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Sample records for bacterial metabolic networks

  1. Identifying essential genes in bacterial metabolic networks with machine learning methods

    Directory of Open Access Journals (Sweden)

    Eils Roland

    2010-05-01

    Full Text Available Abstract Background Identifying essential genes in bacteria supports to identify potential drug targets and an understanding of minimal requirements for a synthetic cell. However, experimentally assaying the essentiality of their coding genes is resource intensive and not feasible for all bacterial organisms, in particular if they are infective. Results We developed a machine learning technique to identify essential genes using the experimental data of genome-wide knock-out screens from one bacterial organism to infer essential genes of another related bacterial organism. We used a broad variety of topological features, sequence characteristics and co-expression properties potentially associated with essentiality, such as flux deviations, centrality, codon frequencies of the sequences, co-regulation and phyletic retention. An organism-wise cross-validation on bacterial species yielded reliable results with good accuracies (area under the receiver-operator-curve of 75% - 81%. Finally, it was applied to drug target predictions for Salmonella typhimurium. We compared our predictions to the viability of experimental knock-outs of S. typhimurium and identified 35 enzymes, which are highly relevant to be considered as potential drug targets. Specifically, we detected promising drug targets in the non-mevalonate pathway. Conclusions Using elaborated features characterizing network topology, sequence information and microarray data enables to predict essential genes from a bacterial reference organism to a related query organism without any knowledge about the essentiality of genes of the query organism. In general, such a method is beneficial for inferring drug targets when experimental data about genome-wide knockout screens is not available for the investigated organism.

  2. Attractor metabolic networks.

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    Ildefonso M De la Fuente

    Full Text Available 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

  3. Prebiotic metabolic networks?

    OpenAIRE

    2014-01-01

    A prebiotic origin of metabolism has been proposed as one of several scenarios for the origin of life. In their recent work, Ralser and colleagues (Keller et al, 2014) observe an enzyme‐free, metabolism‐like reaction network under conditions reproducing a possible prebiotic environment.

  4. Dye Fluorescence Analysis from Bacterial Metabolism.

    Science.gov (United States)

    1984-04-01

    M were reported for the cell-free extracts of the cultured mouse lymphoma cells mentioned above and an in vitAo solution of porcine pancreas lipase ...fluorescence Fluorescent product Diacetyl fluorescein Lipase Bacterial metabolism 20. ABTRACT fCauhw a o de dif rNooeel md ~Id1)fp by block number) A...nonfluorescing dye is metabolized intracel- lularly by an organism through an enzyme-specific reaction . This produces a fluorescent product which when

  5. Impact of genome reduction on bacterial metabolism and its regulation.

    Science.gov (United States)

    Yus, Eva; Maier, Tobias; Michalodimitrakis, Konstantinos; van Noort, Vera; Yamada, Takuji; Chen, Wei-Hua; Wodke, Judith A H; Güell, Marc; Martínez, Sira; Bourgeois, Ronan; Kühner, Sebastian; Raineri, Emanuele; Letunic, Ivica; Kalinina, Olga V; Rode, Michaela; Herrmann, Richard; Gutiérrez-Gallego, Ricardo; Russell, Robert B; Gavin, Anne-Claude; Bork, Peer; Serrano, Luis

    2009-11-27

    To understand basic principles of bacterial metabolism organization and regulation, but also the impact of genome size, we systematically studied one of the smallest bacteria, Mycoplasma pneumoniae. A manually curated metabolic network of 189 reactions catalyzed by 129 enzymes allowed the design of a defined, minimal medium with 19 essential nutrients. More than 1300 growth curves were recorded in the presence of various nutrient concentrations. Measurements of biomass indicators, metabolites, and 13C-glucose experiments provided information on directionality, fluxes, and energetics; integration with transcription profiling enabled the global analysis of metabolic regulation. Compared with more complex bacteria, the M. pneumoniae metabolic network has a more linear topology and contains a higher fraction of multifunctional enzymes; general features such as metabolite concentrations, cellular energetics, adaptability, and global gene expression responses are similar, however.

  6. Metabolic aspects of bacterial persisters

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    Marcel ePrax

    2014-10-01

    Full Text Available Persister cells form a multi-drug tolerant subpopulation within an isogenic culture of bacteria that are genetically susceptible to antibiotics. Studies with different Gram negative and Gram positive bacteria have identified a large number of genes associated with the persister state. In contrast, the revelation of persister metabolism has only been addressed recently. We here summarize metabolic aspects of persisters, which includes an overview about the bifunctional role of selected carbohydrates as both triggers for the exit from the drug tolerant state and metabolites which persisters feed on. Also alarmones as indicators for starvation have been shown to influence persister levels via different signaling cascades involving the activation of toxin-antitoxin systems and other regulatory factors. Finally, recent data obtained by 13C-isotopologue profiling demonstrated an active amino acid anabolism in Staphylococcus aureus cultures challenged with high drug concentrations. Understanding the metabolism of persister cells poses challenges but also paves the way for the development of anti-persister compounds.

  7. The role of metabolism in bacterial persistence

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    Stephanie M. Amato

    2014-03-01

    Full Text Available Bacterial persisters are phenotypic variants with extraordinary tolerances toward antibiotics. Persister survival has been attributed to inhibition of essential cell functions during antibiotic stress, followed by reversal of the process and resumption of growth upon removal of the antibiotic. Metabolism plays a critical role in this process, since it participates in the entry, maintenance, and exit from the persister phenotype. Here, we review the experimental evidence that demonstrates the importance of metabolism to persistence, highlight the successes and potential for targeting metabolism in the search for anti-persister therapies, and discuss the current methods and challenges to understand persister physiology.

  8. Genetic and computational identification of a conserved bacterial metabolic module.

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    Cara C Boutte

    2008-12-01

    Full Text Available We have experimentally and computationally defined a set of genes that form a conserved metabolic module in the alpha-proteobacterium Caulobacter crescentus and used this module to illustrate a schema for the propagation of pathway-level annotation across bacterial genera. Applying comprehensive forward and reverse genetic methods and genome-wide transcriptional analysis, we (1 confirmed the presence of genes involved in catabolism of the abundant environmental sugar myo-inositol, (2 defined an operon encoding an ABC-family myo-inositol transmembrane transporter, and (3 identified a novel myo-inositol regulator protein and cis-acting regulatory motif that control expression of genes in this metabolic module. Despite being encoded from non-contiguous loci on the C. crescentus chromosome, these myo-inositol catabolic enzymes and transporter proteins form a tightly linked functional group in a computationally inferred network of protein associations. Primary sequence comparison was not sufficient to confidently extend annotation of all components of this novel metabolic module to related bacterial genera. Consequently, we implemented the Graemlin multiple-network alignment algorithm to generate cross-species predictions of genes involved in myo-inositol transport and catabolism in other alpha-proteobacteria. Although the chromosomal organization of genes in this functional module varied between species, the upstream regions of genes in this aligned network were enriched for the same palindromic cis-regulatory motif identified experimentally in C. crescentus. Transposon disruption of the operon encoding the computationally predicted ABC myo-inositol transporter of Sinorhizobium meliloti abolished growth on myo-inositol as the sole carbon source, confirming our cross-genera functional prediction. Thus, we have defined regulatory, transport, and catabolic genes and a cis-acting regulatory sequence that form a conserved module required for myo

  9. Metabolism links bacterial biofilms and colon carcinogenesis.

    Science.gov (United States)

    Johnson, Caroline H; Dejea, Christine M; Edler, David; Hoang, Linh T; Santidrian, Antonio F; Felding, Brunhilde H; Ivanisevic, Julijana; Cho, Kevin; Wick, Elizabeth C; Hechenbleikner, Elizabeth M; Uritboonthai, Winnie; Goetz, Laura; Casero, Robert A; Pardoll, Drew M; White, James R; Patti, Gary J; Sears, Cynthia L; Siuzdak, Gary

    2015-06-02

    Bacterial biofilms in the colon alter the host tissue microenvironment. A role for biofilms in colon cancer metabolism has been suggested but to date has not been evaluated. Using metabolomics, we investigated the metabolic influence that microbial biofilms have on colon tissues and the related occurrence of cancer. Patient-matched colon cancers and histologically normal tissues, with or without biofilms, were examined. We show the upregulation of polyamine metabolites in tissues from cancer hosts with significant enhancement of N(1), N(12)-diacetylspermine in both biofilm-positive cancer and normal tissues. Antibiotic treatment, which cleared biofilms, decreased N(1), N(12)-diacetylspermine levels to those seen in biofilm-negative tissues, indicating that host cancer and bacterial biofilm structures contribute to the polyamine metabolite pool. These results show that colonic mucosal biofilms alter the cancer metabolome to produce a regulator of cellular proliferation and colon cancer growth potentially affecting cancer development and progression.

  10. Metabolism links bacterial biofilms and colon carcinogenesis

    Science.gov (United States)

    Johnson, Caroline H.; Dejea, Christine M.; Edler, David; Hoang, Linh T.; Santidrian, Antonio F.; Felding, Brunhilde H.; Cho, Kevin; Wick, Elizabeth C.; Hechenbleikner, Elizabeth M.; Uritboonthai, Winnie; Goetz, Laura; Casero, Robert A.; Pardoll, Drew M.; White, James R.; Patti, Gary J.; Sears, Cynthia L.; Siuzdak, Gary

    2015-01-01

    SUMMARY Bacterial biofilms in the colon alter the host tissue microenvironment. A role for biofilms in colon cancer metabolism has been suggested but to date has not been evaluated. Using metabolomics, we investigated the metabolic influence that microbial biofilms have on colon tissues and the related occurrence of cancer. Patient-matched colon cancers and histologically normal tissues, with or without biofilms, were examined. We show the upregulation of polyamine metabolites in tissues from cancer hosts with significant enhancement of N1, N12-diacetylspermine in both biofilm positive cancer and normal tissues. Antibiotic treatment, which cleared biofilms, decreased N1, N12-diacetylspermine levels to those seen in biofilm negative tissues, indicating that host cancer and bacterial biofilm structures contribute to the polyamine metabolite pool. These results show that colonic mucosal biofilms alter the cancer metabolome, to produce a regulator of cellular proliferation and colon cancer growth potentially affecting cancer development and progression. PMID:25959674

  11. Context-dependent metabolic networks

    CERN Document Server

    Beguerisse-Díaz, Mariano; Oyarzún, Diego; Picó, Jesús; Barahona, Mauricio

    2016-01-01

    Cells adapt their metabolism to survive changes in their environment. We present a framework for the construction and analysis of metabolic reaction networks that can be tailored to reflect different environmental conditions. Using context-dependent flux distributions from Flux Balance Analysis (FBA), we produce directed networks with weighted links representing the amount of metabolite flowing from a source reaction to a target reaction per unit time. Such networks are analyzed with tools from network theory to reveal salient features of metabolite flows in each biological context. We illustrate our approach with the directed network of the central carbon metabolism of Escherichia coli, and study its properties in four relevant biological scenarios. Our results show that both flow and network structure depend drastically on the environment: networks produced from the same metabolic model in different contexts have different edges, components, and flow communities, capturing the biological re-routing of metab...

  12. Optimal flux patterns in cellular metabolic networks

    Energy Technology Data Exchange (ETDEWEB)

    Almaas, E

    2007-01-20

    The availability of whole-cell level metabolic networks of high quality has made it possible to develop a predictive understanding of bacterial metabolism. Using the optimization framework of flux balance analysis, I investigate metabolic response and activity patterns to variations in the availability of nutrient and chemical factors such as oxygen and ammonia by simulating 30,000 random cellular environments. The distribution of reaction fluxes is heavy-tailed for the bacteria H. pylori and E. coli, and the eukaryote S. cerevisiae. While the majority of flux balance investigations have relied on implementations of the simplex method, it is necessary to use interior-point optimization algorithms to adequately characterize the full range of activity patterns on metabolic networks. The interior-point activity pattern is bimodal for E. coli and S. cerevisiae, suggesting that most metabolic reaction are either in frequent use or are rarely active. The trimodal activity pattern of H. pylori indicates that a group of its metabolic reactions (20%) are active in approximately half of the simulated environments. Constructing the high-flux backbone of the network for every environment, there is a clear trend that the more frequently a reaction is active, the more likely it is a part of the backbone. Finally, I briefly discuss the predicted activity patterns of the central-carbon metabolic pathways for the sample of random environments.

  13. Complex networks theory for analyzing metabolic networks

    Institute of Scientific and Technical Information of China (English)

    ZHAO Jing; YU Hong; LUO Jianhua; CAO Z.W.; LI Yixue

    2006-01-01

    One of the main tasks of post-genomic informatics is to systematically investigate all molecules and their interactions within a living cell so as to understand how these molecules and the interactions between them relate to the function of the organism,while networks are appropriate abstract description of all kinds of interactions. In the past few years, great achievement has been made in developing theory of complex networks for revealing the organizing principles that govern the formation and evolution of various complex biological, technological and social networks. This paper reviews the accomplishments in constructing genome-based metabolic networks and describes how the theory of complex networks is applied to analyze metabolic networks.

  14. Evolution of metabolic network organization

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    Bonchev Danail

    2010-05-01

    Full Text Available Abstract Background Comparison of metabolic networks across species is a key to understanding how evolutionary pressures shape these networks. By selecting taxa representative of different lineages or lifestyles and using a comprehensive set of descriptors of the structure and complexity of their metabolic networks, one can highlight both qualitative and quantitative differences in the metabolic organization of species subject to distinct evolutionary paths or environmental constraints. Results We used a novel representation of metabolic networks, termed network of interacting pathways or NIP, to focus on the modular, high-level organization of the metabolic capabilities of the cell. Using machine learning techniques we identified the most relevant aspects of cellular organization that change under evolutionary pressures. We considered the transitions from prokarya to eukarya (with a focus on the transitions among the archaea, bacteria and eukarya, from unicellular to multicellular eukarya, from free living to host-associated bacteria, from anaerobic to aerobic, as well as the acquisition of cell motility or growth in an environment of various levels of salinity or temperature. Intuitively, we expect organisms with more complex lifestyles to have more complex and robust metabolic networks. Here we demonstrate for the first time that such organisms are not only characterized by larger, denser networks of metabolic pathways but also have more efficiently organized cross communications, as revealed by subtle changes in network topology. These changes are unevenly distributed among metabolic pathways, with specific categories of pathways being promoted to more central locations as an answer to environmental constraints. Conclusions Combining methods from graph theory and machine learning, we have shown here that evolutionary pressures not only affects gene and protein sequences, but also specific details of the complex wiring of functional modules

  15. Metabolic networks: beyond the graph.

    Science.gov (United States)

    Bernal, Andrés; Daza, Edgar

    2011-06-01

    Drugs are devised to enter into the metabolism of an organism in order to produce a desired effect. From the chemical point of view, cellular metabolism is constituted by a complex network of reactions transforming metabolites one in each other. Knowledge on the structure of this network could help to develop novel methods for drug design, and to comprehend the root of known unexpected side effects. Many large-scale studies on the structure of metabolic networks have been developed following models based on different kinds of graphs as the fundamental image of the reaction network. Graphs models, however, comport wrong assumptions regarding the structure of reaction networks that may lead into wrong conclusions if they are not taken into account. In this article we critically review some graph-theoretical approaches to the analysis of centrality, vulnerability and modularity of metabolic networks, analyzing their limitations in estimating these key network properties, consider some proposals explicit or implicitly based on directed hypergraphs regarding their ability to overcome these issues, and review some recent implementation improvements that make the application of these models in increasingly large networks a viable option.

  16. Carbon and phosphorus regulating bacterial metabolism in oligotrophic boreal lakes

    DEFF Research Database (Denmark)

    Vidal, L. O.; Graneli, W.; Daniel, C. B.;

    2011-01-01

    -P and glucose-C alone or in combination (0.01 and 0.3 mg L(-1), respectively) was added to 1.0 mu m filtered lake water and incubated in darkness at 20 degrees C. Additions of glucose (C) and phosphorus (P) alone did not lead to changes in the rates of bacterial metabolic processes, whereas bacterial...... respiration and bacterial production responded positively to C + P enrichment for most of the lakes sampled. Bacterial growth efficiency showed a wide range (2.5-28.7%) and low mean value (12%). These variations were not correlated with the DOC concentration. Our results show that heterotrophic bacterial...

  17. Arginine Metabolism in Bacterial Pathogenesis and Cancer Therapy

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    Lifeng Xiong

    2016-03-01

    Full Text Available Antibacterial resistance to infectious diseases is a significant global concern for health care organizations; along with aging populations and increasing cancer rates, it represents a great burden for government healthcare systems. Therefore, the development of therapies against bacterial infection and cancer is an important strategy for healthcare research. Pathogenic bacteria and cancer have developed a broad range of sophisticated strategies to survive or propagate inside a host and cause infection or spread disease. Bacteria can employ their own metabolism pathways to obtain nutrients from the host cells in order to survive. Similarly, cancer cells can dysregulate normal human cell metabolic pathways so that they can grow and spread. One common feature of the adaption and disruption of metabolic pathways observed in bacterial and cancer cell growth is amino acid pathways; these have recently been targeted as a novel approach to manage bacterial infections and cancer therapy. In particular, arginine metabolism has been illustrated to be important not only for bacterial pathogenesis but also for cancer therapy. Therefore, greater insights into arginine metabolism of pathogenic bacteria and cancer cells would provide possible targets for controlling of bacterial infection and cancer treatment. This review will summarize the recent progress on the relationship of arginine metabolism with bacterial pathogenesis and cancer therapy, with a particular focus on arginase and arginine deiminase pathways of arginine catabolism.

  18. Bacterial microcompartments and the modular construction of microbial metabolism.

    Science.gov (United States)

    Kerfeld, Cheryl A; Erbilgin, Onur

    2015-01-01

    Bacterial microcompartments (BMCs) are protein-bound organelles predicted to be present across 23 bacterial phyla. BMCs facilitate carbon fixation as well as the aerobic and anaerobic catabolism of a variety of organic compounds. These functions have been linked to ecological nutrient cycling, symbiosis, pathogenesis, and cardiovascular disease. Within bacterial cells, BMCs are metabolic modules that can be further dissociated into their constituent structural and functional protein domains. Viewing BMCs as genetic, structural, functional, and evolutionary modules provides a framework for understanding both BMC-mediated metabolism and for adapting their architectures for applications in synthetic biology.

  19. You Are What You Eat: Metabolic Control of Bacterial Division.

    Science.gov (United States)

    Monahan, Leigh G; Harry, Elizabeth J

    2016-03-01

    Fluctuations in nutrient availability are a fact of life for bacterial cells in the 'wild'. To survive and compete, bacteria must rapidly modulate cell-cycle processes to accommodate changing nutritional conditions and concomitant changes in cell growth. Our understanding of how this is achieved has been transformed in recent years, with cellular metabolism emerging as a central player. Several metabolic enzymes, in addition to their normal catalytic functions, have been shown to directly modulate cell-cycle processes in response to changing nutrient levels. Here we focus on cell division, the final event in the bacterial cell cycle, and discuss recent compelling evidence connecting division regulation to nutritional status and metabolic activity.

  20. Profiling metabolic networks to study cancer metabolism.

    Science.gov (United States)

    Hiller, Karsten; Metallo, Christian M

    2013-02-01

    Cancer is a disease of unregulated cell growth and survival, and tumors reprogram biochemical pathways to aid these processes. New capabilities in the computational and bioanalytical characterization of metabolism have now emerged, facilitating the identification of unique metabolic dependencies that arise in specific cancers. By understanding the metabolic phenotype of cancers as a function of their oncogenic profiles, metabolic engineering may be applied to design synthetically lethal therapies for some tumors. This process begins with accurate measurement of metabolic fluxes. Here we review advanced methods of quantifying pathway activity and highlight specific examples where these approaches have uncovered potential opportunities for therapeutic intervention.

  1. Uncovering transcriptional regulation of metabolism by using metabolic network topology

    DEFF Research Database (Denmark)

    Patil, Kiran Raosaheb; Nielsen, Jens

    2005-01-01

    therefore developed an algorithm that is based on hypothesis-driven data analysis to uncover the transcriptional regulatory architecture of metabolic networks. By using information on the metabolic network topology from genome-scale metabolic reconstruction, we show that it is possible to reveal patterns...... in the metabolic network that follow a common transcriptional response. Thus, the algorithm enables identification of so-called reporter metabolites (metabolites around which the most significant transcriptional changes occur) and a set of connected genes with significant and coordinated response to genetic...... changes induced by complex regulatory mechanisms coordinating the activity of different metabolic pathways. It is difficult to map such global transcriptional responses by using traditional methods, because many genes in the metabolic network have relatively small changes at their transcription level. We...

  2. Chemical basis of metabolic network organization.

    Directory of Open Access Journals (Sweden)

    Qiang Zhu

    2011-10-01

    Full Text Available Although the metabolic networks of the three domains of life consist of different constituents and metabolic pathways, they exhibit the same scale-free organization. This phenomenon has been hypothetically explained by preferential attachment principle that the new-recruited metabolites attach preferentially to those that are already well connected. However, since metabolites are usually small molecules and metabolic processes are basically chemical reactions, we speculate that the metabolic network organization may have a chemical basis. In this paper, chemoinformatic analyses on metabolic networks of Kyoto Encyclopedia of Genes and Genomes (KEGG, Escherichia coli and Saccharomyces cerevisiae were performed. It was found that there exist qualitative and quantitative correlations between network topology and chemical properties of metabolites. The metabolites with larger degrees of connectivity (hubs are of relatively stronger polarity. This suggests that metabolic networks are chemically organized to a certain extent, which was further elucidated in terms of high concentrations required by metabolic hubs to drive a variety of reactions. This finding not only provides a chemical explanation to the preferential attachment principle for metabolic network expansion, but also has important implications for metabolic network design and metabolite concentration prediction.

  3. Role of apolipoprotein CI in lipid metabolism and bacterial sepsis

    NARCIS (Netherlands)

    Berbée, Jimmy Fransiscus Paulus

    2007-01-01

    The research described in this thesis focussed on the role of apolipoproteins in lipid metabolism, inflammation and bacterial sepsis, with specific emphasis on apoCI. From studies in human APOC1¬-transgenic and apoc1-/- mice, we were able to identify apoCI as a potent inhibitor of triglyceride hydro

  4. Bacterial adaptation through distributed sensing of metabolic fluxes

    NARCIS (Netherlands)

    Kotte, Oliver; Zaugg, Judith B.; Heinemann, Matthias

    2010-01-01

    The recognition of carbon sources and the regulatory adjustments to recognized changes are of particular importance for bacterial survival in fluctuating environments. Despite a thorough knowledge base of Escherichia coli’s central metabolism and its regulation, fundamental aspects of the employed s

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

    Science.gov (United States)

    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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Henry, Christopher S. [Division of Mathematics and Computer Science, Argonne National Laboratory, Argonne Illinois; Computation Institute, University of Chicago, Chicago Illinois; Bernstein, Hans C. [Biodetection Sciences, National Security Directorate, Pacific Northwest National Laboratory Richland Washington; Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland Washington; The Gene and Linda Voiland School of Chemical Engineering and Bioengineering, Washington State University, Pullman Washington; Weisenhorn, Pamela [Division of Mathematics and Computer Science, Argonne National Laboratory, Argonne Illinois; Division of Biosciences, Argonne National Laboratory, Argonne Illinois; Taylor, Ronald C. [Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland Washington; Lee, Joon-Yong [Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland Washington; Zucker, Jeremy [Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland Washington; Song, Hyun-Seob [Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland Washington

    2016-06-02

    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 network 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.

  7. Predicting effects of structural stress in a genome-reduced model bacterial metabolism

    Science.gov (United States)

    Güell, Oriol; Sagués, Francesc; Serrano, M. Ángeles

    2012-08-01

    Mycoplasma pneumoniae is a human pathogen recently proposed as a genome-reduced model for bacterial systems biology. Here, we study the response of its metabolic network to different forms of structural stress, including removal of individual and pairs of reactions and knockout of genes and clusters of co-expressed genes. Our results reveal a network architecture as robust as that of other model bacteria regarding multiple failures, although less robust against individual reaction inactivation. Interestingly, metabolite motifs associated to reactions can predict the propagation of inactivation cascades and damage amplification effects arising in double knockouts. We also detect a significant correlation between gene essentiality and damages produced by single gene knockouts, and find that genes controlling high-damage reactions tend to be expressed independently of each other, a functional switch mechanism that, simultaneously, acts as a genetic firewall to protect metabolism. Prediction of failure propagation is crucial for metabolic engineering or disease treatment.

  8. A network perspective on metabolic inconsistency

    Directory of Open Access Journals (Sweden)

    Sonnenschein Nikolaus

    2012-05-01

    Full Text Available Abstract Background Integrating gene expression profiles and metabolic pathways under different experimental conditions is essential for understanding the coherence of these two layers of cellular organization. The network character of metabolic systems can be instrumental in developing concepts of agreement between expression data and pathways. A network-driven interpretation of gene expression data has the potential of suggesting novel classifiers for pathological cellular states and of contributing to a general theoretical understanding of gene regulation. Results Here, we analyze the coherence of gene expression patterns and a reconstruction of human metabolism, using consistency scores obtained from network and constraint-based analysis methods. We find a surprisingly strong correlation between the two measures, demonstrating that a substantial part of inconsistencies between metabolic processes and gene expression can be understood from a network perspective alone. Prompted by this finding, we investigate the topological context of the individual biochemical reactions responsible for the observed inconsistencies. On this basis, we are able to separate the differential contributions that bear physiological information about the system, from the unspecific contributions that unravel gaps in the metabolic reconstruction. We demonstrate the biological potential of our network-driven approach by analyzing transcriptome profiles of aldosterone producing adenomas that have been obtained from a cohort of Primary Aldosteronism patients. We unravel systematics in the data that could not have been resolved by conventional microarray data analysis. In particular, we discover two distinct metabolic states in the adenoma expression patterns. Conclusions The methodology presented here can help understand metabolic inconsistencies from a network perspective. It thus serves as a mediator between the topology of metabolic systems and their dynamical

  9. Flux-dependent graphs for metabolic networks

    OpenAIRE

    Beguerisse-Díaz, Mariano; Bosque, Gabriel; Oyarzún, Diego; Picó, Jesús; Barahona, Mauricio

    2016-01-01

    Cells adapt their metabolic fluxes in response to changes in the environment. We present a systematic flux-based framework for the construction of graphs to represent organism-wide metabolic networks. Our graphs encode the directionality of metabolic fluxes via links that represent the flow of metabolites from source to target reactions. The methodology can be applied in the absence of a specific biological context by modelling fluxes as probabilities, or tailored to different environmental c...

  10. Dispersal networks for enhancing bacterial degradation in heterogeneous environments

    Energy Technology Data Exchange (ETDEWEB)

    Banitz, Thomas, E-mail: thomas.banitz@ufz.de [Department of Ecological Modelling, UFZ - Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig (Germany); Wick, Lukas Y.; Fetzer, Ingo [Department of Environmental Microbiology, UFZ - Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig (Germany); Frank, Karin [Department of Ecological Modelling, UFZ - Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig (Germany); Harms, Hauke [Department of Environmental Microbiology, UFZ - Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig (Germany); Johst, Karin [Department of Ecological Modelling, UFZ - Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig (Germany)

    2011-10-15

    Successful biodegradation of organic soil pollutants depends on their bioavailability to catabolically active microorganisms. In particular, environmental heterogeneities often limit bacterial access to pollutants. Experimental and modelling studies revealed that fungal networks can facilitate bacterial dispersal and may thereby improve pollutant bioavailability. Here, we investigate the influence of such bacterial dispersal networks on biodegradation performance under spatially heterogeneous abiotic conditions using a process-based simulation model. To match typical situations in polluted soils, two types of abiotic conditions are studied: heterogeneous bacterial dispersal conditions and heterogeneous initial resource distributions. The model predicts that networks facilitating bacterial dispersal can enhance biodegradation performance for a wide range of these conditions. Additionally, the time horizon over which this performance is assessed and the network's spatial configuration are key factors determining the degree of biodegradation improvement. Our results support the idea of stimulating the establishment of fungal mycelia for enhanced bioremediation of polluted soils. - Highlights: > Bacterial dispersal networks can considerably improve biodegradation performance. > They facilitate bacterial access to dispersal-limited areas and remote resources. > Abiotic conditions, time horizon and network structure govern the improvements. > Stimulating the establishment of fungal mycelia promises enhanced soil remediation. - Simulation modelling demonstrates that fungus-mediated bacterial dispersal can considerably improve the bioavailability of organic pollutants under spatially heterogeneous abiotic conditions typical for water-unsaturated soils.

  11. Computational Methods for Modification of Metabolic Networks

    Directory of Open Access Journals (Sweden)

    Takeyuki Tamura

    2015-01-01

    Full Text Available In metabolic engineering, modification of metabolic networks is an important biotechnology and a challenging computational task. In the metabolic network modification, we should modify metabolic networks by newly adding enzymes or/and knocking-out genes to maximize the biomass production with minimum side-effect. In this mini-review, we briefly review constraint-based formalizations for Minimum Reaction Cut (MRC problem where the minimum set of reactions is deleted so that the target compound becomes non-producible from the view point of the flux balance analysis (FBA, elementary mode (EM, and Boolean models. Minimum Reaction Insertion (MRI problem where the minimum set of reactions is added so that the target compound newly becomes producible is also explained with a similar formalization approach. The relation between the accuracy of the models and the risk of overfitting is also discussed.

  12. Bacterial molecular networks: bridging the gap between functional genomics and dynamical modelling.

    Science.gov (United States)

    van Helden, Jacques; Toussaint, Ariane; Thieffry, Denis

    2012-01-01

    This introductory review synthesizes the contents of the volume Bacterial Molecular Networks of the series Methods in Molecular Biology. This volume gathers 9 reviews and 16 method chapters describing computational protocols for the analysis of metabolic pathways, protein interaction networks, and regulatory networks. Each protocol is documented by concrete case studies dedicated to model bacteria or interacting populations. Altogether, the chapters provide a representative overview of state-of-the-art methods for data integration and retrieval, network visualization, graph analysis, and dynamical modelling.

  13. Bacterial community structure and predicted alginate metabolic pathway in an alginate-degrading bacterial consortium.

    Science.gov (United States)

    Kita, Akihisa; Miura, Toyokazu; Kawata, Satoshi; Yamaguchi, Takeshi; Okamura, Yoshiko; Aki, Tsunehiro; Matsumura, Yukihiko; Tajima, Takahisa; Kato, Junichi; Nishio, Naomichi; Nakashimada, Yutaka

    2016-03-01

    Methane fermentation is one of the effective approaches for utilization of brown algae; however, this process is limited by the microbial capability to degrade alginate, a main polysaccharide found in these algae. Despite its potential, little is known about anaerobic microbial degradation of alginate. Here we constructed a bacterial consortium able to anaerobically degrade alginate. Taxonomic classification of 16S rRNA gene, based on high-throughput sequencing data, revealed that this consortium included two dominant strains, designated HUA-1 and HUA-2; these strains were related to Clostridiaceae bacterium SK082 (99%) and Dysgonomonas capnocytophagoides (95%), respectively. Alginate lyase activity and metagenomic analyses, based on high-throughput sequencing data, revealed that this bacterial consortium possessed putative genes related to a predicted alginate metabolic pathway. However, HUA-1 and 2 did not grow on agar medium with alginate by using roll-tube method, suggesting the existence of bacterial interactions like symbiosis for anaerobic alginate degradation.

  14. Metabolic flux analysis of Gluconacetobacter xylinus for bacterial cellulose production.

    Science.gov (United States)

    Zhong, Cheng; Zhang, Gui-Cai; Liu, Miao; Zheng, Xin-Tong; Han, Pei-Pei; Jia, Shi-Ru

    2013-07-01

    Metabolic flux analysis was used to reveal the metabolic distributions in Gluconacetobacter xylinus (CGMCC no. 2955) cultured on different carbon sources. Compared with other sources, glucose, fructose, and glycerol could achieve much higher bacterial cellulose (BC) yields from G. xylinus (CGMCC no. 2955). The glycerol led to the highest BC production with a metabolic yield of 14.7 g/mol C, which was approximately 1.69-fold and 2.38-fold greater than that produced using fructose and glucose medium, respectively. The highest BC productivity from G. xylinus CGMCC 2955 was 5.97 g BC/L (dry weight) when using glycerol as the sole carbon source. Metabolic flux analysis for the central carbon metabolism revealed that about 47.96 % of glycerol was transformed into BC, while only 19.05 % of glucose and 24.78 % of fructose were transformed into BC. Instead, when glucose was used as the sole carbon source, 40.03 % of glucose was turned into the by-product gluconic acid. Compared with BC from glucose and fructose, BC from the glycerol medium showed the highest tensile strength at 83.5 MPa, with thinner fibers and lower porosity. As a main byproduct of biodiesel production, glycerol holds great potential to produce BC with superior mechanical and microstructural characteristics.

  15. First mass spectrometry metabolic fingerprinting of bacterial metabolism in a model cheese.

    Science.gov (United States)

    Le Boucher, C; Courant, F; Jeanson, S; Chereau, S; Maillard, M-B; Royer, A-L; Thierry, A; Dervilly-Pinel, G; Le Bizec, B; Lortal, S

    2013-11-15

    Metabolic fingerprinting is an untargeted approach which has not yet been undertaken to investigate cheese. This study is a proof of concept, concerning the ability of mass spectrometry (MS) metabolic fingerprinting to investigate modifications induced by bacterial metabolism in cheese over time. An ultrafiltrated milk concentrate was used to manufacture model cheeses inoculated with Lactococcus lactis LD61. Metabolic fingerprints were acquired after 0, 8 and 48h from two different fractions of the metabolome: the water-soluble fraction using liquid chromatography-high resolution-MS and a volatile fraction using gas chromatography-MS. Metabolic fingerprints differed significantly over time. Forty-five metabolites were identified, including well-known cheese metabolites, such as 12 amino acids and 25 volatile metabolites, and less studied ones, such as four vitamins, uric acid, creatine and l-carnitine. These results showed the relevance of cheese MS fingerprinting to generate new findings and to detect even slight differences between two conditions.

  16. Some metabolic effects of bacterial endotoxins in salmonid fishes

    Science.gov (United States)

    Wedemeyer, G.A.; Ross, A.J.; Smith, L.

    1968-01-01

    Coho salmon (Oncorhynchus kisutch) and rainbow trout (Salmo gairdneri) were highly resistant to endotoxins from both Escherichia coli and Aeromonas salmonicida (a fish pathogen) at 14 and 18 C.This resistance was investigated with liver tryptophan pyrrolase, liver glycogen depletion in vitro, and the arterial blood pressure as indicators. Liver glycogen depletion was accelerated by both endotoxins, but there was no significant cardiovascular response or effect on liver tryptophan pyrrolase activity. Since the cardiovascular effects of histamine were also limited, it was concluded that the metabolic effects of bacterial endotoxins in salmonids are qualitatively different from those of the higher vertebrates.

  17. Basic concepts and principles of stoichiometric modeling of metabolic networks

    NARCIS (Netherlands)

    Maarleveld, T.R.; Khandelwal, R.A.; Olivier, B.E.; Teusink, B.; Bruggeman, F.J.

    2013-01-01

    Metabolic networks supply the energy and building blocks for cell growth and maintenance. Cells continuously rewire their metabolic networks in response to changes in environmental conditions to sustain fitness. Studies of the systemic properties of metabolic networks give insight into metabolic pla

  18. Metabolic engineering of Arabidopsis for butanetriol production using bacterial genes.

    Science.gov (United States)

    Abdel-Ghany, Salah E; Day, Irene; Heuberger, Adam L; Broeckling, Corey D; Reddy, Anireddy S N

    2013-11-01

    1,2,4-butanetriol (butanetriol) is a useful precursor for the synthesis of the energetic material butanetriol trinitrate and several pharmaceutical compounds. Bacterial synthesis of butanetriol from xylose or arabinose takes place in a pathway that requires four enzymes. To produce butanetriol in plants by expressing bacterial enzymes, we cloned native bacterial or codon optimized synthetic genes under different promoters into a binary vector and stably transformed Arabidopsis plants. Transgenic lines expressing introduced genes were analyzed for the production of butanetriol using gas chromatography coupled to mass spectrometry (GC-MS). Soil-grown transgenic plants expressing these genes produced up to 20 µg/g of butanetriol. To test if an exogenous supply of pentose sugar precursors would enhance the butanetriol level, transgenic plants were grown in a medium supplemented with either xylose or arabinose and the amount of butanetriol was quantified. Plants expressing synthetic genes in the arabinose pathway showed up to a forty-fold increase in butanetriol levels after arabinose was added to the medium. Transgenic plants expressing either bacterial or synthetic xylose pathways, or the arabinose pathway showed toxicity symptoms when xylose or arabinose was added to the medium, suggesting that a by-product in the pathway or butanetriol affected plant growth. Furthermore, the metabolite profile of plants expressing arabinose and xylose pathways was altered. Our results demonstrate that bacterial pathways that produce butanetriol can be engineered into plants to produce this chemical. This proof-of-concept study for phytoproduction of butanetriol paves the way to further manipulate metabolic pathways in plants to enhance the level of butanetriol production.

  19. Metabolic network alignment in large scale by network compression

    Directory of Open Access Journals (Sweden)

    Ay Ferhat

    2012-03-01

    Full Text Available Abstract Metabolic network alignment is a system scale comparative analysis that discovers important similarities and differences across different metabolisms and organisms. Although the problem of aligning metabolic networks has been considered in the past, the computational complexity of the existing solutions has so far limited their use to moderately sized networks. In this paper, we address the problem of aligning two metabolic networks, particularly when both of them are too large to be dealt with using existing methods. We develop a generic framework that can significantly improve the scale of the networks that can be aligned in practical time. Our framework has three major phases, namely the compression phase, the alignment phase and the refinement phase. For the first phase, we develop an algorithm which transforms the given networks to a compressed domain where they are summarized using fewer nodes, termed supernodes, and interactions. In the second phase, we carry out the alignment in the compressed domain using an existing network alignment method as our base algorithm. This alignment results in supernode mappings in the compressed domain, each of which are smaller instances of network alignment problem. In the third phase, we solve each of the instances using the base alignment algorithm to refine the alignment results. We provide a user defined parameter to control the number of compression levels which generally determines the tradeoff between the quality of the alignment versus how fast the algorithm runs. Our experiments on the networks from KEGG pathway database demonstrate that the compression method we propose reduces the sizes of metabolic networks by almost half at each compression level which provides an expected speedup of more than an order of magnitude. We also observe that the alignments obtained by only one level of compression capture the original alignment results with high accuracy. Together, these suggest that our

  20. Dissecting Germ Cell Metabolism through Network Modeling.

    Directory of Open Access Journals (Sweden)

    Leanne S Whitmore

    Full Text Available Metabolic pathways are increasingly postulated to be vital in programming cell fate, including stemness, differentiation, proliferation, and apoptosis. The commitment to meiosis is a critical fate decision for mammalian germ cells, and requires a metabolic derivative of vitamin A, retinoic acid (RA. Recent evidence showed that a pulse of RA is generated in the testis of male mice thereby triggering meiotic commitment. However, enzymes and reactions that regulate this RA pulse have yet to be identified. We developed a mouse germ cell-specific metabolic network with a curated vitamin A pathway. Using this network, we implemented flux balance analysis throughout the initial wave of spermatogenesis to elucidate important reactions and enzymes for the generation and degradation of RA. Our results indicate that primary RA sources in the germ cell include RA import from the extracellular region, release of RA from binding proteins, and metabolism of retinal to RA. Further, in silico knockouts of genes and reactions in the vitamin A pathway predict that deletion of Lipe, hormone-sensitive lipase, disrupts the RA pulse thereby causing spermatogenic defects. Examination of other metabolic pathways reveals that the citric acid cycle is the most active pathway. In addition, we discover that fatty acid synthesis/oxidation are the primary energy sources in the germ cell. In summary, this study predicts enzymes, reactions, and pathways important for germ cell commitment to meiosis. These findings enhance our understanding of the metabolic control of germ cell differentiation and will help guide future experiments to improve reproductive health.

  1. Metabolic Complementation in Bacterial Communities: Necessary Conditions and Optimality

    Science.gov (United States)

    Mori, Matteo; Ponce-de-León, Miguel; Peretó, Juli; Montero, Francisco

    2016-01-01

    Bacterial communities may display metabolic complementation, in which different members of the association partially contribute to the same biosynthetic pathway. In this way, the end product of the pathway is synthesized by the community as a whole. However, the emergence and the benefits of such complementation are poorly understood. Herein, we present a simple model to analyze the metabolic interactions among bacteria, including the host in the case of endosymbiotic bacteria. The model considers two cell populations, with both cell types encoding for the same linear biosynthetic pathway. We have found that, for metabolic complementation to emerge as an optimal strategy, both product inhibition and large permeabilities are needed. In the light of these results, we then consider the patterns found in the case of tryptophan biosynthesis in the endosymbiont consortium hosted by the aphid Cinara cedri. Using in-silico computed physicochemical properties of metabolites of this and other biosynthetic pathways, we verified that the splitting point of the pathway corresponds to the most permeable intermediate. PMID:27774085

  2. Kinetic analysis of complex metabolic networks

    Energy Technology Data Exchange (ETDEWEB)

    Stephanopoulos, G. [MIT, Cambridge, MA (United States)

    1996-12-31

    A new methodology is presented for the analysis of complex metabolic networks with the goal of metabolite overproduction. The objective is to locate a small number of reaction steps in a network that have maximum impact on network flux amplification and whose rate can also be increased without functional network derangement. This method extends the concepts of Metabolic Control Analysis to groups of reactions and offers the means for calculating group control coefficients as measures of the control exercised by groups of reactions on the overall network fluxes and intracellular metabolite pools. It is further demonstrated that the optimal strategy for the effective increase of network fluxes, while maintaining an uninterrupted supply of intermediate metabolites, is through the coordinated amplification of multiple (as opposed to a single) reaction steps. Satisfying this requirement invokes the concept of the concentration control to coefficient, which emerges as a critical parameter in the identification of feasible enzymatic modifications with maximal impact on the network flux. A case study of aromatic aminoacid production is provided to illustrate these concepts.

  3. On Functional Module Detection in Metabolic Networks

    Directory of Open Access Journals (Sweden)

    Ina Koch

    2013-08-01

    Full Text Available 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.

  4. Metabolic activity of bacterial cells enumerated by direct viable count

    Energy Technology Data Exchange (ETDEWEB)

    Roszak, D.B.; Colwell, R.R.

    1987-12-01

    The direct viable count (DVC) method was modified by incorporating radiolabeled substrates in microautoradiographic analyses to assess bacterial survival in controlled laboratory microcosms. The DVC method, which permits enumeration of culturable and nonculturable cells, discriminates those cells that are responsive to added nutrients but in which division is inhibited by the addition of nalidixic acid. The resulting elongated cells represent all viable cells; this includes those that are culturable on routine media and those that are not. Escherichia coli and Salmonella enteritidis were employed in the microcosm studies, and radiolabeled substrates included (methyl-tritium thymidine or (Uranium-Carbon 14) glutamic acid. Samples taken at selected intervals during the survival experiments were examined by epifluorescence microscopy to enumerate cells by the DVC and acridine orange direct count methods, as well as by culture methods. Good correlation was obtained for cell-associated metabolic activity, measured by microautoradiography and substrate responsiveness (by the DVC method) at various stages of survival. Of the cells responsive to nutrients by the DVC method, ca 90% were metabolically active by the microautoradiographic method. No significant difference was observed between DVC enumerations with or without added radiolabeled substrate.

  5. Complex network perspective on structure and function of Staphylococcus aureus metabolic network

    Indian Academy of Sciences (India)

    L Ying; D W Ding

    2013-02-01

    With remarkable advances in reconstruction of genome-scale metabolic networks, uncovering complex network structure and function from these networks is becoming one of the most important topics in system biology. This work aims at studying the structure and function of Staphylococcus aureus (S. aureus) metabolic network by complex network methods. We first generated a metabolite graph from the recently reconstructed high-quality S. aureus metabolic network model. Then, based on `bow tie' structure character, we explain and discuss the global structure of S. aureus metabolic network. The functional significance, global structural properties, modularity and centrality analysis of giant strong component in S. aureus metabolic networks are studied.

  6. Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network

    DEFF Research Database (Denmark)

    Förster, Jochen; Famili, I.; Fu, P.;

    2003-01-01

    The metabolic network in the yeast Saccharomyces cerevisiae was reconstructed using currently available genomic, biochemical, and physiological information. The metabolic reactions were compartmentalized between the cytosol and the mitochondria, and transport steps between the compartments...... containing 1175 metabolic reactions and 584 metabolites. The number of gene functions included in the reconstructed network corresponds to similar to16% of all characterized ORFs in S. cerevisiae. Using the reconstructed network, the metabolic capabilities of S. cerevisiae were calculated and compared...

  7. Human Metabolic Network: Reconstruction, Simulation, and Applications in Systems Biology

    Science.gov (United States)

    Wu, Ming; Chan, Christina

    2012-01-01

    Metabolism is crucial to cell growth and proliferation. Deficiency or alterations in metabolic functions are known to be involved in many human diseases. Therefore, understanding the human metabolic system is important for the study and treatment of complex diseases. Current reconstructions of the global human metabolic network provide a computational platform to integrate genome-scale information on metabolism. The platform enables a systematic study of the regulation and is applicable to a wide variety of cases, wherein one could rely on in silico perturbations to predict novel targets, interpret systemic effects, and identify alterations in the metabolic states to better understand the genotype-phenotype relationships. In this review, we describe the reconstruction of the human metabolic network, introduce the constraint based modeling approach to analyze metabolic networks, and discuss systems biology applications to study human physiology and pathology. We highlight the challenges and opportunities in network reconstruction and systems modeling of the human metabolic system. PMID:24957377

  8. The topology of metabolic isotope labeling networks

    Directory of Open Access Journals (Sweden)

    Wiechert Wolfgang

    2007-08-01

    Full Text Available Abstract Background Metabolic Flux Analysis (MFA based on isotope labeling experiments (ILEs is a widely established tool for determining fluxes in metabolic pathways. Isotope labeling networks (ILNs contain all essential information required to describe the flow of labeled material in an ILE. Whereas recent experimental progress paves the way for high-throughput MFA, large network investigations and exact statistical methods, these developments are still limited by the poor performance of computational routines used for the evaluation and design of ILEs. In this context, the global analysis of ILN topology turns out to be a clue for realizing large speedup factors in all required computational procedures. Results With a strong focus on the speedup of algorithms the topology of ILNs is investigated using graph theoretic concepts and algorithms. A rigorous determination of all cyclic and isomorphic subnetworks, accompanied by the global analysis of ILN connectivity is performed. Particularly, it is proven that ILNs always brake up into a large number of small strongly connected components (SCCs and, moreover, there are natural isomorphisms between many of these SCCs. All presented techniques are universal, i.e. they do not require special assumptions on the network structure, bidirectionality of fluxes, measurement configuration, or label input. The general results are exemplified with a practically relevant metabolic network which describes the central metabolism of E. coli comprising 10390 isotopomer pools. Conclusion Exploiting the topological features of ILNs leads to a significant speedup of all universal algorithms for ILE evaluation. It is proven in theory and exemplified with the E. coli example that a speedup factor of about 1000 compared to standard algorithms is achieved. This widely opens the door for new high performance algorithms suitable for high throughput applications and large ILNs. Moreover, for the first time the global

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

    Science.gov (United States)

    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.

  10. Steady states and stability in metabolic networks without regulation.

    Science.gov (United States)

    Ivanov, Oleksandr; van der Schaft, Arjan; Weissing, Franz J

    2016-07-21

    Metabolic networks are often extremely complex. Despite intensive efforts many details of these networks, e.g., exact kinetic rates and parameters of metabolic reactions, are not known, making it difficult to derive their properties. Considerable effort has been made to develop theory about properties of steady states in metabolic networks that are valid for any values of parameters. General results on uniqueness of steady states and their stability have been derived with specific assumptions on reaction kinetics, stoichiometry and network topology. For example, deep results have been obtained under the assumptions of mass-action reaction kinetics, continuous flow stirred tank reactors (CFSTR), concordant reaction networks and others. Nevertheless, a general theory about properties of steady states in metabolic networks is still missing. Here we make a step further in the quest for such a theory. Specifically, we study properties of steady states in metabolic networks with monotonic kinetics in relation to their stoichiometry (simple and general) and the number of metabolites participating in every reaction (single or many). Our approach is based on the investigation of properties of the Jacobian matrix. We show that stoichiometry, network topology, and the number of metabolites that participate in every reaction have a large influence on the number of steady states and their stability in metabolic networks. Specifically, metabolic networks with single-substrate-single-product reactions have disconnected steady states, whereas in metabolic networks with multiple-substrates-multiple-product reactions manifolds of steady states arise. Metabolic networks with simple stoichiometry have either a unique globally asymptotically stable steady state or asymptotically stable manifolds of steady states. In metabolic networks with general stoichiometry the steady states are not always stable and we provide conditions for their stability. In order to demonstrate the biological

  11. A guide to integrating transcriptional regulatory and metabolic networks using PROM (probabilistic regulation of metabolism).

    Science.gov (United States)

    Simeonidis, Evangelos; Chandrasekaran, Sriram; Price, Nathan D

    2013-01-01

    The integration of transcriptional regulatory and metabolic networks is a crucial step in the process of predicting metabolic behaviors that emerge from either genetic or environmental changes. Here, we present a guide to PROM (probabilistic regulation of metabolism), an automated method for the construction and simulation of integrated metabolic and transcriptional regulatory networks that enables large-scale phenotypic predictions for a wide range of model organisms.

  12. Evolution of Intra-specific Regulatory Networks in a Multipartite Bacterial Genome.

    Directory of Open Access Journals (Sweden)

    Marco Galardini

    2015-09-01

    Full Text Available Reconstruction of the regulatory network is an important step in understanding how organisms control the expression of gene products and therefore phenotypes. Recent studies have pointed out the importance of regulatory network plasticity in bacterial adaptation and evolution. The evolution of such networks within and outside the species boundary is however still obscure. Sinorhizobium meliloti is an ideal species for such study, having three large replicons, many genomes available and a significant knowledge of its transcription factors (TF. Each replicon has a specific functional and evolutionary mark; which might also emerge from the analysis of their regulatory signatures. Here we have studied the plasticity of the regulatory network within and outside the S. meliloti species, looking for the presence of 41 TFs binding motifs in 51 strains and 5 related rhizobial species. We have detected a preference of several TFs for one of the three replicons, and the function of regulated genes was found to be in accordance with the overall replicon functional signature: house-keeping functions for the chromosome, metabolism for the chromid, symbiosis for the megaplasmid. This therefore suggests a replicon-specific wiring of the regulatory network in the S. meliloti species. At the same time a significant part of the predicted regulatory network is shared between the chromosome and the chromid, thus adding an additional layer by which the chromid integrates itself in the core genome. Furthermore, the regulatory network distance was found to be correlated with both promoter regions and accessory genome evolution inside the species, indicating that both pangenome compartments are involved in the regulatory network evolution. We also observed that genes which are not included in the species regulatory network are more likely to belong to the accessory genome, indicating that regulatory interactions should also be considered to predict gene conservation in

  13. Evolution of Intra-specific Regulatory Networks in a Multipartite Bacterial Genome.

    Science.gov (United States)

    Galardini, Marco; Brilli, Matteo; Spini, Giulia; Rossi, Matteo; Roncaglia, Bianca; Bani, Alessia; Chiancianesi, Manuela; Moretto, Marco; Engelen, Kristof; Bacci, Giovanni; Pini, Francesco; Biondi, Emanuele G; Bazzicalupo, Marco; Mengoni, Alessio

    2015-09-01

    Reconstruction of the regulatory network is an important step in understanding how organisms control the expression of gene products and therefore phenotypes. Recent studies have pointed out the importance of regulatory network plasticity in bacterial adaptation and evolution. The evolution of such networks within and outside the species boundary is however still obscure. Sinorhizobium meliloti is an ideal species for such study, having three large replicons, many genomes available and a significant knowledge of its transcription factors (TF). Each replicon has a specific functional and evolutionary mark; which might also emerge from the analysis of their regulatory signatures. Here we have studied the plasticity of the regulatory network within and outside the S. meliloti species, looking for the presence of 41 TFs binding motifs in 51 strains and 5 related rhizobial species. We have detected a preference of several TFs for one of the three replicons, and the function of regulated genes was found to be in accordance with the overall replicon functional signature: house-keeping functions for the chromosome, metabolism for the chromid, symbiosis for the megaplasmid. This therefore suggests a replicon-specific wiring of the regulatory network in the S. meliloti species. At the same time a significant part of the predicted regulatory network is shared between the chromosome and the chromid, thus adding an additional layer by which the chromid integrates itself in the core genome. Furthermore, the regulatory network distance was found to be correlated with both promoter regions and accessory genome evolution inside the species, indicating that both pangenome compartments are involved in the regulatory network evolution. We also observed that genes which are not included in the species regulatory network are more likely to belong to the accessory genome, indicating that regulatory interactions should also be considered to predict gene conservation in bacterial

  14. Differential producibility analysis (DPA) of transcriptomic data with metabolic networks: deconstructing the metabolic response of M. tuberculosis.

    Science.gov (United States)

    Bonde, Bhushan K; Beste, Dany J V; Laing, Emma; Kierzek, Andrzej M; McFadden, Johnjoe

    2011-06-01

    A general paucity of knowledge about the metabolic state of Mycobacterium tuberculosis within the host environment is a major factor impeding development of novel drugs against tuberculosis. Current experimental methods do not allow direct determination of the global metabolic state of a bacterial pathogen in vivo, but the transcriptional activity of all encoded genes has been investigated in numerous microarray studies. We describe a novel algorithm, Differential Producibility Analysis (DPA) that uses a metabolic network to extract metabolic signals from transcriptome data. The method utilizes Flux Balance Analysis (FBA) to identify the set of genes that affect the ability to produce each metabolite in the network. Subsequently, Rank Product Analysis is used to identify those metabolites predicted to be most affected by a transcriptional signal. We first apply DPA to investigate the metabolic response of E. coli to both anaerobic growth and inactivation of the FNR global regulator. DPA successfully extracts metabolic signals that correspond to experimental data and provides novel metabolic insights. We next apply DPA to investigate the metabolic response of M. tuberculosis to the macrophage environment, human sputum and a range of in vitro environmental perturbations. The analysis revealed a previously unrecognized feature of the response of M. tuberculosis to the macrophage environment: a down-regulation of genes influencing metabolites in central metabolism and concomitant up-regulation of genes that influence synthesis of cell wall components and virulence factors. DPA suggests that a significant feature of the response of the tubercle bacillus to the intracellular environment is a channeling of resources towards remodeling of its cell envelope, possibly in preparation for attack by host defenses. DPA may be used to unravel the mechanisms of virulence and persistence of M. tuberculosis and other pathogens and may have general application for extracting

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

    Science.gov (United States)

    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.

  16. Biofilm Formation Mechanisms of Pseudomonas aeruginosa Predicted via Genome-Scale Kinetic Models of Bacterial Metabolism.

    Science.gov (United States)

    Vital-Lopez, Francisco G; Reifman, Jaques; Wallqvist, Anders

    2015-10-01

    A hallmark of Pseudomonas aeruginosa is its ability to establish biofilm-based infections that are difficult to eradicate. Biofilms are less susceptible to host inflammatory and immune responses and have higher antibiotic tolerance than free-living planktonic cells. Developing treatments against biofilms requires an understanding of bacterial biofilm-specific physiological traits. Research efforts have started to elucidate the intricate mechanisms underlying biofilm development. However, many aspects of these mechanisms are still poorly understood. Here, we addressed questions regarding biofilm metabolism using a genome-scale kinetic model of the P. aeruginosa metabolic network and gene expression profiles. Specifically, we computed metabolite concentration differences between known mutants with altered biofilm formation and the wild-type strain to predict drug targets against P. aeruginosa biofilms. We also simulated the altered metabolism driven by gene expression changes between biofilm and stationary growth-phase planktonic cultures. Our analysis suggests that the synthesis of important biofilm-related molecules, such as the quorum-sensing molecule Pseudomonas quinolone signal and the exopolysaccharide Psl, is regulated not only through the expression of genes in their own synthesis pathway, but also through the biofilm-specific expression of genes in pathways competing for precursors to these molecules. Finally, we investigated why mutants defective in anthranilate degradation have an impaired ability to form biofilms. Alternative to a previous hypothesis that this biofilm reduction is caused by a decrease in energy production, we proposed that the dysregulation of the synthesis of secondary metabolites derived from anthranilate and chorismate is what impaired the biofilms of these mutants. Notably, these insights generated through our kinetic model-based approach are not accessible from previous constraint-based model analyses of P. aeruginosa biofilm

  17. Cerebral blood flow and metabolism in adults with acute bacterial meningitis

    DEFF Research Database (Denmark)

    Møller, Kirsten

    2007-01-01

    The intense intrathecal inflammation observed in acute bacterial meningitis (ABM) is associated with pronounced changes in cerebral blood flow (CBF) and metabolism. In seven substudies, CBF and metabolism were measured in adults with ABM as well as healthy volunteers during various interventions...

  18. Microbial diversity and metabolic networks in acid mine drainage habitats

    Directory of Open Access Journals (Sweden)

    Celia eMendez-Garcia

    2015-05-01

    Full Text Available Acid mine drainage (AMD emplacements are low-complexity natural systems. Low-pH conditions appear to be the main factor underlying the limited diversity of the microbial populations thriving in these environments, although temperature, ionic composition, total organic carbon and dissolved oxygen are also considered to significantly influence their microbial life. This natural reduction in diversity driven by extreme conditions was reflected in several studies on the microbial populations inhabiting the various micro-environments present in such ecosystems. Early studies based on the physiology of the autochthonous microbiota and the growing success of omics technologies have enabled a better understanding of microbial ecology and function in low-pH mine outflows; however, complementary omics-derived data should be included to completely describe their microbial ecology. Furthermore, recent updates on the distribution of eukaryotes and ultra-micro-archaea demand their inclusion in the microbial characterisation of AMD systems. In this review, we present a complete overview of the bacterial, archaeal (including ultra-micro-archaeal and eukaryotic diversity in these ecosystems and include a thorough depiction of the metabolism and element cycling in AMD habitats. We also review different metabolic network structures at the organismal level, which is necessary to disentangle the role of each member of the AMD communities described thus far.

  19. Steady states and stability in metabolic networks without regulation

    NARCIS (Netherlands)

    Ivanov, Oleksandr; van der Schaft, Arjan; Weissing, Franz J

    2016-01-01

    Metabolic networks are often extremely complex. Despite intensive efforts many details of these networks, e.g., exact kinetic rates and parameters of metabolic reactions, are not known, making it difficult to derive their properties. Considerable effort has been made to develop theory about properti

  20. Intestinal microbiota in metabolic diseases: from bacterial community structure and functions to species of pathophysiological relevance.

    Science.gov (United States)

    Clavel, Thomas; Desmarchelier, Charles; Haller, Dirk; Gérard, Philippe; Rohn, Sascha; Lepage, Patricia; Daniel, Hannelore

    2014-07-01

    The trillions of bacterial cells that colonize the mammalian digestive tract influence both host physiology and the fate of dietary compounds. Gnotobionts and fecal transplantation have been instrumental in revealing the causal role of intestinal bacteria in energy homeostasis and metabolic dysfunctions such as type-2 diabetes. However, the exact contribution of gut bacterial metabolism to host energy balance is still unclear and knowledge about underlying molecular mechanisms is scant. We have previously characterized cecal bacterial community functions and host responses in diet-induced obese mice using omics approaches. Based on these studies, we here discuss issues on the relevance of mouse models, give evidence that the metabolism of cholesterol-derived compounds by gut bacteria is of particular importance in the context of metabolic disorders and that dominant species of the family Coriobacteriaceae are good models to study these functions.

  1. Dynamic metabolic flux analysis--tools for probing transient states of metabolic networks.

    Science.gov (United States)

    Antoniewicz, Maciek R

    2013-12-01

    Computational approaches for analyzing dynamic states of metabolic networks provide a practical framework for design, control, and optimization of biotechnological processes. In recent years, two promising modeling approaches have emerged for characterizing transients in cellular metabolism, dynamic metabolic flux analysis (DMFA), and dynamic flux balance analysis (DFBA). Both approaches combine metabolic network analysis based on pseudo steady-state (PSS) assumption for intracellular metabolism with dynamic models for extracellular environment. One strategy to capture dynamics is by combining network analysis with a kinetic model. Predictive models are thus established that can be used to optimize bioprocessing conditions and identify useful genetic manipulations. Alternatively, by combining network analysis with methods for analyzing extracellular time-series data, transients in intracellular metabolic fluxes can be determined and applied for process monitoring and control.

  2. Hydrological pulse regulating the bacterial heterotrophic metabolism between Amazonian mainstems and floodplain lakes

    Directory of Open Access Journals (Sweden)

    Luciana Oliveira Vidal

    2015-09-01

    Full Text Available We evaluated in situ rates of bacterial carbon processing in Amazonian floodplain lakes and mainstems, during both high and low water phases (p < 0.05. Our results showed that Bacterial Production (BP was lower and more variable than Bacterial Respiration (BR, determined as total respiration. Bacterial Carbon Demand (BCD was mostly accounted by BR and presented the same pattern that BR in both water phases. Bacterial growth efficiency showed a wide range (0.2–23% and low mean value of 3 and 6 %, (in high and low water respectively suggesting that dissolved organic carbon (DOC was mostly allocated to catabolic metabolism. However, BGE was regulated by BP in low water phase. Consequently, changes in BGE showed the same pattern that BP. In addition, the hydrological pulse effects on mainstems and floodplains lakes connectivity were found for BP and BGE in low water. Multiple correlation analyses revealed that indexes of organic matter quality (chlorophyll-a, N stable isotopes and C/N ratios were the strongest seasonal drivers of bacterial carbon metabolism. Our work indicated that: (1 the bacterial metabolism was mostly driven by respiration in Amazonian aquatic ecosystems resulting in low BGE in either high and low water phase; (2 the hydrological pulse regulated

  3. Graph methods for the investigation of metabolic networks in parasitology.

    Science.gov (United States)

    Cottret, Ludovic; Jourdan, Fabien

    2010-08-01

    Recently, a way was opened with the development of many mathematical methods to model and analyze genome-scale metabolic networks. Among them, methods based on graph models enable to us quickly perform large-scale analyses on large metabolic networks. However, it could be difficult for parasitologists to select the graph model and methods adapted to their biological questions. In this review, after briefly addressing the problem of the metabolic network reconstruction, we propose an overview of the graph-based approaches used in whole metabolic network analyses. Applications highlight the usefulness of this kind of approach in the field of parasitology, especially by suggesting metabolic targets for new drugs. Their development still represents a major challenge to fight against the numerous diseases caused by parasites.

  4. Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli

    Science.gov (United States)

    De Martino, Daniele; Capuani, Fabrizio; De Martino, Andrea

    2016-06-01

    The solution space of genome-scale models of cellular metabolism provides a map between physically viable flux configurations and cellular metabolic phenotypes described, at the most basic level, by the corresponding growth rates. By sampling the solution space of E. coli's metabolic network, we show that empirical growth rate distributions recently obtained in experiments at single-cell resolution can be explained in terms of a trade-off between the higher fitness of fast-growing phenotypes and the higher entropy of slow-growing ones. Based on this, we propose a minimal model for the evolution of a large bacterial population that captures this trade-off. The scaling relationships observed in experiments encode, in such frameworks, for the same distance from the maximum achievable growth rate, the same degree of growth rate maximization, and/or the same rate of phenotypic change. Being grounded on genome-scale metabolic network reconstructions, these results allow for multiple implications and extensions in spite of the underlying conceptual simplicity.

  5. Cerebral blood flow, oxidative metabolism and cerebrovascular carbon dioxide reactivity in patients with acute bacterial meningitis

    DEFF Research Database (Denmark)

    Møller, Kirsten; Strauss, Gitte Irene; Thomsen, Gerda;

    2002-01-01

    BACKGROUND: The optimal arterial carbon dioxide tension (P(a)CO(2)) in patients with acute bacterial meningitis (ABM) is unknown and controversial. The objective of this study was to measure global cerebral blood flow (CBF), cerebrovascular CO(2) reactivity (CO(2)R), and cerebral metabolic rates...... to baseline ventilation, whereas CMR(glu) increased. CONCLUSION: In patients with acute bacterial meningitis, we found variable levels of CBF and cerebrovascular CO(2) reactivity, a low a-v DO(2), low cerebral metabolic rates of oxygen and glucose, and a cerebral lactate efflux. In these patients...

  6. Metabolic constraint-based refinement of transcriptional regulatory networks.

    Science.gov (United States)

    Chandrasekaran, Sriram; Price, Nathan D

    2013-01-01

    There is a strong need for computational frameworks that integrate different biological processes and data-types to unravel cellular regulation. Current efforts to reconstruct transcriptional regulatory networks (TRNs) focus primarily on proximal data such as gene co-expression and transcription factor (TF) binding. While such approaches enable rapid reconstruction of TRNs, the overwhelming combinatorics of possible networks limits identification of mechanistic regulatory interactions. Utilizing growth phenotypes and systems-level constraints to inform regulatory network reconstruction is an unmet challenge. We present our approach Gene Expression and Metabolism Integrated for Network Inference (GEMINI) that links a compendium of candidate regulatory interactions with the metabolic network to predict their systems-level effect on growth phenotypes. We then compare predictions with experimental phenotype data to select phenotype-consistent regulatory interactions. GEMINI makes use of the observation that only a small fraction of regulatory network states are compatible with a viable metabolic network, and outputs a regulatory network that is simultaneously consistent with the input genome-scale metabolic network model, gene expression data, and TF knockout phenotypes. GEMINI preferentially recalls gold-standard interactions (p-value = 10(-172)), significantly better than using gene expression alone. We applied GEMINI to create an integrated metabolic-regulatory network model for Saccharomyces cerevisiae involving 25,000 regulatory interactions controlling 1597 metabolic reactions. The model quantitatively predicts TF knockout phenotypes in new conditions (p-value = 10(-14)) and revealed potential condition-specific regulatory mechanisms. Our results suggest that a metabolic constraint-based approach can be successfully used to help reconstruct TRNs from high-throughput data, and highlights the potential of using a biochemically-detailed mechanistic framework to

  7. Global network reorganization during dynamic adaptations of Bacillus subtilis metabolism

    DEFF Research Database (Denmark)

    Buescher, Joerg Martin; Liebermeister, Wolfram; Jules, Matthieu

    2012-01-01

    Adaptation of cells to environmental changes requires dynamic interactions between metabolic and regulatory networks, but studies typically address only one or a few layers of regulation. For nutritional shifts between two preferred carbon sources of Bacillus subtilis, we combined statistical...

  8. Metabolic disease network and its implication for disease comorbidity

    Science.gov (United States)

    Lee, Deok-Sun; Oltvai, Zoltan; Christakis, Nicholas; Barabasi, Albert-Laszlo

    2008-03-01

    Given that most diseases are the result of the breakdown of some cellular processes, a key aim of modern medicine is to establish the relationship between disease phenotypes and the various disruptions in the underlying cellular networks. Here we show that our current understanding of the structure of the human metabolic network can provide insight into potential relationships among often distinct disease phenotypes. Using the known enzyme-disease associations, we construct a human metabolic disease network in which nodes are diseases and two diseases are linked if the enzymes associated with them catalyze adjacent metabolic reactions. We find that the more connected a disease is, the higher is its prevalence and the chance that it is associated with a high mortality. The results indicate that the cellular network-level relationships between metabolic pathways and the associated disease provide insights into disease comorbidity, with potential important consequences on disease diagnosis and prevention.

  9. Computing autocatalytic sets to unravel inconsistencies in metabolic network reconstructions

    DEFF Research Database (Denmark)

    Schmidt, R.; Waschina, S.; Boettger-Schmidt, D.

    2015-01-01

    by inherent inconsistencies and gaps. RESULTS: Here we present a novel method to validate metabolic network reconstructions based on the concept of autocatalytic sets. Autocatalytic sets correspond to collections of metabolites that, besides enzymes and a growth medium, are required to produce all biomass......MOTIVATION: Genome-scale metabolic network reconstructions have been established as a powerful tool for the prediction of cellular phenotypes and metabolic capabilities of organisms. In recent years, the number of network reconstructions has been constantly increasing, mostly because...... of the availability of novel (semi-)automated procedures, which enabled the reconstruction of metabolic models based on individual genomes and their annotation. The resulting models are widely used in numerous applications. However, the accuracy and predictive power of network reconstructions are commonly limited...

  10. Efficient searching and annotation of metabolic networks using chemical similarity

    OpenAIRE

    Pertusi, Dante A.; Stine, Andrew E.; Broadbelt, Linda J.; Keith E J Tyo

    2014-01-01

    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 ...

  11. Horizontal and vertical growth of S. cerevisiae metabolic network

    Directory of Open Access Journals (Sweden)

    Tramontano Anna

    2011-10-01

    Full Text Available Abstract Background The growth and development of a biological organism is reflected by its metabolic network, the evolution of which relies on the essential gene duplication mechanism. There are two current views about the evolution of metabolic networks. The retrograde model hypothesizes that a pathway evolves by recruiting novel enzymes in a direction opposite to the metabolic flow. The patchwork model is instead based on the assumption that the evolution is based on the exploitation of broad-specificity enzymes capable of catalysing a variety of metabolic reactions. Results We analysed a well-studied unicellular eukaryotic organism, S. cerevisiae, and studied the effect of the removal of paralogous gene products on its metabolic network. Our results, obtained using different paralog and network definitions, show that, after an initial period when gene duplication was indeed instrumental in expanding the metabolic space, the latter reached an equilibrium and subsequent gene duplications were used as a source of more specialized enzymes rather than as a source of novel reactions. We also show that the switch between the two evolutionary strategies in S. cerevisiae can be dated to about 350 million years ago. Conclusions Our data, obtained through a novel analysis methodology, strongly supports the hypothesis that the patchwork model better explains the more recent evolution of the S. cerevisiae metabolic network. Interestingly, the effects of a patchwork strategy acting before the Euascomycete-Hemiascomycete divergence are still detectable today.

  12. Horizontal and vertical growth of S. cerevisiae metabolic network.

    KAUST Repository

    Grassi, Luigi

    2011-10-14

    BACKGROUND: The growth and development of a biological organism is reflected by its metabolic network, the evolution of which relies on the essential gene duplication mechanism. There are two current views about the evolution of metabolic networks. The retrograde model hypothesizes that a pathway evolves by recruiting novel enzymes in a direction opposite to the metabolic flow. The patchwork model is instead based on the assumption that the evolution is based on the exploitation of broad-specificity enzymes capable of catalysing a variety of metabolic reactions. RESULTS: We analysed a well-studied unicellular eukaryotic organism, S. cerevisiae, and studied the effect of the removal of paralogous gene products on its metabolic network. Our results, obtained using different paralog and network definitions, show that, after an initial period when gene duplication was indeed instrumental in expanding the metabolic space, the latter reached an equilibrium and subsequent gene duplications were used as a source of more specialized enzymes rather than as a source of novel reactions. We also show that the switch between the two evolutionary strategies in S. cerevisiae can be dated to about 350 million years ago. CONCLUSIONS: Our data, obtained through a novel analysis methodology, strongly supports the hypothesis that the patchwork model better explains the more recent evolution of the S. cerevisiae metabolic network. Interestingly, the effects of a patchwork strategy acting before the Euascomycete-Hemiascomycete divergence are still detectable today.

  13. MIRAGE: a functional genomics-based approach for metabolic network model reconstruction and its application to cyanobacteria networks.

    Science.gov (United States)

    Vitkin, Edward; Shlomi, Tomer

    2012-11-29

    Genome-scale metabolic network reconstructions are considered a key step in quantifying the genotype-phenotype relationship. We present a novel gap-filling approach, MetabolIc Reconstruction via functionAl GEnomics (MIRAGE), which identifies missing network reactions by integrating metabolic flux analysis and functional genomics data. MIRAGE's performance is demonstrated on the reconstruction of metabolic network models of E. coli and Synechocystis sp. and validated via existing networks for these species. Then, it is applied to reconstruct genome-scale metabolic network models for 36 sequenced cyanobacteria amenable for constraint-based modeling analysis and specifically for metabolic engineering. The reconstructed network models are supplied via standard SBML files.

  14. Integration of metabolome data with metabolic networks reveals reporter reactions

    DEFF Research Database (Denmark)

    Çakir, Tunahan; Patil, Kiran Raosaheb; Önsan, Zeynep Ilsen;

    2006-01-01

    Interpreting quantitative metabolome data is a difficult task owing to the high connectivity in metabolic networks and inherent interdependency between enzymatic regulation, metabolite levels and fluxes. Here we present a hypothesis-driven algorithm for the integration of such data with metabolic...

  15. Estimating Metabolic Fluxes Using a Maximum Network Flexibility Paradigm

    NARCIS (Netherlands)

    Megchelenbrink, W.; Rossell, S.; Huynen, M.A.; Notebaart, R.A.; Marchiori, E.

    2015-01-01

    MOTIVATION: Genome-scale metabolic networks can be modeled in a constraint-based fashion. Reaction stoichiometry combined with flux capacity constraints determine the space of allowable reaction rates. This space is often large and a central challenge in metabolic modeling is finding the biologicall

  16. Slave nodes and the controllability of metabolic networks

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Dong-Hee; Motter, Adilson E [Department of Physics and Astronomy and Northwestern Institute on Complex Systems (NICO), Northwestern University, Evanston, IL 60208 (United States)], E-mail: dong.kim@tkk.fi, E-mail: motter@northwestern.edu

    2009-11-15

    Recent work on synthetic rescues has shown that the targeted deletion of specific metabolic genes can often be used to rescue otherwise non-viable mutants. This raises a fundamental biophysical question: to what extent can the whole-cell behavior of a large metabolic network be controlled by constraining the flux of one or more reactions in the network? This touches upon the issue of the number of degrees of freedom contained by one such network. Using the metabolic network of Escherichia coli as a model system, here we address this question theoretically by exploring not only reaction deletions, but also a continuum of all possible reaction expression levels. We show that the behavior of the metabolic network can be largely manipulated by the pinned expression of a single reaction. In particular, a relevant fraction of the metabolic reactions exhibits canalizing interactions, in that the specification of one reaction flux determines cellular growth as well as the fluxes of most other reactions in optimal steady states. The activity of individual reactions can thus be used as surrogates to monitor and possibly control cellular growth and other whole-cell behaviors. In addition to its implications for the study of control processes, our methodology provides a new approach to study how the integrated dynamics of the entire metabolic network emerges from the coordinated behavior of its component parts.

  17. Thiosulfate as a metabolic product: the bacterial fermentation of taurine.

    Science.gov (United States)

    Denger, K; Laue, H; Cook, A M

    1997-10-01

    Thiosulfate (S2O32-) is a natural product that is widely utilized in natural ecosystems as an electron sink or as an electron donor. However, the major biological source(s) of this thiosulfate is unknown. We present the first report that taurine (2-aminoethanesulfonate), the major mammalian solute, is subject to fermentation. This bacterial fermentation was found to be catalyzed by a new isolate, strain GKNTAU, a strictly anaerobic, gram-positive, motile rod that formed subterminal spores. Thiosulfate was a quantitative fermentation product. The other fermentation products were ammonia and acetate, and all could be formed by cell-free extracts.

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

    Science.gov (United States)

    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

  19. Dynamic Metabolic Modeling of Denitrifying Bacterial Growth: The Cybernetic Approach

    Energy Technology Data Exchange (ETDEWEB)

    Song, Hyun-Seob; Liu, Chongxuan

    2015-06-29

    Denitrification is a multistage reduction process converting nitrate ultimately to nitrogen gas, carried out mostly by facultative bacteria. Modeling of the denitrification process is challenging due to the complex metabolic regulation that modulates sequential formation and consumption of a series of nitrogen oxide intermediates, which serve as the final electron acceptors for denitrifying bacteria. In this work, we examined the effectiveness and accuracy of the cybernetic modeling framework in simulating the growth dynamics of denitrifying bacteria in comparison with kinetic models. In four different case studies using the literature data, we successfully simulated diauxic and triauxic growth patterns observed in anoxic and aerobic conditions, only by tuning two or three parameters. In order to understand the regulatory structure of the cybernetic model, we systematically analyzed the effect of cybernetic control variables on simulation accuracy. The results showed that the consideration of both enzyme synthesis and activity control through u- and v-variables is necessary and relevant and that uvariables are of greater importance in comparison to v-variables. In contrast, simple kinetic models were unable to accurately capture dynamic metabolic shifts across alternative electron acceptors, unless an inhibition term was additionally incorporated. Therefore, the denitrification process represents a reasonable example highlighting the criticality of considering dynamic regulation for successful metabolic modeling.

  20. Unified theory of bacterial sialometabolism: how and why bacteria metabolize host sialic acids.

    Science.gov (United States)

    Vimr, Eric R

    2013-01-01

    Sialic acids are structurally diverse nine-carbon ketosugars found mostly in humans and other animals as the terminal units on carbohydrate chains linked to proteins or lipids. The sialic acids function in cell-cell and cell-molecule interactions necessary for organismic development and homeostasis. They not only pose a barrier to microorganisms inhabiting or invading an animal mucosal surface, but also present a source of potential carbon, nitrogen, and cell wall metabolites necessary for bacterial colonization, persistence, growth, and, occasionally, disease. The explosion of microbial genomic sequencing projects reveals remarkable diversity in bacterial sialic acid metabolic potential. How bacteria exploit host sialic acids includes a surprisingly complex array of metabolic and regulatory capabilities that is just now entering a mature research stage. This paper attempts to describe the variety of bacterial sialometabolic systems by focusing on recent advances at the molecular and host-microbe-interaction levels. The hope is that this focus will provide a framework for further research that holds promise for better understanding of the metabolic interplay between bacterial growth and the host environment. An ability to modify or block this interplay has already yielded important new insights into potentially new therapeutic approaches for modifying or blocking bacterial colonization or infection.

  1. Bedside Evaluation of Cerebral Energy Metabolism in Severe Community-Acquired Bacterial Meningitis

    DEFF Research Database (Denmark)

    Rom Poulsen, Frantz; Schulz, Mette; Jacobsen, Anne;

    2015-01-01

    BACKGROUND: Mortality and morbidity have remained high in bacterial meningitis. Impairment of cerebral energy metabolism probably contributes to unfavorable outcome. Intracerebral microdialysis is routinely used to monitor cerebral energy metabolism, and recent experimental studies indicate...... that this technique may separate ischemia and non-ischemic mitochondrial dysfunction. The present study is a retrospective interpretation of biochemical data obtained in a series of patients with severe community-acquired meningitis. METHODS: Cerebral energy metabolism was monitored in 15 patients with severe...... community-acquired meningitis utilizing intracerebral microdialysis and bedside biochemical analysis. According to previous studies, cerebral ischemia was defined as lactate/pyruvate (LP) ratio >30 with intracerebral pyruvate level

  2. Recombinant bacterial hemoglobin alters metabolism of Aspergillus niger

    DEFF Research Database (Denmark)

    Hofmann, Gerald; Diano, Audrey; Nielsen, Jens

    2009-01-01

    The filamentous fungus Aspergillus niger is used extensively for the production of enzymes and organic acids. A major problem in industrial fermentations with this fungus is to ensure sufficient supply of oxygen required for respiratory metabolism of the fungus. In case of oxygen limitation...... behind the strong gpdA promoter from Aspergillus nidulans. Analysis of secreted metabolites, oxygen uptake, CO2 evolution and biomass formation points towards a relief of stress in the mutant expressing VHB when it is exposed to oxygen limitation. Our findings therefore point to an interesting strategy...

  3. "Clicking" on the lights to reveal bacterial social networking.

    Science.gov (United States)

    Clevenger, Kenneth D; Fast, Walter

    2012-03-05

    "No man is an island." With apologies to John Donne, the same could be said for a bacterium. The discovery of bacterial quorum sensing and its relevance to microbial ecology and pathogenesis have fueled the increasing scrutiny of the molecular mechanisms responsible for the apparent group behavior of microbes. A number of chemically diverse small molecules act as diffusible signaling molecules that regulate gene expression in a population-dependent manner. Some of these signals, such as the N-acyl-L-homoserine lactones, are produced and sensed by others in the same or closely related species, and other chemical classes of signals are used more broadly for interspecies and even interkingdom communication. As a field, the study of these microbial social networks has been termed "sociomicrobiology".

  4. Metabolic resting-state brain networks in health and disease.

    Science.gov (United States)

    Spetsieris, Phoebe G; Ko, Ji Hyun; Tang, Chris C; Nazem, Amir; Sako, Wataru; Peng, Shichun; Ma, Yilong; Dhawan, Vijay; Eidelberg, David

    2015-02-24

    The delineation of resting state networks (RSNs) in the human brain relies on the analysis of temporal fluctuations in functional MRI signal, representing a small fraction of total neuronal activity. Here, we used metabolic PET, which maps nonfluctuating signals related to total activity, to identify and validate reproducible RSN topographies in healthy and disease populations. In healthy subjects, the dominant (first component) metabolic RSN was topographically similar to the default mode network (DMN). In contrast, in Parkinson's disease (PD), this RSN was subordinated to an independent disease-related pattern. Network functionality was assessed by quantifying metabolic RSN expression in cerebral blood flow PET scans acquired at rest and during task performance. Consistent task-related deactivation of the "DMN-like" dominant metabolic RSN was observed in healthy subjects and early PD patients; in contrast, the subordinate RSNs were activated during task performance. Network deactivation was reduced in advanced PD; this abnormality was partially corrected by dopaminergic therapy. Time-course comparisons of DMN loss in longitudinal resting metabolic scans from PD and Alzheimer's disease subjects illustrated that significant reductions appeared later for PD, in parallel with the development of cognitive dysfunction. In contrast, in Alzheimer's disease significant reductions in network expression were already present at diagnosis, progressing over time. Metabolic imaging can directly provide useful information regarding the resting organization of the brain in health and disease.

  5. Metabolic network analysis-based identification of antimicrobial drug targets in category A bioterrorism agents.

    Directory of Open Access Journals (Sweden)

    Yong-Yeol Ahn

    Full Text Available The 2001 anthrax mail attacks in the United States demonstrated the potential threat of bioterrorism, hence driving the need to develop sophisticated treatment and diagnostic protocols to counter biological warfare. Here, by performing flux balance analyses on the fully-annotated metabolic networks of multiple, whole genome-sequenced bacterial strains, we have identified a large number of metabolic enzymes as potential drug targets for each of the three Category A-designated bioterrorism agents including Bacillus anthracis, Francisella tularensis and Yersinia pestis. Nine metabolic enzymes- belonging to the coenzyme A, folate, phosphatidyl-ethanolamine and nucleic acid pathways common to all strains across the three distinct genera were identified as targets. Antimicrobial agents against some of these enzymes are available. Thus, a combination of cross species-specific antibiotics and common antimicrobials against shared targets may represent a useful combinatorial therapeutic approach against all Category A bioterrorism agents.

  6. Metabolic network analysis-based identification of antimicrobial drug targets in category A bioterrorism agents.

    Science.gov (United States)

    Ahn, Yong-Yeol; Lee, Deok-Sun; Burd, Henry; Blank, William; Kapatral, Vinayak

    2014-01-01

    The 2001 anthrax mail attacks in the United States demonstrated the potential threat of bioterrorism, hence driving the need to develop sophisticated treatment and diagnostic protocols to counter biological warfare. Here, by performing flux balance analyses on the fully-annotated metabolic networks of multiple, whole genome-sequenced bacterial strains, we have identified a large number of metabolic enzymes as potential drug targets for each of the three Category A-designated bioterrorism agents including Bacillus anthracis, Francisella tularensis and Yersinia pestis. Nine metabolic enzymes- belonging to the coenzyme A, folate, phosphatidyl-ethanolamine and nucleic acid pathways common to all strains across the three distinct genera were identified as targets. Antimicrobial agents against some of these enzymes are available. Thus, a combination of cross species-specific antibiotics and common antimicrobials against shared targets may represent a useful combinatorial therapeutic approach against all Category A bioterrorism agents.

  7. An integrated text mining framework for metabolic interaction network reconstruction.

    Science.gov (United States)

    Patumcharoenpol, Preecha; Doungpan, Narumol; Meechai, Asawin; Shen, Bairong; Chan, Jonathan H; Vongsangnak, Wanwipa

    2016-01-01

    Text mining (TM) in the field of biology is fast becoming a routine analysis for the extraction and curation of biological entities (e.g., genes, proteins, simple chemicals) as well as their relationships. Due to the wide applicability of TM in situations involving complex relationships, it is valuable to apply TM to the extraction of metabolic interactions (i.e., enzyme and metabolite interactions) through metabolic events. Here we present an integrated TM framework containing two modules for the extraction of metabolic events (Metabolic Event Extraction module-MEE) and for the construction of a metabolic interaction network (Metabolic Interaction Network Reconstruction module-MINR). The proposed integrated TM framework performed well based on standard measures of recall, precision and F-score. Evaluation of the MEE module using the constructed Metabolic Entities (ME) corpus yielded F-scores of 59.15% and 48.59% for the detection of metabolic events for production and consumption, respectively. As for the testing of the entity tagger for Gene and Protein (GP) and metabolite with the test corpus, the obtained F-score was greater than 80% for the Superpathway of leucine, valine, and isoleucine biosynthesis. Mapping of enzyme and metabolite interactions through network reconstruction showed a fair performance for the MINR module on the test corpus with F-score >70%. Finally, an application of our integrated TM framework on a big-scale data (i.e., EcoCyc extraction data) for reconstructing a metabolic interaction network showed reasonable precisions at 69.93%, 70.63% and 46.71% for enzyme, metabolite and enzyme-metabolite interaction, respectively. This study presents the first open-source integrated TM framework for reconstructing a metabolic interaction network. This framework can be a powerful tool that helps biologists to extract metabolic events for further reconstruction of a metabolic interaction network. The ME corpus, test corpus, source code, and virtual

  8. An integrated text mining framework for metabolic interaction network reconstruction

    Directory of Open Access Journals (Sweden)

    Preecha Patumcharoenpol

    2016-03-01

    Full Text Available Text mining (TM in the field of biology is fast becoming a routine analysis for the extraction and curation of biological entities (e.g., genes, proteins, simple chemicals as well as their relationships. Due to the wide applicability of TM in situations involving complex relationships, it is valuable to apply TM to the extraction of metabolic interactions (i.e., enzyme and metabolite interactions through metabolic events. Here we present an integrated TM framework containing two modules for the extraction of metabolic events (Metabolic Event Extraction module—MEE and for the construction of a metabolic interaction network (Metabolic Interaction Network Reconstruction module—MINR. The proposed integrated TM framework performed well based on standard measures of recall, precision and F-score. Evaluation of the MEE module using the constructed Metabolic Entities (ME corpus yielded F-scores of 59.15% and 48.59% for the detection of metabolic events for production and consumption, respectively. As for the testing of the entity tagger for Gene and Protein (GP and metabolite with the test corpus, the obtained F-score was greater than 80% for the Superpathway of leucine, valine, and isoleucine biosynthesis. Mapping of enzyme and metabolite interactions through network reconstruction showed a fair performance for the MINR module on the test corpus with F-score >70%. Finally, an application of our integrated TM framework on a big-scale data (i.e., EcoCyc extraction data for reconstructing a metabolic interaction network showed reasonable precisions at 69.93%, 70.63% and 46.71% for enzyme, metabolite and enzyme–metabolite interaction, respectively. This study presents the first open-source integrated TM framework for reconstructing a metabolic interaction network. This framework can be a powerful tool that helps biologists to extract metabolic events for further reconstruction of a metabolic interaction network. The ME corpus, test corpus, source

  9. Flux analysis in plant metabolic networks: increasing throughput and coverage.

    Science.gov (United States)

    Junker, Björn H

    2014-04-01

    Quantitative information about metabolic networks has been mainly obtained at the level of metabolite contents, transcript abundance, and enzyme activities. However, the active process of metabolism is represented by the flow of matter through the pathways. These metabolic fluxes can be predicted by Flux Balance Analysis or determined experimentally by (13)C-Metabolic Flux Analysis. These relatively complicated and time-consuming methods have recently seen significant improvements at the level of coverage and throughput. Metabolic models have developed from single cell models into whole-organism dynamic models. Advances in lab automation and data handling have significantly increased the throughput of flux measurements. This review summarizes advances to increase coverage and throughput of metabolic flux analysis in plants.

  10. Signatures of arithmetic simplicity in metabolic network architecture.

    Directory of Open Access Journals (Sweden)

    William J Riehl

    2010-04-01

    Full Text Available Metabolic networks perform some of the most fundamental functions in living cells, including energy transduction and building block biosynthesis. While these are the best characterized networks in living systems, understanding their evolutionary history and complex wiring constitutes one of the most fascinating open questions in biology, intimately related to the enigma of life's origin itself. Is the evolution of metabolism subject to general principles, beyond the unpredictable accumulation of multiple historical accidents? Here we search for such principles by applying to an artificial chemical universe some of the methodologies developed for the study of genome scale models of cellular metabolism. In particular, we use metabolic flux constraint-based models to exhaustively search for artificial chemistry pathways that can optimally perform an array of elementary metabolic functions. Despite the simplicity of the model employed, we find that the ensuing pathways display a surprisingly rich set of properties, including the existence of autocatalytic cycles and hierarchical modules, the appearance of universally preferable metabolites and reactions, and a logarithmic trend of pathway length as a function of input/output molecule size. Some of these properties can be derived analytically, borrowing methods previously used in cryptography. In addition, by mapping biochemical networks onto a simplified carbon atom reaction backbone, we find that properties similar to those predicted for the artificial chemistry hold also for real metabolic networks. These findings suggest that optimality principles and arithmetic simplicity might lie beneath some aspects of biochemical complexity.

  11. Ensemble Kinetic Modeling of Metabolic Networks from Dynamic Metabolic Profiles

    Directory of Open Access Journals (Sweden)

    Gengjie Jia

    2012-11-01

    Full Text Available Kinetic modeling of metabolic pathways has important applications in metabolic engineering, but significant challenges still remain. The difficulties faced vary from finding best-fit parameters in a highly multidimensional search space to incomplete parameter identifiability. To meet some of these challenges, an ensemble modeling method is developed for characterizing a subset of kinetic parameters that give statistically equivalent goodness-of-fit to time series concentration data. The method is based on the incremental identification approach, where the parameter estimation is done in a step-wise manner. Numerical efficacy is achieved by reducing the dimensionality of parameter space and using efficient random parameter exploration algorithms. The shift toward using model ensembles, instead of the traditional “best-fit” models, is necessary to directly account for model uncertainty during the application of such models. The performance of the ensemble modeling approach has been demonstrated in the modeling of a generic branched pathway and the trehalose pathway in Saccharomyces cerevisiae using generalized mass action (GMA kinetics.

  12. Metabolic networks evolve towards states of maximum entropy production.

    Science.gov (United States)

    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.

  13. The Activity Reaction Core and Plasticity of Metabolic Networks.

    Directory of Open Access Journals (Sweden)

    2005-12-01

    Full Text Available Understanding the system-level adaptive changes taking place in an organism in response to variations in the environment is a key issue of contemporary biology. Current modeling approaches, such as constraint-based flux-balance analysis, have proved highly successful in analyzing the capabilities of cellular metabolism, including its capacity to predict deletion phenotypes, the ability to calculate the relative flux values of metabolic reactions, and the capability to identify properties of optimal growth states. Here, we use flux-balance analysis to thoroughly assess the activity of Escherichia coli, Helicobacter pylori, and Saccharomyces cerevisiae metabolism in 30,000 diverse simulated environments. We identify a set of metabolic reactions forming a connected metabolic core that carry non-zero fluxes under all growth conditions, and whose flux variations are highly correlated. Furthermore, we find that the enzymes catalyzing the core reactions display a considerably higher fraction of phenotypic essentiality and evolutionary conservation than those catalyzing noncore reactions. Cellular metabolism is characterized by a large number of species-specific conditionally active reactions organized around an evolutionary conserved, but always active, metabolic core. Finally, we find that most current antibiotics interfering with bacterial metabolism target the core enzymes, indicating that our findings may have important implications for antimicrobial drug-target discovery.

  14. Network reconstruction of platelet metabolism identifies metabolic signature for aspirin resistance

    Science.gov (United States)

    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.

  15. Fast reconstruction of compact context-specific metabolic network models.

    Directory of Open Access Journals (Sweden)

    Nikos Vlassis

    2014-01-01

    Full Text Available Systemic approaches to the study of a biological cell or tissue rely increasingly on the use of context-specific metabolic network models. The reconstruction of such a model from high-throughput data can routinely involve large numbers of tests under different conditions and extensive parameter tuning, which calls for fast algorithms. We present fastcore, a generic algorithm for reconstructing context-specific metabolic network models from global genome-wide metabolic network models such as Recon X. fastcore takes as input a core set of reactions that are known to be active in the context of interest (e.g., cell or tissue, and it searches for a flux consistent subnetwork of the global network that contains all reactions from the core set and a minimal set of additional reactions. Our key observation is that a minimal consistent reconstruction can be defined via a set of sparse modes of the global network, and fastcore iteratively computes such a set via a series of linear programs. Experiments on liver data demonstrate speedups of several orders of magnitude, and significantly more compact reconstructions, over a rival method. Given its simplicity and its excellent performance, fastcore can form the backbone of many future metabolic network reconstruction algorithms.

  16. Phylogeny of metabolic networks: a spectral graph theoretical approach.

    Science.gov (United States)

    Deyasi, Krishanu; Banerjee, Anirban; Deb, Bony

    2015-10-01

    Many methods have been developed for finding the commonalities between different organisms in order to study their phylogeny. The structure of metabolic networks also reveals valuable insights into metabolic capacity of species as well as into the habitats where they have evolved. We constructed metabolic networks of 79 fully sequenced organisms and compared their architectures. We used spectral density of normalized Laplacian matrix for comparing the structure of networks. The eigenvalues of this matrix reflect not only the global architecture of a network but also the local topologies that are produced by different graph evolutionary processes like motif duplication or joining. A divergence measure on spectral densities is used to quantify the distances between various metabolic networks, and a split network is constructed to analyse the phylogeny from these distances. In our analysis, we focused on the species that belong to different classes, but appear more related to each other in the phylogeny. We tried to explore whether they have evolved under similar environmental conditions or have similar life histories. With this focus, we have obtained interesting insights into the phylogenetic commonality between different organisms.

  17. Network-based analysis of the sphingolipid metabolism in hypertension

    DEFF Research Database (Denmark)

    Fenger, Mogens; Linneberg, Allan; Jeppesen, Jørgen

    2015-01-01

    revealed that epistasis does not necessarily reflects the topology of the metabolic pathways i.e., the flow of metabolites. Rather, the enzymes and proteins are integrated in complex cellular substructures where communication flows between the components of the networks, which may be composite in structure......-step procedure is presented in which physiological heterogeneity is disentangled and genetic effects are analyzed by variance decomposition of genetic interactions and by an information theoretical approach including 162 single nucleotide polymorphisms (SNP) in 84 genes in the sphingolipid metabolism and related...... networks in blood pressure regulation. As expected, almost no genetic main effects were detected. In contrast, two-gene interactions established the entire sphingolipid metabolic and related genetic network to be highly involved in the regulation of blood pressure. The pattern of interaction clearly...

  18. GROWTH AND METABOLISM OF INDIVIDUAL BACTERIAL CELLS UTILIZING NANOSIMS

    Energy Technology Data Exchange (ETDEWEB)

    NEALSON, H. K.

    2007-08-03

    This work involved the use of the Nano-SIMS Instrument at Lawrence Livermore Laboratory, in an effort to utilize this unique tool for experiments in Biology. The work consisted primarily of experiments to measure in real time, C and N fixation in cyanobacteria. The work revealed a number of the difficulties in using the nano-SIMS approach with biological material, but with collaboration from a number of individuals at USC and LLNL, major progress was made. The collaborators from LLNL were from the Chemistry Group (Dr. Peter Weber), and the Biology Group (Dr. Jennifer Pett-Ridge). In addition, there were a number of other scientists involved from LLNL. The USC group consisted of Dr. K.H. Nealson, the PI on the grant, Dr. R. Popa, a postdoctoral fellow and research associate at USC, Professor Douglas Capone, and Juliet Finze, a graduate student in biology. Two major experiments were done, both of which yielded new and exciting data. (1) We studied nitrogen and carbon fixation in Anabaena, demonstrating that fixation ofN occurred rapidly in the heterocysts, and that the fixed N was transported rapidly and completely to the vegetative cells. C fixation occurred in the vegetative cells, with labeled C remaining in these cells in support of their growth and metabolism. This work was accepted in the ISME Journal (Nature Publication), and published last month. (2) We studied nitrogen and carbon fixation in Trichodesmium, a non-heterocystous cyanobacterium that also fixes nitrogen. Interestingly, the nitrogen fixation was confined to regions within the filaments that seem to be identical to the so-called cyanophycaen granules. The fixed N is then transported to other parts of the cyanobacterium, as judged by movement of the heavy N throughout the filaments. On the basis of these very exciting results, we have applied for funding from the NSF to continue the collaboration with LLNL. The results of both studies were presented in the summer of 2007 at the Gordon Research

  19. Non-thermal effects of 94 GHz radiation on bacterial metabolism

    Science.gov (United States)

    Raitt, Brittany J.

    Bacillus subtilis, Escherichia coli, Staphylococcus aureus, and Klebsiella pneumoniae were used to investigate the non-thermal effects of terahertz (THz) radiation exposure on bacterial cells. The THz source used was a 94 GHz (0.94 THz) Millitech Gunn Diode Oscillator with a power density of 1.3 mW/cm2. The cultures were placed in the middle sixty wells of two 96-well microplates, one serving as the experimental plate and one serving as a control. The experimental plate was placed on the radiation source for either two, eighteen, or twenty-four hours and the metabolism of the cells was measured in a spectrophotometer using the tetrazolium dye XTT. The results showed no consistent significant differences in either the growth rates or the metabolism of any of the bacterial species at this frequency and power density.

  20. Depth Profile of Bacterial Metabolism and PAH Biodegradation in Bioturbated and Unbioturbated Marine Sediments

    Science.gov (United States)

    2007-11-02

    Reactivity of Terriginous Organic Matter in this Study Phenolic Group Phenol suite (code) Remarks Vanillyl vanillin (Vl), acetovanillone (Vn), vanillic...is degraded in the sediment has been measured using a degradation index (Fig. 4). The ratio of acid to aldehyde for the vanillin family of lignin...is not statistically significant. The ratio of 3,5-dihydroxybenzoic acid to vanillin (DHBd/V) is Depth Profile of Bacterial Metabolism 5

  1. FUNCTIONAL AND METABOLIC ACTIVITY OF NEUTROPHILIC GRANULOCYTES IN CASE OF ACUTE BACTERIAL RHINOSINUSITIS

    Directory of Open Access Journals (Sweden)

    O. A. Kolenchukova

    2013-01-01

    Full Text Available Abstract. The functional and metabolic activities of neutrophilic granulocytes in patients with acute bacterial rhinosinusitis (ABRS have been studied. Characteristics of the indices of chemiluminescence and bioluminescence for neutrophils, extracted from venous blood and maxillary sinus were compared. It was demonstrated the decrease of intensity of APK production in neutrophils, extracted from inflammation point, with simultaneous decrease of intensity of plastic processes and increasing of energy processes in compare with the same indices in blood cells.

  2. Piperine metabolically regulates peritoneal resident macrophages to potentiate their functions against bacterial infection.

    Science.gov (United States)

    Pan, Hao; Xu, Li-Hui; Huang, Mei-Yun; Zha, Qing-Bing; Zhao, Gao-Xiang; Hou, Xiao-Feng; Shi, Zi-Jian; Lin, Qiu-Ru; Ouyang, Dong-Yun; He, Xian-Hui

    2015-10-20

    Pepper, a daily-used seasoning for promoting appetite, is widely used in folk medicine for treating gastrointestinal diseases. Piperine is the major alkaloid in pepper and possesses a wide range of pharmacological activities. However, the mechanism for linking metabolic and medicinal activities of piperine remains unknown. Here we report that piperine robustly boosts mTORC1 activity by recruiting more system L1 amino acid transporter (SLC7A5/SLC3A2) to the cell membrane, thus promoting amino acid metabolism. Piperine-induced increase of mTORC1 activity in resident peritoneal macrophages (pMΦs) is correlated with enhanced production of IL-6 and TNF-α upon LPS stimulation. Such an enhancement of cytokine production could be abrogated by inhibitors of the mTOR signaling pathway, indicating mTOR's action in this process. Moreover, piperine treatment protected resident pMΦs from bacterium-induced apoptosis and disappearance, and increased their bacterial phagocytic ability. Consequently, piperine administration conferred mice resistance against bacterial infection and even sepsis. Our data highlight that piperine has the capacity to metabolically reprogram peritoneal resident macrophages to fortify their innate functions against bacterial infection.

  3. Parallel labeling experiments validate Clostridium acetobutylicum metabolic network model for (13)C metabolic flux analysis.

    Science.gov (United States)

    Au, Jennifer; Choi, Jungik; Jones, Shawn W; Venkataramanan, Keerthi P; Antoniewicz, Maciek R

    2014-11-01

    In this work, we provide new insights into the metabolism of Clostridium acetobutylicum ATCC 824 obtained using a systematic approach for quantifying fluxes based on parallel labeling experiments and (13)C-metabolic flux analysis ((13)C-MFA). Here, cells were grown in parallel cultures with [1-(13)C]glucose and [U-(13)C]glucose as tracers and (13)C-MFA was used to quantify intracellular metabolic fluxes. Several metabolic network models were compared: an initial model based on current knowledge, and extended network models that included additional reactions that improved the fits of experimental data. While the initial network model did not produce a statistically acceptable fit of (13)C-labeling data, an extended network model with five additional reactions was able to fit all data with 292 redundant measurements. The model was subsequently trimmed to produce a minimal network model of C. acetobutylicum for (13)C-MFA, which could still reproduce all of the experimental data. The flux results provided valuable new insights into the metabolism of C. acetobutylicum. First, we found that TCA cycle was effectively incomplete, as there was no measurable flux between α-ketoglutarate and succinyl-CoA, succinate and fumarate, and malate and oxaloacetate. Second, an active pathway was identified from pyruvate to fumarate via aspartate. Third, we found that isoleucine was produced exclusively through the citramalate synthase pathway in C. acetobutylicum and that CAC3174 was likely responsible for citramalate synthase activity. These model predictions were confirmed in several follow-up tracer experiments. The validated metabolic network model established in this study can be used in future investigations for unbiased (13)C-flux measurements in C. acetobutylicum.

  4. Relationship between topology and functions in metabolic network evolution

    Institute of Scientific and Technical Information of China (English)

    WANG Zhuo; CHEN Qi; LIU Lei

    2009-01-01

    What is the relationship between the topological connections among enzymes and their functions during metabolic network evolution? Does this relationship show similarity among closely related or-ganisms? Here we investigated the relationship between enzyme connectivity and functions in meta-bolic networks of chloroplast and its endosymbiotic ancestor, cyanobacteria (Synechococcus sp. WH8102). Also several other species, including E. coil, Arabidopsis thaliana and Cyanidioschyzon merolae, were used for the comparison. We found that the average connectivity among different func-tional pathways and enzyme classifications (EC) was different in all the species examined. However, the average connectivity of enzymes in the same functional classification was quite similar between chloroplast and one representative of cyanobacteria, syw. In addition, the enzymes in the highly con-served modules between chloroplast and syw, such as amino acid metabolism, were highly connected compared with other modules. We also discovered that the isozymes of chloroplast and syw often had higher connectivity, corresponded to primary metabolism and also existed in conserved module. In conclusion, despite the drastic re-organization of metabolism in chloroplast during endosymbiosis, the relationship between network topology and functions is very similar between chloroplast and its pre-cursor cyanobacteria, which demonstrates that the relationship may be used as an indicator of the closeness in evolution.

  5. Evolution of enzymes in metabolism: a network perspective.

    Science.gov (United States)

    Alves, Rui; Chaleil, Raphael A G; Sternberg, Michael J E

    2002-07-19

    Several models have been proposed to explain the origin and evolution of enzymes in metabolic pathways. Initially, the retro-evolution model proposed that, as enzymes at the end of pathways depleted their substrates in the primordial soup, there was a pressure for earlier enzymes in pathways to be created, using the later ones as initial template, in order to replenish the pools of depleted metabolites. Later, the recruitment model proposed that initial templates from other pathways could be used as long as those enzymes were similar in chemistry or substrate specificity. These two models have dominated recent studies of enzyme evolution. These studies are constrained by either the small scale of the study or the artificial restrictions imposed by pathway definitions. Here, a network approach is used to study enzyme evolution in fully sequenced genomes, thus removing both constraints. We find that homologous pairs of enzymes are roughly twice as likely to have evolved from enzymes that are less than three steps away from each other in the reaction network than pairs of non-homologous enzymes. These results, together with the conservation of the type of chemical reaction catalyzed by evolutionarily related enzymes, suggest that functional blocks of similar chemistry have evolved within metabolic networks. One possible explanation for these observations is that this local evolution phenomenon is likely to cause less global physiological disruptions in metabolism than evolution of enzymes from other enzymes that are distant from them in the metabolic network.

  6. Global Network Reorganization During Dynamic Adaptations of Bacillus subtilis Metabolism

    NARCIS (Netherlands)

    Buescher, Joerg Martin; Liebermeister, Wolfram; Jules, Matthieu; Uhr, Markus; Muntel, Jan; Botella, Eric; Hessling, Bernd; Kleijn, Roelco Jacobus; Le Chat, Ludovic; Lecointe, Francois; Maeder, Ulrike; Nicolas, Pierre; Piersma, Sjouke; Ruegheimer, Frank; Becher, Doerte; Bessieres, Philippe; Bidnenko, Elena; Denham, Emma L.; Dervyn, Etienne; Devine, Kevin M.; Doherty, Geoff; Drulhe, Samuel; Felicori, Liza; Fogg, Mark J.; Goelzer, Anne; Hansen, Annette; Harwood, Colin R.; Hecker, Michael; Hubner, Sebastian; Hultschig, Claus; Jarmer, Hanne; Klipp, Edda; Leduc, Aurelie; Lewis, Peter; Molina, Frank; Noirot, Philippe; Peres, Sabine; Pigeonneau, Nathalie; Pohl, Susanne; Rasmussen, Simon; Rinn, Bernd; Schaffer, Marc; Schnidder, Julian; Schwikowski, Benno; Van Dijl, Jan Maarten; Veiga, Patrick; Walsh, Sean; Wilkinson, Anthony J.; Stelling, Joerg; Aymerich, Stephane; Sauer, Uwe

    2012-01-01

    Adaptation of cells to environmental changes requires dynamic interactions between metabolic and regulatory networks, but studies typically address only one or a few layers of regulation. For nutritional shifts between two preferred carbon sources of Bacillus subtilis, we combined statistical and mo

  7. Systematic assignment of thermodynamic constraints in metabolic network models

    NARCIS (Netherlands)

    Kümmel, Anne; Panke, Sven; Heinemann, Matthias

    2006-01-01

    Background: The availability of genome sequences for many organisms enabled the reconstruction of several genome-scale metabolic network models. Currently, significant efforts are put into the automated reconstruction of such models. For this, several computational tools have been developed that par

  8. Metagenomics reveals flavour metabolic network of cereal vinegar microbiota.

    Science.gov (United States)

    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.

  9. Comparative genomic reconstruction of transcriptional networks controlling central metabolism in the Shewanella genus

    Directory of Open Access Journals (Sweden)

    Kovaleva Galina

    2011-06-01

    Full Text Available Abstract Background Genome-scale prediction of gene regulation and reconstruction of transcriptional regulatory networks in bacteria is one of the critical tasks of modern genomics. The Shewanella genus is comprised of metabolically versatile gamma-proteobacteria, whose lifestyles and natural environments are substantially different from Escherichia coli and other model bacterial species. The comparative genomics approaches and computational identification of regulatory sites are useful for the in silico reconstruction of transcriptional regulatory networks in bacteria. Results To explore conservation and variations in the Shewanella transcriptional networks we analyzed the repertoire of transcription factors and performed genomics-based reconstruction and comparative analysis of regulons in 16 Shewanella genomes. The inferred regulatory network includes 82 transcription factors and their DNA binding sites, 8 riboswitches and 6 translational attenuators. Forty five regulons were newly inferred from the genome context analysis, whereas others were propagated from previously characterized regulons in the Enterobacteria and Pseudomonas spp.. Multiple variations in regulatory strategies between the Shewanella spp. and E. coli include regulon contraction and expansion (as in the case of PdhR, HexR, FadR, numerous cases of recruiting non-orthologous regulators to control equivalent pathways (e.g. PsrA for fatty acid degradation and, conversely, orthologous regulators to control distinct pathways (e.g. TyrR, ArgR, Crp. Conclusions We tentatively defined the first reference collection of ~100 transcriptional regulons in 16 Shewanella genomes. The resulting regulatory network contains ~600 regulated genes per genome that are mostly involved in metabolism of carbohydrates, amino acids, fatty acids, vitamins, metals, and stress responses. Several reconstructed regulons including NagR for N-acetylglucosamine catabolism were experimentally validated in S

  10. Transcriptional regulation and steady-state modeling of metabolic networks

    DEFF Research Database (Denmark)

    Zelezniak, Aleksej

    with the changes in gene expression of both reactions that produce and reactions that consume a given metabolite. Analysis of a large compendium of gene expression data further suggested that, contrary to previous thinking, transcriptional regulation at metabolic branch points is highly plastic and, in several...... gene expression data with the human cellular metabolic network. The approach is demonstrated by analysis of two skeletal muscle gene expression datasets. The proposed methodology identified transcription factors and metabolites that represent potential targets for therapeutic agents and future clinical...... diagnostics for type 2 diabetes and impaired glucose metabolism. In a broader context, the study provides a framework for analysis of gene expression datasets from complex heterogeneous diseases, genetic, and environmental perturbations that are reflected in and/or mediated through changes in metabolism...

  11. A fragile metabolic network adapted for cooperation in the symbiotic bacterium Buchnera aphidicola

    Directory of Open Access Journals (Sweden)

    Goryanin Igor

    2009-02-01

    Full Text Available Abstract Background In silico analyses provide valuable insight into the biology of obligately intracellular pathogens and symbionts with small genomes. There is a particular opportunity to apply systems-level tools developed for the model bacterium Escherichia coli to study the evolution and function of symbiotic bacteria which are metabolically specialised to overproduce specific nutrients for their host and, remarkably, have a gene complement that is a subset of the E. coli genome. Results We have reconstructed and analysed the metabolic network of the γ-proteobacterium Buchnera aphidicola (symbiont of the pea aphid as a model for using systems-level approaches to discover key traits of symbionts with small genomes. The metabolic network is extremely fragile with > 90% of the reactions essential for viability in silico; and it is structured so that the bacterium cannot grow without producing the essential amino acid, histidine, which is released to the insect host. Further, the amount of essential amino acid produced by the bacterium in silico can be controlled by host supply of carbon and nitrogen substrates. Conclusion This systems-level analysis predicts that the fragility of the bacterial metabolic network renders the symbiotic bacterium intolerant of drastic environmental fluctuations, whilst the coupling of histidine production to growth prevents the bacterium from exploiting host nutrients without reciprocating. These metabolic traits underpin the sustained nutritional contribution of B. aphidicola to the host and, together with the impact of host-derived substrates on the profile of nutrients released from the bacteria, point to a dominant role of the host in controlling the symbiosis.

  12. Optimality Principles in the Regulation of Metabolic Networks

    Directory of Open Access Journals (Sweden)

    Jan Berkhout

    2012-08-01

    Full Text Available One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks need to be identified in addition to the characterisation of the molecular components and interactions. Then, the cellular “task” of the network—its function—should be identified. A network contributes to organismal fitness through its function. The premise is that the same functions are often implemented in different organisms by the same type of network; hence, the concept of design principles. In biology, due to the strong forces of selective pressure and natural selection, network functions can often be understood as the outcome of fitness optimisation. The hypothesis of fitness optimisation to understand the design of a network has proven to be a powerful strategy. Here, we outline the use of several optimisation principles applied to biological networks, with an emphasis on metabolic regulatory networks. We discuss the different objective functions and constraints that are considered and the kind of understanding that they provide.

  13. What can causal networks tell us about metabolic pathways?

    Directory of Open Access Journals (Sweden)

    Rachael Hageman Blair

    Full Text Available 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 Bay[Formula: see text]Sha 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.

  14. Analysis of Data on Xanthan Fermentation in Stationary Phase Using Black Box and Metabolic Network Models

    Institute of Scientific and Technical Information of China (English)

    马红武; 赵学明; 唐寅杰

    1999-01-01

    The xanthan fermentation data in the stationary phase was analyzed using the black box and the metabolic network models. The data consistency ls checked through the elemental balance in the black box model. In the metabolic network model, the metabolic flux distribution in the cell is calculated using the metabolic flux analysis method, then the maintenance coefficients is calculated.

  15. Parameter estimation in tree graph metabolic networks

    Directory of Open Access Journals (Sweden)

    Laura Astola

    2016-09-01

    Full Text Available We study the glycosylation processes that convert initially toxic substrates to nutritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs to model the enzyme kinetics. A popular choice is to use a system of linear ODEs with constant kinetic rates or to use Michaelis–Menten kinetics. In reality, the catalytic rates, which are affected among other factors by kinetic constants and enzyme concentrations, are changing in time and with the approaches just mentioned, this phenomenon cannot be described. Another problem is that, in general these kinetic coefficients are not always identifiable. A third problem is that, it is not precisely known which enzymes are catalyzing the observed glycosylation processes. With several hundred potential gene candidates, experimental validation using purified target proteins is expensive and time consuming. We aim at reducing this task via mathematical modeling to allow for the pre-selection of most potential gene candidates. In this article we discuss a fast and relatively simple approach to estimate time varying kinetic rates, with three favorable properties: firstly, it allows for identifiable estimation of time dependent parameters in networks with a tree-like structure. Secondly, it is relatively fast compared to usually applied methods that estimate the model derivatives together with the network parameters. Thirdly, by combining the metabolite concentration data with a corresponding microarray data, it can help in detecting the genes related to the enzymatic processes. By comparing the estimated time dynamics of the catalytic rates with time series gene expression data we may assess potential candidate genes behind enzymatic reactions. As an example, we show how to apply this method to select prominent glycosyltransferase genes in tomato seedlings.

  16. Expanded flux variability analysis on metabolic network of Escherichia coli

    Institute of Scientific and Technical Information of China (English)

    CHEN Tong; XIE ZhengWei; OUYANG Qi

    2009-01-01

    Flux balance analysis,based on the mass conservation law in a cellular organism,has been extensively employed to study the interplay between structures and functions of cellular metabolic networks.Consequently,the phenotypes of the metabolism can be well elucidated.In this paper,we introduce the Expanded Flux Variability Analysis (EFVA) to characterize the intrinsic nature of metabolic reactions,such as flexibility,modularity and essentiality,by exploring the trend of the range,the maximum and the minimum flux of reactions.We took the metabolic network of Escherichia coli as an example and analyzed the variability of reaction fluxes under different growth rate constraints.The average variability of all reactions decreases dramatically when the growth rate increases.Consider the noise effect on the metabolic system,we thus argue that the microorganism may practically grow under a suboptimal state.Besides,under the EFVA framework,the reactions are easily to be grouped into catabolic and anabolic groups.And the anabolic groups can be further assigned to specific biomass constitute.We also discovered the growth rate dependent essentiality of reactions.

  17. Topological peculiarities of mammalian networks with different functionalities: transcription, signal transduction and metabolic networks

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    Bjorn Goemann

    2011-12-01

    Full Text Available We have comparatively investigated three different mammalian networks - on transcription, signal transduction and metabolic processes - with respect to their common and individual topological traits. The networks have been constructed based on genome- wide data collected from human, mouse and rat. None of these three networks exhibits a pure power-law degree distribution and, therefore, could be considered scalefree. Rather, the degree distributions of all three networks were best fitted by mixed models of a power law with an exponential tail. The networks differ from one another in the quantitative parameters of the models. Moreover, the transcription network can also be very well approximated by an exponential law. The connectivity within each network is rather robust, as is seen when removing individual nodes and computing the values of their pairwise disconnectivity index (PDI, which characterizes the topological significance of each node v by the number of direct or indirect connections in the network that critically depend on the presence of v. The results evidence that the networks are not centralized: none of nodes globally controls the integrity of each network. Just a few vertices appeared to strongly affect the coherence of the networks. These nodes are characterized by a broad range of degrees, thereby indicating that the degree alone is not the decisive criteria of a node's importance. The networks reveal distinct architectures: The transcriptional network exhibits a hierarchical modularity, whereas the signaling network is mainly comprised of semi-autonomous modules. The metabolic network seems to be made by a more complex mixture of substructures. Thus, despite being encoded by the same genomes, the networks significantly differ from one another in their general architectural design. Altogether, our results indicate that the subsets of genes and relationships that constitute these networks have co-evolved very differently and

  18. Thermodynamics-based Metabolite Sensitivity Analysis in metabolic networks.

    Science.gov (United States)

    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.

  19. Radial Basis Function Networks Applied in Bacterial Classification Based on MALDI-TOF-MS

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    The radial basis function networks were applied to bacterial classification based on the matrix-assisted laser desorption/ionization time-of-flight mass spectrometric (MALDI-TOF-MS) data. The classification of bacteria cultured at different time was discussed and the effect of the network parameters on the classification was investigated. The cross-validation method was used to test the trained networks. The correctness of the classification of different bacteria investigated changes in a wide range from 61.5% to 92.8%. Owing to the complexity of biological effects in bacterial growth, the more rigid control of bacterial culture conditions seems to be a critical factor for improving the rate of correctness for bacterial classification.

  20. Context-specific metabolic networks are consistent with experiments.

    Directory of Open Access Journals (Sweden)

    Scott A Becker

    2008-05-01

    Full Text Available 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.

  1. Effect of bacterial protein meal on protein and energy metabolism in growing chickens

    DEFF Research Database (Denmark)

    Hellwing, Anne Louise Frydendahl; Tauson, Anne-Helene; Skrede, Anders

    2006-01-01

    This experiment investigates the effect of increasing the dietary content of bacterial protein meal (BPM) on the protein and energy metabolism, and carcass chemical composition of growing chickens. Seventy-two Ross male chickens were allocated to four diets, each in three replicates with 0% (D0), 2......% (D2), 4% D4), and 6% BPM (D6), BPM providing up to 20% of total dietary N. Five balance experiments were conducted when the chickens were 3-7, 10-14, 17-21, 23-27, and 30-34 days old. During the same periods, 22-h respiration experiments (indirect calorimetry) were performed with troups of 6 chickens...

  2. The evolution of metabolic networks of E. coli

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    Baumler David J

    2011-11-01

    Full Text Available Abstract 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

  3. Estimating Metabolic Fluxes Using a Maximum Network Flexibility Paradigm

    Science.gov (United States)

    Megchelenbrink, Wout; Rossell, Sergio; Huynen, Martijn A.

    2015-01-01

    Motivation Genome-scale metabolic networks can be modeled in a constraint-based fashion. Reaction stoichiometry combined with flux capacity constraints determine the space of allowable reaction rates. This space is often large and a central challenge in metabolic modeling is finding the biologically most relevant flux distributions. A widely used method is flux balance analysis (FBA), which optimizes a biologically relevant objective such as growth or ATP production. Although FBA has proven to be highly useful for predicting growth and byproduct secretion, it cannot predict the intracellular fluxes under all environmental conditions. Therefore, alternative strategies have been developed to select flux distributions that are in agreement with experimental “omics” data, or by incorporating experimental flux measurements. The latter, unfortunately can only be applied to a limited set of reactions and is currently not feasible at the genome-scale. On the other hand, it has been observed that micro-organisms favor a suboptimal growth rate, possibly in exchange for a more “flexible” metabolic network. Instead of dedicating the internal network state to an optimal growth rate in one condition, a suboptimal growth rate is used, that allows for an easier switch to other nutrient sources. A small decrease in growth rate is exchanged for a relatively large gain in metabolic capability to adapt to changing environmental conditions. Results Here, we propose Maximum Metabolic Flexibility (MMF) a computational method that utilizes this observation to find the most probable intracellular flux distributions. By mapping measured flux data from central metabolism to the genome-scale models of Escherichia coli and Saccharomyces cerevisiae we show that i) indeed, most of the measured fluxes agree with a high adaptability of the network, ii) this result can be used to further reduce the space of feasible solutions iii) this reduced space improves the quantitative predictions

  4. Systematic assignment of thermodynamic constraints in metabolic network models

    Directory of Open Access Journals (Sweden)

    Heinemann Matthias

    2006-11-01

    Full Text Available Abstract Background The availability of genome sequences for many organisms enabled the reconstruction of several genome-scale metabolic network models. Currently, significant efforts are put into the automated reconstruction of such models. For this, several computational tools have been developed that particularly assist in identifying and compiling the organism-specific lists of metabolic reactions. In contrast, the last step of the model reconstruction process, which is the definition of the thermodynamic constraints in terms of reaction directionalities, still needs to be done manually. No computational method exists that allows for an automated and systematic assignment of reaction directions in genome-scale models. Results We present an algorithm that – based on thermodynamics, network topology and heuristic rules – automatically assigns reaction directions in metabolic models such that the reaction network is thermodynamically feasible with respect to the production of energy equivalents. It first exploits all available experimentally derived Gibbs energies of formation to identify irreversible reactions. As these thermodynamic data are not available for all metabolites, in a next step, further reaction directions are assigned on the basis of network topology considerations and thermodynamics-based heuristic rules. Briefly, the algorithm identifies reaction subsets from the metabolic network that are able to convert low-energy co-substrates into their high-energy counterparts and thus net produce energy. Our algorithm aims at disabling such thermodynamically infeasible cyclic operation of reaction subnetworks by assigning reaction directions based on a set of thermodynamics-derived heuristic rules. We demonstrate our algorithm on a genome-scale metabolic model of E. coli. The introduced systematic direction assignment yielded 130 irreversible reactions (out of 920 total reactions, which corresponds to about 70% of all irreversible

  5. Estimating Metabolic Fluxes Using a Maximum Network Flexibility Paradigm.

    Directory of Open Access Journals (Sweden)

    Wout Megchelenbrink

    Full Text Available Genome-scale metabolic networks can be modeled in a constraint-based fashion. Reaction stoichiometry combined with flux capacity constraints determine the space of allowable reaction rates. This space is often large and a central challenge in metabolic modeling is finding the biologically most relevant flux distributions. A widely used method is flux balance analysis (FBA, which optimizes a biologically relevant objective such as growth or ATP production. Although FBA has proven to be highly useful for predicting growth and byproduct secretion, it cannot predict the intracellular fluxes under all environmental conditions. Therefore, alternative strategies have been developed to select flux distributions that are in agreement with experimental "omics" data, or by incorporating experimental flux measurements. The latter, unfortunately can only be applied to a limited set of reactions and is currently not feasible at the genome-scale. On the other hand, it has been observed that micro-organisms favor a suboptimal growth rate, possibly in exchange for a more "flexible" metabolic network. Instead of dedicating the internal network state to an optimal growth rate in one condition, a suboptimal growth rate is used, that allows for an easier switch to other nutrient sources. A small decrease in growth rate is exchanged for a relatively large gain in metabolic capability to adapt to changing environmental conditions.Here, we propose Maximum Metabolic Flexibility (MMF a computational method that utilizes this observation to find the most probable intracellular flux distributions. By mapping measured flux data from central metabolism to the genome-scale models of Escherichia coli and Saccharomyces cerevisiae we show that i indeed, most of the measured fluxes agree with a high adaptability of the network, ii this result can be used to further reduce the space of feasible solutions iii this reduced space improves the quantitative predictions made by FBA and

  6. Sensitivity of chemical reaction networks: a structural approach. 1. Examples and the carbon metabolic network.

    Science.gov (United States)

    Mochizuki, Atsushi; Fiedler, Bernold

    2015-02-21

    In biological cells, chemical reaction pathways lead to complex network systems like metabolic networks. One experimental approach to the dynamics of such systems examines their "sensitivity": each enzyme mediating a reaction in the system is increased/decreased or knocked out separately, and the responses in the concentrations of chemicals or their fluxes are observed. In this study, we present a mathematical method, named structural sensitivity analysis, to determine the sensitivity of reaction systems from information on the network alone. We investigate how the sensitivity responses of chemicals in a reaction network depend on the structure of the network, and on the position of the perturbed reaction in the network. We establish and prove some general rules which relate the sensitivity response to the structure of the underlying network. We describe a hierarchical pattern in the flux response which is governed by branchings in the network. We apply our method to several hypothetical and real life chemical reaction networks, including the metabolic network of the Escherichia coli TCA cycle.

  7. Genetic networks of liver metabolism revealed by integration of metabolic and transcriptional profiling.

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    Christine T Ferrara

    2008-03-01

    Full Text Available Although numerous quantitative trait loci (QTL influencing disease-related phenotypes have been detected through gene mapping and positional cloning, identification of the individual gene(s and molecular pathways leading to those phenotypes is often elusive. One way to improve understanding of genetic architecture is to classify phenotypes in greater depth by including transcriptional and metabolic profiling. In the current study, we have generated and analyzed mRNA expression and metabolic profiles in liver samples obtained in an F2 intercross between the diabetes-resistant C57BL/6 leptin(ob/ob and the diabetes-susceptible BTBR leptin(ob/ob mouse strains. This cross, which segregates for genotype and physiological traits, was previously used to identify several diabetes-related QTL. Our current investigation includes microarray analysis of over 40,000 probe sets, plus quantitative mass spectrometry-based measurements of sixty-seven intermediary metabolites in three different classes (amino acids, organic acids, and acyl-carnitines. We show that liver metabolites map to distinct genetic regions, thereby indicating that tissue metabolites are heritable. We also demonstrate that genomic analysis can be integrated with liver mRNA expression and metabolite profiling data to construct causal networks for control of specific metabolic processes in liver. As a proof of principle of the practical significance of this integrative approach, we illustrate the construction of a specific causal network that links gene expression and metabolic changes in the context of glutamate metabolism, and demonstrate its validity by showing that genes in the network respond to changes in glutamine and glutamate availability. Thus, the methods described here have the potential to reveal regulatory networks that contribute to chronic, complex, and highly prevalent diseases and conditions such as obesity and diabetes.

  8. Second Law of Thermodynamics Applied to Metabolic Networks

    Science.gov (United States)

    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.

  9. Current understanding of the formation and adaptation of metabolic systems based on network theory.

    Science.gov (United States)

    Takemoto, Kazuhiro

    2012-07-12

    Formation and adaptation of metabolic networks has been a long-standing question in biology. With recent developments in biotechnology and bioinformatics, the understanding of metabolism is progressively becoming clearer from a network perspective. This review introduces the comprehensive metabolic world that has been revealed by a wide range of data analyses and theoretical studies; in particular, it illustrates the role of evolutionary events, such as gene duplication and horizontal gene transfer, and environmental factors, such as nutrient availability and growth conditions, in evolution of the metabolic network. Furthermore, the mathematical models for the formation and adaptation of metabolic networks have also been described, according to the current understanding from a perspective of metabolic networks. These recent findings are helpful in not only understanding the formation of metabolic networks and their adaptation, but also metabolic engineering.

  10. Developmental changes in the metabolic network of snapdragon flowers.

    Directory of Open Access Journals (Sweden)

    Joëlle K Muhlemann

    Full Text Available Evolutionary and reproductive success of angiosperms, the most diverse group of land plants, relies on visual and olfactory cues for pollinator attraction. Previous work has focused on elucidating the developmental regulation of pathways leading to the formation of pollinator-attracting secondary metabolites such as scent compounds and flower pigments. However, to date little is known about how flowers control their entire metabolic network to achieve the highly regulated production of metabolites attracting pollinators. Integrative analysis of transcripts and metabolites in snapdragon sepals and petals over flower development performed in this study revealed a profound developmental remodeling of gene expression and metabolite profiles in petals, but not in sepals. Genes up-regulated during petal development were enriched in functions related to secondary metabolism, fatty acid catabolism, and amino acid transport, whereas down-regulated genes were enriched in processes involved in cell growth, cell wall formation, and fatty acid biosynthesis. The levels of transcripts and metabolites in pathways leading to scent formation were coordinately up-regulated during petal development, implying transcriptional induction of metabolic pathways preceding scent formation. Developmental gene expression patterns in the pathways involved in scent production were different from those of glycolysis and the pentose phosphate pathway, highlighting distinct developmental regulation of secondary metabolism and primary metabolic pathways feeding into it.

  11. Evolvability and hierarchy in rewired bacterial gene networks

    Science.gov (United States)

    Isalan, Mark; Lemerle, Caroline; Michalodimitrakis, Konstantinos; Beltrao, Pedro; Horn, Carsten; Raineri, Emanuele; Garriga-Canut, Mireia; Serrano, Luis

    2009-01-01

    Sequencing DNA from several organisms has revealed that duplication and drift of existing genes have primarily molded the contents of a given genome. Though the effect of knocking out or over-expressing a particular gene has been studied in many organisms, no study has systematically explored the effect of adding new links in a biological network. To explore network evolvability, we constructed 598 recombinations of promoters (including regulatory regions) with different transcription or σ-factor genes in Escherichia coli, added over a wild-type genetic background. Here we show that ~95% of new networks are tolerated by the bacteria, that very few alter growth, and that expression level correlates with factor position in the wild-type network hierarchy. Most importantly, we find that certain networks consistently survive over the wild-type under various selection pressures. Therefore new links in the network are rarely a barrier for evolution and can even confer a fitness advantage. PMID:18421347

  12. Metabolic Requirements of Escherichia coli in Intracellular Bacterial Communities during Urinary Tract Infection Pathogenesis

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    Matt S. Conover

    2016-04-01

    Full Text Available Uropathogenic Escherichia coli (UPEC is the primary etiological agent of over 85% of community-acquired urinary tract infections (UTIs. Mouse models of infection have shown that UPEC can invade bladder epithelial cells in a type 1 pilus-dependent mechanism, avoid a TLR4-mediated exocytic process, and escape into the host cell cytoplasm. The internalized UPEC can clonally replicate into biofilm-like intracellular bacterial communities (IBCs of thousands of bacteria while avoiding many host clearance mechanisms. Importantly, IBCs have been documented in urine from women and children suffering acute UTI. To understand this protected bacterial niche, we elucidated the transcriptional profile of bacteria within IBCs using microarrays. We delineated the upregulation within the IBC of genes involved in iron acquisition, metabolism, and transport. Interestingly, lacZ was highly upregulated, suggesting that bacteria were sensing and/or utilizing a galactoside for metabolism in the IBC. A ΔlacZ strain displayed significantly smaller IBCs than the wild-type strain and was attenuated during competitive infection with a wild-type strain. Similarly, a galK mutant resulted in smaller IBCs and attenuated infection. Further, analysis of the highly upregulated gene yeaR revealed that this gene contributes to oxidative stress resistance and type 1 pilus production. These results suggest that bacteria within the IBC are under oxidative stress and, consistent with previous reports, utilize nonglucose carbon metabolites. Better understanding of the bacterial mechanisms used for IBC development and establishment of infection may give insights into development of novel anti-virulence strategies.

  13. Network-based analysis of the sphingolipid metabolism in hypertension

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    Mogens eFenger

    2015-03-01

    Full Text Available Common diseases like essential hypertension or diabetes mellitus are complex as they are polygenic in nature, such that each genetic variation only has a small influence on the disease. Genes operates in integrated networks providing the blue-print for all biological processes and conditional of the complex genotype determines the state and dynamics of any trait, which may be modified to various extent by non-genetic factors. Thus, diseases are heterogenous ensembles of conditions with a common endpoint. Numerous studies have been performed to define genes of importance for a trait or disease, but only a few genes with small effect have been identified. The major reasons for this modest progress is the unresolved heterogeneity of the regulation of blood pressure and the shortcomings of the prevailing monogenic approach to capture genetic effects in a polygenic condition. Here, a two-step procedure is presented in which physiological heterogeneity is disentangled and genetic effects are analysed by variance decomposition of genetic interactions and by an information theoretical approach including 162 single nucleotide polymorphisms (SNP in 84 genes in the sphingolipid metabolism and related networks in blood pressure regulation. As expected, almost no genetic main effects were detected. In contrast, two-gene interactions established the entire sphingolipid metabolic and related genetic network to be highly involved in the regulation of blood pressure. The pattern of interaction clearly revealed that epistasis does not necessarily reflects the topology of the metabolic pathways i.e. the flow of metabolites. Rather, the enzymes and proteins are integrated in complex cellular substructures where communication flows between the components of the networks, which may be composite in structure. The heritabilities for diastolic and systolic blood pressure were estimated to be 0.63 +/- 0.01 , which may in fact be the maximum heritabilities of these traits.

  14. Early-life exercise may promote lasting brain and metabolic health through gut bacterial metabolites.

    Science.gov (United States)

    Mika, Agnieszka; Fleshner, Monika

    2016-02-01

    The 100 trillion microorganisms residing within our intestines contribute roughly 5 million additional genes to our genetic gestalt, thus posing the potential to influence many aspects of our physiology. Microbial colonization of the gut shortly after birth is vital for the proper development of immune, neural and metabolic systems, while sustaining a balanced, diverse gut flora populated with beneficial bacteria is necessary for maintaining optimal function of these systems. Although symbiotic host-microbial interactions are important throughout the lifespan, these interactions can have greater and longer lasting impacts during certain critical developmental periods. A better understanding of these sensitive periods is necessary to improve the impact and effectiveness of health-promoting interventions that target the microbial ecosystem. We have recently reported that exercise initiated in early life increases gut bacterial species involved in promoting psychological and metabolic health. In this review, we emphasize the ability of exercise during this developmentally receptive time to promote optimal brain and metabolic function across the lifespan through microbial signals.

  15. Bacterial microcompartments: widespread prokaryotic organelles for isolation and optimization of metabolic pathways

    Science.gov (United States)

    Bobik, Thomas A.; Lehman, Brent P.; Yeates, Todd O.

    2016-01-01

    Summary Prokaryotes use subcellular compartments for a variety of purposes. An intriguing example is a family of complex subcellular organelles known as bacterial microcompartments (MCPs). MCPs are widely distributed among bacteria and impact processes ranging global carbon fixation and enteric pathogenesis. Overall, MCPs consist of metabolic enzymes encased within a protein shell, and their function is to optimize biochemical pathways by confining toxic or volatile metabolic intermediates. MCPs are fundamentally different from other organelles in having a complex protein shell rather than a lipid-based membrane as an outer barrier. This unusual feature raises basic questions about organelle assembly, protein targeting and metabolite transport. In this review, we discuss the three best-studied MCPs highlighting atomic-level models for shell assembly, targeting sequences that direct enzyme encapsulation, multivalent proteins that organize the lumen enzymes, the principles of metabolite movement across the shell, internal cofactor recycling, a potential system of allosteric regulation of metabolite transport and the mechanism and rationale behind the functional diversification of the proteins that form the shell. We also touch on some potential biotechnology applications an unusual compartment designed by nature to optimize metabolic processes within a cellular context. PMID:26148529

  16. Flux balance analysis of cyanobacterial metabolism: the metabolic network of Synechocystis sp. PCC 6803.

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    Henning Knoop

    Full Text Available Cyanobacteria are versatile unicellular phototrophic microorganisms that are highly abundant in many environments. Owing to their capability to utilize solar energy and atmospheric carbon dioxide for growth, cyanobacteria are increasingly recognized as a prolific resource for the synthesis of valuable chemicals and various biofuels. To fully harness the metabolic capabilities of cyanobacteria necessitates an in-depth understanding of the metabolic interconversions taking place during phototrophic growth, as provided by genome-scale reconstructions of microbial organisms. Here we present an extended reconstruction and analysis of the metabolic network of the unicellular cyanobacterium Synechocystis sp. PCC 6803. Building upon several recent reconstructions of cyanobacterial metabolism, unclear reaction steps are experimentally validated and the functional consequences of unknown or dissenting pathway topologies are discussed. The updated model integrates novel results with respect to the cyanobacterial TCA cycle, an alleged glyoxylate shunt, and the role of photorespiration in cellular growth. Going beyond conventional flux-balance analysis, we extend the computational analysis to diurnal light/dark cycles of cyanobacterial metabolism.

  17. Improvement of bacterial cellulose production by manipulating the metabolic pathways in which ethanol and sodium citrate involved.

    Science.gov (United States)

    Li, Yuanjing; Tian, Chunjie; Tian, Hua; Zhang, Jiliang; He, Xin; Ping, Wenxiang; Lei, Hong

    2012-12-01

    Nowadays, bacterial cellulose has played more and more important role as new biological material for food industry and medical and industrial products based on its unique properties. However, it is still a difficult task to improve the production of bacterial cellulose, especially a large number of byproducts are produced in the metabolic biosynthesis processes. To improve bacterial cellulose production, ethanol and sodium citrate are added into the medium during the fermentation, and the activities of key enzymes and concentration of extracellular metabolites are measured to assess the changes of the metabolic flux of the hexose monophosphate pathway (HMP), the Embden-Meyerhof-Parnas pathway (EMP), and the tricarboxylic acid cycle (TCA). Our results indicate that ethanol functions as energy source for ATP generation at the early stage of the fermentation in the HMP pathway and the supplementation of ethanol significantly reduces glycerol generation (a major byproduct). While in the EMP pathway, sodium citrate plays a key role, and its supplementation results in the byproducts (mainly acetic acid and pyruvic acid) entering the gluconeogenesis pathway for cellulose synthesis. Furthermore, by adding ethanol and sodium citrate, the main byproduct citric acid in the TCA cycle is also reduced significantly. It is concluded that bacterial cellulose production can be improved by increasing energy metabolism and reducing the formation of metabolic byproducts through the metabolic regulations of the bypasses.

  18. Rapid countermeasure discovery against Francisella tularensis based on a metabolic network reconstruction.

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    Sidhartha Chaudhury

    Full Text Available In the future, we may be faced with the need to provide treatment for an emergent biological threat against which existing vaccines and drugs have limited efficacy or availability. To prepare for this eventuality, our objective was to use a metabolic network-based approach to rapidly identify potential drug targets and prospectively screen and validate novel small-molecule antimicrobials. Our target organism was the fully virulent Francisella tularensis subspecies tularensis Schu S4 strain, a highly infectious intracellular pathogen that is the causative agent of tularemia and is classified as a category A biological agent by the Centers for Disease Control and Prevention. We proceeded with a staggered computational and experimental workflow that used a strain-specific metabolic network model, homology modeling and X-ray crystallography of protein targets, and ligand- and structure-based drug design. Selected compounds were subsequently filtered based on physiological-based pharmacokinetic modeling, and we selected a final set of 40 compounds for experimental validation of antimicrobial activity. We began screening these compounds in whole bacterial cell-based assays in biosafety level 3 facilities in the 20th week of the study and completed the screens within 12 weeks. Six compounds showed significant growth inhibition of F. tularensis, and we determined their respective minimum inhibitory concentrations and mammalian cell cytotoxicities. The most promising compound had a low molecular weight, was non-toxic, and abolished bacterial growth at 13 µM, with putative activity against pantetheine-phosphate adenylyltransferase, an enzyme involved in the biosynthesis of coenzyme A, encoded by gene coaD. The novel antimicrobial compounds identified in this study serve as starting points for lead optimization, animal testing, and drug development against tularemia. Our integrated in silico/in vitro approach had an overall 15% success rate in terms of

  19. Genome-scale reconstruction of the Streptococcus pyogenes M49 metabolic network reveals growth requirements and indicates potential drug targets

    NARCIS (Netherlands)

    Levering, J.; Fiedler, T.; Sieg, A.; van Grinsven, K.W.A.; Hering, S.; Veith, N.; Olivier, B.G.; Klett, L.; Hugenholtz, J.; Teusink, B.; Kreikemeyer, B.; Kummer, U.

    2016-01-01

    Genome-scale metabolic models comprise stoichiometric relations between metabolites, as well as associations between genes and metabolic reactions and facilitate the analysis of metabolism. We computationally reconstructed the metabolic network of the lactic acid bacterium Streptococcus pyogenes M49

  20. Indoor Heating Drives Water Bacterial Growth and Community Metabolic Profile Changes in Building Tap Pipes during the Winter Season

    Directory of Open Access Journals (Sweden)

    Hai-Han Zhang

    2015-10-01

    Full Text Available The growth of the bacterial community harbored in indoor drinking water taps is regulated by external environmental factors, such as indoor temperature. However, the effect of indoor heating on bacterial regrowth associated with indoor drinking water taps is poorly understood. In the present work, flow cytometry and community-level sole-carbon-source utilization techniques were combined to explore the effects of indoor heating on water bacterial cell concentrations and community carbon metabolic profiles in building tap pipes during the winter season. The results showed that the temperature of water stagnated overnight (“before” in the indoor water pipes was 15–17 °C, and the water temperature decreased to 4–6 °C after flushing for 10 min (“flushed”. The highest bacterial cell number was observed in water stagnated overnight, and was 5–11 times higher than that of flushed water. Meanwhile, a significantly higher bacterial community metabolic activity (AWCD590nm was also found in overnight stagnation water samples. The significant “flushed” and “taps” values indicated that the AWCD590nm, and bacterial cell number varied among the taps within the flushed group (p < 0.01. Heatmap fingerprints and principle component analyses (PCA revealed a significant discrimination bacterial community functional metabolic profiles in the water stagnated overnight and flushed water. Serine, threonine, glucose-phosphate, ketobutyric acid, phenylethylamine, glycerol, putrescine were significantly used by “before” water samples. The results suggested that water stagnated at higher temperature should be treated before drinking because of bacterial regrowth. The data from this work provides useful information on reasonable utilization of drinking water after stagnation in indoor pipes during indoor heating periods.

  1. Bow-tie topological features of metabolic networks and the functional significance

    Institute of Scientific and Technical Information of China (English)

    ZHAO Jing; TAO Lin; YU Hong; LUO JianHua; GAO ZhiWei; LI YiXue

    2007-01-01

    Exploring the structural topology of genome-based large-scale metabolic network is essential for in vestigating possible relations between structure and functionality. Visualization would be helpful for obtaining immediate information about structural organization. In this work, metabolic networks of 75 organisms were investigated from a topological point of view. A spread bow-tie model was proposed to give a clear visualization of the bow-tie structure for metabolic networks. The revealed topological pattern helps to design more efficient algorithm specifically for metabolic networks. This coarsegrained graph also visualizes the vulnerable connections in the network, and thus could have important implication for disease studies and drug target identifications. In addition, analysis on the reciprocal links and main cores in the GSC part of bow-tie also reveals that the bow-tie structure of metabolic networks has its own intrinsic and significant features which are significantly different from those of random networks.

  2. Phase resetting reveals network dynamics underlying a bacterial cell cycle.

    Directory of Open Access Journals (Sweden)

    Yihan Lin

    Full Text Available Genomic and proteomic methods yield networks of biological regulatory interactions but do not provide direct insight into how those interactions are organized into functional modules, or how information flows from one module to another. In this work we introduce an approach that provides this complementary information and apply it to the bacterium Caulobacter crescentus, a paradigm for cell-cycle control. Operationally, we use an inducible promoter to express the essential transcriptional regulatory gene ctrA in a periodic, pulsed fashion. This chemical perturbation causes the population of cells to divide synchronously, and we use the resulting advance or delay of the division times of single cells to construct a phase resetting curve. We find that delay is strongly favored over advance. This finding is surprising since it does not follow from the temporal expression profile of CtrA and, in turn, simulations of existing network models. We propose a phenomenological model that suggests that the cell-cycle network comprises two distinct functional modules that oscillate autonomously and couple in a highly asymmetric fashion. These features collectively provide a new mechanism for tight temporal control of the cell cycle in C. crescentus. We discuss how the procedure can serve as the basis for a general approach for probing network dynamics, which we term chemical perturbation spectroscopy (CPS.

  3. Metabolic Investigation in Gluconacetobacter xylinus and Its Bacterial Cellulose Production under a Direct Current Electric Field.

    Science.gov (United States)

    Liu, Miao; Zhong, Cheng; Zhang, Yu Ming; Xu, Ze Ming; Qiao, Chang Sheng; Jia, Shi Ru

    2016-01-01

    The effects of a direct current (DC) electric field on the growth and metabolism of Gluconacetobacter xylinus were investigated in static culture. When a DC electric field at 10 mA was applied using platinum electrodes to the culture broth, bacterial cellulose (BC) production was promoted in 12 h but was inhibited in the last 12 h as compared to the control (without DC electric field). At the cathode, the presence of the hydrogen generated a strong reductive environment that is beneficial to cell growth. As compared to the control, the activities of glycolysis and tricarboxylic acid cycle, as well as BC productivity were observed to be slightly higher in the first 12 h. However, due to the absence of sufficient oxygen, lactic acid was accumulated from pyruvic acid at 18 h, which was not in favor of BC production. At the anode, DC inhibited cell growth in 6 h when compared to the control. The metabolic activity in G. xylinus was inhibited through the suppression of the tricarboxylic acid cycle and glycolysis. At 18-24 h, cell density was observed to decrease, which might be due to the electrolysis of water that significantly dropped the pH of cultural broth far beyond the optimal range. Meanwhile, metabolites for self-protection were accumulated, for instance proline, glutamic acid, gluconic acid, and fatty acids. Notably, the accumulation of gluconic acid and lactic acid made it a really tough acid stress to cells at the anode and finally led to depression of cell growth.

  4. Metabolic activity of bacterial cell enumerated by direct viable count. [Escherichia coli; Salmonella enteritidis

    Energy Technology Data Exchange (ETDEWEB)

    Roszak, D.B.; Colwell, R.R.

    1987-12-01

    The direct viable count (DVC) method was modified by incorporation radiolabeled substrates in microautoradiographic analyses to assess bacterial survival in controlled laboratory microcosms. The DVC method, which permits enumeration of culturable and nonculturable cells, discriminates those cells that are responsive to added nutrients but in which division is inhibited by the addition of nalidixic acid. The resulting elongated cells represent all viable cells; this includes those that are culturable on routine media and those that are not. Escherichia coli and Salmonella enteritidis were employed in the microcosm studies, and radiolabeled substrates included (methyl-/sup 3/H) thymidine or (U-/sup 14/C) glutamic acid. Samples taken at selected intervals during the survival experiments were examined by epifluorescence microscopy to enumerate cells by the DVC and acridine orange direct count methods, as well as by culture methods. Good correlation was obtained for cell-associated metabolic activity, measured by microautoradiography and substrate responsiveness (by the DVC method) at various stages of survival. Of the cells responsive to nutrients by the DVC method, ca. 90% were metabolically active by the microautoradiographic method. No significant difference was observed between DVC enumerations with or without added radiolabeled substrate.

  5. Exploring photosynthesis evolution by comparative analysis of metabolic networks between chloroplasts and photosynthetic bacteria

    Directory of Open Access Journals (Sweden)

    Hou Jing

    2006-04-01

    Full Text Available Abstract Background Chloroplasts descended from cyanobacteria and have a drastically reduced genome following an endosymbiotic event. Many genes of the ancestral cyanobacterial genome have been transferred to the plant nuclear genome by horizontal gene transfer. However, a selective set of metabolism pathways is maintained in chloroplasts using both chloroplast genome encoded and nuclear genome encoded enzymes. As an organelle specialized for carrying out photosynthesis, does the chloroplast metabolic network have properties adapted for higher efficiency of photosynthesis? We compared metabolic network properties of chloroplasts and prokaryotic photosynthetic organisms, mostly cyanobacteria, based on metabolic maps derived from genome data to identify features of chloroplast network properties that are different from cyanobacteria and to analyze possible functional significance of those features. Results The properties of the entire metabolic network and the sub-network that consists of reactions directly connected to the Calvin Cycle have been analyzed using hypergraph representation. Results showed that the whole metabolic networks in chloroplast and cyanobacteria both possess small-world network properties. Although the number of compounds and reactions in chloroplasts is less than that in cyanobacteria, the chloroplast's metabolic network has longer average path length, a larger diameter, and is Calvin Cycle -centered, indicating an overall less-dense network structure with specific and local high density areas in chloroplasts. Moreover, chloroplast metabolic network exhibits a better modular organization than cyanobacterial ones. Enzymes involved in the same metabolic processes tend to cluster into the same module in chloroplasts. Conclusion In summary, the differences in metabolic network properties may reflect the evolutionary changes during endosymbiosis that led to the improvement of the photosynthesis efficiency in higher plants. Our

  6. Characterizing the interplay between multiple levels of organization within bacterial sigma factor regulatory networks.

    Science.gov (United States)

    Qiu, Yu; Nagarajan, Harish; Embree, Mallory; Shieu, Wendy; Abate, Elisa; Juárez, Katy; Cho, Byung-Kwan; Elkins, James G; Nevin, Kelly P; Barrett, Christian L; Lovley, Derek R; Palsson, Bernhard O; Zengler, Karsten

    2013-01-01

    Bacteria contain multiple sigma factors, each targeting diverse, but often overlapping sets of promoters, thereby forming a complex network. The layout and deployment of such a sigma factor network directly impacts global transcriptional regulation and ultimately dictates the phenotype. Here we integrate multi-omic data sets to determine the topology, the operational, and functional states of the sigma factor network in Geobacter sulfurreducens, revealing a unique network topology of interacting sigma factors. Analysis of the operational state of the sigma factor network shows a highly modular structure with σ(N) being the major regulator of energy metabolism. Surprisingly, the functional state of the network during the two most divergent growth conditions is nearly static, with sigma factor binding profiles almost invariant to environmental stimuli. This first comprehensive elucidation of the interplay between different levels of the sigma factor network organization is fundamental to characterize transcriptional regulatory mechanisms in bacteria.

  7. Characterizing the interplay betwen mulitple levels of organization within bacterial sigma factor regulatory networks

    Energy Technology Data Exchange (ETDEWEB)

    Yu, Qiu [University of California, San Diego; Nagarajan, Harish [University of California, San Diego; Embree, Mallory [University of California, San Diego; Shieu, Wendy [University of California, San Diego; Abate, Elisa [University of California, San Diego; Juarez, Katy [Universidad Nacional Autonoma de Mexico (UNAM); Cho, Byung-Kwan [University of California, San Diego; Elkins, James G [ORNL; Nevin, Kelly P. [University of Massachusetts, Amherst; Barrett, Christian [University of California, San Diego; Lovley, Derek [University of Massachusetts, Amherst; Palsson, Bernhard O. [University of California, San Diego; Zengler, Karsten [University of California, San Diego

    2013-01-01

    Bacteria contain multiple sigma factors, each targeting diverse, but often overlapping sets of promoters, thereby forming a complex network. The layout and deployment of such a sigma factor network directly impacts global transcriptional regulation and ultimately dictates the phenotype. Here we integrate multi-omic data sets to determine the topology, the operational, and functional states of the sigma factor network in Geobacter sulfurreducens, revealing a unique network topology of interacting sigma factors. Analysis of the operational state of the sigma factor network shows a highly modular structure with sN being the major regulator of energy metabolism. Surprisingly, the functional state of the network during the two most divergent growth conditions is nearly static, with sigma factor binding profiles almost invariant to environmental stimuli. This first comprehensive elucidation of the interplay between different levels of the sigma factor network organization is fundamental to characterize transcriptional regulatory mechanisms in bacteria.

  8. Comparative genomic reconstruction of transcriptional networks controlling central metabolism in the Shewanella genus

    Energy Technology Data Exchange (ETDEWEB)

    Rodionov, Dmitry A.; Novichkov, Pavel; Stavrovskaya, Elena D.; Rodionova, Irina A.; Li, Xiaoqing; Kazanov, Marat D.; Ravcheev, Dmitry A.; Gerasimova, Anna V.; Kazakov, Alexey E.; Kovaleva, Galina Y.; Permina, Elizabeth A.; Laikova, Olga N.; Overbeek, Ross; Romine, Margaret F.; Fredrickson, Jim K.; Arkin, Adam P.; Dubchak, Inna; Osterman, Andrei L.; Gelfand, Mikhail S.

    2011-06-15

    Genome-scale prediction of gene regulation and reconstruction of transcriptional regulatory networks in bacteria is one of the critical tasks of modern genomics. Despite the growing number of genome-scale gene expression studies, our abilities to convert the results of these studies into accurate regulatory annotations and to project them from model to other organisms are extremely limited. The comparative genomics approaches and computational identification of regulatory sites are useful for the in silico reconstruction of transcriptional regulatory networks in bacteria. The Shewanella genus is comprised of metabolically versatile gamma-proteobacteria, whose lifestyles and natural environments are substantially different from Escherichia coli and other model bacterial species. To explore conservation and variations in the Shewanella transcriptional networks we analyzed the repertoire of transcription factors and performed genomics-based reconstruction and comparative analysis of regulons in 16 Shewanella genomes. The inferred regulatory network includes 82 transcription factors and their DNA binding sites, 8 riboswitches and 6 translational attenuators. Forty five regulons were newly inferred from the genome context analysis, whereas others were propagated from previously characterized regulons in the Enterobacteria and Pseudomonas spp.. However, even orthologous regulators with conserved DNA-binding motifs may control substantially different gene sets, revealing striking differences in regulatory strategies between the Shewanella spp. and E. coli. Multiple examples of regulatory network rewiring include regulon contraction and expansion (as in the case of PdhR, HexR, FadR), and numerous cases of recruiting non-orthologous regulators to control equivalent pathways (e.g. NagR for N-acetylglucosamine catabolism and PsrA for fatty acid degradation) and, conversely, orthologous regulators to control distinct pathways (e.g. TyrR, ArgR, Crp).

  9. Early Changes in Soil Metabolic Diversity and Bacterial Community Structure in Sugarcane under Two Harvest Management Systems

    Directory of Open Access Journals (Sweden)

    Lucas Carvalho Basilio Azevedo

    2015-06-01

    Full Text Available Preharvest burning is widely used in Brazil for sugarcane cropping. However, due to environmental restrictions, harvest without burning is becoming the predominant option. Consequently, changes in the microbial community are expected from crop residue accumulation on the soil surface, as well as alterations in soil metabolic diversity as of the first harvest. Because biological properties respond quickly and can be used to monitor environmental changes, we evaluated soil metabolic diversity and bacterial community structure after the first harvest under sugarcane management without burning compared to management with preharvest burning. Soil samples were collected under three sugarcane varieties (SP813250, SP801842 and RB72454 and two harvest management systems (without and with preharvest burning. Microbial biomass C (MBC, carbon (C substrate utilization profiles, bacterial community structure (based on profiles of 16S rRNA gene amplicons, and soil chemical properties were determined. MBC was not different among the treatments. C-substrate utilization and metabolic diversity were lower in soil without burning, except for the evenness index of C-substrate utilization. Soil samples under the variety SP801842 showed the greatest changes in substrate utilization and metabolic diversity, but showed no differences in bacterial community structure, regardless of the harvest management system. In conclusion, combined analysis of soil chemical and microbiological data can detect early changes in microbial metabolic capacity and diversity, with lower values in management without burning. However, after the first harvest, there were no changes in the soil bacterial community structure detected by PCR-DGGE under the sugarcane variety SP801842. Therefore, the metabolic profile is a more sensitive indicator of early changes in the soil microbial community caused by the harvest management system.

  10. Perspectives for a better understanding of the metabolic integration of photorespiration within a complex plant primary metabolism network.

    Science.gov (United States)

    Hodges, Michael; Dellero, Younès; Keech, Olivier; Betti, Marco; Raghavendra, Agepati S; Sage, Rowan; Zhu, Xin-Guang; Allen, Doug K; Weber, Andreas P M

    2016-05-01

    Photorespiration is an essential high flux metabolic pathway that is found in all oxygen-producing photosynthetic organisms. It is often viewed as a closed metabolic repair pathway that serves to detoxify 2-phosphoglycolic acid and to recycle carbon to fuel the Calvin-Benson cycle. However, this view is too simplistic since the photorespiratory cycle is known to interact with several primary metabolic pathways, including photosynthesis, nitrate assimilation, amino acid metabolism, C1 metabolism and the Krebs (TCA) cycle. Here we will review recent advances in photorespiration research and discuss future priorities to better understand (i) the metabolic integration of the photorespiratory cycle within the complex network of plant primary metabolism and (ii) the importance of photorespiration in response to abiotic and biotic stresses.

  11. A flexible state-space approach for the modeling of metabolic networks II: advanced interrogation of hybridoma metabolism.

    Science.gov (United States)

    Baughman, Adam C; Sharfstein, Susan T; Martin, Lealon L

    2011-03-01

    Having previously introduced the mathematical framework of topological metabolic analysis (TMA) - a novel optimization-based technique for modeling metabolic networks of arbitrary size and complexity - we demonstrate how TMA facilitates unique methods of metabolic interrogation. With the aid of several hybridoma metabolic investigations as case-studies (Bonarius et al., 1995, 1996, 2001), we first establish that the TMA framework identifies biologically important aspects of the metabolic network under investigation. We also show that the use of a structured weighting approach within our objective provides a substantial modeling benefit over an unstructured, uniform, weighting approach. We then illustrate the strength of TAM as an advanced interrogation technique, first by using TMA to prove the existence of (and to quantitatively describe) multiple topologically distinct configurations of a metabolic network that each optimally model a given set of experimental observations. We further show that such alternate topologies are indistinguishable using existing stoichiometric modeling techniques, and we explain the biological significance of the topological variables appearing within our model. By leveraging the manner in which TMA implements metabolite inputs and outputs, we also show that metabolites whose possible metabolic fates are inadequately described by a given network reconstruction can be quickly identified. Lastly, we show how the use of the TMA aggregate objective function (AOF) permits the identification of modeling solutions that can simultaneously consider experimental observations, underlying biological motivations, or even purely engineering- or design-based goals.

  12. A Bayesian approach to the evolution of metabolic networks on a phylogeny.

    Directory of Open Access Journals (Sweden)

    Aziz Mithani

    Full Text Available The availability of genomes of many closely related bacteria with diverse metabolic capabilities offers the possibility of tracing metabolic evolution on a phylogeny relating the genomes to understand the evolutionary processes and constraints that affect the evolution of metabolic networks. Using simple (independent loss/gain of reactions or complex (incorporating dependencies among reactions stochastic models of metabolic evolution, it is possible to study how metabolic networks evolve over time. Here, we describe a model that takes the reaction neighborhood into account when modeling metabolic evolution. The model also allows estimation of the strength of the neighborhood effect during the course of evolution. We present Gibbs samplers for sampling networks at the internal node of a phylogeny and for estimating the parameters of evolution over a phylogeny without exploring the whole search space by iteratively sampling from the conditional distributions of the internal networks and parameters. The samplers are used to estimate the parameters of evolution of metabolic networks of bacteria in the genus Pseudomonas and to infer the metabolic networks of the ancestral pseudomonads. The results suggest that pathway maps that are conserved across the Pseudomonas phylogeny have a stronger neighborhood structure than those which have a variable distribution of reactions across the phylogeny, and that some Pseudomonas lineages are going through genome reduction resulting in the loss of a number of reactions from their metabolic networks.

  13. A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology

    NARCIS (Netherlands)

    Herrgård, Markus J.; Swainston, Neil; Dobson, Paul; Dunn, Warwick B.; Arga, K. Yalçin; Arvas, Mikko; Blüthgen, Nils; Borger, Simon; Costenoble, Roeland; Heinemann, Matthias; Hucka, Michael; Novère, Nicolas Le; Li, Peter; Liebermeister, Wolfram; Mo, Monica L.; Oliveira, Ana Paula; Petranovic, Dina; Pettifer, Stephen; Simeonidis, Evangelos; Smallbone, Kieran; Spasić, Irena; Weichart, Dieter; Brent, Roger; Broomhead, David S.; Westerhoff, Hans V.; Kırdar, Betül; Penttilä, Merja; Klipp, Edda; Palsson, Bernhard Ø.; Sauer, Uwe; Oliver, Stephen G.; Mendes, Pedro; Nielsen, Jens; Kell, Douglas B.

    2008-01-01

    Genomic data allow the large-scale manual or semi-automated assembly of metabolic network reconstructions, which provide highly curated organism-specific knowledge bases. Although several genome-scale network reconstructions describe Saccharomyces cerevisiae metabolism, they differ in scope and cont

  14. Potential changes in bacterial metabolism associated with increased water temperature and nutrient inputs in tropical humic lagoons.

    Science.gov (United States)

    Scofield, Vinicius; Jacques, Saulo M S; Guimarães, Jean R D; Farjalla, Vinicius F

    2015-01-01

    Temperature and nutrient concentrations regulate aquatic bacterial metabolism. However, few studies have focused on the effect of the interaction between these factors on bacterial processes, and none have been performed in tropical aquatic ecosystems. We analyzed the main and interactive effects of changes in water temperature and N and P concentrations on bacterioplankton production (BP), bacterioplankton respiration (BR) and bacterial growth efficiency (BGE) in tropical coastal lagoons. We used a factorial design with three levels of water temperature (25, 30, and 35°C) and four levels of N and/or P additions (Control, N, P, and NP additions) in five tropical humic lagoons. When data for all lagoons were pooled together, a weak interaction was observed between the increase in water temperature and the addition of nutrients. Water temperature alone had the greatest impact on bacterial metabolism by increasing BR, decreasing BP, and decreasing BGE. An increase of 1°C lead to an increase of ~4% in BR, a decrease of ~0.9% in BP, and a decrease of ~4% in BGE. When data were analyzed separately, lagoons responded differently to nutrient additions depending on Dissolved Organic Carbon (DOC) concentration. Lagoons with lowest DOC concentrations showed the strongest responses to nutrient additions: BP increased in response to N, P, and their interaction, BR increased in response to N and the interaction between N and P, and BGE was negatively affected, mainly by the interaction between N and P additions. Lagoons with the highest DOC concentrations showed almost no significant relationship with nutrient additions. Taken together, these results show that different environmental drivers impact bacterial processes at different scales. Changes of bacterial metabolism related to the increase of water temperature are consistent between lagoons, therefore their consequences can be predicted at a regional scale, while the effect of nutrient inputs is specific to different

  15. Metabolic investigation in Gluconacetobacter xylinus and its bacterial cellulose production under a direct current electric field

    Directory of Open Access Journals (Sweden)

    Miao eLiu

    2016-03-01

    Full Text Available The effects of a direct current (DC electric field on the growth and metabolism of Gluconacetobacter xylinus were investigated in static culture. When a DC electric field at 10 mA was applied using platinum electrodes to the culture broth, bacterial cellulose (BC production was promoted in 12 hours (h but was inhibited in the last 12 h as compared to the control (without DC electric field. At the cathode, the presence of the hydrogen generated a strong reductive environment that is beneficial to cell growth. As compared to the control, the activities of glycolysis and tricarboxylic acid cycle, as well as BC productivity were observed to be slightly higher in the first 12 h. However, due to the absence of sufficient oxygen, lactic acid was accumulated from pyruvic acid at 18 h, which was not in favor of BC production. At the anode, DC inhibited cell growth in 6 h when compared to the control. The metabolic activity in G. xylinus was inhibited through the suppression of the tricarboxylic acid cycle and glycolysis. At 18-24 h, cell density was observed to decrease, which might be due to the electrolysis of water that significantly dropped the pH of cultural broth far beyond the optimal range. Meanwhile, metabolites for self-protection were accumulated, for instance proline, glutamic acid, gluconic acid and fatty acids. Notably, the accumulation of gluconic acid and lactic acid made it a really tough acid stress to cells at the anode and finally led to depression of cell growth.

  16. A state of the art of metabolic networks of unicellular microalgae and cyanobacteria for biofuel production.

    Science.gov (United States)

    Baroukh, Caroline; Muñoz-Tamayo, Rafael; Steyer, Jean-Philippe; Bernard, Olivier

    2015-07-01

    The most promising and yet challenging application of microalgae and cyanobacteria is the production of renewable energy: biodiesel from microalgae triacylglycerols and bioethanol from cyanobacteria carbohydrates. A thorough understanding of microalgal and cyanobacterial metabolism is necessary to master and optimize biofuel production yields. To this end, systems biology and metabolic modeling have proven to be very efficient tools if supported by an accurate knowledge of the metabolic network. However, unlike heterotrophic microorganisms that utilize the same substrate for energy and as carbon source, microalgae and cyanobacteria require light for energy and inorganic carbon (CO2 or bicarbonate) as carbon source. This double specificity, together with the complex mechanisms of light capture, makes the representation of metabolic network nonstandard. Here, we review the existing metabolic networks of photoautotrophic microalgae and cyanobacteria. We highlight how these networks have been useful for gaining insight on photoautotrophic metabolism.

  17. Controllability in cancer metabolic networks according to drug targets as driver nodes.

    Science.gov (United States)

    Asgari, Yazdan; Salehzadeh-Yazdi, Ali; Schreiber, Falk; Masoudi-Nejad, Ali

    2013-01-01

    Networks are employed to represent many nonlinear complex systems in the real world. The topological aspects and relationships between the structure and function of biological networks have been widely studied in the past few decades. However dynamic and control features of complex networks have not been widely researched, in comparison to topological network features. In this study, we explore the relationship between network controllability, topological parameters, and network medicine (metabolic drug targets). Considering the assumption that targets of approved anticancer metabolic drugs are driver nodes (which control cancer metabolic networks), we have applied topological analysis to genome-scale metabolic models of 15 normal and corresponding cancer cell types. The results show that besides primary network parameters, more complex network metrics such as motifs and clusters may also be appropriate for controlling the systems providing the controllability relationship between topological parameters and drug targets. Consequently, this study reveals the possibilities of following a set of driver nodes in network clusters instead of considering them individually according to their centralities. This outcome suggests considering distributed control systems instead of nodal control for cancer metabolic networks, leading to a new strategy in the field of network medicine.

  18. Horizontal and vertical growth of S. cerevisiae metabolic network

    OpenAIRE

    Tramontano Anna; Grassi Luigi

    2011-01-01

    Abstract Background The growth and development of a biological organism is reflected by its metabolic network, the evolution of which relies on the essential gene duplication mechanism. There are two current views about the evolution of metabolic networks. The retrograde model hypothesizes that a pathway evolves by recruiting novel enzymes in a direction opposite to the metabolic flow. The patchwork model is instead based on the assumption that the evolution is based on the exploitation of br...

  19. Reconstruction and analysis of human liver-specific metabolic network based on CNHLPP data.

    Science.gov (United States)

    Zhao, Jing; Geng, Chao; Tao, Lin; Zhang, Duanfeng; Jiang, Ying; Tang, Kailin; Zhu, Ruixin; Yu, Hong; Zhang, Weidong; He, Fuchu; Li, Yixue; Cao, Zhiwei

    2010-04-05

    Liver is the largest internal organ in the body that takes central roles in metabolic homeostasis, detoxification of various substances, as well as in the synthesis and storage of nutrients. To fulfill these complex tasks, thousands of biochemical reactions are going on in liver to cope with a wide range of foods and environmental variations, which are densely interconnected into an intricate metabolic network. Here, the first human liver-specific metabolic network was reconstructed according to proteomics data from Chinese Human Liver Proteome Project (CNHLPP), and then investigated in the context of the genome-scale metabolic network of Homo sapiens. Topological analysis shows that this organ-specific metabolic network exhibits similar features as organism-specific networks, such as power-law degree distribution, small-world property, and bow-tie structure. Furthermore, the structure of liver network exhibits a modular organization where the modules are formed around precursors from primary metabolism or hub metabolites from derivative metabolism, respectively. Most of the modules are dominated by one major category of metabolisms, while enzymes within same modules have a tendency of being expressed concertedly at protein level. Network decomposition and comparison suggest that the liver network overlays a predominant area in the global metabolic network of H. sapiens genome; meanwhile the human network may develop extra modules to gain more specialized functionality out of liver. The results of this study would permit a high-level interpretation of the metabolite information flow in human liver and provide a basis for modeling the physiological and pathological metabolic states of liver.

  20. Bacterial DNA Sequence Compression Models Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Armando J. Pinho

    2013-08-01

    Full Text Available It is widely accepted that the advances in DNA sequencing techniques have contributed to an unprecedented growth of genomic data. This fact has increased the interest in DNA compression, not only from the information theory and biology points of view, but also from a practical perspective, since such sequences require storage resources. Several compression methods exist, and particularly, those using finite-context models (FCMs have received increasing attention, as they have been proven to effectively compress DNA sequences with low bits-per-base, as well as low encoding/decoding time-per-base. However, the amount of run-time memory required to store high-order finite-context models may become impractical, since a context-order as low as 16 requires a maximum of 17.2 x 109 memory entries. This paper presents a method to reduce such a memory requirement by using a novel application of artificial neural networks (ANN to build such probabilistic models in a compact way and shows how to use them to estimate the probabilities. Such a system was implemented, and its performance compared against state-of-the art compressors, such as XM-DNA (expert model and FCM-Mx (mixture of finite-context models , as well as with general-purpose compressors. Using a combination of order-10 FCM and ANN, similar encoding results to those of FCM, up to order-16, are obtained using only 17 megabytes of memory, whereas the latter, even employing hash-tables, uses several hundreds of megabytes.

  1. Tapping into Salmonella typhimurium LT2 genome in a quest to explore its therapeutic arsenal: A metabolic network modeling approach.

    Science.gov (United States)

    Mehla, Kusum; Ramana, Jayashree

    2017-02-01

    S. typhimurium, the classical broad-host-range serovar is a widely distributed cause of food-borne illness. Escalating antibiotic resistance and potential of conjugal transmission to other pathogens attributable to its broad spectrum host specificities have aided S. typhimurium to emerge as a global health threat. To keep pace with ever evolving bacterial defenses, there is dire need to restock the antibiotic pipeline. Genome scale metabolic reconstructions present immense possibilities to decipher physiological properties of an organism using constraint-based methods The systems-level approaches of genome scale metabolic networks interrogation open up new avenues of drug target identification against deadly infectious diseases. We performed flux balance analysis and minimization of metabolic adjustment studies of genome scale reconstruction model of S. typhimurium targeted at identifying large number of metabolites with a potential to be utilized as therapeutic drug targets. These constraint based approaches initially predict a set of genes indispensable to bacterial survival by performing gene knockout studies which are then prioritized through a multistep process. Metabolites involved in l-rhamnose biosynthesis, peptidoglycan biosynthesis, fatty acid biosynthesis, and folate biosynthesis pathways were prioritized as candidate drug targets. This study provides a general therapeutic approach which can be effectively applied to other pathogens as well.

  2. Metabolic and phylogenetic profile of bacterial community in Guishan coastal water (Pearl River Estuary), South China Sea

    Science.gov (United States)

    Hu, Xiaojuan; Liu, Qing; Li, Zhuojia; He, Zhili; Gong, Yingxue; Cao, Yucheng; Yang, Yufeng

    2014-10-01

    Characteristics of a microbial community are important as they indicate the status of aquatic ecosystems. In the present study, the metabolic and phylogenetic profile of the bacterioplankton community in Guishan coastal water (Pearl River Estuary), South China Sea, at 12 sites (S1-S12) were explored by community-level physiological profiling (CLPP) with BIOLOG Eco-plate and denaturing gradient gel electrophoresis (DGGE). Our results showed that the core mariculture area (S6, S7 and S8) and the sites associating with human activity and sewage discharge (S11 and S12) had higher microbial metabolic capability and bacterial community diversity than others (S1-5, S9-10). Especially, the diversity index of S11 and S12 calculated from both CLPP and DGGE data ( H>3.2) was higher than that of others as sewage discharge may increase water nitrogen and phosphorus nutrient. The bacterial community structure of S6, S8, S11 and S12 was greatly influenced by total phosphorous, salinity and total nitrogen. Based on DGGE fingerprinting, proteobacteria, especially γ- and α-proteobacteria, were found dominant at all sites. In conclusion, the aquaculture area and wharf had high microbial metabolic capability. The structure and composition of bacterial community were closely related to the level of phosphorus, salinity and nitrogen.

  3. Many means to a common end: the intricacies of (p)ppGpp metabolism and its control of bacterial homeostasis.

    Science.gov (United States)

    Gaca, Anthony O; Colomer-Winter, Cristina; Lemos, José A

    2015-04-01

    In nearly all bacterial species examined so far, amino acid starvation triggers the rapid accumulation of the nucleotide second messenger (p)ppGpp, the effector of the stringent response. While for years the enzymes involved in (p)ppGpp metabolism and the significance of (p)ppGpp accumulation to stress survival were considered well defined, a recent surge of interest in the field has uncovered an unanticipated level of diversity in how bacteria metabolize and utilize (p)ppGpp to rapidly synchronize a variety of biological processes important for growth and stress survival. In addition to the classic activation of the stringent response, it has become evident that (p)ppGpp exerts differential effects on cell physiology in an incremental manner rather than simply acting as a biphasic switch that controls growth or stasis. Of particular interest is the intimate relationship of (p)ppGpp with persister cell formation and virulence, which has spurred the pursuit of (p)ppGpp inhibitors as a means to control recalcitrant infections. Here, we present an overview of the enzymes responsible for (p)ppGpp metabolism, elaborate on the intricacies that link basal production of (p)ppGpp to bacterial homeostasis, and discuss the implications of targeting (p)ppGpp synthesis as a means to disrupt long-term bacterial survival strategies.

  4. Computational approaches to the topology, stability and dynamics of metabolic networks.

    Science.gov (United States)

    Steuer, Ralf

    2007-01-01

    Cellular metabolism is characterized by an intricate network of interactions between biochemical fluxes, metabolic compounds and regulatory interactions. To investigate and eventually understand the emergent global behavior arising from such networks of interaction is not possible by intuitive reasoning alone. This contribution seeks to describe recent computational approaches that aim to asses the topological and functional properties of metabolic networks. In particular, based on a recently proposed method, it is shown that it is possible to acquire a quantitative picture of the possible dynamics of metabolic systems, without assuming detailed knowledge of the underlying enzyme-kinetic rate equations and parameters. Rather, the method builds upon a statistical exploration of the comprehensive parameter space to evaluate the dynamic capabilities of a metabolic system, thus providing a first step towards the transition from topology to function of metabolic pathways. Utilizing this approach, the role of feedback mechanisms in the maintenance of stability is discussed using minimal models of cellular pathways.

  5. Estimation of the number of extreme pathways for metabolic networks

    Directory of Open Access Journals (Sweden)

    Thiele Ines

    2007-09-01

    Full Text Available Abstract Background The set of extreme pathways (ExPa, {pi}, defines the convex basis vectors used for the mathematical characterization of the null space of the stoichiometric matrix for biochemical reaction networks. ExPa analysis has been used for a number of studies to determine properties of metabolic networks as well as to obtain insight into their physiological and functional states in silico. However, the number of ExPas, p = |{pi}|, grows with the size and complexity of the network being studied, and this poses a computational challenge. For this study, we investigated the relationship between the number of extreme pathways and simple network properties. Results We established an estimating function for the number of ExPas using these easily obtainable network measurements. In particular, it was found that log [p] had an exponential relationship with log⁡[∑i=1Rd−id+ici] MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaacyGGSbaBcqGGVbWBcqGGNbWzdaWadaqaamaaqadabaGaemizaq2aaSbaaSqaaiabgkHiTmaaBaaameaacqWGPbqAaeqaaaWcbeaakiabdsgaKnaaBaaaleaacqGHRaWkdaWgaaadbaGaemyAaKgabeaaaSqabaGccqWGJbWydaWgaaWcbaGaemyAaKgabeaaaeaacqWGPbqAcqGH9aqpcqaIXaqmaeaacqWGsbGua0GaeyyeIuoaaOGaay5waiaaw2faaaaa@4414@, where R = |Reff| is the number of active reactions in a network, d−i MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaacqWGKbazdaWgaaWcbaGaeyOeI0YaaSbaaWqaaiabdMgaPbqabaaaleqaaaaa@30A9@ and d+i MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb

  6. Involvement of a bacterial microcompartment in the metabolism of fucose and rhamnose by Clostridium phytofermentans.

    Directory of Open Access Journals (Sweden)

    Elsa Petit

    Full Text Available BACKGROUND: Clostridium phytofermentans, an anaerobic soil bacterium, can directly convert plant biomass into biofuels. The genome of C. phytofermentans contains three loci with genes encoding shell proteins of bacterial microcompartments (BMC, organelles composed entirely of proteins. METHODOLOGY AND PRINCIPAL FINDINGS: One of the BMC loci has homology to a BMC-encoding locus implicated in the conversion of fucose to propanol and propionate in a human gut commensal, Roseburia inulinivorans. We hypothesized that it had a similar role in C. phytofermentans. When C. phytofermentans was grown on fucose, the major products identified were ethanol, propanol and propionate. Transmission electron microscopy of fucose- and rhamnose-grown cultures revealed polyhedral structures, presumably BMCs. Microarray analysis indicated that during growth on fucose, operons coding for the BMC locus, fucose dissimilatory enzymes, and an ATP-binding cassette transporter became the dominant transcripts. These data are consistent with fucose fermentation producing a 1,2-propanediol intermediate that is further metabolized in the microcompartment encoded in the BMC locus. Growth on another deoxyhexose sugar, rhamnose, resulted in the expression of the same BMC locus and similar fermentation products. However, a different set of dissimilatory enzymes and transport system genes were induced. Quite surprisingly, growth on fucose or rhamnose also led to the expression of a diverse array of complex plant polysaccharide-degrading enzymes. CONCLUSIONS/SIGNIFICANCE: Based on physiological, genomic, and microarray analyses, we propose a model for the fermentation of fucose and rhamnose in C. phytofermentans that includes enzymes encoded in the same BMC locus. Comparative genomic analysis suggests that this BMC may be present in other clostridial species.

  7. Metabolic Complementarity and Genomics of the Dual Bacterial Symbiosis of Sharpshooters

    Science.gov (United States)

    Wu, Dongying; Daugherty, Sean C; Van Aken, Susan E; Pai, Grace H; Watkins, Kisha L; Khouri, Hoda; Tallon, Luke J; Zaborsky, Jennifer M; Dunbar, Helen E; Tran, Phat L; Moran, Nancy A

    2006-01-01

    Mutualistic intracellular symbiosis between bacteria and insects is a widespread phenomenon that has contributed to the global success of insects. The symbionts, by provisioning nutrients lacking from diets, allow various insects to occupy or dominate ecological niches that might otherwise be unavailable. One such insect is the glassy-winged sharpshooter (Homalodisca coagulata), which feeds on xylem fluid, a diet exceptionally poor in organic nutrients. Phylogenetic studies based on rRNA have shown two types of bacterial symbionts to be coevolving with sharpshooters: the gamma-proteobacterium Baumannia cicadellinicola and the Bacteroidetes species Sulcia muelleri. We report here the sequencing and analysis of the 686,192–base pair genome of B. cicadellinicola and approximately 150 kilobase pairs of the small genome of S. muelleri, both isolated from H. coagulata. Our study, which to our knowledge is the first genomic analysis of an obligate symbiosis involving multiple partners, suggests striking complementarity in the biosynthetic capabilities of the two symbionts: B. cicadellinicola devotes a substantial portion of its genome to the biosynthesis of vitamins and cofactors required by animals and lacks most amino acid biosynthetic pathways, whereas S. muelleri apparently produces most or all of the essential amino acids needed by its host. This finding, along with other results of our genome analysis, suggests the existence of metabolic codependency among the two unrelated endosymbionts and their insect host. This dual symbiosis provides a model case for studying correlated genome evolution and genome reduction involving multiple organisms in an intimate, obligate mutualistic relationship. In addition, our analysis provides insight for the first time into the differences in symbionts between insects (e.g., aphids) that feed on phloem versus those like H. coagulata that feed on xylem. Finally, the genomes of these two symbionts provide potential targets for

  8. Metabolic complementarity and genomics of the dual bacterial symbiosis of sharpshooters.

    Directory of Open Access Journals (Sweden)

    Dongying Wu

    2006-06-01

    Full Text Available Mutualistic intracellular symbiosis between bacteria and insects is a widespread phenomenon that has contributed to the global success of insects. The symbionts, by provisioning nutrients lacking from diets, allow various insects to occupy or dominate ecological niches that might otherwise be unavailable. One such insect is the glassy-winged sharpshooter (Homalodisca coagulata, which feeds on xylem fluid, a diet exceptionally poor in organic nutrients. Phylogenetic studies based on rRNA have shown two types of bacterial symbionts to be coevolving with sharpshooters: the gamma-proteobacterium Baumannia cicadellinicola and the Bacteroidetes species Sulcia muelleri. We report here the sequencing and analysis of the 686,192-base pair genome of B. cicadellinicola and approximately 150 kilobase pairs of the small genome of S. muelleri, both isolated from H. coagulata. Our study, which to our knowledge is the first genomic analysis of an obligate symbiosis involving multiple partners, suggests striking complementarity in the biosynthetic capabilities of the two symbionts: B. cicadellinicola devotes a substantial portion of its genome to the biosynthesis of vitamins and cofactors required by animals and lacks most amino acid biosynthetic pathways, whereas S. muelleri apparently produces most or all of the essential amino acids needed by its host. This finding, along with other results of our genome analysis, suggests the existence of metabolic codependency among the two unrelated endosymbionts and their insect host. This dual symbiosis provides a model case for studying correlated genome evolution and genome reduction involving multiple organisms in an intimate, obligate mutualistic relationship. In addition, our analysis provides insight for the first time into the differences in symbionts between insects (e.g., aphids that feed on phloem versus those like H. coagulata that feed on xylem. Finally, the genomes of these two symbionts provide potential

  9. Co-regulation of metabolic genes is better explained by flux coupling than by network distance.

    Directory of Open Access Journals (Sweden)

    Richard A Notebaart

    2008-01-01

    Full Text Available To what extent can modes of gene regulation be explained by systems-level properties of metabolic networks? Prior studies on co-regulation of metabolic genes have mainly focused on graph-theoretical features of metabolic networks and demonstrated a decreasing level of co-expression with increasing network distance, a naïve, but widely used, topological index. Others have suggested that static graph representations can poorly capture dynamic functional associations, e.g., in the form of dependence of metabolic fluxes across genes in the network. Here, we systematically tested the relative importance of metabolic flux coupling and network position on gene co-regulation, using a genome-scale metabolic model of Escherichia coli. After validating the computational method with empirical data on flux correlations, we confirm that genes coupled by their enzymatic fluxes not only show similar expression patterns, but also share transcriptional regulators and frequently reside in the same operon. In contrast, we demonstrate that network distance per se has relatively minor influence on gene co-regulation. Moreover, the type of flux coupling can explain refined properties of the regulatory network that are ignored by simple graph-theoretical indices. Our results underline the importance of studying functional states of cellular networks to define physiologically relevant associations between genes and should stimulate future developments of novel functional genomic tools.

  10. Bacterial networks and co-occurrence relationships in the lettuce root microbiota.

    Science.gov (United States)

    Cardinale, Massimiliano; Grube, Martin; Erlacher, Armin; Quehenberger, Julian; Berg, Gabriele

    2015-01-01

    Lettuce is one of the most common raw foods worldwide, but occasionally also involved in pathogen outbreaks. To understand the correlative structure of the bacterial community as a network, we studied root microbiota of eight ancient and modern Lactuca sativa cultivars and the wild ancestor Lactuca serriola by pyrosequencing of 16S rRNA gene amplicon libraries. The lettuce microbiota was dominated by Proteobacteria and Bacteriodetes, as well as abundant Chloroflexi and Actinobacteria. Cultivar specificity comprised 12.5% of the species. Diversity indices were not different between lettuce cultivar groups but higher than in L. serriola, suggesting that domestication lead to bacterial diversification in lettuce root system. Spearman correlations between operational taxonomic units (OTUs) showed that co-occurrence prevailed over co-exclusion, and complementary fluorescence in situ hybridization-confocal laser scanning microscopy (FISH-CLSM) analyses revealed that this pattern results from both potential interactions and habitat sharing. Predominant taxa, such as Pseudomonas, Flavobacterium and Sphingomonadaceae rather suggested interactions, even though these are not necessarily part of significant modules in the co-occurrence networks. Without any need for complex interactions, single organisms are able to invade into this microbial network and to colonize lettuce plants, a fact that can influence the susceptibility to pathogens. The approach to combine co-occurrence analysis and FISH-CLSM allows reliably reconstructing and interpreting microbial interaction networks.

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

    Directory of Open Access Journals (Sweden)

    Tin Yau Pang

    2011-05-01

    Full Text Available 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

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

    Science.gov (United States)

    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.

  13. Limited Influence of Oxygen on the Evolution of Chemical Diversity in Metabolic Networks

    Directory of Open Access Journals (Sweden)

    Kazuhiro Takemoto

    2013-10-01

    Full Text Available Oxygen is thought to promote species and biomolecule diversity. Previous studies have suggested that oxygen expands metabolic networks by acquiring metabolites with different chemical properties (higher hydrophobicity, for example. However, such conclusions are typically based on biased evaluation, and are therefore non-conclusive. Thus, we re-investigated the effect of oxygen on metabolic evolution using a phylogenetic comparative method and metadata analysis to reduce the bias as much as possible. Notably, we found no difference in metabolic network expansion between aerobes and anaerobes when evaluating phylogenetic relationships. Furthermore, we showed that previous studies have overestimated or underestimated the degrees of differences in the chemical properties (e.g., hydrophobicity between oxic and anoxic metabolites in metabolic networks of unicellular organisms; however, such overestimation was not observed when considering the metabolic networks of multicellular organisms. These findings indicate that the contribution of oxygen to increased chemical diversity in metabolic networks is lower than previously thought; rather, phylogenetic signals and cell-cell communication result in increased chemical diversity. However, this conclusion does not contradict the effect of oxygen on metabolic evolution; instead, it provides a deeper understanding of how oxygen contributes to metabolic evolution despite several limitations in data analysis methods.

  14. Rapid determination of bacterial abundance, biovolume, morphology, and growth by neural network-based image analysis

    Science.gov (United States)

    Blackburn; Hagstrom; Wikner; Cuadros-Hansson; Bjornsen

    1998-09-01

    Annual bacterial plankton dynamics at several depths and locations in the Baltic Sea were studied by image analysis. Individual bacteria were classified by using an artificial neural network which also effectively identified nonbacterial objects. Cell counts and frequencies of dividing cells were determined, and the data obtained agreed well with visual observations and previously published values. Cell volumes were measured accurately by comparison with bead standards. The survey included 690 images from a total of 138 samples. Each image contained approximately 200 bacteria. The images were analyzed automatically at a rate of 100 images per h. Bacterial abundance exhibited coherent patterns with time and depth, and there were distinct subsurface peaks in the summer months. Four distinct morphological classes were resolved by the image analyzer, and the dynamics of each could be visualized. The bacterial growth rates estimated from frequencies of dividing cells were different from the bacterial growth rates estimated by the thymidine incorporation method. With minor modifications, the image analysis technique described here can be used to analyze other planktonic classes.

  15. Integrating large-scale functional genomics data to dissect metabolic networks for hydrogen production

    Energy Technology Data Exchange (ETDEWEB)

    Harwood, Caroline S

    2012-12-17

    The goal of this project is to identify gene networks that are critical for efficient biohydrogen production by leveraging variation in gene content and gene expression in independently isolated Rhodopseudomonas palustris strains. Coexpression methods were applied to large data sets that we have collected to define probabilistic causal gene networks. To our knowledge this a first systems level approach that takes advantage of strain-to strain variability to computationally define networks critical for a particular bacterial phenotypic trait.

  16. Gap-filling analysis of the iJO1366 Escherichia coli metabolic network reconstruction for discovery of metabolic functions

    Directory of Open Access Journals (Sweden)

    Orth Jeffrey D

    2012-05-01

    Full Text Available Abstract Background The iJO1366 reconstruction of the metabolic network of Escherichia coli is one of the most complete and accurate metabolic reconstructions available for any organism. Still, because our knowledge of even well-studied model organisms such as this one is incomplete, this network reconstruction contains gaps and possible errors. There are a total of 208 blocked metabolites in iJO1366, representing gaps in the network. Results A new model improvement workflow was developed to compare model based phenotypic predictions to experimental data to fill gaps and correct errors. A Keio Collection based dataset of E. coli gene essentiality was obtained from literature data and compared to model predictions. The SMILEY algorithm was then used to predict the most likely missing reactions in the reconstructed network, adding reactions from a KEGG based universal set of metabolic reactions. The feasibility of these putative reactions was determined by comparing updated versions of the model to the experimental dataset, and genes were predicted for the most feasible reactions. Conclusions Numerous improvements to the iJO1366 metabolic reconstruction were suggested by these analyses. Experiments were performed to verify several computational predictions, including a new mechanism for growth on myo-inositol. The other predictions made in this study should be experimentally verifiable by similar means. Validating all of the predictions made here represents a substantial but important undertaking.

  17. Impact of stoichiometry representation on simulation of genotype-phenotype relationships in metabolic networks

    DEFF Research Database (Denmark)

    Brochado, Ana Rita; Andrejev, Sergej; Maranas, Costas D.;

    2012-01-01

    the formulation of the desired objective functions, by casting objective functions using metabolite turnovers rather than fluxes. By simulating perturbed metabolic networks, we demonstrate that the use of stoichiometry representation independent algorithms is fundamental for unambiguously linking modeling results...

  18. The human-bacterial pathogen protein interaction networks of Bacillus anthracis, Francisella tularensis, and Yersinia pestis.

    Directory of Open Access Journals (Sweden)

    Matthew D Dyer

    Full Text Available BACKGROUND: Bacillus anthracis, Francisella tularensis, and Yersinia pestis are bacterial pathogens that can cause anthrax, lethal acute pneumonic disease, and bubonic plague, respectively, and are listed as NIAID Category A priority pathogens for possible use as biological weapons. However, the interactions between human proteins and proteins in these bacteria remain poorly characterized leading to an incomplete understanding of their pathogenesis and mechanisms of immune evasion. METHODOLOGY: In this study, we used a high-throughput yeast two-hybrid assay to identify physical interactions between human proteins and proteins from each of these three pathogens. From more than 250,000 screens performed, we identified 3,073 human-B. anthracis, 1,383 human-F. tularensis, and 4,059 human-Y. pestis protein-protein interactions including interactions involving 304 B. anthracis, 52 F. tularensis, and 330 Y. pestis proteins that are uncharacterized. Computational analysis revealed that pathogen proteins preferentially interact with human proteins that are hubs and bottlenecks in the human PPI network. In addition, we computed modules of human-pathogen PPIs that are conserved amongst the three networks. Functionally, such conserved modules reveal commonalities between how the different pathogens interact with crucial host pathways involved in inflammation and immunity. SIGNIFICANCE: These data constitute the first extensive protein interaction networks constructed for bacterial pathogens and their human hosts. This study provides novel insights into host-pathogen interactions.

  19. Petri Net Based Metabolic Network Parameters Fitting with GPU Acceleration%Petri Net Based Metabolic Network Parameters Fitting with GPU Acceleration

    Institute of Scientific and Technical Information of China (English)

    Gao, Jun; Zhu, Ruixin; Liu, Qi; Cao, Zhiwei

    2011-01-01

    Classical Petri net has been applied into biological analysis, especially as a qualitative model for biochemical pathways analysis, but lack of the ability for quantitative kinetic simulations. In our study, we presented an integra- tion work of the qualitative model--Petri nets with the quantitative approach-ordinary differential equations (ODEs) for the modeling and analysis of metabolic networks. As an application of our study, the computational modeling of arachidonic acid (AA) biochemical network was provided. A Petri net was set up to present the constraint-based dynamic simulations on AA metabolic network combined with the validated ODEs model. Furthermore, Graphics Processing Unit (GPU) was adopted to accelerate the calculation of kinetic parameters unavailable from experi- ments. Our results have indicated that the proposed simulation method was practicable and useful with GPU accel- eration, and provides new clues for the large-scale qualitative and quantitative models of biochemical networks.

  20. Rearrangements of the transcriptional regulatory networks of metabolic pathways in fungi

    OpenAIRE

    Lavoie, Hugo; Hogues, Hervé; Whiteway, Malcolm

    2009-01-01

    Growing evidence suggests that transcriptional regulatory networks in many organisms are highly flexible. Here, we discuss the evolution of transcriptional regulatory networks governing the metabolic machinery of sequenced ascomycetes. In particular, recent work has shown that transcriptional rewiring is common in regulons controlling processes such as production of ribosome components and metabolism of carbohydrates and lipids. We note that dramatic rearrangements of the transcriptional regu...

  1. Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide.

    Science.gov (United States)

    Orth, Jeffrey D; Fleming, R M T; Palsson, Bernhard Ø

    2010-09-01

    Biochemical network reconstructions have become popular tools in systems biology. Metabolicnetwork reconstructions are biochemically, genetically, and genomically (BiGG) structured databases of biochemical reactions and metabolites. They contain information such as exact reaction stoichiometry, reaction reversibility, and the relationships between genes, proteins, and reactions. Network reconstructions have been used extensively to study the phenotypic behavior of wild-type and mutant stains under a variety of conditions, linking genotypes with phenotypes. Such phenotypic simulations have allowed for the prediction of growth after genetic manipulations, prediction of growth phenotypes after adaptive evolution, and prediction of essential genes. Additionally, because network reconstructions are organism specific, they can be used to understand differences between organisms of species in a functional context.There are different types of reconstructions representing various types of biological networks (metabolic, regulatory, transcription/translation). This chapter serves as an introduction to metabolic and regulatory network reconstructions and models and gives a complete description of the core Escherichia coli metabolic model. This model can be analyzed in any computational format (such as MATLAB or Mathematica) based on the information given in this chapter. The core E. coli model is a small-scale model that can be used for educational purposes. It is meant to be used by senior undergraduate and first-year graduate students learning about constraint-based modeling and systems biology. This model has enough reactions and pathways to enable interesting and insightful calculations, but it is also simple enough that the results of such calculations can be understoodeasily.

  2. Effects of sulfur-metabolizing bacterial community diversity on H2S emission behavior in landfills with different operation modes.

    Science.gov (United States)

    Fang, Yuan; Du, Yao; Hu, Lifang; Xu, Jing; Long, Yuyang; Shen, Dongsheng

    2016-11-01

    Hydrogen sulfide (H2S) is one of the major contributors to offensive odors from landfills, and its concentration differs under different operation modes. This study examined the distribution of H2S emission from different landfill depths under different operation modes (anaerobic, semi-aerobic, semi-aerobic transformation, and the three operation modes with additional leachate recirculation). The microbial community (especially the sulfur-metabolizing bacterial community) was investigated using high-throughput sequencing technology. The results showed that the semi-aerobic mode could substantially lower the risks of H2S pollution in landfills, which might be because of the difference in biological processes related to sulfur metabolism driven by functional microbes. A myriad of factors are responsible for mutually shaping the sulfur-metabolizing bacterial community composition in landfills that might subsequently affect the behavior of H2S emission in landfills. The differences in abundance of the genera Acinetobacter and Paracoccus (phylum Proteobacteria) caused by environmental factors might explain the differences in H2S emission. H2S odor control could be realized if the related functional microbe diversity can be influenced by adjustments to landfill operation.

  3. Metabolic network reconstruction, growth characterization and 13C-metabolic flux analysis of the extremophile Thermus thermophilus HB8.

    Science.gov (United States)

    Swarup, Aditi; Lu, Jing; DeWoody, Kathleen C; Antoniewicz, Maciek R

    2014-07-01

    Thermus thermophilus is an extremely thermophilic bacterium with significant biotechnological potential. In this work, we have characterized aerobic growth characteristics of T. thermophilus HB8 at temperatures between 50 and 85°C, constructed a metabolic network model of its central carbon metabolism and validated the model using (13)C-metabolic flux analysis ((13)C-MFA). First, cells were grown in batch cultures in custom constructed mini-bioreactors at different temperatures to determine optimal growth conditions. The optimal temperature for T. thermophilus grown on defined medium with glucose was 81°C. The maximum growth rate was 0.25h(-1). Between 50 and 81°C the growth rate increased by 7-fold and the temperature dependence was described well by an Arrhenius model with an activation energy of 47kJ/mol. Next, we performed a (13)C-labeling experiment with [1,2-(13)C] glucose as the tracer and calculated intracellular metabolic fluxes using (13)C-MFA. The results provided support for the constructed network model and highlighted several interesting characteristics of T. thermophilus metabolism. We found that T. thermophilus largely uses glycolysis and TCA cycle to produce biosynthetic precursors, ATP and reducing equivalents needed for cells growth. Consistent with its proposed metabolic network model, we did not detect any oxidative pentose phosphate pathway flux or Entner-Doudoroff pathway activity. The biomass precursors erythrose-4-phosphate and ribose-5-phosphate were produced via the non-oxidative pentose phosphate pathway, and largely via transketolase, with little contribution from transaldolase. The high biomass yield on glucose that was measured experimentally was also confirmed independently by (13)C-MFA. The results presented here provide a solid foundation for future studies of T. thermophilus and its metabolic engineering applications.

  4. Integrated analysis of transcript-level regulation of metabolism reveals disease-relevant nodes of the human metabolic network.

    Science.gov (United States)

    Galhardo, Mafalda; Sinkkonen, Lasse; Berninger, Philipp; Lin, Jake; Sauter, Thomas; Heinäniemi, Merja

    2014-02-01

    Metabolic diseases and comorbidities represent an ever-growing epidemic where multiple cell types impact tissue homeostasis. Here, the link between the metabolic and gene regulatory networks was studied through experimental and computational analysis. Integrating gene regulation data with a human metabolic network prompted the establishment of an open-sourced web portal, IDARE (Integrated Data Nodes of Regulation), for visualizing various gene-related data in context of metabolic pathways. Motivated by increasing availability of deep sequencing studies, we obtained ChIP-seq data from widely studied human umbilical vein endothelial cells. Interestingly, we found that association of metabolic genes with multiple transcription factors (TFs) enriched disease-associated genes. To demonstrate further extensions enabled by examining these networks together, constraint-based modeling was applied to data from human preadipocyte differentiation. In parallel, data on gene expression, genome-wide ChIP-seq profiles for peroxisome proliferator-activated receptor (PPAR) γ, CCAAT/enhancer binding protein (CEBP) α, liver X receptor (LXR) and H3K4me3 and microRNA target identification for miR-27a, miR-29a and miR-222 were collected. Disease-relevant key nodes, including mitochondrial glycerol-3-phosphate acyltransferase (GPAM), were exposed from metabolic pathways predicted to change activity by focusing on association with multiple regulators. In both cell types, our analysis reveals the convergence of microRNAs and TFs within the branched chain amino acid (BCAA) metabolic pathway, possibly providing an explanation for its downregulation in obese and diabetic conditions.

  5. A Dynamic State Metabolic Journey: From Mass Spectrometry to Network Analysis via Estimation of Kinetic Parameters

    OpenAIRE

    Dhanasekaran, Arockia R.

    2011-01-01

    In the post-genomic era, there is a dire need for tools to perform metabolic analyses that include the structural, functional, and regulatory analysis of metabolic networks. This need arose because of the lag between the two phases of metabolic engineering, namely, synthesis and analysis. Molecular biological tools for synthesis like recombinant DNA technology and genetic engineering have advanced a lot farther than tools for systemic analysis. Consequently, bioinformatics is poised to play ...

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

    Directory of Open Access Journals (Sweden)

    Gould John

    2007-04-01

    Full Text Available Abstract Background 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. Results 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

  7. Forward selection radial basis function networks applied to bacterial classification based on MALDI-TOF-MS.

    Science.gov (United States)

    Zhang, Zhuoyong; Wang, Dan; Harrington, Peter de B; Voorhees, Kent J; Rees, Jon

    2004-06-17

    Forward selection improved radial basis function (RBF) network was applied to bacterial classification based on the data obtained by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). The classification of each bacterium cultured at different time was discussed and the effect of parameters of the RBF network was investigated. The new method involves forward selection to prevent overfitting and generalized cross-validation (GCV) was used as model selection criterion (MSC). The original data was compressed by using wavelet transformation to speed up the network training and reduce the number of variables of the original MS data. The data was normalized prior training and testing a network to define the area the neural network to be trained in, accelerate the training rate, and reduce the range the parameters to be selected in. The one-out-of-n method was used to split the data set of p samples into a training set of size p-1 and a test set of size 1. With the improved method, the classification correctness for the five bacteria discussed in the present paper are 87.5, 69.2, 80, 92.3, and 92.8%, respectively.

  8. Dissimilatory metabolism of nitrogen oxides in bacteria: comparative reconstruction of transcriptional networks.

    Directory of Open Access Journals (Sweden)

    2005-10-01

    Full Text Available Bacterial response to nitric oxide (NO is of major importance since NO is an obligatory intermediate of the nitrogen cycle. Transcriptional regulation of the dissimilatory nitric oxides metabolism in bacteria is diverse and involves FNR-like transcription factors HcpR, DNR, and NnrR; two-component systems NarXL and NarQP; NO-responsive activator NorR; and nitrite-sensitive repressor NsrR. Using comparative genomics approaches, we predict DNA-binding motifs for these transcriptional factors and describe corresponding regulons in available bacterial genomes. Within the FNR family of regulators, we observed a correlation of two specificity-determining amino acids and contacting bases in corresponding DNA recognition motif. Highly conserved regulon HcpR for the hybrid cluster protein and some other redox enzymes is present in diverse anaerobic bacteria, including Clostridia, Thermotogales, and delta-proteobacteria. NnrR and DNR control denitrification in alpha- and beta-proteobacteria, respectively. Sigma-54-dependent NorR regulon found in some gamma- and beta-proteobacteria contains various enzymes involved in the NO detoxification. Repressor NsrR, which was previously known to control only nitrite reductase operon in Nitrosomonas spp., appears to be the master regulator of the nitric oxides' metabolism, not only in most gamma- and beta-proteobacteria (including well-studied species such as Escherichia coli, but also in Gram-positive Bacillus and Streptomyces species. Positional analysis and comparison of regulatory regions of NO detoxification genes allows us to propose the candidate NsrR-binding motif. The most conserved member of the predicted NsrR regulon is the NO-detoxifying flavohemoglobin Hmp. In enterobacteria, the regulon also includes two nitrite-responsive loci, nipAB (hcp-hcr and nipC (dnrN, thus confirming the identity of the effector, i.e. nitrite. The proposed NsrR regulons in Neisseria and some other species are extended to include

  9. Dissimilatory Metabolism of Nitrogen Oxides in Bacteria:Comparative Reconstruction of Transcriptional Networks

    Energy Technology Data Exchange (ETDEWEB)

    Rodionov, Dmitry A.; Dubchak, Inna L.; Arkin, Adam P.; Alm, EricJ.; Gelfand, Mikhail S.

    2005-09-01

    Bacterial response to nitric oxide (NO) is of major importance since NO is an obligatory intermediate of the nitrogen cycle. Transcriptional regulation of the dissimilatory nitric oxides metabolism in bacteria is diverse and involves FNR-like transcription factors HcpR, DNR and NnrR, two-component systems NarXL and NarQP, NO-responsive activator NorR, and nitrite sensitive repressor NsrR. Using comparative genomics approaches we predict DNA-binding signals for these transcriptional factors and describe corresponding regulons in available bacterial genomes. Within the FNR family of regulators, we observed a correlation of two specificity-determining amino acids and contacting bases in corresponding DNA signal. Highly conserved regulon HcpR for the hybrid cluster protein and some other redox enzymes is present in diverse anaerobic bacteria including Clostridia, Thermotogales and delta-proteobacteria. NnrR and DNR control denitrification in alpha- and beta-proteobacteria, respectively. Sigma-54-dependent NorR regulon found in some gamma- and beta-proteobacteria contains various enzymes involved in the NO detoxification. Repressor NsrR, which was previously known to control only nitrite reductase operon in Nitrosomonas spp., appears to be the master regulator of the nitric oxides metabolism not only in most gamma- and beta-proteobacteria (including well-studied species like Escherichia coli), but also in Gram-positive Bacillus and Streptomyces species. Positional analysis and comparison of regulatory regions of NO detoxification genes allows us to propose the candidate NsrR-binding signal. The most conserved member of the predicted NsrR regulon is the NO-detoxifying flavohemoglobin Hmp. In enterobacteria, the regulon includes also two nitrite-responsive loci, nipAB (hcp-hcr) and nipC(dnrN), thus confirming the identity of the effector, i.e., nitrite. The proposed NsrR regulons in Neisseria and some other species are extended to include denitrification genes. As the

  10. Dissimilatory metabolism of nitrogen oxides in bacteria: comparative reconstruction of transcriptional networks.

    Directory of Open Access Journals (Sweden)

    Dmitry A Rodionov

    2005-10-01

    Full Text Available Bacterial response to nitric oxide (NO is of major importance since NO is an obligatory intermediate of the nitrogen cycle. Transcriptional regulation of the dissimilatory nitric oxides metabolism in bacteria is diverse and involves FNR-like transcription factors HcpR, DNR, and NnrR; two-component systems NarXL and NarQP; NO-responsive activator NorR; and nitrite-sensitive repressor NsrR. Using comparative genomics approaches, we predict DNA-binding motifs for these transcriptional factors and describe corresponding regulons in available bacterial genomes. Within the FNR family of regulators, we observed a correlation of two specificity-determining amino acids and contacting bases in corresponding DNA recognition motif. Highly conserved regulon HcpR for the hybrid cluster protein and some other redox enzymes is present in diverse anaerobic bacteria, including Clostridia, Thermotogales, and delta-proteobacteria. NnrR and DNR control denitrification in alpha- and beta-proteobacteria, respectively. Sigma-54-dependent NorR regulon found in some gamma- and beta-proteobacteria contains various enzymes involved in the NO detoxification. Repressor NsrR, which was previously known to control only nitrite reductase operon in Nitrosomonas spp., appears to be the master regulator of the nitric oxides' metabolism, not only in most gamma- and beta-proteobacteria (including well-studied species such as Escherichia coli, but also in gram-positive Bacillus and Streptomyces species. Positional analysis and comparison of regulatory regions of NO detoxification genes allows us to propose the candidate NsrR-binding motif. The most conserved member of the predicted NsrR regulon is the NO-detoxifying flavohemoglobin Hmp. In enterobacteria, the regulon also includes two nitrite-responsive loci, nipAB (hcp-hcr and nipC (dnrN, thus confirming the identity of the effector, i.e. nitrite. The proposed NsrR regulons in Neisseria and some other species are extended to include

  11. Metabolic pathway of non-alcoholic fatty liver disease: Network properties and robustness

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2017-03-01

    Full Text Available Nonalcoholic fatty liver disease (NAFLD is a systematic and complex disease involving various cytokines/metabolites. In present article, we use methodology of network biology to analyze network properties of NAFLD metabolic pathway. It is found that the metabolic pathway of NAFLD is not a typical complex network with power-law degree distribution, p(x=x^(-4.4275, x>=5. There is only one connected component in the metabolic pathway. The calculated cut cytokines/metabolites of the metabolic pathway are SREBP-1c, ChREBP, ObR, AMPK, IRE1alpha, ROS, PERK, elF2alpha, ATF4, CHOP, Bim, CASP8, Bid, CxII, Lipogenic enzymes, XBP1, and FFAs. The most important cytokine/metabolite for possible network robustness is FFAs, seconded by TNF-alpha. It is concluded that FFAs is the most important cytokine/metabolite in the metabolic pathway, seconded by ROS. FFAs, LEP, ACDC, CYP2E1, and Glucose are the only cytokines/metabolites that affect others without influences from other cytokines/metabolites. Finally, the IDs matrix for identifying possible sub-networks/modules is given. However, jointly combining the results of connectedness analysis and sub-networks/modules identification, we hold that there are not significant sub-networks/modules in the pathway.

  12. Diet-induced bacterial immunogens in the gastrointestinal tract of dairy cows: Impacts on immunity and metabolism

    Directory of Open Access Journals (Sweden)

    Zhou Jun

    2011-08-01

    Full Text Available Abstract Dairy cows are often fed high grain diets to meet the energy demand for high milk production or simply due to a lack of forages at times. As a result, ruminal acidosis, especially subacute ruminal acidosis (SARA, occurs frequently in practical dairy production. When SARA occurs, bacterial endotoxin (or lipopolysaccharide, LPS is released in the rumen and the large intestine in a large amount. Many other bacterial immunogens may also be released in the digestive tract following feeding dairy cows diets containing high proportions of grain. LPS can be translocated into the bloodstream across the epithelium of the digestive tract, especially the lower tract, due to possible alterations of permeability and injuries of the epithelial tissue. As a result, the concentration of blood LPS increases. Immune responses are subsequently caused by circulating LPS, and the systemic effects include increases in concentrations of neutrophils and the acute phase proteins such as serum amyloid-A (SAA, haptoglobin (Hp, LPS binding protein (LBP, and C-reactive protein (CRP in blood. Entry of LPS into blood can also result in metabolic alterations. Blood glucose and nonesterified fatty acid concentrations are enhanced accompanying an increase of blood LPS after increasing the amount of grain in the diet, which adversely affects feed intake of dairy cows. As the proportions of grain in the diet increase, patterns of plasma β-hydoxybutyric acid, cholesterol, and minerals (Ca, Fe, and Zn are also perturbed. The bacterial immunogens can also lead to reduced supply of nutrients for synthesis of milk components and depressed functions of the epithelial cells in the mammary gland. The immune responses and metabolic alterations caused by circulating bacterial immunogens will exert an effect on milk production. It has been demonstrated that increases in concentrations of ruminal LPS and plasma acute phase proteins (CRP, SAA, and LBP are associated with declines in

  13. A new method for determining the metabolic activity of specific bacterial populations in soil using tritiated leucine and immunomagnetic separation

    DEFF Research Database (Denmark)

    Sengeløv, Gitte; Sørensen, Søren Johannes; Frette, Lone

    2000-01-01

    A new assay, using immunomagnetic separation and uptake of tritiated leucine ([3H]-Leu), was developed for measuring the in situ metabolic activity of specific bacterial populations in soil. Such assays are needed to assess the role individual species play in diverse microbial soil communities....... The method was optimized using Pseudomonas putida KT2440¿:¿:Tc+/TOL::gfp inoculated into soil microcosms. Inoculated soil samples were incubated with [3H]-Leu followed by an immunomagnetic separation to recover the target bacteria. Radiolabel incorporated by the target bacteria was then measured. Incubation...

  14. A metabolic-transcriptional network links sleep and cellular energetics in the brain.

    Science.gov (United States)

    Wisor, Jonathan P

    2012-01-01

    This review proposes a mechanistic link between cellular metabolic status, transcriptional regulatory changes and sleep. Sleep loss is associated with changes in cellular metabolic status in the brain. Metabolic sensors responsive to cellular metabolic status regulate the circadian clock transcriptional network. Modifications of the transcriptional activity of circadian clock genes affect sleep/wake state changes. Changes in sleep state reverse sleep loss-induced changes in cellular metabolic status. It is thus proposed that the regulation of circadian clock genes by cellular metabolic sensors is a critical intermediate step in the link between cellular metabolic status and sleep. Studies of this regulatory relationship may offer insights into the function of sleep at the cellular level.

  15. Combining Flux Balance and Energy Balance Analysis for Large-Scale Metabolic Network: Biochemical Circuit Theory for Analysis of Large-Scale Metabolic Networks

    Science.gov (United States)

    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.

  16. Docosahexaenoic Acid, Inflammation, and Bacterial Dysbiosis in Relation to Periodontal Disease, Inflammatory Bowel Disease, and the Metabolic Syndrome

    Directory of Open Access Journals (Sweden)

    Michael F. Roizen

    2013-08-01

    Full Text Available Docosahexaenoic acid (DHA, a long-chain omega-3 polyunsaturated fatty acid, has been used to treat a range of different conditions, including periodontal disease (PD and inflammatory bowel disease (IBD. That DHA helps with these oral and gastrointestinal diseases in which inflammation and bacterial dysbiosis play key roles, raises the question of whether DHA may assist in the prevention or treatment of other inflammatory conditions, such as the metabolic syndrome, which have also been linked with inflammation and alterations in normal host microbial populations. Here we review established and investigated associations between DHA, PD, and IBD. We conclude that by beneficially altering cytokine production and macrophage recruitment, the composition of intestinal microbiota and intestinal integrity, lipopolysaccharide- and adipose-induced inflammation, and insulin signaling, DHA may be a key tool in the prevention of metabolic syndrome.

  17. Dead end metabolites--defining the known unknowns of the E. coli metabolic network.

    Directory of Open Access Journals (Sweden)

    Amanda Mackie

    Full Text Available The EcoCyc database is an online scientific database which provides an integrated view of the metabolic and regulatory network of the bacterium Escherichia coli K-12 and facilitates computational exploration of this important model organism. We have analysed the occurrence of dead end metabolites within the database--these are metabolites which lack the requisite reactions (either metabolic or transport that would account for their production or consumption within the metabolic network. 127 dead end metabolites were identified from the 995 compounds that are contained within the EcoCyc metabolic network. Their presence reflects either a deficit in our representation of the network or in our knowledge of E. coli metabolism. Extensive literature searches resulted in the addition of 38 transport reactions and 3 metabolic reactions to the database and led to an improved representation of the pathway for Vitamin B12 salvage. 39 dead end metabolites were identified as components of reactions that are not physiologically relevant to E. coli K-12--these reactions are properties of purified enzymes in vitro that would not be expected to occur in vivo. Our analysis led to improvements in the software that underpins the database and to the program that finds dead end metabolites within EcoCyc. The remaining dead end metabolites in the EcoCyc database likely represent deficiencies in our knowledge of E. coli metabolism.

  18. FluxExplorer: A general platform for modeling and analyses of metabolic networks based on stoichiometry

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Stoichiometry-based analyses of meta- bolic networks have aroused significant interest of systems biology researchers in recent years. It is necessary to develop a more convenient modeling platform on which users can reconstruct their network models using completely graphical operations, and explore them with powerful analyzing modules to get a better understanding of the properties of metabolic systems. Herein, an in silico platform, FluxExplorer, for metabolic modeling and analyses based on stoichiometry has been developed as a publicly available tool for systems biology research. This platform integrates various analytic approaches, in- cluding flux balance analysis, minimization of meta- bolic adjustment, extreme pathways analysis, shadow prices analysis, and singular value decom- position, providing a thorough characterization of the metabolic system. Using a graphic modeling process, metabolic networks can be reconstructed and modi- fied intuitively and conveniently. The inconsistencies of a model with respect to the FBA principles can be proved automatically. In addition, this platform sup- ports systems biology markup language (SBML). FluxExplorer has been applied to rebuild a metabolic network in mammalian mitochondria, producing meaningful results. Generally, it is a powerful and very convenient tool for metabolic network modeling and analysis.

  19. Discovering missing reactions of metabolic networks by using gene co-expression data

    Science.gov (United States)

    Hosseini, Zhaleh; Marashi, Sayed-Amir

    2017-02-01

    Flux coupling analysis is a computational method which is able to explain co-expression of metabolic genes by analyzing the topological structure of a metabolic network. It has been suggested that if genes in two seemingly fully-coupled reactions are not highly co-expressed, then these two reactions are not fully coupled in reality, and hence, there is a gap or missing reaction in the network. Here, we present GAUGE as a novel approach for gap filling of metabolic networks, which is a two-step algorithm based on a mixed integer linear programming formulation. In GAUGE, the discrepancies between experimental co-expression data and predicted flux coupling relations is minimized by adding a minimum number of reactions to the network. We show that GAUGE is able to predict missing reactions of E. coli metabolism that are not detectable by other popular gap filling approaches. We propose that our algorithm may be used as a complementary strategy for the gap filling problem of metabolic networks. Since GAUGE relies only on gene expression data, it can be potentially useful for exploring missing reactions in the metabolism of non-model organisms, which are often poorly characterized, cannot grow in the laboratory, and lack genetic tools for generating knockouts.

  20. Characterization of the Usage of the Serine Metabolic Network in Human Cancer

    Directory of Open Access Journals (Sweden)

    Mahya Mehrmohamadi

    2014-11-01

    Full Text Available The serine, glycine, one-carbon (SGOC metabolic network is implicated in cancer pathogenesis, but its general functions are unknown. We carried out a computational reconstruction of the SGOC network and then characterized its expression across thousands of cancer tissues. Pathways including methylation and redox metabolism exhibited heterogeneous expression indicating a strong context dependency of their usage in tumors. From an analysis of coexpression, simultaneous up- or downregulation of nucleotide synthesis, NADPH, and glutathione synthesis was found to be a common occurrence in all cancers. Finally, we developed a method to trace the metabolic fate of serine using stable isotopes, high-resolution mass spectrometry, and a mathematical model. Although the expression of single genes didn’t appear indicative of flux, the collective expression of several genes in a given pathway allowed for successful flux prediction. Altogether, these findings identify expansive and heterogeneous functions for the SGOC metabolic network in human cancer.

  1. Soil Parameters Drive the Structure, Diversity and Metabolic Potentials of the Bacterial Communities Across Temperate Beech Forest Soil Sequences.

    Science.gov (United States)

    Jeanbille, M; Buée, M; Bach, C; Cébron, A; Frey-Klett, P; Turpault, M P; Uroz, S

    2016-02-01

    Soil and climatic conditions as well as land cover and land management have been shown to strongly impact the structure and diversity of the soil bacterial communities. Here, we addressed under a same land cover the potential effect of the edaphic parameters on the soil bacterial communities, excluding potential confounding factors as climate. To do this, we characterized two natural soil sequences occurring in the Montiers experimental site. Spatially distant soil samples were collected below Fagus sylvatica tree stands to assess the effect of soil sequences on the edaphic parameters, as well as the structure and diversity of the bacterial communities. Soil analyses revealed that the two soil sequences were characterized by higher pH and calcium and magnesium contents in the lower plots. Metabolic assays based on Biolog Ecoplates highlighted higher intensity and richness in usable carbon substrates in the lower plots than in the middle and upper plots, although no significant differences occurred in the abundance of bacterial and fungal communities along the soil sequences as assessed using quantitative PCR. Pyrosequencing analysis of 16S ribosomal RNA (rRNA) gene amplicons revealed that Proteobacteria, Acidobacteria and Bacteroidetes were the most abundantly represented phyla. Acidobacteria, Proteobacteria and Chlamydiae were significantly enriched in the most acidic and nutrient-poor soils compared to the Bacteroidetes, which were significantly enriched in the soils presenting the higher pH and nutrient contents. Interestingly, aluminium, nitrogen, calcium, nutrient availability and pH appeared to be the best predictors of the bacterial community structures along the soil sequences.

  2. Dynamics of bacterial communities before and after distribution in a full-scale drinking water network

    KAUST Repository

    El Chakhtoura, Joline

    2015-05-01

    Understanding the biological stability of drinking water distribution systems is imperative in the framework of process control and risk management. The objective of this research was to examine the dynamics of the bacterial community during drinking water distribution at high temporal resolution. Water samples (156 in total) were collected over short time-scales (minutes/hours/days) from the outlet of a treatment plant and a location in its corresponding distribution network. The drinking water is treated by biofiltration and disinfectant residuals are absent during distribution. The community was analyzed by 16S rRNA gene pyrosequencing and flow cytometry as well as conventional, culture-based methods. Despite a random dramatic event (detected with pyrosequencing and flow cytometry but not with plate counts), the bacterial community profile at the two locations did not vary significantly over time. A diverse core microbiome was shared between the two locations (58-65% of the taxa and 86-91% of the sequences) and found to be dependent on the treatment strategy. The bacterial community structure changed during distribution, with greater richness detected in the network and phyla such as Acidobacteria and Gemmatimonadetes becoming abundant. The rare taxa displayed the highest dynamicity, causing the major change during water distribution. This change did not have hygienic implications and is contingent on the sensitivity of the applied methods. The concept of biological stability therefore needs to be revised. Biostability is generally desired in drinking water guidelines but may be difficult to achieve in large-scale complex distribution systems that are inherently dynamic.

  3. New approach for phylogenetic tree recovery based on genome-scale metabolic networks.

    Science.gov (United States)

    Gamermann, Daniel; Montagud, Arnaud; Conejero, J Alberto; Urchueguía, Javier F; de Córdoba, Pedro Fernández

    2014-07-01

    A wide range of applications and research has been done with genome-scale metabolic models. In this work, we describe an innovative methodology for comparing metabolic networks constructed from genome-scale metabolic models and how to apply this comparison in order to infer evolutionary distances between different organisms. Our methodology allows a quantification of the metabolic differences between different species from a broad range of families and even kingdoms. This quantification is then applied in order to reconstruct phylogenetic trees for sets of various organisms.

  4. Origin of structural difference in metabolic networks with respect to temperature

    Directory of Open Access Journals (Sweden)

    Akutsu Tatsuya

    2008-09-01

    Full Text Available Abstract Background Metabolism is believed to adaptively shape-shift with changing environment. In recent years, a structural difference with respect to temperature, which is an environmental factor, has been revealed in metabolic networks, implying that metabolic networks transit with temperature. Subsequently, elucidatation of the origin of these structural differences due to temperature is important for understanding the evolution of life. However, the origin has yet to be clarified due to the complexity of metabolic networks. Results Consequently, we propose a simple model with a few parameters to explain the transitions. We first present mathematical solutions of this model using mean-field approximation, and demonstrate that this model can reproduce structural properties, such as heterogeneous connectivity and hierarchical modularity, in real metabolic networks both qualitatively and quantitatively. We next show that the model parameters correlate with optimal growth temperature. In addition, we present a relationship between multiple cyclic properties and optimal growth temperature in metabolic networks. Conclusion From the proposed model, we find that such structural properties are determined by the emergence of a short-cut path, which reduces the minimum distance between two nodes on a graph. Furthermore, we investigate correlations between model parameters and growth temperature; as a result, we find that the emergence of the short-cut path tends to be inhibited with increasing temperature. In addition, we also find that the short-cut path bypasses a relatively long path at high temperature when the emergence of the new path is not inhibited. Even further, additional network analysis provides convincing evidence of the reliability of the proposed model and its conclusions on the possible origins of differences in metabolic network structure.

  5. Effect of substrate competition in kinetic models of metabolic networks.

    Science.gov (United States)

    Schäuble, Sascha; Stavrum, Anne Kristin; Puntervoll, Pål; Schuster, Stefan; Heiland, Ines

    2013-09-02

    Substrate competition can be found in many types of biological processes, ranging from gene expression to signal transduction and metabolic pathways. Although several experimental and in silico studies have shown the impact of substrate competition on these processes, it is still often neglected, especially in modelling approaches. Using toy models that exemplify different metabolic pathway scenarios, we show that substrate competition can influence the dynamics and the steady state concentrations of a metabolic pathway. We have additionally derived rate laws for substrate competition in reversible reactions and summarise existing rate laws for substrate competition in irreversible reactions.

  6. RELATCH: relative optimality in metabolic networks explains robust metabolic and regulatory responses to perturbations.

    Science.gov (United States)

    Kim, Joonhoon; Reed, Jennifer L

    2012-07-05

    Predicting cellular responses to perturbations is an important task in systems biology. We report a new approach, RELATCH, which uses flux and gene expression data from a reference state to predict metabolic responses in a genetically or environmentally perturbed state. Using the concept of relative optimality, which considers relative flux changes from a reference state, we hypothesize a relative metabolic flux pattern is maintained from one state to another, and that cells adapt to perturbations using metabolic and regulatory reprogramming to preserve this relative flux pattern. This constraint-based approach will have broad utility where predictions of metabolic responses are needed.

  7. Metabolic and molecular characterization of bacterial community associated to Patagonian Chilean oligotrophic-lakes of quaternary glacial origin.

    Science.gov (United States)

    Leon, Carla; Campos, Víctor; Urrutia, Roberto; Mondaca, María-Angélica

    2012-04-01

    The Patagonian Lakes have particular environmental conditions with or without intermittent disturbances. The study of the microorganisms present in aquatic ecosystems has increased notably because they can be used as micro-scale bioindicators of, among others, anthropogenic pollution and climatic change. The aim of the work was to compare the composition of the bacterial communities associated with sediments of three Patagonian Lakes with different geomorphologic patterns and disturbances. The lake sediments were characterized by molecular techniques, physiology profiles and physico-chemical analyses. The metabolic and physiological profiles of the microbial community demonstrated that non-impacted Tranquilo Lake is statistically different to impacted Bertrand and Plomo Lakes. Similar results were detected by DGGE profiles. FISH results demonstrated that betaproteobacteria showed the highest count in the Tranquilo Lake while gammaproteobacteria showed the highest counts in the Bertrand and Plomo Lakes, indicating that their sediments are highly dystrophic. The results demonstrate differences in the metabolic activity and structural and functional composition of bacterial communities of the studied lakes, which have different geomorphological patterns due to disturbances such as volcanic activity and the climatic change.

  8. The effects of non-metabolizing bacterial cells on the precipitation of U, Pb and Ca phosphates

    Science.gov (United States)

    Dunham-Cheatham, Sarrah; Rui, Xue; Bunker, Bruce; Menguy, Nicolas; Hellmann, Roland; Fein, Jeremy

    2011-05-01

    In this study, we test the potential for passive cell wall biomineralization by determining the effects of non-metabolizing bacteria on the precipitation of uranyl, lead, and calcium phosphates from a range of over-saturated conditions. Experiments were performed using Gram-positive Bacillus subtilis and Gram-negative Shewanella oneidensis MR-1. After equilibration, the aqueous phases were sampled and the remaining metal and P concentrations were analyzed using inductively coupled plasma-optical emission spectroscopy (ICP-OES); the solid phases were collected and analyzed using X-ray diffractometry (XRD), transmission electron microscopy (TEM), and X-ray absorption spectroscopy (XAS). At the lower degrees of over-saturation studied, bacterial cells exerted no discernable effect on the mode of precipitation of the metal phosphates, with homogeneous precipitation occurring exclusively. However, at higher saturation states in the U system, we observed heterogeneous mineralization and extensive nucleation of hydrogen uranyl phosphate (HUP) mineralization throughout the fabric of the bacterial cell walls. This mineral nucleation effect was observed in both B. subtilis and S. oneidensis cells. In both cases, the biogenic mineral precipitates formed under the higher saturation state conditions were significantly smaller than those that formed in the abiotic controls. The cell wall nucleation effects that occurred in some of the U systems were not observed under any of the saturation state conditions studied in the Pb or Ca systems. The presence of B. subtilis significantly decreased the extent of precipitation in the U system, but had little effect in the Pb and Ca systems. At least part of this effect is due to higher solubility of the nanoscale HUP precipitate relative to macroscopic HUP. This study documents several effects of non-metabolizing bacterial cells on the nature and extent of metal phosphate precipitation. Each of these effects likely contributes to higher

  9. Colonic transit time is related to bacterial metabolism and mucosal turnover in the human gut

    DEFF Research Database (Denmark)

    Roager, Henrik Munch; Hansen, Lea Benedicte Skov; Bahl, Martin Iain

    transit time and the gut microbial composition and metabolism, we assessed the colonic transit time of 98 subjects using radiopaque markers, and profiled their gut microbiota by16S rRNA gene sequencing and their urine metabolome by ultra performance liquid chromatography mass spectrometry. Based......Little is known about how colonic transit time relates to human colonic metabolism, and its importance for host health, although stool consistency, a proxy for colonic transit time, has recently been negatively associated with gut microbial richness. To address the relationships between colonic...... on correlation analyses, we show that colonic transit time is associated with overall gut microbial composition, diversity and metabolism. A relatively prolonged colonic transit time associates with high microbial species richness and a shift in colonic metabolism from carbohydrate fermentation to protein...

  10. A Strategy for Functional Interpretation of Metabolomic Time Series Data in Context of Metabolic Network Information.

    Science.gov (United States)

    Nägele, Thomas; Fürtauer, Lisa; Nagler, Matthias; Weiszmann, Jakob; Weckwerth, Wolfram

    2016-01-01

    The functional connection of experimental metabolic time series data with biochemical network information is an important, yet complex, issue in systems biology. Frequently, experimental analysis of diurnal, circadian, or developmental dynamics of metabolism results in a comprehensive and multidimensional data matrix comprising information about metabolite concentrations, protein levels, and/or enzyme activities. While, irrespective of the type of organism, the experimental high-throughput analysis of the transcriptome, proteome, and metabolome has become a common part of many systems biological studies, functional data integration in a biochemical and physiological context is still challenging. Here, an approach is presented which addresses the functional connection of experimental time series data with biochemical network information which can be inferred, for example, from a metabolic network reconstruction. Based on a time-continuous and variance-weighted regression analysis of experimental data, metabolic functions, i.e., first-order derivatives of metabolite concentrations, were related to time-dependent changes in other biochemically relevant metabolic functions, i.e., second-order derivatives of metabolite concentrations. This finally revealed time points of perturbed dependencies in metabolic functions indicating a modified biochemical interaction. The approach was validated using previously published experimental data on a diurnal time course of metabolite levels, enzyme activities, and metabolic flux simulations. To support and ease the presented approach of functional time series analysis, a graphical user interface including a test data set and a manual is provided which can be run within the numerical software environment Matlab®.

  11. Resource niche overlap promotes stability of bacterial community metabolism in experimental microcosms

    NARCIS (Netherlands)

    E.R. Hunting; M.G. Vijver; H.G. van der Geest; C. Mulder; M.H.S. Kraak; A.M. Breure; W. Admiraal

    2015-01-01

    Decomposition of organic matter is an important ecosystem process governed in part by bacteria. The process of decomposition is expected to benefit from interspecific bacterial interactions such as resource partitioning and facilitation. However, the relative importance of resource niche breadth (me

  12. Effect of CO2 enrichment on bacterial metabolism in an Arctic fjord

    NARCIS (Netherlands)

    C. Motegi; T. Tanaka; J. Piontek; C.P.D. Brussaard; J.P. Gattuso; M.G. Weinbauer

    2013-01-01

    The anthropogenic increase of carbon dioxide (CO2) alters the seawater carbonate chemistry, with a decline of pH and an increase in the partial pressure of CO2 (pCO2). Although bacteria play a major role in carbon cycling, little is known about the impact of rising pCO2 on bacterial carbon metabolis

  13. Metabolomics Approach Reveals Integrated Metabolic Network Associated with Serotonin Deficiency

    Science.gov (United States)

    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.

  14. Study on Incompatibility of Traditional Chinese Medicine: Evidence from Formula Network, Chemical Space, and Metabolism Room

    Directory of Open Access Journals (Sweden)

    Wei Long

    2013-01-01

    Full Text Available A traditional Chinese medicine (TCM formula network including 362 TCM formulas was built by using complex network methodologies. The properties of this network were analyzed including network diameter, average distance, clustering coefficient, and average degree. Meanwhile, we built a TCM chemical space and a TCM metabolism room under the theory of chemical space. The properties of chemical space and metabolism room were calculated and analyzed. The properties of the medicine pairs in “eighteen antagonisms and nineteen mutual inhibitors,” an ancient rule for TCM incompatibility, were studied based on the TCM formula network, chemical space, and metabolism room. The results showed that the properties of these incompatible medicine pairs are different from those of the other TCM based on the analysis of the TCM formula network, chemical space, and metabolism room. The lines of evidence derived from our work demonstrated that the ancient rule of TCM incompatibility, “eighteen antagonisms and nineteen mutual inhibitors,” is probably scientifically based.

  15. FluxSimulator: An R Package to Simulate Isotopomer Distributions in Metabolic Networks

    Directory of Open Access Journals (Sweden)

    Thomas W. Binsl

    2007-01-01

    Full Text Available The representation of biochemical knowledge in terms of fluxes (transformation rates in a metabolic network is often a crucial step in the development of new drugs and efficient bioreactors. Mass spectroscopy (MS and nuclear magnetic resonance spectroscopy (NMRS in combination with 13C labeled substrates are experimental techniques resulting in data that may be used to quantify fluxes in the metabolic network underlying a process. The massive amount of data generated by spectroscopic experiments increasingly requires software which models the dynamics of the underlying biological system. In this work we present an approach to handle isotopomer distributions in metabolic networks using an object-oriented programming approach, implemented using S4 classes in R. The developed package is called FluxSimulator and provides a user friendly interface to specify the topological information of the metabolic network as well as carbon atom transitions in plain text files. The package automatically derives the mathematical representation of the formulated network, and assembles a set of ordinary differential equations (ODEs describing the change of each isotopomer pool over time. These ODEs are subsequently solved numerically. In a case study FluxSimulator was applied to an example network. Our results indicate that the package is able to reproduce exact changes in isotopomer compositions of the metabolite pools over time at given flux rates.

  16. Comparative analysis of Salmonella genomes identifies a metabolic network for escalating growth in the inflamed gut.

    Science.gov (United States)

    Nuccio, Sean-Paul; Bäumler, Andreas J

    2014-03-18

    The Salmonella genus comprises a group of pathogens associated with illnesses ranging from gastroenteritis to typhoid fever. We performed an in silico analysis of comparatively reannotated Salmonella genomes to identify genomic signatures indicative of disease potential. By removing numerous annotation inconsistencies and inaccuracies, the process of reannotation identified a network of 469 genes involved in central anaerobic metabolism, which was intact in genomes of gastrointestinal pathogens but degrading in genomes of extraintestinal pathogens. This large network contained pathways that enable gastrointestinal pathogens to utilize inflammation-derived nutrients as well as many of the biochemical reactions used for the enrichment and biochemical discrimination of Salmonella serovars. Thus, comparative genome analysis identifies a metabolic network that provides clues about the strategies for nutrient acquisition and utilization that are characteristic of gastrointestinal pathogens. IMPORTANCE While some Salmonella serovars cause infections that remain localized to the gut, others disseminate throughout the body. Here, we compared Salmonella genomes to identify characteristics that distinguish gastrointestinal from extraintestinal pathogens. We identified a large metabolic network that is functional in gastrointestinal pathogens but decaying in extraintestinal pathogens. While taxonomists have used traits from this network empirically for many decades for the enrichment and biochemical discrimination of Salmonella serovars, our findings suggest that it is part of a "business plan" for growth in the inflamed gastrointestinal tract. By identifying a large metabolic network characteristic of Salmonella serovars associated with gastroenteritis, our in silico analysis provides a blueprint for potential strategies to utilize inflammation-derived nutrients and edge out competing gut microbes.

  17. Observability of plant metabolic networks is reflected in the correlation of metabolic profiles

    DEFF Research Database (Denmark)

    Schwahn, Kevin; Küken, Anika; Kliebenstein, Daniel James;

    2016-01-01

    to obtain information about the entire system. Yet, the extent to which the data profiles reflect the role of components in the observability of the system remains unexplored. Here we first identify the sensor metabolites in the model plant Arabidopsis (Arabidopsis thaliana) by employing state...... with in silico generated metabolic profiles from a medium-size kinetic model of plant central carbon metabolism. Altogether, due to the small number of identified sensors, our study implies that targeted metabolite analyses may provide the vast majority of relevant information about plant metabolic systems....

  18. TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks

    Directory of Open Access Journals (Sweden)

    Jensen Paul A

    2011-09-01

    Full Text Available Abstract Background Several methods have been developed for analyzing genome-scale models of metabolism and transcriptional regulation. Many of these methods, such as Flux Balance Analysis, use constrained optimization to predict relationships between metabolic flux and the genes that encode and regulate enzyme activity. Recently, mixed integer programming has been used to encode these gene-protein-reaction (GPR relationships into a single optimization problem, but these techniques are often of limited generality and lack a tool for automating the conversion of rules to a coupled regulatory/metabolic model. Results We present TIGER, a Toolbox for Integrating Genome-scale Metabolism, Expression, and Regulation. TIGER converts a series of generalized, Boolean or multilevel rules into a set of mixed integer inequalities. The package also includes implementations of existing algorithms to integrate high-throughput expression data with genome-scale models of metabolism and transcriptional regulation. We demonstrate how TIGER automates the coupling of a genome-scale metabolic model with GPR logic and models of transcriptional regulation, thereby serving as a platform for algorithm development and large-scale metabolic analysis. Additionally, we demonstrate how TIGER's algorithms can be used to identify inconsistencies and improve existing models of transcriptional regulation with examples from the reconstructed transcriptional regulatory network of Saccharomyces cerevisiae. Conclusion The TIGER package provides a consistent platform for algorithm development and extending existing genome-scale metabolic models with regulatory networks and high-throughput data.

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

    Directory of Open Access Journals (Sweden)

    Ai-Di Zhang

    2013-01-01

    Full Text Available 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.

  20. Metabolic network analysis of Bacillus clausii on minimal and semirich medium using C-13-Labeled glucose

    DEFF Research Database (Denmark)

    Christiansen, Torben; Christensen, Bjarke; Nielsen, Jens

    2002-01-01

    from the medium and partly synthesized from glucose. The metabolic network analysis was extended to include analysis of growth on the semirich medium containing amino acids, and the metabolic flux distribution on this medium was estimated and compared with growth on minimal medium....... to increase with increasing specific growth rate but at a much lower level than previously reported for Bacillus subtilis. Two futile cycles in the pyruvate metabolism were included in the metabolic network. A substantial flux in the futile cycle involving malic enzyme was estimated, whereas only a very small......Using C-13-labeled glucose fed to the facultative alkalophilic Bacillus clausii producing the alkaline serine protease Savinase, the intracellular fluxes were quantified in continuous cultivation and in batch cultivation on a minimal medium. The flux through the pentose phosphate pathway was found...

  1. Detection of Bundle Branch Block using Adaptive Bacterial Foraging Optimization and Neural Network

    Directory of Open Access Journals (Sweden)

    Padmavthi Kora

    2017-03-01

    Full Text Available The medical practitioners analyze the electrical activity of the human heart so as to predict various ailments by studying the data collected from the Electrocardiogram (ECG. A Bundle Branch Block (BBB is a type of heart disease which occurs when there is an obstruction along the pathway of an electrical impulse. This abnormality makes the heart beat irregular as there is an obstruction in the branches of heart, this results in pulses to travel slower than the usual. Our current study involved is to diagnose this heart problem using Adaptive Bacterial Foraging Optimization (ABFO Algorithm. The Data collected from MIT/BIH arrhythmia BBB database applied to an ABFO Algorithm for obtaining best(important feature from each ECG beat. These features later fed to Levenberg Marquardt Neural Network (LMNN based classifier. The results show the proposed classification using ABFO is better than some recent algorithms reported in the literature.

  2. Deciphering metabolic networks by blue native polyacrylamide gel electrophoresis: A functional proteomic exploration

    Directory of Open Access Journals (Sweden)

    Christopher Auger

    2015-06-01

    Full Text Available Metabolism is the consortium of reactions within a cell which directs a variety of processes including energy synthesis, signalling and the behaviour of a biological system. Metabolic networks, and more specifically the activity of enzymes within them, provide an accurate status of how cellular information is being executed. The performance of these networks and their ability to siphon metabolites in a number of directions may be the difference between a healthy and diseased state. Blue native polyacrylamide gel electrophoresis (BN-PAGE, owing to its simplicity and wide-ranging applications, permits the inspection of these nodules. The separation of proteins and enzyme complexes in their native format enables the exploration of enzymatic activity in metabolic networks via in-gel assays. These are quick, specific, and amenable to further studies. This electrophoretic technology not only enables the visualization of enzymatic efficacy but reveals the crosstalk among enzymes and their interactions with other organellar partners.

  3. Understanding the control of acyl flux through the lipid metabolic network of plant oil biosynthesis.

    Science.gov (United States)

    Bates, Philip D

    2016-09-01

    Plant oil biosynthesis involves a complex metabolic network with multiple subcellular compartments, parallel pathways, cycles, and pathways that have a dual function to produce essential membrane lipids and triacylglycerol. Modern molecular biology techniques provide tools to alter plant oil compositions through bioengineering, however with few exceptions the final composition of triacylglycerol cannot be predicted. One reason for limited success in oilseed bioengineering is the inadequate understanding of how to control the flux of fatty acids through various fatty acid modification, and triacylglycerol assembly pathways of the lipid metabolic network. This review focuses on the mechanisms of acyl flux through the lipid metabolic network, and highlights where uncertainty resides in our understanding of seed oil biosynthesis. This article is part of a Special Issue entitled: Plant Lipid Biology edited by Kent D. Chapman and Ivo Feussner.

  4. Thermodynamic analysis of regulation in metabolic networks using constraint-based modeling

    Directory of Open Access Journals (Sweden)

    Mahadevan Radhakrishnan

    2010-05-01

    Full Text Available Abstract Background Geobacter sulfurreducens is a member of the Geobacter species, which are capable of oxidation of organic waste coupled to the reduction of heavy metals and electrode with applications in bioremediation and bioenergy generation. While the metabolism of this organism has been studied through the development of a stoichiometry based genome-scale metabolic model, the associated regulatory network has not yet been well studied. In this manuscript, we report on the implementation of a thermodynamics based metabolic flux model for Geobacter sulfurreducens. We use this updated model to identify reactions that are subject to regulatory control in the metabolic network of G. sulfurreducens using thermodynamic variability analysis. Findings As a first step, we have validated the regulatory sites and bottleneck reactions predicted by the thermodynamic flux analysis in E. coli by evaluating the expression ranges of the corresponding genes. We then identified ten reactions in the metabolic network of G. sulfurreducens that are predicted to be candidates for regulation. We then compared the free energy ranges for these reactions with the corresponding gene expression fold changes under conditions of different environmental and genetic perturbations and show that the model predictions of regulation are consistent with data. In addition, we also identify reactions that operate close to equilibrium and show that the experimentally determined exchange coefficient (a measure of reversibility is significant for these reactions. Conclusions Application of the thermodynamic constraints resulted in identification of potential bottleneck reactions not only from the central metabolism but also from the nucleotide and amino acid subsystems, thereby showing the highly coupled nature of the thermodynamic constraints. In addition, thermodynamic variability analysis serves as a valuable tool in estimating the ranges of ΔrG' of every reaction in the model

  5. Colonic transit time relates to bacterial metabolism and mucosal turnover in the human gut

    DEFF Research Database (Denmark)

    Roager, Henrik Munch; Hansen, Lea Benedicte Skov; Bahl, Martin Iain

    transit time and the gut microbial composition and metabolism, we assessed the colonic transit time of 98 subjects using radiopaque markers, and profiled their gut microbiota by16S rRNA gene sequencingand their urine metabolome by ultra performance liquid chromatography mass spectrometry. Based......Little is known about how colonic transit time relates to human colonic metabolism, and its importance for host health, although stool consistency, a proxy for colonic transit time, has recently been negatively associated with gut microbial richness. To address the relationships between colonic...... on correlation analyses,we show that colonic transit time is associated with overall gutmicrobial composition, diversity and metabolism. A relatively prolonged colonic transit time associates with high microbial species richness and a shift in colonic metabolismfrom carbohydrate fermentation to protein...

  6. Colonic transit time is related to bacterial metabolism and mucosal turnover in the gut

    DEFF Research Database (Denmark)

    Roager, Henrik Munch; Hansen, Lea Benedicte Skov; Bahl, Martin Iain;

    2016-01-01

    Little is known about how colonic transit time relates to human colonic metabolism and its importance for host health, although a firm stool consistency, a proxy for a long colonic transit time, has recently been positively associated with gut microbial richness. Here, we show that colonic transit...... time in humans, assessed using radio-opaque markers, is associated with overall gut microbial composition, diversity and metabolism. We find that a long colonic transit time associates with high microbial richness and is accompanied by a shift in colonic metabolism from carbohydrate fermentation...... to protein catabolism as reflected by higher urinary levels of potentially deleterious protein-derived metabolites. Additionally, shorter colonic transit time correlates with metabolites possibly reflecting increased renewal of the colonic mucosa. Together, this suggests that a high gut microbial richness...

  7. Increasing galactose consumption by Saccharomyces cerevisiae through metabolic engineering of the GAL gene regulatory network

    DEFF Research Database (Denmark)

    Østergaard, Simon; Olsson, Lisbeth; Johnston, M.

    2000-01-01

    in the pathway, and ultimately, increasing metabolic flux through the pathway of interest, By manipulating the GAL gene regulatory network of Saccharomyces cerevisiae, which is a tightly regulated system, we produced prototroph mutant strains, which increased the flux through the galactose utilization pathway...... media. The improved galactose consumption of the gal mutants did not favor biomass formation, but rather caused excessive respiro-fermentative metabolism, with the ethanol production rate increasing linearly with glycolytic flux....

  8. Delayed bactericidal response of Mycobacterium tuberculosis to bedaquiline involves remodelling of bacterial metabolism

    DEFF Research Database (Denmark)

    Koul, A.; Vranckx, L.; Dhar, N.;

    2014-01-01

    microfluidic devices and time-lapse microscopy of Mycobacterium tuberculosis, we confirm the absence of significant bacteriolytic activity during the first 3-4 days of exposure to BDQ. BDQ-induced inhibition of ATP synthesis leads to bacteriostasis within hours after drug addition. Transcriptional......Bedaquiline (BDQ), an ATP synthase inhibitor, is the first drug to be approved for treatment of multidrug-resistant tuberculosis in decades. Though BDQ has shown excellent efficacy in clinical trials, its early bactericidal activity during the first week of chemotherapy is minimal. Here, using...... and proteomic analyses reveal that M. tuberculosis responds to BDQ by induction of the dormancy regulon and activation of ATP-generating pathways, thereby maintaining bacterial viability during initial drug exposure. BDQ-induced bacterial killing is significantly enhanced when the mycobacteria are grown on non...

  9. Heterotrophic bacterial production and metabolic balance during the VAHINE mesocosm experiment in the New Caledonia lagoon

    Science.gov (United States)

    Van Wambeke, F.; Pfreundt, U.; Barani, A.; Berthelot, H.; Moutin, T.; Rodier, M.; Hess, W. R.; Bonnet, S.

    2015-12-01

    N2 fixation fuels ~ 50 % of new primary production in the oligotrophic South Pacific Ocean. The VAHINE mesocosm experiment designed to track the fate of diazotroph derived nitrogen (DDN) in the New Caledonia lagoon. Here, we examined the temporal dynamics of heterotrophic bacterial production during this experiment. Three replicate large-volume (~ 50 m3) mesocosms were deployed and were intentionally fertilized with dissolved inorganic phosphorus (DIP) to stimulate N2 fixation. We specifically examined relationships between N2 fixation rates and primary production, determined bacterial growth efficiency and established carbon budgets of the system from the DIP fertilization to the end of the experiment (days 5-23). Heterotrophic bacterioplankton production (BP) and alkaline phosphatase activity (APA) were statistically higher during the second phase of the experiment (P2: days 15-23), when chlorophyll biomass started to increase compared to the first phase (P1: days 5-14). Among autotrophs, Synechococcus abundances increased during P2, possibly related to its capacity to assimilate leucine and to produce alkaline phosphatase. Bacterial growth efficiency based on the carbon budget was notably higher than generally cited for oligotrophic environments (27-43 %), possibly due to a high representation of proteorhodopsin-containing organisms within the picoplanctonic community. The carbon budget showed that the main fate of gross primary production (particulate + dissolved) was respiration (67 %), and export through sedimentation (17 %). BP was highly correlated with particulate primary production and chlorophyll biomass during both phases of the experiment but slightly correlated, and only during P2 phase, with N2 fixation rates. Our results suggest that most of the DDN reached the heterotrophic bacterial community through indirect processes, like mortality, lysis and grazing.

  10. Heterotrophic bacterial production and metabolic balance during the VAHINE mesocosm experiment in the New Caledonia lagoon

    Directory of Open Access Journals (Sweden)

    F. Van Wambeke

    2015-12-01

    Full Text Available N2 fixation fuels ~ 50 % of new primary production in the oligotrophic South Pacific Ocean. The VAHINE mesocosm experiment designed to track the fate of diazotroph derived nitrogen (DDN in the New Caledonia lagoon. Here, we examined the temporal dynamics of heterotrophic bacterial production during this experiment. Three replicate large-volume (~ 50 m3 mesocosms were deployed and were intentionally fertilized with dissolved inorganic phosphorus (DIP to stimulate N2 fixation. We specifically examined relationships between N2 fixation rates and primary production, determined bacterial growth efficiency and established carbon budgets of the system from the DIP fertilization to the end of the experiment (days 5–23. Heterotrophic bacterioplankton production (BP and alkaline phosphatase activity (APA were statistically higher during the second phase of the experiment (P2: days 15–23, when chlorophyll biomass started to increase compared to the first phase (P1: days 5–14. Among autotrophs, Synechococcus abundances increased during P2, possibly related to its capacity to assimilate leucine and to produce alkaline phosphatase. Bacterial growth efficiency based on the carbon budget was notably higher than generally cited for oligotrophic environments (27–43 %, possibly due to a high representation of proteorhodopsin-containing organisms within the picoplanctonic community. The carbon budget showed that the main fate of gross primary production (particulate + dissolved was respiration (67 %, and export through sedimentation (17 %. BP was highly correlated with particulate primary production and chlorophyll biomass during both phases of the experiment but slightly correlated, and only during P2 phase, with N2 fixation rates. Our results suggest that most of the DDN reached the heterotrophic bacterial community through indirect processes, like mortality, lysis and grazing.

  11. The bacterial metabolism of carbohydrates used in tests of intestinal permeability

    OpenAIRE

    Qureishy, Gulzar A.

    1984-01-01

    Carbohydrates have been used for tests of intestinal function for many years and the impaired absorption of carbohydrates in the intestinal lumen is either due to the damaged intestinal absorptive surface, as in coeliac disease etc., in some types of acute gastroenteritis , when the absorptive area is reduced by villous atrophy , or due to the bacterial overgrowth in the small intestinal lumen as in blind loop syndrome , some types of malabsorption , which possibly produce alteration in the m...

  12. Development and analysis of an in vivo-compatible metabolic network of Mycobacterium tuberculosis

    Directory of Open Access Journals (Sweden)

    Reifman Jaques

    2010-11-01

    Full Text Available Abstract Background During infection, Mycobacterium tuberculosis confronts a generally hostile and nutrient-poor in vivo host environment. Existing models and analyses of M. tuberculosis metabolic networks are able to reproduce experimentally measured cellular growth rates and identify genes required for growth in a range of different in vitro media. However, these models, under in vitro conditions, do not provide an adequate description of the metabolic processes required by the pathogen to infect and persist in a host. Results To better account for the metabolic activity of M. tuberculosis in the host environment, we developed a set of procedures to systematically modify an existing in vitro metabolic network by enhancing the agreement between calculated and in vivo-measured gene essentiality data. After our modifications, the new in vivo network contained 663 genes, 838 metabolites, and 1,049 reactions and had a significantly increased sensitivity (0.81 in predicted gene essentiality than the in vitro network (0.31. We verified the modifications generated from the purely computational analysis through a review of the literature and found, for example, that, as the analysis suggested, lipids are used as the main source for carbon metabolism and oxygen must be available for the pathogen under in vivo conditions. Moreover, we used the developed in vivo network to predict the effects of double-gene deletions on M. tuberculosis growth in the host environment, explore metabolic adaptations to life in an acidic environment, highlight the importance of different enzymes in the tricarboxylic acid-cycle under different limiting nutrient conditions, investigate the effects of inhibiting multiple reactions, and look at the importance of both aerobic and anaerobic cellular respiration during infection. Conclusions The network modifications we implemented suggest a distinctive set of metabolic conditions and requirements faced by M. tuberculosis during

  13. Heterotrophic bacterial production and metabolic balance during the VAHINE mesocosm experiment in the New Caledonia lagoon

    Science.gov (United States)

    Van Wambeke, France; Pfreundt, Ulrike; Barani, Aude; Berthelot, Hugo; Moutin, Thierry; Rodier, Martine; Hess, Wolfgang R.; Bonnet, Sophie

    2016-06-01

    Studies investigating the fate of diazotrophs through the microbial food web are lacking, although N2 fixation can fuel up to 50 % of new production in some oligotrophic oceans. In particular, the role played by heterotrophic prokaryotes in this transfer is largely unknown. In the frame of the VAHINE (VAriability of vertical and tropHIc transfer of diazotroph derived N in the south wEst Pacific) experiment, three replicate large-volume (˜ 50 m3) mesocosms were deployed for 23 days in the new Caledonia lagoon and were intentionally fertilized on day 4 with dissolved inorganic phosphorus (DIP) to stimulate N2 fixation. We specifically examined relationships between heterotrophic bacterial production (BP) and N2 fixation or primary production, determined bacterial growth efficiency and established carbon budgets. BP was statistically higher during the second phase of the experiment (P2: days 15-23), when chlorophyll biomass started to increase compared to the first phase (P1: days 5-14). Phosphatase alkaline activity increased drastically during the second phase of the experiment, showing adaptations of microbial populations after utilization of the added DIP. Notably, among autotrophs, Synechococcus abundances increased during P2, possibly related to its capacity to assimilate leucine and to produce alkaline phosphatase. Bacterial growth efficiency based on the carbon budget (27-43 %), was notably higher than generally cited for oligotrophic environments and discussed in links with the presence of abundant species of bacteria expressing proteorhodopsin. The main fates of gross primary production (particulate + dissolved) were respiration (67 %) and export through sedimentation (17 %). BP was highly correlated with particulate primary production and chlorophyll biomass during both phases of the experiment but was slightly correlated, and only during P2 phase, with N2 fixation rates. Heterotrophic bacterial production was strongly stimulated after mineral N enrichment

  14. Predicting epistasis: an experimental test of metabolic control theory with bacterial transcription and translation.

    Science.gov (United States)

    MacLean, R C

    2010-03-01

    Epistatic interactions between mutations are thought to play a crucial role in a number of evolutionary processes, including adaptation and sex. Evidence for epistasis is abundant, but tests of general theoretical models that can predict epistasis are lacking. In this study, I test the ability of metabolic control theory to predict epistasis using a novel experimental approach that combines phenotypic and genetic perturbations of enzymes involved in gene expression and protein synthesis in the bacterium Pseudomonas aeruginosa. These experiments provide experimental support for two key predictions of metabolic control theory: (i) epistasis between genes involved in the same pathway is antagonistic; (ii) epistasis becomes increasingly antagonistic as mutational severity increases. Metabolic control theory is a general theory that applies to any set of genes that are involved in the same linear processing chain, not just metabolic pathways, and I argue that this theory is likely to have important implications for predicting epistasis between functionally coupled genes, such as those involved in antibiotic resistance. Finally, this study highlights the fact that phenotypic manipulations of gene activity provide a powerful method for studying epistasis that complements existing genetic methods.

  15. Enhanced plasmid loss in bacterial populations exposed to the antimicrobial compound irgasan delivered from interpenetrating polymer network silicone hydrogels

    DEFF Research Database (Denmark)

    Riber, Leise; Burmølle, Mette; Alm, Martin;

    2016-01-01

    into liquid growth medium from irgasan-impregnated Interpenetrating Polymer Network (IPN) silicone hydrogel objects, a novel technology developed as drug-delivery platform. IPN-mediated irgasan-release was indirectly monitored as the extent of plasmid loss from bacterial cells during a 24-hour period...

  16. Environmental factors shaping cultured free-living amoebae and their associated bacterial community within drinking water network.

    Science.gov (United States)

    Delafont, Vincent; Bouchon, Didier; Héchard, Yann; Moulin, Laurent

    2016-09-01

    Free-living amoebae (FLA) constitute an important part of eukaryotic populations colonising drinking water networks. However, little is known about the factors influencing their ecology in such environments. Because of their status as reservoir of potentially pathogenic bacteria, understanding environmental factors impacting FLA populations and their associated bacterial community is crucial. Through sampling of a large drinking water network, the diversity of cultivable FLA and their bacterial community were investigated by an amplicon sequencing approach, and their correlation with physicochemical parameters was studied. While FLA ubiquitously colonised the water network all year long, significant changes in population composition were observed. These changes were partially explained by several environmental parameters, namely water origin, temperature, pH and chlorine concentration. The characterisation of FLA associated bacterial community reflected a diverse but rather stable consortium composed of nearly 1400 OTUs. The definition of a core community highlighted the predominance of only few genera, majorly dominated by Pseudomonas and Stenotrophomonas. Co-occurrence analysis also showed significant patterns of FLA-bacteria association, and allowed uncovering potentially new FLA - bacteria interactions. From our knowledge, this study is the first that combines a large sampling scheme with high-throughput identification of FLA together with associated bacteria, along with their influencing environmental parameters. Our results demonstrate the importance of physicochemical parameters in the ecology of FLA and their bacterial community in water networks.

  17. Towards Kinetic Modeling of Global Metabolic Networks Methylobacterium extorquens AM1 Growth as Validation

    Institute of Scientific and Technical Information of China (English)

    Ping Ao; Lik Wee Lee; Mary E. Lidstrom; Lan Yin; Xiaomei Zhu

    2008-01-01

    Here we report a systematic method for constructing a large scale kinetic metabolic model and its initial application to the modeling of central metabolism of Methylobacterium extorquens AM1, a methylotrophic and environmental important bacterium. Its central metabolic network includes formaldehyde metabolism, serine cycle, citric acid cycle, pentose phosphate pathway, ghiconeogensis, PHB synthesis and acetyl-CoA conversion pathway, respiration and energy metabolism. Through a systematic and consistent procedure of finding a set of parameters in the physiological range we overcome an outstanding difficulty in large scale kinetic modeling: the requirement for a massive number of enzymatic reaction parameters. We are able to construct the kinetic model based on general biological considerations and incomplete experimental kinetic parameters. Our method consists of the following major steps: 1) using a generic enzymatic rate equation to reduce the number of enzymatic parameters to a minimum set while still preserving their characteristics; 2) using a set of steady state fluxes and metabolite concenwations in the physiological range as the expected output steady state fluxes and metabolite concentrations for the kinetic model to restrict the parametric space of enzymatic reactions; 3) choosing enzyme constants K's and K'eqs optimized for reactions under physiological concentrations, if their experimental values are unknown; 4) for models which do not cover the entire metabolic network of the organisms, designing a dynamical exchange for the coupling between the metabolism represented in the model and the rest not included.

  18. Genetic and metabolic signals during acute enteric bacterial infection alter the microbiota and drive progression to chronic inflammatory disease

    Energy Technology Data Exchange (ETDEWEB)

    Kamdar, Karishma; Khakpour, Samira; Chen, Jingyu; Leone, Vanessa; Brulc, Jennifer; Mangatu, Thomas; Antonopoulos, Dionysios A.; Chang, Eugene B; Kahn, Stacy A.; Kirschner, Barbara S; Young, Glenn; DePaolo, R. William

    2016-01-13

    Chronic inflammatory disorders are thought to arise due to an interplay between predisposing host genetics and environmental factors. For example, the onset of inflammatory bowel disease is associated with enteric proteobacterial infection, yet the mechanistic basis for this association is unclear. We have shown previously that genetic defiency in TLR1 promotes acute enteric infection by the proteobacteria Yersinia enterocolitica. Examining that model further, we uncovered an altered cellular immune response that promotes the recruitment of neutrophils which in turn increases metabolism of the respiratory electron acceptor tetrathionate by Yersinia. These events drive permanent alterations in anti-commensal immunity, microbiota composition, and chronic inflammation, which persist long after Yersinia clearence. Deletion of the bacterial genes involved in tetrathionate respiration or treatment using targeted probiotics could prevent microbiota alterations and inflammation. Thus, acute infection can drive long term immune and microbiota alterations leading to chronic inflammatory disease in genetically predisposed individuals.

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

    Science.gov (United States)

    2016-04-01

    profiles, and metabolic risk factors in intervention patients and controls in both programs. Results: In the on-treatment analysis , both...from further analysis (Supporting Information Table S2). A flow diagram showing participant enrollment, attrition, and subgroup analysis is presented in...sufficient RNA remaining for analysis were subjected to qRT-PCR Figure 1 Flow diagram showing participant enrollment, attrition, and subgroup analysis

  20. Double network bacterial cellulose hydrogel to build a biology-device interface.

    Science.gov (United States)

    Shi, Zhijun; Li, Ying; Chen, Xiuli; Han, Hongwei; Yang, Guang

    2014-01-21

    Establishing a biology-device interface might enable the interaction between microelectronics and biotechnology. In this study, electroactive hydrogels have been produced using bacterial cellulose (BC) and conducting polymer (CP) deposited on the BC hydrogel surface to cover the BC fibers. The structures of these composites thus have double networks, one of which is a layer of electroactive hydrogels combined with BC and CP. The electroconductivity provides the composites with capabilities for voltage and current response, and the BC hydrogel layer provides good biocompatibility, biodegradability, bioadhesion and mass transport properties. Such a system might allow selective biological functions such as molecular recognition and specific catalysis and also for probing the detailed genetic and molecular mechanisms of life. A BC-CP composite hydrogel could then lead to a biology-device interface. Cyclic voltammetry and electrochemical impedance spectroscopy (EIS) are used here to study the composite hydrogels' electroactive property. BC-PAni and BC-PPy respond to voltage changes. This provides a mechanism to amplify electrochemical signals for analysis or detection. BC hydrogels were found to be able to support the growth, spreading and migration of human normal skin fibroblasts without causing any cytotoxic effect on the cells in the cell culture. These double network BC-CP hydrogels are biphasic Janus hydrogels which integrate electroactivity with biocompatibility, and might provide a biology-device interface to produce implantable devices for personalized and regenerative medicine.

  1. Hypoxia determines survival outcomes of bacterial infection through HIF-1alpha dependent re-programming of leukocyte metabolism *

    Science.gov (United States)

    Thompson, A.A.R.; Dickinson, R.S.; Murphy, F.; Thomson, J. P.; Marriott, H.M.; Tavares, A.; Willson, J.; Williams, L.; Lewis, A.; Mirchandani, A.; Dos Santos Coelho, P.; Doherty, C.; Ryan, E.; Watts, E.; Morton, N. M.; Forbes, S.; Stimson, R. H.; Hameed, A. G.; Arnold, N.; Preston, J.A.; Lawrie, A.; Finisguerra, V.; Mazzone, M.; Sadiku, P.; Goveia, J.; Taverna, F.; Carmeliet, P.; Foster, S.J.; Chilvers, E.R.; Cowburn, A.S.; Dockrell, D.H.; Johnson, R.S.; Meehan, R. R.; Whyte, M.K.B.; Walmsley, S.R.

    2017-01-01

    Hypoxia and bacterial infection frequently co-exist, in both acute and chronic clinical settings, and typically result in adverse clinical outcomes. To ameliorate this morbidity, we investigated the interaction between hypoxia and the host response. In the context of acute hypoxia, both S. aureus and S. pneumoniae infections rapidly induced progressive neutrophil mediated morbidity and mortality, with associated hypothermia and cardiovascular compromise. Preconditioning animals through longer exposures to hypoxia, prior to infection, prevented these pathophysiological responses and profoundly dampened the transcriptome of circulating leukocytes. Specifically, perturbation of HIF pathway and glycolysis genes by hypoxic preconditioning was associated with reduced leukocyte glucose utilisation, resulting in systemic rescue from a global negative energy state and myocardial protection. Thus we demonstrate that hypoxia preconditions the innate immune response and determines survival outcomes following bacterial infection through suppression of HIF-1α and neutrophil metabolism. The therapeutic implications of this work are that in the context of systemic or tissue hypoxia therapies that target the host response could improve infection associated morbidity and mortality. PMID:28386604

  2. Topological Properties of Protein-Protein and Metabolic Interaction Networks of Drosophila melanogaster

    Institute of Scientific and Technical Information of China (English)

    Thanigaimani Rajarathinam; Yen-Han Lin

    2006-01-01

    The underlying principle governing the natural phenomena of life is one of the critical issues receiving due importance in recent years. A key feature of the scale-free architecture is the vitality of the most connected nodes (hubs). The major objective of this article was to analyze the protein-protein and metabolic interaction networks of Drosophila melanogaster by considering the architectural patterns and the consequence of removal of hubs on the topological parameter of the two interaction systems. Analysis showed that both interaction networks follow a scale-free model, establishing the fact that most real world networks,from varied situations, conform to the small world pattern. The average path length showed a two-fold and a three-fold increase (changing from 9.42 to 20.93 and from 5.29 to 17.75, respectively) for the protein-protein and metabolic interaction networks, respectively, due to the deletion of hubs. On the contrary, the arbitrary elimination of nodes did not show any remarkable disparity in the topological parameter of the protein-protein and metabolic interaction networks (average path length: 9.42±0.02 and 5.27±0.01, respectively). This aberrant behavior for the two cases underscores the significance of the most linked nodes to the natural topology of the networks.

  3. Single-cell level based approach to investigate bacterial metabolism during batch industrial fermentation

    DEFF Research Database (Denmark)

    Nierychlo, Marta; Larsen, Poul; Eriksen, Niels T.

    Escherichia coli fermentations have been studied for decades, but most results are based on average measurements of the whole populations of cells, whilst averaged data can mask the distribution of activities at the sub-population or single-cell level. A population of genetically identical cells...... can exhibit different phenotypes under specific environmental conditions that show significant differences in physiological parameters from the population average. However, studies concerning segregation of populations into metabolically diversified subpopulations are scarce. Acetate is a product...... of Escherichia coli overflow metabolism when the bacteria are grown under aerobic conditions and glucose is present in excessive concentrations. Acetate accumulation is of the utmost importance in batch fermentation processes as it is an undesirable byproduct that negatively affects growth, physiology...

  4. Dynamic Metabolic Footprinting Reveals the Key Components of Metabolic Network in Yeast Saccharomyces cerevisiae

    DEFF Research Database (Denmark)

    Chumnanpuen, Pramote; Hansen, Michael Adsetts Edberg; Smedsgaard, Jørn;

    2014-01-01

    relies on analysis at a single time point. Using direct infusion-mass spectrometry (DI-MS), we could observe the dynamic metabolic footprinting in yeast S. cerevisiae BY4709 (wild type) cultured on 3 different C-sources (glucose, glycerol, and ethanol) and sampled along 10 time points with 5 biological...

  5. Anthropogenic N deposition slows decay by favoring bacterial metabolism: Insights from metagenomic analyses

    Directory of Open Access Journals (Sweden)

    Zachary B. Freedman

    2016-03-01

    Full Text Available Litter decomposition is an enzymatically-complex process that is mediated by a diverse assemblage of saprophytic microorganisms. It is a globally important biogeochemical process that can be suppressed by anthropogenic N deposition. In a northern hardwood forest ecosystem located in Michigan, USA, 20 years of experimentally increased atmospheric N deposition has reduced forest floor decay and increased soil C storage. Here, we paired extracellular enzyme assays with shotgun metagenomics to assess if anthropogenic N deposition has altered the functional potential of microbial communities inhabiting decaying forest floor. Experimental N deposition significantly reduced the activity of extracellular enzymes mediating plant cell wall decay, which occurred concurrently with changes in the relative abundance of metagenomic functional gene pathways mediating the metabolism of carbohydrates, aromatic compounds, as well as microbial respiration. Moreover, experimental N deposition increased the relative abundance of 50 of the 60 gene pathways, the majority of which were associated with saprotrophic bacteria. Conversely, the relative abundance and composition of fungal genes mediating the metabolism of plant litter was not affected by experimental N deposition. Future rates of atmospheric N deposition have favored saprotrophic soil bacteria, whereas the metabolic potential of saprotrophic fungi appears resilient to this agent of environmental change. Results presented here provide evidence that changes in the functional capacity of saprotrophic soil microorganisms mediate how anthropogenic N deposition increases C storage in soil.

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

    Science.gov (United States)

    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.

  7. Intravenous lipopolysaccharide challenge alters ruminal bacterial microbiota and disrupts ruminal metabolism in dairy cattle.

    Science.gov (United States)

    Jing, Longhui; Zhang, Ruiyang; Liu, Yujie; Zhu, Weiyun; Mao, Shengyong

    2014-07-28

    In the present study, three primiparous lactating Holstein cows (260-285 d in lactation) were used in a 3 × 3 Latin square design to assess the effects of three doses (0.0, 0.4 and 0.8 μg/kg body weight) of lipopolysaccharide (LPS, Escherichia coli 0111:B4) on changes in ruminal microbiota and ruminal fermentation. Ruminal pH was linearly decreased (Pinfusion linearly decreased (Phay and soyabean meal in the rumen, but did not affect (P>0.10) the gene expression of Na⁺/K⁺-ATPase and monocarboxylic acid transporter-1, -2 and -4. A plot of principal coordinate analysis based on unweighted UniFrac values and analysis of molecular variance revealed that the structure of ruminal bacterial communities in the control was distinct from that of the ruminal microbiota in the cattle exposed to LPS. At the phylum level, when compared with the control group, LPS infusion in the tested cows linearly increased (P< 0.05) the abundance of Firmicutes, and linearly decreased (P< 0.05) the percentage of Bacteroidetes, Tenericutes, Spirochaetes, Chlorobi and Lentisphaerae. To our knowledge, this is the first study to report that intravenously LPS challenge altered the ruminal bacterial microbiota and fermentation profiles. The present data suggest that systemic LPS could alter ruminal environment and ruminal microbiota composition, leading to a general decrease in fermentative activity.

  8. Bacterial degradation of synthetic and kraft lignin by axenic and mixed culture and their metabolic products.

    Science.gov (United States)

    Chandra, Ram; Bharagava, Ram Naresh

    2013-11-01

    Pulp paper mill effluent has high pollution load due to presence of lignin and its derivatives as major colouring and polluting constituents. In this study, two lignin degrading bacteria IITRL1 and IITRSU7 were isolated and identified as Citrobacter freundii (FJ581026) and Citrobacter sp. (FJ581023), respectively. In degradation study by axenic and mixed culture, mixed bacterial culture was found more effective compared to axenic culture as it decolourized 85 and 62% of synthetic and kraft lignin whereas in axenic conditions, bacterium IITRL1 and IITRSU7 decolourized 61 and 64% synthetic and 49 and 54% kraft lignin, respectively. Further, the mixed bacterial culture also showed the removal of 71, 58% TOC; 78, 53% AOX; 70, 58% COD and 74, 58% lignin from synthetic and kraft lignin, respectively. The ligninolytic enzyme was characterized as manganese peroxidase by SDS-PAGE yielding a single band of 43 KDa. The HPLC analysis of degraded samples showed reduction as well as shifting of peaks compared to control indicating the degradation as well as transformation of compounds. Further, in GC-MS analysis of synthetic and kraft lignin degraded samples, hexadecanoic acid was found as recalcitrant compounds while 2,4,6-trichloro-phenol, 2,3,4,5-tetrachloro-phenol and pentachloro-phenol were detected as new metabolites.

  9. Effects of bacterially produced precipitates on the metabolism of sulfate reducing bacteria during the bio-treatment process of copper-containing wastewater

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    A large volume of bacterially produced precipitates are generated during the bio-treatment of heavy metal wastewater.The composition of the bacterially produced precipitates and its effects on sulfate reducing bacteria (SRB) in copper-containing waste stream were evaluated in this study.The elemental composition of the microbial precipitate was studied using electrodispersive X-ray spectroscopy (EDX),and it was found that the ratio of S:Cu was 1.12.Combining with the results of copper distribution in the SRB metabolism culture,which was analyzed by the sequential extraction procedure,copper in the precipitates was determined as covellite (CuS).The bacterially produced precipitates caused a decrease of the sulfate reduction rate,and the more precipitates were generated,the lower the sulfate reduction rate was.The particle sizes of bacterially generated covellite were ranging from 0.03 to 2 m by particles size distribution (PSD) analysis,which was smaller than that of the SRB cells.Transmission electron microscopy (TEM) analysis showed that the microbial covellite was deposited on the surface of the cell.The effects of the microbial precipitate on SRB metabolism were found to be weakened by increasing the precipitation time and adding microbial polymeric substances in later experiments.These results provided direct evidence that the SRB activity was inhibited by the bacterially produced covellite,which enveloped the bacterium and thus affected the metabolism of SRB on mass transfer.

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

    Directory of Open Access Journals (Sweden)

    Qingying Meng

    2016-05-01

    Full Text Available 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.

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

    Science.gov (United States)

    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.

  12. Hyperoxaluria leads to dysbiosis and drives selective enrichment of oxalate metabolizing bacterial species in recurrent kidney stone endures

    Science.gov (United States)

    Suryavanshi, Mangesh V.; Bhute, Shrikant S.; Jadhav, Swapnil D.; Bhatia, Manish S.; Gune, Rahul P.; Shouche, Yogesh S.

    2016-01-01

    Hyperoxaluria due to endogenously synthesized and exogenously ingested oxalates is a leading cause of recurrent oxalate stone formations. Even though, humans largely rely on gut microbiota for oxalate homeostasis, hyperoxaluria associated gut microbiota features remain largely unknown. Based on 16S rRNA gene amplicons, targeted metagenomic sequencing of formyl-CoA transferase (frc) gene and qPCR assay, we demonstrate a selective enrichment of Oxalate Metabolizing Bacterial Species (OMBS) in hyperoxaluria condition. Interestingly, higher than usual concentration of oxalate was found inhibitory to many gut microbes, including Oxalobacter formigenes, a well-characterized OMBS. In addition a concomitant enrichment of acid tolerant pathobionts in recurrent stone sufferers is observed. Further, specific enzymes participating in oxalate metabolism are found augmented in stone endures. Additionally, hyperoxaluria driven dysbiosis was found to be associated with oxalate content, stone episodes and colonization pattern of Oxalobacter formigenes. Thus, we rationalize the first in-depth surveillance of OMBS in the human gut and their association with hyperoxaluria. Our findings can be utilized in the treatment of hyperoxaluria associated recurrent stone episodes. PMID:27708409

  13. The influence of Glyceria maxima and nitrate input on the composition and nitrate metabolism of the dissimilatory nitrate-reducing bacterial community

    NARCIS (Netherlands)

    Nijburg, J.W.; Laanbroek, H.J.

    1997-01-01

    The influence of nitrate addition and the presence of Glyceria maxima (reed sweetgrass) on the composition and nitrate metabolism of the dissimilatory nitrate-reducing bacterial community was investigated. Anoxic freshwater sediment was incubated in pots with or without G. maxima and with or without

  14. Quantitative Tools for Dissection of Hydrogen-Producing Metabolic Networks-Final Report

    Energy Technology Data Exchange (ETDEWEB)

    Rabinowitz, Joshua D.; Dismukes, G.Charles.; Rabitz, Herschel A.; Amador-Noguez, Daniel

    2012-10-19

    During this project we have pioneered the development of integrated experimental-computational technologies for the quantitative dissection of metabolism in hydrogen and biofuel producing microorganisms (i.e. C. acetobutylicum and various cyanobacteria species). The application of these new methodologies resulted in many significant advances in the understanding of the metabolic networks and metabolism of these organisms, and has provided new strategies to enhance their hydrogen or biofuel producing capabilities. As an example, using mass spectrometry, isotope tracers, and quantitative flux-modeling we mapped the metabolic network structure in C. acetobutylicum. This resulted in a comprehensive and quantitative understanding of central carbon metabolism that could not have been obtained using genomic data alone. We discovered that biofuel production in this bacterium, which only occurs during stationary phase, requires a global remodeling of central metabolism (involving large changes in metabolite concentrations and fluxes) that has the effect of redirecting resources (carbon and reducing power) from biomass production into solvent production. This new holistic, quantitative understanding of metabolism is now being used as the basis for metabolic engineering strategies to improve solvent production in this bacterium. In another example, making use of newly developed technologies for monitoring hydrogen and NAD(P)H levels in vivo, we dissected the metabolic pathways for photobiological hydrogen production by cyanobacteria Cyanothece sp. This investigation led to the identification of multiple targets for improving hydrogen production. Importantly, the quantitative tools and approaches that we have developed are broadly applicable and we are now using them to investigate other important biofuel producers, such as cellulolytic bacteria.

  15. Identification of regulatory network hubs that control lipid metabolism in Chlamydomonas reinhardtii.

    Science.gov (United States)

    Gargouri, Mahmoud; Park, Jeong-Jin; Holguin, F Omar; Kim, Min-Jeong; Wang, Hongxia; Deshpande, Rahul R; Shachar-Hill, Yair; Hicks, Leslie M; Gang, David R

    2015-08-01

    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 combined 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. 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.

  16. Deciphering Fur transcriptional regulatory network highlights its complex role beyond iron metabolism in Escherichia coli

    DEFF Research Database (Denmark)

    Seo, Sang Woo; Kim, Donghyuk; Latif, Haythem;

    2014-01-01

    The ferric uptake regulator (Fur) plays a critical role in the transcriptional regulation of iron metabolism. However, the full regulatory potential of Fur remains undefined. Here we comprehensively reconstruct the Fur transcriptional regulatory network in Escherichia coli K-12 MG1655 in response...

  17. Changes in bacterial community metabolism and composition during the degradation of dissolved organic matter from the jellyfish Aurelia aurita in a Mediterranean coastal lagoon.

    Science.gov (United States)

    Blanchet, Marine; Pringault, Olivier; Bouvy, Marc; Catala, Philippe; Oriol, Louise; Caparros, Jocelyne; Ortega-Retuerta, Eva; Intertaglia, Laurent; West, Nyree; Agis, Martin; Got, Patrice; Joux, Fabien

    2015-09-01

    Spatial increases and temporal shifts in outbreaks of gelatinous plankton have been observed over the past several decades in many estuarine and coastal ecosystems. The effects of these blooms on marine ecosystem functioning and particularly on the dynamics of the heterotrophic bacteria are still unclear. The response of the bacterial community from a Mediterranean coastal lagoon to the addition of dissolved organic matter (DOM) from the jellyfish Aurelia aurita, corresponding to an enrichment of dissolved organic carbon (DOC) by 1.4, was assessed for 22 days in microcosms (8 l). The high bioavailability of this material led to (i) a rapid mineralization of the DOC and dissolved organic nitrogen from the jellyfish and (ii) the accumulation of high concentrations of ammonium and orthophosphate in the water column. DOM from jellyfish greatly stimulated heterotrophic prokaryotic production and respiration rates during the first 2 days; then, these activities showed a continuous decay until reaching those measured in the control microcosms (lagoon water only) at the end of the experiment. Bacterial growth efficiency remained below 20%, indicating that most of the DOM was respired and a minor part was channeled to biomass production. Changes in bacterial diversity were assessed by tag pyrosequencing of partial bacterial 16S rRNA genes, DNA fingerprints, and a cultivation approach. While bacterial diversity in control microcosms showed little changes during the experiment, the addition of DOM from the jellyfish induced a rapid growth of Pseudoalteromonas and Vibrio species that were isolated. After 9 days, the bacterial community was dominated by Bacteroidetes, which appeared more adapted to metabolize high-molecular-weight DOM. At the end of the experiment, the bacterial community shifted toward a higher proportion of Alphaproteobacteria. Resilience of the bacterial community after the addition of DOM from the jellyfish was higher for metabolic functions than diversity

  18. Metabolic network modeling of microbial interactions in natural and engineered environmental systems

    Directory of Open Access Journals (Sweden)

    Octavio ePerez-Garcia

    2016-05-01

    Full Text Available 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

  19. Metabolic Network Modeling of Microbial Interactions in Natural and Engineered Environmental Systems.

    Science.gov (United States)

    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

  20. Metabolic Network Modeling of Microbial Interactions in Natural and Engineered Environmental Systems

    Science.gov (United States)

    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

  1. Studying the Relationship between Robustness against Mutations in Metabolic Networks and Lifestyle of Organisms

    Directory of Open Access Journals (Sweden)

    Sayed-Amir Marashi

    2013-01-01

    Full Text Available Robustness is the key feature of biological networks that enables living organisms to keep their homeostatic state and to survive against external and internal perturbations. Variations in environmental conditions or nutrients and intracellular changes such as genetic mutations have the potential to change stability and efficiency of an organism. Structural robustness helps biological systems to choose alternative routes of adaptation to varying conditions. In this study, in order to estimate the structural robustness in metabolic networks we presented a novel flux balance-based approach inspired by bond percolation theory. Fourteen in silico metabolic models were studied in this work in order to examine the possible relationship between the lifestyle of organisms and their metabolic robustness. The results of this study confirm that in organisms which are highly adapted to their environment robustness to mutations may decrease compared to other organisms.

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

    Science.gov (United States)

    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.

  3. Hepatokines: unlocking the multi-organ network in metabolic diseases.

    Science.gov (United States)

    Iroz, Alison; Couty, Jean-Pierre; Postic, Catherine

    2015-08-01

    In the face of urbanisation, surplus energy intake, sedentary habits and obesity, type 2 diabetes has developed into a major health concern worldwide. Commonly overlooked in contemporary obesity research, the liver is emerging as a central regulator of whole body energy homeostasis. Liver-derived proteins known as hepatokines are now considered attractive targets for the development of novel type 2 diabetes treatments. This commentary presents examples of three leading hepatokines: fetuin-A, the first to be described and correlated with increased inflammation and insulin resistance; angiopoietin-like protein (ANGPTL)8/betatrophin, initially proposed for its action on beta cell proliferation, although this effect has recently been brought into question; and fibroblast growth factor 21 (FGF21), an insulin-sensitising hormone that is an appealing drug target because of its beneficial metabolic actions. Novel discoveries in hepatokine research may lead to promising biomarkers and treatments for metabolic disorders and type 2 diabetes. This is one of a series of commentaries under the banner '50 years forward', giving personal opinions on future perspectives in diabetes, to celebrate the 50th anniversary of Diabetologia (1965-2015).

  4. The long-chain alkane metabolism network of Alcanivorax dieselolei.

    Science.gov (United States)

    Wang, Wanpeng; Shao, Zongze

    2014-12-12

    Alkane-degrading bacteria are ubiquitous in marine environments, but little is known about how alkane degradation is regulated. Here we investigate alkane sensing, chemotaxis, signal transduction, uptake and pathway regulation in Alcanivorax dieselolei. The outer membrane protein OmpS detects the presence of alkanes and triggers the expression of an alkane chemotaxis complex. The coupling protein CheW2 of the chemotaxis complex, which is induced only by long-chain (LC) alkanes, sends signals to trigger the expression of Cyo, which participates in modulating the expression of the negative regulator protein AlmR. This change in turn leads to the expression of ompT1 and almA, which drive the selective uptake and hydroxylation of LC alkanes, respectively. AlmA is confirmed as a hydroxylase of LC alkanes. Additional factors responsible for the metabolism of medium-chain-length alkanes are also identified, including CheW1, OmpT1 and OmpT2. These results provide new insights into alkane metabolism pathways from alkane sensing to degradation.

  5. Dose-dependent effects of dietary zinc oxide on bacterial communities and metabolic profiles in the ileum of weaned pigs.

    Science.gov (United States)

    Pieper, R; Vahjen, W; Neumann, K; Van Kessel, A G; Zentek, J

    2012-10-01

    Pharmacological levels of zinc oxide (ZnO) can improve the health of weaning piglets and influence the intestinal microbiota. This experiment aimed at studying the dose-response effect of five dietary concentrations of ZnO on small intestinal bacteria and metabolite profiles. Fifteen piglets, weaned at 25 ± 1 days of age, were allocated into five groups according to body weight and litter. Diets were formulated to contain 50 (basal diet), 150, 250, 1000 and 2500 mg zinc/kg by adding analytical-grade (>98% purity) ZnO to the basal diet and fed ad libitum for 14 days after a 7-day adaptation period on the basal diet. Ileal bacterial community profiles were analysed by denaturing gradient gel electrophoresis and selected bacterial groups quantified by real-time PCR. Concentrations of ileal volatile fatty acids (VFA), D- and L-lactate and ammonia were determined. Species richness, Shannon diversity and evenness were significantly higher at high ZnO levels. Quantitative PCR revealed lowest total bacterial counts in the 50 mg/kg group. Increasing ZnO levels led to an increase (p = 0.017) in enterobacteria from log 4.0 cfu/g digesta (50 mg/kg) to log 6.7 cfu/g digesta (2500 mg/kg). Lactic acid bacteria were not influenced (p = 0.687) and clostridial cluster XIVa declined (p = 0.035) at highest ZnO level. Concentration of total, D- and L-lactate and propionate was not affected (p = 0.736, p = 0.290 and p = 0.630), but concentrations of ileal total VFA, acetate and butyrate increased markedly from 50 to 150 mg/kg and decreased with further increasing zinc levels and reached low levels again at 2500 mg/kg (p = 0.048, p = 0.048 and p = 0.097). Ammonia decreased (p < 0.006) with increasing dietary ZnO level. In conclusion, increasing levels of dietary ZnO had strong and dose-dependent effects on ileal bacterial community composition and activity, suggesting taxonomic variation in metabolic response to ZnO.

  6. Impact of targeted counseling on reported vaginal hygiene practices and bacterial vaginosis: the HIV Prevention Trials Network 035 study.

    Science.gov (United States)

    Kasaro, Margaret P; Husnik, Marla J; Chi, Benjamin H; Reid, Cheri; Magure, Tsitsi; Makanani, Bonus; Tembo, Tchangani; Ramjee, Gita; Maslankowski, Lisa; Rabe, Lorna; Brad Guffey, M

    2017-04-01

    The objective of this study was to describe the impact of intense counseling to reduce vaginal hygiene practices and its effect on bacterial vaginosis. A secondary data analysis of the HIV Prevention Trials Network 035 study was undertaken, focusing on HIV-negative, nonpregnant women who were at least 18 years old, in seven African sites and one US site. At enrollment and during follow-up quarterly visits, vaginal hygiene practices were determined by face-to-face administration of a behavioral assessment questionnaire. Vaginal hygiene practices were categorized as insertion into the vagina of (1) nothing, (2) water only, and (3) other substances with or without water. Each practice was quantified by frequency and type/combination of inserted substances. At quarterly visits, diagnosis of bacterial vaginosis was made using the Nugent score. Trends for vaginal hygiene practices and bacterial vaginosis were evaluated using generalized estimating equation models. A total of 3087 participants from the HIV Prevention Trials Network 035 study were eligible for this analysis. At enrollment, 1859 (60%) reported recent vaginal hygiene practices. By one year, this figure had decreased to 1019 (33%) with counseling. However, bacterial vaginosis prevalence remained consistent across the study observation period, with 36%-38% of women testing positive for the condition ( p for trend = 0.27). Overall, those who reported douching with water only (AOR = 1.03, 95%CI: 0.94-1.13) and those who reported inserting other substances (AOR= 0.98, 95%CI: 0.88-1.09) in the past quarter were not more likely to have bacterial vaginosis compared to those who reported no insertions. However, in South Africa, an increase in bacterial vaginosis was seen among those who reported inserting other substances (AOR: 1.48, 95%CI: 1.17, 1.88). In conclusion, targeted counseling against vaginal hygiene practices resulted in change in self-reported behavior but did not have an impact on bacterial vaginosis

  7. FoodMicrobionet: A database for the visualisation and exploration of food bacterial communities based on network analysis.

    Science.gov (United States)

    Parente, Eugenio; Cocolin, Luca; De Filippis, Francesca; Zotta, Teresa; Ferrocino, Ilario; O'Sullivan, Orla; Neviani, Erasmo; De Angelis, Maria; Cotter, Paul D; Ercolini, Danilo

    2016-02-16

    Amplicon targeted high-throughput sequencing has become a popular tool for the culture-independent analysis of microbial communities. Although the data obtained with this approach are portable and the number of sequences available in public databases is increasing, no tool has been developed yet for the analysis and presentation of data obtained in different studies. This work describes an approach for the development of a database for the rapid exploration and analysis of data on food microbial communities. Data from seventeen studies investigating the structure of bacterial communities in dairy, meat, sourdough and fermented vegetable products, obtained by 16S rRNA gene targeted high-throughput sequencing, were collated and analysed using Gephi, a network analysis software. The resulting database, which we named FoodMicrobionet, was used to analyse nodes and network properties and to build an interactive web-based visualisation. The latter allows the visual exploration of the relationships between Operational Taxonomic Units (OTUs) and samples and the identification of core- and sample-specific bacterial communities. It also provides additional search tools and hyperlinks for the rapid selection of food groups and OTUs and for rapid access to external resources (NCBI taxonomy, digital versions of the original articles). Microbial interaction network analysis was carried out using CoNet on datasets extracted from FoodMicrobionet: the complexity of interaction networks was much lower than that found for other bacterial communities (human microbiome, soil and other environments). This may reflect both a bias in the dataset (which was dominated by fermented foods and starter cultures) and the lower complexity of food bacterial communities. Although some technical challenges exist, and are discussed here, the net result is a valuable tool for the exploration of food bacterial communities by the scientific community and food industry.

  8. Data-driven integration of genome-scale regulatory and metabolic network models

    Directory of Open Access Journals (Sweden)

    Saheed eImam

    2015-05-01

    Full Text Available 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 more 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. 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.

  9. Information theory in systems biology. Part I: Gene regulatory and metabolic networks.

    Science.gov (United States)

    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.

  10. Using atom mapping rules for an improved detection of relevant routes in weighted metabolic networks.

    Science.gov (United States)

    Blum, Torsten; Kohlbacher, Oliver

    2008-01-01

    Computational analysis of pathways in metabolic networks has numerous applications in systems biology. While graph theory-based approaches have been presented that find biotransformation routes from one metabolite to another in these networks, most of these approaches suffer from finding too many routes, most of which are biologically infeasible or meaningless. We present a novel approach for finding relevant routes based on atom mapping rules (describing which educt atoms are mapped onto which product atoms in a chemical reaction). This leads to a reformulation of the problem as a lightest path search in a degree-weighted metabolic network. The key component of the approach is a new method of computing optimal atom mapping rules.

  11. Kinetic model of metabolic network for xiamenmycin biosynthetic optimisation.

    Science.gov (United States)

    Xu, Min-juan; Chen, Yong-cong; Xu, Jun; Ao, Ping; Zhu, Xiao-mei

    2016-02-01

    Xiamenmycins, a series of prenylated benzopyran compounds with anti-fibrotic bioactivities, were isolated from a mangrove-derived Streptomyces xiamenensis. To fulfil the requirements of pharmaceutical investigations, a high production of xiamenmycin is needed. In this study, the authors present a kinetic metabolic model to evaluate fluxes in an engineered Streptomyces lividans with xiamenmycin-oriented genetic modification based on generic enzymatic rate equations and stability constraints. Lyapunov function was used for a viability optimisation. From their kinetic model, the flux distributions for the engineered S. lividans fed on glucose and glycerol as carbon sources were calculated. They found that if the bacterium can utilise glucose simultaneously with glycerol, xiamenmycin production can be enhanced by 40% theoretically, while maintaining the same growth rate. Glycerol may increase the flux for phosphoenolpyruvate synthesis without interfering citric acid cycle. They therefore believe this study demonstrates a possible new direction for bioengineering of S. lividans.

  12. Metabolic Network Topology Reveals Transcriptional Regulatory Signatures of Type 2 Diabetes

    DEFF Research Database (Denmark)

    Zelezniak, Aleksej; Pers, Tune Hannes; Pinho Soares, Simao Pedro;

    2010-01-01

    Type 2 diabetes mellitus (T2DM) is a disorder characterized by both insulin resistance and impaired insulin secretion. Recent transcriptomics studies related to T2DM have revealed changes in expression of a large number of metabolic genes in a variety of tissues. Identification of the molecular...... with human metabolic network reconstructions to identify key metabolic regulatory features of T2DM. These features include reporter metabolites—metabolites with significant collective transcriptional response in the associated enzyme-coding genes, and transcription factors with significant enrichment...... of binding sites in the promoter regions of these genes. In addition to metabolites from TCA cycle, oxidative phosphorylation, and lipid metabolism (known to be associated with T2DM), we identified several reporter metabolites representing novel biomarker candidates. For example, the highly connected...

  13. Biochemical characterization of human gluconokinase and the proposed metabolic impact of gluconic Acid as determined by constraint based metabolic network analysis

    DEFF Research Database (Denmark)

    Rohatgi, Neha; Nielsen, Tine Kragh; Bjørn, Sara Petersen

    2014-01-01

    and strict specificity towards gluconate out of 122 substrates tested. In order to evaluate the metabolic impact of gluconate in humans we modeled gluconate metabolism using steady state metabolic network analysis. The results indicate that significant metabolic flux changes in anabolic pathways linked......The metabolism of gluconate is well characterized in prokaryotes where it is known to be degraded following phosphorylation by gluconokinase. Less is known of gluconate metabolism in humans. Human gluconokinase activity was recently identified proposing questions about the metabolic role...... to the hexose monophosphate shunt (HMS) are induced through a small increase in gluconate concentration. We argue that the enzyme takes part in a context specific carbon flux route into the HMS that, in humans, remains incompletely explored. Apart from the biochemical description of human gluconokinase...

  14. Determination of the hydrocarbon-degrading metabolic capabilities of tropical bacterial isolates

    Energy Technology Data Exchange (ETDEWEB)

    Marquez-Rocha, F.J.; Olmos-Soto, J. [Centro de Investigacion Cientifica y de Educacion Superior de Ensenada, San Diego, CA (United States). Departamento de Biotecnologia Marina; Rosano-Hernandez, M.A.; Muriel-Garcia, M. [Instituto Mexicano del Petroleo, CD Carmen Camp (Mexico). Zona Marina/Tecnologia Ambiental

    2005-01-01

    Of more than 20 bacteria isolated from a tropical soil using minimal medium supplemented with hydrocarbons, 11 grew well on diesel as sole carbon source, and another 11 grew in the presence of polynuclear aromatic hydrocarbons (PAHs). Ten isolates were identified phenotypically as Pseudomonas sp. and eight as Bacillus sp. Gene sequences representing the catabolic genes (alkM, todM, ndoM, and xylM) and 16S rRNA gene sequences characteristic for Pseudomona and Bacillus were amplified by PCR, using DNA recovered from the supernatant of hydrocarbon-contaminated soil suspensions. Based on their rapid growth characteristics in the presence of hydrocarbons and the formation of PCR products for the catabolic genes alkM and ndoM six isolates were selected for biodegradation assays. After 30 days a mixed culture of two isolates achieved close to 70% hydrocarbon removal and apparent mineralization of 16% of the hydrocarbons present in the soil. Biodegradation rates varied from 275 to 387 mg hydrocarbon kg{sup -1} day{sup -1}. Several bacterial isolates obtained in this study have catabolic capabilities for the biodegradation of alkanes and aromatic hydrocarbons including PAHs. (author)

  15. GAM: a web-service for integrated transcriptional and metabolic network analysis.

    Science.gov (United States)

    Sergushichev, Alexey A; Loboda, Alexander A; Jha, Abhishek K; Vincent, Emma E; Driggers, Edward M; Jones, Russell G; Pearce, Edward J; Artyomov, Maxim N

    2016-07-08

    Novel techniques for high-throughput steady-state metabolomic profiling yield information about changes of nearly thousands of metabolites. Such metabolomic profiles, when analyzed together with transcriptional profiles, can reveal novel insights about underlying biological processes. While a number of conceptual approaches have been developed for data integration, easily accessible tools for integrated analysis of mammalian steady-state metabolomic and transcriptional data are lacking. Here we present GAM ('genes and metabolites'): a web-service for integrated network analysis of transcriptional and steady-state metabolomic data focused on identification of the most changing metabolic subnetworks between two conditions of interest. In the web-service, we have pre-assembled metabolic networks for humans, mice, Arabidopsis and yeast and adapted exact solvers for an optimal subgraph search to work in the context of these metabolic networks. The output is the most regulated metabolic subnetwork of size controlled by false discovery rate parameters. The subnetworks are then visualized online and also can be downloaded in Cytoscape format for subsequent processing. The web-service is available at: https://artyomovlab.wustl.edu/shiny/gam/.

  16. Genome-scale reconstruction of metabolic networks of Lactobacillus casei ATCC 334 and 12A.

    Directory of Open Access Journals (Sweden)

    Elena Vinay-Lara

    Full Text Available Lactobacillus casei strains are widely used in industry and the utility of this organism in these industrial applications is strain dependent. Hence, tools capable of predicting strain specific phenotypes would have utility in the selection of strains for specific industrial processes. Genome-scale metabolic models can be utilized to better understand genotype-phenotype relationships and to compare different organisms. To assist in the selection and development of strains with enhanced industrial utility, genome-scale models for L. casei ATCC 334, a well characterized strain, and strain 12A, a corn silage isolate, were constructed. Draft models were generated from RAST genome annotations using the Model SEED database and refined by evaluating ATP generating cycles, mass-and-charge-balances of reactions, and growth phenotypes. After the validation process was finished, we compared the metabolic networks of these two strains to identify metabolic, genetic and ortholog differences that may lead to different phenotypic behaviors. We conclude that the metabolic capabilities of the two networks are highly similar. The L. casei ATCC 334 model accounts for 1,040 reactions, 959 metabolites and 548 genes, while the L. casei 12A model accounts for 1,076 reactions, 979 metabolites and 640 genes. The developed L. casei ATCC 334 and 12A metabolic models will enable better understanding of the physiology of these organisms and be valuable tools in the development and selection of strains with enhanced utility in a variety of industrial applications.

  17. Genome –Scale Reconstruction of Metabolic Networks of Lactobacillus casei ATCC 334 and 12A

    Science.gov (United States)

    Vinay-Lara, Elena; Hamilton, Joshua J.; Stahl, Buffy; Broadbent, Jeff R.; Reed, Jennifer L.; Steele, James L.

    2014-01-01

    Lactobacillus casei strains are widely used in industry and the utility of this organism in these industrial applications is strain dependent. Hence, tools capable of predicting strain specific phenotypes would have utility in the selection of strains for specific industrial processes. Genome-scale metabolic models can be utilized to better understand genotype-phenotype relationships and to compare different organisms. To assist in the selection and development of strains with enhanced industrial utility, genome-scale models for L. casei ATCC 334, a well characterized strain, and strain 12A, a corn silage isolate, were constructed. Draft models were generated from RAST genome annotations using the Model SEED database and refined by evaluating ATP generating cycles, mass-and-charge-balances of reactions, and growth phenotypes. After the validation process was finished, we compared the metabolic networks of these two strains to identify metabolic, genetic and ortholog differences that may lead to different phenotypic behaviors. We conclude that the metabolic capabilities of the two networks are highly similar. The L. casei ATCC 334 model accounts for 1,040 reactions, 959 metabolites and 548 genes, while the L. casei 12A model accounts for 1,076 reactions, 979 metabolites and 640 genes. The developed L. casei ATCC 334 and 12A metabolic models will enable better understanding of the physiology of these organisms and be valuable tools in the development and selection of strains with enhanced utility in a variety of industrial applications. PMID:25365062

  18. Metagenomic signatures of a tropical mining-impacted stream reveal complex microbial and metabolic networks.

    Science.gov (United States)

    Reis, Mariana P; Dias, Marcela F; Costa, Patrícia S; Ávila, Marcelo P; Leite, Laura R; de Araújo, Flávio M G; Salim, Anna C M; Bucciarelli-Rodriguez, Mônica; Oliveira, Guilherme; Chartone-Souza, Edmar; Nascimento, Andréa M A

    2016-10-01

    Bacteria from aquatic ecosystems significantly contribute to biogeochemical cycles, but details of their community structure in tropical mining-impacted environments remain unexplored. In this study, we analyzed a bacterial community from circumneutral-pH tropical stream sediment by 16S rRNA and shotgun deep sequencing. Carrapatos stream sediment, which has been exposed to metal stress due to gold and iron mining (21 [g Fe]/kg), revealed a diverse community, with predominance of Proteobacteria (39.4%), Bacteroidetes (12.2%), and Parcubacteria (11.4%). Among Proteobacteria, the most abundant reads were assigned to neutrophilic iron-oxidizing taxa, such as Gallionella, Sideroxydans, and Mariprofundus, which are involved in Fe cycling and harbor several metal resistance genes. Functional analysis revealed a large number of genes participating in nitrogen and methane metabolic pathways despite the low concentrations of inorganic nitrogen in the Carrapatos stream. Our findings provide important insights into bacterial community interactions in a mining-impacted environment.

  19. Modeling and Robustness Analysis of Biochemical Networks of Glycerol Metabolism by Klebsiella Pneumoniae

    Science.gov (United States)

    Ye, Jianxiong; Feng, Enmin; Wang, Lei; Xiu, Zhilong; Sun, Yaqin

    Glycerol bioconversion to 1,3-propanediol (1,3-PD) by Klebsiella pneumoniae (K. pneumoniae) can be characterized by an intricate network of interactions among biochemical fluxes, metabolic compounds, key enzymes and genetic regulatory. To date, there still exist some uncertain factors in this complex network because of the limitation in bio-techniques, especially in measuring techniques for intracellular substances. In this paper, among these uncertain factors, we aim to infer the transport mechanisms of glycerol and 1,3-PD across the cell membrane, which have received intensive interest in recent years. On the basis of different inferences of the transport mechanisms, we reconstruct various metabolic networks correspondingly and subsequently develop their dynamical systems (S-systems). To determine the most reasonable metabolic network from all possible ones, we establish a quantitative definition of biological robustness and undertake parameter identification and robustness analysis for each system. Numerical results show that it is most possible that both glycerol and 1,3-PD pass the cell membrane by active transport and passive diffusion.

  20. Distinct metabolic network states manifest in the gene expression profiles of pediatric inflammatory bowel disease patients and controls

    Science.gov (United States)

    Knecht, Carolin; Fretter, Christoph; Rosenstiel, Philip; Krawczak, Michael; Hütt, Marc-Thorsten

    2016-09-01

    Information on biological networks can greatly facilitate the function-orientated interpretation of high-throughput molecular data. Genome-wide metabolic network models of human cells, in particular, can be employed to contextualize gene expression profiles of patients with the goal of both, a better understanding of individual etiologies and an educated reclassification of (clinically defined) phenotypes. We analyzed publicly available expression profiles of intestinal tissues from treatment-naive pediatric inflammatory bowel disease (IBD) patients and age-matched control individuals, using a reaction-centric metabolic network derived from the Recon2 model. By way of defining a measure of ‘coherence’, we quantified how well individual patterns of expression changes matched the metabolic network. We observed a bimodal distribution of metabolic network coherence in both patients and controls, albeit at notably different mixture probabilities. Multidimensional scaling analysis revealed a bisectional pattern as well that overlapped widely with the metabolic network-based results. Expression differences driving the observed bimodality were related to cellular transport of thiamine and bile acid metabolism, thereby highlighting the crosstalk between metabolism and other vital pathways. We demonstrated how classical data mining and network analysis can jointly identify biologically meaningful patterns in gene expression data.

  1. On the determining role of network structure titania in silicone against bacterial colonization: Mechanism and disruption of biofilm

    Energy Technology Data Exchange (ETDEWEB)

    Depan, D.; Misra, R.D.K., E-mail: dmisra@louisiana.edu

    2014-01-01

    Silicone-based biomedical devices are prone to microbial adhesion, which is the primary cause of concern in the functioning of the artificial device. Silicone exhibiting long-term and effective antibacterial ability is highly desirable to prevent implant related infections. In this regard, nanophase titania was incorporated in silicone as an integral part of the silicone network structure through cross-link mechanism, with the objective to reduce bacterial adhesion to a minimum. The bacterial adhesion was studied using crystal violet assay, while the mechanism of inhibition of biofilm formation was studied via electron microscopy. The incorporation of nanophase titania in silicone dramatically reduced the viability of Staphylococcus aureus (S. aureus) and the capability to adhere on the surface of hybrid silicone by ∼ 93% in relation to stand alone silicone. The conclusion of dramatic reduction in the viability of S. aureus is corroborated by different experimental approaches including biofilm inhibition assay, zone of inhibition, and through a novel experiment that involved incubation of biofilm with titania nanoparticles. It is proposed that the mechanism of disruption of bacterial film in the presence of titania involves puncturing of the bacterial cell membrane. - Highlights: • Network structure titania in silicone imparts antimicrobial activity. • Ability to microbial adhesion is significantly reduced. • Antimicrobial mechanism involves rupture of biofilm.

  2. Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters.

    Science.gov (United States)

    Adadi, Roi; Volkmer, Benjamin; Milo, Ron; Heinemann, Matthias; Shlomi, Tomer

    2012-01-01

    Identifying the factors that determine microbial growth rate under various environmental and genetic conditions is a major challenge of systems biology. While current genome-scale metabolic modeling approaches enable us to successfully predict a variety of metabolic phenotypes, including maximal biomass yield, the prediction of actual growth rate is a long standing goal. This gap stems from strictly relying on data regarding reaction stoichiometry and directionality, without accounting for enzyme kinetic considerations. Here we present a novel metabolic network-based approach, MetabOlic Modeling with ENzyme kineTics (MOMENT), which predicts metabolic flux rate and growth rate by utilizing prior data on enzyme turnover rates and enzyme molecular weights, without requiring measurements of nutrient uptake rates. The method is based on an identified design principle of metabolism in which enzymes catalyzing high flux reactions across different media tend to be more efficient in terms of having higher turnover numbers. Extending upon previous attempts to utilize kinetic data in genome-scale metabolic modeling, our approach takes into account the requirement for specific enzyme concentrations for catalyzing predicted metabolic flux rates, considering isozymes, protein complexes, and multi-functional enzymes. MOMENT is shown to significantly improve the prediction accuracy of various metabolic phenotypes in E. coli, including intracellular flux rates and changes in gene expression levels under different growth rates. Most importantly, MOMENT is shown to predict growth rates of E. coli under a diverse set of media that are correlated with experimental measurements, markedly improving upon existing state-of-the art stoichiometric modeling approaches. These results support the view that a physiological bound on cellular enzyme concentrations is a key factor that determines microbial growth rate.

  3. Phosphatidylkojibiosyl Diglyceride: metabolism and function as an anchor in bacterial cell membrane.

    Science.gov (United States)

    Pieringer, R A

    1975-07-01

    The recently discovered phosphoglycolipid, phosphatidylkojibiosyl diglyceride (PKD), was first observed as a biosynthetic by-product of glycosyl diglyceride metabolism in Streptococcus faecalis (faecium) ATCC 9790. Its structure is 1, 2-diacyl-3-O-alpha-Dglucopyranosyl-6'-O-phosphoryl- [1'', 2''-diacyl-3''-O-sn-glycerol]-alpha-D-glucopyranosyl)-sn-glycerol. The biosynthesis of phosphatidyl-kojibiosyl diglyceride occurs by a novel transphosphatidylation reaction in which a phosphatidyl glycerol to the primary alcohol function at the 6 position of the internal glucose of kojibiosyl diglyceride. The reaction is catalyzed by a membrane-derived enzyme. Phosphatidyl-kojibiosyl diglyceride is bound covalently through a phosphodiester bond to the polyglycerol phosphate moiety of membrane lipoteichoic acid from S. faecalis. Phosphatidylkojibiosyl diglyceride has four nonpolar long chain fatty acyl groups and appears to have the necessary physico-chemical properties to anchor the long hydrophilic glycerol phosphate polymer of lipoteichoic acid to the hydrophobic enviroment of the membrane of S. faecalis and probably other gram-positive bacteria as well.

  4. Yeast 5 – an expanded reconstruction of the Saccharomyces cerevisiae metabolic network

    Directory of Open Access Journals (Sweden)

    Heavner Benjamin D

    2012-06-01

    Full Text Available Abstract Background Efforts to improve the computational reconstruction of the Saccharomyces cerevisiae biochemical reaction network and to refine the stoichiometrically constrained metabolic models that can be derived from such a reconstruction have continued since the first stoichiometrically constrained yeast genome scale metabolic model was published in 2003. Continuing this ongoing process, we have constructed an update to the Yeast Consensus Reconstruction, Yeast 5. The Yeast Consensus Reconstruction is a product of efforts to forge a community-based reconstruction emphasizing standards compliance and biochemical accuracy via evidence-based selection of reactions. It draws upon models published by a variety of independent research groups as well as information obtained from biochemical databases and primary literature. Results Yeast 5 refines the biochemical reactions included in the reconstruction, particularly reactions involved in sphingolipid metabolism; updates gene-reaction annotations; and emphasizes the distinction between reconstruction and stoichiometrically constrained model. Although it was not a primary goal, this update also improves the accuracy of model prediction of viability and auxotrophy phenotypes and increases the number of epistatic interactions. This update maintains an emphasis on standards compliance, unambiguous metabolite naming, and computer-readable annotations available through a structured document format. Additionally, we have developed MATLAB scripts to evaluate the model’s predictive accuracy and to demonstrate basic model applications such as simulating aerobic and anaerobic growth. These scripts, which provide an independent tool for evaluating the performance of various stoichiometrically constrained yeast metabolic models using flux balance analysis, are included as Additional files 1, 2 and 3. Additional file 1 Function testYeastModel.m.m. Click here for file Additional file 2 Function model

  5. Computing smallest intervention strategies for multiple metabolic networks in a boolean model.

    Science.gov (United States)

    Lu, Wei; Tamura, Takeyuki; Song, Jiangning; Akutsu, Tatsuya

    2015-02-01

    This article considers the problem whereby, given two metabolic networks N1 and N2, a set of source compounds, and a set of target compounds, we must find the minimum set of reactions whose removal (knockout) ensures that the target compounds are not producible in N1 but are producible in N2. Similar studies exist for the problem of finding the minimum knockout with the smallest side effect for a single network. However, if technologies of external perturbations are advanced in the near future, it may be important to develop methods of computing the minimum knockout for multiple networks (MKMN). Flux balance analysis (FBA) is efficient if a well-polished model is available. However, that is not always the case. Therefore, in this article, we study MKMN in Boolean models and an elementary mode (EM)-based model. Integer linear programming (ILP)-based methods are developed for these models, since MKMN is NP-complete for both the Boolean model and the EM-based model. Computer experiments are conducted with metabolic networks of clostridium perfringens SM101 and bifidobacterium longum DJO10A, respectively known as bad bacteria and good bacteria for the human intestine. The results show that larger networks are more likely to have MKMN solutions. However, solving for these larger networks takes a very long time, and often the computation cannot be completed. This is reasonable, because small networks do not have many alternative pathways, making it difficult to satisfy the MKMN condition, whereas in large networks the number of candidate solutions explodes. Our developed software minFvskO is available online.

  6. Computing Smallest Intervention Strategies for Multiple Metabolic Networks in a Boolean Model

    Science.gov (United States)

    Lu, Wei; Song, Jiangning; Akutsu, Tatsuya

    2015-01-01

    Abstract This article considers the problem whereby, given two metabolic networks N1 and N2, a set of source compounds, and a set of target compounds, we must find the minimum set of reactions whose removal (knockout) ensures that the target compounds are not producible in N1 but are producible in N2. Similar studies exist for the problem of finding the minimum knockout with the smallest side effect for a single network. However, if technologies of external perturbations are advanced in the near future, it may be important to develop methods of computing the minimum knockout for multiple networks (MKMN). Flux balance analysis (FBA) is efficient if a well-polished model is available. However, that is not always the case. Therefore, in this article, we study MKMN in Boolean models and an elementary mode (EM)-based model. Integer linear programming (ILP)-based methods are developed for these models, since MKMN is NP-complete for both the Boolean model and the EM-based model. Computer experiments are conducted with metabolic networks of clostridium perfringens SM101 and bifidobacterium longum DJO10A, respectively known as bad bacteria and good bacteria for the human intestine. The results show that larger networks are more likely to have MKMN solutions. However, solving for these larger networks takes a very long time, and often the computation cannot be completed. This is reasonable, because small networks do not have many alternative pathways, making it difficult to satisfy the MKMN condition, whereas in large networks the number of candidate solutions explodes. Our developed software minFvskO is available online. PMID:25684199

  7. An X-ray Absorption Fine Structure study of Au adsorbed onto the non-metabolizing cells of two soil bacterial species

    Energy Technology Data Exchange (ETDEWEB)

    Song, Zhen; Kenney, Janice P.L.; Fein, Jeremy B.; Bunker, Bruce A. (Notre)

    2015-02-09

    Gram-positive and Gram-negative bacterial cells can remove Au from Au(III)-chloride solutions, and the extent of removal is strongly pH dependent. In order to determine the removal mechanisms, X-ray Absorption Fine Structure (XAFS) spectroscopy experiments were conducted on non-metabolizing biomass of Bacillus subtilis and Pseudomonas putida with fixed Au(III) concentrations over a range of bacterial concentrations and pH values. X-ray Absorption Near Edge Structure (XANES) and Extended X-ray Absorption Fine Structure (EXAFS) data on both bacterial species indicate that more than 90% of the Au atoms on the bacterial cell walls were reduced to Au(I). In contrast to what has been observed for Au(III) interaction with metabolizing bacterial cells, no Au(0) or Au-Au nearest neighbors were observed in our experimental systems. All of the removed Au was present as adsorbed bacterial surface complexes. For both species, the XAFS data suggest that although Au-chloride-hydroxide aqueous complexes dominate the speciation of Au in solution, Au on the bacterial cell wall is characterized predominantly by binding of Au atoms to sulfhydryl functional groups and amine and/or carboxyl functional groups, and the relative importance of the sulfhydryl groups increases with increasing pH and with decreasing Au loading. The XAFS data for both microorganism species suggest that adsorption is the first step in the formation of Au nanoparticles by bacteria, and the results enhance our ability to account for the behavior of Au in bacteria-bearing geologic systems.

  8. Cj1411c GENE OF CAMPYLOBACTER JEJUNI 11168 ENCODES FOR A CYTOCHROME P450 INVOLVED IN BACTERIAL CAPSULE SUGAR METABOLISM

    Directory of Open Access Journals (Sweden)

    N. CORCIONIVOSCHI

    2013-12-01

    Full Text Available After isolation in 1970s, Campylobacter jejuni become the most commonlyrecognized cause of bacterial gastroenteritis in man. In animals is frequently foundin bovines on ovines. Publishing of the genome sequence of Campylobacter jejuni11168 (Parkhill, 2000 revealed the presence of only one cytochrome P450 in anoperon involved in sugar and cell surface biosynthesis. The gene name is Cj1411c, is1359 bp long and encodes 453 aa. The sequence is strictly conserved inCampylobacter jejuni RM221. Similarities with two cytochrome P450s, one formSilicobacter sp. and one form Poloromonas sp., were identified. These two enzymesare known to be involved in ascorbate and aldarate metabolism. The recombinantconstruct allowed the expression of active P450 enzyme with a 450 nm peak whenbinds CO. The protein was purified in proportion of ~ 70 %. By deleting the P450gene from the Campylobacter jejuni 11168 genome clear changes in cellmorphology were identified cells becoming wider and shorter. The capsular sugarprofile of the NCI strain reveals the presence of arabinose which was not found inthe wild type strain. The arabinose was identified by both High Performance LiquidChromatography (HPLC and Nuclear Magnetic Resonance (NMR.

  9. The cockroach Blattella germanica obtains nitrogen from uric acid through a metabolic pathway shared with its bacterial endosymbiont.

    Science.gov (United States)

    Patiño-Navarrete, Rafael; Piulachs, Maria-Dolors; Belles, Xavier; Moya, Andrés; Latorre, Amparo; Peretó, Juli

    2014-07-01

    Uric acid stored in the fat body of cockroaches is a nitrogen reservoir mobilized in times of scarcity. The discovery of urease in Blattabacterium cuenoti, the primary endosymbiont of cockroaches, suggests that the endosymbiont may participate in cockroach nitrogen economy. However, bacterial urease may only be one piece in the entire nitrogen recycling process from insect uric acid. Thus, in addition to the uricolytic pathway to urea, there must be glutamine synthetase assimilating the released ammonia by the urease reaction to enable the stored nitrogen to be metabolically usable. None of the Blattabacterium genomes sequenced to date possess genes encoding for those enzymes. To test the host's contribution to the process, we have sequenced and analysed Blattella germanica transcriptomes from the fat body. We identified transcripts corresponding to all genes necessary for the synthesis of uric acid and its catabolism to urea, as well as for the synthesis of glutamine, asparagine, proline and glycine, i.e. the amino acids required by the endosymbiont. We also explored the changes in gene expression with different dietary protein levels. It appears that the ability to use uric acid as a nitrogen reservoir emerged in cockroaches after its age-old symbiotic association with bacteria.

  10. Cj1411c GENE OF CAMPYLOBACTER JEJUNI 11168 ENCODES FOR A CYTOCHROME P450 INVOLVED IN BACTERIAL CAPSULE SUGAR METABOLISM

    Directory of Open Access Journals (Sweden)

    CORCIONIVOSCHI N.

    2007-01-01

    Full Text Available After isolation in 1970s, Campylobacter jejuni become the most commonlyrecognized cause of bacterial gastroenteritis in man. In animals is frequently foundin bovines on ovines. Publishing of the genome sequence of Campylobacter jejuni11168 (Parkhill, 2000 revealed the presence of only one cytochrome P450 in anoperon involved in sugar and cell surface biosynthesis. The gene name is Cj1411c, is1359 bp long and encodes 453 aa. The sequence is strictly conserved inCampylobacter jejuni RM221. Similarities with two cytochrome P450s, one formSilicobacter sp. and one form Poloromonas sp., were identified. These two enzymesare known to be involved in ascorbate and aldarate metabolism. The recombinantconstruct allowed the expression of active P450 enzyme with a 450 nm peak whenbinds CO. The protein was purified in proportion of ~ 70 %. By deleting the P450gene from the Campylobacter jejuni 11168 genome clear changes in cellmorphology were identified cells becoming wider and shorter. The capsular sugarprofile of the NCI strain reveals the presence of arabinose which was not found inthe wild type strain. The arabinose was identified by both High Performance LiquidChromatography (HPLC and Nuclear Magnetic Resonance (NMR.

  11. Integration of expression data in genome-scale metabolic network reconstructions

    Directory of Open Access Journals (Sweden)

    Anna S. Blazier

    2012-08-01

    Full Text Available With the advent of high-throughput technologies, the field of systems biology has amassed an abundance of omics data, quantifying thousands of cellular components across a variety of scales, ranging from mRNA transcript levels to metabolite quantities. Methods are needed to not only integrate this omics data but to also use this data to heighten the predictive capabilities of computational models. Several recent studies have successfully demonstrated how flux balance analysis (FBA, a constraint-based modeling approach, can be used to integrate transcriptomic data into genome-scale metabolic network reconstructions to generate predictive computational models. In this review, we summarize such FBA-based methods for integrating expression data into genome-scale metabolic network reconstructions, highlighting their advantages as well as their limitations.

  12. Bacterial degradation of recalcitrant PAHs: metabolic studies and application to pyrene degradation in a freshwater sediment

    Energy Technology Data Exchange (ETDEWEB)

    Jouanneau, Y.; Demaneche, S.; Meyer, Ch.; Willison, J.C. [CEA-Grenoble, UMR 5092 CNRS-CEA-UJF, 38 - Grenoble (France)

    2005-07-01

    Cost-effective bio-remediation strategies have been proposed to remove toxic chemicals, including polycyclic aromatic hydrocarbons (PAHs), from contaminated sites. However, the efficiency of these strategies is often limited, due to the resistance of certain chemicals to microbial degradation. Our studies deal with the biodegradation of four-ring PAHs using two recently isolated bacteria, Mycobacterium strain 6PY1, which can mineralize pyrene and phenanthrene, and Sphingomonas strain CHY-1, which mineralizes chrysene and various three-ring PAHs. The metabolic pathways for the biodegradation of PAHs have been investigated using GC-MS to identify and assay metabolites. Also, several enzymes involved in PAH catabolism have been identified by a combination of proteomic and genetic approaches. In Mycobacterium 6PY1, two ring-hydroxylating di-oxygenases which catalyze the initial attack of PAHs have been overproduced in E. coli, isolated and characterized. The selectivity of the two enzymes showed marked differences, since one di-oxygenase preferentially oxidized 2- or 3- ring PAHs whereas the other attacked pyrene and 3-ring PAHs exclusively. In Sphingomonas CHY-1, a single di-oxygenase, called PhnI, was found to convert seven PAHs, including chrysene, to the corresponding dihydro-diols. It is the first enzyme to be described which is able to attack the four-ring PAHs chrysene and benz[a]anthracene.. The fate of pyrene was examined in a sediment taken from a freshwater lake of the French Alps. Experiments were carried out in microcosms containing a layer of sediment which was spiked with {sup 14}C-pyrene. Pyrene mineralization was monitored over 61 days by measuring the {sup 14}CO{sub 2} evolved from the microcosms. Some microcosms were planted with young reeds (Phragmites australis), while other were inoculated with Mycobacterium 6PY1. P. australis reeds promoted a significant increase of pyrene degradation, which most likely resulted from a root-mediated increase of

  13. Quantitative mass spectrometry reveals plasticity of metabolic networks in Mycobacterium smegmatis.

    Science.gov (United States)

    Chopra, Tarun; Hamelin, Romain; Armand, Florence; Chiappe, Diego; Moniatte, Marc; McKinney, John D

    2014-11-01

    Mycobacterium tuberculosis has a remarkable ability to persist within the human host as a clinically inapparent or chronically active infection. Fatty acids are thought to be an important carbon source used by the bacteria during long term infection. Catabolism of fatty acids requires reprogramming of metabolic networks, and enzymes central to this reprogramming have been targeted for drug discovery. Mycobacterium smegmatis, a nonpathogenic relative of M. tuberculosis, is often used as a model system because of the similarity of basic cellular processes in these two species. Here, we take a quantitative proteomics-based approach to achieve a global view of how the M. smegmatis metabolic network adjusts to utilization of fatty acids as a carbon source. Two-dimensional liquid chromatography and mass spectrometry of isotopically labeled proteins identified a total of 3,067 proteins with high confidence. This number corresponds to 44% of the predicted M. smegmatis proteome and includes most of the predicted metabolic enzymes. Compared with glucose-grown cells, 162 proteins showed differential abundance in acetate- or propionate-grown cells. Among these, acetate-grown cells showed a higher abundance of proteins that could constitute a functional glycerate pathway. Gene inactivation experiments confirmed that both the glyoxylate shunt and the glycerate pathway are operational in M. smegmatis. In addition to proteins with annotated functions, we demonstrate carbon source-dependent differential abundance of proteins that have not been functionally characterized. These proteins might play as-yet-unidentified roles in mycobacterial carbon metabolism. This study reveals several novel features of carbon assimilation in M. smegmatis, which suggests significant functional plasticity of metabolic networks in this organism.

  14. Metabolism

    Science.gov (United States)

    ... Are More Common in People With Type 1 Diabetes Metabolic Syndrome Your Child's Weight Healthy Eating Endocrine System Blood Test: Basic Metabolic Panel (BMP) Activity: Endocrine System Growth Disorders Diabetes Center Thyroid Disorders Your Endocrine System Movie: Endocrine ...

  15. Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli

    DEFF Research Database (Denmark)

    McCloskey, Douglas; Palsson, Bernhard; Feist, Adam

    2013-01-01

    The genome-scale model (GEM) of metabolism in the bacterium Escherichia coli K-12 has been in development for over a decade and is now in wide use. GEM-enabled studies of E. coli have been primarily focused on six applications: (1) metabolic engineering, (2) model-driven discovery, (3) prediction...... of cellular phenotypes, (4) analysis of biological network properties, (5) studies of evolutionary processes, and (6) models of interspecies interactions. In this review, we provide an overview of these applications along with a critical assessment of their successes and limitations, and a perspective...... on likely future developments in the field. Taken together, the studies performed over the past decade have established a genome-scale mechanistic understanding of genotype-phenotype relationships in E. coli metabolism that forms the basis for similar efforts for other microbial species. Future challenges...

  16. Plasticity of metabolic networks and the evolution of C4 photosynthesis

    Science.gov (United States)

    Bogart, Eli; Myers, Chris

    2012-02-01

    Over 50 groups of plants have independently developed a common mechanism (C4 photosynthesis) for increasing the efficiency of photosynthetic carbon dioxide assimilation. Understanding the high degree of evolvability of the C4 system could offer useful guidance for attempts to introduce it artificially to other plants. Previously, the nonlinear relationship between carbon dioxide levels and rates of carbon assimilation and photorespiration has prevented the application of genome-scale metabolic models to the problem of the evolution of the pathway. We apply a nonlinear optimization method to find feasible flux distributions in a plant metabolic model, allowing us to explore the plasticity of the metabolic network and characterize the fitness landscape of the transition from C3 to C4 photosynthesis.

  17. A proof for loop-law constraints in stoichiometric metabolic networks

    Directory of Open Access Journals (Sweden)

    Noor Elad

    2012-11-01

    Full Text Available Abstract 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.

  18. Visualization and quantification of archaeal and bacterial metabolically active cells in soil using fluorescence in situ hybridization method

    Science.gov (United States)

    Semenov, Mikhail; Manucharova, Natalia; Stepanov, Alexey

    2015-04-01

    The method of in situ hybridization using fluorescent labeled 16S rRNA-targeted oligonucleotide probes (FISH - fluorescence in situ hybridization) combines identification and quantification of groups of microorganisms at different phylogenetic levels, from domain to species. The FISH method enables to study the soil microbial community in situ, avoiding plating on nutrient media, and allows to identify and quantify living, metabolically active cells of Bacteria and Archaea. The full procedure consists of the following steps: desorption of the cells from the soil particles, fixation of cells, coating a fixed sample on the glass slide, hybridization with the specific probes and, finally, microscopic observation and cell counting. For the FISH analysis of Bacteria and Archaea, the paraformaldehyde-fixed samples were hybridized with Cy3-labeled Archaea-specific probe(Arc915) and 6-carboxyfluorescein (FAM)-labeled Bacteria-specific probe(EUB338). When a molecular probe is incorporated into a cell, it can hybridize solely with a complementary rRNA sequence. The hybridization can be visualized under the fluorescent microscope and counted. The application of FISH will be demonstrated by the abundance of metabolically active cells of Archaea and Bacteria depending on soil properties, depth and land use. The research was carried out at field and natural ecosystems of European part of Russia. Samples were collected within the soil profiles (3-6 horizons) of Chernozem and Kastanozem with distinct land use. Quantification of metabolically active cells in virgin and arable Chernozem revealed that the abundance of Archaea in topsoil of virgin Chernozem was doubled as compared with arable soil, but it leveled off in the deeper horizons. Plowing of Chernozem decreased an amount of archaeal and bacterial active cells simultaneously, however, Bacteria were more resistant to agrogenic impact than Archaea. In Kastanozem, a significant change in the abundance of metabolically active

  19. In Silico Genome-Scale Reconstruction and Validation of the Corynebacterium glutamicum Metabolic Network

    DEFF Research Database (Denmark)

    Kjeldsen, Kjeld Raunkjær; Nielsen, J.

    2009-01-01

    A genome-scale metabolic model of the Gram-positive bacteria Corynebacterium glutamicum ATCC 13032 was constructed comprising 446 reactions and 411 metabolite, based on the annotated genome and available biochemical information. The network was analyzed using constraint based methods. The model...... and lactate. Comparable flux values between in silico model and experimental values were seen, although some differences in the phenotypic behavior between the model and the experimental data were observed,...

  20. The genome-scale metabolic extreme pathway structure in Haemophilus influenzae shows significant network redundancy.

    Science.gov (United States)

    Papin, Jason A; Price, Nathan D; Edwards, Jeremy S; Palsson B, Bernhard Ø

    2002-03-07

    Genome-scale metabolic networks can be characterized by a set of systemically independent and unique extreme pathways. These extreme pathways span a convex, high-dimensional space that circumscribes all potential steady-state flux distributions achievable by the defined metabolic network. Genome-scale extreme pathways associated with the production of non-essential amino acids in Haemophilus influenzae were computed. They offer valuable insight into the functioning of its metabolic network. Three key results were obtained. First, there were multiple internal flux maps corresponding to externally indistinguishable states. It was shown that there was an average of 37 internal states per unique exchange flux vector in H. influenzae when the network was used to produce a single amino acid while allowing carbon dioxide and acetate as carbon sinks. With the inclusion of succinate as an additional output, this ratio increased to 52, a 40% increase. Second, an analysis of the carbon fates illustrated that the extreme pathways were non-uniformly distributed across the carbon fate spectrum. In the detailed case study, 45% of the distinct carbon fate values associated with lysine production represented 85% of the extreme pathways. Third, this distribution fell between distinct systemic constraints. For lysine production, the carbon fate values that represented 85% of the pathways described above corresponded to only 2 distinct ratios of 1:1 and 4:1 between carbon dioxide and acetate. The present study analysed single outputs from one organism, and provides a start to genome-scale extreme pathways studies. These emergent system-level characterizations show the significance of metabolic extreme pathway analysis at the genome-scale.

  1. Cross-talk between Circadian clocks, Sleep-wake Cycles and Metabolic Networks: Dispelling the Darkness

    OpenAIRE

    Ray, Sandipan; Reddy, Akhilesh B.

    2016-01-01

    This is the final version of the article. It first appeared from Wiley via https://doi.org/10.1002/bies.201500056 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 i...

  2. Metabolism

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    2008255 Serum adiponectin level declines in the elderly with metabolic syndrome.WU Xiaoyan(吴晓琰),et al.Dept Geriatr,Huashan Hosp,Fudan UnivShanghai200040.Chin J Geriatr2008;27(3):164-167.Objective To investigate the correlation between ser-um adiponectin level and metabolic syndrome in the elderly·Methods Sixty-one subjects with metabolic syndrome and140age matched subjects without metabolic

  3. Metabolic network rewiring of propionate flux compensates vitamin B12 deficiency in C. elegans

    Science.gov (United States)

    Watson, Emma; Olin-Sandoval, Viridiana; Hoy, Michael J; Li, Chi-Hua; Louisse, Timo; Yao, Victoria; Mori, Akihiro; Holdorf, Amy D; Troyanskaya, Olga G; Ralser, Markus; Walhout, Albertha JM

    2016-01-01

    Metabolic network rewiring is the rerouting of metabolism through the use of alternate enzymes to adjust pathway flux and accomplish specific anabolic or catabolic objectives. Here, we report the first characterization of two parallel pathways for the breakdown of the short chain fatty acid propionate in Caenorhabditis elegans. Using genetic interaction mapping, gene co-expression analysis, pathway intermediate quantification and carbon tracing, we uncover a vitamin B12-independent propionate breakdown shunt that is transcriptionally activated on vitamin B12 deficient diets, or under genetic conditions mimicking the human diseases propionic- and methylmalonic acidemia, in which the canonical B12-dependent propionate breakdown pathway is blocked. Our study presents the first example of transcriptional vitamin-directed metabolic network rewiring to promote survival under vitamin deficiency. The ability to reroute propionate breakdown according to B12 availability may provide C. elegans with metabolic plasticity and thus a selective advantage on different diets in the wild. DOI: http://dx.doi.org/10.7554/eLife.17670.001 PMID:27383050

  4. Integration of Posttranscriptional Gene Networks into Metabolic Adaptation and Biofilm Maturation in Candida albicans.

    Directory of Open Access Journals (Sweden)

    Jiyoti Verma-Gaur

    2015-10-01

    Full Text Available The yeast Candida albicans is a human commensal and opportunistic pathogen. Although both commensalism and pathogenesis depend on metabolic adaptation, the regulatory pathways that mediate metabolic processes in C. albicans are incompletely defined. For example, metabolic change is a major feature that distinguishes community growth of C. albicans in biofilms compared to suspension cultures, but how metabolic adaptation is functionally interfaced with the structural and gene regulatory changes that drive biofilm maturation remains to be fully understood. We show here that the RNA binding protein Puf3 regulates a posttranscriptional mRNA network in C. albicans that impacts on mitochondrial biogenesis, and provide the first functional data suggesting evolutionary rewiring of posttranscriptional gene regulation between the model yeast Saccharomyces cerevisiae and C. albicans. A proportion of the Puf3 mRNA network is differentially expressed in biofilms, and by using a mutant in the mRNA deadenylase CCR4 (the enzyme recruited to mRNAs by Puf3 to control transcript stability we show that posttranscriptional regulation is important for mitochondrial regulation in biofilms. Inactivation of CCR4 or dis-regulation of mitochondrial activity led to altered biofilm structure and over-production of extracellular matrix material. The extracellular matrix is critical for antifungal resistance and immune evasion, and yet of all biofilm maturation pathways extracellular matrix biogenesis is the least understood. We propose a model in which the hypoxic biofilm environment is sensed by regulators such as Ccr4 to orchestrate metabolic adaptation, as well as the regulation of extracellular matrix production by impacting on the expression of matrix-related cell wall genes. Therefore metabolic changes in biofilms might be intimately linked to a key biofilm maturation mechanism that ultimately results in untreatable fungal disease.

  5. Comparative genome-scale reconstruction of gapless metabolic networks for present and ancestral species.

    Directory of Open Access Journals (Sweden)

    Esa Pitkänen

    2014-02-01

    Full Text Available We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/.

  6. Coevolution trumps pleiotropy: carbon assimilation traits are independent of metabolic network structure in budding yeast.

    Science.gov (United States)

    Opulente, Dana A; Morales, Christopher M; Carey, Lucas B; Rest, Joshua S

    2013-01-01

    Phenotypic traits may be gained and lost together because of pleiotropy, the involvement of common genes and networks, or because of simultaneous selection for multiple traits across environments (multiple-trait coevolution). However, the extent to which network pleiotropy versus environmental coevolution shapes shared responses has not been addressed. To test these alternatives, we took advantage of the fact that the genus Saccharomyces has variation in habitat usage and diversity in the carbon sources that a given strain can metabolize. We examined patterns of gain and loss in carbon utilization traits across 488 strains of Saccharomyces to investigate whether the structure of metabolic pathways or selection pressure from common environments may have caused carbon utilization traits to be gained and lost together. While most carbon sources were gained and lost independently of each other, we found four clusters that exhibit non-random patterns of gain and loss across strains. Contrary to the network pleiotropy hypothesis, we did not find that these patterns are explained by the structure of metabolic pathways or shared enzymes. Consistent with the hypothesis that common environments shape suites of phenotypes, we found that the environment a strain was isolated from partially predicts the carbon sources it can assimilate.

  7. Coevolution trumps pleiotropy: carbon assimilation traits are independent of metabolic network structure in budding yeast.

    Directory of Open Access Journals (Sweden)

    Dana A Opulente

    Full Text Available Phenotypic traits may be gained and lost together because of pleiotropy, the involvement of common genes and networks, or because of simultaneous selection for multiple traits across environments (multiple-trait coevolution. However, the extent to which network pleiotropy versus environmental coevolution shapes shared responses has not been addressed. To test these alternatives, we took advantage of the fact that the genus Saccharomyces has variation in habitat usage and diversity in the carbon sources that a given strain can metabolize. We examined patterns of gain and loss in carbon utilization traits across 488 strains of Saccharomyces to investigate whether the structure of metabolic pathways or selection pressure from common environments may have caused carbon utilization traits to be gained and lost together. While most carbon sources were gained and lost independently of each other, we found four clusters that exhibit non-random patterns of gain and loss across strains. Contrary to the network pleiotropy hypothesis, we did not find that these patterns are explained by the structure of metabolic pathways or shared enzymes. Consistent with the hypothesis that common environments shape suites of phenotypes, we found that the environment a strain was isolated from partially predicts the carbon sources it can assimilate.

  8. Comparative Genome-Scale Reconstruction of Gapless Metabolic Networks for Present and Ancestral Species

    Science.gov (United States)

    Pitkänen, Esa; Jouhten, Paula; Hou, Jian; Syed, Muhammad Fahad; Blomberg, Peter; Kludas, Jana; Oja, Merja; Holm, Liisa; Penttilä, Merja; Rousu, Juho; Arvas, Mikko

    2014-01-01

    We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. PMID:24516375

  9. 代谢网络的重构%Reconstruction of Metabolic Networks

    Institute of Scientific and Technical Information of China (English)

    邓世果; 吴干华; 杨会杰

    2012-01-01

    A new method was proposed to reconstruct metabolic networks on the basis of the existing method. As an illustration example, the method was used to build the metabolic network for a species of anabaena. By use of the data of organism's biochemical reaction,enzyme and gene in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, the preliminary metabolic network for the anabaena was reconstructed. The structural behaviors such as degree distribution, hierarchy, and community were discussed.%在已有的构建代谢网络方法的基础上,提出了构造代谢网络的改进方法.以鱼腥藻作为实例,利用KEGG数据库中生物体的生化反应、酶、基因数据重构出鱼腥藻的初步代谢网络,并对初步代谢网络进行了修正,讨论了该网络的拓扑结构性质.

  10. Hierarchical organization of fluxes in Escherichia coli metabolic network: using flux coupling analysis for understanding the physiological properties of metabolic genes.

    Science.gov (United States)

    Hosseini, Zhaleh; Marashi, Sayed-Amir

    2015-05-01

    Flux coupling analysis is a method for investigating the connections between reactions of metabolic networks. Here, we construct the hierarchical flux coupling graph for the reactions of the Escherichia coli metabolic network model to determine the level of each reaction in the graph. This graph is constructed based on flux coupling analysis of metabolic network: if zero flux through reaction a results in zero flux through reaction b (and not vice versa), then reaction a is located at the top of reaction b in the flux coupling graph. We show that in general, more important, older and essential reactions are located at the top of the graph. Strikingly, genes corresponding to these reactions are found to be the genes which are most regulated.

  11. Nutrient metabolism and optimization method for bacterial culture medium%细菌培养基中营养物质的代谢及优化方法

    Institute of Scientific and Technical Information of China (English)

    杨明明(综述); 李国晏; 应莲芳(审校)

    2016-01-01

    细菌培养基是细菌体外生长的基质,其中营养物质是细菌生长、繁殖的基本成分。因此,探索培养基中营养物质的代谢,优化培养基配方,对细菌体外培养的研究及规模化生产有着重要的指导意义。现就细菌培养基分类、营养物质、细菌在营养物质中的代谢及培养基优化方法等方面予以总结。%The bacterial culture medium is an essential growth substrate on bacterial growth and reproduction in vitro . Thus, it has a great siginificance in development of bacterial growth in vitro and in a related large scale production by inves-tigation of nutrient metabolism and searching for optimization formulation for the medium . This article sumirizes a general introduction about classification , nutrient , bacterial metabolism and optimization method for the medium.

  12. Antimalarial drug targets in Plasmodium falciparum predicted by stage-specific metabolic network analysis

    Directory of Open Access Journals (Sweden)

    Huthmacher Carola

    2010-08-01

    Full Text Available Abstract Background Despite enormous efforts to combat malaria the disease still afflicts up to half a billion people each year of which more than one million die. Currently no approved vaccine is available and resistances to antimalarials are widely spread. Hence, new antimalarial drugs are urgently needed. Results Here, we present a computational analysis of the metabolism of Plasmodium falciparum, the deadliest malaria pathogen. We assembled a compartmentalized metabolic model and predicted life cycle stage specific metabolism with the help of a flux balance approach that integrates gene expression data. Predicted metabolite exchanges between parasite and host were found to be in good accordance with experimental findings when the parasite's metabolic network was embedded into that of its host (erythrocyte. Knock-out simulations identified 307 indispensable metabolic reactions within the parasite. 35 out of 57 experimentally demonstrated essential enzymes were recovered and another 16 enzymes, if additionally the assumption was made that nutrient uptake from the host cell is limited and all reactions catalyzed by the inhibited enzyme are blocked. This predicted set of putative drug targets, shown to be enriched with true targets by a factor of at least 2.75, was further analyzed with respect to homology to human enzymes, functional similarity to therapeutic targets in other organisms and their predicted potency for prophylaxis and disease treatment. Conclusions The results suggest that the set of essential enzymes predicted by our flux balance approach represents a promising starting point for further drug development.

  13. Counting and Correcting Thermodynamically Infeasible Flux Cycles in Genome-Scale Metabolic Networks

    Directory of Open Access Journals (Sweden)

    Andrea De Martino

    2013-10-01

    Full Text Available Thermodynamics constrains the flow of matter in a reaction network to occur through routes along which the Gibbs energy decreases, implying that viable steady-state flux patterns should be void of closed reaction cycles. Identifying and removing cycles in large reaction networks can unfortunately be a highly challenging task from a computational viewpoint. We propose here a method that accomplishes it by combining a relaxation algorithm and a Monte Carlo procedure to detect loops, with ad hoc rules (discussed in detail to eliminate them. As test cases, we tackle (a the problem of identifying infeasible cycles in the E. coli metabolic network and (b the problem of correcting thermodynamic infeasibilities in the Flux-Balance-Analysis solutions for 15 human cell-type-specific metabolic networks. Results for (a are compared with previous analyses of the same issue, while results for (b are weighed against alternative methods to retrieve thermodynamically viable flux patterns based on minimizing specific global quantities. Our method, on the one hand, outperforms previous techniques and, on the other, corrects loopy solutions to Flux Balance Analysis. As a byproduct, it also turns out to be able to reveal possible inconsistencies in model reconstructions.

  14. New insights into Dehalococcoides mccartyi metabolism from a reconstructed metabolic network-based systems-level analysis of D. mccartyi transcriptomes.

    Directory of Open Access Journals (Sweden)

    M Ahsanul Islam

    Full Text Available Organohalide respiration, mediated by Dehalococcoides mccartyi, is a useful bioremediation process that transforms ground water pollutants and known human carcinogens such as trichloroethene and vinyl chloride into benign ethenes. Successful application of this process depends on the fundamental understanding of the respiration and metabolism of D. mccartyi. Reductive dehalogenases, encoded by rdhA genes of these anaerobic bacteria, exclusively catalyze organohalide respiration and drive metabolism. To better elucidate D. mccartyi metabolism and physiology, we analyzed available transcriptomic data for a pure isolate (Dehalococcoides mccartyi strain 195 and a mixed microbial consortium (KB-1 using the previously developed pan-genome-scale reconstructed metabolic network of D. mccartyi. The transcriptomic data, together with available proteomic data helped confirm transcription and expression of the majority genes in D. mccartyi genomes. A composite genome of two highly similar D. mccartyi strains (KB-1 Dhc from the KB-1 metagenome sequence was constructed, and operon prediction was conducted for this composite genome and other single genomes. This operon analysis, together with the quality threshold clustering analysis of transcriptomic data helped generate experimentally testable hypotheses regarding the function of a number of hypothetical proteins and the poorly understood mechanism of energy conservation in D. mccartyi. We also identified functionally enriched important clusters (13 for strain 195 and 11 for KB-1 Dhc of co-expressed metabolic genes using information from the reconstructed metabolic network. This analysis highlighted some metabolic genes and processes, including lipid metabolism, energy metabolism, and transport that potentially play important roles in organohalide respiration. Overall, this study shows the importance of an organism's metabolic reconstruction in analyzing various "omics" data to obtain improved understanding

  15. Integrative Analysis of Circadian Transcriptome and Metabolic Network Reveals the Role of De Novo Purine Synthesis in Circadian Control of Cell Cycle

    OpenAIRE

    Ying Li; Guang Li; Benjamin Görling; Burkhard Luy; Jiulin Du; Jun Yan

    2015-01-01

    Metabolism is the major output of the circadian clock in many organisms. We developed a computational method to integrate both circadian gene expression and metabolic network. Applying this method to zebrafish circadian transcriptome, we have identified large clusters of metabolic genes containing mostly genes in purine and pyrimidine metabolism in the metabolic network showing similar circadian phases. Our metabolomics analysis found that the level of inosine 5'-monophosphate (IMP), an inter...

  16. Difference in the distribution pattern of substrate enzymes in the metabolic network of Escherichia coli, according to chaperonin requirement

    Directory of Open Access Journals (Sweden)

    Niwa Tatsuya

    2011-06-01

    Full Text Available Abstract Background Chaperonins are important in living systems because they play a role in the folding of proteins. Earlier comprehensive analyses identified substrate proteins for which folding requires the chaperonin GroEL/GroES (GroE in Escherichia coli, and they revealed that many chaperonin substrates are metabolic enzymes. This result implies the importance of chaperonins in metabolism. However, the relationship between chaperonins and metabolism is still unclear. Results We investigated the distribution of chaperonin substrate enzymes in the metabolic network using network analysis techniques as a first step towards revealing this relationship, and found that as chaperonin requirement increases, substrate enzymes are more laterally distributed in the metabolic. In addition, comparative genome analysis showed that the chaperonin-dependent substrates were less conserved, suggesting that these substrates were acquired later on in evolutionary history. Conclusions This result implies the expansion of metabolic networks due to this chaperonin, and it supports the existing hypothesis of acceleration of evolution by chaperonins. The distribution of chaperonin substrate enzymes in the metabolic network is inexplicable because it does not seem to be associated with individual protein features such as protein abundance, which has been observed characteristically in chaperonin substrates in previous works. However, it becomes clear by considering this expansion process due to chaperonin. This finding provides new insights into metabolic evolution and the roles of chaperonins in living systems.

  17. A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology

    OpenAIRE

    Feist Adam M; Bordbar Aarash; Usaite-Black Renata; Woodcock Joseph; Palsson Bernhard O; Famili Iman

    2011-01-01

    Abstract 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 ut...

  18. Identification of genes and networks driving cardiovascular and metabolic phenotypes in a mouse F2 intercross.

    Directory of Open Access Journals (Sweden)

    Jonathan M J Derry

    Full Text Available To identify the genes and pathways that underlie cardiovascular and metabolic phenotypes we performed an integrated analysis of a mouse C57BL/6JxA/J F2 (B6AF2 cross by relating genome-wide gene expression data from adipose, kidney, and liver tissues to physiological endpoints measured in the population. We have identified a large number of trait QTLs including loci driving variation in cardiac function on chromosomes 2 and 6 and a hotspot for adiposity, energy metabolism, and glucose traits on chromosome 8. Integration of adipose gene expression data identified a core set of genes that drive the chromosome 8 adiposity QTL. This chromosome 8 trans eQTL signature contains genes associated with mitochondrial function and oxidative phosphorylation and maps to a subnetwork with conserved function in humans that was previously implicated in human obesity. In addition, human eSNPs corresponding to orthologous genes from the signature show enrichment for association to type II diabetes in the DIAGRAM cohort, supporting the idea that the chromosome 8 locus perturbs a molecular network that in humans senses variations in DNA and in turn affects metabolic disease risk. We functionally validate predictions from this approach by demonstrating metabolic phenotypes in knockout mice for three genes from the trans eQTL signature, Akr1b8, Emr1, and Rgs2. In addition we show that the transcriptional signatures for knockout of two of these genes, Akr1b8 and Rgs2, map to the F2 network modules associated with the chromosome 8 trans eQTL signature and that these modules are in turn very significantly correlated with adiposity in the F2 population. Overall this study demonstrates how integrating gene expression data with QTL analysis in a network-based framework can aid in the elucidation of the molecular drivers of disease that can be translated from mice to humans.

  19. Genome-scale reconstruction and analysis of the metabolic network in the hyperthermophilic archaeon Sulfolobus solfataricus.

    Directory of Open Access Journals (Sweden)

    Thomas Ulas

    Full Text Available We describe the reconstruction of a genome-scale metabolic model of the crenarchaeon Sulfolobus solfataricus, a hyperthermoacidophilic microorganism. It grows in terrestrial volcanic hot springs with growth occurring at pH 2-4 (optimum 3.5 and a temperature of 75-80°C (optimum 80°C. The genome of Sulfolobus solfataricus P2 contains 2,992,245 bp on a single circular chromosome and encodes 2,977 proteins and a number of RNAs. The network comprises 718 metabolic and 58 transport/exchange reactions and 705 unique metabolites, based on the annotated genome and available biochemical data. Using the model in conjunction with constraint-based methods, we simulated the metabolic fluxes induced by different environmental and genetic conditions. The predictions were compared to experimental measurements and phenotypes of S. solfataricus. Furthermore, the performance of the network for 35 different carbon sources known for S. solfataricus from the literature was simulated. Comparing the growth on different carbon sources revealed that glycerol is the carbon source with the highest biomass flux per imported carbon atom (75% higher than glucose. Experimental data was also used to fit the model to phenotypic observations. In addition to the commonly known heterotrophic growth of S. solfataricus, the crenarchaeon is also able to grow autotrophically using the hydroxypropionate-hydroxybutyrate cycle for bicarbonate fixation. We integrated this pathway into our model and compared bicarbonate fixation with growth on glucose as sole carbon source. Finally, we tested the robustness of the metabolism with respect to gene deletions using the method of Minimization of Metabolic Adjustment (MOMA, which predicted that 18% of all possible single gene deletions would be lethal for the organism.

  20. A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology

    Directory of Open Access Journals (Sweden)

    Feist Adam M

    2011-10-01

    Full Text Available Abstract 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.

  1. Effects of dietary inulin on bacterial growth, short-chain fatty acid production and hepatic lipid metabolism in gnotobiotic mice.

    Science.gov (United States)

    Weitkunat, Karolin; Schumann, Sara; Petzke, Klaus Jürgen; Blaut, Michael; Loh, Gunnar; Klaus, Susanne

    2015-09-01

    In literature, contradictory effects of dietary fibers and their fermentation products, short-chain fatty acids (SCFA), are described: On one hand, they increase satiety, but on the other hand, they provide additional energy and promote obesity development. We aimed to answer this paradox by investigating the effects of fermentable and non-fermentable fibers on obesity induced by high-fat diet in gnotobiotic C3H/HeOuJ mice colonized with a simplified human microbiota. Mice were fed a high-fat diet supplemented either with 10% cellulose (non-fermentable) or inulin (fermentable) for 6 weeks. Feeding the inulin diet resulted in an increased diet digestibility and reduced feces energy, compared to the cellulose diet with no differences in food intake, suggesting an increased intestinal energy extraction from inulin. However, we observed no increase in body fat/weight. The additional energy provided by the inulin diet led to an increased bacterial proliferation in this group. Supplementation of inulin resulted further in significantly elevated concentrations of total SCFA in cecum and portal vein plasma, with a reduced cecal acetate:propionate ratio. Hepatic expression of genes involved in lipogenesis (Fasn, Gpam) and fatty acid elongation/desaturation (Scd1, Elovl3, Elovl6, Elovl5, Fads1 and Fads2) were decreased in inulin-fed animals. Accordingly, plasma and liver phospholipid composition were changed between the different feeding groups. Concentrations of omega-3 and odd-chain fatty acids were increased in inulin-fed mice, whereas omega-6 fatty acids were reduced. Taken together, these data indicate that, during this short-term feeding, inulin has mainly positive effects on the lipid metabolism, which could cause beneficial effects during obesity development in long-term studies.

  2. 细菌应激反应中(p)ppGpp代谢的调控%Modulation of (p)ppGpp metabolism during bacterial stringent response

    Institute of Scientific and Technical Information of China (English)

    刘彪; 宁德刚

    2011-01-01

    (p)ppGpp is a well known intracellular signal that mediates bacterial stringent response to environment stresses through the change of its concentration level thus controls many important cellular processes for bacterial survival. In this review, we outline the mechanism of (p)ppGpp action, the enzyme system involved in (p)ppGpp metabolism, and summarize the signal transmission, the regulation and the diversity of (p)ppGpp metabolism. Moreover, we give a brief introduction on our achieved results recently about (p)ppGpp metabolism in cyanobacteria, and predict that a (p)ppGpp new metabolic mechanism different from those known exists in cyanobacteria.%(p)ppGpp是介导细菌细胞对环境胁迫产生应激反应的重要胞内信号,通过控制一系列重要的细胞活动使细菌得以生存.通过对蓝细菌中(p)ppGpp代谢的研究,对(p)ppGpp作用机制、控制(p)ppGpp代谢的酶系统、环境胁迫信号传递、细胞中(p)ppGpp水平的调控及其多样性进行了总结.

  3. Metabolic networks to generate pyruvate, PEP and ATP from glycerol in Pseudomonas fluorescens.

    Science.gov (United States)

    Alhasawi, Azhar; Thomas, Sean C; Appanna, Vasu D

    2016-04-01

    Glycerol is a major by-product of the biodiesel industry. In this study we report on the metabolic networks involved in its transformation into pyruvate, phosphoenolpyruvate (PEP) and ATP. When the nutritionally-versatile Pseudomonas fluorescens was exposed to hydrogen peroxide (H2O2) in a mineral medium with glycerol as the sole carbon source, the microbe reconfigured its metabolism to generate adenosine triphosphate (ATP) primarily via substrate-level phosphorylation (SLP). This alternative ATP-producing stratagem resulted in the synthesis of copious amounts of PEP and pyruvate. The production of these metabolites was mediated via the enhanced activities of such enzymes as pyruvate carboxylase (PC) and phosphoenolpyruvate carboxylase (PEPC). The high energy PEP was subsequently converted into ATP with the aid of pyruvate phosphate dikinase (PPDK), phosphoenolpyruvate synthase (PEPS) and pyruvate kinase (PK) with the concomitant formation of pyruvate. The participation of the phospho-transfer enzymes like adenylate kinase (AK) and acetate kinase (ACK) ensured the efficiency of this O2-independent energy-generating machinery. The increased activity of glycerol dehydrogenase (GDH) in the stressed bacteria provided the necessary precursors to fuel this process. This H2O2-induced anaerobic life-style fortuitously evokes metabolic networks to an effective pathway that can be harnessed into the synthesis of ATP, PEP and pyruvate. The bioconversion of glycerol to pyruvate will offer interesting economic benefit.

  4. Insight into bacterial virulence mechanisms against host immune response via the Yersinia pestis-human protein-protein interaction network.

    Science.gov (United States)

    Yang, Huiying; Ke, Yuehua; Wang, Jian; Tan, Yafang; Myeni, Sebenzile K; Li, Dong; Shi, Qinghai; Yan, Yanfeng; Chen, Hui; Guo, Zhaobiao; Yuan, Yanzhi; Yang, Xiaoming; Yang, Ruifu; Du, Zongmin

    2011-11-01

    A Yersinia pestis-human protein interaction network is reported here to improve our understanding of its pathogenesis. Up to 204 interactions between 66 Y. pestis bait proteins and 109 human proteins were identified by yeast two-hybrid assay and then combined with 23 previously published interactions to construct a protein-protein interaction network. Topological analysis of the interaction network revealed that human proteins targeted by Y. pestis were significantly enriched in the proteins that are central in the human protein-protein interaction network. Analysis of this network showed that signaling pathways important for host immune responses were preferentially targeted by Y. pestis, including the pathways involved in focal adhesion, regulation of cytoskeleton, leukocyte transendoepithelial migration, and Toll-like receptor (TLR) and mitogen-activated protein kinase (MAPK) signaling. Cellular pathways targeted by Y. pestis are highly relevant to its pathogenesis. Interactions with host proteins involved in focal adhesion and cytoskeketon regulation pathways could account for resistance of Y. pestis to phagocytosis. Interference with TLR and MAPK signaling pathways by Y. pestis reflects common characteristics of pathogen-host interaction that bacterial pathogens have evolved to evade host innate immune response by interacting with proteins in those signaling pathways. Interestingly, a large portion of human proteins interacting with Y. pestis (16/109) also interacted with viral proteins (Epstein-Barr virus [EBV] and hepatitis C virus [HCV]), suggesting that viral and bacterial pathogens attack common cellular functions to facilitate infections. In addition, we identified vasodilator-stimulated phosphoprotein (VASP) as a novel interaction partner of YpkA and showed that YpkA could inhibit in vitro actin assembly mediated by VASP.

  5. The DtxR protein acting as dual transcriptional regulator directs a global regulatory network involved in iron metabolism of Corynebacterium glutamicum

    Directory of Open Access Journals (Sweden)

    Hüser Andrea T

    2006-02-01

    Full Text Available Abstract Background The knowledge about complete bacterial genome sequences opens the way to reconstruct the qualitative topology and global connectivity of transcriptional regulatory networks. Since iron is essential for a variety of cellular processes but also poses problems in biological systems due to its high toxicity, bacteria have evolved complex transcriptional regulatory networks to achieve an effective iron homeostasis. Here, we apply a combination of transcriptomics, bioinformatics, in vitro assays, and comparative genomics to decipher the regulatory network of the iron-dependent transcriptional regulator DtxR of Corynebacterium glutamicum. Results A deletion of the dtxR gene of C. glutamicum ATCC 13032 led to the mutant strain C. glutamicum IB2103 that was able to grow in minimal medium only under low-iron conditions. By performing genome-wide DNA microarray hybridizations, differentially expressed genes involved in iron metabolism of C. glutamicum were detected in the dtxR mutant. Bioinformatics analysis of the genome sequence identified a common 19-bp motif within the upstream region of 31 genes, whose differential expression in C. glutamicum IB2103 was verified by real-time reverse transcription PCR. Binding of a His-tagged DtxR protein to oligonucleotides containing the 19-bp motifs was demonstrated in vitro by DNA band shift assays. At least 64 genes encoding a variety of physiological functions in iron transport and utilization, in central carbohydrate metabolism and in transcriptional regulation are controlled directly by the DtxR protein. A comparison with the bioinformatically predicted networks of C. efficiens, C. diphtheriae and C. jeikeium identified evolutionary conserved elements of the DtxR network. Conclusion This work adds considerably to our currrent understanding of the transcriptional regulatory network of C. glutamicum genes that are controlled by DtxR. The DtxR protein has a major role in controlling the

  6. Pseudomonas fluorescens induces strain-dependent and strain-independent host plant responses in defense networks, primary metabolism and photosynthesis

    Energy Technology Data Exchange (ETDEWEB)

    Pelletier, Dale A [ORNL; Morrell-Falvey, Jennifer L [ORNL; Karve, Abhijit A [ORNL; Lu, Tse-Yuan S [ORNL; Tschaplinski, Timothy J [ORNL; Tuskan, Gerald A [ORNL; Chen, Jay [ORNL; Martin, Madhavi Z [ORNL; Jawdy, Sara [ORNL; Weston, David [ORNL; Doktycz, Mitchel John [ORNL; Schadt, Christopher Warren [ORNL

    2012-01-01

    Colonization of plants by nonpathogenic Pseudomonas fluorescens strains can confer enhanced defense capacity against a broad spectrum of pathogens. Few studies, however, have linked defense pathway regulation to primary metabolism and physiology. In this study, physiological data, metabolites, and transcript profiles are integrated to elucidate how molecular networks initiated at the root-microbe interface influence shoot metabolism and whole-plant performance. Experiments with Arabidopsis thaliana were performed using the newly identified P. fluorescens GM30 or P. fluorescens Pf-5 strains. Co-expression networks indicated that Pf-5 and GM30 induced a subnetwork specific to roots enriched for genes participating in RNA regulation, protein degradation, and hormonal metabolism. In contrast, only GM30 induced a subnetwork enriched for calcium signaling, sugar and nutrient signaling, and auxin metabolism, suggesting strain dependence in network architecture. In addition, one subnetwork present in shoots was enriched for genes in secondary metabolism, photosynthetic light reactions, and hormone metabolism. Metabolite analysis indicated that this network initiated changes in carbohydrate and amino acid metabolism. Consistent with this, we observed strain-specific responses in tryptophan and phenylalanine abundance. Both strains reduced host plant carbon gain and fitness, yet provided a clear fitness benefit when plants were challenged with the pathogen P. syringae DC3000.

  7. Network environ perspective for urban metabolism and carbon emissions: a case study of Vienna, Austria.

    Science.gov (United States)

    Chen, Shaoqing; Chen, Bin

    2012-04-17

    Cities are considered major contributors to global warming, where carbon emissions are highly embedded in the overall urban metabolism. To examine urban metabolic processes and emission trajectories we developed a carbon flux model based on Network Environ Analysis (NEA). The mutual interactions and control situation within the urban ecosystem of Vienna were examined, and the system-level properties of the city's carbon metabolism were assessed. Regulatory strategies to minimize carbon emissions were identified through the tracking of the possible pathways that affect these emission trajectories. Our findings suggest that indirect flows have a strong bearing on the mutual and control relationships between urban sectors. The metabolism of a city is considered self-mutualistic and sustainable only when the local and distal environments are embraced. Energy production and construction were found to be two factors with a major impact on carbon emissions, and whose regulation is only effective via ad-hoc pathways. In comparison with the original life-cycle tracking, the application of NEA was better at revealing details from a mechanistic aspect, which is crucial for informed sustainable urban management.

  8. Web-based metabolic network visualization with a zooming user interface

    Directory of Open Access Journals (Sweden)

    Karp Peter D

    2011-05-01

    Full Text Available Abstract 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.

  9. A Network Flow Analysis of the Nitrogen Metabolism in Beijing, China.

    Science.gov (United States)

    Zhang, Yan; Lu, Hanjing; Fath, Brian D; Zheng, Hongmei; Sun, Xiaoxi; Li, Yanxian

    2016-08-16

    Rapid urbanization results in high nitrogen flows and subsequent environmental consequences. In this study, we identified the main metabolic components (nitrogen inputs, flows, and outputs) and used ecological network analysis to track the direct and integral (direct + indirect) metabolic flows of nitrogen in Beijing, China, from 1996 to 2012 and to quantify the structure of Beijing's nitrogen metabolic processes. We found that Beijing's input of new reactive nitrogen (Q, which represents nitrogen obtained from the atmosphere or nitrogen-containing materials used in production and consumption to support human activities) increased from 431 Gg in 1996 to 507 Gg in 2012. Flows to the industry, atmosphere, and household, and components of the system were clearly largest, with total integrated inputs plus outputs from these nodes accounting for 31, 29, and 15%, respectively, of the total integral flows for all paths. The flows through the sewage treatment and transportation components showed marked growth, with total integrated inputs plus outputs increasing to 3.7 and 5.2 times their 1996 values, respectively. Our results can help policymakers to locate the key nodes and pathways in an urban nitrogen metabolic system so they can monitor and manage these components of the system.

  10. Genome-scale reconstruction of metabolic network for a halophilic extremophile, Chromohalobacter salexigens DSM 3043

    Directory of Open Access Journals (Sweden)

    Oner Ebru

    2011-01-01

    Full Text Available Abstract Background Chromohalobacter salexigens (formerly Halomonas elongata DSM 3043 is a halophilic extremophile with a very broad salinity range and is used as a model organism to elucidate prokaryotic osmoadaptation due to its strong euryhaline phenotype. Results C. salexigens DSM 3043's metabolism was reconstructed based on genomic, biochemical and physiological information via a non-automated but iterative process. This manually-curated reconstruction accounts for 584 genes, 1386 reactions, and 1411 metabolites. By using flux balance analysis, the model was extensively validated against literature data on the C. salexigens phenotypic features, the transport and use of different substrates for growth as well as against experimental observations on the uptake and accumulation of industrially important organic osmolytes, ectoine, betaine, and its precursor choline, which play important roles in the adaptive response to osmotic stress. Conclusions This work presents the first comprehensive genome-scale metabolic model of a halophilic bacterium. Being a useful guide for identification and filling of knowledge gaps, the reconstructed metabolic network iOA584 will accelerate the research on halophilic bacteria towards application of systems biology approaches and design of metabolic engineering strategies.

  11. Comparative metabolic network analysis of two xylose fermenting recombinant Saccharomyces cerevisiae strains.

    Science.gov (United States)

    Grotkjaer, Thomas; Christakopoulos, Paul; Nielsen, Jens; Olsson, Lisbeth

    2005-01-01

    The recombinant xylose fermenting strain Saccharomyces cerevisiae TMB3001 can grow on xylose, but the xylose utilisation rate is low. One important reason for the inefficient fermentation of xylose to ethanol is believed to be the imbalance of redox co-factors. In the present study, a metabolic flux model was constructed for two recombinant S. cerevisiae strains: TMB3001 and CPB.CR4 which in addition to xylose metabolism have a modulated redox metabolism, i.e. ammonia assimilation was shifted from being NADPH to NADH dependent by deletion of gdh1 and over-expression of GDH2. The intracellular fluxes were estimated for both strains in anaerobic continuous cultivations when the growth limiting feed consisted of glucose (2.5 g L-1) and xylose (13 g L-1). The metabolic network analysis with 13C labelled glucose showed that there was a shift in the specific xylose reductase activity towards use of NADH as co-factor rather than NADPH. This shift is beneficial for solving the redox imbalance and it can therefore partly explain the 25% increase in the ethanol yield observed for CPB.CR4. Furthermore, the analysis indicated that the glyoxylate cycle was activated in CPB.CR4.

  12. Combining Genomics, Metabolome Analysis, and Biochemical Modelling to Understand Metabolic Networks

    Directory of Open Access Journals (Sweden)

    Oliver Fiehn

    2006-04-01

    Full Text Available Now that complete genome sequences are available for a variety of organisms, the elucidation of gene functions involved in metabolism necessarily includes a better understanding of cellular responses upon mutations on all levels of gene products, mRNA, proteins, and metabolites. Such progress is essential since the observable properties of organisms – the phenotypes – are produced by the genotype in juxtaposition with the environment. Whereas much has been done to make mRNA and protein profiling possible, considerably less effort has been put into profiling the end products of gene expression, metabolites. To date, analytical approaches have been aimed primarily at the accurate quantification of a number of pre-defined target metabolites, or at producing fingerprints of metabolic changes without individually determining metabolite identities. Neither of these approaches allows the formation of an in-depth understanding of the biochemical behaviour within metabolic networks. Yet, by carefully choosing protocols for sample preparation and analytical techniques, a number of chemically different classes of compounds can be quantified simultaneously to enable such understanding. In this review, the terms describing various metabolite-oriented approaches are given, and the differences among these approaches are outlined. Metabolite target analysis, metabolite profiling, metabolomics, and metabolic fingerprinting are considered. For each approach, a number of examples are given, and potential applications are discussed.

  13. Systematic construction of kinetic models from genome-scale metabolic networks.

    Directory of Open Access Journals (Sweden)

    Natalie J Stanford

    Full Text Available The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments.

  14. Metabolic flux network analysis of fermentative hydrogen production: using Clostridium tyrobutyricum as an example.

    Science.gov (United States)

    Cheng, Hai-Hsuan; Whang, Liang-Ming; Lin, Che-An; Liu, I-Chun; Wu, Chao-Wei

    2013-08-01

    This study applies metabolic flux network analysis (MFA) to evaluate the metabolic flux of fermentative hydrogen production (FHP) with the use of Clostridium tyrobutyricum fed with either glucose or lactate/acetate as substrates. The MFA results suggest that hydraulic retention time (HRT) presents significant impact on hydrogen production from glucose. At HRT between 4 and 18 h, increase of HRT increased hydrogen production but decreased lactate production, while at HRT below 4 h decrease of HRT increased hydrogen production but decreased lactate production. The flux for lactate, butyrate and acetate seemed to affect H₂ production, due presumably to their impacts on the balance of NADH, ferredoxin and ATP. It is suggested that the MFA can be a useful tool to provide valuable information for optimization and design of the fermentative hydrogen production process.

  15. Towards finding the linkage between metabolic and age-related disorders using semantic gene data network analysis

    Science.gov (United States)

    Uzzal Hossain, Mohammad; Zaffar Shibly, Abu; Md. Omar, Taimur; Tous Zohora, Fatama; Sara Santona, Umme; Hossain, Md. Jakir; Hosen Khoka, Md. Sadek; Ara Keya, Chaman; Salimullah, Md.

    2016-01-01

    A metabolic disorder (MD) occurs when the metabolic process is disturbed. This process is carried out by thousands of enzymes participating in numerous inter-dependent metabolic pathways. Critical biochemical reactions that involve the processing and transportation of carbohydrates, proteins and lipids are affected in metabolic diseases. Therefore, it is of interest to identify the common pathways of metabolic disorders by building protein-protein interactions (PPI) for network analysis. The molecular network linkages between MD and age related diseases (ARD) are intriguing. Hence, we created networks of protein-protein interactions that are related with MD and ARD using relevant known data in the public domain. The network analysis identified known MD associated proteins and predicted genes and or its products of ARD in common pathways. The genes in the common pathways were isolated from the network and further analyzed for their co-localization and shared domains. Thus, a model hypothesis is proposed using interaction networks that are linked between MD and ARD. This data even if less conclusive finds application in understanding the molecular mechanism of known diseases in relation to observed molecular events PMID:27212841

  16. Integrated Regulatory and Metabolic Networks of the Marine Diatom Phaeodactylum tricornutum Predict the Response to Rising CO2 Levels.

    Science.gov (United States)

    Levering, Jennifer; Dupont, Christopher L; Allen, Andrew E; Palsson, Bernhard O; Zengler, Karsten

    2017-01-01

    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 shared 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. IMPORTANCE 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.

  17. Reconstruction and in silico analysis of metabolic network for an oleaginous yeast, Yarrowia lipolytica.

    Directory of Open Access Journals (Sweden)

    Pengcheng Pan

    Full Text Available With the emergence of energy scarcity, the use of renewable energy sources such as biodiesel is becoming increasingly necessary. Recently, many researchers have focused their minds on Yarrowia lipolytica, a model oleaginous yeast, which can be employed to accumulate large amounts of lipids that could be further converted to biodiesel. In order to understand the metabolic characteristics of Y. lipolytica at a systems level and to examine the potential for enhanced lipid production, a genome-scale compartmentalized metabolic network was reconstructed based on a combination of genome annotation and the detailed biochemical knowledge from multiple databases such as KEGG, ENZYME and BIGG. The information about protein and reaction associations of all the organisms in KEGG and Expasy-ENZYME database was arranged into an EXCEL file that can then be regarded as a new useful database to generate other reconstructions. The generated model iYL619_PCP accounts for 619 genes, 843 metabolites and 1,142 reactions including 236 transport reactions, 125 exchange reactions and 13 spontaneous reactions. The in silico model successfully predicted the minimal media and the growing abilities on different substrates. With flux balance analysis, single gene knockouts were also simulated to predict the essential genes and partially essential genes. In addition, flux variability analysis was applied to design new mutant strains that will redirect fluxes through the network and may enhance the production of lipid. This genome-scale metabolic model of Y. lipolytica can facilitate system-level metabolic analysis as well as strain development for improving the production of biodiesels and other valuable products by Y. lipolytica and other closely related oleaginous yeasts.

  18. Metabolism

    Science.gov (United States)

    ... a particular food provides to the body. A chocolate bar has more calories than an apple, so ... acid phenylalanine, needed for normal growth and protein production). Inborn errors of metabolism can sometimes lead to ...

  19. Recent advances in elementary flux modes and yield space analysis as useful tools in metabolic network studies.

    Science.gov (United States)

    Horvat, Predrag; Koller, Martin; Braunegg, Gerhart

    2015-09-01

    A review of the use of elementary flux modes (EFMs) and their applications in metabolic engineering covered with yield space analysis (YSA) is presented. EFMs are an invaluable tool in mathematical modeling of biochemical processes. They are described from their inception in 1994, followed by various improvements of their computation in later years. YSA constitutes another precious tool for metabolic network modeling, and is presented in details along with EFMs in this article. The application of these techniques is discussed for several case studies of metabolic network modeling provided in respective original articles. The article is concluded by some case studies in which the application of EFMs and YSA turned out to be most useful, such as the analysis of intracellular polyhydroxyalkanoate (PHA) formation and consumption in Cupriavidus necator, including the constraint-based description of the steady-state flux cone of the strain's metabolic network, the profound analysis of a continuous five-stage bioreactor cascade for PHA production by C. necator using EFMs and, finally, the study of metabolic fluxes in the metabolic network of C. necator cultivated on glycerol.

  20. Kriging-Based Parameter Estimation Algorithm for Metabolic Networks Combined with Single-Dimensional Optimization and Dynamic Coordinate Perturbation.

    Science.gov (United States)

    Wang, Hong; Wang, Xicheng; Li, Zheng; Li, Keqiu

    2016-01-01

    The metabolic network model allows for an in-depth insight into the molecular mechanism of a particular organism. Because most parameters of the metabolic network cannot be directly measured, they must be estimated by using optimization algorithms. However, three characteristics of the metabolic network model, i.e., high nonlinearity, large amount parameters, and huge variation scopes of parameters, restrict the application of many traditional optimization algorithms. As a result, there is a growing demand to develop efficient optimization approaches to address this complex problem. In this paper, a Kriging-based algorithm aiming at parameter estimation is presented for constructing the metabolic networks. In the algorithm, a new infill sampling criterion, named expected improvement and mutual information (EI&MI), is adopted to improve the modeling accuracy by selecting multiple new sample points at each cycle, and the domain decomposition strategy based on the principal component analysis is introduced to save computing time. Meanwhile, the convergence speed is accelerated by combining a single-dimensional optimization method with the dynamic coordinate perturbation strategy when determining the new sample points. Finally, the algorithm is applied to the arachidonic acid metabolic network to estimate its parameters. The obtained results demonstrate the effectiveness of the proposed algorithm in getting precise parameter values under a limited number of iterations.

  1. Evolutionary remodeling of global regulatory networks during long-term bacterial adaptation to human hosts

    DEFF Research Database (Denmark)

    Pedersen, Søren Damkiær; Yang, Lei; Molin, Søren

    2013-01-01

    The genetic basis of bacterial adaptation to a natural environment has been investigated in a highly successful Pseudomonas aeruginosa lineage (DK2) that evolved within the airways of patients with cystic fibrosis (CF) for more than 35 y. During evolution in the CF airways, the DK2 lineage underw...

  2. Systematic analysis of transcription-level effects of neurodegenerative diseases on human brain metabolism by a newly reconstructed brain-specific metabolic network

    Directory of Open Access Journals (Sweden)

    Mustafa Sertbaş

    2014-01-01

    Full Text Available Network-oriented analysis is essential to identify those parts of a cell affected by a given perturbation. The effect of neurodegenerative perturbations in the form of diseases of brain metabolism was investigated by using a newly reconstructed brain-specific metabolic network. The developed stoichiometric model correctly represents healthy brain metabolism, and includes 630 metabolic reactions in and between astrocytes and neurons, which are controlled by 570 genes. The integration of transcriptome data of six neurodegenerative diseases (Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis, multiple sclerosis, schizophrenia with the model was performed to identify reporter features specific and common for these diseases, which revealed metabolites and pathways around which the most significant changes occur. The identified metabolites are potential biomarkers for the pathology of the related diseases. Our model indicated perturbations in oxidative stress, energy metabolism including TCA cycle and lipid metabolism as well as several amino acid related pathways, in agreement with the role of these pathways in the studied diseases. The computational prediction of transcription factors that commonly regulate the reporter metabolites was achieved through binding-site analysis. Literature support for the identified transcription factors such as USF1, SP1 and those from FOX families are known from the literature to have regulatory roles in the identified reporter metabolic pathways as well as in the neurodegenerative diseases. In essence, the reconstructed brain model enables the elucidation of effects of a perturbation on brain metabolism and the illumination of possible machineries in which a specific metabolite or pathway acts as a regulatory spot for cellular reorganization.

  3. Milk- and solid-feeding practices and daycare attendance are associated with differences in bacterial diversity, predominant communities, and metabolic and immune function of the infant gut microbiome.

    Science.gov (United States)

    Thompson, Amanda L; Monteagudo-Mera, Andrea; Cadenas, Maria B; Lampl, Michelle L; Azcarate-Peril, M A

    2015-01-01

    The development of the infant intestinal microbiome in response to dietary and other exposures may shape long-term metabolic and immune function. We examined differences in the community structure and function of the intestinal microbiome between four feeding groups, exclusively breastfed infants before introduction of solid foods (EBF), non-exclusively breastfed infants before introduction of solid foods (non-EBF), EBF infants after introduction of solid foods (EBF+S), and non-EBF infants after introduction of solid foods (non-EBF+S), and tested whether out-of-home daycare attendance was associated with differences in relative abundance of gut bacteria. Bacterial 16S rRNA amplicon sequencing was performed on 49 stool samples collected longitudinally from a cohort of 9 infants (5 male, 4 female). PICRUSt metabolic inference analysis was used to identify metabolic impacts of feeding practices on the infant gut microbiome. Sequencing data identified significant differences across groups defined by feeding and daycare attendance. Non-EBF and daycare-attending infants had higher diversity and species richness than EBF and non-daycare attending infants. The gut microbiome of EBF infants showed increased proportions of Bifidobacterium and lower abundance of Bacteroidetes and Clostridiales than non-EBF infants. PICRUSt analysis indicated that introduction of solid foods had a marginal impact on the microbiome of EBF infants (24 enzymes overrepresented in EBF+S infants). In contrast, over 200 bacterial gene categories were overrepresented in non-EBF+S compared to non-EBF infants including several bacterial methyl-accepting chemotaxis proteins (MCP) involved in signal transduction. The identified differences between EBF and non-EBF infants suggest that breast milk may provide the gut microbiome with a greater plasticity (despite having a lower phylogenetic diversity) that eases the transition into solid foods.

  4. The tricarboxylic acid cycle, an ancient metabolic network with a novel twist.

    Directory of Open Access Journals (Sweden)

    Ryan J Mailloux

    Full Text Available The tricarboxylic acid (TCA cycle is an essential metabolic network in all oxidative organisms and provides precursors for anabolic processes and reducing factors (NADH and FADH(2 that drive the generation of energy. Here, we show that this metabolic network is also an integral part of the oxidative defence machinery in living organisms and alpha-ketoglutarate (KG is a key participant in the detoxification of reactive oxygen species (ROS. Its utilization as an anti-oxidant can effectively diminish ROS and curtail the formation of NADH, a situation that further impedes the release of ROS via oxidative phosphorylation. Thus, the increased production of KG mediated by NADP-dependent isocitrate dehydrogenase (NADP-ICDH and its decreased utilization via the TCA cycle confer a unique strategy to modulate the cellular redox environment. Activities of alpha-ketoglutarate dehydrogenase (KGDH, NAD-dependent isocitrate dehydrogenase (NAD-ICDH, and succinate dehydrogenase (SDH were sharply diminished in the cellular systems exposed to conditions conducive to oxidative stress. These findings uncover an intricate link between TCA cycle and ROS homeostasis and may help explain the ineffective TCA cycle that characterizes various pathological conditions and ageing.

  5. From the Cover: Design of artificial cell-cell communication using gene and metabolic networks

    Science.gov (United States)

    Bulter, Thomas; Lee, Sun-Gu; Waichun Wong, Wilson; Fung, Eileen; Connor, Michael R.; Liao, James C.

    2004-02-01

    Artificial transcriptional networks have been used to achieve novel, nonnative behavior in bacteria. Typically, these artificial circuits are isolated from cellular metabolism and are designed to function without intercellular communication. To attain concerted biological behavior in a population, synchronization through intercellular communication is highly desirable. Here we demonstrate the design and construction of a gene-metabolic circuit that uses a common metabolite to achieve tunable artificial cell-cell communication. This circuit uses a threshold concentration of acetate to induce gene expression by acetate kinase and part of the nitrogen-regulation two-component system. As one application of the cell-cell communication circuit we created an artificial quorum sensor. Engineering of carbon metabolism in Escherichia coli made acetate secretion proportional to cell density and independent of oxygen availability. In these cells the circuit induced gene expression in response to a threshold cell density. This threshold can be tuned effectively by controlling pH over the cell membrane, which determines the partition of acetate between medium and cells. Mutagenesis of the enhancer sequence of the glnAp2 promoter produced variants of the circuit with changed sensitivity demonstrating tunability of the circuit by engineering of its components. The behavior of the circuit shows remarkable predictability based on a mathematical design model.

  6. Neuron-Specific Enolase Is Correlated to Compromised Cerebral Metabolism in Patients Suffering from Acute Bacterial Meningitis; An Observational Cohort Study

    DEFF Research Database (Denmark)

    Bartek, Jiri; Thelin, Eric Peter; Ghatan, Per Hamid;

    2016-01-01

    INTRODUCTION: Patients suffering from acute bacterial meningitis (ABM) with a decreased level of consciousness have been shown to have an improved clinical outcome if treated with an intracranial pressure (ICP) guided therapy. By using intracranial microdialysis (MD) to monitor cerebral metabolism......) guided therapy. Serum biomarkers were obtained at admission and every 12 hours. The MD parameters glucose, lactate, pyruvate and glycerol were analyzed. Outcome was assessed at 12-55 months after discharge from hospital. Mann-Whitney U-Test and Wilcoxon matched-pairs signed rank test were applied...

  7. Detection of denitrification genes by in situ rolling circle amplification - fluorescence in situ hybridization (in situ RCA-FISH) to link metabolic potential with identity inside bacterial cells

    DEFF Research Database (Denmark)

    Hoshino, Tatsuhiko; Schramm, Andreas

    2010-01-01

    A target-primed in situ rolling circle amplification (in situ RCA) protocol was developed for detection of single-copy genes inside bacterial cells and optimized with Pseudomonas stutzeri, targeting nitrite and nitrous oxide reductase genes (nirS and nosZ). Two padlock probes were designed per gene...... as Candidatus Accumulibacter phosphatis by combining in situ RCA-FISH with 16S rRNA-targeted FISH. While not suitable for quantification because of its low detection frequency, in situ RCA-FISH will allow to link metabolic potential with 16S rRNA (gene)-based identification of single microbial cells....

  8. Clinical Presentation and Prevalence of Spontaneous Bacterial Peritonitis in Patients with Cryptogenic Cirrhosis and Features of Metabolic Syndrome

    Directory of Open Access Journals (Sweden)

    P Sorrentino

    2004-01-01

    Full Text Available BACKGROUND: Nonalcoholic steatohepatitis (NASH may progress to cirrhosis. The prevalence and clinical relevance that spontaneous bacterial peritonitis may have in complicating ascites due to NASH-related cirrhosis have yet to be defined.

  9. Metabolic network analysis on Phaffia rhodozyma yeast using C-13-labeled glucose and gas chromatography-mass spectrometry

    DEFF Research Database (Denmark)

    Cannizzaro, C.; Christensen, B.; Nielsen, Jens

    2004-01-01

    . In order to have a better understanding of its metabolism, labeling experiments with [1-C-13]glucose were conducted with the wildtype strain (CBS5905T) and a hyper-producing carotenoid strain (J4-3) in order to determine their metabolic network structure and estimate intracellular fluxes. Amino acid...... labeling patterns, as determined by GC-MS, were in accordance with a metabolic network consisting of the Embden-Meyerhof-Parnas pathway, the pentose phosphate pathway, and the TCA cycle. Glucose was mainly consumed along the pentose phosphate pathway (similar to65% for wildtype strain), which reflected...... high NADPH requirements for lipid biosynthesis. Although common to other oleaginous yeast, there was no, or very little, malic enzyme activity for carbon-limited growth. In addition, there was no evidence of phosphoketolase activity. The central carbon metabolism of the mutant strain was similar...

  10. Convergent evolution of modularity in metabolic networks through different community structures

    Directory of Open Access Journals (Sweden)

    Zhou Wanding

    2012-09-01

    Full Text Available Abstract Background 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. Results 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 taxomony. Conclusions 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

  11. Analysis of lifestyle and metabolic predictors of visceral obesity with Bayesian Networks

    Directory of Open Access Journals (Sweden)

    de Morais Sérgio

    2010-09-01

    Full Text Available Abstract Background The aim of this study was to provide a framework for the analysis of visceral obesity and its determinants in women, where complex inter-relationships are observed among lifestyle, nutritional and metabolic predictors. Thirty-four predictors related to lifestyle, adiposity, body fat distribution, blood lipids and adipocyte sizes have been considered as potential correlates of visceral obesity in women. To properly address the difficulties in managing such interactions given our limited sample of 150 women, bootstrapped Bayesian networks were constructed based on novel constraint-based learning methods that appeared recently in the statistical learning community. Statistical significance of edge strengths was evaluated and the less reliable edges were pruned to increase the network robustness. To allow accessible interpretation and integrate biological knowledge into the final network, several undirected edges were afterwards directed with physiological expertise according to relevant literature. Results Extensive experiments on synthetic data sampled from a known Bayesian network show that the algorithm, called Recursive Hybrid Parents and Children (RHPC, outperforms state-of-the-art algorithms that appeared in the recent literature. Regarding biological plausibility, we found that the inference results obtained with the proposed method were in excellent agreement with biological knowledge. For example, these analyses indicated that visceral adipose tissue accumulation is strongly related to blood lipid alterations independent of overall obesity level. Conclusions Bayesian Networks are a useful tool for investigating and summarizing evidence when complex relationships exist among predictors, in particular, as in the case of multifactorial conditions like visceral obesity, when there is a concurrent incidence for several variables, interacting in a complex manner. The source code and the data sets used for the empirical tests

  12. METABOLISM

    Institute of Scientific and Technical Information of China (English)

    1999-01-01

    Objective: To determine the allele frequencies of genetic variants 373 Ala→Pro and 451 Arg→Gln of cholesteryl ester transfer protein (CETP) and to explore their potential impacts on serum lipid metabolism. Methods: The genotypes in CETP codon 373 and 451 in 91 German healthy students and 409 an-

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

    DEFF Research Database (Denmark)

    Montagud, Arnau; Zelezniak, Aleksej; Navarro, Emilio

    2011-01-01

    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...... 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......-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...

  14. Metabolic flux analysis of hydrogen production network by Clostridium butyricum W5: Effect of pH and glucose concentrations

    Energy Technology Data Exchange (ETDEWEB)

    Cai, Guiqin [School of Earth and Environmental Sciences, The University of Adelaide, Adelaide, SA 5005 (Australia); Jin, Bo [School of Earth and Environmental Sciences, The University of Adelaide, Adelaide, SA 5005 (Australia); School of Chemical Engineering, The University of Adelaide, Adelaide, SA 5005 (Australia); Australian Water Quality Centre, SA Water Corporation, 250 Victoria Square, Adelaide, SA 5100 (Australia); Saint, Chris [School of Earth and Environmental Sciences, The University of Adelaide, Adelaide, SA 5005 (Australia); Australian Water Quality Centre, SA Water Corporation, 250 Victoria Square, Adelaide, SA 5100 (Australia); Monis, Paul [Australian Water Quality Centre, SA Water Corporation, 250 Victoria Square, Adelaide, SA 5100 (Australia)

    2010-07-15

    Fermentative hydrogen production by strict anaerobes has been widely reported. There is a lack of information related to metabolic flux distribution and its variation with respect to fermentation conditions in the metabolic production system. This study aimed to get a better understanding of the metabolic network and to conduct metabolic flux analysis (MFA) of fermentative hydrogen production by a recently isolated Clostridium butyricum strain W5. We chose the specific growth rate as the objective function and used specific H{sub 2} production rate as the criterion to evaluate the experimental results with the in silico MFA. For the first time, we constructed an in silico metabolic flux model for the anaerobic glucose metabolism of C. butyricum W5 with assistance of a modeling program MetaFluxNet. The model was used to evaluate metabolic flux distribution in the fermentative hydrogen production network, and to study the fractional flux response to variations in initial glucose concentration and operational pH. The MFA results suggested that pH has a more significant effect on hydrogen production yield compared to the glucose concentration. The MFA is a useful tool to provide valuable information for optimization and design of the fermentative hydrogen production process. (author)

  15. Perspectives for a better understanding of the metabolic integration of photorespiration within a complex plant primary metabolism network

    Science.gov (United States)

    Photorespiration is an important high flux metabolic pathway that is found in all oxygen-producing photosynthetic organisms. It is often viewed as a closed loop that recycles carbon to fuel the Calvin cycle. However, the photorespiratory cycle is known to interact with several primary metabolic path...

  16. Integer programming-based method for designing synthetic metabolic networks by Minimum Reaction Insertion in a Boolean model.

    Science.gov (United States)

    Lu, Wei; Tamura, Takeyuki; Song, Jiangning; Akutsu, Tatsuya

    2014-01-01

    In this paper, we consider the Minimum Reaction Insertion (MRI) problem for finding the minimum number of additional reactions from a reference metabolic network to a host metabolic network so that a target compound becomes producible in the revised host metabolic network in a Boolean model. Although a similar problem for larger networks is solvable in a flux balance analysis (FBA)-based model, the solution of the FBA-based model tends to include more reactions than that of the Boolean model. However, solving MRI using the Boolean model is computationally more expensive than using the FBA-based model since the Boolean model needs more integer variables. Therefore, in this study, to solve MRI for larger networks in the Boolean model, we have developed an efficient Integer Programming formalization method in which the number of integer variables is reduced by the notion of feedback vertex set and minimal valid assignment. As a result of computer experiments conducted using the data of metabolic networks of E. coli and reference networks downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we have found that the developed method can appropriately solve MRI in the Boolean model and is applicable to large scale-networks for which an exhaustive search does not work. We have also compared the developed method with the existing connectivity-based methods and FBA-based methods, and show the difference between the solutions of our method and the existing methods. A theoretical analysis of MRI is also conducted, and the NP-completeness of MRI is proved in the Boolean model. Our developed software is available at "http://sunflower.kuicr.kyoto-u.ac.jp/~rogi/minRect/minRect.html."

  17. Integer programming-based method for designing synthetic metabolic networks by Minimum Reaction Insertion in a Boolean model.

    Directory of Open Access Journals (Sweden)

    Wei Lu

    Full Text Available In this paper, we consider the Minimum Reaction Insertion (MRI problem for finding the minimum number of additional reactions from a reference metabolic network to a host metabolic network so that a target compound becomes producible in the revised host metabolic network in a Boolean model. Although a similar problem for larger networks is solvable in a flux balance analysis (FBA-based model, the solution of the FBA-based model tends to include more reactions than that of the Boolean model. However, solving MRI using the Boolean model is computationally more expensive than using the FBA-based model since the Boolean model needs more integer variables. Therefore, in this study, to solve MRI for larger networks in the Boolean model, we have developed an efficient Integer Programming formalization method in which the number of integer variables is reduced by the notion of feedback vertex set and minimal valid assignment. As a result of computer experiments conducted using the data of metabolic networks of E. coli and reference networks downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG database, we have found that the developed method can appropriately solve MRI in the Boolean model and is applicable to large scale-networks for which an exhaustive search does not work. We have also compared the developed method with the existing connectivity-based methods and FBA-based methods, and show the difference between the solutions of our method and the existing methods. A theoretical analysis of MRI is also conducted, and the NP-completeness of MRI is proved in the Boolean model. Our developed software is available at "http://sunflower.kuicr.kyoto-u.ac.jp/~rogi/minRect/minRect.html."

  18. Integer Programming-Based Method for Designing Synthetic Metabolic Networks by Minimum Reaction Insertion in a Boolean Model

    Science.gov (United States)

    Song, Jiangning; Akutsu, Tatsuya

    2014-01-01

    In this paper, we consider the Minimum Reaction Insertion (MRI) problem for finding the minimum number of additional reactions from a reference metabolic network to a host metabolic network so that a target compound becomes producible in the revised host metabolic network in a Boolean model. Although a similar problem for larger networks is solvable in a flux balance analysis (FBA)-based model, the solution of the FBA-based model tends to include more reactions than that of the Boolean model. However, solving MRI using the Boolean model is computationally more expensive than using the FBA-based model since the Boolean model needs more integer variables. Therefore, in this study, to solve MRI for larger networks in the Boolean model, we have developed an efficient Integer Programming formalization method in which the number of integer variables is reduced by the notion of feedback vertex set and minimal valid assignment. As a result of computer experiments conducted using the data of metabolic networks of E. coli and reference networks downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we have found that the developed method can appropriately solve MRI in the Boolean model and is applicable to large scale-networks for which an exhaustive search does not work. We have also compared the developed method with the existing connectivity-based methods and FBA-based methods, and show the difference between the solutions of our method and the existing methods. A theoretical analysis of MRI is also conducted, and the NP-completeness of MRI is proved in the Boolean model. Our developed software is available at “http://sunflower.kuicr.kyoto-u.ac.jp/~rogi/minRect/minRect.html.” PMID:24651476

  19. Rhinal hypometabolism on FDG PET in healthy APO-E4 carriers: impact on memory function and metabolic networks

    Energy Technology Data Exchange (ETDEWEB)

    Didic, Mira; Felician, Olivier; Gour, Natalina; Ceccaldi, Mathieu [Pole de Neurosciences Cliniques, Centre Hospitalo-Universitaire de la Timone, AP-HM, Service de Neurologie and Neuropsychologie, Marseille (France); Aix Marseille Universite, Inserm, INS UMRS 1106, Marseille (France); Bernard, Rafaelle; Pecheux, Christophe [Centre Hospitalo-Universitaire de la Timone, AP-HM, et INSERM UMRS 910: ' ' Genetique Medicale et Genomique fonctionnelle' ' , Departement de Genetique Medicale, Marseille (France); Mundler, Olivier; Guedj, Eric [Centre Hospitalo-Universitaire de la Timone, AP-HM, Service Central de Biophysique et Medecine Nucleaire, Marseille (France); Aix Marseille Universite, CERIMED, CNRS UMR7289, INT, Marseille (France); Aix Marseille Universite, CNRS UMR7289, INT, Marseille (France)

    2015-09-15

    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

  20. Metabolism of DMSP, DMS and DMSO by the cultivable bacterial community associated with the DMSP-producing dinoflagellate Scrippsiella trochoidea

    Digital Repository Service at National Institute of Oceanography (India)

    Hatton, A.D.; Shenoy, D.M.; Hart, M.C.; Mogg, A.; Green, D.H.

    of carbon, reduced sulphur and energy for bacterioplankton (Kiene and Linn 2000a). The precursor of DMS, dimethylsulfoniopropionate (DMSP), is produced by marine micro- and macro-algae. Studies examining the distribution of chlorophyll a as a measure... by Green et al. (2004). To gain a fuller understanding of the total bacterial diversity of this culture, a culture independent approach was also used. Total bacterial 16S ribosomal RNA genes (16S rRNA) were amplified by PCR and cloned into pGEM-T Easy...

  1. Material balance studies on animal cell metabolism using a stoichiometrically based reaction network.

    Science.gov (United States)

    Xie, L; Wang, D I

    1996-12-05

    A detailed reaction network of mammalian cell metabolism contains hundreds of enzymatic reactions. By grouping serial reactions into single overall reactions and separating overlapped pathways into independent reactions, the total number of reactions of the network is significantly reduced. This strategy of manipulating the reaction network avoids the manipulations of a large number of reactions otherwise needed to determine the reaction extents. A stoichiometric material balance model is developed based on the stoichiometry of the simplified reaction network. Closures of material balances on glucose and each of the 20 amino acids are achieved using experimental data from three controlled fed-batch and one-batch hybridoma cultures. Results show that the critical role of essential amino acids, except glutamine, is to provide precursors for protein synthesis. The catabolism of some of the essential amino acids, particularly isoleucine and leucine, is observed when an excess amount of these amino acids is available in the culture medium. It was found that the reduction of glutamine utilization (for reducing ammonia production) is accompanied by an increase in the uptake of nonessential amino acids (NAAs) from the culture medium. This suggests that NAAs are necessary even though they are not essential for cell growth. A glutamine balance shows that less than 20% of the glutamine nitrogen is utilized for essential roles, such as protein and nucleotide syntheses. A relatively constant percentage (about 45%) of the glutamine nitrogen is utilized for NAA biosynthesis, despite the fact that the absolute amount varies among the four experiments. As to the carbon skeleton of glutamine, a significant portion enters the tricarboxylic acid (TCA) cycle. A material balance on glucose shows that most of the glucose (81%) is converted into lactate when glucose is in excess. On the other hand, when glucose is limited, lactate production is considerably reduced, while a major portion

  2. Extension of the IsaViz software for the representation of metabolic and regulatory networks

    Directory of Open Access Journals (Sweden)

    Diogo Fernando Veiga

    2005-06-01

    Full Text Available In this work we developed an extension of IsaViz software, a RDF (Resource Description Framework authoring tool, designed to be a graphical environment to build models of metabolic and regulatory networks. This environment, called Metabolic IsaViz, was linked to a genomic library of types and was modeled on the basis of ontologies. Biochemical pathways included data at sequence level (e.g., the amino acid sequence of enzymes, besides kinetic and thermodynamic parameters for the reactions. Models created with Metabolic IsaViz could be exported to pathways simulators through SBML (Systems Biology Markup Language, which allowed to analyze the pathway dynamics of target chemicals.A determinação de vias metabólicas e regulatórias de microrganismos é essencial para estudos pós-sequenciamento de DNA, com aplicações diretas em várias áreas da biotecnologia, em especial em engenharia metabólica. Neste trabalho desenvolvemos uma extensão do software IsaViz, editor de grafos RDF (Resource Description Framework, com a finalidade de criar um ambiente gráfico para a construção de modelos de vias metabólicas e regulatórias. Este ambiente, o Metabolic IsaViz, foi integrado a uma biblioteca de tipos genômicos, modelada com base em ontologias, sendo que as vias bioquímicas podem incluir dados ao nível de seqüência (como a seqüência de aminoácidos das enzimas, além de parâmetros cinéticos e termodinâmicos. Os modelos criados com o Metabolic IsaViz podem ser exportados para simuladores de vias metabólicas através da linguagem SBML (Systems Biology Markup Language para análise da formação de metabólitos de interesse.

  3. Reverse and Multiple Stable Isotope Probing to Study Bacterial Metabolism and Interactions at the Single Cell Level.

    Science.gov (United States)

    Wang, Yun; Song, Yizhi; Tao, Yifan; Muhamadali, Howbeer; Goodacre, Royston; Zhou, Ning-Yi; Preston, Gail M; Xu, Jian; Huang, Wei E

    2016-10-04

    The interactions between microorganisms driven by substrate metabolism and energy flow are important to shape diversity, abundance, and structure of a microbial community. Single cell technologies are useful tools for dissecting the functions of individual members and their interactions in microbial communities. Here, we developed a novel Raman stable isotope probing (Raman-SIP), which uses Raman microspectroscopy coupled with reverse and D2O colabeling to study metabolic interactions in a two-species community consisting of Acinetobacter baylyi ADP1 and Escherichia coli DH5α-GFP. This Raman-SIP approach is able to detect carbon assimilation and general metabolic activity simultaneously. Taking advantage of Raman shift of single cell Raman spectra (SCRS) mediated by incorporation of stable-isotopic substrates, Raman-SIP with reverse labeling has been applied to detect initially (13)C-labeled bands of ADP1 SCRS reverting back to (12)C positions in the presence of (12)C citrate. Raman-SIP with D2O labeling has been employed to probe metabolic activity of single cells without the need of cell replication. Our results show that E. coli alone in minimal medium with citrate as the sole carbon source had no metabolic activity, but became metabolically active in the presence of ADP1. Mass spectrometry-based metabolite footprint analysis suggests that putrescine and phenylalanine excreted by ADP1 cells may support the metabolic activity of E. coli. This study demonstrates that Raman-SIP with reverse labeling would be a useful tool to probe metabolism of any carbon substrate, overcoming limitations when stable isotopic substrates are not readily available. It is also found that Raman-SIP with D2O labeling is a sensitive and reliable approach to distinguish metabolically active cells but not quiescent cells. This novel approach extends the application of Raman-SIP and demonstrates its potential application as a valuable strategic approach for probing cellular metabolism

  4. The fractal architecture of cytoplasmic organization: scaling, kinetics and emergence in metabolic networks.

    Science.gov (United States)

    Aon, Miguel Antonio; O'Rourke, Brian; Cortassa, Sonia

    2004-01-01

    In this work, we highlight the links between fractals and scaling in cells and explore the kinetic consequences for biochemical reactions operating in fractal media. Based on the proposal that the cytoskeletal architecture is organized as a percolation lattice, with clusters emerging as fractal forms, the analysis of kinetics in percolation clusters is especially emphasized. A key consequence of this spatiotemporal cytoplasmic organization is that enzyme reactions following Michaelis-Menten or allosteric type kinetics exhibit higher rates in fractal media (for short times and at lower substrate concentrations) at the percolation threshold than in Euclidean media. As a result, considerably faster and higher amplification of enzymatic activity is obtained. Finally, we describe some of the properties bestowed by cytoskeletal organization and dynamics on metabolic networks.

  5. Salmonella proteomics under oxidative stress reveals coordinated regulation of antioxidant defense with iron metabolism and bacterial virulence.

    Science.gov (United States)

    Fu, Jiaqi; Qi, Linlu; Hu, Mo; Liu, Yanhua; Yu, Kaiwen; Liu, Qian; Liu, Xiaoyun

    2017-02-11

    Salmonella Typhimurium is a bacterial pathogen that can cause widespread gastroenteritis. Salmonella encounters reactive oxygen species both under free-living conditions and within their mammalian host during infection. To study its response to oxidative stress, we performed the first large-scale proteomic profiling of Salmonella upon exposure to H2O2. Among 1600 detected proteins, 83 proteins showed significantly altered abundance. Interestingly, only a subset of known antioxidants was induced, likely due to distinct regulatory mechanisms. In addition, we found elevation of several Salmonella acquired phage products with potential contribution to DNA repair under oxidative stress. Furthermore, we observed robust induction of iron-uptake systems and disruption of these pathways led to bacterial survival defects under H2O2 challenge. Importantly, this work is the first to report that oxidative stress severely repressed the Salmonella type III secretion system (T3SS), reducing its virulence. Biological significance Salmonella, a Gram-negative bacterial pathogen, encounters reactive oxygen species (ROS) both endogenously and exogenously. To better understand its response to oxidative stress, we performed the first large-scale profiling of Salmonella protein expression upon H2O2 treatment. Among 1600 quantified proteins, the abundance of 116 proteins was altered significantly. Notably, iron acquisition systems were induced to promote bacterial survival under oxidative stress. Furthermore, we are the first to report that oxidative stress severely repressed Salmonella type III secretion system and hence reduced its virulence. We believe that these findings will not only help us better understand the molecular mechanisms that Salmonella has evolved to counteract ROS but also the global impact of oxidative stress on bacterial physiology.

  6. Automated Image Analysis for the Detection of Benthic Crustaceans and Bacterial Mat Coverage Using the VENUS Undersea Cabled Network

    Directory of Open Access Journals (Sweden)

    Jacopo Aguzzi

    2011-11-01

    Full Text Available The development and deployment of sensors for undersea cabled observatories is presently biased toward the measurement of habitat variables, while sensor technologies for biological community characterization through species identification and individual counting are less common. The VENUS cabled multisensory network (Vancouver Island, Canada deploys seafloor camera systems at several sites. Our objective in this study was to implement new automated image analysis protocols for the recognition and counting of benthic decapods (i.e., the galatheid squat lobster, Munida quadrispina, as well as for the evaluation of changes in bacterial mat coverage (i.e., Beggiatoa spp., using a camera deployed in Saanich Inlet (103 m depth. For the counting of Munida we remotely acquired 100 digital photos at hourly intervals from 2 to 6 December 2009. In the case of bacterial mat coverage estimation, images were taken from 2 to 8 December 2009 at the same time frequency. The automated image analysis protocols for both study cases were created in MatLab 7.1. Automation for Munida counting incorporated the combination of both filtering and background correction (Median- and Top-Hat Filters with Euclidean Distances (ED on Red-Green-Blue (RGB channels. The Scale-Invariant Feature Transform (SIFT features and Fourier Descriptors (FD of tracked objects were then extracted. Animal classifications were carried out with the tools of morphometric multivariate statistic (i.e., Partial Least Square Discriminant Analysis; PLSDA on Mean RGB (RGBv value for each object and Fourier Descriptors (RGBv+FD matrices plus SIFT and ED. The SIFT approach returned the better results. Higher percentages of images were correctly classified and lower misclassification errors (an animal is present but not detected occurred. In contrast, RGBv+FD and ED resulted in a high incidence of records being generated for non-present animals. Bacterial mat coverage was estimated in terms of Percent

  7. Automated image analysis for the detection of benthic crustaceans and bacterial mat coverage using the VENUS undersea cabled network.

    Science.gov (United States)

    Aguzzi, Jacopo; Costa, Corrado; Robert, Katleen; Matabos, Marjolaine; Antonucci, Francesca; Juniper, S Kim; Menesatti, Paolo

    2011-01-01

    The development and deployment of sensors for undersea cabled observatories is presently biased toward the measurement of habitat variables, while sensor technologies for biological community characterization through species identification and individual counting are less common. The VENUS cabled multisensory network (Vancouver Island, Canada) deploys seafloor camera systems at several sites. Our objective in this study was to implement new automated image analysis protocols for the recognition and counting of benthic decapods (i.e., the galatheid squat lobster, Munida quadrispina), as well as for the evaluation of changes in bacterial mat coverage (i.e., Beggiatoa spp.), using a camera deployed in Saanich Inlet (103 m depth). For the counting of Munida we remotely acquired 100 digital photos at hourly intervals from 2 to 6 December 2009. In the case of bacterial mat coverage estimation, images were taken from 2 to 8 December 2009 at the same time frequency. The automated image analysis protocols for both study cases were created in MatLab 7.1. Automation for Munida counting incorporated the combination of both filtering and background correction (Median- and Top-Hat Filters) with Euclidean Distances (ED) on Red-Green-Blue (RGB) channels. The Scale-Invariant Feature Transform (SIFT) features and Fourier Descriptors (FD) of tracked objects were then extracted. Animal classifications were carried out with the tools of morphometric multivariate statistic (i.e., Partial Least Square Discriminant Analysis; PLSDA) on Mean RGB (RGBv) value for each object and Fourier Descriptors (RGBv+FD) matrices plus SIFT and ED. The SIFT approach returned the better results. Higher percentages of images were correctly classified and lower misclassification errors (an animal is present but not detected) occurred. In contrast, RGBv+FD and ED resulted in a high incidence of records being generated for non-present animals. Bacterial mat coverage was estimated in terms of Percent Coverage

  8. Coexpression network analysis in abdominal and gluteal adipose tissue reveals regulatory genetic loci for metabolic syndrome and related phenotypes

    DEFF Research Database (Denmark)

    Min, Josine L; Nicholson, George; Halgrimsdottir, Ingileif

    2012-01-01

    Metabolic Syndrome (MetS) is highly prevalent and has considerable public health impact, but its underlying genetic factors remain elusive. To identify gene networks involved in MetS, we conducted whole-genome expression and genotype profiling on abdominal (ABD) and gluteal (GLU) adipose tissue, ...

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

    Science.gov (United States)

    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...

  10. Bringing metabolic networks to life: convenience rate law and thermodynamic constraints

    Directory of Open Access Journals (Sweden)

    Klipp Edda

    2006-12-01

    Full Text Available Abstract Background Translating a known metabolic network into a dynamic model requires rate laws for all chemical reactions. The mathematical expressions depend on the underlying enzymatic mechanism; they can become quite involved and may contain a large number of parameters. Rate laws and enzyme parameters are still unknown for most enzymes. Results We introduce a simple and general rate law called "convenience kinetics". It can be derived from a simple random-order enzyme mechanism. Thermodynamic laws can impose dependencies on the kinetic parameters. Hence, to facilitate model fitting and parameter optimisation for large networks, we introduce thermodynamically independent system parameters: their values can be varied independently, without violating thermodynamical constraints. We achieve this by expressing the equilibrium constants either by Gibbs free energies of formation or by a set of independent equilibrium constants. The remaining system parameters are mean turnover rates, generalised Michaelis-Menten constants, and constants for inhibition and activation. All parameters correspond to molecular energies, for instance, binding energies between reactants and enzyme. Conclusion Convenience kinetics can be used to translate a biochemical network – manually or automatically - into a dynamical model with plausible biological properties. It implements enzyme saturation and regulation by activators and inhibitors, covers all possible reaction stoichiometries, and can be specified by a small number of parameters. Its mathematical form makes it especially suitable for parameter estimation and optimisation. Parameter estimates can be easily computed from a least-squares fit to Michaelis-Menten values, turnover rates, equilibrium constants, and other quantities that are routinely measured in enzyme assays and stored in kinetic databases.

  11. The Nuclear Receptor LXR Limits Bacterial Infection of Host Macrophages through a Mechanism that Impacts Cellular NAD Metabolism

    Directory of Open Access Journals (Sweden)

    Jonathan Matalonga

    2017-01-01

    Full Text Available Macrophages exert potent effector functions against invading microorganisms but constitute, paradoxically, a preferential niche for many bacterial strains to replicate. Using a model of infection by Salmonella Typhimurium, we have identified a molecular mechanism regulated by the nuclear receptor LXR that limits infection of host macrophages through transcriptional activation of the multifunctional enzyme CD38. LXR agonists reduced the intracellular levels of NAD+ in a CD38-dependent manner, counteracting pathogen-induced changes in macrophage morphology and the distribution of the F-actin cytoskeleton and reducing the capability of non-opsonized Salmonella to infect macrophages. Remarkably, pharmacological treatment with an LXR agonist ameliorated clinical signs associated with Salmonella infection in vivo, and these effects were dependent on CD38 expression in bone-marrow-derived cells. Altogether, this work reveals an unappreciated role for CD38 in bacterial-host cell interaction that can be pharmacologically exploited by activation of the LXR pathway.

  12. Linkage of organic anion transporter-1 to metabolic pathways through integrated "omics"-driven network and functional analysis.

    Science.gov (United States)

    Ahn, Sun-Young; Jamshidi, Neema; Mo, Monica L; Wu, Wei; Eraly, Satish A; Dnyanmote, Ankur; Bush, Kevin T; Gallegos, Tom F; Sweet, Douglas H; Palsson, Bernhard Ø; Nigam, Sanjay K

    2011-09-09

    The main kidney transporter of many commonly prescribed drugs (e.g. penicillins, diuretics, antivirals, methotrexate, and non-steroidal anti-inflammatory drugs) is organic anion transporter-1 (OAT1), originally identified as NKT (Lopez-Nieto, C. E., You, G., Bush, K. T., Barros, E. J., Beier, D. R., and Nigam, S. K. (1997) J. Biol. Chem. 272, 6471-6478). Targeted metabolomics in knockouts have shown that OAT1 mediates the secretion or reabsorption of many important metabolites, including intermediates in carbohydrate, fatty acid, and amino acid metabolism. This observation raises the possibility that OAT1 helps regulate broader metabolic activities. We therefore examined the potential roles of OAT1 in metabolic pathways using Recon 1, a functionally tested genome-scale reconstruction of human metabolism. A computational approach was used to analyze in vivo metabolomic as well as transcriptomic data from wild-type and OAT1 knock-out animals, resulting in the implication of several metabolic pathways, including the citric acid cycle, polyamine, and fatty acid metabolism. Validation by in vitro and ex vivo analysis using Xenopus oocyte, cell culture, and kidney tissue assays demonstrated interactions between OAT1 and key intermediates in these metabolic pathways, including previously unknown substrates, such as polyamines (e.g. spermine and spermidine). A genome-scale metabolic network reconstruction generated some experimentally supported predictions for metabolic pathways linked to OAT1-related transport. The data support the possibility that the SLC22 and other families of transporters, known to be expressed in many tissues and primarily known for drug and toxin clearance, are integral to a number of endogenous pathways and may be involved in a larger remote sensing and signaling system (Ahn, S. Y., and Nigam, S. K. (2009) Mol. Pharmacol. 76, 481-490, and Wu, W., Dnyanmote, A. V., and Nigam, S. K. (2011) Mol. Pharmacol. 79, 795-805). Drugs may alter metabolism by

  13. Systems-level modeling of mycobacterial metabolism for the identification of new (multi-)drug targets

    NARCIS (Netherlands)

    Rienksma, R.A.; Suarez Diez, M.; Spina, L.; Schaap, P.J.; Martins dos Santos, V.A.P.

    2014-01-01

    Systems-level metabolic network reconstructions and the derived constraint-based (CB) mathematical models are efficient tools to explore bacterial metabolism. Approximately one-fourth of the Mycobacterium tuberculosis (Mtb) genome contains genes that encode proteins directly involved in its metaboli

  14. SS-mPMG and SS-GA: tools for finding pathways and dynamic simulation of metabolic networks.

    Science.gov (United States)

    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.

  15. Complex metabolic network of 1,3-propanediol transport mechanisms and its system identification via biological robustness.

    Science.gov (United States)

    Guo, Yanjie; Feng, Enmin; Wang, Lei; Xiu, Zhilong

    2014-04-01

    The bioconversion of glycerol to 1,3-propanediol (1,3-PD) by Klebsiella pneumoniae (K. pneumoniae) can be characterized by an intricate metabolic network of interactions among biochemical fluxes, metabolic compounds, key enzymes and genetic regulation. Since there are some uncertain factors in the fermentation, especially the transport mechanisms of 1,3-PD across cell membrane, the metabolic network contains multiple possible metabolic systems. Considering the genetic regulation of dha regulon and inhibition of 3-hydroxypropionaldehyde to the growth of cells, we establish a 14-dimensional nonlinear hybrid dynamical system aiming to determine the most possible metabolic system and the corresponding optimal parameter. The existence, uniqueness and continuity of solutions are discussed. Taking the robustness index of the intracellular substances together as a performance index, a system identification model is proposed, in which 1,395 continuous variables and 90 discrete variables are involved. The identification problem is decomposed into two subproblems and a parallel particle swarm optimization procedure is constructed to solve them. Numerical results show that it is most possible that 1,3-PD passes the cell membrane by active transport coupled with passive diffusion.

  16. Using co-metabolism to accelerate synthetic starch wastewater degradation and nutrient recovery in photosynthetic bacterial wastewater treatment technology.

    Science.gov (United States)

    Lu, Haifeng; Zhang, Guangming; Lu, Yufeng; Zhang, Yuanhui; Li, Baoming; Cao, Wei

    2016-01-01

    Starch wastewater is a type of nutrient-rich wastewater that contains numerous macromolecular polysaccharides. Using photosynthetic bacteria (PSB) to treat starch wastewater can reduce pollutants and enhance useful biomass production. However, PSB cannot directly degrade macromolecular polysaccharides, which weakens the starch degradation effect. Therefore, co-metabolism with primary substances was employed in PSB wastewater treatment to promote starch degradation. The results indicated that co-metabolism is a highly effective method in synthetic starch degradation by PSB. When malic acid was used as the optimal primary substrate, the chemical oxygen demand, total sugar, macromolecules removal and biomass yield were considerably higher than when primary substances were not used, respectively. Malic acid was the primary substrate that played a highly important role in starch degradation. It promoted the alpha-amylase activity to 46.8 U and the PSB activity, which induced the degradation of macromolecules. The products in the wastewater were ethanol, acetic acid and propionic acid. Ethanol was the primary product throughout the degradation process. The introduction of co-metabolism with malic acid to treat wastewater can accelerate macromolecules degradation and bioresource production and weaken the acidification effect. This method provides another pathway for bioresource recovery from wastewater. This approach is a sustainable and environmentally friendly wastewater treatment technology.

  17. In vitro fermentation of B-GOS: impact on faecal bacterial populations and metabolic activity in autistic and non-autistic children.

    Science.gov (United States)

    Grimaldi, Roberta; Cela, Drinalda; Swann, Jonathan R; Vulevic, Jelena; Gibson, Glenn R; Tzortzis, George; Costabile, Adele

    2017-02-01

    Children with autism spectrum disorders (ASD) often suffer gastrointestinal problems consistent with imbalances in the gut microbial population. Treatment with antibiotics or pro/prebiotics has been postulated to regulate microbiota and improve gut symptoms, but there is a lack of evidence for such approaches, especially for prebiotics. This study assessed the influence of a prebiotic galactooligosaccharide (B-GOS) on gut microbial ecology and metabolic function using faecal samples from autistic and non-autistic children in an in vitro gut model system. Bacteriology was analysed using flow cytometry combined with fluorescence in situ hybridization and metabolic activity by HPLC and (1)H-NMR. Consistent with previous studies, the microbiota of children with ASD contained a higher number of Clostridium spp. and a lower number of bifidobacteria compared with non-autistic children. B-GOS administration significantly increased bifidobacterial populations in each compartment of the models, both with autistic and non-autistic-derived samples, and lactobacilli in the final vessel of non-autistic models. In addition, changes in other bacterial population have been seen in particular for Clostridium, Rosburia, Bacteroides, Atopobium, Faecalibacterium prausnitzii, Sutterella spp. and Veillonellaceae. Furthermore, the addition of B-GOS to the models significantly altered short-chain fatty acid production in both groups, and increased ethanol and lactate in autistic children.

  18. Strain Dependent Genetic Networks for Antibiotic-Sensitivity in a Bacterial Pathogen with a Large Pan-Genome.

    Science.gov (United States)

    van Opijnen, Tim; Dedrick, Sandra; Bento, José

    2016-09-01

    The interaction between an antibiotic and bacterium is not merely restricted to the drug and its direct target, rather antibiotic induced stress seems to resonate through the bacterium, creating selective pressures that drive the emergence of adaptive mutations not only in the direct target, but in genes involved in many different fundamental processes as well. Surprisingly, it has been shown that adaptive mutations do not necessarily have the same effect in all species, indicating that the genetic background influences how phenotypes are manifested. However, to what extent the genetic background affects the manner in which a bacterium experiences antibiotic stress, and how this stress is processed is unclear. Here we employ the genome-wide tool Tn-Seq to construct daptomycin-sensitivity profiles for two strains of the bacterial pathogen Streptococcus pneumoniae. Remarkably, over half of the genes that are important for dealing with antibiotic-induced stress in one strain are dispensable in another. By confirming over 100 genotype-phenotype relationships, probing potassium-loss, employing genetic interaction mapping as well as temporal gene-expression experiments we reveal genome-wide conditionally important/essential genes, we discover roles for genes with unknown function, and uncover parts of the antibiotic's mode-of-action. Moreover, by mapping the underlying genomic network for two query genes we encounter little conservation in network connectivity between strains as well as profound differences in regulatory relationships. Our approach uniquely enables genome-wide fitness comparisons across strains, facilitating the discovery that antibiotic responses are complex events that can vary widely between strains, which suggests that in some cases the emergence of resistance could be strain specific and at least for species with a large pan-genome less predictable.

  19. Reconstruction of the yeast protein-protein interaction network involved in nutrient sensing and global metabolic regulation

    DEFF Research Database (Denmark)

    Nandy, Subir Kumar; Jouhten, Paula; Nielsen, Jens

    2010-01-01

    proteins. Despite the value of BioGRID for studying protein-protein interactions, there is a need for manual curation of these interactions in order to remove false positives. RESULTS: Here we describe an annotated reconstruction of the protein-protein interactions around four key nutrient......-sensing and metabolic regulatory signal transduction pathways (STP) operating in Saccharomyces cerevisiae. The reconstructed STP network includes a full protein-protein interaction network including the key nodes Snf1, Tor1, Hog1 and Pka1. The network includes a total of 623 structural open reading frames (ORFs......) and for all the interactions between them (edges). The annotated information is readily available utilizing the functionalities of network modelling tools such as Cytoscape and CellDesigner. CONCLUSIONS: The reported fully annotated interaction model serves as a platform for integrated systems biology studies...

  20. Different biochemical mechanisms ensure network-wide balancing of reducing equivalents in microbial metabolism.

    Science.gov (United States)

    Fuhrer, Tobias; Sauer, Uwe

    2009-04-01

    To sustain growth, the catabolic formation of the redox equivalent NADPH must be balanced with the anabolic demand. The mechanisms that ensure such network-wide balancing, however, are presently not understood. Based on 13C-detected intracellular fluxes, metabolite concentrations, and cofactor specificities for all relevant central metabolic enzymes, we have quantified catabolic NADPH production in Agrobacterium tumefaciens, Bacillus subtilis, Escherichia coli, Paracoccus versutus, Pseudomonas fluorescens, Rhodobacter sphaeroides, Sinorhizobium meliloti, and Zymomonas mobilis. For six species, the estimated NADPH production from glucose catabolism exceeded the requirements for biomass synthesis. Exceptions were P. fluorescens, with balanced rates, and E. coli, with insufficient catabolic production, in which about one-third of the NADPH is supplied via the membrane-bound transhydrogenase PntAB. P. versutus and B. subtilis were the only species that appear to rely on transhydrogenases for balancing NADPH overproduction during growth on glucose. In the other four species, the main but not exclusive redox-balancing mechanism appears to be the dual cofactor specificities of several catabolic enzymes and/or the existence of isoenzymes with distinct cofactor specificities, in particular glucose 6-phosphate dehydrogenase. An unexpected key finding for all species, except E. coli and B. subtilis, was the lack of cofactor specificity in the oxidative pentose phosphate pathway, which contrasts with the textbook view of the pentose phosphate pathway dehydrogenases as being NADP+ dependent.

  1. Dissecting and engineering metabolic and regulatory networks of thermophilic bacteria for biofuel production.

    Science.gov (United States)

    Lin, Lu; Xu, Jian

    2013-11-01

    Interest in thermophilic bacteria as live-cell catalysts in biofuel and biochemical industry has surged in recent years, due to their tolerance of high temperature and wide spectrum of carbon-sources that include cellulose. However their direct employment as microbial cellular factories in the highly demanding industrial conditions has been hindered by uncompetitive biofuel productivity, relatively low tolerance to solvent and osmic stresses, and limitation in genome engineering tools. In this work we review recent advances in dissecting and engineering the metabolic and regulatory networks of thermophilic bacteria for improving the traits of key interest in biofuel industry: cellulose degradation, pentose-hexose co-utilization, and tolerance of thermal, osmotic, and solvent stresses. Moreover, new technologies enabling more efficient genetic engineering of thermophiles were discussed, such as improved electroporation, ultrasound-mediated DNA delivery, as well as thermo-stable plasmids and functional selection systems. Expanded applications of such technological advancements in thermophilic microbes promise to substantiate a synthetic biology perspective, where functional parts, module, chassis, cells and consortia were modularly designed and rationally assembled for the many missions at industry and nature that demand the extraordinary talents of these extremophiles.

  2. Ecosystem biogeochemistry considered as a distributed metabolic network ordered by maximum entropy production.

    Science.gov (United States)

    Vallino, Joseph J

    2010-05-12

    We examine the application of the maximum entropy production principle for describing ecosystem biogeochemistry. Since ecosystems can be functionally stable despite changes in species composition, we use a distributed metabolic network for describing biogeochemistry, which synthesizes generic biological structures that catalyse reaction pathways, but is otherwise organism independent. Allocation of biological structure and regulation of biogeochemical reactions is determined via solution of an optimal control problem in which entropy production is maximized. However, because synthesis of biological structures cannot occur if entropy production is maximized instantaneously, we propose that information stored within the metagenome allows biological systems to maximize entropy production when averaged over time. This differs from abiotic systems that maximize entropy production at a point in space-time, which we refer to as the steepest descent pathway. It is the spatio-temporal averaging that allows biological systems to outperform abiotic processes in entropy production, at least in many situations. A simulation of a methanotrophic system is used to demonstrate the approach. We conclude with a brief discussion on the implications of viewing ecosystems as self-organizing molecular machines that function to maximize entropy production at the ecosystem level of organization.

  3. An arginine-aspartate network in the active site of bacterial TruB is critical for catalyzing pseudouridine formation.

    Science.gov (United States)

    Friedt, Jenna; Leavens, Fern M V; Mercier, Evan; Wieden, Hans-Joachim; Kothe, Ute

    2014-04-01

    Pseudouridine synthases introduce the most common RNA modification and likely use the same catalytic mechanism. Besides a catalytic aspartate residue, the contributions of other residues for catalysis of pseudouridine formation are poorly understood. Here, we have tested the role of a conserved basic residue in the active site for catalysis using the bacterial pseudouridine synthase TruB targeting U55 in tRNAs. Substitution of arginine 181 with lysine results in a 2500-fold reduction of TruB's catalytic rate without affecting tRNA binding. Furthermore, we analyzed the function of a second-shell aspartate residue (D90) that is conserved in all TruB enzymes and interacts with C56 of tRNA. Site-directed mutagenesis, biochemical and kinetic studies reveal that this residue is not critical for substrate binding but influences catalysis significantly as replacement of D90 with glutamate or asparagine reduces the catalytic rate 30- and 50-fold, respectively. In agreement with molecular dynamics simulations of TruB wild type and TruB D90N, we propose an electrostatic network composed of the catalytic aspartate (D48), R181 and D90 that is important for catalysis by fine-tuning the D48-R181 interaction. Conserved, negatively charged residues similar to D90 are found in a number of pseudouridine synthases, suggesting that this might be a general mechanism.

  4. LmSmdB: an integrated database for metabolic and gene regulatory network in Leishmania major and Schistosoma mansoni.

    Science.gov (United States)

    Patel, Priyanka; Mandlik, Vineetha; Singh, Shailza

    2016-03-01

    A database that integrates all the information required for biological processing is essential to be stored in one platform. We have attempted to create one such integrated database that can be a one stop shop for the essential features required to fetch valuable result. LmSmdB (L. major and S. mansoni database) is an integrated database that accounts for the biological networks and regulatory pathways computationally determined by integrating the knowledge of the genome sequences of the mentioned organisms. It is the first database of its kind that has together with the network designing showed the simulation pattern of the product. This database intends to create a comprehensive canopy for the regulation of lipid metabolism reaction in the parasite by integrating the transcription factors, regulatory genes and the protein products controlled by the transcription factors and hence operating the metabolism at genetic level.

  5. Metabolic network analysis of an adipoyl-7-ADCA-producing strain of Penicillium chrysogenum: Elucidation of adipate degradation

    DEFF Research Database (Denmark)

    Thykær, Jette; Christensen, Bjarke; Nielsen, Jens

    2002-01-01

    An adipoyl-7-ADCA-producing, recombinant strain of Penicillium chrysogenum was characterized by metabolic network analysis, with special focus on the degradation of adipate and determination of the metabolic fluxes. Degradation of the side-chain precursor, adipate, causes an undesired consumption...... and the formed acetyl-CoA was metabolized in the glyoxylate shunt. This hypothesis was further substantiated by an enzyme assay, which showed activity of the key enzyme in the glyoxylate shunt. Flux estimations in two chemostat cultures, one with and one without adipate in the feed, revealed that degradation...... of adipate in the production of 7-ADCA. Using C-13-labeled glucose and measurement of metabolite labeling patterns, it was shown that adipate was degraded by beta-oxidation to succinyl-CoA and acetyl-CoA. The labeling analysis indicated that degradation of adipate was taking place in the microbodies...

  6. Toward metabolic engineering in the context of system biology and synthetic biology: advances and prospects.

    Science.gov (United States)

    Liu, Yanfeng; Shin, Hyun-dong; Li, Jianghua; Liu, Long

    2015-02-01

    Metabolic engineering facilitates the rational development of recombinant bacterial strains for metabolite overproduction. Building on enormous advances in system biology and synthetic biology, novel strategies have been established for multivariate optimization of metabolic networks in ensemble, spatial, and dynamic manners such as modular pathway engineering, compartmentalization metabolic engineering, and metabolic engineering guided by genome-scale metabolic models, in vitro reconstitution, and systems and synthetic biology. Herein, we summarize recent advances in novel metabolic engineering strategies. Combined with advancing kinetic models and synthetic biology tools, more efficient new strategies for improving cellular properties can be established and applied for industrially important biochemical production.

  7. Increased interictal cerebral glucose metabolism in a cortical-subcortical network in drug naive patients with cryptogenic temporal lobe epilepsy.

    Science.gov (United States)

    Franceschi, M; Lucignani, G; Del Sole, A; Grana, C; Bressi, S; Minicucci, F; Messa, C; Canevini, M P; Fazio, F

    1995-01-01

    Positron emission tomography with [18F]-2-fluoro-2-deoxy-D-glucose ([18F]FDG) has been used to assess the pattern of cerebral metabolism in different types of epilepsies. However, PET with [18F]FDG has never been used to evaluate drug naive patients with cryptogenic temporal lobe epilepsy, in whom the mechanism of origin and diffusion of the epileptic discharge may differ from that underlying other epilepsies. In a group of patients with cryptogenic temporal lobe epilepsy, never treated with antiepileptic drugs, evidence has been found of significant interictal glucose hypermetabolism in a bilateral neural network including the temporal lobes, thalami, basal ganglia, and cingular cortices. The metabolism in these areas and frontal lateral cortex enables the correct classification of all patients with temporal lobe epilepsy and controls by discriminant function analysis. Other cortical areas--namely, frontal basal and lateral, temporal mesial, and cerebellar cortices--had bilateral increases of glucose metabolism ranging from 10 to 15% of normal controls, although lacking stringent statistical significance. This metabolic pattern could represent a pathophysiological state of hyperactivity predisposing to epileptic discharge generation or diffusion, or else a network of inhibitory circuits activated to prevent the diffusion of the epileptic discharge. PMID:7561924

  8. Circadian rhythms, metabolism, and insulin sensitivity: transcriptional networks in animal models.

    Science.gov (United States)

    Kitazawa, Masashi

    2013-04-01

    Homeostatic systems have adapted to respond to the diurnal light/dark cycle. Numerous physiological pathways, including metabolism, are coordinated by this 24-h cycle. Animals with mutations in clock genes show abnormal glucose and lipid metabolism, indicating a critical relationship between the circadian clock and metabolism. Energy homeostasis is achieved through circadian regulation of the expression and activity of several key metabolic enzymes. Temporal organization of tissue metabolism is coordinated by reciprocal cross-talk between the core clock mechanism and key metabolic enzymes and transcriptional activators. The aim of this review is to define the role of the circadian clock in the regulation of insulin sensitivity by describing the interconnection between the circadian clock and metabolic pathways.

  9. A Bacterial Glucanotransferase Can Replace the Complex Maltose Metabolism Required for Starch to Sucrose Conversion in Leaves at Night

    DEFF Research Database (Denmark)

    Ruzanski, Christian; Smirnova, Julia; Rejzek, Martin

    2013-01-01

    Controlled conversion of leaf starch to sucrose at night is essential for the normal growth of Arabidopsis. The conversion involves the cytosolic metabolism of maltose to hexose phosphates via an unusual, multidomain protein with 4-glucanotransferase activity, DPE2, believed to transfer glucosyl...... moieties to a complex heteroglycan prior to their conversion to hexose phosphate via a cytosolic phosphorylase. The significance of this complex pathway is unclear; conversion of maltose to hexose phosphate in bacteria proceeds via a more typical 4-glucanotransferase that does not require a heteroglycan...... approaches suggested that it can potentially generate a glucosyl buffer between maltose and hexose phosphate because, unlike DPE2, it can generate polydisperse malto-oligosaccharides from maltose. Consistent with this suggestion, MalQ is capable of restoring an essentially wild-type phenotype when expressed...

  10. Augmentation of bacterial homeostasis by regulating in situ buffer capacity: Significance of total dissolved salts over acidogenic metabolism.

    Science.gov (United States)

    Venkata Mohan, S; Srikanth, S; Nikhil, G N

    2017-02-01

    During anaerobic fermentation, consequent accumulation of acidic fermented products leads to the failure of pH homeostasis. The present study aimed to comprehend the changes in buffering capacity with addition of sodium salts of hydroxide, bicarbonate and phosphate. The results showed notable augmentation in buffer capacity and cumulative hydrogen production (CHP) compared to control. The influential factor is the amount of undissociated volatile fatty acids released that affected the cell metabolism and consequently biohydrogen generation. It is inferred that among the tested salts, sodium bicarbonate has substantial buffering capacity (β, 0.035± mol) ensuing maximum CHP (468± mL). Besides, bioelectrochemical analysis revealed variations in redox currents that aligned with biohydrogen production. The study provides valuable information on the role of inorganic dissolved salts that would be required to regulate H2 generation and acidogenesis in the aspects of acid-gas phase system.

  11. [Effect of plasmid pKM101 on the expression of bacterial genes not related to DNa metabolism].

    Science.gov (United States)

    Skavronskaya, A G; Tiganova, I G; Andreeva, I V; Rusina, O Iu

    1999-02-01

    An experimental system ensuring fusion of bacterial genes to the lac operon of the Mu dl(Aplac) phage was used. Fusion operons in which the lac operon was under the control of promoters of the elt gene, responsible for synthesis of the LT toxin, of the tetracyclin-resistance tet gene, and sfiA gene encoding filament production, was studied. Using this experimental system, plasmid pKM101 was shown to be capable of activating the expression of the above Escherichia coli and Salmonella typhimurium genes, which is manifested as the activation of beta-galactosidase synthesis. The activation of the elt gene expression by the pKM101 plasmid was also confirmed in experiments on detecting the LT toxin synthesized by bacteria carrying this plasmid. Effect of the plasmid on the activation of elt operon expression, unlike the effect of this plasmid on mutability, does not depend on the functioning of the lexA and recA genes, i.e., this is not a SOS-regulated process. The mutant plasmid pGW12, a derivative of pKM101, deficient in the mucAB genes responsible for mutagenesis, causes a more pronounced activation of the elt gene than plasmid pKM101.

  12. The metabolism of neonicotinoid insecticide thiamethoxam by soil enrichment cultures, and the bacterial diversity and plant growth-promoting properties of the cultured isolates.

    Science.gov (United States)

    Zhou, Guang-Can; Wang, Ying; Ma, Yuan; Zhai, Shan; Zhou, Ling-Yan; Dai, Yi-Jun; Yuan, Sheng

    2014-01-01

    A soil enrichment culture (SEC) rapidly degraded 96% of 200 mg L(-1) neonicotinoid insecticide thiamethoxam (TMX) in MSM broth within 30 d; therefore, its metabolic pathway of TMX, bacterial diversity and plant growth-promoting rhizobacteria (PGPR) activities of the cultured isolates were studied. The SEC transformed TMX via the nitro reduction pathway to form nitrso, urea metabolites and via cleavage of the oxadiazine cycle to form a new metabolite, hydroxyl CLO-tri. In addition, 16S rRNA gene-denaturing gradient gel electrophoresis analysis revealed that uncultured rhizobacteria are predominant in the SEC broth and that 77.8% of the identified bacteria belonged to uncultured bacteria. A total of 31 cultured bacterial strains including six genera (Achromobacter, Agromyces, Ensifer, Mesorhizobium, Microbacterium and Pseudoxanthomonas) were isolated from the SEC broth. The 12 strains of Ensifer adhaerens have the ability to degrade TMX. All six selected bacteria showed PGPR activities. E. adhaerens TMX-23 and Agromyces mediolanus TMX-25 produced indole-3-acetic acid, whereas E. adhaerens TMX-23 and Mesorhizobium alhagi TMX-36 are N2-fixing bacteria. The six-isolated microbes were tolerant to 200 mg L(-1) TMX, and the growth of E. adhaerens was significantly enhanced by TMX, whereas that of Achromobacter sp. TMX-5 and Microbacterium sp.TMX-6 were enhanced slightly. The present study will help to explain the fate of TMX in the environment and its microbial degradation mechanism, as well as to facilitate future investigations of the mechanism through which TMX enhances plant vigor.

  13. Intestinal concentrations of free and encapsulated dietary medium-chain fatty acids and effects on gastric microbial ecology and bacterial metabolic products in the digestive tract of piglets.

    Science.gov (United States)

    Zentek, Jürgen; Buchheit-Renko, Susanne; Männer, Klaus; Pieper, Robert; Vahjen, Wilfried

    2012-02-01

    The influence of low dietary levels of free and encapsulated medium-chain fatty acids on their concentrations in the digesta, the gastric microbial ecology and bacterial metabolic products in the gastrointestinal tract (GIT) in weaned piglets was studied. Starting after weaning, 36 piglets were fed a diet without (Control) or with medium-chain fatty acids uncoated (MCFA) or coated with vegetable fat and lecithin (MCFAc). After 4 weeks, the animals were killed, and digesta from the stomach and different sections of the GIT were collected. The concentrations of caprylic (p < 0.001) and capric (p = 0.001) acids were higher in the stomachs of piglets fed diets MCFA and MCFAc compared to the Control group. The concentrations dropped rapidly along the GIT, regardless of encapsulation, and tended to be higher in groups MCFA and MCFAc compared to the Control. Compared to the Control group, ingestion of diet MCFAc led to an increase in the number of eubacteria (p = 0.001), enterobacteriaceae (p < 0.001), clostridial clusters I (p = 0.001) and IV (p = 0.019), Lactobacillus johnsonii (p < 0.001) and Lactobacillus amylovorus (p = 0.001) in gastric contents. A similar trend was seen with diet MCFA. Relative concentrations of short-chain fatty acids were characterised by lower propionic acid levels (p = 0.045), numerically (p < 0.1) higher acetic, lower n-butyric and i-valeric acid concentrations in the small intestine. Lactic acid concentrations were not significantly changed in the GIT, but ammonia concentrations increased (p < 0.001) in the distal small intestine in the MCFA and MCFAc groups. In conclusion, medium-chain fatty acids affected microbial ecology parameters in the gastric contents and bacterial metabolites in the small intestine. At low dietary levels, medium-chain fatty acids may be regarded as modulators of the gastric microbiota in weaned piglets.

  14. Transient exposure to low levels of insecticide affects metabolic networks of honeybee larvae.

    Science.gov (United States)

    Derecka, Kamila; Blythe, Martin J; Malla, Sunir; Genereux, Diane P; Guffanti, Alessandro; Pavan, Paolo; Moles, Anna; Snart, Charles; Ryder, Thomas; Ortori, Catharine A; Barrett, David A; Schuster, Eugene; Stöger, Reinhard

    2013-01-01

    The survival of a species depends on its capacity to adjust to changing environmental conditions, and new stressors. Such new, anthropogenic stressors include the neonicotinoid class of crop-protecting agents, which have been implicated in the population declines of pollinating insects, including honeybees (Apis mellifera). The low-dose effects of these compounds on larval development and physiological responses have remained largely unknown. Over a period of 15 days, we provided syrup tainted with low levels (2 µg/L(-1)) of the neonicotinoid insecticide imidacloprid to beehives located in the field. We measured transcript levels by RNA sequencing and established lipid profiles using liquid chromatography coupled with mass spectrometry from worker-bee larvae of imidacloprid-exposed (IE) and unexposed, control (C) hives. Within a catalogue of 300 differentially expressed transcripts in larvae from IE hives, we detect significant enrichment of genes functioning in lipid-carbohydrate-mitochondrial metabolic networks. Myc-involved transcriptional response to exposure of this neonicotinoid is indicated by overrepresentation of E-box elements in the promoter regions of genes with altered expression. RNA levels for a cluster of genes encoding detoxifying P450 enzymes are elevated, with coordinated downregulation of genes in glycolytic and sugar-metabolising pathways. Expression of the environmentally responsive Hsp90 gene is also reduced, suggesting diminished buffering and stability of the developmental program. The multifaceted, physiological response described here may be of importance to our general understanding of pollinator health. Muscles, for instance, work at high glycolytic rates and flight performance could be impacted should low levels of this evolutionarily novel stressor likewise induce downregulation of energy metabolising genes in adult pollinators.

  15. Untangling the role of one-carbon metabolism in colorectal cancer risk: a comprehensive Bayesian network analysis

    Science.gov (United States)

    Myte, Robin; Gylling, Björn; Häggström, Jenny; Schneede, Jörn; Magne Ueland, Per; Hallmans, Göran; Johansson, Ingegerd; Palmqvist, Richard; Van Guelpen, Bethany

    2017-01-01

    The role of one-carbon metabolism (1CM), particularly folate, in colorectal cancer (CRC) development has been extensively studied, but with inconclusive results. Given the complexity of 1CM, the conventional approach, investigating components individually, may be insufficient. We used a machine learning-based Bayesian network approach to study, simultaneously, 14 circulating one-carbon metabolites, 17 related single nucleotide polymorphisms (SNPs), and several environmental factors in relation to CRC risk in 613 cases and 1190 controls from the prospective Northern Sweden Health and Disease Study. The estimated networks corresponded largely to known biochemical relationships. Plasma concentrations of folate (direct), vitamin B6 (pyridoxal 5-phosphate) (inverse), and vitamin B2 (riboflavin) (inverse) had the strongest independent associations with CRC risk. Our study demonstrates the importance of incorporating B-vitamins in future studies of 1CM and CRC development, and the usefulness of Bayesian network learning for investigating complex biological systems in relation to disease. PMID:28233834

  16. Metabolic engineering of potato carotenoid content through tuber-specific overexpression of a bacterial mini-pathway.

    Directory of Open Access Journals (Sweden)

    Gianfranco Diretto

    Full Text Available BACKGROUND: Since the creation of "Golden Rice", biofortification of plant-derived foods is a promising strategy for the alleviation of nutritional deficiencies. Potato is the most important staple food for mankind after the cereals rice, wheat and maize, and is extremely poor in provitamin A carotenoids. METHODOLOGY: We transformed potato with a mini-pathway of bacterial origin, driving the synthesis of beta-carotene (Provitamin A from geranylgeranyl diphosphate. Three genes, encoding phytoene synthase (CrtB, phytoene desaturase (CrtI and lycopene beta-cyclase (CrtY from Erwinia, under tuber-specific or constitutive promoter control, were used. 86 independent transgenic lines, containing six different promoter/gene combinations, were produced and analyzed. Extensive regulatory effects on the expression of endogenous genes for carotenoid biosynthesis are observed in transgenic lines. Constitutive expression of the CrtY and/or CrtI genes interferes with the establishment of transgenosis and with the accumulation of leaf carotenoids. Expression of all three genes, under tuber-specific promoter control, results in tubers with a deep yellow ("golden" phenotype without any adverse leaf phenotypes. In these tubers, carotenoids increase approx. 20-fold, to 114 mcg/g dry weight and beta-carotene 3600-fold, to 47 mcg/g dry weight. CONCLUSIONS: This is the highest carotenoid and beta-carotene content reported for biofortified potato as well as for any of the four major staple foods (the next best event being "Golden Rice 2", with 31 mcg/g dry weight beta-carotene. Assuming a beta-carotene to retinol conversion of 6ratio1, this is sufficient to provide 50% of the Recommended Daily Allowance of Vitamin A with 250 gms (fresh weight of "golden" potatoes.

  17. The origin of modern metabolic networks inferred from phylogenomic analysis of protein architecture.

    Science.gov (United States)

    Caetano-Anollés, Gustavo; Kim, Hee Shin; Mittenthal, Jay E

    2007-05-29

    Metabolism represents a complex collection of enzymatic reactions and transport processes that convert metabolites into molecules capable of supporting cellular life. Here we explore the origins and evolution of modern metabolism. Using phylogenomic information linked to the structure of metabolic enzymes, we sort out recruitment processes and discover that most enzymatic activities were associated with the nine most ancient and widely distributed protein fold architectures. An analysis of newly discovered functions showed enzymatic diversification occurred early, during the onset of the modern protein world. Most importantly, phylogenetic reconstruction exercises and other evidence suggest strongly that metabolism originated in enzymes with the P-loop hydrolase fold in nucleotide metabolism, probably in pathways linked to the purine metabolic subnetwork. Consequently, the first enzymatic takeover of an ancient biochemistry or prebiotic chemistry was related to the synthesis of nucleotides for the RNA world.

  18. Loss of variation of state detected in soybean metabolic and human myelomonocytic leukaemia cell transcriptional networks under external stimuli.

    Science.gov (United States)

    Sakata, Katsumi; Saito, Toshiyuki; Ohyanagi, Hajime; Okumura, Jun; Ishige, Kentaro; Suzuki, Harukazu; Nakamura, Takuji; Komatsu, Setsuko

    2016-10-24

    Soybean (Glycine max) is sensitive to flooding stress, and flood damage at the seedling stage is a barrier to growth. We constructed two mathematical models of the soybean metabolic network, a control model and a flooded model, from metabolic profiles in soybean plants. We simulated the metabolic profiles with perturbations before and after the flooding stimulus using the two models. We measured the variation of state that the system could maintain from a state-space description of the simulated profiles. The results showed a loss of variation of state during the flooding response in the soybean plants. Loss of variation of state was also observed in a human myelomonocytic leukaemia cell transcriptional network in response to a phorbol-ester stimulus. Thus, we detected a loss of variation of state under external stimuli in two biological systems, regardless of the regulation and stimulus types. Our results suggest that a loss of robustness may occur concurrently with the loss of variation of state in biological systems. We describe the possible applications of the quantity of variation of state in plant genetic engineering and cell biology. Finally, we present a hypothetical "external stimulus-induced information loss" model of biological systems.

  19. Loss of variation of state detected in soybean metabolic and human myelomonocytic leukaemia cell transcriptional networks under external stimuli

    KAUST Repository

    Sakata, Katsumi

    2016-10-24

    Soybean (Glycine max) is sensitive to flooding stress, and flood damage at the seedling stage is a barrier to growth. We constructed two mathematical models of the soybean metabolic network, a control model and a flooded model, from metabolic profiles in soybean plants. We simulated the metabolic profiles with perturbations before and after the flooding stimulus using the two models. We measured the variation of state that the system could maintain from a state–space description of the simulated profiles. The results showed a loss of variation of state during the flooding response in the soybean plants. Loss of variation of state was also observed in a human myelomonocytic leukaemia cell transcriptional network in response to a phorbol-ester stimulus. Thus, we detected a loss of variation of state under external stimuli in two biological systems, regardless of the regulation and stimulus types. Our results suggest that a loss of robustness may occur concurrently with the loss of variation of state in biological systems. We describe the possible applications of the quantity of variation of state in plant genetic engineering and cell biology. Finally, we present a hypothetical “external stimulus-induced information loss” model of biological systems.

  20. Shotgun proteome analysis of Bordetella pertussis reveals a distinct influence of iron availability on the bacterial metabolism, virulence, and defense response.

    Science.gov (United States)

    Alvarez Hayes, Jimena; Lamberti, Yanina; Surmann, Kristin; Schmidt, Frank; Völker, Uwe; Rodriguez, Maria Eugenia

    2015-07-01

    One of the mechanisms involved in host immunity is the limitation of iron accessibility to pathogens, which in turn provokes the corresponding physiological adaptation of pathogens. This study reports a gel-free nanoLC-MS/MS-based comparative proteome analysis of Bordetella pertussis grown under iron-excess and iron-depleted conditions. Out of the 926 proteins covered 98 displayed a shift in their abundance in response to low iron availability. Forty-seven of them were found to be increased in level while 58 were found with decreased protein levels under iron starvation. In addition to proteins previously reported to be influenced by iron in B. pertussis, we observed changes in metabolic proteins involved in fatty acid utilization and poly-hydroxybutyrate production. Additionally, many bacterial virulence factors regulated by the BvgAS two-component system were found at decreased levels in response to iron limitation. These results, together with the increased production of proteins potentially involved in oxidative stress resistance, seem to indicate that iron starvation provokes changes in B. pertussis phenotype that might shape host-pathogen interaction.

  1. Prediction of Microbial Growth Rate versus Biomass Yield by a Metabolic Network with Kinetic Parameters

    NARCIS (Netherlands)

    Adadi, Roi; Volkmer, Benjamin; Milo, Ron; Heinemann, Matthias; Shlomi, Tomer

    2012-01-01

    Identifying the factors that determine microbial growth rate under various environmental and genetic conditions is a major challenge of systems biology. While current genome-scale metabolic modeling approaches enable us to successfully predict a variety of metabolic phenotypes, including maximal bio

  2. Investigating genotype-phenotype relationships in Saccharomyces cerevisiae metabolic network through stoichiometric modeling

    DEFF Research Database (Denmark)

    Brochado, Ana Rita

    . This chapter aims at providing the reader with relevant state-of-the-art information concerning Systems Biology, Genome-Scale Metabolic Modeling and Metabolic Engineering. Particular attention is given to the yeast Saccharomyces cerevisiae, the eukaryotic model organism used thought the thesis....

  3. Designer labels for plant metabolism: statistical design of isotope labeling experiments for improved quantification of flux in complex plant metabolic networks.

    Science.gov (United States)

    Nargund, Shilpa; Sriram, Ganesh

    2013-01-27

    Metabolic fluxes are powerful indicators of cell physiology and can be estimated by isotope-assisted metabolic flux analysis (MFA). The complexity of the compartmented metabolic networks of plants has constrained the application of isotope-assisted MFA to them, principally because of poor identifiability of fluxes from the measured isotope labeling patterns. However, flux identifiability can be significantly improved by a priori design of isotope labeling experiments (ILEs). This computational design involves evaluating the effect of different isotope label and isotopomer measurement combinations on flux identifiability, and thereby identifying optimal labels and measurements toward evaluating the fluxes of interest with the highest confidence. This article reports ILE designs for two major, compartmented plant metabolic pathways - the pentose phosphate pathway (PPP) and γ-aminobutyric acid (GABA) shunt. Together, these pathways represent common motifs in plant metabolism including duplication of pathways in different subcellular compartments, reversible reactions and cyclic carbon flow. To compare various ILE designs, we employed statistical A- and D-optimality criteria. Our computations showed that 1,2-(13)C Glc is a powerful and robust label for the plant PPPs, given currently popular isotopomer measurement techniques (single quadrupole mass spectrometry [MS] and 2-D nuclear magnetic resonance [NMR]). Further analysis revealed that this label can estimate several PPP fluxes better than the popular label 1-(13)C Glc. Furthermore, the concurrent measurement of the isotopomers of hexose and pentose moieties synthesized exclusively in the cytosol or the plastid compartments (measurable through intracellular glucose or sucrose, starch, RNA ribose and histidine) considerably improves the identifiability of PPP fluxes in the individual compartments. Additionally, MS-derived isotopomer measurements outperform NMR-derived measurements in identifying PPP fluxes. The

  4. Using isotopic tracers to assess the impact of tillage and straw management on the microbial metabolic network in soil

    Science.gov (United States)

    Van Groenigen, K.; Forristal, D.; Jones, M. B.; Schwartz, E.; Hungate, B. A.; Dijkstra, P.

    2013-12-01

    By decomposing soil organic matter, microbes gain energy and building blocks for biosynthesis and release CO2 to the atmosphere. Therefore, insight into the effect of management practices on microbial metabolic pathways and C use efficiency (CUE; microbial C produced per substrate C utilized) may help to predict long term changes in soil C stocks. We studied the effects of reduced (RT) and conventional tillage (CT) on the microbial central C metabolic network, using soil samples from a 12-year-old field experiment in an Irish winter wheat cropping system. Each year after harvest, straw was removed from half of the RT and CT plots or incorporated into the soil in the other half, resulting in four treatment combinations. We added 1-13C and 2,3-13C pyruvate and 1-13C and U-13C glucose as metabolic tracer isotopomers to composite soil samples taken at two depths (0-15 cm and 15-30 cm) from each treatment and used the rate of position-specific respired 13CO2 to parameterize a metabolic model. Model outcomes were then used to calculate CUE of the microbial community. We found that the composite samples differed in CUE, but the changes were small, with values ranging between 0.757-0.783 across treatments and soil depth. Increases in CUE were associated with a decrease in tricarboxylic acid cycle and reductive pentose phosphate pathway activity and increased consumption of metabolic intermediates for biosynthesis. Our results indicate that RT and straw incorporation promote soil C storage without substantially changing CUE or any of the microbial metabolic pathways. This suggests that at our site, RT and straw incorporation promote soil C storage mostly through direct effects such as increased soil C input and physical protection from decomposition, rather than by feedback responses of the microbial community.

  5. [Bacterial vaginosis].

    Science.gov (United States)

    Romero Herrero, Daniel; Andreu Domingo, Antonia

    2016-07-01

    Bacterial vaginosis (BV) is the main cause of vaginal dysbacteriosis in the women during the reproductive age. It is an entity in which many studies have focused for years and which is still open for discussion topics. This is due to the diversity of microorganisms that cause it and therefore, its difficult treatment. Bacterial vaginosis is probably the result of vaginal colonization by complex bacterial communities, many of them non-cultivable and with interdependent metabolism where anaerobic populations most likely play an important role in its pathogenesis. The main symptoms are an increase of vaginal discharge and the unpleasant smell of it. It can lead to serious consequences for women, such as an increased risk of contracting sexually transmitted infections including human immunodeficiency virus and upper genital tract and pregnancy complications. Gram stain is the gold standard for microbiological diagnosis of BV, but can also be diagnosed using the Amsel clinical criteria. It should not be considered a sexually transmitted disease but it is highly related to sex. Recurrence is the main problem of medical treatment. Apart from BV, there are other dysbacteriosis less characterized like aerobic vaginitis of which further studies are coming slowly but are achieving more attention and consensus among specialists.

  6. Toward development of an autonomous network of bacteria-based delivery systems (BacteriaBots): spatiotemporally high-throughput characterization of bacterial quorum-sensing response.

    Science.gov (United States)

    Sahari, Ali; Traore, Mahama A; Stevens, Ann M; Scharf, Birgit E; Behkam, Bahareh

    2014-12-02

    Characterization of bacterial innate and engineered cooperative behavior, regulated through chemical signaling in a process known as quorum sensing, is critical to development of a myriad of bacteria-enabled systems including biohybrid drug delivery systems and biohybrid mobile sensor networks. Here, we demonstrate, for the first time, that microfluidic diffusive mixers can be used for spatiotemporally high-throughput characterization of bacterial quorum-sensing response. Using this batch characterization method, the quorum-sensing response in Escherichia coli MG1655, transformed with a truncated lux operon from Vibrio fischeri, in the presence of 1-100 nM exogenous acyl-homoserine lactone molecules has been quantified. This method provides a rapid and facile tool for high-throughput characterization of the quorum-sensing response of genetically modified bacteria in the presence of a wide concentration range of signaling molecules with a precision of ±0.5 nM. Furthermore, the quorum-sensing response of BacteriaBots has been characterized to determine if the results obtained from a large bacterial population can serve as a robust predictive tool for the small bacterial population attached to each BacteriaBot.

  7. Ecological network analysis of an urban water metabolic system: model development, and a case study for Beijing.

    Science.gov (United States)

    Zhang, Yan; Yang, Zhifeng; Fath, Brian D

    2010-09-15

    Using ecological network analysis, we analyzed the network structure and ecological relationships in an urban water metabolic system. We developed an ecological network model for the system, and used Beijing as an example of analysis based on the model. We used network throughflow analysis to determine the flows among components, and measured both indirect and direct flows. Using a network utility matrix, we determined the relationships and degrees of mutualism among six compartments--1) local environment, 2) rainwater collection, 3) industry, 4) agriculture, 5) domestic sector, and 6) wastewater recycling--which represent producer, consumer, and reducer trophic levels. The capacity of producers to provide water for Beijing decreased from 2003 to 2007, and consumer demand for water decreased due to decreasing industrial and agricultural demand; the recycling capacity of reducers also improved, decreasing the discharge pressure on the environment. The ecological relationships associated with the local environment or the wastewater recycling sector changed little from 2003 to 2007. From 2003 to 2005, the main changes in the ecological relationships among components of Beijing's water metabolic system mostly occurred between the local environment, the industrial and agricultural sectors, and the domestic sector, but by 2006 and 2007, the major change was between the local environment, the agricultural sector, and the industrial sector. The other ecological relationships did not change during the study period. Although Beijing's mutualism indices remained generally stable, the ecological relationships among compartments changed greatly. Our analysis revealed ways to further optimize this system and the relationships among compartments, thereby optimizing future urban water resources development.

  8. Low contribution of internal metabolism to carbon dioxide emissions along lotic and lentic environments of a Mediterranean fluvial network

    Science.gov (United States)

    Gómez-Gener, Lluís.; Schiller, Daniel; Marcé, Rafael; Arroita, Maite; Casas-Ruiz, Joan Pere; Staehr, Peter Anton; Acuña, Vicenç; Sabater, Sergi; Obrador, Biel

    2016-12-01

    Inland waters are significant sources of carbon dioxide (CO2) to the atmosphere. CO2 supersaturation and subsequent CO2 emissions from inland waters can be driven by internal metabolism, external inputs of dissolved inorganic carbon (DIC) derived from the catchment, and other processes (e.g., internal geochemical reactions of calcite precipitation or photochemical mineralization of organic solutes). However, the sensitivity of the magnitude and sources of CO2 emissions to fluvial network hydromorphological alterations is still poorly understood. Here we investigated both the magnitude and sources of CO2 emissions from lotic (i.e., running waters) and lentic (i.e., stagnant waters associated to small dams) waterbodies of a Mediterranean fluvial network by computing segment-scale mass balances of CO2. Our results showed that sources other than internal metabolism sustained most (82%) of the CO2 emissions from the studied fluvial network. The magnitude and sources of CO2 emissions in lotic waterbodies were highly dependent on hydrology, with higher emissions dominated by DIC inputs derived from the catchment during high flows and lower emissions partially fueled by CO2 produced biologically within the river during low flows. In contrast, CO2 emissions in lentic waterbodies were low, relatively stable over the time and the space, and dominated by DIC inputs from the catchment regardless of the different hydrological situations. Overall, our results stress the sensitivity of fluvial networks to human activities and climate change and particularly highlight the role of hydromorphological conditions on modulating the magnitude and sources of CO2 emissions from fluvial networks.

  9. Integrated in silico Analyses of Regulatory and Metabolic Networks of Synechococcus sp. PCC 7002 Reveal Relationships between Gene Centrality and Essentiality

    Directory of Open Access Journals (Sweden)

    Hyun-Seob Song

    2015-03-01

    Full Text Available 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 termed 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.

  10. Candidate states of Helicobacter pylori's genome-scale metabolic network upon application of "loop law" thermodynamic constraints.

    Science.gov (United States)

    Price, Nathan D; Thiele, Ines; Palsson, Bernhard Ø

    2006-06-01

    Constraint-based modeling has proven to be a useful tool in the analysis of biochemical networks. To date, most studies in this field have focused on the use of linear constraints, resulting from mass balance and capacity constraints, which lead to the definition of convex solution spaces. One additional constraint arising out of thermodynamics is known as the "loop law" for reaction fluxes, which states that the net flux around a closed biochemical loop must be zero because no net thermodynamic driving force exists. The imposition of the loop-law can lead to nonconvex solution spaces making the analysis of the consequences of its imposition challenging. A four-step approach is developed here to apply the loop-law to study metabolic network properties: 1), determine linear equality constraints that are necessary (but not necessarily sufficient) for thermodynamic feasibility; 2), tighten V(max) and V(min) constraints to enclose the remaining nonconvex space; 3), uniformly sample the convex space that encloses the nonconvex space using standard Monte Carlo techniques; and 4), eliminate from the resulting set all solutions that violate the loop-law, leaving a subset of steady-state solutions. This subset of solutions represents a uniform random sample of the space that is defined by the additional imposition of the loop-law. This approach is used to evaluate the effect of imposing the loop-law on predicted candidate states of the genome-scale metabolic network of Helicobacter pylori.

  11. Comparison of Metabolic Network between Muscle and Intramuscular Adipose Tissues in Hanwoo Beef Cattle Using a Systems Biology Approach.

    Science.gov (United States)

    Lee, Hyun-Jeong; Park, Hye-Sun; Kim, Woonsu; Yoon, Duhak; Seo, Seongwon

    2014-01-01

    The interrelationship between muscle and adipose tissues plays a major role in determining the quality of carcass traits. The objective of this study was to compare metabolic differences between muscle and intramuscular adipose (IMA) tissues in the longissimus dorsi (LD) of Hanwoo (Bos taurus coreanae) using the RNA-seq technology and a systems biology approach. The LD sections between the 6th and 7th ribs were removed from nine (each of three cows, steers, and bulls) Hanwoo beef cattle (carcass weight of 430.2 ± 40.66 kg) immediately after slaughter. The total mRNA from muscle, IMA, and subcutaneous adipose and omental adipose tissues were isolated and sequenced. The reads that passed quality control were mapped onto the bovine reference genome (build bosTau6), and differentially expressed genes across tissues were identified. The KEGG pathway enrichment tests revealed the opposite direction of metabolic regulation between muscle and IMA. Metabolic gene network analysis clearly indicated that oxidative metabolism was upregulated in muscle and downregulated in IMA. Interestingly, pathways for regulating cell adhesion, structure, and integrity and chemokine signaling pathway were upregulated in IMA and downregulated in muscle. It is thus inferred that IMA may play an important role in the regulation of development and structure of the LD tissues and muscle/adipose communication.

  12. Identification of altered metabolic pathways of γ-irradiated rice mutant via network-based transcriptome analysis.

    Science.gov (United States)

    Hwang, Sun-Goo; Kim, Dong Sub; Hwang, Jung Eun; Park, Hyeon Mi; Jang, Cheol Seong

    2015-12-01

    In order to develop rice mutants for crop improvement, we applied γ-irradiation mutagenesis and selected a rice seed color mutant (MT) in the M14 targeting-induced local lesions in genome lines. This mutant exhibited differences in germination rate, plant height, and root length in seedlings compared to the wild-type plants. We found 1645 different expressed probes of MT by microarray hybridization. To identify the modified metabolic pathways, we conducted integrated genomic analysis such as weighted correlation network analysis with a module detection method of differentially expressed genes (DEGs) in MT on the basis of large-scale microarray transcriptional profiling. These modules are largely divided into three subnetworks and mainly exhibit overrepresented gene ontology functions such as oxidation-related function, ion-binding, and kinase activity (phosphorylation), and the expressional coherences of module genes mainly exhibited in vegetative and maturation stages. Through a metabolic pathway analysis, we detected the significant DEGs involved in the major carbohydrate metabolism (starch degradation), protein degradation (aspartate protease), and signaling in sugars and nutrients. Furthermore, the accumulation of amino acids (asparagine and glutamic acid), sucrose, and starch in MT were affected by gamma rays. Our results provide an effective approach for identification of metabolic pathways associated with useful agronomic traits in mutation breeding.

  13. Comparative analysis of false discovery rate methods in constructing metabolic association networks.

    Science.gov (United States)

    Koo, Imhoi; Yao, Sen; Zhang, Xiang; Kim, Seongho

    2014-08-01

    Gaussian graphical model (GGM)-based method, a key approach to reverse engineering biological networks, uses partial correlation to measure conditional dependence between two variables by controlling the contribution from other variables. After estimating partial correlation coefficients, one of the most critical processes in network construction is to control the false discovery rate (FDR) to assess the significant associations among variables. Various FDR methods have been proposed mainly for biomarker discovery, but it still remains unclear which FDR method performs better for network construction. Furthermore, there is no study to see the effect of the network structure on network construction. We selected the six FDR methods, the linear step-up procedure (BH95), the adaptive linear step-up procedure (BH00), Efron's local FDR (LFDR), Benjamini-Yekutieli's step-up procedure (BY01), Storey's q-value procedure (Storey01), and Storey-Taylor-Siegmund's adaptive step-up procedure (STS04), to evaluate their performances on network construction. We further considered two network structures, random and scale-free networks, to investigate their influence on network construction. Both simulated data and real experimental data suggest that STS04 provides the highest true positive rate (TPR) or F1 score, while BY01 has the highest positive predictive value (PPV) in network construction. In addition, no significant effect of the network structure is found on FDR methods.

  14. Challenges and Opportunities of Long-Term Continuous Stream Metabolism Measurements at the National Ecological Observatory Network

    Science.gov (United States)

    Goodman, K. J.; Lunch, C. K.; Baxter, C.; Hall, R.; Holtgrieve, G. W.; Roberts, B. J.; Marcarelli, A. M.; Tank, J. L.

    2013-12-01

    Recent advances in dissolved oxygen sensing and modeling have made continuous measurements of whole-stream metabolism relatively easy to make, allowing ecologists to quantify and evaluate stream ecosystem health at expanded temporal and spatial scales. Long-term monitoring of continuous stream metabolism will enable a better understanding of the integrated and complex effects of anthropogenic change (e.g., land-use, climate, atmospheric deposition, invasive species, etc.) on stream ecosystem function. In addition to their value in the particular streams measured, information derived from long-term data will improve the ability to extrapolate from shorter-term data. With the need to better understand drivers and responses of whole-stream metabolism come difficulties in interpreting the results. Long-term trends will encompass physical changes in stream morphology and flow regime (e.g., variable flow conditions and changes in channel structure) combined with changes in biota. Additionally long-term data sets will require an organized database structure, careful quantification of errors and uncertainties, as well as propagation of error as a result of the calculation of metabolism metrics. Parsing of continuous data and the choice of modeling approaches can also have a large influence on results and on error estimation. The two main modeling challenges include 1) obtaining unbiased, low-error daily estimates of gross primary production (GPP) and ecosystem respiration (ER), and 2) interpreting GPP and ER measurements over extended time periods. The National Ecological Observatory Network (NEON), in partnership with academic and government scientists, has begun to tackle several of these challenges as it prepares for the collection and calculation of 30 years of continuous whole-stream metabolism data. NEON is a national-scale research platform that will use consistent procedures and protocols to standardize measurements across the United States, providing long

  15. Generation and Evaluation of a Genome-Scale Metabolic Network Model of Synechococcus elongatus PCC7942

    Directory of Open Access Journals (Sweden)

    Julián Triana

    2014-08-01

    Full Text Available The reconstruction of genome-scale metabolic models and their applications represent a great advantage of systems biology. Through their use as metabolic flux simulation models, production of industrially-interesting metabolites can be predicted. Due to the growing number of studies of metabolic models driven by the increasing genomic sequencing projects, it is important to conceptualize steps of reconstruction and analysis. We have focused our work in the cyanobacterium Synechococcus elongatus PCC7942, for which several analyses and insights are unveiled. A comprehensive approach has been used, which can be of interest to lead the process of manual curation and genome-scale metabolic analysis. The final model, iSyf715 includes 851 reactions and 838 metabolites. A biomass equation, which encompasses elementary building blocks to allow cell growth, is also included. The applicability of the model is finally demonstrated by simulating autotrophic growth conditions of Synechococcus elongatus PCC7942.

  16. Generation and Evaluation of a Genome-Scale Metabolic Network Model of Synechococcus elongatus PCC7942

    Science.gov (United States)

    Triana, Julián; Montagud†, Arnau; Siurana, Maria; Fuente, David; Urchueguía, Arantxa; Gamermann, Daniel; Torres, Javier; Tena, Jose; de Córdoba, Pedro Fernández; Urchueguía, Javier F.

    2014-01-01

    The reconstruction of genome-scale metabolic models and their applications represent a great advantage of systems biology. Through their use as metabolic flux simulation models, production of industrially-interesting metabolites can be predicted. Due to the growing number of studies of metabolic models driven by the increasing genomic sequencing projects, it is important to conceptualize steps of reconstruction and analysis. We have focused our work in the cyanobacterium Synechococcus elongatus PCC7942, for which several analyses and insights are unveiled. A comprehensive approach has been used, which can be of interest to lead the process of manual curation and genome-scale metabolic analysis. The final model, iSyf715 includes 851 reactions and 838 metabolites. A biomass equation, which encompasses elementary building blocks to allow cell growth, is also included. The applicability of the model is finally demonstrated by simulating autotrophic growth conditions of Synechococcus elongatus PCC7942. PMID:25141288

  17. A systems approach to predict oncometabolites via context-specific genome-scale metabolic networks.

    Directory of Open Access Journals (Sweden)

    Hojung Nam

    2014-09-01

    Full Text Available Altered metabolism in cancer cells has been viewed as a passive response required for a malignant transformation. However, this view has changed through the recently described metabolic oncogenic factors: mutated isocitrate dehydrogenases (IDH, succinate dehydrogenase (SDH, and fumarate hydratase (FH that produce oncometabolites that competitively inhibit epigenetic regulation. In this study, we demonstrate in silico predictions of oncometabolites that have the potential to dysregulate epigenetic controls in nine types of cancer by incorporating massive scale genetic mutation information (collected from more than 1,700 cancer genomes, expression profiling data, and deploying Recon 2 to reconstruct context-specific genome-scale metabolic models. Our analysis predicted 15 compounds and 24 substructures of potential oncometabolites that could result from the loss-of-function and gain-of-function mutations of metabolic enzymes, respectively. These results suggest a substantial potential for discovering unidentified oncometabolites in various forms of cancers.

  18. Metabolic network modeling of redox balancing and biohydrogen production in purple nonsulfur bacteria

    Directory of Open Access Journals (Sweden)

    Grammel Hartmut

    2011-09-01

    Full Text Available Abstract Background Purple nonsulfur bacteria (PNSB are facultative photosynthetic bacteria and exhibit an extremely versatile metabolism. A central focus of research on PNSB dealt with the elucidation of mechanisms by which they manage to balance cellular redox under diverse conditions, in particular under photoheterotrophic growth. Results Given the complexity of the central metabolism of PNSB, metabolic modeling becomes crucial for an integrated analysis of the accumulated biological knowledge. We reconstructed a stoichiometric model capturing the central metabolism of three important representatives of PNSB (Rhodospirillum rubrum, Rhodobacter sphaeroides and Rhodopseudomonas palustris. Using flux variability analysis, the model reveals key metabolic constraints related to redox homeostasis in these bacteria. With the help of the model we can (i give quantitative explanations for non-intuitive, partially species-specific phenomena of photoheterotrophic growth of PNSB, (ii reproduce various quantitative experimental data, and (iii formulate several new hypotheses. For example, model analysis of photoheterotrophic growth reveals that - despite a large number of utilizable catabolic pathways - substrate-specific biomass and CO2 yields are fixed constraints, irrespective of the assumption of optimal growth. Furthermore, our model explains quantitatively why a CO2 fixing pathway such as the Calvin cycle is required by PNSB for many substrates (even if CO2 is released. We also analyze the role of other pathways potentially involved in redox metabolism and how they affect quantitatively the required capacity of the Calvin cycle. Our model also enables us to discriminate between different acetate assimilation pathways that were proposed recently for R. sphaeroides and R. rubrum, both lacking the isocitrate lyase. Finally, we demonstrate the value of the metabolic model also for potential biotechnological applications: we examine the theoretical

  19. Protein design in systems metabolic engineering for industrial strain development.

    Science.gov (United States)

    Chen, Zhen; Zeng, An-Ping

    2013-05-01

    Accelerating the process of industrial bacterial host strain development, aimed at increasing productivity, generating new bio-products or utilizing alternative feedstocks, requires the integration of complementary approaches to manipulate cellular metabolism and regulatory networks. Systems metabolic engineering extends the concept of classical metabolic engineering to the systems level by incorporating the techniques used in systems biology and synthetic biology, and offers a framework for the development of the next generation of industrial strains. As one of the most useful tools of systems metabolic engineering, protein design allows us to design and optimize cellular metabolism at a molecular level. Here, we review the current strategies of protein design for engineering cellular synthetic pathways, metabolic control systems and signaling pathways, and highlight the challenges of this subfield within the context of systems metabolic engineering.

  20. Comparing Metabolic Energy Expenditure Estimation Using Wearable Multi-Sensor Network and Single Accelerometer

    Science.gov (United States)

    Dong, Bo; Biswas, Subir; Montoye, Alexander; Pfeiffer, Karin

    2014-01-01

    This paper presents the implementation details, system architecture and performance of a wearable sensor network that was designed for human activity recognition and energy expenditure estimation. We also included ActiGraph GT3X+ as a popular single sensor solution for detailed comparison with the proposed wearable sensor network. Linear regression and Artificial Neural Network are implemented and tested. Through a rigorous system study and experiment, it is shown that the wearable multi-sensor network outperforms the single sensor solution in terms of energy expenditure estimation. PMID:24110325

  1. Ecological network analysis of an urban metabolic system based on input-output tables: model development and case study for Beijing.

    Science.gov (United States)

    Zhang, Yan; Zheng, Hongmei; Fath, Brian D; Liu, Hong; Yang, Zhifeng; Liu, Gengyuan; Su, Meirong

    2014-01-15

    If cities are considered as "superorganisms", then disorders of their metabolic processes cause something analogous to an "urban disease". It is therefore helpful to identify the causes of such disorders by analyzing the inner mechanisms that control urban metabolic processes. Combining input-output analysis with ecological network analysis lets researchers study the functional relationships and hierarchy of the urban metabolic processes, thereby providing direct support for the analysis of urban disease. In this paper, using Beijing as an example, we develop a model of an urban metabolic system that accounts for the intensity of the embodied ecological elements using monetary input-output tables from 1997, 2000, 2002, 2005, and 2007, and use this data to compile the corresponding physical input-output tables. This approach described the various flows of ecological elements through urban metabolic processes and let us build an ecological network model with 32 components. Then, using two methods from ecological network analysis (flow analysis and utility analysis), we quantitatively analyzed the physical input-output relationships among urban components, determined the ecological hierarchy of the components of the metabolic system, and determined the distribution of advantage-dominated and disadvantage-dominated relationships, thereby providing scientific support to guide restructuring of the urban metabolic system in an effort to prevent or cure urban "diseases".

  2. The topology of the bacterial co-conserved protein network and its implications for predicting protein function

    Directory of Open Access Journals (Sweden)

    Leach Sonia M

    2008-06-01

    Full Text Available Abstract Background Protein-protein interactions networks are most often generated from physical protein-protein interaction data. Co-conservation, also known as phylogenetic profiles, is an alternative source of information for generating protein interaction networks. Co-conservation methods generate interaction networks among proteins that are gained or lost together through evolution. Co-conservation is a particularly useful technique in the compact bacteria genomes. Prior studies in yeast suggest that the topology of protein-protein interaction networks generated from physical interaction assays can offer important insight into protein function. Here, we hypothesize that in bacteria, the topology of protein interaction networks derived via co-conservation information could similarly improve methods for predicting protein function. Since the topology of bacteria co-conservation protein-protein interaction networks has not previously been studied in depth, we first perform such an analysis for co-conservation networks in E. coli K12. Next, we demonstrate one way in which network connectivity measures and global and local function distribution can be exploited to predict protein function for previously uncharacterized proteins. Results Our results showed, like most biological networks, our bacteria co-conserved protein-protein interaction networks had scale-free topologies. Our results indicated that some properties of the physical yeast interaction network hold in our bacteria co-conservation networks, such as high connectivity for essential proteins. However, the high connectivity among protein complexes in the yeast physical network was not seen in the co-conservation network which uses all bacteria as the reference set. We found that the distribution of node connectivity varied by functional category and could be informative for function prediction. By integrating of functional information from different annotation sources and using the

  3. Hybrid dynamic modeling of Escherichia coli central metabolic network combining Michaelis-Menten and approximate kinetic equations.

    Science.gov (United States)

    Costa, Rafael S; Machado, Daniel; Rocha, Isabel; Ferreira, Eugénio C

    2010-05-01

    The construction of dynamic metabolic models at reaction network level requires the use of mechanistic enzymatic rate equations that comprise a large number of parameters. The lack of knowledge on these equations and the difficulty in the experimental identification of their associated parameters, represent nowadays the limiting factor in the construction of such models. In this study, we compare four alternative modeling approaches based on Michaelis-Menten kinetics for the bi-molecular reactions and different types of simplified rate equations for the remaining reactions (generalized mass action, convenience kinetics, lin-log and power-law). Using the mechanistic model for Escherichia coli central carbon metabolism as a benchmark, we investigate the alternative modeling approaches through comparative simulations analyses. The good dynamic behavior and the powerful predictive capabilities obtained using the hybrid model composed of Michaelis-Menten and the approximate lin-log kinetics indicate that this is a possible suitable approach to model complex large-scale networks where the exact rate laws are unknown.

  4. ReacKnock: identifying reaction deletion strategies for microbial strain optimization based on genome-scale metabolic network.

    Directory of Open Access Journals (Sweden)

    Zixiang Xu

    Full Text Available Gene knockout has been used as a common strategy to improve microbial strains for producing chemicals. Several algorithms are available to predict the target reactions to be deleted. Most of them apply mixed integer bi-level linear programming (MIBLP based on metabolic networks, and use duality theory to transform bi-level optimization problem of large-scale MIBLP to single-level programming. However, the validity of the transformation was not proved. Solution of MIBLP depends on the structure of inner problem. If the inner problem is continuous, Karush-Kuhn-Tucker (KKT method can be used to reformulate the MIBLP to a single-level one. We adopt KKT technique in our algorithm ReacKnock to attack the intractable problem of the solution of MIBLP, demonstrated with the genome-scale metabolic network model of E. coli for producing various chemicals such as succinate, ethanol, threonine and etc. Compared to the previous methods, our algorithm is fast, stable and reliable to find the optimal solutions for all the chemical products tested, and able to provide all the alternative deletion strategies which lead to the same industrial objective.

  5. Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics

    Science.gov (United States)

    Bordbar, Aarash; Yurkovich, James T.; Paglia, Giuseppe; Rolfsson, Ottar; Sigurjónsson, Ólafur E.; Palsson, Bernhard O.

    2017-01-01

    The increasing availability of metabolomics data necessitates novel methods for deeper data analysis and interpretation. We present a flux balance analysis method that allows for the computation of dynamic intracellular metabolic changes at the cellular scale through integration of time-course absolute quantitative metabolomics. This approach, termed “unsteady-state flux balance analysis” (uFBA), is applied to four cellular systems: three dynamic and one steady-state as a negative control. uFBA and FBA predictions are contrasted, and uFBA is found to be more accurate in predicting dynamic metabolic flux states for red blood cells, platelets, and Saccharomyces cerevisiae. Notably, only uFBA predicts that stored red blood cells metabolize TCA intermediates to regenerate important cofactors, such as ATP, NADH, and NADPH. These pathway usage predictions were subsequently validated through 13C isotopic labeling and metabolic flux analysis in stored red blood cells. Utilizing time-course metabolomics data, uFBA provides an accurate method to predict metabolic physiology at the cellular scale for dynamic systems. PMID:28387366

  6. Systems biology study of mucopolysaccharidosis using a human metabolic reconstruction network.

    Science.gov (United States)

    Salazar, Diego A; Rodríguez-López, Alexander; Herreño, Angélica; Barbosa, Hector; Herrera, Juliana; Ardila, Andrea; Barreto, George E; González, Janneth; Alméciga-Díaz, Carlos J

    2016-02-01

    Mucopolysaccharidosis (MPS) is a group of lysosomal storage diseases (LSD), characterized by the deficiency of a lysosomal enzyme responsible for the degradation of glycosaminoglycans (GAG). This deficiency leads to the lysosomal accumulation of partially degraded GAG. Nevertheless, deficiency of a single lysosomal enzyme has been associated with impairment in other cell mechanism, such as apoptosis and redox balance. Although GAG analysis represents the main biomarker for MPS diagnosis, it has several limitations that can lead to a misdiagnosis, whereby the identification of new biomarkers represents an important issue for MPS. In this study, we used a system biology approach, through the use of a genome-scale human metabolic reconstruction to understand the effect of metabolism alterations in cell homeostasis and to identify potential new biomarkers in MPS. In-silico MPS models were generated by silencing of MPS-related enzymes, and were analyzed through a flux balance and variability analysis. We found that MPS models used approximately 2286 reactions to satisfy the objective function. Impaired reactions were mainly involved in cellular respiration, mitochondrial process, amino acid and lipid metabolism, and ion exchange. Metabolic changes were similar for MPS I and II, and MPS III A to C; while the remaining MPS showed unique metabolic profiles. Eight and thirteen potential high-confidence biomarkers were identified for MPS IVB and VII, respectively, which were associated with the secondary pathologic process of LSD. In vivo evaluation of predicted intermediate confidence biomarkers (β-hexosaminidase and β-glucoronidase) for MPS IVA and VI correlated with the in-silico prediction. These results show the potential of a computational human metabolic reconstruction to understand the molecular mechanisms this group of diseases, which can be used to identify new biomarkers for MPS.

  7. Bistability in a Metabolic Network Underpins the De Novo Evolution of Colony Switching in Pseudomonas fluorescens

    DEFF Research Database (Denmark)

    Gallie, Jenna; Libby, Eric; Bertels, Frederic;

    2015-01-01

    levels favour nucleotide metabolism (capsule OFF), while cells with lower pyrimidine levels divert resources towards polymer biosynthesis (capsule ON). This decision point is present and functional in the wild-type strain. Finally, we present a simple mathematical model demonstrating that the molecular...... in central metabolism (carB) generates such a striking phenotype. We show that colony switching is underpinned by ON/OFF expression of capsules consisting of a colanic acid-like polymer. We use molecular genetics, biochemical analyses, and experimental evolution to establish that capsule switching results...

  8. IMG-ABC: new features for bacterial secondary metabolism analysis and targeted biosynthetic gene cluster discovery in thousands of microbial genomes

    Science.gov (United States)

    Hadjithomas, Michalis; Chen, I-Min A.; Chu, Ken; Huang, Jinghua; Ratner, Anna; Palaniappan, Krishna; Andersen, Evan; Markowitz, Victor; Kyrpides, Nikos C.; Ivanova, Natalia N.

    2017-01-01

    Secondary metabolites produced by microbes have diverse biological functions, which makes them a great potential source of biotechnologically relevant compounds with antimicrobial, anti-cancer and other activities. The proteins needed to synthesize these natural products are often encoded by clusters of co-located genes called biosynthetic gene clusters (BCs). In order to advance the exploration of microbial secondary metabolism, we developed the largest publically available database of experimentally verified and predicted BCs, the Integrated Microbial Genomes Atlas of Biosynthetic gene Clusters (IMG-ABC) (https://img.jgi.doe.gov/abc/). Here, we describe an update of IMG-ABC, which includes ClusterScout, a tool for targeted identification of custom biosynthetic gene clusters across 40 000 isolate microbial genomes, and a new search capability to query more than 700 000 BCs from isolate genomes for clusters with similar Pfam composition. Additional features enable fast exploration and analysis of BCs through two new interactive visualization features, a BC function heatmap and a BC similarity network graph. These new tools and features add to the value of IMG-ABC's vast body of BC data, facilitating their in-depth analysis and accelerating secondary metabolite discovery. PMID:27903896

  9. A new method for assembling metabolic networks, with application to the Krebs citric acid cycle.

    Science.gov (United States)

    Mittenthal, J E; Clarke, B; Waddell, T G; Fawcett, G

    2001-02-01

    To understand why a molecular network has a particular connectivity one can generate an ensemble of alternative networks, all of which meet the same performance criteria as the real network. We have generated alternatives to the Krebs cycle, allowing group transfers and B(12)-mediated shifts that were excluded in previous work. Our algorithm does not use a reaction list, but determines the reactants and products in generic reactions. It generates networks in order of increasing number of reaction steps. We find that alternatives to the Krebs cycle are very likely to be cycles. Many of the alternatives produce toxic or unstable compounds and use group transfer reactions, which have unfavorable consequences. Although alternatives are better than the Krebs cycle in some respects, the Krebs cycle has the most favorable combination of traits.

  10. Lack of evolvability in self-sustaining autocatalytic networks constraints metabolism-first scenarios for the origin of life.

    Science.gov (United States)

    Vasas, Vera; Szathmáry, Eörs; Santos, Mauro

    2010-01-26

    A basic property of life is its capacity to experience Darwinian evolution. The replicator concept is at the core of genetics-first theories of the origin of life, which suggest that self-replicating oligonucleotides or their similar ancestors may have been the first "living" systems and may have led to the evolution of an RNA world. But problems with the nonenzymatic synthesis of biopolymers and the origin of template replication have spurred the alternative metabolism-first scenario, where self-reproducing and evolving proto-metabolic networks are assumed to have predated self-replicating genes. Recent theoretical work shows that "compositional genomes" (i.e., the counts of different molecular species in an assembly) are able to propagate compositional information and can provide a setup on which natural selection acts. Accordingly, if we stick to the notion of replicator as an entity that passes on its structure largely intact in successive replications, those macromolecular aggregates could be dubbed "ensemble replicators" (composomes) and quite different from the more familiar genes and memes. In sharp contrast with template-dependent replication dynamics, we demonstrate here that replication of compositional information is so inaccurate that fitter compositional genomes cannot be maintained by selection and, therefore, the system lacks evolvability (i.e., it cannot substantially depart from the asymptotic steady-state solution already built-in in the dynamical equations). We conclude that this fundamental limitation of ensemble replicators cautions against metabolism-first theories of the origin of life, although ancient metabolic systems could have provided a stable habitat within which polymer replicators later evolved.

  11. Ultraperformance liquid chromatography-mass spectrometry based comprehensive metabolomics combined with pattern recognition and network analysis methods for characterization of metabolites and metabolic pathways from biological data sets.

    Science.gov (United States)

    Zhang, Ai-hua; Sun, Hui; Han, Ying; Yan, Guang-li; Yuan, Ye; Song, Gao-chen; Yuan, Xiao-xia; Xie, Ning; Wang, Xi-jun

    2013-08-06

    Metabolomics is the study of metabolic changes in biological systems and provides the small molecule fingerprints related to the disease. Extracting biomedical information from large metabolomics data sets by multivariate data analysis is of considerable complexity. Therefore, more efficient and optimizing metabolomics data processing technologies are needed to improve mass spectrometry applications in biomarker discovery. Here, we report the findings of urine metabolomic investigation of hepatitis C virus (HCV) patients by high-throughput ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) coupled with pattern recognition methods (principal component analysis, partial least-squares, and OPLS-DA) and network pharmacology. A total of 20 urinary differential metabolites (13 upregulated and 7 downregulated) were identified and contributed to HCV progress, involve several key metabolic pathways such as taurine and hypotaurine metabolism, glycine, serine and threonine metabolism, histidine metabolism, arginine and proline metabolism, and so forth. Metabolites identified through metabolic profiling may facilitate the development of more accurate marker algorithms to better monitor disease progression. Network analysis validated close contact between these metabolites and implied the importance of the metabolic pathways. Mapping altered metabolites to KEGG pathways identified alterations in a variety of biological processes mediated through complex networks. These findings may be promising to yield a valuable and noninvasive tool that insights into the pathophysiology of HCV and to advance the early diagnosis and monitor the progression of disease. Overall, this investigation illustrates the power of the UPLC-MS platform combined with the pattern recognition and network analysis methods that can engender new insights into HCV pathobiology.

  12. Berberine Ameliorates Nonbacterial Prostatitis via Multi-Target Metabolic Network Regulation

    Science.gov (United States)

    Sun, Hui; Wang, Huiyu; Zhang, Aihua; Yan, Guangli; Zhang, Yue; An, Na

    2015-01-01

    Abstract Metabolomics has been increasingly applied to discovering biomarkers and identifying perturbed pathways. Berberine has been shown to exhibit anti-inflammatory, antioxidant, and anticancer properties, but its mechanisms for treating nonbacterial prostatitis (NBP) remain unclear completely. We developed the untargeted metabolomics approach based on UPLC-Q-TOF-HDMS to profile the metabolite changes in urine samples in order to discover novel potential biomarkers to clarify mechanisms of berberine in treating a rat model of capsaicin-induced nonbacterial prostatitis (NBP). The changes in metabolic profiling were restored to their base-line values after berberine treatment according to the principal component analysis (PCA) score plots. Fourteen different potential biomarkers and five acutely perturbed metabolic pathways contributing to the treatment of NBP were discovered and identified. Specifically, the berberine-treated rats are located closer to the normal group, indicating that the NBP-induced disturbances to the metabolic profile were partially reversed by berberine treatment. After treatment with berberine, the relative contents of 12 potential biomarkers were effectively regulated, which suggested that the therapeutic effects of berberine on NBP may involve regulating disturbances to the metabolism. Our results show that the protective effect of berberine occurs in part through a reversal of the NBP-caused disturbances. PMID:25588034

  13. An experimentally-supported genome-scale metabolic network reconstruction for Yersinia pestis CO92

    Directory of Open Access Journals (Sweden)

    Motin Vladimir L

    2011-10-01

    Full Text Available Abstract Background Yersinia pestis is a gram-negative bacterium that causes plague, a disease linked historically to the Black Death in Europe during the Middle Ages and to several outbreaks during the modern era. Metabolism in Y. pestis displays remarkable flexibility and robustness, allowing the bacterium to proliferate in both warm-blooded mammalian hosts and cold-blooded insect vectors such as fleas. Results Here we report a genome-scale reconstruction and mathematical model of metabolism for Y. pestis CO92 and supporting experimental growth and metabolite measurements. The model contains 815 genes, 678 proteins, 963 unique metabolites and 1678 reactions, accurately simulates growth on a range of carbon sources both qualitatively and quantitatively, and identifies gaps in several key biosynthetic pathways and suggests how those gaps might be filled. Furthermore, our model presents hypotheses to explain certain known nutritional requirements characteristic of this strain. Conclusions Y. pestis continues to be a dangerous threat to human health during modern times. The Y. pestis genome-scale metabolic reconstruction presented here, which has been benchmarked against experimental data and correctly reproduces known phenotypes, provides an in silico platform with which to investigate the metabolism of this important human pathogen.

  14. Flavonoids: a metabolic network mediating plants adaptation to their real estate.

    Science.gov (United States)

    Mouradov, Aidyn; Spangenberg, German

    2014-01-01

    From an evolutionary perspective, the emergence of the sophisticated chemical scaffolds of flavonoid molecules represents a key step in the colonization of Earth's terrestrial environment by vascular plants nearly 500 million years ago. The subsequent evolution of flavonoids through recruitment and modification of ancestors involved in primary metabolism has allowed vascular plants to cope with pathogen invasion and damaging UV light. The functional properties of flavonoids as a unique combination of different classes of compounds vary significantly depending on the demands of their local real estate. Apart from geographical location, the composition of flavonoids is largely dependent on the plant species, their developmental stage, tissue type, subcellular localization, and key ecological influences of both biotic and abiotic origin. Molecular and metabolic cross-talk between flavonoid and other pathways as a result of the re-direction of intermediate molecules have been well investigated. This metabolic plasticity is a key factor in plant adaptive strength and is of paramount importance for early land plants adaptation to their local ecosystems. In human and animal health the biological and pharmacological activities of flavonoids have been investigated in great depth and have shown a wide range of anti-inflammatory, anti-oxidant, anti-microbial, and anti-cancer properties. In this paper we review the application of advanced gene technologies for targeted reprogramming of the flavonoid pathway in plants to understand its molecular functions and explore opportunities for major improvements in forage plants enhancing animal health and production.

  15. Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering.

    Science.gov (United States)

    He, Fei; Murabito, Ettore; Westerhoff, Hans V

    2016-04-01

    Metabolic pathways can be engineered to maximize the synthesis of various products of interest. With the advent of computational systems biology, this endeavour is usually carried out through in silico theoretical studies with the aim to guide and complement further in vitro and in vivo experimental efforts. Clearly, what counts is the result in vivo, not only in terms of maximal productivity but also robustness against environmental perturbations. Engineering an organism towards an increased production flux, however, often compromises that robustness. In this contribution, we review and investigate how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed by systems and control engineering. While trade-offs between production optimality and cellular robustness have already been studied diagnostically and statically, the dynamics also matter. Integration of the dynamic design aspects of control engineering with the more diagnostic aspects of metabolic, hierarchical control and regulation analysis is leading to the new, conceptual and operational framework required for the design of robust and productive dynamic pathways.

  16. Stoichiometric network reconstruction and analysis of yeast sphingolipid metabolism incorporating different states of hydroxylation.

    Science.gov (United States)

    Kavun Ozbayraktar, Fatma Betul; Ulgen, Kutlu O

    2011-04-01

    The first elaborate metabolic model of Saccharomyces cerevisiae sphingolipid metabolism was reconstructed in silico. The model considers five different states of sphingolipid hydroxylation, rendering it unique among other models. It is aimed to clarify the significance of hydroxylation on sphingolipids and hence to interpret the preferences of the cell between different metabolic pathway branches under different stress conditions. The newly constructed model was validated by single, double and triple gene deletions with experimentally verified phenotypes. Calcium sensitivity and deletion mutations that may suppress calcium sensitivity were examined by CSG1 and CSG2 related deletions. The model enabled the analysis of complex sphingolipid content of the plasma membrane coupled with diacylglycerol and phosphatidic acid biosynthesis and ATP consumption in in silico cell. The flux data belonging to these critically important key metabolites are integrated with the fact of phytoceramide induced cell death to propose novel potential drug targets for cancer therapeutics. In conclusion, we propose that IPT1, GDA1, CSG and AUR1 gene deletions may be novel candidates of drug targets for cancer therapy according to the results of flux balance and variability analyses coupled with robustness analysis.

  17. Enhancing Carbon Fixation by Metabolic Engineering: A Model System of Complex Network Modulation

    Energy Technology Data Exchange (ETDEWEB)

    Dr. Gregory Stephanopoulos

    2008-04-10

    In the first two years of this research we focused on the development of a DNA microarray for transcriptional studies in the photosynthetic organism Synechocystis and the elucidation of the metabolic pathway for biopolymer synthesis in this organism. In addition we also advanced the molecular biological tools for metabolic engineering of biopolymer synthesis in Synechocystis and initiated a series of physiological studies for the elucidation of the carbon fixing pathways and basic central carbon metabolism of these organisms. During the last two-year period we focused our attention on the continuation and completion of the last task, namely, the development of tools for basic investigations of the physiology of these cells through, primarily, the determination of their metabolic fluxes. The reason for this decision lies in the importance of fluxes as key indicators of physiology and the high level of information content they carry in terms of identifying rate limiting steps in a metabolic pathway. While flux determination is a well-advanced subject for heterotrophic organisms, for the case of autotrophic bacteria, like Synechocystis, some special challenges had to be overcome. These challenges stem mostly from the fact that if one uses {sup 13}C labeled CO{sub 2} for flux determination, the {sup 13}C label will mark, at steady state, all carbon atoms of all cellular metabolites, thus eliminating the necessary differentiation required for flux determination. This peculiarity of autotrophic organisms makes it imperative to carry out flux determination under transient conditions, something that had not been accomplished before. We are pleased to report that we have solved this problem and we are now able to determine fluxes in photosynthetic organisms from stable isotope labeling experiments followed by measurements of label enrichment in cellular metabolites using Gas Chromatography-Mass Spectrometry. We have conducted extensive simulations to test the method and

  18. Hybrid dynamic modeling of Escherichia coli central metabolic network combining Michaelis–Menten and approximate kinetic equations

    DEFF Research Database (Denmark)

    Costa, Rafael S.; Machado, Daniel; Rocha, Isabel;

    2010-01-01

    , represent nowadays the limiting factor in the construction of such models. In this study, we compare four alternative modeling approaches based on Michaelis–Menten kinetics for the bi-molecular reactions and different types of simplified rate equations for the remaining reactions (generalized mass action...... using the hybrid model composed of Michaelis–Menten and the approximate lin-log kinetics indicate that this is a possible suitable approach to model complex large-scale networks where the exact rate laws are unknown......., convenience kinetics, lin-log and power-law). Using the mechanistic model for Escherichia coli central carbon metabolism as a benchmark, we investigate the alternative modeling approaches through comparative simulations analyses. The good dynamic behavior and the powerful predictive capabilities obtained...

  19. Change in network connectivity during fictive-gasping generation in hypoxia: Prevention by a metabolic intermediate

    Directory of Open Access Journals (Sweden)

    Andrés eNieto-Posadas

    2014-07-01

    Full Text Available The neuronal circuit in charge of generating the respiratory rhythms, localized in the pre-Bötzinger complex (preBötC, is configured to produce fictive-eupnea during normoxia and reconfigures to produce fictive-gasping during hypoxic conditions in vitro. The mechanisms involved in such reconfiguration have been extensively investigated by cell-focused studies, but the actual changes at the network level remain elusive. Since a failure to generate gasping has been linked to Sudden Infant Death Syndrome, the study of gasping generation and pharmacological approaches to promote it may have clinical relevance. Here, we study the changes in network dynamics and circuit reconfiguration that occur during the transition to fictive-gasping generation in the brainstem slice preparation by recording the preBötC with multi-electrode arrays and assessing correlated firing among respiratory neurons or clusters of respiratory neurons (multiunits. We studied whether the respiratory network reconfiguration in hypoxia involves changes in either the number of active respiratory elements, the number of functional connections among elements, or the strength of these connections. Moreover, we tested the influence of isocitrate, a Krebs cycle intermediate that has recently been shown to promote breathing, on the configuration of the preBötC circuit during normoxia and on its reconfiguration during hypoxia. We found that, in contrast to previous suggestions based on cell-focused studies, the number and the overall activity of respiratory neurons change only slightly during hypoxia. However, hypoxia induces a reduction in the strength of functional connectivity within the circuit without reducing the number of connections. Isocitrate prevented this reduction during hypoxia while increasing the strength of network connectivity. In conclusion, we provide an overview of the configuration of the respiratory network under control conditions and how it is reconfigured

  20. Effects of chlorinated drinking water on the xenobiotic metabolism in Cyprinus carpio treated with samples from two Italian municipal networks.

    Science.gov (United States)

    Cirillo, Silvia; Canistro, Donatella; Vivarelli, Fabio; Paolini, Moreno

    2016-09-01

    Drinking water (DW) disinfection represents a milestone of the past century, thanks to its efficacy in the reduction of risks of epidemic forms by water micro-organisms. Nevertheless, such process generates disinfection by-products (DBPs), some of which are genotoxic both in animals and in humans and carcinogenic in animals. At present, chlorination is one of the most employed strategies but the toxicological effects of several classes of DBPs are unknown. In this investigation, a multidisciplinary approach foreseeing the chemical analysis of chlorinated DW samples and the study of its effects on mixed function oxidases (MFOs) belonging to the superfamily of cytochrome P450-linked monooxygenases of Cyprinus carpio hepatopancreas, was employed. The experimental samples derived from aquifers of two Italian towns (plant 1, river water and plant 2, spring water) were obtained immediately after the disinfection (A) and along the network (R1). Animals treated with plant 1 DW-processed fractions showed a general CYP-associated MFO induction. By contrast, in plant 2, a complex modulation pattern was achieved, with a general up-regulation for the point A and a marked MFO inactivation in the R1 group, particularly for the testosterone metabolism. Together, the toxicity and co-carcinogenicity (i.e. unremitting over-generation of free radicals and increased bioactivation capability) of DW linked to the recorded metabolic manipulation, suggests that a prolonged exposure to chlorine-derived disinfectants may produce adverse health effects.

  1. A method for estimation of elasticities in metabolic networks using steady state and dynamic metabolomics data and linlog kinetics

    Directory of Open Access Journals (Sweden)

    van Gulik Walter M

    2006-12-01

    Full Text Available Abstract Background Dynamic modeling of metabolic reaction networks under in vivo conditions is a crucial step in order to obtain a better understanding of the (disfunctioning of living cells. So far dynamic metabolic models generally have been based on mechanistic rate equations which often contain so many parameters that their identifiability from experimental data forms a serious problem. Recently, approximative rate equations, based on the linear logarithmic (linlog format have been proposed as a suitable alternative with fewer parameters. Results In this paper we present a method for estimation of the kinetic model parameters, which are equal to the elasticities defined in Metabolic Control Analysis, from metabolite data obtained from dynamic as well as steady state perturbations, using the linlog kinetic format. Additionally, we address the question of parameter identifiability from dynamic perturbation data in the presence of noise. The method is illustrated using metabolite data generated with a dynamic model of the glycolytic pathway of Saccharomyces cerevisiae based on mechanistic rate equations. Elasticities are estimated from the generated data, which define the complete linlog kinetic model of the glycolysis. The effect of data noise on the accuracy of the estimated elasticities is presented. Finally, identifiable subset of parameters is determined using information on the standard deviations of the estimated elasticities through Monte Carlo (MC simulations. Conclusion The parameter estimation within the linlog kinetic framework as presented here allows the determination of the elasticities directly from experimental data from typical dynamic and/or steady state experiments. These elasticities allow the reconstruction of the full kinetic model of Saccharomyces cerevisiae, and the determination of the control coefficients. MC simulations revealed that certain elasticities are potentially unidentifiable from dynamic data only

  2. Genome-scale reconstruction of the metabolic network in Yersinia pestis, strain 91001

    Energy Technology Data Exchange (ETDEWEB)

    Navid, A; Almaas, E

    2009-01-13

    The gram-negative bacterium Yersinia pestis, the aetiological agent of bubonic plague, is one the deadliest pathogens known to man. Despite its historical reputation, plague is a modern disease which annually afflicts thousands of people. Public safety considerations greatly limit clinical experimentation on this organism and thus development of theoretical tools to analyze the capabilities of this pathogen is of utmost importance. Here, we report the first genome-scale metabolic model of Yersinia pestis biovar Mediaevalis based both on its recently annotated genome, and physiological and biochemical data from literature. Our model demonstrates excellent agreement with Y. pestis known metabolic needs and capabilities. Since Y. pestis is a meiotrophic organism, we have developed CryptFind, a systematic approach to identify all candidate cryptic genes responsible for known and theoretical meiotrophic phenomena. In addition to uncovering every known cryptic gene for Y. pestis, our analysis of the rhamnose fermentation pathway suggests that betB is the responsible cryptic gene. Despite all of our medical advances, we still do not have a vaccine for bubonic plague. Recent discoveries of antibiotic resistant strains of Yersinia pestis coupled with the threat of plague being used as a bioterrorism weapon compel us to develop new tools for studying the physiology of this deadly pathogen. Using our theoretical model, we can study the cell's phenotypic behavior under different circumstances and identify metabolic weaknesses which may be harnessed for the development of therapeutics. Additionally, the automatic identification of cryptic genes expands the usage of genomic data for pharmaceutical purposes.

  3. Emerging Roles of Toxin-Antitoxin Modules in Bacterial Pathogenesis

    Directory of Open Access Journals (Sweden)

    Barbara Kędzierska

    2016-06-01

    Full Text Available Toxin-antitoxin (TA cassettes are encoded widely by bacteria. The modules typically comprise a protein toxin and protein or RNA antitoxin that sequesters the toxin factor. Toxin activation in response to environmental cues or other stresses promotes a dampening of metabolism, most notably protein translation, which permits survival until conditions improve. Emerging evidence also implicates TAs in bacterial pathogenicity. Bacterial persistence involves entry into a transient semi-dormant state in which cells survive unfavorable conditions including killing by antibiotics, which is a significant clinical problem. TA complexes play a fundamental role in inducing persistence by downregulating cellular metabolism. Bacterial biofilms are important in numerous chronic inflammatory and infectious diseases and cause serious therapeutic problems due to their multidrug tolerance and resistance to host immune system actions. Multiple TAs influence biofilm formation through a network of interactions with other factors that mediate biofilm production and maintenance. Moreover, in view of their emerging contributions to bacterial virulence, TAs are potential targets for novel prophylactic and therapeutic approaches that are required urgently in an era of expanding antibiotic resistance. This review summarizes the emerging evidence that implicates TAs in the virulence profiles of a diverse range of key bacterial pathogens that trigger serious human disease.

  4. Correlation-based network analysis of metabolite and enzyme profiles reveals a role of citrate biosynthesis in modulating N and C metabolism in zea mays

    Science.gov (United States)

    To investigate the natural variability of leaf metabolism and enzymatic activity in a maize inbred population, statistical and network analyses were employed on metabolite and enzyme profiles. The test of coefficient of variation showed that sugars and amino acids displayed opposite trends in their ...

  5. Genealogy profiling through strain improvement by using metabolic network analysis: metabolic flux genealogy of several generations of lysine-producing corynebacteria.

    Science.gov (United States)

    Wittmann, Christoph; Heinzle, Elmar

    2002-12-01

    A comprehensive approach of metabolite balancing, (13)C tracer studies, gas chromatography-mass spectrometry, matrix-assisted laser desorption ionization-time of flight mass spectrometry, and isotopomer modeling was applied for comparative metabolic network analysis of a genealogy of five successive generations of lysine-producing Corynebacterium glutamicum. The five strains examined (C. glutamicum ATCC 13032, 13287, 21253, 21526, and 21543) were previously obtained by random mutagenesis and selection. Throughout the genealogy, the lysine yield in batch cultures increased markedly from 1.2 to 24.9% relative to the glucose uptake flux. Strain optimization was accompanied by significant changes in intracellular flux distributions. The relative pentose phosphate pathway (PPP) flux successively increased, clearly corresponding to the product yield. Moreover, the anaplerotic net flux increased almost twofold as a consequence of concerted regulation of C(3) carboxylation and C(4) decarboxylation fluxes to cover the increased demand for lysine formation; thus, the overall increase was a consequence of concerted regulation of C(3) carboxylation and C(4) decarboxylation fluxes. The relative flux through isocitrate dehydrogenase dropped from 82.7% in the wild type to 59.9% in the lysine-producing mutants. In contrast to the NADPH demand, which increased from 109 to 172% due to the increasing lysine yield, the overall NADPH supply remained constant between 185 and 196%, resulting in a decrease in the apparent NADPH excess through strain optimization. Extrapolated to industrial lysine producers, the NADPH supply might become a limiting factor. The relative contributions of PPP and the tricarboxylic acid cycle to NADPH generation changed markedly, indicating that C. glutamicum is able to maintain a constant supply of NADPH under completely different flux conditions. Statistical analysis by a Monte Carlo approach revealed high precision for the estimated fluxes, underlining the

  6. To supplement or not to supplement: a metabolic network framework for human nutritional supplements.

    Directory of Open Access Journals (Sweden)

    Christopher D Nogiec

    Full Text Available Flux balance analysis and constraint based modeling have been successfully used in the past to elucidate the metabolism of single cellular organisms. However, limited work has been done with multicellular organisms and even less with humans. The focus of this paper is to present a novel use of this technique by investigating human nutrition, a challenging field of study. Specifically, we present a steady state constraint based model of skeletal muscle tissue to investigate amino acid supplementation's effect on protein synthesis. We implement several in silico supplementation strategies to study whether amino acid supplementation might be beneficial for increasing muscle contractile protein synthesis. Concurrent with published data on amino acid supplementation's effect on protein synthesis in a post resistance exercise state, our results suggest that increasing bioavailability of methionine, arginine, and the branched-chain amino acids can increase the flux of contractile protein synthesis. The study also suggests that a common commercial supplement, glutamine, is not an effective supplement in the context of increasing protein synthesis and thus, muscle mass. Similar to any study in a model organism, the computational modeling of this research has some limitations. Thus, this paper introduces the prospect of using systems biology as a framework to formally investigate how supplementation and nutrition can affect human metabolism and physiology.

  7. Identifying genes and gene networks involved in chromium metabolism and detoxification in Crambe abyssinica

    Energy Technology Data Exchange (ETDEWEB)

    Zulfiqar, Asma, E-mail: asmazulfiqar08@yahoo.com [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States); Paulose, Bibin, E-mail: bpaulose@psis.umass.edu [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States); Chhikara, Sudesh, E-mail: sudesh@psis.umass.edu [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States); Dhankher, Om Parkash, E-mail: parkash@psis.umass.edu [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States)

    2011-10-15

    Chromium pollution is a serious environmental problem with few cost-effective remediation strategies available. Crambe abyssinica (a member of Brassicaseae), a non-food, fast growing high biomass crop, is an ideal candidate for phytoremediation of heavy metals contaminated soils. The present study used a PCR-Select Suppression Subtraction Hybridization approach in C. abyssinica to isolate differentially expressed genes in response to Cr exposure. A total of 72 differentially expressed subtracted cDNAs were sequenced and found to represent 43 genes. The subtracted cDNAs suggest that Cr stress significantly affects pathways related to stress/defense, ion transporters, sulfur assimilation, cell signaling, protein degradation, photosynthesis and cell metabolism. The regulation of these genes in response to Cr exposure was further confirmed by semi-quantitative RT-PCR. Characterization of these differentially expressed genes may enable the engineering of non-food, high-biomass plants, including C. abyssinica, for phytoremediation of Cr-contaminated soils and sediments. - Highlights: > Molecular mechanism of Cr uptake and detoxification in plants is not well known. > We identified differentially regulated genes upon Cr exposure in Crambe abyssinica. > 72 Cr-induced subtracted cDNAs were sequenced and found to represent 43 genes. > Pathways linked to stress, ion transport, and sulfur assimilation were affected. > This is the first Cr transcriptome study in a crop with phytoremediation potential. - This study describes the identification and isolation of differentially expressed genes involved in chromium metabolism and detoxification in a non-food industrial oil crop Crambe abyssinica.

  8. Screening of potential targets in Plasmodium falciparum using stage-specific metabolic network analysis.

    Science.gov (United States)

    Dholakia, Neel; Dhandhukia, Pinakin; Roy, Nilanjan

    2015-11-01

    The Apicomplexa parasite Plasmodium is a major cause of death in developing countries which are less equipped to bring new medicines to the market. Currently available drugs used for treatment of malaria are limited either by inadequate efficacy, toxicity and/or increased resistance. Availability of the genome sequence, microarray data and metabolic profile of Plasmodium parasite offers an opportunity for the identification of stage-specific genes important to the organism's lifecycle. In this study, microarray data were analysed for differential expression and overlapped onto metabolic pathways to identify differentially regulated pathways essential for transition to successive erythrocytic stages. The results obtained indicate that S-adenosylmethionine decarboxylase/ornithine decarboxylase, a bifunctional enzyme required for polyamine synthesis, is important for the Plasmodium cell growth in the absence of exogenous polyamines. S-adenosylmethionine decarboxylase/ornithine decarboxylase is a valuable target for designing therapeutically useful inhibitors. One such inhibitor, [Formula: see text]-difluoromethyl ornithine, is currently in use for the treatment of African sleeping sickness caused by Trypanosoma brucei. Structural studies of ornithine decarboxylase along with known inhibitors and their analogues were carried out to screen drug databases for more effective and less toxic compounds.

  9. Integrative analysis of circadian transcriptome and metabolic network reveals the role of de novo purine synthesis in circadian control of cell cycle.

    Science.gov (United States)

    Li, Ying; Li, Guang; Görling, Benjamin; Luy, Burkhard; Du, Jiulin; Yan, Jun

    2015-02-01

    Metabolism is the major output of the circadian clock in many organisms. We developed a computational method to integrate both circadian gene expression and metabolic network. Applying this method to zebrafish circadian transcriptome, we have identified large clusters of metabolic genes containing mostly genes in purine and pyrimidine metabolism in the metabolic network showing similar circadian phases. Our metabolomics analysis found that the level of inosine 5'-monophosphate (IMP), an intermediate metabolite in de novo purine synthesis, showed significant circadian oscillation in larval zebrafish. We focused on IMP dehydrogenase (impdh), a rate-limiting enzyme in de novo purine synthesis, with three circadian oscillating gene homologs: impdh1a, impdh1b and impdh2. Functional analysis revealed that impdh2 contributes to the daily rhythm of S phase in the cell cycle while impdh1a contributes to ocular development and pigment synthesis. The three zebrafish homologs of impdh are likely regulated by different circadian transcription factors. We propose that the circadian regulation of de novo purine synthesis that supplies crucial building blocks for DNA replication is an important mechanism conferring circadian rhythmicity on the cell cycle. Our method is widely applicable to study the impact of circadian transcriptome on metabolism in complex organisms.

  10. Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors.

    Science.gov (United States)

    Duardo-Sánchez, Aliuska; Munteanu, Cristian R; Riera-Fernández, Pablo; López-Díaz, Antonio; Pazos, Alejandro; González-Díaz, Humberto

    2014-01-27

    The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein interactions, or metabolic networks. However, the applications are not restricted to the world of molecules and can be extended to the study of macroscopic nonliving systems, organisms, or even legal or social networks. On the other hand, the development of the field of Artificial Intelligence has led to the formulation of computational algorithms whose design is based on the structure and functioning of networks of biological neurons. These algorithms, called Artificial Neural Networks (ANNs), can be useful for the study of complex networks, since the numerical parameters that encode information of the network (for example centralities/node descriptors) can be used as inputs for the ANNs. The Wiener index (W) is a graph invariant widely used in chemoinformatics to quantify the molecular structure of drugs and to study complex networks. In this work, we explore for the first time the possibility of using Markov chains to calculate analogues of node distance numbers/W to describe complex networks from the point of view of their nodes. These parameters are called Markov-Wiener node descriptors of order k(th) (W(k)). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the W(k)(i) values for a very high number of nodes (>100,000) in more than 100 different complex networks using the software MI-NODES. These networks were grouped according to the field of application. Molecular networks include the Metabolic Reaction Networks (MRNs) of 40 different organisms. In addition, we analyzed other biological and legal and social networks. These include the Interaction Web Database Biological Networks (IWDBNs), with 75 food webs or ecological systems and the Spanish Financial Law Network (SFLN). The calculated W

  11. Reconstructing a hydrogen-driven microbial metabolic network in Opalinus Clay rock

    Science.gov (United States)

    Bagnoud, Alexandre; Chourey, Karuna; Hettich, Robert L.; de Bruijn, Ino; Andersson, Anders F.; Leupin, Olivier X.; Schwyn, Bernhard; Bernier-Latmani, Rizlan

    2016-10-01

    The Opalinus Clay formation will host geological nuclear waste repositories in Switzerland. It is expected that gas pressure will build-up due to hydrogen production from steel corrosion, jeopardizing the integrity of the engineered barriers. In an in situ experiment located in the Mont Terri Underground Rock Laboratory, we demonstrate that hydrogen is consumed by microorganisms, fuelling a microbial community. Metagenomic binning and metaproteomic analysis of this deep subsurface community reveals a carbon cycle driven by autotrophic hydrogen oxidizers belonging to novel genera. Necromass is then processed by fermenters, followed by complete oxidation to carbon dioxide by heterotrophic sulfate-reducing bacteria, which closes the cycle. This microbial metabolic web can be integrated in the design of geological repositories to reduce pressure build-up. This study shows that Opalinus Clay harbours the potential for chemolithoautotrophic-based system, and provides a model of microbial carbon cycle in deep subsurface environments where hydrogen and sulfate are present.

  12. Evolution of the metabolic and regulatory networks associated with oxygen availability in two phytopathogenic enterobacteria

    Directory of Open Access Journals (Sweden)

    Babujee Lavanya

    2012-03-01

    Full Text Available Abstract Background Dickeya dadantii and Pectobacterium atrosepticum are phytopathogenic enterobacteria capable of facultative anaerobic growth in a wide range of O2 concentrations found in plant and natural environments. The transcriptional response to O2 remains under-explored for these and other phytopathogenic enterobacteria although it has been well characterized for animal-associated genera including Escherichia coli and Salmonella enterica. Knowledge of the extent of conservation of the transcriptional response across orthologous genes in more distantly related species is useful to identify rates and patterns of regulon evolution. Evolutionary events such as loss and acquisition of genes by lateral transfer events along each evolutionary branch results in lineage-specific genes, some of which may have been subsequently incorporated into the O2-responsive stimulon. Here we present a comparison of transcriptional profiles measured using densely tiled oligonucleotide arrays for two phytopathogens, Dickeya dadantii 3937 and Pectobacterium atrosepticum SCRI1043, grown to mid-log phase in MOPS minimal medium (0.1% glucose with and without O2. Results More than 7% of the genes of each phytopathogen are differentially expressed with greater than 3-fold changes under anaerobic conditions. In addition to anaerobic metabolism genes, the O2 responsive stimulon includes a variety of virulence and pathogenicity-genes. Few of these genes overlap with orthologous genes in the anaerobic stimulon of E. coli. We define these as the conserved core, in which the transcriptional pattern as well as genetic architecture are well preserved. This conserved core includes previously described anaerobic metabolic pathways such as fermentation. Other components of the anaerobic stimulon show variation in genetic content, genome architecture and regulation. Notably formate metabolism, nitrate/nitrite metabolism, and fermentative butanediol production, differ between E

  13. Reconstructing a hydrogen-driven microbial metabolic network in Opalinus Clay rock

    Science.gov (United States)

    Bagnoud, Alexandre; Chourey, Karuna; Hettich, Robert L.; de Bruijn, Ino; Andersson, Anders F.; Leupin, Olivier X.; Schwyn, Bernhard; Bernier-Latmani, Rizlan

    2016-01-01

    The Opalinus Clay formation will host geological nuclear waste repositories in Switzerland. It is expected that gas pressure will build-up due to hydrogen production from steel corrosion, jeopardizing the integrity of the engineered barriers. In an in situ experiment located in the Mont Terri Underground Rock Laboratory, we demonstrate that hydrogen is consumed by microorganisms, fuelling a microbial community. Metagenomic binning and metaproteomic analysis of this deep subsurface community reveals a carbon cycle driven by autotrophic hydrogen oxidizers belonging to novel genera. Necromass is then processed by fermenters, followed by complete oxidation to carbon dioxide by heterotrophic sulfate-reducing bacteria, which closes the cycle. This microbial metabolic web can be integrated in the design of geological repositories to reduce pressure build-up. This study shows that Opalinus Clay harbours the potential for chemolithoautotrophic-based system, and provides a model of microbial carbon cycle in deep subsurface environments where hydrogen and sulfate are present. PMID:27739431

  14. Integrative microRNA and proteomic approaches identify novel osteoarthritis genes and their collaborative metabolic and inflammatory networks.

    Directory of Open Access Journals (Sweden)

    Dimitrios Iliopoulos

    Full Text Available BACKGROUND: Osteoarthritis is a multifactorial disease characterized by destruction of the articular cartilage due to genetic, mechanical and environmental components affecting more than 100 million individuals all over the world. Despite the high prevalence of the disease, the absence of large-scale molecular studies limits our ability to understand the molecular pathobiology of osteoathritis and identify targets for drug development. METHODOLOGY/PRINCIPAL FINDINGS: In this study we integrated genetic, bioinformatic and proteomic approaches in order to identify new genes and their collaborative networks involved in osteoarthritis pathogenesis. MicroRNA profiling of patient-derived osteoarthritic cartilage in comparison to normal cartilage, revealed a 16 microRNA osteoarthritis gene signature. Using reverse-phase protein arrays in the same tissues we detected 76 differentially expressed proteins between osteoarthritic and normal chondrocytes. Proteins such as SOX11, FGF23, KLF6, WWOX and GDF15 not implicated previously in the genesis of osteoarthritis were identified. Integration of microRNA and proteomic data with microRNA gene-target prediction algorithms, generated a potential "interactome" network consisting of 11 microRNAs and 58 proteins linked by 414 potential functional associations. Comparison of the molecular and clinical data, revealed specific microRNAs (miR-22, miR-103 and proteins (PPARA, BMP7, IL1B to be highly correlated with Body Mass Index (BMI. Experimental validation revealed that miR-22 regulated PPARA and BMP7 expression and its inhibition blocked inflammatory and catabolic changes in osteoarthritic chondrocytes. CONCLUSIONS/SIGNIFICANCE: Our findings indicate that obesity and inflammation are related to osteoarthritis, a metabolic disease affected by microRNA deregulation. Gene network approaches provide new insights for elucidating the complexity of diseases such as osteoarthritis. The integration of microRNA, proteomic

  15. Investigation of Archaeal and Bacterial community structure of five different small drinking water networks with special regard to the nitrifying microorganisms.

    Science.gov (United States)

    Nagymáté, Zsuzsanna; Homonnay, Zalán G; Márialigeti, Károly

    2016-01-01

    Total microbial community structure, and particularly nitrifying communities inhabiting five different small drinking water networks characterized with different water physical and chemical parameters was investigated, using cultivation-based methods and sequence aided Terminal Restriction Fragment Length Polymorphism (T-RFLP) analysis. Ammonium ion, originated from well water, was only partially oxidized via nitrite to nitrate in the drinking water distribution systems. Nitrification occurred at low ammonium ion concentration (27-46μM), relatively high pH (7.6-8.2) and over a wide range of dissolved oxygen concentrations (0.4-9.0mgL(-1)). The nitrifying communities of the distribution systems were characterized by variable most probable numbers (2×10(2)-7.1×10(4) MPN L(-1)) and probably originated from the non-treated well water. The sequence aided T-RFLP method revealed that ammonia-oxidizing microorganisms and nitrite-oxidizing Bacteria (Nitrosomonas oligotropha, Nitrosopumilus maritimus, and Nitrospira moscoviensis, 'Candidatus Nitrospira defluvii') were present in different ratios in the total microbial communities of the distinct parts of the water network systems. The nitrate generated by nitrification was partly utilized by nitrate-reducing (and denitrifying) Bacteria, present in low MPN and characterized by sequence aided T-RFLP as Comamonas sp. and Pseudomonas spp. Different environmental factors, like pH, chemical oxygen demand, calculated total inorganic nitrogen content (moreover nitrite and nitrate concentration), temperature had important effect on the total bacterial and archaeal community distribution.

  16. Characterization of the periplasmic redox network that sustains the versatile anaerobic metabolism of Shewanella oneidensis MR-1

    Directory of Open Access Journals (Sweden)

    Mónica N. Alves

    2015-06-01

    Full Text Available The versatile anaerobic metabolism of the Gram-negative bacterium Shewanella oneidensis MR-1 (SOMR-1 relies on a multitude of redox proteins found in its periplasm. Most are multiheme cytochromes that carry electrons to terminal reductases of insoluble electron acceptors located at the cell surface, or bona fide terminal reductases of soluble electron acceptors. In this study, the interaction network of several multiheme cytochromes was explored by a combination of NMR spectroscopy, activity assays followed by UV-visible spectroscopy and comparison of surface electrostatic potentials. From these data the small tetraheme cytochrome (STC emerges as the main periplasmic redox shuttle in SOMR-1. It accepts electrons from CymA and distributes them to a number of terminal oxidoreductases involved in the respiration of various compounds. STC is also involved in the electron transfer pathway to reduce nitrite by interaction with the octaheme tetrathionate reductase (OTR, but not with cytochrome c nitrite reductase (ccNiR. In the main pathway leading the metal respiration STC pairs with flavocytochrome c (FccA, the other major periplasmic cytochrome, which provides redundancy in this important pathway. The data reveals that the two proteins compete for the binding site at the surface of MtrA, the decaheme cytochrome inserted on the periplasmic side of the MtrCAB-OmcA outer-membrane complex. However, this is not observed for the MtrA homologues. Indeed, neither STC nor FccA interact with MtrD, the best replacement for MtrA, and only STC is able to interact with the decaheme cytochrome DmsE of the outer-membrane complex DmsEFABGH. Overall, these results shown that STC plays a central role in the anaerobic respiratory metabolism of SOMR-1. Nonetheless, the trans-periplasmic electron transfer chain is functionally resilient as a consequence of redundancies that arise from the presence of alternative pathways that bypass/compete with STC.

  17. Genome-scale metabolic network validation of Shewanella oneidensis using transposon insertion frequency analysis.

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