wave of in silico techniques for identifying promising novel pathways to these compounds in large putative metabolic networks. To date, these approaches have primarily used general graph search algorithms, which are prohibitively slow as putative metabolic networks may exceed 1 million compounds. To alleviate this limitation, we report two methods--SimIndex (SI) and SimZyme--which use chemical similarity of 2D chemical fingerprints to efficiently navigate large metabolic networks and propose enzymatic connections between the constituent nodes. We also report a Byers-Waterman type pathway search algorithm for further paring down pertinent networks. Benchmarking tests run with SI show it can reduce the number of nodes visited in searching a putative network by 100-fold with a computational time improvement of up to 10(5)-fold. Subsequent Byers-Waterman search application further reduces the number of nodes searched by up to 100-fold, while SimZyme demonstrates ∼ 90% accuracy in matching query substrates with enzymes. Using these modules, we have designed and annotated an alternative to the methylerythritol phosphate pathway to produce isopentenyl pyrophosphate with more favorable thermodynamics than the native pathway. These algorithms will have a significant impact on our ability to use large metabolic networks that lack annotation of promiscuous reactions. Python files will be available for download at http://tyolab.northwestern.edu/tools/. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A systems biology approach to reconcile metabolic network models with application to Synechocystis sp. PCC 6803 for biofuel production.

PubMed

Mohammadi, Reza; Fallah-Mehrabadi, Jalil; Bidkhori, Gholamreza; Zahiri, Javad; Javad Niroomand, Mohammad; Masoudi-Nejad, Ali

2016-07-19

Production of biofuels has been one of the promising efforts in biotechnology in the past few decades. The perspective of these efforts can be reduction of increasing demands for fossil fuels and consequently reducing environmental pollution. Nonetheless, most previous approaches did not succeed in obviating many big challenges in this way. In recent years systems biology with the help of microorganisms has been trying to overcome these challenges. Unicellular cyanobacteria are widespread phototrophic microorganisms that have capabilities such as consuming solar energy and atmospheric carbon dioxide for growth and thus can be a suitable chassis for the production of valuable organic materials such as biofuels. For the ultimate use of metabolic potential of cyanobacteria, it is necessary to understand the reactions that are taking place inside the metabolic network of these microorganisms. In this study, we developed a Java tool to reconstruct an integrated metabolic network of a cyanobacterium (Synechocystis sp. PCC 6803). We merged three existing reconstructed metabolic networks of this microorganism. Then, after modeling for biofuel production, the results from flux balance analysis (FBA) disclosed an increased yield in biofuel production for ethanol, isobutanol, 3-methyl-1-butanol, 2-methyl-1-butanol, and propanol. The numbers of blocked reactions were also decreased for 2-methyl-1-butanol production. In addition, coverage of the metabolic network in terms of the number of metabolites and reactions was increased in the new obtained model.

Maneuvering in the Complex Path from Genotype to Phenotype

NASA Astrophysics Data System (ADS)

Strohman, Richard

2002-04-01

Human disease phenotypes are controlled not only by genes but by lawful self-organizing networks that display system-wide dynamics. These networks range from metabolic pathways to signaling pathways that regulate hormone action. When perturbed, networks alter their output of matter and energy which, depending on the environmental context, can produce either a pathological or a normal phenotype. Study of the dynamics of these networks by approaches such as metabolic control analysis may provide new insights into the pathogenesis and treatment of complex diseases.

Virocell Metabolism: Metabolic Innovations During Host-Virus Interactions in the Ocean.

PubMed

Rosenwasser, Shilo; Ziv, Carmit; Creveld, Shiri Graff van; Vardi, Assaf

2016-10-01

Marine viruses are considered to be major ecological, evolutionary, and biogeochemical drivers of the marine environment, responsible for nutrient recycling and determining species composition. Viruses can re-shape their host's metabolic network during infection, generating the virocell-a unique metabolic state that supports their specific requirement. Here we discuss the concept of 'virocell metabolism' and its formation by rewiring of host-encoded metabolic networks, or by introducing virus-encoded auxiliary metabolic genes which provide the virocell with novel metabolic capabilities. The ecological role of marine viruses is commonly assessed by their relative abundance and phylogenetic diversity, lacking the ability to assess the dynamics of active viral infection. The new ability to define a unique metabolic state of the virocell will expand the current virion-centric approaches in order to quantify the impact of marine viruses on microbial food webs. Copyright © 2016. Published by Elsevier Ltd.

Determinism and Contingency Shape Metabolic Complementation in an Endosymbiotic Consortium

PubMed Central

Ponce-de-Leon, Miguel; Tamarit, Daniel; Calle-Espinosa, Jorge; Mori, Matteo; Latorre, Amparo; Montero, Francisco; Pereto, Juli

2017-01-01

Bacterial endosymbionts and their insect hosts establish an intimate metabolic relationship. Bacteria offer a variety of essential nutrients to their hosts, whereas insect cells provide the necessary sources of matter and energy to their tiny metabolic allies. These nutritional complementations sustain themselves on a diversity of metabolite exchanges between the cell host and the reduced yet highly specialized bacterial metabolism—which, for instance, overproduces a small set of essential amino acids and vitamins. A well-known case of metabolic complementation is provided by the cedar aphid Cinara cedri that harbors two co-primary endosymbionts, Buchnera aphidicola BCc and Ca. Serratia symbiotica SCc, and in which some metabolic pathways are partitioned between different partners. Here we present a genome-scale metabolic network (GEM) for the bacterial consortium from the cedar aphid iBSCc. The analysis of this GEM allows us the confirmation of cases of metabolic complementation previously described by genome analysis (i.e., tryptophan and biotin biosynthesis) and the redefinition of an event of metabolic pathway sharing between the two endosymbionts, namely the biosynthesis of tetrahydrofolate. In silico knock-out experiments with iBSCc showed that the consortium metabolism is a highly integrated yet fragile network. We also have explored the evolutionary pathways leading to the emergence of metabolic complementation between reduced metabolisms starting from individual, complete networks. Our results suggest that, during the establishment of metabolic complementation in endosymbionts, adaptive evolution is significant in the case of tryptophan biosynthesis, whereas vitamin production pathways seem to adopt suboptimal solutions. PMID:29213256

Knowledge representation in metabolic pathway databases.

PubMed

Stobbe, Miranda D; Jansen, Gerbert A; Moerland, Perry D; van Kampen, Antoine H C

2014-05-01

The accurate representation of all aspects of a metabolic network in a structured format, such that it can be used for a wide variety of computational analyses, is a challenge faced by a growing number of researchers. Analysis of five major metabolic pathway databases reveals that each database has made widely different choices to address this challenge, including how to deal with knowledge that is uncertain or missing. In concise overviews, we show how concepts such as compartments, enzymatic complexes and the direction of reactions are represented in each database. Importantly, also concepts which a database does not represent are described. Which aspects of the metabolic network need to be available in a structured format and to what detail differs per application. For example, for in silico phenotype prediction, a detailed representation of gene-protein-reaction relations and the compartmentalization of the network is essential. Our analysis also shows that current databases are still limited in capturing all details of the biology of the metabolic network, further illustrated with a detailed analysis of three metabolic processes. Finally, we conclude that the conceptual differences between the databases, which make knowledge exchange and integration a challenge, have not been resolved, so far, by the exchange formats in which knowledge representation is standardized.

Multi-Criteria Optimization of Regulation in Metabolic Networks

PubMed Central

Higuera, Clara; Villaverde, Alejandro F.; Banga, Julio R.; Ross, John; Morán, Federico

2012-01-01

Determining the regulation of metabolic networks at genome scale is a hard task. It has been hypothesized that biochemical pathways and metabolic networks might have undergone an evolutionary process of optimization with respect to several criteria over time. In this contribution, a multi-criteria approach has been used to optimize parameters for the allosteric regulation of enzymes in a model of a metabolic substrate-cycle. This has been carried out by calculating the Pareto set of optimal solutions according to two objectives: the proper direction of flux in a metabolic cycle and the energetic cost of applying the set of parameters. Different Pareto fronts have been calculated for eight different “environments” (specific time courses of end product concentrations). For each resulting front the so-called knee point is identified, which can be considered a preferred trade-off solution. Interestingly, the optimal control parameters corresponding to each of these points also lead to optimal behaviour in all the other environments. By calculating the average of the different parameter sets for the knee solutions more frequently found, a final and optimal consensus set of parameters can be obtained, which is an indication on the existence of a universal regulation mechanism for this system.The implications from such a universal regulatory switch are discussed in the framework of large metabolic networks. PMID:22848435

Metabolomics analysis: Finding out metabolic building blocks

PubMed Central

2017-01-01

In this paper we propose a new methodology for the analysis of metabolic networks. We use the notion of strongly connected components of a graph, called in this context metabolic building blocks. Every strongly connected component is contracted to a single node in such a way that the resulting graph is a directed acyclic graph, called a metabolic DAG, with a considerably reduced number of nodes. The property of being a directed acyclic graph brings out a background graph topology that reveals the connectivity of the metabolic network, as well as bridges, isolated nodes and cut nodes. Altogether, it becomes a key information for the discovery of functional metabolic relations. Our methodology has been applied to the glycolysis and the purine metabolic pathways for all organisms in the KEGG database, although it is general enough to work on any database. As expected, using the metabolic DAGs formalism, a considerable reduction on the size of the metabolic networks has been obtained, specially in the case of the purine pathway due to its relative larger size. As a proof of concept, from the information captured by a metabolic DAG and its corresponding metabolic building blocks, we obtain the core of the glycolysis pathway and the core of the purine metabolism pathway and detect some essential metabolic building blocks that reveal the key reactions in both pathways. Finally, the application of our methodology to the glycolysis pathway and the purine metabolism pathway reproduce the tree of life for the whole set of the organisms represented in the KEGG database which supports the utility of this research. PMID:28493998

DOE Office of Scientific and Technical Information (OSTI.GOV)

James C. Liao

This project is a collaboration with F. R. Tabita of Ohio State. Our major goal is to understand the factors and regulatory mechanisms that influence hydrogen production. The organisms to be utilized in this study, phototrophic microorganisms, in particular nonsulfur purple (NSP) bacteria, catalyze many significant processes including the assimilation of carbon dioxide into organic carbon, nitrogen fixation, sulfur oxidation, aromatic acid degradation, and hydrogen oxidation/evolution. Our part of the project was to develop a modeling technique to investigate the metabolic network in connection to hydrogen production and regulation. Organisms must balance the pathways that generate and consume reducing powermore » in order to maintain redox homeostasis to achieve growth. Maintaining this homeostasis in the nonsulfur purple photosynthetic bacteria is a complex feat with many avenues that can lead to balance, as these organisms possess versatile metabolic capabilities including anoxygenic photosynthesis, aerobic or anaerobic respiration, and fermentation. Growth is achieved by using H{sub 2} as an electron donor and CO{sub 2} as a carbon source during photoautotrophic and chemoautotrophic growth, where CO{sub 2} is fixed via the Calvin-Benson-Bassham (CBB) cycle. Photoheterotrophic growth can also occur when alternative organic carbon compounds are utilized as both the carbon source and electron donor. Regardless of the growth mode, excess reducing equivalents generated as a result of oxidative processes, must be transferred to terminal electron acceptors, thus insuring that redox homeostasis is maintained in the cell. Possible terminal acceptors include O{sub 2}, CO{sub 2}, organic carbon, or various oxyanions. Cells possess regulatory mechanisms to balance the activity of the pathways which supply energy, such as photosynthesis, and those that consume energy, such as CO{sub 2} assimilation or N{sub 2} fixation. The major route for CO{sub 2} assimilation is the CBB reductive pentose phosphate pathway, whose key enzyme is ribulose 1,5-biphosphate carboxylase/oxygenase (RubisCO). In addition to providing virtually all cellular carbon during autotrophic metabolism, RubisCO-mediated CO{sub 2} assimilation is also very important for nonsulfur purple photosynthetic bacteria under photoheterotrophic growth conditions since CO{sub 2} becomes the major electron sink under these conditions. In this work, Ensemble Modeling (EM) was developed to examine the behavior of CBB-compromised RubisCO knockout mutant strains of the nonsulfur purple photosynthetic bacterium Rhodobacter sphaeroides. Mathematical models of metabolism can be a great aid in studying the effects of large perturbations to the system, such as the inactivation of RubisCO. Due to the complex and highly-interconnected nature of these networks, it is not a trivial process to understand what the effect of perturbations to the metabolic network will be, or vice versa, what enzymatic perturbations are necessary to yield a desired effect. Flux distribution is controlled by multiple enzymes in the network, often indirectly linked to the pathways of interest. Further, depending on the state of the cell and the environmental conditions, the effect of a perturbation may center around how it effects the carbon flow in the network, the balancing of cofactors, or both. Thus, it is desirable to develop mathematical models to describe, understand, and predict network behavior. Through the development of such models, one may gain the ability to generate a set of testable hypotheses for system behavior.« less

Transcriptional Regulatory Networks in Saccharomyces cerevisiae

NASA Astrophysics Data System (ADS)

Lee, Tong Ihn; Rinaldi, Nicola J.; Robert, François; Odom, Duncan T.; Bar-Joseph, Ziv; Gerber, Georg K.; Hannett, Nancy M.; Harbison, Christopher T.; Thompson, Craig M.; Simon, Itamar; Zeitlinger, Julia; Jennings, Ezra G.; Murray, Heather L.; Gordon, D. Benjamin; Ren, Bing; Wyrick, John J.; Tagne, Jean-Bosco; Volkert, Thomas L.; Fraenkel, Ernest; Gifford, David K.; Young, Richard A.

2002-10-01

We have determined how most of the transcriptional regulators encoded in the eukaryote Saccharomyces cerevisiae associate with genes across the genome in living cells. Just as maps of metabolic networks describe the potential pathways that may be used by a cell to accomplish metabolic processes, this network of regulator-gene interactions describes potential pathways yeast cells can use to regulate global gene expression programs. We use this information to identify network motifs, the simplest units of network architecture, and demonstrate that an automated process can use motifs to assemble a transcriptional regulatory network structure. Our results reveal that eukaryotic cellular functions are highly connected through networks of transcriptional regulators that regulate other transcriptional regulators.

Global dynamic optimization approach to predict activation in metabolic pathways.

PubMed

de Hijas-Liste, Gundián M; Klipp, Edda; Balsa-Canto, Eva; Banga, Julio R

2014-01-06

During the last decade, a number of authors have shown that the genetic regulation of metabolic networks may follow optimality principles. Optimal control theory has been successfully used to compute optimal enzyme profiles considering simple metabolic pathways. However, applying this optimal control framework to more general networks (e.g. branched networks, or networks incorporating enzyme production dynamics) yields problems that are analytically intractable and/or numerically very challenging. Further, these previous studies have only considered a single-objective framework. In this work we consider a more general multi-objective formulation and we present solutions based on recent developments in global dynamic optimization techniques. We illustrate the performance and capabilities of these techniques considering two sets of problems. First, we consider a set of single-objective examples of increasing complexity taken from the recent literature. We analyze the multimodal character of the associated non linear optimization problems, and we also evaluate different global optimization approaches in terms of numerical robustness, efficiency and scalability. Second, we consider generalized multi-objective formulations for several examples, and we show how this framework results in more biologically meaningful results. The proposed strategy was used to solve a set of single-objective case studies related to unbranched and branched metabolic networks of different levels of complexity. All problems were successfully solved in reasonable computation times with our global dynamic optimization approach, reaching solutions which were comparable or better than those reported in previous literature. Further, we considered, for the first time, multi-objective formulations, illustrating how activation in metabolic pathways can be explained in terms of the best trade-offs between conflicting objectives. This new methodology can be applied to metabolic networks with arbitrary topologies, non-linear dynamics and constraints.

Estrogen receptors and the metabolic network.

PubMed

Barros, Rodrigo P A; Gustafsson, Jan-Åke

2011-09-07

The metabolic syndrome has reached pandemic level worldwide, and evidence is that estradiol plays a key role in its development. The discovery of the second estrogen receptor, ERβ, in tissues previously not considered targets of estradiol was a breakthrough in endocrinology. In the present review, we discuss how the presence of ERβ and the previously described ERα in tissues involved in glucose and lipid homeostasis (brain, skeletal muscle, adipose tissue, pancreas, liver, and heart) may have important implications to risk factors associated with the metabolic syndrome. Imbalance of ERα/ERβ ratio in this "metabolic network" may lead to the metabolic syndrome. Copyright © 2011 Elsevier Inc. All rights reserved.

Photoperiodic responses of depression-like behavior, the brain serotonergic system, and peripheral metabolism in laboratory mice.

PubMed

Otsuka, Tsuyoshi; Kawai, Misato; Togo, Yuki; Goda, Ryosei; Kawase, Takahiro; Matsuo, Haruka; Iwamoto, Ayaka; Nagasawa, Mao; Furuse, Mitsuhiro; Yasuo, Shinobu

2014-02-01

Seasonal affective disorder (SAD) is characterized by depression during specific seasons, generally winter. The pathophysiological mechanisms underlying SAD remain elusive due to a limited number of animal models with high availability and validity. Here we show that laboratory C57BL/6J mice display photoperiodic changes in depression-like behavior and brain serotonin content. C57BL/6J mice maintained under short-day conditions, as compared to those under long-day conditions, demonstrated prolonged immobility times in the forced swimming test with lower brain levels of serotonin and its precursor l-tryptophan. Furthermore, photoperiod altered multiple parameters reflective of peripheral metabolism, including the ratio of plasma l-tryptophan to the sum of other large neutral amino acids that compete for transport across the blood-brain barrier, responses of circulating glucose and insulin to glucose load, sucrose intake under restricted feeding condition, and sensitivity of the brain serotonergic system to peripherally administered glucose. These data suggest that the mechanisms underlying SAD involve the brain-peripheral tissue network, and C57BL/6J mice can serve as a powerful tool for investigating the link between seasons and mood. Copyright © 2013 Elsevier Ltd. All rights reserved.

Glucose Addiction in Cancer Therapy: Advances and Drawbacks.

PubMed

Granja, Sara; Pinheiro, Céline; Reis, Rui Manuel; Martinho, Olga; Baltazar, Fátima

2015-01-01

While normal differentiated cells primarily use mitochondrial respiration to generate the required energy for cellular processes, most cancer cells rely on glycolysis, even in sufficient oxygen conditions. This phenomenon is known as the "Warburg effect" or aerobic glycolysis and the metabolic reprogramming of cancer cells towards this altered energy metabolism is currently recognized as one of the "hallmarks of cancer". Aerobic glycolysis underlies the rapid growth of tumor cells, with high rates of glucose consumption and lactic acid production, leading to cellular acidosis. Metabolic reprogramming renders cancer cells dependent on specific metabolic enzymes or pathways that could be exploited in cancer therapy. The development of treatments that target tumor glucose metabolism is receiving renewed attention, with several drugs targeting metabolic pathways currently in clinical trials. The search for suitable targets, however, is limited by the high plasticity of the metabolic network that can induce compensatory routes. Deregulated glucose metabolism is a prominent feature associated with resistance to classical chemotherapy or oncogene-targeted therapies, strengthening the clinical potential of combining these therapies with glycolysis inhibitors. The aim of this review is to compare the advances of different therapeutic strategies targeting the glucose "addiction" of tumor cells, highlighting their potential as effective weapons against cancer. We further discuss recent evidence for the involvement of glucose metabolism as a compensatory response to the use of drugs that target different signaling pathways, where the combination with glycolysis inhibitors could prove extraordinarily useful.

Blueprint for antimicrobial hit discovery targeting metabolic networks

PubMed Central

Shen, Y.; Liu, J.; Estiu, G.; Isin, B.; Ahn, Y-Y.; Lee, D-S.; Barabási, A-L.; Kapatral, V.; Wiest, O.; Oltvai, Z. N.

2010-01-01

Advances in genome analysis, network biology, and computational chemistry have the potential to revolutionize drug discovery by combining system-level identification of drug targets with the atomistic modeling of small molecules capable of modulating their activity. To demonstrate the effectiveness of such a discovery pipeline, we deduced common antibiotic targets in Escherichia coli and Staphylococcus aureus by identifying shared tissue-specific or uniformly essential metabolic reactions in their metabolic networks. We then predicted through virtual screening dozens of potential inhibitors for several enzymes of these reactions and showed experimentally that a subset of these inhibited both enzyme activities in vitro and bacterial cell viability. This blueprint is applicable for any sequenced organism with high-quality metabolic reconstruction and suggests a general strategy for strain-specific antiinfective therapy. PMID:20080587

Human-Centered Development of an Online Social Network for Metabolic Syndrome Management.

PubMed

Núñez-Nava, Jefersson; Orozco-Sánchez, Paola A; López, Diego M; Ceron, Jesus D; Alvarez-Rosero, Rosa E

2016-01-01

According to the International Diabetes Federation (IDF), a quarter of the world's population has Metabolic Syndrome (MS). To develop (and assess the users' degree of satisfaction of) an online social network for patients who suffer from Metabolic Syndrome, based on the recommendations and requirements of the Human-Centered Design. Following the recommendations of the ISO 9241-210 for Human-Centered Design (HCD), an online social network was designed to promote physical activity and healthy nutrition. In order to guarantee the active participation of the users during the development of the social network, a survey, an in-depth interview, a focal group, and usability tests were carried out with people suffering from MS. The study demonstrated how the different activities, recommendations, and requirements of the ISO 9241-210 are integrated into a traditional software development process. Early usability tests demonstrated that the user's acceptance and the effectiveness and efficiency of the social network are satisfactory.

Technologies and Approaches to Elucidate and Model the Virulence Program of Salmonella.

DOE Office of Scientific and Technical Information (OSTI.GOV)

McDermott, Jason E.; Yoon, Hyunjin; Nakayasu, Ernesto S.

Salmonella is a primary cause of enteric diseases in a variety of animals. During its evolution into a pathogenic bacterium, Salmonella acquired an elaborate regulatory network that responds to multiple environmental stimuli within host animals and integrates them resulting in fine regulation of the virulence program. The coordinated action by this regulatory network involves numerous virulence regulators, necessitating genome-wide profiling analysis to assess and combine efforts from multiple regulons. In this review we discuss recent high-throughput analytic approaches to understand the regulatory network of Salmonella that controls virulence processes. Application of high-throughput analyses have generated a large amount of datamore » and driven development of computational approaches required for data integration. Therefore, we also cover computer-aided network analyses to infer regulatory networks, and demonstrate how genome-scale data can be used to construct regulatory and metabolic systems models of Salmonella pathogenesis. Genes that are coordinately controlled by multiple virulence regulators under infectious conditions are more likely to be important for pathogenesis. Thus, reconstructing the global regulatory network during infection or, at the very least, under conditions that mimic the host cellular environment not only provides a bird’s eye view of Salmonella survival strategy in response to hostile host environments but also serves as an efficient means to identify novel virulence factors that are essential for Salmonella to accomplish systemic infection in the host.« less

NetCooperate: a network-based tool for inferring host-microbe and microbe-microbe cooperation.

PubMed

Levy, Roie; Carr, Rogan; Kreimer, Anat; Freilich, Shiri; Borenstein, Elhanan

2015-05-17

Host-microbe and microbe-microbe interactions are often governed by the complex exchange of metabolites. Such interactions play a key role in determining the way pathogenic and commensal species impact their host and in the assembly of complex microbial communities. Recently, several studies have demonstrated how such interactions are reflected in the organization of the metabolic networks of the interacting species, and introduced various graph theory-based methods to predict host-microbe and microbe-microbe interactions directly from network topology. Using these methods, such studies have revealed evolutionary and ecological processes that shape species interactions and community assembly, highlighting the potential of this reverse-ecology research paradigm. NetCooperate is a web-based tool and a software package for determining host-microbe and microbe-microbe cooperative potential. It specifically calculates two previously developed and validated metrics for species interaction: the Biosynthetic Support Score which quantifies the ability of a host species to supply the nutritional requirements of a parasitic or a commensal species, and the Metabolic Complementarity Index which quantifies the complementarity of a pair of microbial organisms' niches. NetCooperate takes as input a pair of metabolic networks, and returns the pairwise metrics as well as a list of potential syntrophic metabolic compounds. The Biosynthetic Support Score and Metabolic Complementarity Index provide insight into host-microbe and microbe-microbe metabolic interactions. NetCooperate determines these interaction indices from metabolic network topology, and can be used for small- or large-scale analyses. NetCooperate is provided as both a web-based tool and an open-source Python module; both are freely available online at http://elbo.gs.washington.edu/software_netcooperate.html.

Second Law of Thermodynamics Applied to Metabolic Networks

NASA Technical Reports Server (NTRS)

Nigam, R.; Liang, S.

2003-01-01

We present a simple algorithm based on linear programming, that combines Kirchoff's flux and potential laws and applies them to metabolic networks to predict thermodynamically feasible reaction fluxes. These law's represent mass conservation and energy feasibility that are widely used in electrical circuit analysis. Formulating the Kirchoff's potential law around a reaction loop in terms of the null space of the stoichiometric matrix leads to a simple representation of the law of entropy that can be readily incorporated into the traditional flux balance analysis without resorting to non-linear optimization. Our technique is new as it can easily check the fluxes got by applying flux balance analysis for thermodynamic feasibility and modify them if they are infeasible so that they satisfy the law of entropy. We illustrate our method by applying it to the network dealing with the central metabolism of Escherichia coli. Due to its simplicity this algorithm will be useful in studying large scale complex metabolic networks in the cell of different organisms.

Prebiotic synthesis of phosphoenol pyruvate by α-phosphorylation-controlled triose glycolysis

NASA Astrophysics Data System (ADS)

Coggins, Adam J.; Powner, Matthew W.

2017-04-01

Phosphoenol pyruvate is the highest-energy phosphate found in living organisms and is one of the most versatile molecules in metabolism. Consequently, it is an essential intermediate in a wide variety of biochemical pathways, including carbon fixation, the shikimate pathway, substrate-level phosphorylation, gluconeogenesis and glycolysis. Triose glycolysis (generation of ATP from glyceraldehyde 3-phosphate via phosphoenol pyruvate) is among the most central and highly conserved pathways in metabolism. Here, we demonstrate the efficient and robust synthesis of phosphoenol pyruvate from prebiotic nucleotide precursors, glycolaldehyde and glyceraldehyde. Furthermore, phosphoenol pyruvate is derived within an α-phosphorylation controlled reaction network that gives access to glyceric acid 2-phosphate, glyceric acid 3-phosphate, phosphoserine and pyruvate. Our results demonstrate that the key components of a core metabolic pathway central to energy transduction and amino acid, sugar, nucleotide and lipid biosyntheses can be reconstituted in high yield under mild, prebiotically plausible conditions.

Are Tanycytes the Missing Link Between Type 2 Diabetes and Alzheimer's Disease?

PubMed

Raikwar, Sudhanshu P; Bhagavan, Sachin M; Ramaswamy, Swathi Beladakere; Thangavel, Ramasamy; Dubova, Iuliia; Selvakumar, Govindhasamy Pushpavathi; Ahmed, Mohammad Ejaz; Kempuraj, Duraisamy; Zaheer, Smita; Iyer, Shankar; Zaheer, Asgar

2018-05-24

Tanycytes are highly specialized bipolar ependymal cells that line the ventrolateral wall and the floor of the third ventricle in the brain and form a blood-cerebrospinal fluid barrier at the level of the median eminence. They play a pivotal role in regulating metabolic networks that control body weight and energy homeostasis. Due to the glucosensing function of tanycytes, they could be considered as a critical player in the pathogenesis of type 2 diabetes. Genetic fate mapping studies have established the role of tanycytes for the newly detected adult hypothalamic neurogenesis with important implications for metabolism as well as pathophysiology of various neurodegenerative diseases. We believe that a comprehensive understanding of the physiological mechanisms underlying their neuroplasticity, glucosensing, and cross talk with endothelial cells will enable us to achieve metabolic homeostasis in type 2 diabetes patients and possibly delay the progression of Alzheimer's disease and hopefully improve cognitive function.

EGFR Signal-Network Reconstruction Demonstrates Metabolic Crosstalk in EMT

PubMed Central

Choudhary, Kumari Sonal; Rohatgi, Neha; Briem, Eirikur; Gudjonsson, Thorarinn; Gudmundsson, Steinn; Rolfsson, Ottar

2016-01-01

Epithelial to mesenchymal transition (EMT) is an important event during development and cancer metastasis. There is limited understanding of the metabolic alterations that give rise to and take place during EMT. Dysregulation of signalling pathways that impact metabolism, including epidermal growth factor receptor (EGFR), are however a hallmark of EMT and metastasis. In this study, we report the investigation into EGFR signalling and metabolic crosstalk of EMT through constraint-based modelling and analysis of the breast epithelial EMT cell model D492 and its mesenchymal counterpart D492M. We built an EGFR signalling network for EMT based on stoichiometric coefficients and constrained the network with gene expression data to build epithelial (EGFR_E) and mesenchymal (EGFR_M) networks. Metabolic alterations arising from differential expression of EGFR genes was derived from a literature review of AKT regulated metabolic genes. Signaling flux differences between EGFR_E and EGFR_M models subsequently allowed metabolism in D492 and D492M cells to be assessed. Higher flux within AKT pathway in the D492 cells compared to D492M suggested higher glycolytic activity in D492 that we confirmed experimentally through measurements of glucose uptake and lactate secretion rates. The signaling genes from the AKT, RAS/MAPK and CaM pathways were predicted to revert D492M to D492 phenotype. Follow-up analysis of EGFR signaling metabolic crosstalk in three additional breast epithelial cell lines highlighted variability in in vitro cell models of EMT. This study shows that the metabolic phenotype may be predicted by in silico analyses of gene expression data of EGFR signaling genes, but this phenomenon is cell-specific and does not follow a simple trend. PMID:27253373

EGFR Signal-Network Reconstruction Demonstrates Metabolic Crosstalk in EMT.

PubMed

Choudhary, Kumari Sonal; Rohatgi, Neha; Halldorsson, Skarphedinn; Briem, Eirikur; Gudjonsson, Thorarinn; Gudmundsson, Steinn; Rolfsson, Ottar

2016-06-01

Epithelial to mesenchymal transition (EMT) is an important event during development and cancer metastasis. There is limited understanding of the metabolic alterations that give rise to and take place during EMT. Dysregulation of signalling pathways that impact metabolism, including epidermal growth factor receptor (EGFR), are however a hallmark of EMT and metastasis. In this study, we report the investigation into EGFR signalling and metabolic crosstalk of EMT through constraint-based modelling and analysis of the breast epithelial EMT cell model D492 and its mesenchymal counterpart D492M. We built an EGFR signalling network for EMT based on stoichiometric coefficients and constrained the network with gene expression data to build epithelial (EGFR_E) and mesenchymal (EGFR_M) networks. Metabolic alterations arising from differential expression of EGFR genes was derived from a literature review of AKT regulated metabolic genes. Signaling flux differences between EGFR_E and EGFR_M models subsequently allowed metabolism in D492 and D492M cells to be assessed. Higher flux within AKT pathway in the D492 cells compared to D492M suggested higher glycolytic activity in D492 that we confirmed experimentally through measurements of glucose uptake and lactate secretion rates. The signaling genes from the AKT, RAS/MAPK and CaM pathways were predicted to revert D492M to D492 phenotype. Follow-up analysis of EGFR signaling metabolic crosstalk in three additional breast epithelial cell lines highlighted variability in in vitro cell models of EMT. This study shows that the metabolic phenotype may be predicted by in silico analyses of gene expression data of EGFR signaling genes, but this phenomenon is cell-specific and does not follow a simple trend.

Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns.

PubMed

Lezon, Timothy R; Banavar, Jayanth R; Cieplak, Marek; Maritan, Amos; Fedoroff, Nina V

2006-12-12

We describe a method based on the principle of entropy maximization to identify the gene interaction network with the highest probability of giving rise to experimentally observed transcript profiles. In its simplest form, the method yields the pairwise gene interaction network, but it can also be extended to deduce higher-order interactions. Analysis of microarray data from genes in Saccharomyces cerevisiae chemostat cultures exhibiting energy metabolic oscillations identifies a gene interaction network that reflects the intracellular communication pathways that adjust cellular metabolic activity and cell division to the limiting nutrient conditions that trigger metabolic oscillations. The success of the present approach in extracting meaningful genetic connections suggests that the maximum entropy principle is a useful concept for understanding living systems, as it is for other complex, nonequilibrium systems.

The transcriptional regulatory network of Corynebacterium jeikeium K411 and its interaction with metabolic routes contributing to human body odor formation.

PubMed

Barzantny, Helena; Schröder, Jasmin; Strotmeier, Jasmin; Fredrich, Eugenie; Brune, Iris; Tauch, Andreas

2012-06-15

Lipophilic corynebacteria are involved in the generation of volatile odorous products in the process of human body odor formation by degrading skin lipids and specific odor precursors. Therefore, these bacteria represent appropriate model systems for the cosmetic industry to examine axillary malodor formation on the molecular level. To understand the transcriptional control of metabolic pathways involved in this process, the transcriptional regulatory network of the lipophilic axilla isolate Corynebacterium jeikeium K411 was reconstructed from the complete genome sequence. This bioinformatic approach detected a gene-regulatory repertoire of 83 candidate proteins, including 56 DNA-binding transcriptional regulators, nine two-component systems, nine sigma factors, and nine regulators with diverse physiological functions. Furthermore, a cross-genome comparison among selected corynebacterial species of the taxonomic cluster 3 revealed a common gene-regulatory repertoire of 44 transcriptional regulators, including the MarR-like regulator Jk0257, which is exclusively encoded in the genomes of this taxonomical subline. The current network reconstruction comprises 48 transcriptional regulators and 674 gene-regulatory interactions that were assigned to five interconnected functional modules. Most genes involved in lipid degradation are under the combined control of the global cAMP-sensing transcriptional regulator GlxR and the LuxR-family regulator RamA, probably reflecting the essential role of lipid degradation in C. jeikeium. This study provides the first genome-scale in silico analysis of the transcriptional regulation of metabolism in a lipophilic bacterium involved in the formation of human body odor. Copyright © 2012 Elsevier B.V. All rights reserved.

Metagenomics reveals flavour metabolic network of cereal vinegar microbiota.

PubMed

Wu, Lin-Huan; Lu, Zhen-Ming; Zhang, Xiao-Juan; Wang, Zong-Min; Yu, Yong-Jian; Shi, Jin-Song; Xu, Zheng-Hong

2017-04-01

Multispecies microbial community formed through centuries of repeated batch acetic acid fermentation (AAF) is crucial for the flavour quality of traditional vinegar produced from cereals. However, the metabolism to generate and/or formulate the essential flavours by the multispecies microbial community is hardly understood. Here we used metagenomic approach to clarify in situ metabolic network of key microbes responsible for flavour synthesis of a typical cereal vinegar, Zhenjiang aromatic vinegar, produced by solid-state fermentation. First, we identified 3 organic acids, 7 amino acids, and 20 volatiles as dominant vinegar metabolites. Second, we revealed taxonomic and functional composition of the microbiota by metagenomic shotgun sequencing. A total of 86 201 predicted protein-coding genes from 35 phyla (951 genera) were involved in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of Metabolism (42.3%), Genetic Information Processing (28.3%), and Environmental Information Processing (10.1%). Furthermore, a metabolic network for substrate breakdown and dominant flavour formation in vinegar microbiota was constructed, and microbial distribution discrepancy in different metabolic pathways was charted. This study helps elucidating different metabolic roles of microbes during flavour formation in vinegar microbiota. Copyright © 2016 Elsevier Ltd. All rights reserved.

SpirPro: A Spirulina proteome database and web-based tools for the analysis of protein-protein interactions at the metabolic level in Spirulina (Arthrospira) platensis C1.

PubMed

Senachak, Jittisak; Cheevadhanarak, Supapon; Hongsthong, Apiradee

2015-07-29

Spirulina (Arthrospira) platensis is the only cyanobacterium that in addition to being studied at the molecular level and subjected to gene manipulation, can also be mass cultivated in outdoor ponds for commercial use as a food supplement. Thus, encountering environmental changes, including temperature stresses, is common during the mass production of Spirulina. The use of cyanobacteria as an experimental platform, especially for photosynthetic gene manipulation in plants and bacteria, is becoming increasingly important. Understanding the mechanisms and protein-protein interaction networks that underlie low- and high-temperature responses is relevant to Spirulina mass production. To accomplish this goal, high-throughput techniques such as OMICs analyses are used. Thus, large datasets must be collected, managed and subjected to information extraction. Therefore, databases including (i) proteomic analysis and protein-protein interaction (PPI) data and (ii) domain/motif visualization tools are required for potential use in temperature response models for plant chloroplasts and photosynthetic bacteria. A web-based repository was developed including an embedded database, SpirPro, and tools for network visualization. Proteome data were analyzed integrated with protein-protein interactions and/or metabolic pathways from KEGG. The repository provides various information, ranging from raw data (2D-gel images) to associated results, such as data from interaction and/or pathway analyses. This integration allows in silico analyses of protein-protein interactions affected at the metabolic level and, particularly, analyses of interactions between and within the affected metabolic pathways under temperature stresses for comparative proteomic analysis. The developed tool, which is coded in HTML with CSS/JavaScript and depicted in Scalable Vector Graphics (SVG), is designed for interactive analysis and exploration of the constructed network. SpirPro is publicly available on the web at http://spirpro.sbi.kmutt.ac.th . SpirPro is an analysis platform containing an integrated proteome and PPI database that provides the most comprehensive data on this cyanobacterium at the systematic level. As an integrated database, SpirPro can be applied in various analyses, such as temperature stress response networking analysis in cyanobacterial models and interacting domain-domain analysis between proteins of interest.

Random sampling of elementary flux modes in large-scale metabolic networks.

PubMed

Machado, Daniel; Soons, Zita; Patil, Kiran Raosaheb; Ferreira, Eugénio C; Rocha, Isabel

2012-09-15

The description of a metabolic network in terms of elementary (flux) modes (EMs) provides an important framework for metabolic pathway analysis. However, their application to large networks has been hampered by the combinatorial explosion in the number of modes. In this work, we develop a method for generating random samples of EMs without computing the whole set. Our algorithm is an adaptation of the canonical basis approach, where we add an additional filtering step which, at each iteration, selects a random subset of the new combinations of modes. In order to obtain an unbiased sample, all candidates are assigned the same probability of getting selected. This approach avoids the exponential growth of the number of modes during computation, thus generating a random sample of the complete set of EMs within reasonable time. We generated samples of different sizes for a metabolic network of Escherichia coli, and observed that they preserve several properties of the full EM set. It is also shown that EM sampling can be used for rational strain design. A well distributed sample, that is representative of the complete set of EMs, should be suitable to most EM-based methods for analysis and optimization of metabolic networks. Source code for a cross-platform implementation in Python is freely available at http://code.google.com/p/emsampler. dmachado@deb.uminho.pt Supplementary data are available at Bioinformatics online.

Construction and simulation of the Bradyrhizobium diazoefficiens USDA110 metabolic network: a comparison between free-living and symbiotic states.

PubMed

Yang, Yi; Hu, Xiao-Pan; Ma, Bin-Guang

2017-02-28

Bradyrhizobium diazoefficiens is a rhizobium able to convert atmospheric nitrogen into ammonium by establishing mutualistic symbiosis with soybean. It has been recognized as an important parent strain for microbial agents and is widely applied in agricultural and environmental fields. In order to study the metabolic properties of symbiotic nitrogen fixation and the differences between a free-living cell and a symbiotic bacteroid, a genome-scale metabolic network of B. diazoefficiens USDA110 was constructed and analyzed. The metabolic network, iYY1101, contains 1031 reactions, 661 metabolites, and 1101 genes in total. Metabolic models reflecting free-living and symbiotic states were determined by defining the corresponding objective functions and substrate input sets, and were further constrained by high-throughput transcriptomic and proteomic data. Constraint-based flux analysis was used to compare the metabolic capacities and the effects on the metabolic targets of genes and reactions between the two physiological states. The results showed that a free-living rhizobium possesses a steady state flux distribution for sustaining a complex supply of biomass precursors while a symbiotic bacteroid maintains a relatively condensed one adapted to nitrogen-fixation. Our metabolic models may serve as a promising platform for better understanding the symbiotic nitrogen fixation of this species.

A genetically-encoded chloride and pH sensor for dissociating ion dynamics in the nervous system

PubMed Central

Raimondo, Joseph V.; Joyce, Bradley; Kay, Louise; Schlagheck, Theresa; Newey, Sarah E.; Srinivas, Shankar; Akerman, Colin J.

2013-01-01

Within the nervous system, intracellular Cl− and pH regulate fundamental processes including cell proliferation, metabolism, synaptic transmission, and network excitability. Cl− and pH are often co-regulated, and network activity results in the movement of both Cl− and H+. Tools to accurately measure these ions are crucial for understanding their role under physiological and pathological conditions. Although genetically-encoded Cl− and pH sensors have been described previously, these either lack ion specificity or are unsuitable for neuronal use. Here we present ClopHensorN—a new genetically-encoded ratiometric Cl− and pH sensor that is optimized for the nervous system. We demonstrate the ability of ClopHensorN to dissociate and simultaneously quantify Cl− and H+ concentrations under a variety of conditions. In addition, we establish the sensor's utility by characterizing activity-dependent ion dynamics in hippocampal neurons. PMID:24312004

A genetically-encoded chloride and pH sensor for dissociating ion dynamics in the nervous system.

PubMed

Raimondo, Joseph V; Joyce, Bradley; Kay, Louise; Schlagheck, Theresa; Newey, Sarah E; Srinivas, Shankar; Akerman, Colin J

2013-01-01

Within the nervous system, intracellular Cl(-) and pH regulate fundamental processes including cell proliferation, metabolism, synaptic transmission, and network excitability. Cl(-) and pH are often co-regulated, and network activity results in the movement of both Cl(-) and H(+). Tools to accurately measure these ions are crucial for understanding their role under physiological and pathological conditions. Although genetically-encoded Cl(-) and pH sensors have been described previously, these either lack ion specificity or are unsuitable for neuronal use. Here we present ClopHensorN-a new genetically-encoded ratiometric Cl(-) and pH sensor that is optimized for the nervous system. We demonstrate the ability of ClopHensorN to dissociate and simultaneously quantify Cl(-) and H(+) concentrations under a variety of conditions. In addition, we establish the sensor's utility by characterizing activity-dependent ion dynamics in hippocampal neurons.

Networks in Cell Biology

NASA Astrophysics Data System (ADS)

Buchanan, Mark; Caldarelli, Guido; De Los Rios, Paolo; Rao, Francesco; Vendruscolo, Michele

2010-05-01

Introduction; 1. Network views of the cell Paolo De Los Rios and Michele Vendruscolo; 2. Transcriptional regulatory networks Sarath Chandra Janga and M. Madan Babu; 3. Transcription factors and gene regulatory networks Matteo Brilli, Elissa Calistri and Pietro Lió; 4. Experimental methods for protein interaction identification Peter Uetz, Björn Titz, Seesandra V. Rajagopala and Gerard Cagney; 5. Modeling protein interaction networks Francesco Rao; 6. Dynamics and evolution of metabolic networks Daniel Segré; 7. Hierarchical modularity in biological networks: the case of metabolic networks Erzsébet Ravasz Regan; 8. Signalling networks Gian Paolo Rossini; Appendix 1. Complex networks: from local to global properties D. Garlaschelli and G. Caldarelli; Appendix 2. Modelling the local structure of networks D. Garlaschelli and G. Caldarelli; Appendix 3. Higher-order topological properties S. Ahnert, T. Fink and G. Caldarelli; Appendix 4. Elementary mathematical concepts A. Gabrielli and G. Caldarelli; References.

From SNP co-association to RNA co-expression: novel insights into gene networks for intramuscular fatty acid composition in porcine.

PubMed

Ramayo-Caldas, Yuliaxis; Ballester, Maria; Fortes, Marina R S; Esteve-Codina, Anna; Castelló, Anna; Noguera, Jose L; Fernández, Ana I; Pérez-Enciso, Miguel; Reverter, Antonio; Folch, Josep M

2014-03-26

Fatty acids (FA) play a critical role in energy homeostasis and metabolic diseases; in the context of livestock species, their profile also impacts on meat quality for healthy human consumption. Molecular pathways controlling lipid metabolism are highly interconnected and are not fully understood. Elucidating these molecular processes will aid technological development towards improvement of pork meat quality and increased knowledge of FA metabolism, underpinning metabolic diseases in humans. The results from genome-wide association studies (GWAS) across 15 phenotypes were subjected to an Association Weight Matrix (AWM) approach to predict a network of 1,096 genes related to intramuscular FA composition in pigs. To identify the key regulators of FA metabolism, we focused on the minimal set of transcription factors (TF) that the explored the majority of the network topology. Pathway and network analyses pointed towards a trio of TF as key regulators of FA metabolism: NCOA2, FHL2 and EP300. Promoter sequence analyses confirmed that these TF have binding sites for some well-know regulators of lipid and carbohydrate metabolism. For the first time in a non-model species, some of the co-associations observed at the genetic level were validated through co-expression at the transcriptomic level based on real-time PCR of 40 genes in adipose tissue, and a further 55 genes in liver. In particular, liver expression of NCOA2 and EP300 differed between pig breeds (Iberian and Landrace) extreme in terms of fat deposition. Highly clustered co-expression networks in both liver and adipose tissues were observed. EP300 and NCOA2 showed centrality parameters above average in the both networks. Over all genes, co-expression analyses confirmed 28.9% of the AWM predicted gene-gene interactions in liver and 33.0% in adipose tissue. The magnitude of this validation varied across genes, with up to 60.8% of the connections of NCOA2 in adipose tissue being validated via co-expression. Our results recapitulate the known transcriptional regulation of FA metabolism, predict gene interactions that can be experimentally validated, and suggest that genetic variants mapped to EP300, FHL2, and NCOA2 modulate lipid metabolism and control energy homeostasis in pigs.

Mitochondrial-related proteomic changes during obesity and fasting in mice are greater in the liver than skeletal muscles.

PubMed

Nesteruk, Monika; Hennig, Ewa E; Mikula, Michal; Karczmarski, Jakub; Dzwonek, Artur; Goryca, Krzysztof; Rubel, Tymon; Paziewska, Agnieszka; Woszczynski, Marek; Ledwon, Joanna; Dabrowska, Michalina; Dadlez, Michal; Ostrowski, Jerzy

2014-03-01

Although mitochondrial dysfunction is implicated in the pathogenesis of obesity, the molecular mechanisms underlying obesity-related metabolic abnormalities are not well established. We performed mitochondrial quantitative proteomic and whole transcriptome analysis followed by functional annotations within liver and skeletal muscles, using fasted and non-fasted 16- and 48-week-old high-fat diet (HFD)-fed and normal diet-fed (control group) wild-type C56BL/6J mice, and hyperphagic ob/ob and db/db obese mice. Our study identified 1,675 and 704 mitochondria-associated proteins with at least two peptides in liver and muscle, respectively. Of these, 221 liver and 44 muscle proteins were differentially expressed (adjusted p values ≤ 0.05) between control and all obese mice, while overnight fasting altered expression of 107 liver and 35 muscle proteins. In the liver, we distinguished a network of 27 proteins exhibiting opposite direction of expression changes in HFD-fed and hyperphagic mice when compared to control. The network centered on cytochromes P450 3a11 (Cyp3a11) and 4a14 (Cyp4a14), and fructose-bisphosphate aldolase B (Aldob) proteins which bridged proteins cluster involved in Metabolism of xenobiotics with proteins engaged in Fatty acid metabolism and PPAR signaling pathways. Functional annotations revealed that most of the hepatic molecular alterations, which characterized both obesity and fasting, related to different aspects of energy metabolism (such as Fatty acid metabolism, Peroxisome, and PPAR signaling); however, only a limited number of functional annotations could be selected from skeletal muscle data sets. Thus, our comprehensive molecular overview revealed that both obesity and fasting states induce more pronounced mitochondrial proteome changes in the liver than in the muscles.

[18F]Fludeoxyglucose-Positron Emission Tomography Evidence for Cerebral Hypermetabolism in the Awake State in Narcolepsy and Idiopathic Hypersomnia.

PubMed

Dauvilliers, Yves; Evangelista, Elisa; de Verbizier, Delphine; Barateau, Lucie; Peigneux, Philippe

2017-01-01

Changes in structural and functional central nervous system have been reported in narcolepsy, with large discrepancies between studies. No study has investigated yet spontaneous brain activity at wake in idiopathic hypersomnia (IH). We compared relative changes in regional brain metabolism in two central hypersomnia conditions with different clinical features, namely narcolepsy type 1 (NT1) and IH, and in healthy controls. Sixteen patients [12 males, median age 30 years (17-78)] with NT1, nine patients [2 males, median age 27 years (20-60)] with IH and 19 healthy controls [16 males, median age 36 years (17-78)] were included. 18 F-fludeoxyglucose positron emission tomography (PET) was performed in all drug-free subjects under similar conditions and instructions to stay in a wake resting state. We found increased metabolism in the anterior and middle cingulate and the insula in the two pathological conditions as compared to healthy controls. The reverse contrast failed to evidence hypometabolism in patients vs. controls. Comparisons between patient groups were non-significant. At sub-statistical threshold, we found higher right superior occipital gyrus glucose metabolism in narcolepsy and higher middle orbital cortex and supplementary motor area metabolism in IH, findings that require further confirmation. There is significant hypermetabolism in narcolepsy and IH in the wake resting state in a set of brain regions constitutive of the salience cortical network that may reflect a compensatory neurocircuitry activity secondary to sleepiness. Metabolic differences between the two disorders within the executive-control network may be a signature of abnormally functioning neural system leading to persistent drowsiness typical of IH.

Whole-genome transcriptomic insights into protective molecular mechanisms in metabolically healthy obese African Americans.

PubMed

Gaye, Amadou; Doumatey, Ayo P; Davis, Sharon K; Rotimi, Charles N; Gibbons, Gary H

2018-01-01

Several clinical guidelines have been proposed to distinguish metabolically healthy obesity (MHO) from other subgroups of obesity but the molecular mechanisms by which MHO individuals remain metabolically healthy despite having a high fat mass are yet to be elucidated. We conducted the first whole blood transcriptomic study designed to identify specific sets of genes that might shed novel insights into the molecular mechanisms that protect or delay the occurrence of obesity-related co-morbidities in MHO. The study included 29 African-American obese individuals, 8 MHO and 21 metabolically abnormal obese (MAO). Unbiased transcriptome-wide network analysis was carried out to identify molecular modules of co-expressed genes that are collectively associated with MHO. Network analysis identified a group of 23 co-expressed genes, including ribosomal protein genes (RPs), which were significantly downregulated in MHO subjects. The three pathways enriched in the group of co-expressed genes are EIF2 signaling, regulation of eIF4 and p70S6K signaling, and mTOR signaling. The expression of ten of the RPs collectively predicted MHO status with an area under the curve of 0.81. Triglycerides/HDL (TG/HDL) ratio, an index of insulin resistance, was the best predictor of the expression of genes in the MHO group. The higher TG/HDL values observed in the MAO subjects may underlie the activation of endoplasmic reticulum (ER) and related-stress pathways that lead to a chronic inflammatory state. In summary, these findings suggest that controlling ER stress and/or ribosomal stress by downregulating RPs or controlling TG/HDL ratio may represent effective strategies to prevent or delay the occurrence of metabolic disorders in obese individuals.

Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions

PubMed Central

Blais, Edik M.; Rawls, Kristopher D.; Dougherty, Bonnie V.; Li, Zhuo I.; Kolling, Glynis L.; Ye, Ping; Wallqvist, Anders; Papin, Jason A.

2017-01-01

The laboratory rat has been used as a surrogate to study human biology for more than a century. Here we present the first genome-scale network reconstruction of Rattus norvegicus metabolism, iRno, and a significantly improved reconstruction of human metabolism, iHsa. These curated models comprehensively capture metabolic features known to distinguish rats from humans including vitamin C and bile acid synthesis pathways. After reconciling network differences between iRno and iHsa, we integrate toxicogenomics data from rat and human hepatocytes, to generate biomarker predictions in response to 76 drugs. We validate comparative predictions for xanthine derivatives with new experimental data and literature-based evidence delineating metabolite biomarkers unique to humans. Our results provide mechanistic insights into species-specific metabolism and facilitate the selection of biomarkers consistent with rat and human biology. These models can serve as powerful computational platforms for contextualizing experimental data and making functional predictions for clinical and basic science applications. PMID:28176778

Pancreatic regulation of glucose homeostasis

PubMed Central

Röder, Pia V; Wu, Bingbing; Liu, Yixian; Han, Weiping

2016-01-01

In order to ensure normal body function, the human body is dependent on a tight control of its blood glucose levels. This is accomplished by a highly sophisticated network of various hormones and neuropeptides released mainly from the brain, pancreas, liver, intestine as well as adipose and muscle tissue. Within this network, the pancreas represents a key player by secreting the blood sugar-lowering hormone insulin and its opponent glucagon. However, disturbances in the interplay of the hormones and peptides involved may lead to metabolic disorders such as type 2 diabetes mellitus (T2DM) whose prevalence, comorbidities and medical costs take on a dramatic scale. Therefore, it is of utmost importance to uncover and understand the mechanisms underlying the various interactions to improve existing anti-diabetic therapies and drugs on the one hand and to develop new therapeutic approaches on the other. This review summarizes the interplay of the pancreas with various other organs and tissues that maintain glucose homeostasis. Furthermore, anti-diabetic drugs and their impact on signaling pathways underlying the network will be discussed. PMID:26964835

A Prochlorococcus proving ground for constraint-based metabolic modeling and multi-`omics data integration

NASA Astrophysics Data System (ADS)

Casey, J.; Ji, B.; Shaoie, S.; Mardinoglu, A.; Sarathi Sen, P.; Jahn, O.; Reda, K.; Leigh, J.; Follows, M. J.; Nielsen, J.; Karl, D. M.

2016-02-01

Representatives of the oligotrophic marine cyanobacterium Prochlorococcus marinus are the smallest free-living photosynthetic organisms, both in terms of physical size and genome size, yet are the most abundant photoautotrophic microbes in the oceans and profoundly influence global biogeochemical cycles. Physiological and regulatory control of nutrient and light stress has been observed in MED4 in culture and in its closely related `ecotype' eMED4 in the field, however its metabolism has not been investigated in detail. We present a genome-scale metabolic network reconstruction of the high-light adapted axenic strain MED4ax ("iJCMED4") for the quantitative analysis of a range of its metabolic phenotypes. The resulting structure is a proving ground for the incorporation of enzyme kinetics, biochemical and elemental compositional data, transcriptomic, proteomic, metabolomic, and fluxomic datasets which can be implemented within a constraint-based metabolic modeling environment. The iJCMED4 stoichiometric model consists of 523 metabolic genes encoding 787 reactions with 673 unique metabolites distributed in 5 sub-cellular compartments and is mass, charge, and thermodynamically balanced. Several variants of flux balance analysis were used to simulate growth and metabolic fluxes over the diel cycle, under various stress conditions (e.g., nitrogen, phosphorus, light), and within the framework of a global biogeochemical model (DARWIN). Model simulations accurately predicted growth rates in culture under a variety of defined medium compositions and there was close agreement of photosynthetic performance, biomass and energy yields and efficiencies, and transporter fluxes for iJCMED4 and culture experiments. In addition to a nearly optimal photosynthetic quotient and central carbon metabolism efficiency, MED4 has made dramatic alterations to redox and phosphorus metabolism across biosynthetic and intermediate pathways. We propose that reductions in phosphate reaction participation reflects the P-deplete environment where MED4 was originally isolated.

Reconstruction of metabolic networks in a fluoranthene-degrading enrichments from polycyclic aromatic hydrocarbon polluted soil.

PubMed

Zhao, Jian-Kang; Li, Xiao-Ming; Ai, Guo-Min; Deng, Ye; Liu, Shuang-Jiang; Jiang, Cheng-Ying

2016-11-15

Microbial degradation of polycyclic aromatic hydrocarbons (PAHs) is the primary process of removing PAHs from environments. The metabolic pathway of PAHs in pure cultures has been intensively studied, but cooperative metabolisms at community-level remained to be explored. In this study, we determined the dynamic composition of a microbial community and its metabolic intermediates during fluoranthene degradation using high-throughput metagenomics and gas chromatography-mass spectrometry (GC-MS), respectively. Subsequently, a cooperative metabolic network for fluoranthene degradation was constructed. The network shows that Mycobacterium contributed the majority of ring-hydroxylating and -cleavage dioxygenases, while Diaphorobacter contributed most of the dehydrogenases. Hyphomicrobium, Agrobacterium, and Sphingopyxis contributed to genes encoding enzymes involved in downstream reactions of fluoranthene degradation. The contributions of various microbial groups were calculated with the PICRUSt program. The contributions of Hyphomicrobium to alcohol dehydrogenases were 62.4% in stage 1 (i.e., when fluoranthene was rapidly removed) and 76.8% in stage 3 (i.e., when fluoranthene was not detectable), respectively; the contribution of Pseudomonas were 6.6% in stage 1 and decreased to 1.2% in subsequent stages. To the best of the author's knowledge, this report describes the first cooperative metabolic network to predict the contributions of various microbial groups during PAH-degradation at community-level. Copyright © 2016 Elsevier B.V. All rights reserved.

ChloroKB: A Web Application for the Integration of Knowledge Related to Chloroplast Metabolic Network1[OPEN

PubMed Central

Gloaguen, Pauline; Alban, Claude; Ravanel, Stéphane; Seigneurin-Berny, Daphné; Matringe, Michel; Ferro, Myriam; Bruley, Christophe; Rolland, Norbert; Vandenbrouck, Yves

2017-01-01

Higher plants, as autotrophic organisms, are effective sources of molecules. They hold great promise for metabolic engineering, but the behavior of plant metabolism at the network level is still incompletely described. Although structural models (stoichiometry matrices) and pathway databases are extremely useful, they cannot describe the complexity of the metabolic context, and new tools are required to visually represent integrated biocurated knowledge for use by both humans and computers. Here, we describe ChloroKB, a Web application (http://chlorokb.fr/) for visual exploration and analysis of the Arabidopsis (Arabidopsis thaliana) metabolic network in the chloroplast and related cellular pathways. The network was manually reconstructed through extensive biocuration to provide transparent traceability of experimental data. Proteins and metabolites were placed in their biological context (spatial distribution within cells, connectivity in the network, participation in supramolecular complexes, and regulatory interactions) using CellDesigner software. The network contains 1,147 reviewed proteins (559 localized exclusively in plastids, 68 in at least one additional compartment, and 520 outside the plastid), 122 proteins awaiting biochemical/genetic characterization, and 228 proteins for which genes have not yet been identified. The visual presentation is intuitive and browsing is fluid, providing instant access to the graphical representation of integrated processes and to a wealth of refined qualitative and quantitative data. ChloroKB will be a significant support for structural and quantitative kinetic modeling, for biological reasoning, when comparing novel data with established knowledge, for computer analyses, and for educational purposes. ChloroKB will be enhanced by continuous updates following contributions from plant researchers. PMID:28442501

Systems Level Analyses Reveal Multiple Regulatory Activities of CodY Controlling Metabolism, Motility and Virulence in Listeria monocytogenes

PubMed Central

Lobel, Lior; Herskovits, Anat A.

2016-01-01

Bacteria sense and respond to many environmental cues, rewiring their regulatory network to facilitate adaptation to new conditions/niches. Global transcription factors that co-regulate multiple pathways simultaneously are essential to this regulatory rewiring. CodY is one such global regulator, controlling expression of both metabolic and virulence genes in Gram-positive bacteria. Branch chained amino acids (BCAAs) serve as a ligand for CodY and modulate its activity. Classically, CodY was considered to function primarily as a repressor under rich growth conditions. However, our previous studies of the bacterial pathogen Listeria monocytogenes revealed that CodY is active also when the bacteria are starved for BCAAs. Under these conditions, CodY loses the ability to repress genes (e.g., metabolic genes) and functions as a direct activator of the master virulence regulator gene, prfA. This observation raised the possibility that CodY possesses multiple functions that allow it to coordinate gene expression across a wide spectrum of metabolic growth conditions, and thus better adapt bacteria to the mammalian niche. To gain a deeper understanding of CodY’s regulatory repertoire and identify direct target genes, we performed a genome wide analysis of the CodY regulon and DNA binding under both rich and minimal growth conditions, using RNA-Seq and ChIP-Seq techniques. We demonstrate here that CodY is indeed active (i.e., binds DNA) under both conditions, serving as a repressor and activator of different genes. Further, we identified new genes and pathways that are directly regulated by CodY (e.g., sigB, arg, his, actA, glpF, gadG, gdhA, poxB, glnR and fla genes), integrating metabolism, stress responses, motility and virulence in L. monocytogenes. This study establishes CodY as a multifaceted factor regulating L. monocytogenes physiology in a highly versatile manner. PMID:26895237

A Systems Biology Approach Reveals Converging Molecular Mechanisms that Link Different POPs to Common Metabolic Diseases.

PubMed

Ruiz, Patricia; Perlina, Ally; Mumtaz, Moiz; Fowler, Bruce A

2016-07-01

A number of epidemiological studies have identified statistical associations between persistent organic pollutants (POPs) and metabolic diseases, but testable hypotheses regarding underlying molecular mechanisms to explain these linkages have not been published. We assessed the underlying mechanisms of POPs that have been associated with metabolic diseases; three well-known POPs [2,3,7,8-tetrachlorodibenzodioxin (TCDD), 2,2´,4,4´,5,5´-hexachlorobiphenyl (PCB 153), and 4,4´-dichlorodiphenyldichloroethylene (p,p´-DDE)] were studied. We used advanced database search tools to delineate testable hypotheses and to guide laboratory-based research studies into underlying mechanisms by which this POP mixture could produce or exacerbate metabolic diseases. For our searches, we used proprietary systems biology software (MetaCore™/MetaDrug™) to conduct advanced search queries for the underlying interactions database, followed by directional network construction to identify common mechanisms for these POPs within two or fewer interaction steps downstream of their primary targets. These common downstream pathways belong to various cytokine and chemokine families with experimentally well-documented causal associations with type 2 diabetes. Our systems biology approach allowed identification of converging pathways leading to activation of common downstream targets. To our knowledge, this is the first study to propose an integrated global set of step-by-step molecular mechanisms for a combination of three common POPs using a systems biology approach, which may link POP exposure to diseases. Experimental evaluation of the proposed pathways may lead to development of predictive biomarkers of the effects of POPs, which could translate into disease prevention and effective clinical treatment strategies. Ruiz P, Perlina A, Mumtaz M, Fowler BA. 2016. A systems biology approach reveals converging molecular mechanisms that link different POPs to common metabolic diseases. Environ Health Perspect 124:1034-1041; http://dx.doi.org/10.1289/ehp.1510308.

Metabolomic and proteomic analysis of D-lactate-producing Lactobacillus delbrueckii under various fermentation conditions.

PubMed

Liang, Shaoxiong; Gao, Dacheng; Liu, Huanhuan; Wang, Cheng; Wen, Jianping

2018-05-28

As an important feedstock monomer for the production of biodegradable stereo-complex poly-lactic acid polymer, D-lactate has attracted much attention. To improve D-lactate production by microorganisms such as Lactobacillus delbrueckii, various fermentation conditions were performed, such as the employment of anaerobic fermentation, the utilization of more suitable neutralizing agents, and exploitation of alternative nitrogen sources. The highest D-lactate titer could reach 133 g/L under the optimally combined fermentation condition, increased by 70.5% compared with the control. To decipher the potential mechanisms of D-lactate overproduction, the time-series response of intracellular metabolism to different fermentation conditions was investigated by GC-MS and LC-MS/MS-based metabolomic analysis. Then the metabolomic datasets were subjected to weighted correlation network analysis (WGCNA), and nine distinct metabolic modules and eight hub metabolites were identified to be specifically associated with D-lactate production. Moreover, a quantitative iTRAQ-LC-MS/MS proteomic approach was employed to further analyze the change of intracellular metabolism under the combined fermentation condition, identifying 97 up-regulated and 42 down-regulated proteins compared with the control. The in-depth analysis elucidated how the key factors exerted influence on D-lactate biosynthesis. The results revealed that glycolysis and pentose phosphate pathways, transport of glucose, amino acids and peptides, amino acid metabolism, peptide hydrolysis, synthesis of nucleotides and proteins, and cell division were all strengthened, while ATP consumption for exporting proton, cell damage, metabolic burden caused by stress response, and bypass of pyruvate were decreased under the combined condition. These might be the main reasons for significantly improved D-lactate production. These findings provide the first omics view of cell growth and D-lactate overproduction in L. delbrueckii, which can be a theoretical basis for further improving the production of D-lactate.

Sequence-based Network Completion Reveals the Integrality of Missing Reactions in Metabolic Networks*

PubMed Central

Krumholz, Elias W.; Libourel, Igor G. L.

2015-01-01

Genome-scale metabolic models are central in connecting genotypes to metabolic phenotypes. However, even for well studied organisms, such as Escherichia coli, draft networks do not contain a complete biochemical network. Missing reactions are referred to as gaps. These gaps need to be filled to enable functional analysis, and gap-filling choices influence model predictions. To investigate whether functional networks existed where all gap-filling reactions were supported by sequence similarity to annotated enzymes, four draft networks were supplemented with all reactions from the Model SEED database for which minimal sequence similarity was found in their genomes. Quadratic programming revealed that the number of reactions that could partake in a gap-filling solution was vast: 3,270 in the case of E. coli, where 72% of the metabolites in the draft network could connect a gap-filling solution. Nonetheless, no network could be completed without the inclusion of orphaned enzymes, suggesting that parts of the biochemistry integral to biomass precursor formation are uncharacterized. However, many gap-filling reactions were well determined, and the resulting networks showed improved prediction of gene essentiality compared with networks generated through canonical gap filling. In addition, gene essentiality predictions that were sensitive to poorly determined gap-filling reactions were of poor quality, suggesting that damage to the network structure resulting from the inclusion of erroneous gap-filling reactions may be predictable. PMID:26041773

Metabolite profiling and associated gene expression reveal two metabolic shifts during the seed-to-seedling transition in Arabidopsis thaliana.

PubMed

Silva, Anderson Tadeu; Ligterink, Wilco; Hilhorst, Henk W M

2017-11-01

Metabolic and transcriptomic correlation analysis identified two distinctive profiles involved in the metabolic preparation for seed germination and seedling establishment, respectively. Transcripts were identified that may control metabolic fluxes. The transition from a quiescent metabolic state (dry seed) to the active state of a vigorous seedling is crucial in the plant's life cycle. We analysed this complex physiological trait by measuring the changes in primary metabolism that occur during the transition in order to determine which metabolic networks are operational. The transition involves several developmental stages from seed germination to seedling establishment, i.e. between imbibition of the mature dry seed and opening of the cotyledons, the final stage of seedling establishment. We hypothesized that the advancement of growth is associated with certain signature metabolite profiles. Metabolite-metabolite correlation analysis underlined two specific profiles which appear to be involved in the metabolic preparation for seed germination and efficient seedling establishment, respectively. Metabolite profiles were also compared to transcript profiles and although transcriptional changes did not always equate to a proportional metabolic response, in depth correlation analysis identified several transcripts that may directly influence the flux through metabolic pathways during the seed-to-seedling transition. This correlation analysis also pinpointed metabolic pathways which are significant for the seed-to-seedling transition, and metabolite contents that appeared to be controlled directly by transcript abundance. This global view of the transcriptional and metabolic changes during the seed-to-seedling transition in Arabidopsis opens up new perspectives for understanding the complex regulatory mechanism underlying this transition.

Development of constraint-based system-level models of microbial metabolism.

PubMed

Navid, Ali

2012-01-01

Genome-scale models of metabolism are valuable tools for using genomic information to predict microbial phenotypes. System-level mathematical models of metabolic networks have been developed for a number of microbes and have been used to gain new insights into the biochemical conversions that occur within organisms and permit their survival and proliferation. Utilizing these models, computational biologists can (1) examine network structures, (2) predict metabolic capabilities and resolve unexplained experimental observations, (3) generate and test new hypotheses, (4) assess the nutritional requirements of the organism and approximate its environmental niche, (5) identify missing enzymatic functions in the annotated genome, and (6) engineer desired metabolic capabilities in model organisms. This chapter details the protocol for developing genome-scale models of metabolism in microbes as well as tips for accelerating the model building process.

Metabolomic strategies to map functions of metabolic pathways

PubMed Central

Mulvihill, Melinda M.

2014-01-01

Genome sequencing efforts have revealed a strikingly large number of unannotated and uncharacterized genes that fall into metabolic enzymes classes, likely indicating that our current knowledge of biochemical pathways in normal physiology, let alone in disease states, remains largely incomplete. This realization presents a daunting challenge for post-genomic-era scientists in deciphering the biochemical and (patho)physiological roles of these enzymes and their metabolites and metabolic networks. This is further complicated by many recent studies showing a rewiring of normal metabolic networks in disease states to give rise to unique pathophysiological functions of enzymes, metabolites, and metabolic pathways. This review focuses on recent discoveries made using metabolic mapping technologies to uncover novel pathways and metabolite-mediated posttranslational modifications and epigenetic alterations and their impact on physiology and disease. PMID:24918200

Regulatory mechanisms of metabolic flexibility in the dark-eyed junco (Junco hyemalis).

PubMed

Stager, Maria; Swanson, David L; Cheviron, Zachary A

2015-03-01

Small temperate birds reversibly modify their aerobic performance to maintain thermoregulatory homeostasis under seasonally changing environmental conditions and these physiological adjustments may be attributable to changes in the expression of genes in the underlying regulatory networks. Here, we report the results of an experimental procedure designed to gain insight into the fundamental mechanisms of metabolic flexibility in the dark-eyed junco (Junco hyemalis). We combined genomic transcriptional profiles with measures of metabolic enzyme activities and whole-animal thermogenic performance from juncos exposed to four 6-week acclimation treatments that varied in temperature (cold, 3°C; warm, 24°C) and photoperiod (short day, 8 h light:16 h dark; long day, 16 h light:8 h dark). Cold-acclimated birds increased thermogenic capacity compared with warm-acclimated birds, and this enhanced performance was associated with upregulation of genes involved in muscle hypertrophy, angiogenesis, and lipid transport and oxidation, as well as with catabolic enzyme activities. These physiological changes occurred over ecologically relevant timescales, suggesting that birds make regulatory adjustments to interacting, hierarchical pathways in order to seasonally enhance thermogenic capacity. © 2015. Published by The Company of Biologists Ltd.

Injured Brains and Adaptive Networks: The Benefits and Costs of Hyperconnectivity.

PubMed

Hillary, Frank G; Grafman, Jordan H

2017-05-01

A common finding in human functional brain-imaging studies is that damage to neural systems paradoxically results in enhanced functional connectivity between network regions, a phenomenon commonly referred to as 'hyperconnectivity'. Here, we describe the various ways that hyperconnectivity operates to benefit a neural network following injury while simultaneously negotiating the trade-off between metabolic cost and communication efficiency. Hyperconnectivity may be optimally expressed by increasing connections through the most central and metabolically efficient regions (i.e., hubs). While adaptive in the short term, we propose that chronic hyperconnectivity may leave network hubs vulnerable to secondary pathological processes over the life span due to chronically elevated metabolic stress. We conclude by offering novel, testable hypotheses for advancing our understanding of the role of hyperconnectivity in systems-level brain plasticity in neurological disorders. Copyright © 2017 Elsevier Ltd. All rights reserved.

Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns

PubMed Central

Lezon, Timothy R.; Banavar, Jayanth R.; Cieplak, Marek; Maritan, Amos; Fedoroff, Nina V.

2006-01-01

We describe a method based on the principle of entropy maximization to identify the gene interaction network with the highest probability of giving rise to experimentally observed transcript profiles. In its simplest form, the method yields the pairwise gene interaction network, but it can also be extended to deduce higher-order interactions. Analysis of microarray data from genes in Saccharomyces cerevisiae chemostat cultures exhibiting energy metabolic oscillations identifies a gene interaction network that reflects the intracellular communication pathways that adjust cellular metabolic activity and cell division to the limiting nutrient conditions that trigger metabolic oscillations. The success of the present approach in extracting meaningful genetic connections suggests that the maximum entropy principle is a useful concept for understanding living systems, as it is for other complex, nonequilibrium systems. PMID:17138668

Information theory in systems biology. Part I: Gene regulatory and metabolic networks.

PubMed

Mousavian, Zaynab; Kavousi, Kaveh; Masoudi-Nejad, Ali

2016-03-01

"A Mathematical Theory of Communication", was published in 1948 by Claude Shannon to establish a framework that is now known as information theory. In recent decades, information theory has gained much attention in the area of systems biology. The aim of this paper is to provide a systematic review of those contributions that have applied information theory in inferring or understanding of biological systems. Based on the type of system components and the interactions between them, we classify the biological systems into 4 main classes: gene regulatory, metabolic, protein-protein interaction and signaling networks. In the first part of this review, we attempt to introduce most of the existing studies on two types of biological networks, including gene regulatory and metabolic networks, which are founded on the concepts of information theory. Copyright © 2015 Elsevier Ltd. All rights reserved.

Combining Genome-Scale Experimental and Computational Methods To Identify Essential Genes in Rhodobacter sphaeroides

DOE PAGES

Burger, Brian T.; Imam, Saheed; Scarborough, Matthew J.; ...

2017-06-06

Rhodobacter sphaeroides is one of the best-studied alphaproteobacteria from biochemical, genetic, and genomic perspectives. To gain a better systems-level understanding of this organism, we generated a large transposon mutant library and used transposon sequencing (Tn-seq) to identify genes that are essential under several growth conditions. Using newly developed Tn-seq analysis software (TSAS), we identified 493 genes as essential for aerobic growth on a rich medium. We then used the mutant library to identify conditionally essential genes under two laboratory growth conditions, identifying 85 additional genes required for aerobic growth in a minimal medium and 31 additional genes required for photosyntheticmore » growth. In all instances, our analyses confirmed essentiality for many known genes and identified genes not previously considered to be essential. We used the resulting Tn-seq data to refine and improve a genome-scale metabolic network model (GEM) for R. sphaeroides. Together, we demonstrate how genetic, genomic, and computational approaches can be combined to obtain a systems-level understanding of the genetic framework underlying metabolic diversity in bacterial species.« less

Important Role of FTO in the Survival of Rare Panresistant Triple-Negative Inflammatory Breast Cancer Cells Facing a Severe Metabolic Challenge

PubMed Central

Singh, Balraj; Kinne, Hannah E.; Milligan, Ryan D.; Washburn, Laura J.; Olsen, Mark; Lucci, Anthony

2016-01-01

We have previously shown that only 0.01% cells survive a metabolic challenge involving lack of glutamine in culture medium of SUM149 triple-negative Inflammatory Breast Cancer cell line. These cells, designated as SUM149-MA for metabolic adaptability, are resistant to chemotherapeutic drugs, and they efficiently metastasize to multiple organs in nude mice. We hypothesized that obesity-related molecular networks, which normally help in cellular and organismal survival under metabolic challenges, may help in the survival of MA cells. The fat mass and obesity-associated protein FTO is overexpressed in MA cells. Obesity-associated cis-acting elements in non-coding region of FTO regulate the expression of IRX3 gene, thus activating obesity networks. Here we found that IRX3 protein is significantly overexpressed in MA cells (5 to 6-fold) as compared to the parental SUM149 cell line, supporting our hypothesis. We also obtained evidence that additional key regulators of energy balance such as ARID5B, IRX5, and CUX1 P200 repressor could potentially help progenitor-like TNBC cells survive in glutamine-free medium. MO-I-500, a pharmacological inhibitor of FTO, significantly (>90%) inhibited survival and/or colony formation of SUM149-MA cells as compared to untreated cells or those treated with a control compound MO-I-100. Curiously, MO-I-500 treatment also led to decreased levels of FTO and IRX3 proteins in the SUM149 cells initially surviving in glutamine-free medium as compared to MO-I-100 treatment. Interestingly, MO-I-500 treatment had a relatively little effect on cell growth of either the SUM149 or SUM149-MA cell line when added to a complete medium containing glutamine that does not pose a metabolic challenge. Importantly, once selected and cultured in glutamine-free medium, SUM149-MA cells were no longer affected by MO-I-500 even in Gln-free medium. We conclude that panresistant MA cells contain interconnected molecular networks that govern developmental status and energy balance, and genetic and epigenetic alterations that are selected during cancer evolution. PMID:27390851

Enumeration of Smallest Intervention Strategies in Genome-Scale Metabolic Networks

PubMed Central

von Kamp, Axel; Klamt, Steffen

2014-01-01

One ultimate goal of metabolic network modeling is the rational redesign of biochemical networks to optimize the production of certain compounds by cellular systems. Although several constraint-based optimization techniques have been developed for this purpose, methods for systematic enumeration of intervention strategies in genome-scale metabolic networks are still lacking. In principle, Minimal Cut Sets (MCSs; inclusion-minimal combinations of reaction or gene deletions that lead to the fulfilment of a given intervention goal) provide an exhaustive enumeration approach. However, their disadvantage is the combinatorial explosion in larger networks and the requirement to compute first the elementary modes (EMs) which itself is impractical in genome-scale networks. We present MCSEnumerator, a new method for effective enumeration of the smallest MCSs (with fewest interventions) in genome-scale metabolic network models. For this we combine two approaches, namely (i) the mapping of MCSs to EMs in a dual network, and (ii) a modified algorithm by which shortest EMs can be effectively determined in large networks. In this way, we can identify the smallest MCSs by calculating the shortest EMs in the dual network. Realistic application examples demonstrate that our algorithm is able to list thousands of the most efficient intervention strategies in genome-scale networks for various intervention problems. For instance, for the first time we could enumerate all synthetic lethals in E.coli with combinations of up to 5 reactions. We also applied the new algorithm exemplarily to compute strain designs for growth-coupled synthesis of different products (ethanol, fumarate, serine) by E.coli. We found numerous new engineering strategies partially requiring less knockouts and guaranteeing higher product yields (even without the assumption of optimal growth) than reported previously. The strength of the presented approach is that smallest intervention strategies can be quickly calculated and screened with neither network size nor the number of required interventions posing major challenges. PMID:24391481

Genome-scale fluxes predicted under the guidance of enzyme abundance using a novel hyper-cube shrink algorithm.

PubMed

Xie, Zhengwei; Zhang, Tianyu; Ouyang, Qi

2018-02-01

One of the long-expected goals of genome-scale metabolic modelling is to evaluate the influence of the perturbed enzymes on flux distribution. Both ordinary differential equation (ODE) models and constraint-based models, like Flux balance analysis (FBA), lack the capacity to perform metabolic control analysis (MCA) for large-scale networks. In this study, we developed a hyper-cube shrink algorithm (HCSA) to incorporate the enzymatic properties into the FBA model by introducing a pseudo reaction V constrained by enzymatic parameters. Our algorithm uses the enzymatic information quantitatively rather than qualitatively. We first demonstrate the concept by applying HCSA to a simple three-node network, whereby we obtained a good correlation between flux and enzyme abundance. We then validate its prediction by comparison with ODE and with a synthetic network producing voilacein and analogues in Saccharomyces cerevisiae. We show that HCSA can mimic the state-state results of ODE. Finally, we show its capability of predicting the flux distribution in genome-scale networks by applying it to sporulation in yeast. We show the ability of HCSA to operate without biomass flux and perform MCA to determine rate-limiting reactions. Algorithm was implemented by Matlab and C ++. The code is available at https://github.com/kekegg/HCSA. xiezhengwei@hsc.pku.edu.cn or qi@pku.edu.cn. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Thermodynamics-based Metabolite Sensitivity Analysis in metabolic networks.

PubMed

Kiparissides, A; Hatzimanikatis, V

2017-01-01

The increasing availability of large metabolomics datasets enhances the need for computational methodologies that can organize the data in a way that can lead to the inference of meaningful relationships. Knowledge of the metabolic state of a cell and how it responds to various stimuli and extracellular conditions can offer significant insight in the regulatory functions and how to manipulate them. Constraint based methods, such as Flux Balance Analysis (FBA) and Thermodynamics-based flux analysis (TFA), are commonly used to estimate the flow of metabolites through genome-wide metabolic networks, making it possible to identify the ranges of flux values that are consistent with the studied physiological and thermodynamic conditions. However, unless key intracellular fluxes and metabolite concentrations are known, constraint-based models lead to underdetermined problem formulations. This lack of information propagates as uncertainty in the estimation of fluxes and basic reaction properties such as the determination of reaction directionalities. Therefore, knowledge of which metabolites, if measured, would contribute the most to reducing this uncertainty can significantly improve our ability to define the internal state of the cell. In the present work we combine constraint based modeling, Design of Experiments (DoE) and Global Sensitivity Analysis (GSA) into the Thermodynamics-based Metabolite Sensitivity Analysis (TMSA) method. TMSA ranks metabolites comprising a metabolic network based on their ability to constrain the gamut of possible solutions to a limited, thermodynamically consistent set of internal states. TMSA is modular and can be applied to a single reaction, a metabolic pathway or an entire metabolic network. This is, to our knowledge, the first attempt to use metabolic modeling in order to provide a significance ranking of metabolites to guide experimental measurements. Copyright © 2016 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Reconstruction and visualization of carbohydrate, N-glycosylation pathways in Pichia pastoris CBS7435 using computational and system biology approaches.

PubMed

Srivastava, Akriti; Somvanshi, Pallavi; Mishra, Bhartendu Nath

2013-06-01

Pichia pastoris is an efficient expression system for production of recombinant proteins. To understand its physiology for building novel applications it is important to understand and reconstruct its metabolic network. The metabolic reconstruction approach connects genotype with phenotype. Here, we have attempted to reconstruct carbohydrate metabolism pathways responsible for high biomass density and N-glycosylation pathways involved in the post translational modification of proteins of P. pastoris CBS7435. Both these metabolic pathways play a crucial role in heterologous protein production. We report novel, missing and unannotated enzymes involved in the target metabolic pathways. A strong possibility of cellulose and xylose metabolic processes in P. pastoris CBS7435 suggests its use in the area of biofuels. The reconstructed metabolic networks can be used for increased yields and improved product quality, for designing appropriate growth medium, for production of recombinant therapeutics and for making biofuels.

On the thermodynamic origin of metabolic scaling.

PubMed

Ballesteros, Fernando J; Martinez, Vicent J; Luque, Bartolo; Lacasa, Lucas; Valor, Enric; Moya, Andrés

2018-01-23

The origin and shape of metabolic scaling has been controversial since Kleiber found that basal metabolic rate of animals seemed to vary as a power law of their body mass with exponent 3/4, instead of 2/3, as a surface-to-volume argument predicts. The universality of exponent 3/4 -claimed in terms of the fractal properties of the nutrient network- has recently been challenged according to empirical evidence that observed a wealth of robust exponents deviating from 3/4. Here we present a conceptually simple thermodynamic framework, where the dependence of metabolic rate with body mass emerges from a trade-off between the energy dissipated as heat and the energy efficiently used by the organism to maintain its metabolism. This balance tunes the shape of an additive model from which different effective scalings can be recovered as particular cases, thereby reconciling previously inconsistent empirical evidence in mammals, birds, insects and even plants under a unified framework. This model is biologically motivated, fits remarkably well the data, and also explains additional features such as the relation between energy lost as heat and mass, the role and influence of different climatic environments or the difference found between endotherms and ectotherms.

Synergy between (13)C-metabolic flux analysis and flux balance analysis for understanding metabolic adaptation to anaerobiosis in E. coli.

PubMed

Chen, Xuewen; Alonso, Ana P; Allen, Doug K; Reed, Jennifer L; Shachar-Hill, Yair

2011-01-01

Genome-based Flux Balance Analysis (FBA) and steady-state isotopic-labeling-based Metabolic Flux Analysis (MFA) are complimentary approaches to predicting and measuring the operation and regulation of metabolic networks. Here, genome-derived models of Escherichia coli (E. coli) metabolism were used for FBA and ¹³C-MFA analyses of aerobic and anaerobic growths of wild-type E. coli (K-12 MG1655) cells. Validated MFA flux maps reveal that the fraction of maintenance ATP consumption in total ATP production is about 14% higher under anaerobic (51.1%) than aerobic conditions (37.2%). FBA revealed that an increased ATP utilization is consumed by ATP synthase to secrete protons from fermentation. The TCA cycle is shown to be incomplete in aerobically growing cells and submaximal growth is due to limited oxidative phosphorylation. An FBA was successful in predicting product secretion rates in aerobic culture if both glucose and oxygen uptake measurement were constrained, but the most-frequently predicted values of internal fluxes yielded from sampling the feasible space differ substantially from MFA-derived fluxes. © 2010 Elsevier Inc. All rights reserved.

A link between lipid metabolism and epithelial-mesenchymal transition provides a target for colon cancer therapy

PubMed Central

Sánchez-Martínez, Ruth; Álvarez-Fernández, Mónica; Vargas, Teodoro; Molina, Susana; García, Belén; Herranz, Jesús; Moreno-Rubio, Juan; Reglero, Guillermo; Pérez-Moreno, Mirna; Feliu, Jaime; Malumbres, Marcos; de Molina, Ana Ramírez

2015-01-01

The alterations in carbohydrate metabolism that fuel tumor growth have been extensively studied. However, other metabolic pathways involved in malignant progression, demand further understanding. Here we describe a metabolic acyl-CoA synthetase/stearoyl-CoA desaturase ACSL/SCD network causing an epithelial-mesenchymal transition (EMT) program that promotes migration and invasion of colon cancer cells. The mesenchymal phenotype produced upon overexpression of these enzymes is reverted through reactivation of AMPK signaling. Furthermore, this network expression correlates with poorer clinical outcome of stage-II colon cancer patients. Finally, combined treatment with chemical inhibitors of ACSL/SCD selectively decreases cancer cell viability without reducing normal cells viability. Thus, ACSL/SCD network stimulates colon cancer progression through conferring increased energetic capacity and invasive and migratory properties to cancer cells, and might represent a new therapeutic opportunity for colon cancer treatment. PMID:26451612

Regulation of Primary Metabolism in Response to Low Oxygen Availability as Revealed by Carbon and Nitrogen Isotope Redistribution.

PubMed

António, Carla; Päpke, Carola; Rocha, Marcio; Diab, Houssein; Limami, Anis M; Obata, Toshihiro; Fernie, Alisdair R; van Dongen, Joost T

2016-01-01

Based on enzyme activity assays and metabolic responses to waterlogging of the legume Lotus japonicus, it was previously suggested that, during hypoxia, the tricarboxylic acid cycle switches to a noncyclic operation mode. Hypotheses were postulated to explain the alternative metabolic pathways involved, but as yet, a direct analysis of the relative redistribution of label through the corresponding pathways was not made. Here, we describe the use of stable isotope-labeling experiments for studying metabolism under hypoxia using wild-type roots of the crop legume soybean (Glycine max). [(13)C]Pyruvate labeling was performed to compare metabolism through the tricarboxylic acid cycle, fermentation, alanine metabolism, and the γ-aminobutyric acid shunt, while [(13)C]glutamate and [(15)N]ammonium labeling were performed to address the metabolism via glutamate to succinate. Following these labelings, the time course for the redistribution of the (13)C/(15)N label throughout the metabolic network was evaluated with gas chromatography-time of flight-mass spectrometry. Our combined labeling data suggest the inhibition of the tricarboxylic acid cycle enzyme succinate dehydrogenase, also known as complex II of the mitochondrial electron transport chain, providing support for the bifurcation of the cycle and the down-regulation of the rate of respiration measured during hypoxic stress. Moreover, up-regulation of the γ-aminobutyric acid shunt and alanine metabolism explained the accumulation of succinate and alanine during hypoxia. © 2016 American Society of Plant Biologists. All Rights Reserved.

Metabolic modelling to support long term strategic decisions on water supply systems

NASA Astrophysics Data System (ADS)

Ciriello, Valentina; Felisa, Giada; Lauriola, Ilaria; Pomanti, Flavio; Di Federico, Vittorio

2017-04-01

Water resources are essential for the economic development and sustenance of anthropic activities belonging to the civil, agricultural and industrial sectors. Nevertheless, availability of water resources is not uniformly distributed in space and time. Moreover, the increasing water demand, mainly due to population growth and expansion of agricultural crops, may cause increasing water stress conditions, if combined with the effects of climate change. Under these circumstances, it is necessary to improve the resilience of water supply systems both in terms of infrastructures and environmental compliance. Metabolic modelling approaches represent a flexible tool able to provide support to decision making in the long term, based on sustainability criteria. These approaches mimic the water supply network through a set of material and energy fluxes that interact and influence each other. By analyzing these fluxes, a suite of key performance indicators is evaluated in order to identify which kind of interventions may be applied to increase the sustainability of the system. Here, we adopt these concepts to analyze the water supply network of Reggio-Emilia (Italy) which is supported by water withdrawals from both surface water and groundwater bodies. We analyze different scenarios, including possible reduction of water withdrawals from one of the different sources as a consequence of a decrease in water availability under present and future scenarios. On these basis, we identify preventive strategies for a dynamic management of the water supply system.

Analysis of drought-responsive signalling network in two contrasting rice cultivars using transcriptome-based approach

PubMed Central

Borah, Pratikshya; Sharma, Eshan; Kaur, Amarjot; Chandel, Girish; Mohapatra, Trilochan; Kapoor, Sanjay; Khurana, Jitendra P.

2017-01-01

Traditional cultivars of rice in India exhibit tolerance to drought stress due to their inherent genetic variations. Here we present comparative physiological and transcriptome analyses of two contrasting cultivars, drought tolerant Dhagaddeshi (DD) and susceptible IR20. Microarray analysis revealed several differentially expressed genes (DEGs) exclusively in DD as compared to IR20 seedlings exposed to 3 h drought stress. Physiologically, DD seedlings showed higher cell membrane stability and differential ABA accumulation in response to dehydration, coupled with rapid changes in gene expression. Detailed analyses of metabolic pathways enriched in expression data suggest interplay of ABA dependent along with secondary and redox metabolic networks that activate osmotic and detoxification signalling in DD. By co-localization of DEGs with QTLs from databases or published literature for physiological traits of DD and IR20, candidate genes were identified including those underlying major QTL qDTY1.1 in DD. Further, we identified previously uncharacterized genes from both DD and IR20 under drought conditions including OsWRKY51, OsVP1 and confirmed their expression by qPCR in multiple rice cultivars. OsFBK1 was also functionally validated in susceptible PB1 rice cultivar and Arabidopsis for providing drought tolerance. Some of the DEGs mapped to the known QTLs could thus, be of potential significance for marker-assisted breeding. PMID:28181537

Glucose Metabolism during Resting State Reveals Abnormal Brain Networks Organization in the Alzheimer’s Disease and Mild Cognitive Impairment

PubMed Central

Martínez-Montes, Eduardo

2013-01-01

This paper aims to study the abnormal patterns of brain glucose metabolism co-variations in Alzheimer disease (AD) and Mild Cognitive Impairment (MCI) patients compared to Normal healthy controls (NC) using the Alzheimer Disease Neuroimaging Initiative (ADNI) database. The local cerebral metabolic rate for glucose (CMRgl) in a set of 90 structures belonging to the AAL atlas was obtained from Fluro-Deoxyglucose Positron Emission Tomography data in resting state. It is assumed that brain regions whose CMRgl values are significantly correlated are functionally associated; therefore, when metabolism is altered in a single region, the alteration will affect the metabolism of other brain areas with which it interrelates. The glucose metabolism network (represented by the matrix of the CMRgl co-variations among all pairs of structures) was studied using the graph theory framework. The highest concurrent fluctuations in CMRgl were basically identified between homologous cortical regions in all groups. Significant differences in CMRgl co-variations in AD and MCI groups as compared to NC were found. The AD and MCI patients showed aberrant patterns in comparison to NC subjects, as detected by global and local network properties (global and local efficiency, clustering index, and others). MCI network’s attributes showed an intermediate position between NC and AD, corroborating it as a transitional stage from normal aging to Alzheimer disease. Our study is an attempt at exploring the complex association between glucose metabolism, CMRgl covariations and the attributes of the brain network organization in AD and MCI. PMID:23894356

Metabolomics Approach Reveals Integrated Metabolic Network Associated with Serotonin Deficiency

PubMed Central

Weng, Rui; Shen, Sensen; Tian, Yonglu; Burton, Casey; Xu, Xinyuan; Liu, Yi; Chang, Cuilan; Bai, Yu; Liu, Huwei

2015-01-01

Serotonin is an important neurotransmitter that broadly participates in various biological processes. While serotonin deficiency has been associated with multiple pathological conditions such as depression, schizophrenia, Alzheimer’s disease and Parkinson’s disease, the serotonin-dependent mechanisms remain poorly understood. This study therefore aimed to identify novel biomarkers and metabolic pathways perturbed by serotonin deficiency using metabolomics approach in order to gain new metabolic insights into the serotonin deficiency-related molecular mechanisms. Serotonin deficiency was achieved through pharmacological inhibition of tryptophan hydroxylase (Tph) using p-chlorophenylalanine (pCPA) or genetic knockout of the neuronal specific Tph2 isoform. This dual approach improved specificity for the serotonin deficiency-associated biomarkers while minimizing nonspecific effects of pCPA treatment or Tph2 knockout (Tph2-/-). Non-targeted metabolic profiling and a targeted pCPA dose-response study identified 21 biomarkers in the pCPA-treated mice while 17 metabolites in the Tph2-/- mice were found to be significantly altered compared with the control mice. These newly identified biomarkers were associated with amino acid, energy, purine, lipid and gut microflora metabolisms. Oxidative stress was also found to be significantly increased in the serotonin deficient mice. These new biomarkers and the overall metabolic pathways may provide new understanding for the serotonin deficiency-associated mechanisms under multiple pathological states. PMID:26154191

Metabolomics Approach Reveals Integrated Metabolic Network Associated with Serotonin Deficiency.

PubMed

Weng, Rui; Shen, Sensen; Tian, Yonglu; Burton, Casey; Xu, Xinyuan; Liu, Yi; Chang, Cuilan; Bai, Yu; Liu, Huwei

2015-07-08

Serotonin is an important neurotransmitter that broadly participates in various biological processes. While serotonin deficiency has been associated with multiple pathological conditions such as depression, schizophrenia, Alzheimer's disease and Parkinson's disease, the serotonin-dependent mechanisms remain poorly understood. This study therefore aimed to identify novel biomarkers and metabolic pathways perturbed by serotonin deficiency using metabolomics approach in order to gain new metabolic insights into the serotonin deficiency-related molecular mechanisms. Serotonin deficiency was achieved through pharmacological inhibition of tryptophan hydroxylase (Tph) using p-chlorophenylalanine (pCPA) or genetic knockout of the neuronal specific Tph2 isoform. This dual approach improved specificity for the serotonin deficiency-associated biomarkers while minimizing nonspecific effects of pCPA treatment or Tph2 knockout (Tph2-/-). Non-targeted metabolic profiling and a targeted pCPA dose-response study identified 21 biomarkers in the pCPA-treated mice while 17 metabolites in the Tph2-/- mice were found to be significantly altered compared with the control mice. These newly identified biomarkers were associated with amino acid, energy, purine, lipid and gut microflora metabolisms. Oxidative stress was also found to be significantly increased in the serotonin deficient mice. These new biomarkers and the overall metabolic pathways may provide new understanding for the serotonin deficiency-associated mechanisms under multiple pathological states.

Environmental and genetic factors that contribute to Escherichia coli K-12 biofilm formation

PubMed Central

Prüß, Birgit M.; Verma, Karan; Samanta, Priyankar; Sule, Preeti; Kumar, Sunil; Wu, Jianfei; Christianson, David; Horne, Shelley M.; Stafslien, Shane J.; Wolfe, Alan J.; Denton, Anne

2010-01-01

Biofilms are communities of bacteria whose formation on surfaces requires a large portion of the bacteria’s transcriptional network. To identify environmental conditions and transcriptional regulators that contribute to sensing these conditions, we used a high-throughput approach to monitor biofilm biomass produced by an isogenic set of Escherichia coli K-12 strains grown under combinations of environmental conditions. Of the environmental combinationsd, growth in tryptic soy broth at 37°C supported the most biofilm production. To analyze the complex relationships between the diverse cell surface organelles, transcriptional regulators, and metabolic enzymes represented by the tested mutant set, we used a novel vector-item pattern-mining algorithm. The algorithm related biofilm amounts to the functional annotations of each mutated protein. The pattern with the best statistical significance was the gene ontology ‘pyruvate catabolic process,’ which is associated with enzymes of acetate metabolism. Phenotype microarray experiments illustrated that carbon sources that are metabolized to acetyl-coenzyme A, acetyl phosphate, and acetate are particularly supportive of biofilm formation. Scanning electron microscopy revealed structural differences between mutants that lack acetate metabolism enzymes and their parent and confirmed the quantitative differences. We conclude that acetate metabolism functions as a metabolic sensor, transmitting changes in environmental conditions to biofilm biomass and structure. PMID:20559621

Pharmacogenetics of Vascular Risk Factors in Alzheimer’s Disease

PubMed Central

Cacabelos, Ramón; Meyyazhagan, Arun; Carril, Juan C.; Cacabelos, Pablo; Teijido, Óscar

2018-01-01

Alzheimer’s disease (AD) is a polygenic/complex disorder in which genomic, epigenomic, cerebrovascular, metabolic, and environmental factors converge to define a progressive neurodegenerative phenotype. Pharmacogenetics is a major determinant of therapeutic outcome in AD. Different categories of genes are potentially involved in the pharmacogenetic network responsible for drug efficacy and safety, including pathogenic, mechanistic, metabolic, transporter, and pleiotropic genes. However, most drugs exert pleiotropic effects that are promiscuously regulated for different gene products. Only 20% of the Caucasian population are extensive metabolizers for tetragenic haplotypes integrating CYP2D6-CYP2C19-CYP2C9-CYP3A4/5 variants. Patients harboring CYP-related poor (PM) and/or ultra-rapid (UM) geno-phenotypes display more irregular profiles in drug metabolism than extensive (EM) or intermediate (IM) metabolizers. Among 111 pentagenic (APOE-APOB-APOC3-CETP-LPL) haplotypes associated with lipid metabolism, carriers of the H26 haplotype (23-TT-CG-AG-CC) exhibit the lowest cholesterol levels, and patients with the H104 haplotype (44-CC-CC-AA-CC) are severely hypercholesterolemic. Furthermore, APOE, NOS3, ACE, AGT, and CYP variants influence the therapeutic response to hypotensive drugs in AD patients with hypertension. Consequently, the implementation of pharmacogenetic procedures may optimize therapeutics in AD patients under polypharmacy regimes for the treatment of concomitant vascular disorders. PMID:29301387

Pharmacogenetics of Vascular Risk Factors in Alzheimer's Disease.

PubMed

Cacabelos, Ramón; Meyyazhagan, Arun; Carril, Juan C; Cacabelos, Pablo; Teijido, Óscar

2018-01-03

Alzheimer's disease (AD) is a polygenic/complex disorder in which genomic, epigenomic, cerebrovascular, metabolic, and environmental factors converge to define a progressive neurodegenerative phenotype. Pharmacogenetics is a major determinant of therapeutic outcome in AD. Different categories of genes are potentially involved in the pharmacogenetic network responsible for drug efficacy and safety, including pathogenic, mechanistic, metabolic, transporter, and pleiotropic genes. However, most drugs exert pleiotropic effects that are promiscuously regulated for different gene products. Only 20% of the Caucasian population are extensive metabolizers for tetragenic haplotypes integrating CYP2D6-CYP2C19-CYP2C9-CYP3A4/5 variants. Patients harboring CYP-related poor (PM) and/or ultra-rapid (UM) geno-phenotypes display more irregular profiles in drug metabolism than extensive (EM) or intermediate (IM) metabolizers. Among 111 pentagenic ( APOE-APOB-APOC3-CETP-LPL ) haplotypes associated with lipid metabolism, carriers of the H26 haplotype (23-TT-CG-AG-CC) exhibit the lowest cholesterol levels, and patients with the H104 haplotype (44-CC-CC-AA-CC) are severely hypercholesterolemic. Furthermore, APOE , NOS3 , ACE , AGT , and CYP variants influence the therapeutic response to hypotensive drugs in AD patients with hypertension. Consequently, the implementation of pharmacogenetic procedures may optimize therapeutics in AD patients under polypharmacy regimes for the treatment of concomitant vascular disorders.

Metabolomics Approach Reveals Integrated Metabolic Network Associated with Serotonin Deficiency

NASA Astrophysics Data System (ADS)

Weng, Rui; Shen, Sensen; Tian, Yonglu; Burton, Casey; Xu, Xinyuan; Liu, Yi; Chang, Cuilan; Bai, Yu; Liu, Huwei

2015-07-01

Serotonin is an important neurotransmitter that broadly participates in various biological processes. While serotonin deficiency has been associated with multiple pathological conditions such as depression, schizophrenia, Alzheimer’s disease and Parkinson’s disease, the serotonin-dependent mechanisms remain poorly understood. This study therefore aimed to identify novel biomarkers and metabolic pathways perturbed by serotonin deficiency using metabolomics approach in order to gain new metabolic insights into the serotonin deficiency-related molecular mechanisms. Serotonin deficiency was achieved through pharmacological inhibition of tryptophan hydroxylase (Tph) using p-chlorophenylalanine (pCPA) or genetic knockout of the neuronal specific Tph2 isoform. This dual approach improved specificity for the serotonin deficiency-associated biomarkers while minimizing nonspecific effects of pCPA treatment or Tph2 knockout (Tph2-/-). Non-targeted metabolic profiling and a targeted pCPA dose-response study identified 21 biomarkers in the pCPA-treated mice while 17 metabolites in the Tph2-/- mice were found to be significantly altered compared with the control mice. These newly identified biomarkers were associated with amino acid, energy, purine, lipid and gut microflora metabolisms. Oxidative stress was also found to be significantly increased in the serotonin deficient mice. These new biomarkers and the overall metabolic pathways may provide new understanding for the serotonin deficiency-associated mechanisms under multiple pathological states.

Identification of regulatory network hubs that control lipid metabolism in Chlamydomonas reinhardtii

DOE PAGES

Gargouri, Mahmoud; Park, Jeong -Jin; Holguin, F. Omar; ...

2015-05-28

Microalgae-based biofuels are promising sources of alternative energy, but improvements throughout the production process are required to establish them as economically feasible. One of the most influential improvements would be a significant increase in lipid yields, which could be achieved by altering the regulation of lipid biosynthesis and accumulation. Chlamydomonas reinhardtii accumulates oil (triacylglycerols, TAG) in response to nitrogen (N) deprivation. Although a few important regulatory genes have been identified that are involved in controlling this process, a global understanding of the larger regulatory network has not been developed. In order to uncover this network in this species, a combinedmore » omics (transcriptomic, proteomic and metabolomic) analysis was applied to cells grown in a time course experiment after a shift from N-replete to N-depleted conditions. Changes in transcript and protein levels of 414 predicted transcription factors (TFs) and transcriptional regulators (TRs) were monitored relative to other genes. The TF and TR genes were thus classified by two separate measures: up-regulated versus down-regulated and early response versus late response relative to two phases of polar lipid synthesis (before and after TAG biosynthesis initiation). Lipidomic and primary metabolite profiling generated compound accumulation levels that were integrated with the transcript dataset and TF profiling to produce a transcriptional regulatory network. In conclusion, evaluation of this proposed regulatory network led to the identification of several regulatory hubs that control many aspects of cellular metabolism, from N assimilation and metabolism, to central metabolism, photosynthesis and lipid metabolism.« less