WorldWideScience

Sample records for bacterial metabolic network

  1. Structural correlations in bacterial metabolic networks

    Directory of Open Access Journals (Sweden)

    Lizana Ludvig

    2011-01-01

    Full Text Available Abstract Background Evolution of metabolism occurs through the acquisition and loss of genes whose products acts as enzymes in metabolic reactions, and from a presumably simple primordial metabolism the organisms living today have evolved complex and highly variable metabolisms. We have studied this phenomenon by comparing the metabolic networks of 134 bacterial species with known phylogenetic relationships, and by studying a neutral model of metabolic network evolution. Results We consider the 'union-network' of 134 bacterial metabolisms, and also the union of two smaller subsets of closely related species. Each reaction-node is tagged with the number of organisms it belongs to, which we denote organism degree (OD, a key concept in our study. Network analysis shows that common reactions are found at the centre of the network and that the average OD decreases as we move to the periphery. Nodes of the same OD are also more likely to be connected to each other compared to a random OD relabelling based on their occurrence in the real data. This trend persists up to a distance of around five reactions. A simple growth model of metabolic networks is used to investigate the biochemical constraints put on metabolic-network evolution. Despite this seemingly drastic simplification, a 'union-network' of a collection of unrelated model networks, free of any selective pressure, still exhibit similar structural features as their bacterial counterpart. Conclusions The OD distribution quantifies topological properties of the evolutionary history of bacterial metabolic networks, and lends additional support to the importance of horizontal gene transfer during bacterial metabolic evolution where new reactions are attached at the periphery of the network. The neutral model of metabolic network growth can reproduce the main features of real networks, but we observe that the real networks contain a smaller common core, while they are more similar at the periphery

  2. Structural correlations in bacterial metabolic networks.

    Science.gov (United States)

    Bernhardsson, Sebastian; Gerlee, Philip; Lizana, Ludvig

    2011-01-20

    Evolution of metabolism occurs through the acquisition and loss of genes whose products acts as enzymes in metabolic reactions, and from a presumably simple primordial metabolism the organisms living today have evolved complex and highly variable metabolisms. We have studied this phenomenon by comparing the metabolic networks of 134 bacterial species with known phylogenetic relationships, and by studying a neutral model of metabolic network evolution. We consider the 'union-network' of 134 bacterial metabolisms, and also the union of two smaller subsets of closely related species. Each reaction-node is tagged with the number of organisms it belongs to, which we denote organism degree (OD), a key concept in our study. Network analysis shows that common reactions are found at the centre of the network and that the average OD decreases as we move to the periphery. Nodes of the same OD are also more likely to be connected to each other compared to a random OD relabelling based on their occurrence in the real data. This trend persists up to a distance of around five reactions. A simple growth model of metabolic networks is used to investigate the biochemical constraints put on metabolic-network evolution. Despite this seemingly drastic simplification, a 'union-network' of a collection of unrelated model networks, free of any selective pressure, still exhibit similar structural features as their bacterial counterpart. The OD distribution quantifies topological properties of the evolutionary history of bacterial metabolic networks, and lends additional support to the importance of horizontal gene transfer during bacterial metabolic evolution where new reactions are attached at the periphery of the network. The neutral model of metabolic network growth can reproduce the main features of real networks, but we observe that the real networks contain a smaller common core, while they are more similar at the periphery of the network. This suggests that natural selection and

  3. The evolution of modularity in bacterial metabolic networks.

    Science.gov (United States)

    Kreimer, Anat; Borenstein, Elhanan; Gophna, Uri; Ruppin, Eytan

    2008-05-13

    Deciphering the modular organization of metabolic networks and understanding how modularity evolves have attracted tremendous interest in recent years. Here, we present a comprehensive large scale characterization of modularity across the bacterial tree of life, systematically quantifying the modularity of the metabolic networks of >300 bacterial species. Three main determinants of metabolic network modularity are identified. First, network size is an important topological determinant of network modularity. Second, several environmental factors influence network modularity, with endosymbionts and mammal-specific pathogens having lower modularity scores than bacterial species that occupy a wider range of niches. Moreover, even among the pathogens, those that alternate between two distinct niches, such as insect and mammal, tend to have relatively high metabolic network modularity. Third, horizontal gene transfer is an important force that contributes significantly to metabolic modularity. We additionally reconstruct the metabolic network of ancestral bacterial species and examine the evolution of modularity across the tree of life. This reveals a trend of modularity decrease from ancestors to descendants that is likely the outcome of niche specialization and the incorporation of peripheral metabolic reactions.

  4. Bacterial Unculturability and the Formation of Intercellular Metabolic Networks.

    Science.gov (United States)

    Pande, Samay; Kost, Christian

    2017-05-01

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

  5. Trade-offs between efficiency and robustness in bacterial metabolic networks are associated with niche breadth.

    Science.gov (United States)

    Morine, Melissa J; Gu, Hong; Myers, Ransom A; Bielawski, Joseph P

    2009-05-01

    The relation between structure and function in biologic networks is a central point of systems biology research. Key functional features--notably, efficiency and robustness--are linked to the topologic structure of a network, and there appears to be a degree of trade-off between these features, i.e., simulation studies indicate that more efficient networks tend to be less robust. Here, we investigate this issue in metabolic networks from 105 lineages of bacteria having a wide range of ecologies. We take quantitative measurements on each network and integrate this network data with ecologic data using a phylogenetic comparative model. In this setting, we find that biologic conclusions obtained with classical phylogenetic comparative methods are sensitive to correlations between model covariates and phylogenetic branch length. To avoid this problem, we propose a revised statistical framework--hierarchical mixed-effect regression--to accommodate phylogenetic nonindependence. Using this approach, we show that the cartography of metabolic networks does indeed reflect a trade-off between efficiency and robustness. Furthermore, ecologic characteristics related to niche breadth are strong predictors of network shape. Given the broad variation in niche breadth seen among species, we predict that there is no universally optimal balance between efficiency and robustness in bacterial metabolic networks and, thus, no universally optimal network structure. These results highlight the biologic relevance of variation in network structure and the potential role of niche breadth in shaping metabolic strategies of efficiency and robustness.

  6. Identification of a human neonatal immune-metabolic network associated with bacterial infection.

    Science.gov (United States)

    Smith, Claire L; Dickinson, Paul; Forster, Thorsten; Craigon, Marie; Ross, Alan; Khondoker, Mizanur R; France, Rebecca; Ivens, Alasdair; Lynn, David J; Orme, Judith; Jackson, Allan; Lacaze, Paul; Flanagan, Katie L; Stenson, Benjamin J; Ghazal, Peter

    2014-08-14

    Understanding how human neonates respond to infection remains incomplete. Here, a system-level investigation of neonatal systemic responses to infection shows a surprisingly strong but unbalanced homeostatic immune response; developing an elevated set-point of myeloid regulatory signalling and sugar-lipid metabolism with concomitant inhibition of lymphoid responses. Innate immune-negative feedback opposes innate immune activation while suppression of T-cell co-stimulation is coincident with selective upregulation of CD85 co-inhibitory pathways. By deriving modules of co-expressed RNAs, we identify a limited set of networks associated with bacterial infection that exhibit high levels of inter-patient variability. Whereas, by integrating immune and metabolic pathways, we infer a patient-invariant 52-gene-classifier that predicts bacterial infection with high accuracy using a new independent patient population. This is further shown to have predictive value in identifying infection in suspected cases with blood culture-negative tests. Our results lay the foundation for future translation of host pathways in advancing diagnostic, prognostic and therapeutic strategies for neonatal sepsis.

  7. Bacterial metabolism in immediate response to nutritional perturbation with temporal and network view of metabolites.

    Science.gov (United States)

    Yukihira, Daichi; Fujimura, Yoshinori; Wariishi, Hiroyuki; Miura, Daisuke

    2015-09-01

    In this study, the initial propagation of metabolic perturbation in Escherichia coli was visualized to understand the dynamic characteristics of the metabolic pathways without the association of transcription alterations. E. coli cells were exposed to the sudden relief of glucose starvation, and time-dependent variances in metabolite balances were traced in the second scale. The acquired time-course data were represented by structural variations of the metabolite-metabolite correlation network. The initial correlation structure was altered immediately by the glucose pulse, followed by further structural variations within a few minutes. It was demonstrated that one metabolite temporally correlated with distinct metabolites with different timings, and such a behavior could imply a regulatory role for the metabolite in the metabolic network. Centrality analysis of the networks and partial correlation analysis indicated that preparation for growth and oxidative stress could be coupled as a structural property of the metabolic pathways.

  8. Context-specific metabolic network reconstruction of a naphthalene-degrading bacterial community guided by metaproteomic data.

    Science.gov (United States)

    Tobalina, Luis; Bargiela, Rafael; Pey, Jon; Herbst, Florian-Alexander; Lores, Iván; Rojo, David; Barbas, Coral; Peláez, Ana I; Sánchez, Jesús; von Bergen, Martin; Seifert, Jana; Ferrer, Manuel; Planes, Francisco J

    2015-06-01

    With the advent of meta-'omics' data, the use of metabolic networks for the functional analysis of microbial communities became possible. However, while network-based methods are widely developed for single organisms, their application to bacterial communities is currently limited. Herein, we provide a novel, context-specific reconstruction procedure based on metaproteomic and taxonomic data. Without previous knowledge of a high-quality, genome-scale metabolic networks for each different member in a bacterial community, we propose a meta-network approach, where the expression levels and taxonomic assignments of proteins are used as the most relevant clues for inferring an active set of reactions. Our approach was applied to draft the context-specific metabolic networks of two different naphthalene-enriched communities derived from an anthropogenically influenced, polyaromatic hydrocarbon contaminated soil, with (CN2) or without (CN1) bio-stimulation. We were able to capture the overall functional differences between the two conditions at the metabolic level and predict an important activity for the fluorobenzoate degradation pathway in CN1 and for geraniol metabolism in CN2. Experimental validation was conducted, and good agreement with our computational predictions was observed. We also hypothesize different pathway organizations at the organismal level, which is relevant to disentangle the role of each member in the communities. The approach presented here can be easily transferred to the analysis of genomic, transcriptomic and metabolomic data. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  9. A novel proposal of a simplified bacterial gene set and the neo-construction of a general minimized metabolic network.

    Science.gov (United States)

    Ye, Yuan-Nong; Ma, Bin-Guang; Dong, Chuan; Zhang, Hong; Chen, Ling-Ling; Guo, Feng-Biao

    2016-10-07

    A minimal gene set (MGS) is critical for the assembly of a minimal artificial cell. We have developed a proposal of simplifying bacterial gene set to approximate a bacterial MGS by the following procedure. First, we base our simplified bacterial gene set (SBGS) on experimentally determined essential genes to ensure that the genes included in the SBGS are critical. Second, we introduced a half-retaining strategy to extract persistent essential genes to ensure stability. Third, we constructed a viable metabolic network to supplement SBGS. The proposed SBGS includes 327 genes and required 431 reactions. This report describes an SBGS that preserves both self-replication and self-maintenance systems. In the minimized metabolic network, we identified five novel hub metabolites and confirmed 20 known hubs. Highly essential genes were found to distribute the connecting metabolites into more reactions. Based on our SBGS, we expanded the pool of targets for designing broad-spectrum antibacterial drugs to reduce pathogen resistance. We also suggested a rough semi-de novo strategy to synthesize an artificial cell, with potential applications in industry.

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

    Science.gov (United States)

    2010-01-01

    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. PMID:20438628

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

    Science.gov (United States)

    Plaimas, Kitiporn; Eils, Roland; König, Rainer

    2010-05-03

    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. 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. 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.

  12. Radiometric detection of bacterial metabolism

    International Nuclear Information System (INIS)

    Camargo, E.E.; Wagner Junior, H.N.

    1979-01-01

    The measurement of 14 CO 2 produced by the bacterial oxidation of labelled compounds is discussed as a means of evaluating the bacterial metabolism. The following items are discussed:automated radiometric detection, types of graphs, clinical applications of the radiometric system and influential factors. Complementary studies on bacterial assimilation of substances are presented. (M.A.) [pt

  13. Urban infrastructure influences dissolved organic matter quality and bacterial metabolism in an urban stream network

    Science.gov (United States)

    Urban streams are degraded by a suite of factors, including burial beneath urban infrastructure (i.e., roads, parking lots) that eliminates light and reduces direct organic matter inputs to streams, with likely consequences for organic matter metabolism by microbes and carbon lim...

  14. Bacterial Networks in Cells and Communities.

    Science.gov (United States)

    Sourjik, Victor; Vorholt, Julia A

    2015-11-20

    Research on the bacterial regulatory networks is currently experiencing a true revival, driven by advances in methodology and by emergence of novel concepts. The biannual conference Bacterial Networks (BacNet15) held in May 2015, in Sant Feliu de Guíxols, Spain, covered progress in the studies of regulatory networks that control bacterial physiology, cell biology, stress responses, metabolism, collective behavior and evolution. It demonstrated how interdisciplinary approaches that combine molecular biology and biochemistry with the latest microscopy developments, whole cell (-omics) approaches and mathematical modeling can help understand design principles relevant in microbiology. It further showed how current biotechnology and medical microbiology could profit from our knowledge of and ability to engineer regulatory networks of bacteria. Copyright © 2015 Elsevier Ltd. All rights reserved.

  15. IdentiCS – Identification of coding sequence and in silico reconstruction of the metabolic network directly from unannotated low-coverage bacterial genome sequence

    Directory of Open Access Journals (Sweden)

    Zeng An-Ping

    2004-08-01

    Full Text Available Abstract Background A necessary step for a genome level analysis of the cellular metabolism is the in silico reconstruction of the metabolic network from genome sequences. The available methods are mainly based on the annotation of genome sequences including two successive steps, the prediction of coding sequences (CDS and their function assignment. The annotation process takes time. The available methods often encounter difficulties when dealing with unfinished error-containing genomic sequence. Results In this work a fast method is proposed to use unannotated genome sequence for predicting CDSs and for an in silico reconstruction of metabolic networks. Instead of using predicted genes or CDSs to query public databases, entries from public DNA or protein databases are used as queries to search a local database of the unannotated genome sequence to predict CDSs. Functions are assigned to the predicted CDSs simultaneously. The well-annotated genome of Salmonella typhimurium LT2 is used as an example to demonstrate the applicability of the method. 97.7% of the CDSs in the original annotation are correctly identified. The use of SWISS-PROT-TrEMBL databases resulted in an identification of 98.9% of CDSs that have EC-numbers in the published annotation. Furthermore, two versions of sequences of the bacterium Klebsiella pneumoniae with different genome coverage (3.9 and 7.9 fold, respectively are examined. The results suggest that a 3.9-fold coverage of the bacterial genome could be sufficiently used for the in silico reconstruction of the metabolic network. Compared to other gene finding methods such as CRITICA our method is more suitable for exploiting sequences of low genome coverage. Based on the new method, a program called IdentiCS (Identification of Coding Sequences from Unfinished Genome Sequences is delivered that combines the identification of CDSs with the reconstruction, comparison and visualization of metabolic networks (free to download

  16. Metabolic signatures of bacterial vaginosis.

    Science.gov (United States)

    Srinivasan, Sujatha; Morgan, Martin T; Fiedler, Tina L; Djukovic, Danijel; Hoffman, Noah G; Raftery, Daniel; Marrazzo, Jeanne M; Fredricks, David N

    2015-04-14

    Bacterial vaginosis (BV) is characterized by shifts in the vaginal microbiota from Lactobacillus dominant to a microbiota with diverse anaerobic bacteria. Few studies have linked specific metabolites with bacteria found in the human vagina. Here, we report dramatic differences in metabolite compositions and concentrations associated with BV using a global metabolomics approach. We further validated important metabolites using samples from a second cohort of women and a different platform to measure metabolites. In the primary study, we compared metabolite profiles in cervicovaginal lavage fluid from 40 women with BV and 20 women without BV. Vaginal bacterial representation was determined using broad-range PCR with pyrosequencing and concentrations of bacteria by quantitative PCR. We detected 279 named biochemicals; levels of 62% of metabolites were significantly different in women with BV. Unsupervised clustering of metabolites separated women with and without BV. Women with BV have metabolite profiles marked by lower concentrations of amino acids and dipeptides, concomitant with higher levels of amino acid catabolites and polyamines. Higher levels of the signaling eicosanoid 12-hydroxyeicosatetraenoic acid (12-HETE), a biomarker for inflammation, were noted in BV. Lactobacillus crispatus and Lactobacillus jensenii exhibited similar metabolite correlation patterns, which were distinct from correlation patterns exhibited by BV-associated bacteria. Several metabolites were significantly associated with clinical signs and symptoms (Amsel criteria) used to diagnose BV, and no metabolite was associated with all four clinical criteria. BV has strong metabolic signatures across multiple metabolic pathways, and these signatures are associated with the presence and concentrations of particular bacteria. Bacterial vaginosis (BV) is a common but highly enigmatic condition that is associated with adverse outcomes for women and their neonates. Small molecule metabolites in the

  17. VRML metabolic network visualizer.

    Science.gov (United States)

    Rojdestvenski, Igor

    2003-03-01

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

  18. Robustness of metabolic networks

    Science.gov (United States)

    Jeong, Hawoong

    2009-03-01

    We investigated the robustness of cellular metabolism by simulating the system-level computational models, and also performed the corresponding experiments to validate our predictions. We address the cellular robustness from the ``metabolite''-framework by using the novel concept of ``flux-sum,'' which is the sum of all incoming or outgoing fluxes (they are the same under the pseudo-steady state assumption). By estimating the changes of the flux-sum under various genetic and environmental perturbations, we were able to clearly decipher the metabolic robustness; the flux-sum around an essential metabolite does not change much under various perturbations. We also identified the list of the metabolites essential to cell survival, and then ``acclimator'' metabolites that can control the cell growth were discovered. Furthermore, this concept of ``metabolite essentiality'' should be useful in developing new metabolic engineering strategies for improved production of various bioproducts and designing new drugs that can fight against multi-antibiotic resistant superbacteria by knocking-down the enzyme activities around an essential metabolite. Finally, we combined a regulatory network with the metabolic network to investigate its effect on dynamic properties of cellular metabolism.

  19. Metabolic host responses to infection by intracellular bacterial pathogens

    Directory of Open Access Journals (Sweden)

    Wolfgang eEisenreich

    2013-07-01

    Full Text Available The interaction of bacterial pathogens with mammalian hosts leads to a variety of physiological responses of the interacting partners aimed at an adaptation to the new situation. These responses include multiple metabolic changes in the affected host cells which are most obvious when the pathogen replicates within host cells as in case of intracellular bacterial pathogens. While the pathogen tries to deprive nutrients from the host cell, the host cell in return takes various metabolic countermeasures against the nutrient theft. During this conflicting interaction, the pathogen triggers metabolic host cell responses by means of common cell envelope components and specific virulence-associated factors. These host reactions generally promote replication of the pathogen. There is growing evidence that pathogen-specific factors may interfere in different ways with the complex regulatory network that controls the carbon and nitrogen metabolism of mammalian cells. The host cell defence answers include general metabolic reactions, like the generation of oxygen- and/or nitrogen-reactive species, and more specific measures aimed to prevent access to essential nutrients for the respective pathogen. Accurate results on metabolic host cell responses are often hampered by the use of cancer cell lines that already exhibit various de-regulated reactions in the primary carbon metabolism. Hence, there is an urgent need for cellular models that more closely reflect the in vivo infection conditions. The exact knowledge of the metabolic host cell responses may provide new interesting concepts for antibacterial therapies.

  20. The role of metabolism in bacterial persistence

    Directory of Open Access Journals (Sweden)

    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.

  1. A bacterial quercetin oxidoreductase QuoA-mediated perturbation in the phenylpropanoid metabolic network increases lignification with a concomitant decrease in phenolamides in Arabidopsis

    Science.gov (United States)

    Swarup, Sanjay

    2013-01-01

    Metabolic perturbations by a gain-of-function approach provide a means to alter steady states of metabolites and query network properties, while keeping enzyme complexes intact. A combination of genetic and targeted metabolomics approach was used to understand the network properties of phenylpropanoid secondary metabolism pathways. A novel quercetin oxidoreductase, QuoA, from Pseudomonas putida, which converts quercetin to naringenin, thus effectively reversing the biosynthesis of quercetin through a de novo pathway, was expressed in Arabidopsis thaliana. QuoA transgenic lines selected for low, medium, and high expression levels of QuoA RNA had corresponding levels of QuoA activity and hypocotyl coloration resulting from increased anthocyanin accumulation. Stems of all three QuoA lines had increased tensile strength resulting from increased lignification. Sixteen metabolic intermediates from anthocyanin, lignin, and shikimate pathways had increased accumulation, of which 11 paralleled QuoA expression levels in the transgenic lines. The concomitant upregulation of the above pathways was explained by a significant downregulation of the phenolamide pathway and its precursor, spermidine. In a tt6 mutant line, lignifications as well as levels of the lignin pathway metabolites were much lower than those of QuoA transgenic lines. Unlike QuoA lines, phenolamides and spermidine were not affected in the tt6 line. Taken together, these results suggest that phenolamide pathway plays a major role in directing metabolic intermediates into the lignin pathway. Metabolic perturbations were accompanied by downregulation of five genes associated with branch-point enzymes and upregulation of their corresponding products. These results suggest that gene–metabolite pairs are likely to be co-ordinately regulated at critical branch points. Thus, these perturbations by a gain-of-function approach have uncovered novel properties of the phenylpropanoid metabolic network. PMID:24085580

  2. A bacterial quercetin oxidoreductase QuoA-mediated perturbation in the phenylpropanoid metabolic network increases lignification with a concomitant decrease in phenolamides in Arabidopsis.

    Science.gov (United States)

    Reuben, Sheela; Rai, Amit; Pillai, Bhinu V S; Rodrigues, Amrith; Swarup, Sanjay

    2013-11-01

    Metabolic perturbations by a gain-of-function approach provide a means to alter steady states of metabolites and query network properties, while keeping enzyme complexes intact. A combination of genetic and targeted metabolomics approach was used to understand the network properties of phenylpropanoid secondary metabolism pathways. A novel quercetin oxidoreductase, QuoA, from Pseudomonas putida, which converts quercetin to naringenin, thus effectively reversing the biosynthesis of quercetin through a de novo pathway, was expressed in Arabidopsis thaliana. QuoA transgenic lines selected for low, medium, and high expression levels of QuoA RNA had corresponding levels of QuoA activity and hypocotyl coloration resulting from increased anthocyanin accumulation. Stems of all three QuoA lines had increased tensile strength resulting from increased lignification. Sixteen metabolic intermediates from anthocyanin, lignin, and shikimate pathways had increased accumulation, of which 11 paralleled QuoA expression levels in the transgenic lines. The concomitant upregulation of the above pathways was explained by a significant downregulation of the phenolamide pathway and its precursor, spermidine. In a tt6 mutant line, lignifications as well as levels of the lignin pathway metabolites were much lower than those of QuoA transgenic lines. Unlike QuoA lines, phenolamides and spermidine were not affected in the tt6 line. Taken together, these results suggest that phenolamide pathway plays a major role in directing metabolic intermediates into the lignin pathway. Metabolic perturbations were accompanied by downregulation of five genes associated with branch-point enzymes and upregulation of their corresponding products. These results suggest that gene-metabolite pairs are likely to be co-ordinately regulated at critical branch points. Thus, these perturbations by a gain-of-function approach have uncovered novel properties of the phenylpropanoid metabolic network.

  3. 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.

  4. Evolution of metabolic network organization

    Directory of Open Access Journals (Sweden)

    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

  5. Cooperative Metabolism in a Three-Partner Insect-Bacterial Symbiosis Revealed by Metabolic Modeling.

    Science.gov (United States)

    Ankrah, Nana Y D; Luan, Junbo; Douglas, Angela E

    2017-08-01

    An important factor determining the impact of microbial symbionts on their animal hosts is the balance between the cost of nutrients consumed by the symbionts and the benefit of nutrients released back to the host, but the quantitative significance of nutrient exchange in symbioses involving multiple microbial partners has rarely been addressed. In this study on the association between two intracellular bacterial symbionts, " Candidatus Portiera aleyrodidarum" and " Candidatus Hamiltonella defensa," and their animal host, the whitefly Bemisia tabaci , we apply metabolic modeling to investigate host-symbiont nutrient exchange. Our in silico analysis revealed that >60% of the essential amino acids and related metabolites synthesized by " Candidatus Portiera aleyrodidarum" are utilized by the host, including a substantial contribution of nitrogen recycled from host nitrogenous waste, and that these interactions are required for host growth. In contrast, " Candidatus Hamiltonella defensa" retains most or all of the essential amino acids and B vitamins that it is capable of synthesizing. Furthermore, " Candidatus Hamiltonella defensa" suppresses host growth in silico by competition with " Candidatus Portiera aleyrodidarum" for multiple host nutrients, by suppressing " Candidatus Portiera aleyrodidarum" growth and metabolic function, and also by consumption of host nutrients that would otherwise be allocated to host growth. The interpretation from these modeling outputs that " Candidatus Hamiltonella defensa" is a nutritional parasite could not be inferred reliably from gene content alone but requires consideration of constraints imposed by the structure of the metabolic network. Furthermore, these quantitative models offer precise predictions for future experimental study and the opportunity to compare the functional organization of metabolic networks in different symbioses. IMPORTANCE The metabolic functions of unculturable intracellular bacteria with much reduced

  6. Bacterial Metabolism Shapes the Host-Pathogen Interface.

    Science.gov (United States)

    Passalacqua, Karla D; Charbonneau, Marie-Eve; O'Riordan, Mary X D

    2016-06-01

    Bacterial pathogens have evolved to exploit humans as a rich source of nutrients to support survival and replication. The pathways of bacterial metabolism that permit successful colonization are surprisingly varied and highlight remarkable metabolic flexibility. The constraints and immune pressures of distinct niches within the human body set the stage for understanding the mechanisms by which bacteria acquire critical nutrients. In this article we discuss how different bacterial pathogens carry out carbon and energy metabolism in the host and how they obtain or use key nutrients for replication and immune evasion.

  7. Bacterial metabolism shapes the host:pathogen interface

    Science.gov (United States)

    Passalacqua, Karla D.; Charbonneau, Marie-Eve; O'Riordan, Mary X.D.

    2016-01-01

    Summary Bacterial pathogens have evolved to exploit humans as a rich source of nutrients to support survival and replication. The pathways of bacterial metabolism that permit successful colonization are surprisingly varied and highlight remarkable metabolic flexibility. The constraints and immune pressures of distinct niches within the human body set the stage for understanding the mechanisms by which bacteria acquire critical nutrients. Here we discuss how different bacterial pathogens carry out carbon and energy metabolism in the host, and how they obtain or use key nutrients for replication and immune evasion. PMID:27337445

  8. 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...

  9. IDENTIFICATION AND ANALYSIS OF BACTERIAL GENOMIC METABOLIC SIGNATURES.

    Science.gov (United States)

    Bowerman, Nathaniel; Tintle, Nathan; Dejongh, Matthew; Best, Aaron A

    2017-01-01

    With continued rapid growth in the number and quality of fully sequenced and accurately annotated bacterial genomes, we have unprecedented opportunities to understand metabolic diversity. We selected 101 diverse and representative completely sequenced bacteria and implemented a manual curation effort to identify 846 unique metabolic variants present in these bacteria. The presence or absence of these variants act as a metabolic signature for each of the bacteria, which can then be used to understand similarities and differences between and across bacterial groups. We propose a novel and robust method of summarizing metabolic diversity using metabolic signatures and use this method to generate a metabolic tree, clustering metabolically similar organisms. Resulting analysis of the metabolic tree confirms strong associations with well-established biological results along with direct insight into particular metabolic variants which are most predictive of metabolic diversity. The positive results of this manual curation effort and novel method development suggest that future work is needed to further expand the set of bacteria to which this approach is applied and use the resulting tree to test broad questions about metabolic diversity and complexity across the bacterial tree of life.

  10. 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. Copyright © 2012 Elsevier Ltd. All rights reserved.

  11. Networked Chemoreceptors Benefit Bacterial Chemotaxis Performance

    Directory of Open Access Journals (Sweden)

    Vered Frank

    2016-12-01

    Full Text Available Motile bacteria use large receptor arrays to detect and follow chemical gradients in their environment. Extended receptor arrays, composed of networked signaling complexes, promote cooperative stimulus control of their associated signaling kinases. Here, we used structural lesions at the communication interface between core complexes to create an Escherichia coli strain with functional but dispersed signaling complexes. This strain allowed us to directly study how networking of signaling complexes affects chemotactic signaling and gradient-tracking performance. We demonstrate that networking of receptor complexes provides bacterial cells with about 10-fold-heightened detection sensitivity to attractants while maintaining a wide dynamic range over which receptor adaptational modifications can tune response sensitivity. These advantages proved especially critical for chemotaxis toward an attractant source under conditions in which bacteria are unable to alter the attractant gradient.

  12. 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...... 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...... 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...

  13. An Advance Organizer for Teaching Bacterial Metabolism

    Science.gov (United States)

    Barbosa, Heloiza R.; Marques, Marilis V.; Torres, Bayardo B.

    2005-01-01

    The metabolic versatility of bacteria is a source of learning difficulty for students in classical microbiology courses. To facilitate the learning process, the authors developed an advance organizer. It consists of a set of six diagrams of metabolic pathways describing the basic living requirements of several types of bacteria: energy, carbon…

  14. Bacterial colony counting by Convolutional Neural Networks.

    Science.gov (United States)

    Ferrari, Alessandro; Lombardi, Stefano; Signoroni, Alberto

    2015-01-01

    Counting bacterial colonies on microbiological culture plates is a time-consuming, error-prone, nevertheless fundamental task in microbiology. Computer vision based approaches can increase the efficiency and the reliability of the process, but accurate counting is challenging, due to the high degree of variability of agglomerated colonies. In this paper, we propose a solution which adopts Convolutional Neural Networks (CNN) for counting the number of colonies contained in confluent agglomerates, that scored an overall accuracy of the 92.8% on a large challenging dataset. The proposed CNN-based technique for estimating the cardinality of colony aggregates outperforms traditional image processing approaches, becoming a promising approach to many related applications.

  15. Lethal Consequences of Overcoming Metabolic Restrictions Imposed on a Cooperative Bacterial Population.

    Science.gov (United States)

    Goo, Eunhye; Kang, Yongsung; Lim, Jae Yun; Ham, Hyeonheui; Hwang, Ingyu

    2017-02-28

    Quorum sensing (QS) controls cooperative activities in many Proteobacteria In some species, QS-dependent specific metabolism contributes to the stability of the cooperation. However, the mechanism by which QS and metabolic networks have coevolved to support stable public good cooperation and maintenance of the cooperative group remains unknown. Here we explored the underlying mechanisms of QS-controlled central metabolism in the evolutionary aspects of cooperation. In Burkholderia glumae , the QS-dependent glyoxylate cycle plays an important role in cooperativity. A bifunctional QS-dependent transcriptional regulator, QsmR, rewired central metabolism to utilize the glyoxylate cycle rather than the tricarboxylic acid cycle. Defects in the glyoxylate cycle caused metabolic imbalance and triggered high expression of the stress-responsive chaperonin GroEL. High-level expression of GroEL in glyoxylate cycle mutants interfered with the biosynthesis of a public resource, oxalate, by physically interrupting the oxalate biosynthetic enzyme ObcA. Under such destabilized cooperativity conditions, spontaneous mutations in the qsmR gene in glyoxylate cycle mutants occurred to relieve metabolic stresses, but these mutants lost QsmR-mediated pleiotropy. Overcoming the metabolic restrictions imposed on the population of cooperators among glyoxylate cycle mutants resulted in the occurrence and selection of spontaneous qsmR mutants despite the loss of other important functions. These results provide insight into how QS bacteria have evolved to maintain stable cooperation via QS-mediated metabolic coordination. IMPORTANCE We address how quorum sensing (QS) has coevolved with metabolic networks to maintain bacterial sociality. We found that QS-mediated metabolic rewiring is critical for sustainable bacterial cooperation in Burkholderia glumae The loss of the glyoxylate cycle triggered the expression of the stress-responsive molecular chaperonin GroEL. Excessive biosynthesis of Gro

  16. Cerebral Metabolic Changes Related to Oxidative Metabolism in a Model of Bacterial Meningitis Induced by Lipopolysaccharide

    DEFF Research Database (Denmark)

    Munk, Michael; Rom Poulsen, Frantz; Larsen, Lykke

    2018-01-01

    BACKGROUND: Cerebral mitochondrial dysfunction is prominent in the pathophysiology of severe bacterial meningitis. In the present study, we hypothesize that the metabolic changes seen after intracisternal lipopolysaccharide (LPS) injection in a piglet model of meningitis is compatible...... with mitochondrial dysfunction and resembles the metabolic patterns seen in patients with bacterial meningitis. METHODS: Eight pigs received LPS injection in cisterna magna, and four pigs received NaCl in cisterna magna as a control. Biochemical variables related to energy metabolism were monitored by intracerebral...... dysfunction with increasing cerebral LPR due to increased lactate and normal pyruvate, PbtO2, and ICP. The metabolic pattern resembles the one observed in patients with bacterial meningitis. Metabolic monitoring in these patients is feasible to monitor for cerebral metabolic derangements otherwise missed...

  17. Metabolic competition as a driver of bacterial population structure.

    Science.gov (United States)

    Watkins, Eleanor R; Maiden, Martin Cj; Gupta, Sunetra

    2016-10-01

    Understanding the processes whereby diversity arises and is maintained in pathogen populations is pivotal for designing disease control interventions. A particular problem is the maintenance of strain structure in bacterial pathogen populations despite frequent genetic exchange. Although several theoretical frameworks have been put forward to explain this widespread phenomenon, few have focused on the role of genes encoding metabolic functions, despite an increasing recognition of their importance in pathogenesis and transmission. In this article, we review the literature for evidence of metabolic niches within the host and discuss theoretical frameworks which examine ecological interactions between metabolic genes. We contend that metabolic competition is an important phenomenon which contributes to the maintenance of population structure and diversity of many bacterial pathogens.

  18. Stability from Structure : Metabolic Networks Are Unlike Other Biological Networks

    NARCIS (Netherlands)

    Van Nes, P.; Bellomo, D.; Reinders, M.J.T.; De Ridder, D.

    2009-01-01

    In recent work, attempts have been made to link the structure of biochemical networks to their complex dynamics. It was shown that structurally stable network motifs are enriched in such networks. In this work, we investigate to what extent these findings apply to metabolic networks. To this end, we

  19. Bacterial fatty acid metabolism in modern antibiotic discovery.

    Science.gov (United States)

    Yao, Jiangwei; Rock, Charles O

    2017-11-01

    Bacterial fatty acid synthesis is essential for many pathogens and different from the mammalian counterpart. These features make bacterial fatty acid synthesis a desirable target for antibiotic discovery. The structural divergence of the conserved enzymes and the presence of different isozymes catalyzing the same reactions in the pathway make bacterial fatty acid synthesis a narrow spectrum target rather than the traditional broad spectrum target. Furthermore, bacterial fatty acid synthesis inhibitors are single-targeting, rather than multi-targeting like traditional monotherapeutic, broad-spectrum antibiotics. The single-targeting nature of bacterial fatty acid synthesis inhibitors makes overcoming fast-developing, target-based resistance a necessary consideration for antibiotic development. Target-based resistance can be overcome through multi-targeting inhibitors, a cocktail of single-targeting inhibitors, or by making the single targeting inhibitor sufficiently high affinity through a pathogen selective approach such that target-based mutants are still susceptible to therapeutic concentrations of drug. Many of the pathogens requiring new antibiotic treatment options encode for essential bacterial fatty acid synthesis enzymes. This review will evaluate the most promising targets in bacterial fatty acid metabolism for antibiotic therapeutics development and review the potential and challenges in advancing each of these targets to the clinic and circumventing target-based resistance. This article is part of a Special Issue entitled: Bacterial Lipids edited by Russell E. Bishop. Copyright © 2016 Elsevier B.V. All rights reserved.

  20. 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

  1. Bacterial adaptation through distributed sensing of metabolic fluxes

    NARCIS (Netherlands)

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

    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

  2. Occurrence, metabolism, metabolic role, and industrial uses of bacterial polyhydroxyalkanoates.

    OpenAIRE

    Anderson, A J; Dawes, E A

    1990-01-01

    Polyhydroxyalkanoates (PHAs), of which polyhydroxybutyrate (PHB) is the most abundant, are bacterial carbon and energy reserve materials of widespread occurrence. They are composed of 3-hydroxyacid monomer units and exist as a small number of cytoplasmic granules per cell. The properties of the C4 homopolymer PHB as a biodegradable thermoplastic first attracted industrial attention more than 20 years ago. Copolymers of C4 (3-hydroxybutyrate [3HB]) and C5 (3-hydroxyvalerate [3HV]) monomer unit...

  3. Bacterial metabolism of 2,6-xylenol.

    Science.gov (United States)

    Ewers, J; Rubio, M A; Knackmuss, H J; Freier-Schröder, D

    1989-11-01

    Strain DM1, a Mycobacterium sp. that utilizes 2,6-xylenol, 2,3,6-trimethylphenol, and o-cresol as sources of carbon and energy, was isolated. Intact cells of Mycobacterium strain DM1 grown with 2,6-xylenol cooxidized 2,4,6-trimethylphenol to 2,4,6-trimethylresorcinol. 4-Chloro-3,5-dimethylphenol prevents 2,6-xylenol from being totally degraded; it was quantitatively converted to 2,6-dimethylhydroquinone by resting cells. 2,6-Dimethylhydroquinone, citraconate, and an unidentified metabolite were detected as products of 2,6-xylenol oxidation in cells that were partially inactivated by EDTA. Under oxygen limitation, 2,6-dimethylhy-droquinone, citraconate, and an unidentified metabolite were released during 2,6-xylenol turnover by resting cells. Cell extracts of 2,6-xylenol-grown cells contained a 2,6-dimethylhydroquinone-converting enzyme. When supplemented with NADH, cell extracts catalyzed the reduction of 2,6-dimethyl-3-hydroxyquinone to 2,6-dimethyl-3-hydroxyhydroquinone. Since a citraconase was also demonstrated in cell extracts, a new metabolic pathway with 2,6-dimethyl-3-hydroxyhydroquinone as the ring fission substrate is proposed.

  4. Occurrence, metabolism, metabolic role, and industrial uses of bacterial polyhydroxyalkanoates.

    Science.gov (United States)

    Anderson, A J; Dawes, E A

    1990-12-01

    Polyhydroxyalkanoates (PHAs), of which polyhydroxybutyrate (PHB) is the most abundant, are bacterial carbon and energy reserve materials of widespread occurrence. They are composed of 3-hydroxyacid monomer units and exist as a small number of cytoplasmic granules per cell. The properties of the C4 homopolymer PHB as a biodegradable thermoplastic first attracted industrial attention more than 20 years ago. Copolymers of C4 (3-hydroxybutyrate [3HB]) and C5 (3-hydroxyvalerate [3HV]) monomer units have modified physical properties; e.g., the plastic is less brittle than PHB, whereas PHAs containing C8 to C12 monomers behave as elastomers. This family of materials is the centre of considerable commercial interest, and 3HB-co-3HV copolymers have been marketed by ICI plc as Biopol. The known polymers exist as 2(1) helices with the fiber repeat decreasing from 0.596 nm for PHB to about 0.45 nm for C8 to C10 polymers. Novel copolymers with a backbone of 3HB and 4HB have been obtained. The native granules contain noncrystalline polymer, and water may possibly act as a plasticizer. Although the biosynthesis and regulation of PHB are generally well understood, the corresponding information for the synthesis of long-side-chain PHAs from alkanes, alcohols, and organic acids is still incomplete. The precise mechanisms of action of the polymerizing and depolymerizing enzymes also remain to be established. The structural genes for the three key enzymes of PHB synthesis from acetyl coenzyme A in Alcaligenes eutrophus have been cloned, sequenced, and expressed in Escherichia coli. Polymer molecular weights appear to be species specific. The factors influencing the commercial choice of organism, substrate, and isolation process are discussed. The physiological functions of PHB as a reserve material and in symbiotic nitrogen fixation and its presence in bacterial plasma membranes and putative role in transformability and calcium signaling are also considered.

  5. 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

  6. Cerebral oxygenation and energy metabolism in bacterial meningitis

    DEFF Research Database (Denmark)

    Larsen, Lykke

    Introduction: In a recent retrospective study of patients with severe bacterial meningitis we demonstrated that cerebral oxidative metabolism was affected in approximately 50% of the cases. An increase of lactate/pyruvate (LP) ratio above the upper normal limit, defined according to according...... bacterial meningitis; secondly to examine whether it is correct to separate the diagnosis of cerebral ischemia from mitochondrial dysfunction based exclusively on the biochemical pattern obtained during intracerebral microdialysis. Method: A prospective clinical study including patients with severe...... community acquired bacterial meningitis admitted to the Department of Infectious Diseases, Odense University Hospital, during the period January 2014 to June 2016. We relate data from measurements of brain tissue oxygen tension (PbtO2) to simultaneously recorded data reflecting cerebral cytoplasmic redox...

  7. Hierarchical analysis of dependency in metabolic networks.

    Science.gov (United States)

    Gagneur, Julien; Jackson, David B; Casari, Georg

    2003-05-22

    Elucidation of metabolic networks for an increasing number of organisms reveals that even small networks can contain thousands of reactions and chemical species. The intimate connectivity between components complicates their decomposition into biologically meaningful sub-networks. Moreover, traditional higher-order representations of metabolic networks as metabolic pathways, suffers from the lack of rigorous definition, yielding pathways of disparate content and size. We introduce a hierarchical representation that emphasizes the gross organization of metabolic networks in largely independent pathways and sub-systems at several levels of independence. The approach highlights the coupling of different pathways and the shared compounds responsible for those couplings. By assessing our results on Escherichia coli (E.coli metabolic reactions, Genetic Circuits Research Group, University of California, San Diego, http://gcrg.ucsd.edu/organisms/ecoli.html, 'model v 1.01. reactions') against accepted biochemical annotations, we provide the first systematic synopsis of an organism's metabolism. Comparison with operons of E.coli shows that low-level clusters are reflected in genome organization and gene regulation. Source code, data sets and supplementary information are available at http://www.mas.ecp.fr/labo/equipe/gagneur/hierarchy/hierarchy.html

  8. Cell-to-cell variation and specialization in sugar metabolism in clonal bacterial populations

    OpenAIRE

    Nikolic, Nela; Schreiber, Frank; Dal Co, Alma; Kiviet, Daniel J.; Bergmiller, Tobias; Littmann, Sten; Kuypers, Marcel M. M.; Ackermann, Martin

    2017-01-01

    Author summary This study addresses a fundamental question in bacterial metabolism: do all individuals in a clonal population express the same metabolic functions, or do individuals specialize on different metabolic functions and assimilate different substrates? Reports about stochastic gene expression in bacterial populations raise the possibility that transcriptional differences between individuals translate into different metabolic behaviors, but the prevalence and magnitude of such effect...

  9. Influence of nanophase titania topography on bacterial attachment and metabolism

    Directory of Open Access Journals (Sweden)

    Margaret R Park

    2008-12-01

    Full Text Available Margaret R Park1, Michelle K Banks1, Bruce Applegate2, Thomas J Webster31School of Civil Engineering; 2Department of Food Science; 3School of Biomedical Engineering, Purdue University, West Lafayette, IN, USAAbstract: Surfaces with nanophase compared to conventional (or nanometer smooth topographies are known to have different properties of area, charge, and reactivity. Previously published research indicates that the attachment of certain bacteria (such as Pseudomonas fluorescens 5RL is higher on surfaces with nanophase compared to conventional topographies, however, their effect on bacterial metabolism is unclear. Results presented here show that the adhesion of Pseudomonas fluorescens 5RL and Pseudomonas putida TVA8 was higher on nanophase than conventional titania. Importantly, in terms of metabolism, bacteria attached to the nanophase surfaces had higher bioluminescence rates than on the conventional surfaces under all nutrient conditions. Thus, the results from this study show greater select bacterial metabolism on nanometer than conventional topographies, critical results with strong consequences for the design of improved biosensors for bacteria detection.Keywords: bacteria, attachment, nanophase, topography, metabolism

  10. High resolution deuterium NMR studies of bacterial metabolism

    International Nuclear Information System (INIS)

    Aguayo, J.B.; Gamcsik, M.P.; Dick, J.D.

    1988-01-01

    High resolution deuterium NMR spectra were obtained from suspensions of five bacterial strains: Escherichia coli, Clostridium perfringens, Klebsiella pneumoniae, Proteus mirabilis, and Staphylococcus aureus. Deuterium-labeled D-glucose at C-1, C-2, and C-6 was used to monitor dynamically anaerobic metabolism. The flux of glucose through the various bacterial metabolic pathways could be determined by following the disappearance of glucose and the appearance of the major end products in the 2H NMR spectrum. The presence of both labeled and unlabeled metabolites could be detected using 1H NMR spectroscopy since the proton resonances in the labeled species are shifted upfield due to an isotopic chemical shift effect. The 1H-1H scalar coupling observed in both the 2H and 1H NMR spectra was used to assign definitively the resonances of labeled species. An increase in the intensity of natural abundance deuterium signal of water can be used to monitor pathways in which a deuteron is lost from the labeled metabolite. The steps in which label loss can occur are outlined, and the influence these processes have on the ability of 2H NMR spectroscopy to monitor metabolism are assessed

  11. Cerebral Metabolic Changes Related to Oxidative Metabolism in a Model of Bacterial Meningitis Induced by Lipopolysaccharide.

    Science.gov (United States)

    Munk, M; Poulsen, F R; Larsen, L; Nordström, C H; Nielsen, T H

    2018-03-05

    Cerebral mitochondrial dysfunction is prominent in the pathophysiology of severe bacterial meningitis. In the present study, we hypothesize that the metabolic changes seen after intracisternal lipopolysaccharide (LPS) injection in a piglet model of meningitis is compatible with mitochondrial dysfunction and resembles the metabolic patterns seen in patients with bacterial meningitis. Eight pigs received LPS injection in cisterna magna, and four pigs received NaCl in cisterna magna as a control. Biochemical variables related to energy metabolism were monitored by intracerebral microdialysis technique and included interstitial glucose, lactate, pyruvate, glutamate, and glycerol. The intracranial pressure (ICP) and brain tissue oxygen tension (PbtO 2 ) were also monitored along with physiological variables including mean arterial pressure, blood glucose, lactate, and partial pressure of O 2 and CO 2 . Pigs were monitored for 60 min at baseline and 240 min after LPS/NaCl injection. After LPS injection, a significant increase in cerebral lactate/pyruvate ratio (LPR) compared to control group was registered (p = 0.01). This increase was due to a significant increased lactate with stable and normal values of pyruvate. No significant change in PbtO 2 or ICP was registered. No changes in physiological variables were observed. The metabolic changes after intracisternal LPS injection is compatible with disturbance in the oxidative metabolism and partly due to mitochondrial dysfunction with increasing cerebral LPR due to increased lactate and normal pyruvate, PbtO 2 , and ICP. The metabolic pattern resembles the one observed in patients with bacterial meningitis. Metabolic monitoring in these patients is feasible to monitor for cerebral metabolic derangements otherwise missed by conventional intensive care monitoring.

  12. 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......, the fungus will produce various by-products like organic acids and polyols. In order to circumvent this problem we here study the effects of the expression of a bacterial hemoglobin protein on the metabolism of A. niger. We integrated the vgb gene from Vitreoscilla sp. into the genome at the pyrA locus...... 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...

  13. Metabolic networks of Sodalis glossinidius: a systems biology approach to reductive evolution.

    Science.gov (United States)

    Belda, Eugeni; Silva, Francisco J; Peretó, Juli; Moya, Andrés

    2012-01-01

    Genome reduction is a common evolutionary process affecting bacterial lineages that establish symbiotic or pathogenic associations with eukaryotic hosts. Such associations yield highly reduced genomes with greatly streamlined metabolic abilities shaped by the type of ecological association with the host. Sodalis glossinidius, the secondary endosymbiont of tsetse flies, represents one of the few complete genomes available of a bacterium at the initial stages of this process. In the present study, genome reduction is studied from a systems biology perspective through the reconstruction and functional analysis of genome-scale metabolic networks of S. glossinidius. The functional profile of ancestral and extant metabolic networks sheds light on the evolutionary events underlying transition to a host-dependent lifestyle. Meanwhile, reductive evolution simulations on the extant metabolic network can predict possible future evolution of S. glossinidius in the context of genome reduction. Finally, knockout simulations in different metabolic systems reveal a gradual decrease in network robustness to different mutational events for bacterial endosymbionts at different stages of the symbiotic association. Stoichiometric analysis reveals few gene inactivation events whose effects on the functionality of S. glossinidius metabolic systems are drastic enough to account for the ecological transition from a free-living to host-dependent lifestyle. The decrease in network robustness across different metabolic systems may be associated with the progressive integration in the more stable environment provided by the insect host. Finally, reductive evolution simulations reveal the strong influence that external conditions exert on the evolvability of metabolic systems.

  14. Stability of multispecies bacterial communities: signaling networks may stabilize microbiomes.

    Directory of Open Access Journals (Sweden)

    Ádám Kerényi

    Full Text Available Multispecies bacterial communities can be remarkably stable and resilient even though they consist of cells and species that compete for environmental resources. In silico models suggest that common signals released into the environment may help selected bacterial species cluster at common locations and that sharing of public goods (i.e. molecules produced and released for mutual benefit can stabilize this coexistence. In contrast, unilateral eavesdropping on signals produced by a potentially invading species may protect a community by keeping invaders away from limited resources. Shared bacterial signals, such as those found in quorum sensing systems, may thus play a key role in fine tuning competition and cooperation within multi-bacterial communities. We suggest that in addition to metabolic complementarity, signaling dynamics may be important in further understanding complex bacterial communities such as the human, animal as well as plant microbiomes.

  15. On Functional Module Detection in Metabolic Networks

    Science.gov (United States)

    Koch, Ina; Ackermann, Jörg

    2013-01-01

    Functional modules of metabolic networks are essential for understanding the metabolism of an organism as a whole. With the vast amount of experimental data and the construction of complex and large-scale, often genome-wide, models, the computer-aided identification of functional modules becomes more and more important. Since steady states play a key role in biology, many methods have been developed in that context, for example, elementary flux modes, extreme pathways, transition invariants and place invariants. Metabolic networks can be studied also from the point of view of graph theory, and algorithms for graph decomposition have been applied for the identification of functional modules. A prominent and currently intensively discussed field of methods in graph theory addresses the Q-modularity. In this paper, we recall known concepts of module detection based on the steady-state assumption, focusing on transition-invariants (elementary modes) and their computation as minimal solutions of systems of Diophantine equations. We present the Fourier-Motzkin algorithm in detail. Afterwards, we introduce the Q-modularity as an example for a useful non-steady-state method and its application to metabolic networks. To illustrate and discuss the concepts of invariants and Q-modularity, we apply a part of the central carbon metabolism in potato tubers (Solanum tuberosum) as running example. The intention of the paper is to give a compact presentation of known steady-state concepts from a graph-theoretical viewpoint in the context of network decomposition and reduction and to introduce the application of Q-modularity to metabolic Petri net models. PMID:24958145

  16. Metabolic Regulation of a Bacterial Cell System with Emphasis on Escherichia coli Metabolism

    Science.gov (United States)

    Shimizu, Kazuyuki

    2013-01-01

    It is quite important to understand the overall metabolic regulation mechanism of bacterial cells such as Escherichia coli from both science (such as biochemistry) and engineering (such as metabolic engineering) points of view. Here, an attempt was made to clarify the overall metabolic regulation mechanism by focusing on the roles of global regulators which detect the culture or growth condition and manipulate a set of metabolic pathways by modulating the related gene expressions. For this, it was considered how the cell responds to a variety of culture environments such as carbon (catabolite regulation), nitrogen, and phosphate limitations, as well as the effects of oxygen level, pH (acid shock), temperature (heat shock), and nutrient starvation. PMID:25937963

  17. Bistable responses in bacterial genetic networks: Designs and dynamical consequences

    Science.gov (United States)

    Tiwari, Abhinav; Ray, J. Christian J.; Narula, Jatin; Igoshin, Oleg A.

    2011-01-01

    A key property of living cells is their ability to react to stimuli with specific biochemical responses. These responses can be understood through the dynamics of underlying biochemical and genetic networks. Evolutionary design principles have been well studied in networks that display graded responses, with a continuous relationship between input signal and system output. Alternatively, biochemical networks can exhibit bistable responses so that over a range of signals the network possesses two stable steady states. In this review, we discuss several conceptual examples illustrating network designs that can result in a bistable response of the biochemical network. Next, we examine manifestations of these designs in bacterial master-regulatory genetic circuits. In particular, we discuss mechanisms and dynamic consequences of bistability in three circuits: two-component systems, sigma-factor networks, and a multistep phosphorelay. Analyzing these examples allows us to expand our knowledge of evolutionary design principles for networks with bistable responses. PMID:21385588

  18. Ecological network analysis of China's societal metabolism.

    Science.gov (United States)

    Zhang, Yan; Liu, Hong; Li, Yating; Yang, Zhifeng; Li, Shengsheng; Yang, Naijin

    2012-01-01

    Uncontrolled socioeconomic development has strong negative effects on the ecological environment, including pollution and the depletion and waste of natural resources. These serious consequences result from the high flows of materials and energy through a socioeconomic system produced by exchanges between the system and its surroundings, causing the disturbance of metabolic processes. In this paper, we developed an ecological network model for a societal system, and used China in 2006 as a case study to illustrate application of the model. We analyzed China's basic metabolic processes and used ecological network analysis to study the network relationships within the system. Basic components comprised the internal environment, five sectors (agriculture, exploitation, manufacturing, domestic, and recycling), and the external environment. We defined 21 pairs of ecological relationships in China's societal metabolic system (excluding self-mutualism within a component). Using utility and throughflow analysis, we found that exploitation, mutualism, and competition relationships accounted for 76.2, 14.3, and 9.5% of the total relationships, respectively. In our trophic level analysis, the components were divided into producers, consumers, and decomposers according to their positions in the system. Our analyses revealed ways to optimize the system's structure and adjust its functions, thereby promoting healthier socioeconomic development, and suggested ways to apply ecological network analysis in future socioeconomic research. Copyright © 2011 Elsevier Ltd. All rights reserved.

  19. Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network.

    Science.gov (United States)

    Kim, Hyun Uk; Kim, Tae Yong; Lee, Sang Yup

    2011-01-01

    Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology. We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism's metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model. After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis.

  20. 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. Copyright © 2015 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

  1. Genotype networks, innovation, and robustness in sulfur metabolism

    Science.gov (United States)

    2011-01-01

    Background A metabolism is a complex network of chemical reactions. This network synthesizes multiple small precursor molecules of biomass from chemicals that occur in the environment. The metabolic network of any one organism is encoded by a metabolic genotype, defined as the set of enzyme-coding genes whose products catalyze the network's reactions. Each metabolic genotype has a metabolic phenotype. We define this metabolic phenotype as the spectrum of different sources of a chemical element that a metabolism can use to synthesize biomass. We here focus on the element sulfur. We study properties of the space of all possible metabolic genotypes in sulfur metabolism by analyzing random metabolic genotypes that are viable on different numbers of sulfur sources. Results We show that metabolic genotypes with the same phenotype form large connected genotype networks - networks of metabolic networks - that extend far through metabolic genotype space. How far they reach through this space depends linearly on the number of super-essential reactions. A super-essential reaction is an essential reaction that occurs in all networks viable in a given environment. Metabolic networks can differ in how robust their phenotype is to the removal of individual reactions. We find that this robustness depends on metabolic network size, and on other variables, such as the size of minimal metabolic networks whose reactions are all essential in a specific environment. We show that different neighborhoods of any genotype network harbor very different novel phenotypes, metabolic innovations that can sustain life on novel sulfur sources. We also analyze the ability of evolving populations of metabolic networks to explore novel metabolic phenotypes. This ability is facilitated by the existence of genotype networks, because different neighborhoods of these networks contain very different novel phenotypes. Conclusions We show that the space of metabolic genotypes involved in sulfur metabolism

  2. Applications of CRISPR/Cas System to Bacterial Metabolic Engineering

    Directory of Open Access Journals (Sweden)

    Suhyung Cho

    2018-04-01

    Full Text Available The clustered regularly interspaced short palindromic repeats/CRISPR-associated (CRISPR/Cas adaptive immune system has been extensively used for gene editing, including gene deletion, insertion, and replacement in bacterial and eukaryotic cells owing to its simple, rapid, and efficient activities in unprecedented resolution. Furthermore, the CRISPR interference (CRISPRi system including deactivated Cas9 (dCas9 with inactivated endonuclease activity has been further investigated for regulation of the target gene transiently or constitutively, avoiding cell death by disruption of genome. This review discusses the applications of CRISPR/Cas for genome editing in various bacterial systems and their applications. In particular, CRISPR technology has been used for the production of metabolites of high industrial significance, including biochemical, biofuel, and pharmaceutical products/precursors in bacteria. Here, we focus on methods to increase the productivity and yield/titer scan by controlling metabolic flux through individual or combinatorial use of CRISPR/Cas and CRISPRi systems with introduction of synthetic pathway in industrially common bacteria including Escherichia coli. Further, we discuss additional useful applications of the CRISPR/Cas system, including its use in functional genomics.

  3. Metabolic activity of bacterial cell enumerated by direct viable count

    International Nuclear Information System (INIS)

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

    1987-01-01

    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- 3 H] thymidine or [U- 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

  4. Bacterial microcompartments as metabolic modules for plant synthetic biology.

    Science.gov (United States)

    Gonzalez-Esquer, C Raul; Newnham, Sarah E; Kerfeld, Cheryl A

    2016-07-01

    Bacterial microcompartments (BMCs) are megadalton-sized protein assemblies that enclose segments of metabolic pathways within cells. They increase the catalytic efficiency of the encapsulated enzymes while sequestering volatile or toxic intermediates from the bulk cytosol. The first BMCs discovered were the carboxysomes of cyanobacteria. Carboxysomes compartmentalize the enzyme ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) with carbonic anhydrase. They enhance the carboxylase activity of RuBisCO by increasing the local concentration of CO2 in the vicinity of the enzyme's active site. As a metabolic module for carbon fixation, carboxysomes could be transferred to eukaryotic organisms (e.g. plants) to increase photosynthetic efficiency. Within the scope of synthetic biology, carboxysomes and other BMCs hold even greater potential when considered a source of building blocks for the development of nanoreactors or three-dimensional scaffolds to increase the efficiency of either native or heterologously expressed enzymes. The carboxysome serves as an ideal model system for testing approaches to engineering BMCs because their expression in cyanobacteria provides a sensitive screen for form (appearance of polyhedral bodies) and function (ability to grow on air). We recount recent progress in the re-engineering of the carboxysome shell and core to offer a conceptual framework for the development of BMC-based architectures for applications in plant synthetic biology. © 2016 The Authors The Plant Journal © 2016 John Wiley & Sons Ltd.

  5. The reconstruction and analysis of tissue specific human metabolic networks.

    Science.gov (United States)

    Hao, Tong; Ma, Hong-Wu; Zhao, Xue-Ming; Goryanin, Igor

    2012-02-01

    Human tissues have distinct biological functions. Many proteins/enzymes are known to be expressed only in specific tissues and therefore the metabolic networks in various tissues are different. Though high quality global human metabolic networks and metabolic networks for certain tissues such as liver have already been studied, a systematic study of tissue specific metabolic networks for all main tissues is still missing. In this work, we reconstruct the tissue specific metabolic networks for 15 main tissues in human based on the previously reconstructed Edinburgh Human Metabolic Network (EHMN). The tissue information is firstly obtained for enzymes from Human Protein Reference Database (HPRD) and UniprotKB databases and transfers to reactions through the enzyme-reaction relationships in EHMN. As our knowledge of tissue distribution of proteins is still very limited, we replenish the tissue information of the metabolic network based on network connectivity analysis and thorough examination of the literature. Finally, about 80% of proteins and reactions in EHMN are determined to be in at least one of the 15 tissues. To validate the quality of the tissue specific network, the brain specific metabolic network is taken as an example for functional module analysis and the results reveal that the function of the brain metabolic network is closely related with its function as the centre of the human nervous system. The tissue specific human metabolic networks are available at .

  6. Bacterial metabolism of human polymorphonuclear leukocyte-derived arachidonic acid.

    Science.gov (United States)

    Sorrell, T C; Muller, M; Sztelma, K

    1992-05-01

    Evidence for transcellular bacterial metabolism of phagocyte-derived arachidonic acid was sought by exposing human blood polymorphonuclear leukocytes, prelabelled with [3H]arachidonic acid, to opsonized, stationary-phase Pseudomonas aeruginosa (bacteria-to-phagocyte ratio of 50:1) for 90 min at 37 degrees C. Control leukocytes were stimulated with the calcium ionophore A23187 (5 microM) for 5 min. Radiochromatograms of arachidonic acid metabolites, extracted from A23187-stimulated cultures and then separated by reverse-phase high-performance liquid chromatography, revealed leukotriene B4, its omega-oxidation products, and 5-hydroxy-eicosatetraenoic acid. In contrast, two major metabolite peaks, distinct from known polymorphonuclear leukocyte arachidonic acid products by high-performance liquid chromatography or by thin-layer chromatography, were identified in cultures of P. aeruginosa with [3H]arachidonic acid-labelled polymorphonuclear leukocytes. Respective chromatographic characteristics of these novel products were identical to those of two major metabolite peaks produced by incubation of stationary-phase P. aeruginosa with [3H]arachidonic acid. Production of the metabolites was dependent upon pseudomonal viability. UV spectral data were consistent with a conjugated diene structure. Metabolism of arachidonic acid by P. aeruginosa was not influenced by the presence of catalase, superoxide dismutase, nordihydroguaiaretic acid, ethanol, dimethyl sulfoxide, or ferrous ions but was inhibited by carbon monoxide, ketoconazole, and 1,2-epoxy-3,3,3-trichloropropane. Our data suggest that pseudomonal metabolism of polymorphonuclear leukocyte-derived arachidonic acid occurs during phagocytosis, probably by enzymatic epoxidation and hydroxylation via an oxygenase. By this means, potential proinflammatory effects of arachidonic acid or its metabolites may be modulated by P. aeruginosa at sites of infection in vivo.

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

    DEFF Research Database (Denmark)

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

    2003-01-01

    and the environment were included. A total of 708 structural open reading frames (ORFs) were accounted for in the reconstructed network, corresponding to 1035 metabolic reactions. Further, 140 reactions were included on the basis of biochemical evidence resulting in a genome-scale reconstructed metabolic network...... with Escherichia coli. The reconstructed metabolic network is the first comprehensive network for a eukaryotic organism, and it may be used as the basis for in silico analysis of phenotypic functions....

  8. WUFlux: an open-source platform for 13C metabolic flux analysis of bacterial metabolism.

    Science.gov (United States)

    He, Lian; Wu, Stephen G; Zhang, Muhan; Chen, Yixin; Tang, Yinjie J

    2016-11-04

    Flux analyses, including flux balance analysis (FBA) and 13 C-metabolic flux analysis ( 13 C-MFA), offer direct insights into cell metabolism, and have been widely used to characterize model and non-model microbial species. Nonetheless, constructing the 13 C-MFA model and performing flux calculation are demanding for new learners, because they require knowledge of metabolic networks, carbon transitions, and computer programming. To facilitate and standardize the 13 C-MFA modeling work, we set out to publish a user-friendly and programming-free platform (WUFlux) for flux calculations in MATLAB ® . We constructed an open-source platform for steady-state 13 C-MFA. Using GUIDE (graphical user interface design environment) in MATLAB, we built a user interface that allows users to modify models based on their own experimental conditions. WUFlux is capable of directly correcting mass spectrum data of TBDMS (N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide)-derivatized proteinogenic amino acids by removing background noise. To simplify 13 C-MFA of different prokaryotic species, the software provides several metabolic network templates, including those for chemoheterotrophic bacteria and mixotrophic cyanobacteria. Users can modify the network and constraints, and then analyze the microbial carbon and energy metabolisms of various carbon substrates (e.g., glucose, pyruvate/lactate, acetate, xylose, and glycerol). WUFlux also offers several ways of visualizing the flux results with respect to the constructed network. To validate our model's applicability, we have compared and discussed the flux results obtained from WUFlux and other MFA software. We have also illustrated how model constraints of cofactor and ATP balances influence fluxome results. Open-source software for 13 C-MFA, WUFlux, with a user-friendly interface and easy-to-modify templates, is now available at http://www.13cmfa.org /or ( http://tang.eece.wustl.edu/ToolDevelopment.htm ). We will continue

  9. Wholly Rickettsia! Reconstructed Metabolic Profile of the Quintessential Bacterial Parasite of Eukaryotic Cells

    Directory of Open Access Journals (Sweden)

    Timothy P. Driscoll

    2017-09-01

    Full Text Available Reductive genome evolution has purged many metabolic pathways from obligate intracellular Rickettsia (Alphaproteobacteria; Rickettsiaceae. While some aspects of host-dependent rickettsial metabolism have been characterized, the array of host-acquired metabolites and their cognate transporters remains unknown. This dearth of information has thwarted efforts to obtain an axenic Rickettsia culture, a major impediment to conventional genetic approaches. Using phylogenomics and computational pathway analysis, we reconstructed the Rickettsia metabolic and transport network, identifying 51 host-acquired metabolites (only 21 previously characterized needed to compensate for degraded biosynthesis pathways. In the absence of glycolysis and the pentose phosphate pathway, cell envelope glycoconjugates are synthesized from three imported host sugars, with a range of additional host-acquired metabolites fueling the tricarboxylic acid cycle. Fatty acid and glycerophospholipid pathways also initiate from host precursors, and import of both isoprenes and terpenoids is required for the synthesis of ubiquinone and the lipid carrier of lipid I and O-antigen. Unlike metabolite-provisioning bacterial symbionts of arthropods, rickettsiae cannot synthesize B vitamins or most other cofactors, accentuating their parasitic nature. Six biosynthesis pathways contain holes (missing enzymes; similar patterns in taxonomically diverse bacteria suggest alternative enzymes that await discovery. A paucity of characterized and predicted transporters emphasizes the knowledge gap concerning how rickettsiae import host metabolites, some of which are large and not known to be transported by bacteria. Collectively, our reconstructed metabolic network offers clues to how rickettsiae hijack host metabolic pathways. This blueprint for growth determinants is an important step toward the design of axenic media to rescue rickettsiae from the eukaryotic cell.

  10. 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

  11. Phylogeny of metabolic networks: A spectral graph theoretical ...

    Indian Academy of Sciences (India)

    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 ...

  12. Shared metabolic pathways in a coevolved insect-bacterial symbiosis.

    Science.gov (United States)

    Russell, Calum W; Bouvaine, Sophie; Newell, Peter D; Douglas, Angela E

    2013-10-01

    The symbiotic bacterium Buchnera aphidicola lacks key genes in the biosynthesis of five essential amino acids (EAAs), and yet its animal hosts (aphids) depend on the symbiosis for the synthesis of these EAAs (isoleucine, leucine, methionine, phenylalanine, and valine). We tested the hypothesis, derived from genome annotation, that the missing Buchnera reactions are mediated by host enzymes, with the exchange of metabolic intermediates between the partners. The specialized host cells bearing Buchnera were separated into a Buchnera fraction and a Buchnera-free host cell fraction (HF). Addition of HF to isolated Buchnera preparations significantly increased the production of leucine and phenylalanine, and recombinant enzymes mediating the final reactions in branched-chain amino acid and phenylalanine synthesis rescued the production of these EAAs by Buchnera preparations without HF. The likely precursors for the missing proximal reactions in isoleucine and methionine synthesis were identified, and they differed from predictions based on genome annotations: synthesis of 2-oxobutanoate, the aphid-derived precursor of isoleucine synthesis, was stimulated by homoserine and not threonine via threonine dehydratase, and production of the homocysteine precursor of methionine was driven by cystathionine, not cysteine, via reversal of the transsulfuration pathway. The evolution of shared metabolic pathways in this symbiosis can be attributed to host compensation for genomic deterioration in the symbiont, involving changes in host gene expression networks to recruit specific enzymes to the host cell.

  13. Ultraviolet irradiated water containing humic substances inhibits bacterial metabolism

    International Nuclear Information System (INIS)

    Lund, V.; Hongve, D.

    1994-01-01

    Disinfection of drinking water by u.v. irradiation has been observed to reduce the biofilm formation in the pipes in a pilot plant. An apparently inhibitory effect that persists in the water after the u.v. treatment has been studied in the laboratory. Reduced numbers of viable bacteria and reduced bacterial metabolism were observed when irradiated waters were inoculated with fresh bacteria. Approximately 60% of the heterotrophic bacteria in the water samples were inactivated within a 1 h contact time with freshly u.v. disinfected water. The uptake rates of labelled tracer substances were significantly reduced when the bacteria were exposed to irradiated water. The inhibitory effect seems to last for at least 1 week. High concentrations of organic matter seem to counteract the inhibitory effect. No relationship was found between u.v. dose and effect within the dose range tested. The observed effects may be explained by the action of oxidizing reagents such as hydroxyl radicals, produced in photochemical reactions between u.v. irradiation and humic substances in the water. (author)

  14. THE EFFECTS OF BACTERIAL ENDOTOXIN ON LIPIDE METABOLISM

    Science.gov (United States)

    LeQuire, V. S.; Hutcherson, J. D.; Hamilton, R. L.; Gray, M. E.

    1959-01-01

    Single intravenous injections of Shear's polysaccharide in varying dosages invariably produced an elevation in the levels of the total serum lipides 24 hours after injection of endotoxin. The total serum cholesterol and lipide phosphorus were also affected, although they did not change with smaller doses of endotoxin and were rarely elevated to the same degree as were the total serum lipides. The degree of elevation of the serum lipides was apparently related to the amount of endotoxin injected up to a certain point, beyond which there was no further increase. There were two types of response to endotoxin by the serum lipides, a moderate increase and an uncontrolled increase. Higher dosages of endotoxin and fasting apparently increased the incidence of the latter response. No direct correlation could be made between serum lipide responses and histologic evidence typical of the generalized Shwartzman reaction following this regimen of endotoxin injection. The Shwartzman reaction did occur with greater frequency and with lower dosages of endotoxin in fasted animals. Animals given repeated injections of endotoxin showed an initial increase in serum lipides followed by a progressive decrease to normal levels as tolerance to the febrile action of endotoxin appeared. The febrile tolerance as well as the unresponsiveness of the serum lipides to endotoxin was abolished by thorium dioxide (thorotrast) in these animals. In similar experiments a "breakthrough" of lipide unresponsiveness to endotoxin was obtained by increasing the amount of endotoxin injected. Some of the implications of these results for the metabolic alterations produced by bacterial endotoxins are discussed. PMID:13673140

  15. 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.

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

    Science.gov (United States)

    Vidal, Luciana O.; Abril, Gwenäel; Artigas, Luiz F.; Melo, Michaela L.; Bernardes, Marcelo C.; Lobão, Lúcia M.; Reis, Mariana C.; Moreira-Turcq, Patrícia; Benedetti, Marc; Tornisielo, Valdemar L.; Roland, Fabio

    2015-01-01

    We evaluated in situ rates of bacterial carbon processing in Amazonian floodplain lakes and mainstems, during both high water (HW) and low water (LW) phases (p < 0.05). Our results showed that bacterial production (BP) was lower and more variable than bacterial respiration, determined as total respiration. Bacterial carbon demand was mostly accounted by BR and presented the same pattern that BR in both water phases. Bacterial growth efficiency (BGE) showed a wide range (0.2–23%) and low mean value of 3 and 6%, (in HW and LW, respectively) suggesting that dissolved organic carbon was mostly allocated to catabolic metabolism. However, BGE was regulated by BP in LW 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 LW. Multiple correlation analyses revealed that indexes of organic matter (OM) quality (chlorophyll-a, N stable isotopes and C/N ratios) were the strongest seasonal drivers of bacterial carbon metabolism. Our work indicated that: (i) the bacterial metabolism was mostly driven by respiration in Amazonian aquatic ecosystems resulting in low BGE in either high or LW phase; (ii) the hydrological pulse regulated the bacterial heterotrophic metabolism between Amazonian mainstems and floodplain lakes mostly driven by OM quality. PMID:26483776

  17. Metabolic networks of Cucurbita maxima phloem.

    Science.gov (United States)

    Fiehn, Oliver

    2003-03-01

    Metabolomic analysis aims at a comprehensive characterization of biological samples. Yet, biologically meaningful interpretations are often limited by the poor spatial and temporal resolution of the acquired data sets. One way to remedy this is to limit the complexity of the cell types being studied. Cucurbita maxima Duch. vascular exudates provide an excellent material for metabolomics in this regard. Using automated mass spectral deconvolution, over 400 components have been detected in these exudates, but only 90 of them were tentatively identified. Many amino compounds were found in vascular exudates from leaf petioles at concentrations several orders of magnitude higher than in tissue disks from the same leaves, whereas hexoses and sucrose were found in far lower amounts. In order to find the expected impact of assimilation rates on sugar levels, total phloem composition of eight leaves from four plants was followed over 4.5 days. Surprisingly, no diurnal rhythm was found for any of the phloem metabolites that was statistically valid for all eight leaves. Instead, each leaf had its own distinct vascular exudate profile similar to leaves from the same plant, but clearly different from leaves harvested from plants at the same developmental stage. Thirty to forty per cent of all metabolite levels of individual leaves were different from the average of all metabolite profiles. Using metabolic co-regulation analysis, similarities and differences between the exudate profiles were more accurately characterized through network computation, specifically with respect to nitrogen metabolism.

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

    Science.gov (United States)

    Méndez-García, Celia; Peláez, Ana I; Mesa, Victoria; Sánchez, Jesús; Golyshina, Olga V; Ferrer, Manuel

    2015-01-01

    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-based methodologies 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 archaea recovered through sterile filtering (herein referred to as filterable fraction) in these environments demand their inclusion in the microbial characterization of AMD systems. In this review, we present a complete overview of the bacterial, archaeal (including filterable fraction), 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. 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.

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

    Science.gov (United States)

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

    2017-12-01

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

  1. From genomes to in silico cells via metabolic networks

    DEFF Research Database (Denmark)

    Borodina, Irina; Nielsen, Jens

    2005-01-01

    Genome-scale metabolic models are the focal point of systems biology as they allow the collection of various data types in a form suitable for mathematical analysis. High-quality metabolic networks and metabolic networks with incorporated regulation have been successfully used for the analysis of...... approaches to obtain an in silico prediction of cellular function based on the interaction of all of the cellular components....

  2. 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...

  3. Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network

    OpenAIRE

    Mart?n-Jim?nez, Cynthia A.; Salazar-Barreto, Diego; Barreto, George E.; Gonz?lez, Janneth

    2017-01-01

    Astrocytes are the most abundant cells of the central nervous system; they have a predominant role in maintaining brain metabolism. In this sense, abnormal metabolic states have been found in different neuropathological diseases. Determination of metabolic states of astrocytes is difficult to model using current experimental approaches given the high number of reactions and metabolites present. Thus, genome-scale metabolic networks derived from transcriptomic data can be used as a framework t...

  4. 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.

  5. Metabolic pathways variability and sequence/networks comparisons

    Science.gov (United States)

    Tun, Kyaw; Dhar, Pawan K; Palumbo, Maria Concetta; Giuliani, Alessandro

    2006-01-01

    Background In this work a simple method for the computation of relative similarities between homologous metabolic network modules is presented. The method is similar to classical sequence alignment and allows for the generation of phenotypic trees amenable to be compared with correspondent sequence based trees. The procedure can be applied to both single metabolic modules and whole metabolic network data without the need of any specific assumption. Results We demonstrate both the ability of the proposed method to build reliable biological classification of a set of microrganisms and the strong correlation between the metabolic network wiringand involved enzymes sequence space. Conclusion The method represents a valuable tool for the investigation of genotype/phenotype correlationsallowing for a direct comparison of different species as for their metabolic machinery. In addition the detection of enzymes whose sequence space is maximally correlated with the metabolicnetwork space gives an indication of the most crucial (on an evolutionary viewpoint) steps of the metabolic process. PMID:16420696

  6. Metabolic pathways variability and sequence/networks comparisons

    Directory of Open Access Journals (Sweden)

    Palumbo Maria

    2006-01-01

    Full Text Available Abstract Background In this work a simple method for the computation of relative similarities between homologous metabolic network modules is presented. The method is similar to classical sequence alignment and allows for the generation of phenotypic trees amenable to be compared with correspondent sequence based trees. The procedure can be applied to both single metabolic modules and whole metabolic network data without the need of any specific assumption. Results We demonstrate both the ability of the proposed method to build reliable biological classification of a set of microrganisms and the strong correlation between the metabolic network wiringand involved enzymes sequence space. Conclusion The method represents a valuable tool for the investigation of genotype/phenotype correlationsallowing for a direct comparison of different species as for their metabolic machinery. In addition the detection of enzymes whose sequence space is maximally correlated with the metabolicnetwork space gives an indication of the most crucial (on an evolutionary viewpoint steps of the metabolic process.

  7. 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

  8. Integration of Genome Scale Metabolic Networks and Gene Regulation of Metabolic Enzymes With Physiologically Based Pharmacokinetics

    Science.gov (United States)

    Maldonado, Elaina M.; Leoncikas, Vytautas; Fisher, Ciarán P.; Moore, J. Bernadette; Plant, Nick J.

    2017-01-01

    The scope of physiologically based pharmacokinetic (PBPK) modeling can be expanded by assimilation of the mechanistic models of intracellular processes from systems biology field. The genome scale metabolic networks (GSMNs) represent a whole set of metabolic enzymes expressed in human tissues. Dynamic models of the gene regulation of key drug metabolism enzymes are available. Here, we introduce GSMNs and review ongoing work on integration of PBPK, GSMNs, and metabolic gene regulation. We demonstrate example models. PMID:28782239

  9. Comparing the impact of ultrafine particles from petrodiesel and biodiesel combustion to bacterial metabolism by targeted HPLC-MS/MS metabolic profiling.

    Science.gov (United States)

    Zhong, Fanyi; Xu, Mengyang; Schelli, Katie; Rutowski, Joshua; Holmén, Britt A; Zhu, Jiangjiang

    2017-08-01

    Alterations of gut bacterial metabolism play an important role in their host metabolism, and can result in diseases such as obesity and diabetes. While many factors were discovered influencing the gut bacterial metabolism, exposure to ultrafine particles (UFPs) from engine combustions were recently proposed to be a potential risk factor for the perturbation of gut bacterial metabolism, and consequentially to obesity and diabetes development. This study focused on evaluation of how UFPs from diesel engine combustions impact gut bacterial metabolism. We hypothesize that UFPs from different type of diesel (petrodiesel vs. biodiesel) will both impact bacterial metabolism, and the degree of impact is also diesel type-dependent. Targeted metabolic profiling of 221 metabolites were applied to three model gut bacteria in vitro, Streptococcus salivarius, Lactobacillus acidophilus and Lactobacillus fermentum. UFPs from two types of fuels, petrodiesel (B0) and a biodiesel blend (B20: 20% soy biodiesel/80% B0 by volume), were exposed to the bacteria and their metabolic changes were compared. For each bacterial strain, metabolites with significantly changed abundance were observed in both perturbations, and all three strains have increased number of altered metabolites detected from B20 UFPs perturbation in comparison to B0 UFPs. Multivariate statistical analysis further confirmed that the metabolic profiles were clearly different between testing groups. Metabolic pathway analyses also demonstrated several important metabolic pathways, including pathways involves amino acids biosynthesis and sugar metabolism, were significantly impacted by UFPs exposure. Copyright © 2017 Elsevier Inc. All rights reserved.

  10. Hologenome theory supported by cooccurrence networks of species-specific bacterial communities in siphonous algae (Caulerpa).

    Science.gov (United States)

    Aires, Tania; Moalic, Yann; Serrao, Ester A; Arnaud-Haond, Sophie

    2015-07-01

    The siphonous algae of the Caulerpa genus harbor internal microbial communities hypothesized to play important roles in development, defense and metabolic activities of the host. Here, we characterize the endophytic bacterial community of four Caulerpa taxa in the Mediterranean Sea, through 16S rRNA amplicon sequencing. Results reveal a striking alpha diversity of the bacterial communities, similar to levels found in sponges and coral holobionts. These comprise (1) a very small core community shared across all hosts (70%) species-specific fraction of the community, forming very specific clusters revealed by modularity in networks of cooccurrence, even in areas where distinct Caulerpa taxa occurred in sympatry. Indirect inferences based on sequence homology suggest that these communities may play an important role in the metabolism of their host, in particular on their ability to grow on anoxic sediment. These findings support the hologenome theory and the need for a holistic framework in ecological and evolutionary studies of these holobionts that frequently become invasive. © FEMS 2015. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  11. Metabolic bacterial genes and the construction of high-level composite lineages of life.

    Science.gov (United States)

    Méheust, Raphaël; Lopez, Philippe; Bapteste, Eric

    2015-03-01

    Understanding how major organismal lineages originated is fundamental for understanding processes by which life evolved. Major evolutionary transitions, like eukaryogenesis, merging genetic material from distantly related organisms, are rare events, hence difficult ones to explain causally. If most archaeal lineages emerged after massive acquisitions of bacterial genes, a rule however arises: metabolic bacterial genes contributed to all major evolutionary transitions. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  12. Efficient aquatic bacterial metabolism of dissolved low-molecular-weight compounds from terrestrial sources.

    Science.gov (United States)

    Berggren, Martin; Laudon, Hjalmar; Haei, Mahsa; Ström, Lena; Jansson, Mats

    2010-03-01

    Carboxylic acids (CAs), amino acids (AAs) and carbohydrates (CHs) in dissolved free forms can be readily assimilated by aquatic bacteria and metabolized at high growth efficiencies. Previous studies have shown that these low-molecular-weight (LMW) substrates are released by phytoplankton but also that unidentified LMW compounds of terrestrial origin is a subsidy for bacterial metabolism in unproductive freshwater systems. We tested the hypothesis that different terrestrially derived CA, AA and CH compounds can offer substantial support for aquatic bacterial metabolism in fresh waters that are dominated by allochthonous dissolved organic matter (DOM). Drainage water from three catchments of different characters in the Krycklan experimental area in Northern Sweden were studied at the rising and falling limb of the spring flood, using a 2-week bioassay approach. A variety of CA, AA and CH compounds were significantly assimilated by bacteria, meeting 15-100% of the bacterial carbon demand and explaining most of the observed variation in bacterial growth efficiency (BGE; R(2)=0.66). Of the 29 chemical species that was detected, acetate was the most important, representing 45% of the total bacterial consumption of all LMW compounds. We suggest that LMW organic compounds in boreal spring flood drainage could potentially support all in situ bacterial production in receiving lake waters during periods of weeks to months after the spring flood.

  13. The impact of metabolic state on Cd adsorption onto bacterial cells

    Science.gov (United States)

    Johnson, K.J.; Ams, D.A.; Wedel, A.N.; Szymanowski, J.E.S.; Weber, D.L.; Schneegurt, M.A.; Fein, J.B.

    2007-01-01

    This study examines the effect of bacterial metabolism on the adsorption of Cd onto Gram-positive and Gram-negative bacterial cells. Metabolically active Gram-positive cells adsorbed significantly less Cd than non-metabolizing cells. Gram-negative cells, however, showed no systematic difference in Cd adsorption between metabolizing and non-metabolizing cells. The effect of metabolism on Cd adsorption to Gram-positive cells was likely due to an influx of protons in and around the cell wall from the metabolic proton motive force, promoting competition between Cd and protons for adsorption sites on the cell wall. The relative lack of a metabolic effect on Cd adsorption onto Gram-negative compared to Gram-positive cells suggests that Cd binding in Gram-negative cells is focused in a region of the cell wall that is not reached, or is unaffected by this proton flux. Thermodynamic modeling was used to estimate that proton pumping causes the pH in the cell wall of metabolizing Gram-positive bacteria to decrease from the bulk solution value of 7.0 to approximately 5.7. ?? 2007 The Authors.

  14. Integrated co-regulation of bacterial arsenic and phosphorus metabolisms.

    Science.gov (United States)

    Kang, Yoon-Suk; Heinemann, Joshua; Bothner, Brian; Rensing, Christopher; McDermott, Timothy R

    2012-12-01

    Arsenic ranks first on the US Environmental Protection Agency Superfund List of Hazardous Substances. Its mobility and toxicity depend upon chemical speciation, which is significantly driven by microbial redox transformations. Genome sequence-enabled surveys reveal that in many microorganisms genes essential to arsenite (AsIII) oxidation are located immediately adjacent to genes coding for functions associated with phosphorus (Pi) acquisition, implying some type of functional importance to the metabolism of As, Pi or both. We extensively document how expression of genes key to AsIII oxidation and the Pi stress response are intricately co-regulated in the soil bacterium Agrobacterium tumefaciens. These observations significantly expand our understanding of how environmental factors influence microbial AsIII metabolism and contribute to the current discussion of As and P metabolism in the microbial cell. © 2012 Society for Applied Microbiology and Blackwell Publishing Ltd.

  15. 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...

  16. Marine bacterial, archaeal and protistan association networks reveal ecological linkages.

    Science.gov (United States)

    Steele, Joshua A; Countway, Peter D; Xia, Li; Vigil, Patrick D; Beman, J Michael; Kim, Diane Y; Chow, Cheryl-Emiliane T; Sachdeva, Rohan; Jones, Adriane C; Schwalbach, Michael S; Rose, Julie M; Hewson, Ian; Patel, Anand; Sun, Fengzhu; Caron, David A; Fuhrman, Jed A

    2011-09-01

    Microbes have central roles in ocean food webs and global biogeochemical processes, yet specific ecological relationships among these taxa are largely unknown. This is in part due to the dilute, microscopic nature of the planktonic microbial community, which prevents direct observation of their interactions. Here, we use a holistic (that is, microbial system-wide) approach to investigate time-dependent variations among taxa from all three domains of life in a marine microbial community. We investigated the community composition of bacteria, archaea and protists through cultivation-independent methods, along with total bacterial and viral abundance, and physico-chemical observations. Samples and observations were collected monthly over 3 years at a well-described ocean time-series site of southern California. To find associations among these organisms, we calculated time-dependent rank correlations (that is, local similarity correlations) among relative abundances of bacteria, archaea, protists, total abundance of bacteria and viruses and physico-chemical parameters. We used a network generated from these statistical correlations to visualize and identify time-dependent associations among ecologically important taxa, for example, the SAR11 cluster, stramenopiles, alveolates, cyanobacteria and ammonia-oxidizing archaea. Negative correlations, perhaps suggesting competition or predation, were also common. The analysis revealed a progression of microbial communities through time, and also a group of unknown eukaryotes that were highly correlated with dinoflagellates, indicating possible symbioses or parasitism. Possible 'keystone' species were evident. The network has statistical features similar to previously described ecological networks, and in network parlance has non-random, small world properties (that is, highly interconnected nodes). This approach provides new insights into the natural history of microbes.

  17. 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.

  18. Slave nodes and the controllability of metabolic networks

    International Nuclear Information System (INIS)

    Kim, Dong-Hee; Motter, Adilson E

    2009-01-01

    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.

  19. Does habitat variability really promote metabolic network modularity?

    Science.gov (United States)

    Takemoto, Kazuhiro

    2013-01-01

    The hypothesis that variability in natural habitats promotes modular organization is widely accepted for cellular networks. However, results of some data analyses and theoretical studies have begun to cast doubt on the impact of habitat variability on modularity in metabolic networks. Therefore, we re-evaluated this hypothesis using statistical data analysis and current metabolic information. We were unable to conclude that an increase in modularity was the result of habitat variability. Although horizontal gene transfer was also considered because it may contribute for survival in a variety of environments, closely related to habitat variability, and is known to be positively correlated with network modularity, such a positive correlation was not concluded in the latest version of metabolic networks. Furthermore, we demonstrated that the previously observed increase in network modularity due to habitat variability and horizontal gene transfer was probably due to a lack of available data on metabolic reactions. Instead, we determined that modularity in metabolic networks is dependent on species growth conditions. These results may not entirely discount the impact of habitat variability and horizontal gene transfer. Rather, they highlight the need for a more suitable definition of habitat variability and a more careful examination of relationships of the network modularity with horizontal gene transfer, habitats, and environments.

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

    Directory of Open Access Journals (Sweden)

    Sylvain Prigent

    2017-01-01

    Full Text Available 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

  1. A compendium of inborn errors of metabolism mapped onto the human metabolic network.

    Science.gov (United States)

    Sahoo, Swagatika; Franzson, Leifur; Jonsson, Jon J; Thiele, Ines

    2012-10-01

    Inborn errors of metabolism (IEMs) are hereditary metabolic defects, which are encountered in almost all major metabolic pathways occurring in man. Many IEMs are screened for in neonates through metabolomic analysis of dried blood spot samples. To enable the mapping of these metabolomic data onto the published human metabolic reconstruction, we added missing reactions and pathways involved in acylcarnitine (AC) and fatty acid oxidation (FAO) metabolism. Using literary data, we reconstructed an AC/FAO module consisting of 352 reactions and 139 metabolites. When this module was combined with the human metabolic reconstruction, the synthesis of 39 acylcarnitines and 22 amino acids, which are routinely measured, was captured and 235 distinct IEMs could be mapped. We collected phenotypic and clinical features for each IEM enabling comprehensive classification. We found that carbohydrate, amino acid, and lipid metabolism were most affected by the IEMs, while the brain was the most commonly affected organ. Furthermore, we analyzed the IEMs in the context of metabolic network topology to gain insight into common features between metabolically connected IEMs. While many known examples were identified, we discovered some surprising IEM pairs that shared reactions as well as clinical features but not necessarily causal genes. Moreover, we could also re-confirm that acetyl-CoA acts as a central metabolite. This network based analysis leads to further insight of hot spots in human metabolism with respect to IEMs. The presented comprehensive knowledge base of IEMs will provide a valuable tool in studying metabolic changes involved in inherited metabolic diseases.

  2. Structuring evolution: biochemical networks and metabolic diversification in birds.

    Science.gov (United States)

    Morrison, Erin S; Badyaev, Alexander V

    2016-08-25

    Recurrence and predictability of evolution are thought to reflect the correspondence between genomic and phenotypic dimensions of organisms, and the connectivity in deterministic networks within these dimensions. Direct examination of the correspondence between opportunities for diversification imbedded in such networks and realized diversity is illuminating, but is empirically challenging because both the deterministic networks and phenotypic diversity are modified in the course of evolution. Here we overcome this problem by directly comparing the structure of a "global" carotenoid network - comprising of all known enzymatic reactions among naturally occurring carotenoids - with the patterns of evolutionary diversification in carotenoid-producing metabolic networks utilized by birds. We found that phenotypic diversification in carotenoid networks across 250 species was closely associated with enzymatic connectivity of the underlying biochemical network - compounds with greater connectivity occurred the most frequently across species and were the hotspots of metabolic pathway diversification. In contrast, we found no evidence for diversification along the metabolic pathways, corroborating findings that the utilization of the global carotenoid network was not strongly influenced by history in avian evolution. The finding that the diversification in species-specific carotenoid networks is qualitatively predictable from the connectivity of the underlying enzymatic network points to significant structural determinism in phenotypic evolution.

  3. Phylogeny of metabolic networks: A spectral graph theoretical ...

    Indian Academy of Sciences (India)

    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 ...

  4. Metabolic network segmentation: A probabilistic graphical modeling approach to identify the sites and sequential order of metabolic regulation from non-targeted metabolomics data.

    Directory of Open Access Journals (Sweden)

    Andreas Kuehne

    2017-06-01

    Full Text Available In recent years, the number of large-scale metabolomics studies on various cellular processes in different organisms has increased drastically. However, it remains a major challenge to perform a systematic identification of mechanistic regulatory events that mediate the observed changes in metabolite levels, due to complex interdependencies within metabolic networks. We present the metabolic network segmentation (MNS algorithm, a probabilistic graphical modeling approach that enables genome-scale, automated prediction of regulated metabolic reactions from differential or serial metabolomics data. The algorithm sections the metabolic network into modules of metabolites with consistent changes. Metabolic reactions that connect different modules are the most likely sites of metabolic regulation. In contrast to most state-of-the-art methods, the MNS algorithm is independent of arbitrary pathway definitions, and its probabilistic nature facilitates assessments of noisy and incomplete measurements. With serial (i.e., time-resolved data, the MNS algorithm also indicates the sequential order of metabolic regulation. We demonstrated the power and flexibility of the MNS algorithm with three, realistic case studies with bacterial and human cells. Thus, this approach enables the identification of mechanistic regulatory events from large-scale metabolomics data, and contributes to the understanding of metabolic processes and their interplay with cellular signaling and regulation processes.

  5. Inferring the metabolism of human orphan metabolites from their metabolic network context affirms human gluconokinase activity.

    Science.gov (United States)

    Rolfsson, Óttar; Paglia, Giuseppe; Magnusdóttir, Manuela; Palsson, Bernhard Ø; Thiele, Ines

    2013-01-15

    Metabolic network reconstructions define metabolic information within a target organism and can therefore be used to address incomplete metabolic information. In the present study we used a computational approach to identify human metabolites whose metabolism is incomplete on the basis of their detection in humans but exclusion from the human metabolic network reconstruction RECON 1. Candidate solutions, composed of metabolic reactions capable of explaining the metabolism of these compounds, were then identified computationally from a global biochemical reaction database. Solutions were characterized with respect to how metabolites were incorporated into RECON 1 and their biological relevance. Through detailed case studies we show that biologically plausible non-intuitive hypotheses regarding the metabolism of these compounds can be proposed in a semi-automated manner, in an approach that is similar to de novo network reconstruction. We subsequently experimentally validated one of the proposed hypotheses and report that C9orf103, previously identified as a candidate tumour suppressor gene, encodes a functional human gluconokinase. The results of the present study demonstrate how semi-automatic gap filling can be used to refine and extend metabolic reconstructions, thereby increasing their biological scope. Furthermore, we illustrate how incomplete human metabolic knowledge can be coupled with gene annotation in order to prioritize and confirm gene functions.

  6. 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

    network topology. The algorithm thus enables identification of reporter reactions, which are reactions where there are significant coordinated changes in the level of surrounding metabolites following environmental/genetic perturbations. Applicability of the algorithm is demonstrated by using data from......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...... is measured. By combining the results with transcriptome data, we further show that it is possible to infer whether the reactions are hierarchically or metabolically regulated. Hereby, the reported approach represents an attempt to map different layers of regulation within metabolic networks through...

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

    International Nuclear Information System (INIS)

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

    2014-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Amphun eChaiboonchoe

    2014-12-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

  10. Identification of Conserved Moieties in Metabolic Networks by Graph Theoretical Analysis of Atom Transition Networks

    Science.gov (United States)

    Haraldsdóttir, Hulda S.; Fleming, Ronan M. T.

    2016-01-01

    Conserved moieties are groups of atoms that remain intact in all reactions of a metabolic network. Identification of conserved moieties gives insight into the structure and function of metabolic networks and facilitates metabolic modelling. All moiety conservation relations can be represented as nonnegative integer vectors in the left null space of the stoichiometric matrix corresponding to a biochemical network. Algorithms exist to compute such vectors based only on reaction stoichiometry but their computational complexity has limited their application to relatively small metabolic networks. Moreover, the vectors returned by existing algorithms do not, in general, represent conservation of a specific moiety with a defined atomic structure. Here, we show that identification of conserved moieties requires data on reaction atom mappings in addition to stoichiometry. We present a novel method to identify conserved moieties in metabolic networks by graph theoretical analysis of their underlying atom transition networks. Our method returns the exact group of atoms belonging to each conserved moiety as well as the corresponding vector in the left null space of the stoichiometric matrix. It can be implemented as a pipeline of polynomial time algorithms. Our implementation completes in under five minutes on a metabolic network with more than 4,000 mass balanced reactions. The scalability of the method enables extension of existing applications for moiety conservation relations to genome-scale metabolic networks. We also give examples of new applications made possible by elucidating the atomic structure of conserved moieties. PMID:27870845

  11. 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

    -of-the-art genome-scale metabolic networks. By using metabolic data profiles from a set of seven environmental perturbations as well as from natural variability, we demonstrate that the data profiles of sensor metabolites are more correlated than those of nonsensor metabolites. This pattern was confirmed...

  12. 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

  13. Bacterial metabolism of fluorene, dibenzofuran, dibenzothiophene, and carbazole

    Energy Technology Data Exchange (ETDEWEB)

    Bressier, D. C.; Fedorak, P. M. [Alberta Univ., Dept. of biological Sciences, Edmonton, AB (Canada)

    2000-05-01

    Fluorene and its analogs, dibenzofuran, dibenzothiophene and carbazole are environmental contaminants in areas impacted by spills of creosote. These compounds are susceptible to to three different modes of initial oxidation, namely (1) the naphthalene-like attack, (2) an angular dioxygenase attack and (3) the five-membered ring attack. This paper reviews the bacterial degradation of fluorene and its analogs by summarizing the metabolites, enzymology, and the genetics of these of these transformations. Data from the literature is presented, indicating that the electronegativity of the atom connecting the two aromatic rings influences the attack of the angular dioxygenase. In dibenzofuran and carbazole the connecting atoms have high electronegativities, whereas in dibenzothiopene and fluorene the two atoms, sulphur and carbon, have lower negativities, requiring oxidisation before the angular dioxygenases attack these compounds. These observations demonstrate the similarities among the microbial mechanisms to oxidize fluorene and its analogs found in coal tar. 87 refs., 1 tab., 9 figs.

  14. 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.

  15. Preferential attachment in the evolution of metabolic networks

    Directory of Open Access Journals (Sweden)

    Elofsson Arne

    2005-11-01

    Full Text Available Abstract Background Many biological networks show some characteristics of scale-free networks. Scale-free networks can evolve through preferential attachment where new nodes are preferentially attached to well connected nodes. In networks which have evolved through preferential attachment older nodes should have a higher average connectivity than younger nodes. Here we have investigated preferential attachment in the context of metabolic networks. Results The connectivities of the enzymes in the metabolic network of Escherichia coli were determined and representatives for these enzymes were located in 11 eukaryotes, 17 archaea and 46 bacteria. E. coli enzymes which have representatives in eukaryotes have a higher average connectivity while enzymes which are represented only in the prokaryotes, and especially the enzymes only present in βγ-proteobacteria, have lower connectivities than expected by chance. Interestingly, the enzymes which have been proposed as candidates for horizontal gene transfer have a higher average connectivity than the other enzymes. Furthermore, It was found that new edges are added to the highly connected enzymes at a faster rate than to enzymes with low connectivities which is consistent with preferential attachment. Conclusion Here, we have found indications of preferential attachment in the metabolic network of E. coli. A possible biological explanation for preferential attachment growth of metabolic networks is that novel enzymes created through gene duplication maintain some of the compounds involved in the original reaction, throughout its future evolution. In addition, we found that enzymes which are candidates for horizontal gene transfer have a higher average connectivity than other enzymes. This indicates that while new enzymes are attached preferentially to highly connected enzymes, these highly connected enzymes have sometimes been introduced into the E. coli genome by horizontal gene transfer. We speculate

  16. 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

  17. 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.

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

    Science.gov (United States)

    Jia, Gengjie; Stephanopoulos, Gregory; Gunawan, Rudiyanto

    2012-01-01

    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. PMID:24957767

  19. 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.

  20. 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.

  1. Metabolic Coevolution in the Bacterial Symbiosis of Whiteflies and Related Plant Sap-Feeding Insects.

    Science.gov (United States)

    Luan, Jun-Bo; Chen, Wenbo; Hasegawa, Daniel K; Simmons, Alvin M; Wintermantel, William M; Ling, Kai-Shu; Fei, Zhangjun; Liu, Shu-Sheng; Douglas, Angela E

    2015-09-15

    Genomic decay is a common feature of intracellular bacteria that have entered into symbiosis with plant sap-feeding insects. This study of the whitefly Bemisia tabaci and two bacteria (Portiera aleyrodidarum and Hamiltonella defensa) cohoused in each host cell investigated whether the decay of Portiera metabolism genes is complemented by host and Hamiltonella genes, and compared the metabolic traits of the whitefly symbiosis with other sap-feeding insects (aphids, psyllids, and mealybugs). Parallel genomic and transcriptomic analysis revealed that the host genome contributes multiple metabolic reactions that complement or duplicate Portiera function, and that Hamiltonella may contribute multiple cofactors and one essential amino acid, lysine. Homologs of the Bemisia metabolism genes of insect origin have also been implicated in essential amino acid synthesis in other sap-feeding insect hosts, indicative of parallel coevolution of shared metabolic pathways across multiple symbioses. Further metabolism genes coded in the Bemisia genome are of bacterial origin, but phylogenetically distinct from Portiera, Hamiltonella and horizontally transferred genes identified in other sap-feeding insects. Overall, 75% of the metabolism genes of bacterial origin are functionally unique to one symbiosis, indicating that the evolutionary history of metabolic integration in these symbioses is strongly contingent on the pattern of horizontally acquired genes. Our analysis, further, shows that bacteria with genomic decay enable host acquisition of complex metabolic pathways by multiple independent horizontal gene transfers from exogenous bacteria. Specifically, each horizontally acquired gene can function with other genes in the pathway coded by the symbiont, while facilitating the decay of the symbiont gene coding the same reaction. © The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

  2. Comparative Analysis of Yeast Metabolic Network Models Highlights Progress, Opportunities for Metabolic Reconstruction.

    Directory of Open Access Journals (Sweden)

    Benjamin D Heavner

    2015-11-01

    Full Text Available We have compared 12 genome-scale models of the Saccharomyces cerevisiae metabolic network published since 2003 to evaluate progress in reconstruction of the yeast metabolic network. We compared the genomic coverage, overlap of annotated metabolites, predictive ability for single gene essentiality with a selection of model parameters, and biomass production predictions in simulated nutrient-limited conditions. We have also compared pairwise gene knockout essentiality predictions for 10 of these models. We found that varying approaches to model scope and annotation reflected the involvement of multiple research groups in model development; that single-gene essentiality predictions were affected by simulated medium, objective function, and the reference list of essential genes; and that predictive ability for single-gene essentiality did not correlate well with predictive ability for our reference list of synthetic lethal gene interactions (R = 0.159. We conclude that the reconstruction of the yeast metabolic network is indeed gradually improving through the iterative process of model development, and there remains great opportunity for advancing our understanding of biology through continued efforts to reconstruct the full biochemical reaction network that constitutes yeast metabolism. Additionally, we suggest that there is opportunity for refining the process of deriving a metabolic model from a metabolic network reconstruction to facilitate mechanistic investigation and discovery. This comparative study lays the groundwork for developing improved tools and formalized methods to quantitatively assess metabolic network reconstructions independently of any particular model application, which will facilitate ongoing efforts to advance our understanding of the relationship between genotype and cellular phenotype.

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

    Directory of Open Access Journals (Sweden)

    Karlstädt Anja

    2012-08-01

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

  4. 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....... Global and regional CBF was measured by transcranial Doppler ultrasonography, single-photon emission computed tomography, and the Kety-Schmidt method. Cerebral metabolism and net flux were measured by the Fick principle. Interventions included intravenous norepinephrine infusion, acute hyperventilation......, propofol infusion, and mannitol infusion. Global CBF in patients was lower than that of controls; cerebral oxygen extraction and metabolism was lower in patients than in controls. Regional CBF was more heterogeneous in patients than in controls. Norepinephrine infusion increased mean arterial pressure...

  5. 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...... for protein and energy retention found in the balance and respiration experiments. It was concluded that the overall protein and energy metabolism as well as carcass composition were not influenced by a dietary content of up to 6% BPM corresponding to 20% of dietary N....

  6. Opposing Effects of Fasting Metabolism on Tissue Tolerance in Bacterial and Viral Inflammation.

    Science.gov (United States)

    Wang, Andrew; Huen, Sarah C; Luan, Harding H; Yu, Shuang; Zhang, Cuiling; Gallezot, Jean-Dominique; Booth, Carmen J; Medzhitov, Ruslan

    2016-09-08

    Acute infections are associated with a set of stereotypic behavioral responses, including anorexia, lethargy, and social withdrawal. Although these so-called sickness behaviors are the most common and familiar symptoms of infections, their roles in host defense are largely unknown. Here, we investigated the role of anorexia in models of bacterial and viral infections. We found that anorexia was protective while nutritional supplementation was detrimental in bacterial sepsis. Furthermore, glucose was necessary and sufficient for these effects. In contrast, nutritional supplementation protected against mortality from influenza infection and viral sepsis, whereas blocking glucose utilization was lethal. In both bacterial and viral models, these effects were largely independent of pathogen load and magnitude of inflammation. Instead, we identify opposing metabolic requirements tied to cellular stress adaptations critical for tolerance of differential inflammatory states. VIDEO ABSTRACT. Copyright © 2016 Elsevier Inc. All rights reserved.

  7. Enumeration of minimal stoichiometric precursor sets in metabolic networks

    NARCIS (Netherlands)

    Andrade, R.; Wannagat, M.; Coimbra Klein, C.; Acuna, V.; Marchetti Spaccamela, A.; Vieira Milreu, P.; Stougie, L.; Sagot, M.-F.

    2016-01-01

    Background: What an organism needs at least from its environment to produce a set of metabolites, e.g. target(s) of interest and/or biomass, has been called a minimal precursor set. Early approaches to enumerate all minimal precursor sets took into account only the topology of the metabolic network

  8. 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 and...

  9. 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

  10. Underground metabolism: network-level perspective and biotechnological potential

    DEFF Research Database (Denmark)

    Notebaart, Richard A; Kintses, Bálint; Feist, Adam

    2018-01-01

    A key challenge in molecular systems biology is understanding how new pathways arise during evolution and how to exploit them for biotechnological applications. New pathways in metabolic networks often evolve by recruiting weak promiscuous activities of pre-existing enzymes. Here we describe recent...

  11. Metabolic networks in epilepsy by MR spectroscopic imaging.

    Science.gov (United States)

    Pan, J W; Spencer, D D; Kuzniecky, R; Duckrow, R B; Hetherington, H; Spencer, S S

    2012-12-01

    The concept of an epileptic network has long been suggested from both animal and human studies of epilepsy. Based on the common observation that the MR spectroscopic imaging measure of NAA/Cr is sensitive to neuronal function and injury, we use this parameter to assess for the presence of a metabolic network in mesial temporal lobe epilepsy (MTLE) patients. A multivariate factor analysis is performed with controls and MTLE patients, using NAA/Cr measures from 12 loci: the bilateral hippocampi, thalami, basal ganglia, and insula. The factor analysis determines which and to what extent these loci are metabolically covarying. We extract two independent factors that explain the data's variability in control and MTLE patients. In controls, these factors characterize a 'thalamic' and 'dominant subcortical' function. The MTLE patients also exhibit a 'thalamic' factor, in addition to a second factor involving the ipsilateral insula and bilateral basal ganglia. These data suggest that MTLE patients demonstrate a metabolic network that involves the thalami, also seen in controls. The MTLE patients also display a second set of metabolically covarying regions that may be a manifestation of the epileptic network that characterizes limbic seizure propagation. © 2012 John Wiley & Sons A/S.

  12. Estimating the size of the solution space of metabolic networks

    Directory of Open Access Journals (Sweden)

    Mulet Roberto

    2008-05-01

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

  13. Dual-label radioisotope method for simultaneously measuring bacterial production and metabolism in natural waters

    International Nuclear Information System (INIS)

    Jonas, B.J.; Tuttle, J.H.; Stoner, D.L.; Ducklow, H.W.

    1988-01-01

    Bacterial production and amino acid metabolism in aquatic systems can be estimated by simultaneous incubation of water samples with both tritiated methyl-thymidine and 14 C-labeled amino acids. This dual-label method not only saves time, labor, and materials, but also allows determination of these two parameters in the same microbial subcommunity. Both organic carbon incorporation and respiration can be estimated. The method is particularly suitable for large-scale field programs and has been used successfully with eutrophic estuarine samples as well as with oligotrophic oceanic water. In the mesohaline portion of Chesapeake Bay, thymidine incorporation ranged seasonally from 2 to 635 pmol liter -1 h -1 and amino acid turnover rates ranged from 0.01 to 28.4% h -1 . Comparison of thymidine incorporation with amino acid turnover measurements made at a deep, midbay station in 1985 suggested a close coupling between bacterial production and amino acid metabolism during most of the year. However, production-specific amino acid turnover rates increased dramatically in deep bay waters during the spring phytoplankton bloom, indicating transient decoupling of bacterial production from metabolism. Ecological features such as this are readily detectable with the dual-label method

  14. The metabolic footprint of the airway bacterial community in cystic fibrosis.

    Science.gov (United States)

    Narayanamurthy, Vaishnavi; Sweetnam, John M; Denner, Darcy R; Chen, Lena W; Naureckas, Edward T; Laxman, Bharathi; White, Steven R

    2017-06-30

    Progressive, chronic bacterial infection of the airways is a leading cause of death in cystic fibrosis (CF). Culture-independent methods based on sequencing of the bacterial 16S rRNA gene describe a distinct microbial community that decreases in richness and diversity with disease progression. Understanding the functional characteristics of the microbial community may aid in identifying potential therapies and may assist in management, but current methods are cumbersome. Here, we demonstrate the use of an oxidative metabolic assay as a complement to sequencing methods to describe the microbiome in the airways of patients with CF. Expectorated sputum was collected from 16 CF subjects and 8 control subjects. The Biolog Gen III Microplate was used in a community-level physiological profiling (CLPP)-based assay to examine oxidative metabolic activity. 16S rRNA V4 amplicon sequencing was used to characterize the taxonomy and diversity of the samples. Correlations were then identified among the oxidative activity and taxonomy data. In an additional paired analysis, sputum from seven CF subjects were collected at two separate clinic visits and compared for oxidative activity, taxonomy, and diversity. Significant differences in oxidative metabolic activity, microbial taxonomy, and diversity were found between the CF and control sputum samples. Oxidative activity correlated positively with total genera but not with other measures of diversity or taxonomy, demonstrating that the metabolic assay complements the structural aspects of the microbiome. As expected, Pseudomonas was significantly enriched in CF samples, while Streptococcus and Prevotella were similarly abundant in both CF and control samples. Paired analysis of CF samples at separate clinic visits revealed comparable oxidative activity that correlated with similar stability in taxonomy and diversity. The CLPP assay used in this study complements existing sequencing methods to delineate the oxidative metabolic

  15. 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

  16. 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. Copyright © 2016 International Metabolic Engineering Society. Published by Elsevier

  17. Fungal networks shape dynamics of bacterial dispersal and community assembly in cheese rind microbiomes.

    Science.gov (United States)

    Zhang, Yuanchen; Kastman, Erik K; Guasto, Jeffrey S; Wolfe, Benjamin E

    2018-01-23

    Most studies of bacterial motility have examined small-scale (micrometer-centimeter) cell dispersal in monocultures. However, bacteria live in multispecies communities, where interactions with other microbes may inhibit or facilitate dispersal. Here, we demonstrate that motile bacteria in cheese rind microbiomes use physical networks created by filamentous fungi for dispersal, and that these interactions can shape microbial community structure. Serratia proteamaculans and other motile cheese rind bacteria disperse on fungal networks by swimming in the liquid layers formed on fungal hyphae. RNA-sequencing, transposon mutagenesis, and comparative genomics identify potential genetic mechanisms, including flagella-mediated motility, that control bacterial dispersal on hyphae. By manipulating fungal networks in experimental communities, we demonstrate that fungal-mediated bacterial dispersal can shift cheese rind microbiome composition by promoting the growth of motile over non-motile community members. Our single-cell to whole-community systems approach highlights the interactive dynamics of bacterial motility in multispecies microbiomes.

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

    DEFF Research Database (Denmark)

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

    , and performance of Escherichia coli. An insight into glucose and acetate fate on the level of individual cell can provide the type of information which are valuable for the understanding of bacterial metabolism in fermentation process and can shed more light on the differentiation of isogenic fermenting...... 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...

  19. Metabolic and molecular methods to evaluate the organoclay effects on a bacterial community

    International Nuclear Information System (INIS)

    Abbate, Cristina; Ambrosoli, Roberto; Minati, Josè Luis; Gennari, Mara; Arena, Maria

    2013-01-01

    The aim of this work was to evaluate the influence exerted by two different commercial organoclays (DELLITE 43B and DELLITE 67G) on a model microbial consortium using microbial metabolic characterization with BIOLOG system and denaturing gradient gel electrophoresis (DGGE) molecular approach. The information obtained from the molecular analyses, in their complex, account for the differences in species composition induced on the reference consortium by the contact with the organoclays under study. DELLITE 43B resulted to produce a marked selective effect, stimulating the quantitative increase especially of Pseudomonas pseudoalcaligenes. A weaker effect was found for DELLITE 67G. On the other hand, Biolog analyses indicated a depressing action exerted by DELLITE 43B on the metabolic activity of the model microbial consortium as a whole. The presence of P. pseudoalcaligenes and B. borstelensis in the bacterial community after the treatments confirmed that a positive change in the microbial structure consortium occurred. -- Highlights: •A tool to determine the environmental impact of organoclays on a bacterial community. •A positive change in the microbial structure consortium occurred. •No repressive effect on the original microbial community. -- This work is a good tool to determine the environmental impact of organoclays on a bacterial community

  20. 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. Copyright © 2014 Elsevier Ltd. All rights reserved.

  1. 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.

  2. The evolution of metabolic networks of E. coli

    Directory of Open Access Journals (Sweden)

    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. Consistency Analysis of Genome-Scale Models of Bacterial Metabolism: A Metamodel Approach.

    Science.gov (United States)

    Ponce-de-Leon, Miguel; Calle-Espinosa, Jorge; Peretó, Juli; Montero, Francisco

    2015-01-01

    Genome-scale metabolic models usually contain inconsistencies that manifest as blocked reactions and gap metabolites. With the purpose to detect recurrent inconsistencies in metabolic models, a large-scale analysis was performed using a previously published dataset of 130 genome-scale models. The results showed that a large number of reactions (~22%) are blocked in all the models where they are present. To unravel the nature of such inconsistencies a metamodel was construed by joining the 130 models in a single network. This metamodel was manually curated using the unconnected modules approach, and then, it was used as a reference network to perform a gap-filling on each individual genome-scale model. Finally, a set of 36 models that had not been considered during the construction of the metamodel was used, as a proof of concept, to extend the metamodel with new biochemical information, and to assess its impact on gap-filling results. The analysis performed on the metamodel allowed to conclude: 1) the recurrent inconsistencies found in the models were already present in the metabolic database used during the reconstructions process; 2) the presence of inconsistencies in a metabolic database can be propagated to the reconstructed models; 3) there are reactions not manifested as blocked which are active as a consequence of some classes of artifacts, and; 4) the results of an automatic gap-filling are highly dependent on the consistency and completeness of the metamodel or metabolic database used as the reference network. In conclusion the consistency analysis should be applied to metabolic databases in order to detect and fill gaps as well as to detect and remove artifacts and redundant information.

  4. Sequence-based Network Completion Reveals the Integrality of Missing Reactions in Metabolic Networks*

    Science.gov (United States)

    Krumholz, Elias W.; Libourel, Igor G. L.

    2015-01-01

    Genome-scale metabolic models are central in connecting genotypes to metabolic phenotypes. However, even for well studied organisms, such as Escherichia coli, draft networks do not contain a complete biochemical network. Missing reactions are referred to as gaps. These gaps need to be filled to enable functional analysis, and gap-filling choices influence model predictions. To investigate whether functional networks existed where all gap-filling reactions were supported by sequence similarity to annotated enzymes, four draft networks were supplemented with all reactions from the Model SEED database for which minimal sequence similarity was found in their genomes. Quadratic programming revealed that the number of reactions that could partake in a gap-filling solution was vast: 3,270 in the case of E. coli, where 72% of the metabolites in the draft network could connect a gap-filling solution. Nonetheless, no network could be completed without the inclusion of orphaned enzymes, suggesting that parts of the biochemistry integral to biomass precursor formation are uncharacterized. However, many gap-filling reactions were well determined, and the resulting networks showed improved prediction of gene essentiality compared with networks generated through canonical gap filling. In addition, gene essentiality predictions that were sensitive to poorly determined gap-filling reactions were of poor quality, suggesting that damage to the network structure resulting from the inclusion of erroneous gap-filling reactions may be predictable. PMID:26041773

  5. Sequence-based Network Completion Reveals the Integrality of Missing Reactions in Metabolic Networks.

    Science.gov (United States)

    Krumholz, Elias W; Libourel, Igor G L

    2015-07-31

    Genome-scale metabolic models are central in connecting genotypes to metabolic phenotypes. However, even for well studied organisms, such as Escherichia coli, draft networks do not contain a complete biochemical network. Missing reactions are referred to as gaps. These gaps need to be filled to enable functional analysis, and gap-filling choices influence model predictions. To investigate whether functional networks existed where all gap-filling reactions were supported by sequence similarity to annotated enzymes, four draft networks were supplemented with all reactions from the Model SEED database for which minimal sequence similarity was found in their genomes. Quadratic programming revealed that the number of reactions that could partake in a gap-filling solution was vast: 3,270 in the case of E. coli, where 72% of the metabolites in the draft network could connect a gap-filling solution. Nonetheless, no network could be completed without the inclusion of orphaned enzymes, suggesting that parts of the biochemistry integral to biomass precursor formation are uncharacterized. However, many gap-filling reactions were well determined, and the resulting networks showed improved prediction of gene essentiality compared with networks generated through canonical gap filling. In addition, gene essentiality predictions that were sensitive to poorly determined gap-filling reactions were of poor quality, suggesting that damage to the network structure resulting from the inclusion of erroneous gap-filling reactions may be predictable. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

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

    Directory of Open Access Journals (Sweden)

    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.

  7. Current Understanding of the Formation and Adaptation of Metabolic Systems Based on Network Theory

    Directory of Open Access Journals (Sweden)

    Kazuhiro Takemoto

    2012-07-01

    Full Text Available 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.

  8. Community structure of the metabolically active rumen bacterial and archaeal communities of dairy cows over the transition period

    DEFF Research Database (Denmark)

    Zhu, Zhigang; Noel, Samantha Joan; Difford, Gareth Frank

    2017-01-01

    was extracted from the rumen samples and cDNA thereof was subsequently used for characterizing the metabolically active bacterial (16S rRNA transcript amplicon sequencing) and archaeal (qPCR, T-RFLP and mcrA and 16S rRNA transcript amplicon sequencing) communities. The metabolically active bacterial community......% of the total reads, dominated by the genera Methanobrevibacter (75%) and Methanosphaera (24%), whereas the Methanomassiliicoccales order covered only 0.2% of the total reads. In conclusion, the present study showed that the structure of the metabolically active bacterial and archaeal rumen communities changed...... prepartum to postpartum decrease (from 15% to 2%) was observed in relative abundance of Methanomassiliicoccales 16S rRNA transcripts. In contrast to qPCR analysis of the 16S rRNA transcripts, quantification of mcrA transcripts revealed no change in total abundance of metabolically active methanogens over...

  9. Study of the toxic effect of Cd and Cu on the metabolic processes of bacterial communities

    International Nuclear Information System (INIS)

    Damyanova, A.

    1983-01-01

    The change in the microelement content of aqueous media affects the development and the vital process of microorganisms. The present paper deals with the accumulation and the influence of the toxic elements Cd and Cu on the metabolic processes proceeding in bacterial communities. To trace the dynamics of one of the basic metabolic processes, oxygen consumption was measured polarographically. For tracing the accumulation of the additionally included Cd and Cu in the experimental volume of water, the nondestructive method of neutron activation analysis was used. The results show a correspondence between the inclusion of elements and the change in the breathing as a function of the additionally included concentration of the investigated microelements. The greater toxic effect of Cd on the oxygen consumption is noticeable. (author)

  10. Study of the toxic effect of Cd and Cu on the metabolic processes of bacterial communities

    Energy Technology Data Exchange (ETDEWEB)

    Damyanova, A. (Bylgarska Akademiya na Naukite, Sofia. Inst. za Yadrena Izsledvaniya i Yadrena Energetika)

    1983-01-01

    The change in the microelement content of aqueous media affects the development and the vital process of microorganisms. The present paper deals with the accumulation and the influence of the toxic elements Cd and Cu on the metabolic processes proceeding in bacterial communities. To trace the dynamics of one of the basic metabolic processes, oxygen consumption was measured polarographically. For tracing the accumulation of the additionally included Cd and Cu in the experimental volume of water, the nondestructive method of neutron activation analysis was used. The results show a correspondence between the inclusion of elements and the change in the breathing as a function of the additionally included concentration of the investigated microelements. The greater toxic effect of Cd on the oxygen consumption is noticeable.

  11. 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.

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

    Science.gov (United States)

    Conover, Matt S; Hadjifrangiskou, Maria; Palermo, Joseph J; Hibbing, Michael E; Dodson, Karen W; Hultgren, Scott J

    2016-04-12

    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. Urinary tract infections (UTIs) are one of the most common bacterial infections, impacting mostly women. Every year, millions of UTIs occur in the U.S. with most being caused by uropathogenic E. coli(UPEC). During a UTI, UPEC invade bladder cells and form an intracellular bacterial community

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

    Directory of Open Access Journals (Sweden)

    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.

  14. Thermodynamic calculations for biochemical transport and reaction processes in metabolic networks

    NARCIS (Netherlands)

    Jol, Stefan J; Kümmel, Anne; Hatzimanikatis, Vassily; Beard, Daniel A; Heinemann, Matthias

    2010-01-01

    Thermodynamic analysis of metabolic networks has recently generated increasing interest for its ability to add constraints on metabolic network operation, and to combine metabolic fluxes and metabolite measurements in a mechanistic manner. Concepts for the calculation of the change in Gibbs energy

  15. Predicting selective drug targets in cancer through metabolic networks

    Science.gov (United States)

    Folger, Ori; Jerby, Livnat; Frezza, Christian; Gottlieb, Eyal; Ruppin, Eytan; Shlomi, Tomer

    2011-01-01

    The interest in studying metabolic alterations in cancer and their potential role as novel targets for therapy has been rejuvenated in recent years. Here, we report the development of the first genome-scale network model of cancer metabolism, validated by correctly identifying genes essential for cellular proliferation in cancer cell lines. The model predicts 52 cytostatic drug targets, of which 40% are targeted by known, approved or experimental anticancer drugs, and the rest are new. It further predicts combinations of synthetic lethal drug targets, whose synergy is validated using available drug efficacy and gene expression measurements across the NCI-60 cancer cell line collection. Finally, potential selective treatments for specific cancers that depend on cancer type-specific downregulation of gene expression and somatic mutations are compiled. PMID:21694718

  16. Enumeration of minimal stoichiometric precursor sets in metabolic networks.

    Science.gov (United States)

    Andrade, Ricardo; Wannagat, Martin; Klein, Cecilia C; Acuña, Vicente; Marchetti-Spaccamela, Alberto; Milreu, Paulo V; Stougie, Leen; Sagot, Marie-France

    2016-01-01

    What an organism needs at least from its environment to produce a set of metabolites, e.g. target(s) of interest and/or biomass, has been called a minimal precursor set. Early approaches to enumerate all minimal precursor sets took into account only the topology of the metabolic network (topological precursor sets). Due to cycles and the stoichiometric values of the reactions, it is often not possible to produce the target(s) from a topological precursor set in the sense that there is no feasible flux. Although considering the stoichiometry makes the problem harder, it enables to obtain biologically reasonable precursor sets that we call stoichiometric. Recently a method to enumerate all minimal stoichiometric precursor sets was proposed in the literature. The relationship between topological and stoichiometric precursor sets had however not yet been studied. Such relationship between topological and stoichiometric precursor sets is highlighted. We also present two algorithms that enumerate all minimal stoichiometric precursor sets. The first one is of theoretical interest only and is based on the above mentioned relationship. The second approach solves a series of mixed integer linear programming problems. We compared the computed minimal precursor sets to experimentally obtained growth media of several Escherichia coli strains using genome-scale metabolic networks. The results show that the second approach efficiently enumerates minimal precursor sets taking stoichiometry into account, and allows for broad in silico studies of strains or species interactions that may help to understand e.g. pathotype and niche-specific metabolic capabilities. sasita is written in Java, uses cplex as LP solver and can be downloaded together with all networks and input files used in this paper at http://www.sasita.gforge.inria.fr.

  17. (Im) Perfect robustness and adaptation of metabolic networks subject to metabolic and gene-expression regulation: marrying control engineering with metabolic control analysis

    NARCIS (Netherlands)

    He, F.; Fromion, V.; Westerhoff, H.V.

    2013-01-01

    Background: Metabolic control analysis (MCA) and supply-demand theory have led to appreciable understanding of the systems properties of metabolic networks that are subject exclusively to metabolic regulation. Supply-demand theory has not yet considered gene-expression regulation explicitly whilst a

  18. Desalination Technology Waste Streams: Impact of pH and Brine on Bacterial Metabolism Among Natural Marine Assemblages

    Science.gov (United States)

    2008-09-10

    Naval Research Laboratory Washington, DC 20375-5320 NRL/MR/6110--08-9138 Desalination Technology Waste Streams: Impact of pH and Brine on Bacterial...31 I)ecember 2(X)8 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER 61-5557-M65 Desalination Technology Waste Streams: Impact of pI4 and Brine on...AND BRINE ON BACTERIAL METABOLISM AMONG NATURAL MARINE ASSEMBLAGES ABSTRACT Hydrate formation-based techniques have been proposed as desalination

  19. 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

    2015-10-01

    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 from global carbon fixation to 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 of an unusual compartment designed by nature to optimize metabolic processes within a cellular context. © 2015 John Wiley & Sons Ltd.

  20. Rapid Countermeasure Discovery against Francisella tularensis Based on a Metabolic Network Reconstruction

    Science.gov (United States)

    Chaudhury, Sidhartha; Abdulhameed, Mohamed Diwan M.; Singh, Narender; Tawa, Gregory J.; D’haeseleer, Patrik M.; Zemla, Adam T.; Navid, Ali; Zhou, Carol E.; Franklin, Matthew C.; Cheung, Jonah; Rudolph, Michael J.; Love, James; Graf, John F.; Rozak, David A.; Dankmeyer, Jennifer L.; Amemiya, Kei; Daefler, Simon; Wallqvist, Anders

    2013-01-01

    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 active versus tested

  1. Construction of a genome-scale metabolic network of the plant pathogen Pectobacterium carotovorum provides new strategies for bactericide discovery.

    Science.gov (United States)

    Wang, Cheng; Deng, Zhi-Luo; Xie, Zhi-Ming; Chu, Xin-Yi; Chang, Ji-Wei; Kong, De-Xin; Li, Bao-Ju; Zhang, Hong-Yu; Chen, Ling-Ling

    2015-01-30

    We reconstructed the first genome-scale metabolic network of the plant pathogen Pectobacterium carotovorum subsp. carotovorum PC1 based on its genomic sequence, annotation, and physiological data. Metabolic characteristics were analyzed using flux balance analysis (FBA), and the results were afterwards validated by phenotype microarray (PM) experiments. The reconstructed genome-scale metabolic model, iPC1209, contains 2235 reactions, 1113 metabolites and 1209 genes. We identified 19 potential bactericide targets through a comprehensive in silico gene-deletion study. Next, we performed virtual screening to identify candidate inhibitors for an important potential drug target, alkaline phosphatase, and experimentally verified that three lead compounds were able to inhibit both bacterial cell viability and the activity of alkaline phosphatase in vitro. This study illustrates a new strategy for the discovery of agricultural bactericides. Copyright © 2014 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

  2. 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.

  3. 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

  4. Metabolic fingerprinting of bacterial strains isolated from northern areas of Pakistan

    International Nuclear Information System (INIS)

    Zaheer, A.; Latif, Z.

    2017-01-01

    The diversity of Plant Growth Promoting Rhizobacteria (PGPR) in the rhizosphere plays a key role in the maintenance of sustainable agricultural system. In this study, samples were obtained from northern areas of Pakistan. Thirty bacterial strains were isolated, purified, characterized biochemically and subjected to the metabolic fingerprinting by performing nitrogen fixation, phosphate solubilization, protease, indole acetic acid (IAA) production, antibiotic susceptibility and heavy metal resistance test, lead acetate assay for the H2S production. Strains showing distinct characteristics were further characterized by 16S rDNA sequencing and characterized as Bacillus pumilus (KT273321), Acinetobacter baumanii (KT273323), Acinetobacter junii (KT273324), Pseudomonas aeruginosa (KT273325), Bacillus circulans (KT273326) and Bacillus cereus (KT273327). As most of the strains show positive results for resistance against heavy metals, phosphate solubilization, nitrogen fixation, IAA production, and so these strains might be utilized for the removal of heavy metals from the ecosystem as well as biofertilizer in agriculture lands of northern areas. (author)

  5. Incremental parameter estimation of kinetic metabolic network models

    Directory of Open Access Journals (Sweden)

    Jia Gengjie

    2012-11-01

    Full Text Available Abstract Background An efficient and reliable parameter estimation method is essential for the creation of biological models using ordinary differential equation (ODE. Most of the existing estimation methods involve finding the global minimum of data fitting residuals over the entire parameter space simultaneously. Unfortunately, the associated computational requirement often becomes prohibitively high due to the large number of parameters and the lack of complete parameter identifiability (i.e. not all parameters can be uniquely identified. Results In this work, an incremental approach was applied to the parameter estimation of ODE models from concentration time profiles. Particularly, the method was developed to address a commonly encountered circumstance in the modeling of metabolic networks, where the number of metabolic fluxes (reaction rates exceeds that of metabolites (chemical species. Here, the minimization of model residuals was performed over a subset of the parameter space that is associated with the degrees of freedom in the dynamic flux estimation from the concentration time-slopes. The efficacy of this method was demonstrated using two generalized mass action (GMA models, where the method significantly outperformed single-step estimations. In addition, an extension of the estimation method to handle missing data is also presented. Conclusions The proposed incremental estimation method is able to tackle the issue on the lack of complete parameter identifiability and to significantly reduce the computational efforts in estimating model parameters, which will facilitate kinetic modeling of genome-scale cellular metabolism in the future.

  6. Origins of Specificity and Promiscuity in Metabolic Networks

    Science.gov (United States)

    Carbonell, Pablo; Lecointre, Guillaume; Faulon, Jean-Loup

    2011-01-01

    How enzymes have evolved to their present form is linked to the question of how pathways emerged and evolved into extant metabolic networks. To investigate this mechanism, we have explored the chemical diversity present in a largely unbiased data set of catalytic reactions processed by modern enzymes across the tree of life. In order to get a quantitative estimate of enzyme chemical diversity, we measure enzyme multispecificity or promiscuity using the reaction molecular signatures. Our main finding is that reactions that are catalyzed by a highly specific enzyme are shared by poorly divergent species, suggesting a later emergence of this function during evolution. In contrast, reactions that are catalyzed by highly promiscuous enzymes are more likely to appear uniformly distributed across species in the tree of life. From a functional point of view, promiscuous enzymes are mainly involved in amino acid and lipid metabolisms, which might be associated with the earliest form of biochemical reactions. In this way, results presented in this paper might assist us with the identification of primeval promiscuous catalytic functions contributing to life's minimal metabolism. PMID:22052908

  7. Metabolic pathways of Pseudomonas aeruginosa involved in competition with respiratory bacterial pathogens

    Directory of Open Access Journals (Sweden)

    Marie eBeaume

    2015-04-01

    Full Text Available Background: Chronic airway infection by Pseudomonas aeruginosa considerably contributes to lung tissue destruction and impairment of pulmonary function in cystic-fibrosis (CF patients. Complex interplays between P. aeruginosa and other co-colonizing pathogens including Staphylococcus aureus, Burkholderia spp and Klebsiella pneumoniae may be crucial for pathogenesis and disease progression.Methods: We generated a library of PA14 transposon insertion mutants to identify P. aeruginosa genes required for exploitative and direct competitions with S. aureus, B. cenocepacia, and K. pneumoniae. Results: Whereas wild type PA14 inhibited S. aureus growth, two transposon insertions located in pqsC and carB, resulted in reduced growth inhibition. PqsC is involved in the synthesis of 4-hydroxy-2-alkylquinolines (HAQs, a family of molecules having antibacterial properties, while carB is a key gene in pyrimidine biosynthesis. The carB mutant was also unable to grow in the presence of B. cepacia and K. pneumoniae but not E. coli and S. epidermidis. We further identified a transposon insertion in purF, encoding a key enzyme of purine metabolism. This mutant displayed a severe growth deficiency in the presence of Gram-negative but not of Gram-positive bacteria. We identified a beneficial interaction in a bioA transposon mutant, unable to grow on rich medium. This growth defect could be restored either by addition of biotin or by co-culturing the mutant in the presence of K. pneumoniae or E. coli.Conclusions: Complex interactions take place between the various bacterial species colonizing CF-lungs. This work identified both detrimental and beneficial interactions occurring between P. aeruginosa and three other respiratory pathogens involving several major metabolic pathways. Manipulating these pathways could be used to interfere with bacterial interactions and influence the colonization by respiratory pathogens.

  8. Synthesis and bacterial metabolism of cis- and trans-2-alkyl analogues of sodium cyclamate.

    Science.gov (United States)

    Wiley, R A; Pearson, D A; Schmidt, V; Wesche, S B; Roxon, J J

    1983-07-01

    Sodium cyclamate is an effective artificial sweetner, which has been banned from the U.SD. market because of alleged carcinogenic properties. It appears that cyclohexylamine, liberated from cyclamate as a result of bacterial mtabolism, is the proximate carcinogen. In an effort to elucidate the extent to which analogues of cyclamate would enter into the bacterial metabolic pathway, as well as any stereochemical requirements which might exist, several 2-alkaly analogues of sodium cyclamate were prepared. It was found that trans-N-(2-methylcyclohexyl)sulfamate (trans-2a) and trans-N-(2-ethylcyclohexyl)sulfamate were hydrolyzed by freshly collected fecal suspensions from rats fed cyclamate, but not from control rats, at the same rate as cyclamate itself. trans-N-(2-Isopropylcyclohexyl)sulfamate (trans-2c) was not hydrolyzed at all. Surprisingly, two of the analogous cis compounds (cis-2a and cis-2c, respectively) were hydrolyzed by fecal suspensions from control, as well as from cyclamate-fed, rats. Moreover, cis-2a was hydrolyzed by incubating it in medium only. Thus, it is apparent that stereochemical influences on the chemical properties of these compounds are substantial. These results do not appear to point the way toward a safe, nonmetabolizable sweetening agent.

  9. Feedback Control Architecture and the Bacterial Chemotaxis Network

    Science.gov (United States)

    Hamadeh, Abdullah; Roberts, Mark A. J.; August, Elias; McSharry, Patrick E.; Maini, Philip K.; Armitage, Judith P.; Papachristodoulou, Antonis

    2011-01-01

    Bacteria move towards favourable and away from toxic environments by changing their swimming pattern. This response is regulated by the chemotaxis signalling pathway, which has an important feature: it uses feedback to ‘reset’ (adapt) the bacterial sensing ability, which allows the bacteria to sense a range of background environmental changes. The role of this feedback has been studied extensively in the simple chemotaxis pathway of Escherichia coli. However it has been recently found that the majority of bacteria have multiple chemotaxis homologues of the E. coli proteins, resulting in more complex pathways. In this paper we investigate the configuration and role of feedback in Rhodobacter sphaeroides, a bacterium containing multiple homologues of the chemotaxis proteins found in E. coli. Multiple proteins could produce different possible feedback configurations, each having different chemotactic performance qualities and levels of robustness to variations and uncertainties in biological parameters and to intracellular noise. We develop four models corresponding to different feedback configurations. Using a series of carefully designed experiments we discriminate between these models and invalidate three of them. When these models are examined in terms of robustness to noise and parametric uncertainties, we find that the non-invalidated model is superior to the others. Moreover, it has a ‘cascade control’ feedback architecture which is used extensively in engineering to improve system performance, including robustness. Given that the majority of bacteria are known to have multiple chemotaxis pathways, in this paper we show that some feedback architectures allow them to have better performance than others. In particular, cascade control may be an important feature in achieving robust functionality in more complex signalling pathways and in improving their performance. PMID:21573199

  10. Feedback control architecture and the bacterial chemotaxis network.

    Directory of Open Access Journals (Sweden)

    Abdullah Hamadeh

    2011-05-01

    Full Text Available Bacteria move towards favourable and away from toxic environments by changing their swimming pattern. This response is regulated by the chemotaxis signalling pathway, which has an important feature: it uses feedback to 'reset' (adapt the bacterial sensing ability, which allows the bacteria to sense a range of background environmental changes. The role of this feedback has been studied extensively in the simple chemotaxis pathway of Escherichia coli. However it has been recently found that the majority of bacteria have multiple chemotaxis homologues of the E. coli proteins, resulting in more complex pathways. In this paper we investigate the configuration and role of feedback in Rhodobacter sphaeroides, a bacterium containing multiple homologues of the chemotaxis proteins found in E. coli. Multiple proteins could produce different possible feedback configurations, each having different chemotactic performance qualities and levels of robustness to variations and uncertainties in biological parameters and to intracellular noise. We develop four models corresponding to different feedback configurations. Using a series of carefully designed experiments we discriminate between these models and invalidate three of them. When these models are examined in terms of robustness to noise and parametric uncertainties, we find that the non-invalidated model is superior to the others. Moreover, it has a 'cascade control' feedback architecture which is used extensively in engineering to improve system performance, including robustness. Given that the majority of bacteria are known to have multiple chemotaxis pathways, in this paper we show that some feedback architectures allow them to have better performance than others. In particular, cascade control may be an important feature in achieving robust functionality in more complex signalling pathways and in improving their performance.

  11. Feedback control architecture and the bacterial chemotaxis network.

    Science.gov (United States)

    Hamadeh, Abdullah; Roberts, Mark A J; August, Elias; McSharry, Patrick E; Maini, Philip K; Armitage, Judith P; Papachristodoulou, Antonis

    2011-05-01

    Bacteria move towards favourable and away from toxic environments by changing their swimming pattern. This response is regulated by the chemotaxis signalling pathway, which has an important feature: it uses feedback to 'reset' (adapt) the bacterial sensing ability, which allows the bacteria to sense a range of background environmental changes. The role of this feedback has been studied extensively in the simple chemotaxis pathway of Escherichia coli. However it has been recently found that the majority of bacteria have multiple chemotaxis homologues of the E. coli proteins, resulting in more complex pathways. In this paper we investigate the configuration and role of feedback in Rhodobacter sphaeroides, a bacterium containing multiple homologues of the chemotaxis proteins found in E. coli. Multiple proteins could produce different possible feedback configurations, each having different chemotactic performance qualities and levels of robustness to variations and uncertainties in biological parameters and to intracellular noise. We develop four models corresponding to different feedback configurations. Using a series of carefully designed experiments we discriminate between these models and invalidate three of them. When these models are examined in terms of robustness to noise and parametric uncertainties, we find that the non-invalidated model is superior to the others. Moreover, it has a 'cascade control' feedback architecture which is used extensively in engineering to improve system performance, including robustness. Given that the majority of bacteria are known to have multiple chemotaxis pathways, in this paper we show that some feedback architectures allow them to have better performance than others. In particular, cascade control may be an important feature in achieving robust functionality in more complex signalling pathways and in improving their performance.

  12. Comparison between elementary flux modes analysis and 13C-metabolic fluxes measured in bacterial and plant cells.

    Science.gov (United States)

    Beurton-Aimar, Marie; Beauvoit, Bertrand; Monier, Antoine; Vallée, François; Dieuaide-Noubhani, Martine; Colombié, Sophie

    2011-06-20

    (13)C metabolic flux analysis is one of the pertinent ways to compare two or more physiological states. From a more theoretical standpoint, the structural properties of metabolic networks can be analysed to explore feasible metabolic behaviours and to define the boundaries of steady state flux distributions. Elementary flux mode analysis is one of the most efficient methods for performing this analysis. In this context, recent approaches have tended to compare experimental flux measurements with topological network analysis. Metabolic networks describing the main pathways of central carbon metabolism were set up for a bacteria species (Corynebacterium glutamicum) and a plant species (Brassica napus) for which experimental flux maps were available. The structural properties of each network were then studied using the concept of elementary flux modes. To do this, coefficients of flux efficiency were calculated for each reaction within the networks by using selected sets of elementary flux modes. Then the relative differences - reflecting the change of substrate i.e. a sugar source for C. glutamicum and a nitrogen source for B. napus - of both flux efficiency and flux measured experimentally were compared. For both organisms, there is a clear relationship between these parameters, thus indicating that the network structure described by the elementary flux modes had captured a significant part of the metabolic activity in both biological systems. In B. napus, the extension of the elementary flux mode analysis to an enlarged metabolic network still resulted in a clear relationship between the change in the coefficients and that of the measured fluxes. Nevertheless, the limitations of the method to fit some particular fluxes are discussed. This consistency between EFM analysis and experimental flux measurements, validated on two metabolic systems allows us to conclude that elementary flux mode analysis could be a useful tool to complement (13)C metabolic flux analysis

  13. 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.

  14. 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

  15. Grapevine rootstocks shape underground bacterial microbiome and networking but not potential functionality.

    Science.gov (United States)

    Marasco, Ramona; Rolli, Eleonora; Fusi, Marco; Michoud, Grégoire; Daffonchio, Daniele

    2018-01-03

    The plant compartments of Vitis vinifera, including the rhizosphere, rhizoplane, root endosphere, phyllosphere and carposphere, provide unique niches that drive specific bacterial microbiome associations. The majority of phyllosphere endophytes originate from the soil and migrate up to the aerial compartments through the root endosphere. Thus, the soil and root endosphere partially define the aerial endosphere in the leaves and berries, contributing to the terroir of the fruit. However, V. vinifera cultivars are invariably grafted onto the rootstocks of other Vitis species and hybrids. It has been hypothesized that the plant species determines the microbiome of the root endosphere and, as a consequence, the aerial endosphere. In this work, we test the first part of this hypothesis. We investigate whether different rootstocks influence the bacteria selected from the surrounding soil, affecting the bacterial diversity and potential functionality of the rhizosphere and root endosphere. Bacterial microbiomes from both the root tissues and the rhizosphere of Barbera cultivars, both ungrafted and grafted on four different rootstocks, cultivated in the same soil from the same vineyard, were characterized by 16S rRNA high-throughput sequencing. To assess the influence of the root genotype on the bacterial communities' recruitment in the root system, (i) the phylogenetic diversity coupled with the predicted functional profiles and (ii) the co-occurrence bacterial networks were determined. Cultivation-dependent approaches were used to reveal the plant-growth promoting (PGP) potential associated with the grafted and ungrafted root systems. Richness, diversity and bacterial community networking in the root compartments were significantly influenced by the rootstocks. Complementary to a shared bacterial microbiome, different subsets of soil bacteria, including those endowed with PGP traits, were selected by the root system compartments of different rootstocks. The interaction

  16. Grapevine rootstocks shape underground bacterial microbiome and networking but not potential functionality

    KAUST Repository

    Marasco, Ramona

    2018-01-03

    The plant compartments of Vitis vinifera, including the rhizosphere, rhizoplane, root endosphere, phyllosphere and carposphere, provide unique niches that drive specific bacterial microbiome associations. The majority of phyllosphere endophytes originate from the soil and migrate up to the aerial compartments through the root endosphere. Thus, the soil and root endosphere partially define the aerial endosphere in the leaves and berries, contributing to the terroir of the fruit. However, V. vinifera cultivars are invariably grafted onto the rootstocks of other Vitis species and hybrids. It has been hypothesized that the plant species determines the microbiome of the root endosphere and, as a consequence, the aerial endosphere. In this work, we test the first part of this hypothesis. We investigate whether different rootstocks influence the bacteria selected from the surrounding soil, affecting the bacterial diversity and potential functionality of the rhizosphere and root endosphere.Bacterial microbiomes from both the root tissues and the rhizosphere of Barbera cultivars, both ungrafted and grafted on four different rootstocks, cultivated in the same soil from the same vineyard, were characterized by 16S rRNA high-throughput sequencing. To assess the influence of the root genotype on the bacterial communities\\' recruitment in the root system, (i) the phylogenetic diversity coupled with the predicted functional profiles and (ii) the co-occurrence bacterial networks were determined. Cultivation-dependent approaches were used to reveal the plant-growth promoting (PGP) potential associated with the grafted and ungrafted root systems.Richness, diversity and bacterial community networking in the root compartments were significantly influenced by the rootstocks. Complementary to a shared bacterial microbiome, different subsets of soil bacteria, including those endowed with PGP traits, were selected by the root system compartments of different rootstocks. The interaction

  17. Metatranscriptomic and metagenomic description of the bacterial nitrogen metabolism in waste water wet oxidation effluents

    Directory of Open Access Journals (Sweden)

    Julien Crovadore

    2017-10-01

    Full Text Available Anaerobic digestion is a common method for reducing the amount of sludge solids in used waters and enabling biogas production. The wet oxidation process (WOX improves anaerobic digestion by converting carbon into methane through oxidation of organic compounds. WOX produces effluents rich in ammonia, which must be removed to maintain the activity of methanogens. Ammonia removal from WOX could be biologically operated by aerobic granules. To this end, granulation experiments were conducted in 2 bioreactors containing an activated sludge (AS. For the first time, the dynamics of the microbial community structure and the expression levels of 7 enzymes of the nitrogen metabolism in such active microbial communities were followed in regard to time by metagenomics and metatranscriptomics. It was shown that bacterial communities adapt to the wet oxidation effluent by increasing the expression level of the nitrogen metabolism, suggesting that these biological activities could be a less costly alternative for the elimination of ammonia, resulting in a reduction of the use of chemicals and energy consumption in sewage plants. This study reached a strong sequencing depth (from 4.4 to 7.6 Gb and enlightened a yet unknown diversity of the microorganisms involved in the nitrogen pathway. Moreover, this approach revealed the abundance and expression levels of specialised enzymes involved in nitrification, denitrification, ammonification, dissimilatory nitrate reduction to ammonium (DNRA and nitrogen fixation processes in AS. Keywords: Applied sciences, Biological sciences, Environmental science, Genetics, Microbiology

  18. Metabolism of 2,4-dichlorophenol in tobacco engineered with bacterial degradative genes

    International Nuclear Information System (INIS)

    Perkins, E.J.; Sekine, M.; Gordon, M.P.

    1990-01-01

    The potential use of plants in toxic waste remediation has been overlooked. While chlorophenols are relatively slowly metabolized in Nicotiana tabacum var. Xanthi leaf extracts, chlorocatechols are rapidly metabolized, presumably by polyphenol oxidases. Our initial focus has been the fate of 2,4-dichlorophenol (2,4DCP) in var. Xanthi plants which express a bacterial 2,4DCP hydroxylase, which converts 2,4DCP to 3,5-dichlorocatechol. The roots of wild type and 2,4DCP hydroxylase transgenic plants growing in hydroponics were exposed to 14 C-2,4DCP. Approximately 95% of 14 C-2,4DCP metabolites remained in the roots when exposed to 2,4DCP. Upon extraction of root tissue, three major metabolites were found in untransformed plants and four major metabolites in transformed plants. Upon digestion with beta-D-glucosidase, these metabolites disappeared concomitant with the appearance of free 2,4DCP in wild type plants and 2,4DCP and 3,5-dichlorocatechol in transgenic plants. It is apparent that the chlorophenols are not readily available substrates for polyphenol oxidases in whole plants

  19. 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. Copyright © 2015 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

  20. On Modeling Information Spreading in Bacterial Nano-Networks Based on Plasmid Conjugation.

    Science.gov (United States)

    Castorina, Gaetano; Galluccio, Laura; Palazzo, Sergio

    2016-09-01

    In the last years, nano-communications have attracted much attention as a newly promising research field. In particular, molecular communications, which exploit molecular nodes, are a powerful tool to implement communication functionalities in environments where the use of electromagnetic waves becomes critical, e.g., in the human body. In molecular communications, molecules such as proteins, DNA and RNA sequences are used to carry information. To this aim a novel approach relies on the use of genetically modified bacteria to transport enhanced DNA strands, called plasmids, where information can be encoded. Information transfer is thus based on bacteria motility, i.e., self-propelled motion, which under appropriate circumstances is exhibited by certain bacteria. It has been observed that bacteria motility presents many similarities with opportunistic forwarding. Currently the few studies on opportunistic communications among bacteria are based on simulations only. In this paper we propose an analytical model to characterize information spreading in bacterial nano-networks. To this purpose, an epidemic approach, similar to those used to model Delay Tolerant Networks (DTNs), is employed. We also derive two mathematical models which slightly differ. The first describes bacterial nano-networks where a single plasmid is disseminated according to an epidemic approach; the second, takes into account more complex mechanisms where multiple plasmids are disseminated as in realistic bacterial nano-networks. Numerical results being obtained are finally shown and discussed.

  1. 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...... reduced this unspecific binding, resulting in metabolic activity of the target cells. As expected, a linear relationship...... between activity and temperature was observed, demonstrating the sensitivity of the assay. The method was applied to compare activities of the target strain in bulk soil and in the rhizosphere of barley. Contrary to what was anticipated, no significant difference in metabolic activity was observed....

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

    Science.gov (United States)

    Zhang, Hai-Han; Chen, Sheng-Nan; Huang, Ting-Lin; Shang, Pan-Lu; Yang, Xiao; Ma, Wei-Xing

    2015-10-27

    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 heating periods.

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

    Directory of Open Access Journals (Sweden)

    Aziz Mithani

    2010-08-01

    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.

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

    NARCIS (Netherlands)

    Herrgard, M.J.; Swainston, N.; Dobson, P.; Dunn, W.B.; Arga, K.Y.; Arvas, M.; Bluthgen, N.; Borger, S.; Costenoble, E.R.; Heinemann, M.; Hucka, M.; Li, P.; Liebermeister, W.; Mo, M.L.; Oliveira, A.P.; Petranovic, D.; Pettifer, S.; Simeonides, E.; Smallbone, K.; Spasi, I.; Weichart, D.; Brent, R.; Broomhead, D.S.; Westerhoff, H.V.; Kirdar, B.; Penttila, M.; Klipp, E.; Paton, N.; Palsson, B.O.; Sauer, U.; Oliver, S.G.; Mendes, P.; Nielsen, J.; Kell, D.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

  5. 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

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

    DEFF Research Database (Denmark)

    Herrgard, Markus; Swainston, Neil; Dobson, Paul

    2008-01-01

    a consensus metabolic network reconstruction for S. cerevisiae. In drafting it, we placed special emphasis on referencing molecules to persistent databases or using database-independent forms, such as SMILES or InChI strings, as this permits their chemical structure to be represented unambiguously...... of yeast. Similar strategies should benefit communities studying genome-scale metabolic networks of other organisms....

  7. Effective Young's modulus of bacterial and microfibrillated cellulose fibrils in fibrous networks.

    Science.gov (United States)

    Tanpichai, Supachok; Quero, Franck; Nogi, Masaya; Yano, Hiroyuki; Young, Robert J; Lindström, Tom; Sampson, William W; Eichhorn, Stephen J

    2012-05-14

    The deformation micromechanics of bacterial cellulose (BC) and microfibrillated cellulose (MFC) networks have been investigated using Raman spectroscopy. The Raman spectra of both BC and MFC networks exhibit a band initially located at ≈ 1095 cm(-1). We have used the intensity of this band as a function of rotation angle of the specimens to study the cellulose fibril orientation in BC and MFC networks. We have also used the change in this peak's wavenumber position with applied tensile deformation to probe the stress-transfer behavior of these cellulosic materials. The intensity of this Raman band did not change significantly with rotation angle, indicating an in-plane 2D network of fibrils with uniform random orientation; conversely, a highly oriented flax fiber exhibited a marked change in intensity with rotation angle. Experimental data and theoretical analysis shows that the Raman band shift rate arising from deformation of networks under tension is dependent on the angles between the axis of fibrils, the strain axis, the incident laser polarization direction, and the back scattered polarization configurations. From this analysis, the effective moduli of single fibrils of BC and MFC in the networks were estimated to be in the ranges of 79-88 and 29-36 GPa, respectively. It is shown also that for the model to fit the data it is necessary to use a negative Poisson's ratio for MFC networks and BC networks. Discussion of this in-plane "auxetic" behavior is given.

  8. 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.

  9. To Eat and to Be Eaten: Mutual Metabolic Adaptations of Immune Cells and Intracellular Bacterial Pathogens upon Infection

    Science.gov (United States)

    Eisenreich, Wolfgang; Rudel, Thomas; Heesemann, Jürgen; Goebel, Werner

    2017-01-01

    Intracellular bacterial pathogens (IBPs) invade and replicate in different cell types including immune cells, in particular of the innate immune system (IIS) during infection in the acute phase. However, immune cells primarily function as essential players in the highly effective and integrated host defense systems comprising the IIS and the adaptive immune system (AIS), which cooperatively protect the host against invading microbes including IBPs. As countermeasures, the bacterial pathogens (and in particular the IBPs) have developed strategies to evade or reprogram the IIS at various steps. The intracellular replication capacity and the anti-immune defense responses of the IBP's as well as the specific antimicrobial responses of the immune cells of the innate and the AIS depend on specific metabolic programs of the IBPs and their host cells. The metabolic programs of the immune cells supporting or counteracting replication of the IBPs appear to be mutually exclusive. Indeed, recent studies show that upon interaction of naïve, metabolically quiescent immune cells with IBPs, different metabolic activation processes occur which may result in the provision of a survival and replication niche for the pathogen or its eradication. It is therefore likely that within a possible host cell population subsets exist that are metabolically programmed for pro- or anti-microbial conditions. These metabolic programs may be triggered by the interactions between different bacterial agonistic components and host cell receptors. In this review, we summarize the current status in the field and discuss metabolic adaptation processes within immune cells of the IIS and the IBPs that support or restrict the intracellular replication of the pathogens. PMID:28752080

  10. Using the reconstructed genome-scale human metabolic network to study physiology and pathology

    OpenAIRE

    Bordbar, Aarash; Palsson, Bernhard O.

    2012-01-01

    Metabolism plays a key role in many major human diseases. Generation of high-throughput omics data has ushered in a new era of systems biology. Genome-scale metabolic network reconstructions provide a platform to interpret omics data in a biochemically meaningful manner. The release of the global human metabolic network, Recon 1, in 2007 has enabled new systems biology approaches to study human physiology, pathology, and pharmacology. There are currently over 20 publications that utilize Reco...

  11. Coordinations between gene modules control the operation of plant amino acid metabolic networks

    Directory of Open Access Journals (Sweden)

    Galili Gad

    2009-01-01

    Full Text Available Abstract Background Being sessile organisms, plants should adjust their metabolism to dynamic changes in their environment. Such adjustments need particular coordination in branched metabolic networks in which a given metabolite can be converted into multiple other metabolites via different enzymatic chains. In the present report, we developed a novel "Gene Coordination" bioinformatics approach and use it to elucidate adjustable transcriptional interactions of two branched amino acid metabolic networks in plants in response to environmental stresses, using publicly available microarray results. Results Using our "Gene Coordination" approach, we have identified in Arabidopsis plants two oppositely regulated groups of "highly coordinated" genes within the branched Asp-family network of Arabidopsis plants, which metabolizes the amino acids Lys, Met, Thr, Ile and Gly, as well as a single group of "highly coordinated" genes within the branched aromatic amino acid metabolic network, which metabolizes the amino acids Trp, Phe and Tyr. These genes possess highly coordinated adjustable negative and positive expression responses to various stress cues, which apparently regulate adjustable metabolic shifts between competing branches of these networks. We also provide evidence implying that these highly coordinated genes are central to impose intra- and inter-network interactions between the Asp-family and aromatic amino acid metabolic networks as well as differential system interactions with other growth promoting and stress-associated genome-wide genes. Conclusion Our novel Gene Coordination elucidates that branched amino acid metabolic networks in plants are regulated by specific groups of highly coordinated genes that possess adjustable intra-network, inter-network and genome-wide transcriptional interactions. We also hypothesize that such transcriptional interactions enable regulatory metabolic adjustments needed for adaptation to the stresses.

  12. Comprehensive Mapping of Pluripotent Stem Cell Metabolism Using Dynamic Genome-Scale Network Modeling

    Directory of Open Access Journals (Sweden)

    Sriram Chandrasekaran

    2017-12-01

    Full Text Available Summary: Metabolism is an emerging stem cell hallmark tied to cell fate, pluripotency, and self-renewal, yet systems-level understanding of stem cell metabolism has been limited by the lack of genome-scale network models. Here, we develop a systems approach to integrate time-course metabolomics data with a computational model of metabolism to analyze the metabolic state of naive and primed murine pluripotent stem cells. Using this approach, we find that one-carbon metabolism involving phosphoglycerate dehydrogenase, folate synthesis, and nucleotide synthesis is a key pathway that differs between the two states, resulting in differential sensitivity to anti-folates. The model also predicts that the pluripotency factor Lin28 regulates this one-carbon metabolic pathway, which we validate using metabolomics data from Lin28-deficient cells. Moreover, we identify and validate metabolic reactions related to S-adenosyl-methionine production that can differentially impact histone methylation in naive and primed cells. Our network-based approach provides a framework for characterizing metabolic changes influencing pluripotency and cell fate. : Chandrasekaran et al. use computational modeling, metabolomics, and metabolic inhibitors to discover metabolic differences between various pluripotent stem cell states and infer their impact on stem cell fate decisions. Keywords: systems biology, stem cell biology, metabolism, genome-scale modeling, pluripotency, histone methylation, naive (ground state, primed state, cell fate, metabolic network

  13. Systems biology of bacterial persistence, a metabolism-driven strategy for survival

    NARCIS (Netherlands)

    Radzikowski, Jakub

    2017-01-01

    Bacteriële persisten zijn bacteriën die antibiotica tolereren en kunnen opnieuw vermenigvuldigen na antibiotische behandeling. Ze veroorzaken infecties, bijvoorbeeld tuberculose. Ze zijn verschillend van antibiotica resistente bacteriën, omdat het mechanisme van antibiotische tolerantie niet

  14. 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

    mechanisms underlying these transcriptional changes and their impact on the cellular metabolic phenotype is a challenging task due to the complexity of transcriptional regulation and the highly interconnected nature of the metabolic network. In this study we integrate skeletal muscle gene expression datasets...... 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...... factor regulatory network connecting several parts of metabolism. The identified transcription factors include members of the CREB, NRF1 and PPAR family, among others, and represent regulatory targets for further experimental analysis. Overall, our results provide a holistic picture of key metabolic...

  15. Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection.

    Science.gov (United States)

    Guthke, Reinhard; Möller, Ulrich; Hoffmann, Martin; Thies, Frank; Töpfer, Susanne

    2005-04-15

    The immune response to bacterial infection represents a complex network of dynamic gene and protein interactions. We present an optimized reverse engineering strategy aimed at a reconstruction of this kind of interaction networks. The proposed approach is based on both microarray data and available biological knowledge. The main kinetics of the immune response were identified by fuzzy clustering of gene expression profiles (time series). The number of clusters was optimized using various evaluation criteria. For each cluster a representative gene with a high fuzzy-membership was chosen in accordance with available physiological knowledge. Then hypothetical network structures were identified by seeking systems of ordinary differential equations, whose simulated kinetics could fit the gene expression profiles of the cluster-representative genes. For the construction of hypothetical network structures singular value decomposition (SVD) based methods and a newly introduced heuristic Network Generation Method here were compared. It turned out that the proposed novel method could find sparser networks and gave better fits to the experimental data. Reinhard.Guthke@hki-jena.de.

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

    Science.gov (United States)

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

    2016-01-01

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

  17. 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.

  18. Computational solution to automatically map metabolite libraries in the context of genome scale metabolic networks

    Directory of Open Access Journals (Sweden)

    Benjamin eMerlet

    2016-02-01

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

  19. On the bright side of a forest pest-the metabolic potential of bark beetles' bacterial associates.

    Science.gov (United States)

    Fabryová, Anna; Kostovčík, Martin; Díez-Méndez, Alexandra; Jiménez-Gómez, Alejandro; Celador-Lera, Lorena; Saati-Santamaría, Zaki; Sechovcová, Hana; Menéndez, Esther; Kolařik, Miroslav; García-Fraile, Paula

    2018-04-01

    Bark beetles reproduce and overwinter under the bark of trees, and are associated with bacteria that may influence the fitness of their hosts. As regard the aim of this study was to test the metabolic potential of bacterial strains, isolated from the bark beetle species Cryphalus piceae, Ips typographus and Pityophthorus pityophthorus and collected in the Czech Republic from fir, spruce and pine trees, respectively, to degrade plant cell compounds. The bacterial strains were identified as belonging to the genera Curtobacterium, Erwinia, Pantoea, Pseudomonas, Rahnella, Staphylococcus, and Yersinia. Several activities related to the degradation of lignocellulosic materials, such as cellulose, xylan and starch, were found. Moreover, the genomes of three of these strains were sequenced and analyzed, and the presence of the enzymatic machinery required for biomass hydrolysis was discovered. This finding supports the idea that bacteria aid in the provision of nutrients to the beetle from the hydrolysis of tree compounds, results that are relevant for studying the ecological implication of bacterial strains in the bark beetle life cycle. In addition, the activities found in association with the bacterial strains could be useful in biotechnological processes, such as the production of biofuels from biomass, colorant degradation, in the textile industry and for wastewater treatments. Furthermore, the gene sequences of the lignocellulolytic enzymes found within the genomes serve as a basis for future studies regarding the potential application of these bacteria, and their metabolic machinery, in processes such as biomass hydrolysis and bioremediation. Copyright © 2017 Elsevier B.V. All rights reserved.

  20. Review of metabolic pathways activated in cancer cells as determined through isotopic labeling and network analysis.

    Science.gov (United States)

    Dong, Wentao; Keibler, Mark A; Stephanopoulos, Gregory

    2017-09-01

    Cancer metabolism has emerged as an indispensable part of contemporary cancer research. During the past 10 years, the use of stable isotopic tracers and network analysis have unveiled a number of metabolic pathways activated in cancer cells. Here, we review such pathways along with the particular tracers and labeling observations that led to the discovery of their rewiring in cancer cells. The list of such pathways comprises the reductive metabolism of glutamine, altered glycolysis, serine and glycine metabolism, mutant isocitrate dehydrogenase (IDH) induced reprogramming and the onset of acetate metabolism. Additionally, we demonstrate the critical role of isotopic labeling and network analysis in identifying these pathways. The alterations described in this review do not constitute a complete list, and future research using these powerful tools is likely to discover other cancer-related pathways and new metabolic targets for cancer therapy. Copyright © 2017 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

  1. 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

    Genome-scale metabolic networks provide a comprehensive structural framework for modeling genotype-phenotype relationships through flux simulations. The solution space for the metabolic flux state of the cell is typically very large and optimization-based approaches are often necessary for predic......Genome-scale metabolic networks provide a comprehensive structural framework for modeling genotype-phenotype relationships through flux simulations. The solution space for the metabolic flux state of the cell is typically very large and optimization-based approaches are often necessary...

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

    Directory of Open Access Journals (Sweden)

    Matthew D Dyer

    2010-08-01

    Full Text Available 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.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.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.

  3. 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 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.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.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.

  4. 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

  5. The metabolic pathway of metamifop degradation by consortium ME-1 and its bacterial community structure.

    Science.gov (United States)

    Dong, Weiliang; Liu, Kuan; Wang, Fei; Xin, Fengxue; Zhang, Wenming; Zhang, Min; Wu, Hao; Ma, Jiangfeng; Jiang, Min

    2017-06-01

    Metamifop is universally used in agriculture as a post-emergence aryloxyphenoxy propionate herbicide (AOPP), however its microbial degradation mechanism remains unclear. Consortium ME-1 isolated from AOPP-contaminated soil can degrade metamifop completely after 6 days and utilize it as the carbon source for bacterial growth. Meanwhile, consortium ME-1 possessed the ability to degrade metamifop stably under a wide range of pH (6.0-10.0) or temperature (20-42 °C). HPLC-MS analysis shows that N-(2-fluorophenyl)-2-(4-hydroxyphenoxy)-N-methyl propionamide, 2-(4-hydroxyphenoxy)-propionic acid, 6-chloro-2-benzoxazolinone and N-methyl-2-fluoroaniline, were detected and identified as four intermediate metabolites. Based on the metabolites identified, a putative metabolic pathway of metamifop was proposed for the first time. In addition, the consortium ME-1 was also able to transform or degrade other AOPP such as fenoxaprop-p-ethyl, clodinafop-propargyl, quizalofop-p-ethyl and cyhalofop-butyl. Moreover, the community structure of ME-1 with lower microbial diversity compared with the initial soil sample was investigated by high throughput sequencing. β-Proteobacteria and Sphingobacteria were the largest class with sequence percentages of 46.6% and 27.55% at the class level. In addition, 50 genera were classified in consortium ME-1, of which Methylobacillus, Sphingobacterium, Bordetella and Flavobacterium were the dominant genera with sequence percentages of 25.79, 25.61, 14.68 and 9.55%, respectively.

  6. Rapid Proteomics to Prospect and Validate Novel Bacterial Metabolism Induced by Environmental Burden.

    Science.gov (United States)

    Yu, C L; Brooks, S; Li, Y; Subramanian, M; Summers, R; Pope, M

    2017-01-01

    Understanding the pathophysiology of genes and enzymes involved in caffeine metabolism can have extracurricular benefits, such as providing distinct methylxanthines as intermediates for pharmaceutical synthesis, and also improve environmental waste remediation. The strains Pseudomonas putida CBB5 and CES may provide insights into these applications because they may both be induced to degrade caffeine, yet the latter thrives in concentrations >8.0gL -1 ; threefold higher than any other bacteria. We took a novel approach toward identifying the enzymatic pathways in both Pseudomonas sp. CES and a deletion mutation of strain CBB5, which largely circumvented the need for exhaustive isolation of enzymes and the stepwise reconstitution of their activities to determine caffeine response elements. Here, we describe two optimized, rapid alternative strategies based on multiplexed SIL assays and demonstrate their application by discovering caffeine-degrading enzymes in the CES strain based on quantitative comparison between enriched lysate fractions drawn from bacterial proteomes grown in the absence and presence of caffeine. Comparisons were made using stable isotope dimethyl labeling and expression differences were substantiated by reciprocal labeling experiments. The role of the identified proteins in caffeine degradation was independently verified by genetic sequencing. Multiple new components of N-demethylase system were discovered within a fraction of the lysate enriched specifically for this activity. We also describe how to expand the biological context (and reduce systemic bias) by adapting the protocol for total lysate analysis. We combined off-line prefractionation with the speed and resolution advantages of the Orbitrap LUMOS. The global protocol revealed 2406 proteins 1789 of which were quantified between treatments revealing, among other insights, a new antagonistic degradation pathway for vanillin that is completely suppressed by caffeine treatment. © 2017

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

    Science.gov (United States)

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

    2017-01-01

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

  8. 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...... phenotypes. Our results suggest that adaptation to a highly selective environment, such as the CF airways, is a highly dynamic and complex process, which involves continuous optimization of existing regulatory networks to match the fluctuations in the environment....

  9. 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.

  10. Metabolic Network Discovery by Top-Down and Bottom-Up Approaches and Paths for Reconciliation

    International Nuclear Information System (INIS)

    Çakır, Tunahan; Khatibipour, Mohammad Jafar

    2014-01-01

    The primary focus in the network-centric analysis of cellular metabolism by systems biology approaches is to identify the active metabolic network for the condition of interest. Two major approaches are available for the discovery of the condition-specific metabolic networks. One approach starts from genome-scale metabolic networks, which cover all possible reactions known to occur in the related organism in a condition-independent manner, and applies methods such as the optimization-based Flux-Balance Analysis to elucidate the active network. The other approach starts from the condition-specific metabolome data, and processes the data with statistical or optimization-based methods to extract information content of the data such that the active network is inferred. These approaches, termed bottom-up and top-down, respectively, are currently employed independently. However, considering that both approaches have the same goal, they can both benefit from each other paving the way for the novel integrative analysis methods of metabolome data- and flux-analysis approaches in the post-genomic era. This study reviews the strengths of constraint-based analysis and network inference methods reported in the metabolic systems biology field; then elaborates on the potential paths to reconcile the two approaches to shed better light on how the metabolism functions.

  11. 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. Copyright © 2014 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

  12. INFLUENCE OF ROOT EXUDATES AND BACTERIAL METABOLIC ACTIVITY ON APPARENT CONJUGAL GENE TRANSFER FREQUENCIES IN THE RHIZOSPHERE OF WATER GRASS (ECHINOCLORA CRUSGALLI)

    Science.gov (United States)

    The premise that genetic exchange is primarily localized in niches characterized by dense bacterial populations and high availability of growth substrates was tested by relating conjugal gene transfer of an RP4 derivative to availability of root exudates and bacterial metabolic a...

  13. Changes in bacterial metabolism as a response to dissolved organic matter modification during protozoan grazing in coastal Cantabrian and Mediterranean waters.

    Science.gov (United States)

    Baña, Zuriñe; Ayo, Begoña; Marrasé, Cèlia; Gasol, Josep M; Iriberri, Juan

    2014-02-01

    We explored how marine dissolved organic matter (DOM) altered by bacterial growth and protozoan grazing modify the metabolism of Southeastern Cantabrian Sea (CS) and NW Mediterranean Sea (MS) coastal bacterial communities. Major metabolic features were measured in treatments with half of the natural water replaced by water with different DOM quality, characterized by fluorescent DOM analysis and collected from key times of the predator-prey curve. In both ecosystems, protozoan-altered DOM led to similar increases in bacterial carbon demand (238% and 213%) and decreases in bacterial growth efficiency (BGE: 56% for the CS and 46% for the MS). These low BGEs were caused by similar bacterial production but much higher bacterial respiration rates, which in turn were positively related to aminopeptidase activity. However, in the CS bacterial community dominated by Bacteroidetes (41%), the enhanced hydrolytic activity was produced at a lower metabolic cost than in the MS, dominated by SAR11 (47%), which suggests a better adaptation of Bacteroidetes to the DOM altered during protozoan grazing. These results highlight protozoan grazing as a relevant factor influencing BGE in coastal ecosystems, and relate bacterial community composition to the major metabolic processes that result after a change in the quality of marine DOM. © 2013 Society for Applied Microbiology and John Wiley & Sons Ltd.

  14. 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.

  15. Fire modifies the phylogenetic structure of soil bacterial co-occurrence networks.

    Science.gov (United States)

    Pérez-Valera, Eduardo; Goberna, Marta; Faust, Karoline; Raes, Jeroen; García, Carlos; Verdú, Miguel

    2017-01-01

    Fire alters ecosystems by changing the composition and community structure of soil microbes. The phylogenetic structure of a community provides clues about its main assembling mechanisms. While environmental filtering tends to reduce the community phylogenetic diversity by selecting for functionally (and hence phylogenetically) similar species, processes like competitive exclusion by limiting similarity tend to increase it by preventing the coexistence of functionally (and phylogenetically) similar species. We used co-occurrence networks to detect co-presence (bacteria that co-occur) or exclusion (bacteria that do not co-occur) links indicative of the ecological interactions structuring the community. We propose that inspecting the phylogenetic structure of co-presence or exclusion links allows to detect the main processes simultaneously assembling the community. We monitored a soil bacterial community after an experimental fire and found that fire altered its composition, richness and phylogenetic diversity. Both co-presence and exclusion links were more phylogenetically related than expected by chance. We interpret such a phylogenetic clustering in co-presence links as a result of environmental filtering, while that in exclusion links reflects competitive exclusion by limiting similarity. This suggests that environmental filtering and limiting similarity operate simultaneously to assemble soil bacterial communities, widening the traditional view that only environmental filtering structures bacterial communities. © 2016 Society for Applied Microbiology and John Wiley & Sons Ltd.

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

    Science.gov (United States)

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

    2016-01-01

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

  17. Improving the description of metabolic networks: the TCA cycle as example

    NARCIS (Netherlands)

    Stobbe, Miranda D.; Houten, Sander M.; van Kampen, Antoine H. C.; Wanders, Ronald J. A.; Moerland, Perry D.

    2012-01-01

    To collect the ever-increasing yet scattered knowledge on metabolism, multiple pathway databases like the Kyoto Encyclopedia of Genes and Genomes have been created. A complete and accurate description of the metabolic network for human and other organisms is essential to foster new biological

  18. 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

  19. 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

  20. 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.

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

    Science.gov (United States)

    El-Chakhtoura, Joline; Prest, Emmanuelle; Saikaly, Pascal; van Loosdrecht, Mark; Hammes, Frederik; Vrouwenvelder, Hans

    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. Copyright © 2015 Elsevier Ltd. All rights reserved.

  2. Metabolic network modeling approaches for investigating the "hungry cancer".

    Science.gov (United States)

    Sharma, Ashwini Kumar; König, Rainer

    2013-08-01

    Metabolism is the functional phenotype of a cell, at a given condition, resulting from an intricate interplay of various regulatory processes. The study of these dynamic metabolic processes and their capabilities help to identify the fundamental properties of living systems. Metabolic deregulation is an emerging hallmark of cancer cells. This deregulation results in rewiring of the metabolic circuitry conferring an exploitative metabolic advantage for the tumor cells which leads to a distinct benefit in survival and lays the basis for unbound progression. Metabolism can be considered as a thermodynamic open-system in which source substrates of high value are being processed through a well established interconnected biochemical conversion system, strictly obeying physiochemical principles, generating useful intermediates and finally resulting in the release of byproducts. Based on this basic principle of an input-output balance, various models have been developed to interrogate metabolism elucidating its underlying functional properties. However, only a few modeling approaches have proved computationally feasible in elucidating the metabolic nature of cancer at a systems level. Besides this, statistical approaches have been set up to identify biochemical pathways being more relevant for specific types of tumor cells. In this review, we are briefly introducing the basic statistical approaches followed by the major modeling concepts. We have put an emphasis on the methods and their applications that have been used to a greater extent in understanding the metabolic remodeling of cancer. Copyright © 2013 Elsevier Ltd. All rights reserved.

  3. 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.

  4. The Tripod for Bacterial Natural Product Discovery: Genome Mining, Silent Pathway Induction, and Mass Spectrometry-Based Molecular Networking.

    Science.gov (United States)

    Trivella, Daniela B B; de Felicio, Rafael

    2018-01-01

    Natural products are the richest source of chemical compounds for drug discovery. Particularly, bacterial secondary metabolites are in the spotlight due to advances in genome sequencing and mining, as well as for the potential of biosynthetic pathway manipulation to awake silent (cryptic) gene clusters under laboratory cultivation. Further progress in compound detection, such as the development of the tandem mass spectrometry (MS/MS) molecular networking approach, has contributed to the discovery of novel bacterial natural products. The latter can be applied directly to bacterial crude extracts for identifying and dereplicating known compounds, therefore assisting the prioritization of extracts containing novel natural products, for example. In our opinion, these three approaches-genome mining, silent pathway induction, and MS-based molecular networking-compose the tripod for modern bacterial natural product discovery and will be discussed in this perspective.

  5. 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.

  6. Expression of a bacterial bi-functional chorismate mutase/prephenate dehydratase modulates primary and secondary metabolism associated with aromatic amino acids in Arabidopsis.

    Science.gov (United States)

    Tzin, Vered; Malitsky, Sergey; Aharoni, Asaph; Galili, Gad

    2009-10-01

    Plants can synthesize the aromatic amino acid Phe via arogenate, but it is still not known whether they also use an alternative route for Phe biosynthesis via phenylpyruvate, like many micro-organisms. To examine this possibility, we expressed a bacterial bi-functional PheA (chorismate mutase/prephenate dehydratase) gene in Arabidopsis thaliana that converts chorismate via prephenate into phenylpyruvate. The PheA-expressing plants showed a large increase in the level of Phe, implying that they can convert phenylpyruvate into Phe. In addition, PheA expression rendered the plants more sensitive than wild-type plants to the Trp biosynthesis inhibitor 5-methyl-Trp, implying that Phe biosynthesis competes with Trp biosynthesis from their common precursor chorismate. Surprisingly, GC-MS, LC-MS and microarray analyses showed that this increase in Phe accumulation only had a very minor effect on the levels of other primary metabolites as well as on the transcriptome profile, implying little regulatory cross-interaction between the aromatic amino acid biosynthesis network and the bulk of the Arabidopsis transcriptome and primary metabolism. However, the levels of a number of secondary metabolites derived from all three aromatic amino acids (Phe, Trp and Tyr) were altered in the PheA plants, implying regulatory cross-interactions between the flux of aromatic amino acid biosynthesis from chorismate and their further metabolism into various secondary metabolites. Taken together, our results provide insights into the regulatory mechanisms of aromatic amino acid biosynthesis and their interaction with central primary metabolism, as well as the regulatory interface between primary and secondary metabolism.

  7. 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 (H 2 S) 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 H 2 S 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 H 2 S 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 H 2 S emission in landfills. The differences in abundance of the genera Acinetobacter and Paracoccus (phylum Proteobacteria) caused by environmental factors might explain the differences in H 2 S emission. H 2 S odor control could be realized if the related functional microbe diversity can be influenced by adjustments to landfill operation.

  8. Toward pectin fermentation by Saccharomyces cerevisiae: Expression of the first two steps of a bacterial pathway for d-galacturonate metabolism.

    NARCIS (Netherlands)

    Huisjes, E.H.; Luttik, M.A.; Almering, M.J.; Bisschops, M.M.; Dang, D.H.; Kleerebezem, M.; Siezen, R.J.; Maris, van A.J.; Pronk, J.T.

    2012-01-01

    Saccharomyces cerevisiae cannot metabolize d-galacturonate, an important monomer of pectin. Use of S. cerevisiae for production of ethanol or other compounds of interest from pectin-rich feedstocks therefore requires introduction of a heterologous pathway for d-galacturonate metabolism. Bacterial

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

    Science.gov (United States)

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

    2005-01-01

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

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

    Science.gov (United States)

    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; Le Novère, Nicolas; 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; Kirdar, Betül; Penttilä, Merja; Klipp, Edda; Palsson, Bernhard Ø; Sauer, Uwe; Oliver, Stephen G; Mendes, Pedro; Nielsen, Jens; Kell, Douglas B

    2008-10-01

    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 content, and use different terminologies to describe the same chemical entities. This makes comparisons between them difficult and underscores the desirability of a consolidated metabolic network that collects and formalizes the 'community knowledge' of yeast metabolism. We describe how we have produced a consensus metabolic network reconstruction for S. cerevisiae. In drafting it, we placed special emphasis on referencing molecules to persistent databases or using database-independent forms, such as SMILES or InChI strings, as this permits their chemical structure to be represented unambiguously and in a manner that permits automated reasoning. The reconstruction is readily available via a publicly accessible database and in the Systems Biology Markup Language (http://www.comp-sys-bio.org/yeastnet). It can be maintained as a resource that serves as a common denominator for studying the systems biology of yeast. Similar strategies should benefit communities studying genome-scale metabolic networks of other organisms.

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

    Science.gov (United States)

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

    2017-05-01

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

  12. 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.

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

    Science.gov (United States)

    Dong, Guozhong; Liu, Shimin; Wu, Yongxia; Lei, Chunlong; Zhou, Jun; Zhang, Sen

    2011-08-09

    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-hydroxybutyric 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 milk fat content

  14. 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

  15. 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.

  16. Intracellular Bacterial Pathogens Trigger the Formation of U Small Nuclear RNA Bodies (U Bodies) through Metabolic Stress Induction.

    Science.gov (United States)

    Tsalikis, Jessica; Tattoli, Ivan; Ling, Arthur; Sorbara, Matthew T; Croitoru, David O; Philpott, Dana J; Girardin, Stephen E

    2015-08-21

    Invasive bacterial pathogens induce an amino acid starvation (AAS) response in infected host cells that controls host defense in part by promoting autophagy. However, whether AAS has additional significant effects on the host response to intracellular bacteria remains poorly characterized. Here we showed that Shigella, Salmonella, and Listeria interfere with spliceosomal U snRNA maturation in the cytosol. Bacterial infection resulted in the rerouting of U snRNAs and their cytoplasmic escort, the survival motor neuron (SMN) complex, to processing bodies, thus forming U snRNA bodies (U bodies). This process likely contributes to the decline in the cytosolic levels of U snRNAs and of the SMN complex proteins SMN and DDX20 that we observed in infected cells. U body formation was triggered by membrane damage in infected cells and was associated with the induction of metabolic stresses, such as AAS or endoplasmic reticulum stress. Mechanistically, targeting of U snRNAs to U bodies was regulated by translation initiation inhibition and the ATF4/ATF3 pathway, and U bodies rapidly disappeared upon removal of the stress, suggesting that their accumulation represented an adaptive response to metabolic stress. Importantly, this process likely contributed to shape the host response to invasive bacteria because down-regulation of DDX20 expression using short hairpin RNA (shRNA) amplified ATF3- and NF-κB-dependent signaling. Together, these results identify a critical role for metabolic stress and invasive bacterial pathogens in U body formation and suggest that this process contributes to host defense. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

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

    NARCIS (Netherlands)

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

    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

  18. Construction and analysis of a genome-scale metabolic network for Bacillus licheniformis WX-02.

    Science.gov (United States)

    Guo, Jing; Zhang, Hong; Wang, Cheng; Chang, Ji-Wei; Chen, Ling-Ling

    2016-05-01

    We constructed the genome-scale metabolic network of Bacillus licheniformis (B. licheniformis) WX-02 by combining genomic annotation, high-throughput phenotype microarray (PM) experiments and literature-based metabolic information. The accuracy of the metabolic network was assessed by an OmniLog PM experiment. The final metabolic model iWX1009 contains 1009 genes, 1141 metabolites and 1762 reactions, and the predicted metabolic phenotypes showed an agreement rate of 76.8% with experimental PM data. In addition, key metabolic features such as growth yield, utilization of different substrates and essential genes were identified by flux balance analysis. A total of 195 essential genes were predicted from LB medium, among which 149 were verified with the experimental essential gene set of B. subtilis 168. With the removal of 5 reactions from the network, pathways for poly-γ-glutamic acid (γ-PGA) synthesis were optimized and the γ-PGA yield reached 83.8 mmol/h. Furthermore, the important metabolites and pathways related to γ-PGA synthesis and bacterium growth were comprehensively analyzed. The present study provides valuable clues for exploring the metabolisms and metabolic regulation of γ-PGA synthesis in B. licheniformis WX-02. Copyright © 2016 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

  19. 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.

  20. A compendium of inborn errors of metabolism mapped onto the human metabolic network.

    OpenAIRE

    Sahoo, Swagatika; Franzson, Leifur; Jonsson, Jon J; Thiele, Ines

    2012-01-01

    Efst á síðunni er hægt að nálgast greinina í heild sinni með því að smella á hlekkinn Inborn errors of metabolism (IEMs) are hereditary metabolic defects, which are encountered in almost all major metabolic pathways occurring in man. Many IEMs are screened for in neonates through metabolomic analysis of dried blood spot samples. To enable the mapping of these metabolomic data onto the published human metabolic reconstruction, we added missing reactions and pathways involved in acylcarnitin...

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

    Directory of Open Access Journals (Sweden)

    Alexander Byers Brummer

    2017-03-01

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

  3. 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.

  4. Brucella BioR Regulator Defines a Complex Regulatory Mechanism for Bacterial Biotin Metabolism

    Science.gov (United States)

    Xu, Jie; Zhang, Huimin; Srinivas, Swaminath

    2013-01-01

    The enzyme cofactor biotin (vitamin H or B7) is an energetically expensive molecule whose de novo biosynthesis requires 20 ATP equivalents. It seems quite likely that diverse mechanisms have evolved to tightly regulate its biosynthesis. Unlike the model regulator BirA, a bifunctional biotin protein ligase with the capability of repressing the biotin biosynthetic pathway, BioR has been recently reported by us as an alternative machinery and a new type of GntR family transcriptional factor that can repress the expression of the bioBFDAZ operon in the plant pathogen Agrobacterium tumefaciens. However, quite unusually, a closely related human pathogen, Brucella melitensis, has four putative BioR-binding sites (both bioR and bioY possess one site in the promoter region, whereas the bioBFDAZ [bio] operon contains two tandem BioR boxes). This raised the question of whether BioR mediates the complex regulatory network of biotin metabolism. Here, we report that this is the case. The B. melitensis BioR ortholog was overexpressed and purified to homogeneity, and its solution structure was found to be dimeric. Functional complementation in a bioR isogenic mutant of A. tumefaciens elucidated that Brucella BioR is a functional repressor. Electrophoretic mobility shift assays demonstrated that the four predicted BioR sites of Brucella plus the BioR site of A. tumefaciens can all interact with the Brucella BioR protein. In a reporter strain that we developed on the basis of a double mutant of A. tumefaciens (the ΔbioR ΔbioBFDA mutant), the β-galactosidase (β-Gal) activity of three plasmid-borne transcriptional fusions (bioBbme-lacZ, bioYbme-lacZ, and bioRbme-lacZ) was dramatically decreased upon overexpression of Brucella bioR. Real-time quantitative PCR analyses showed that the expression of bioBFDA and bioY is significantly elevated upon removal of bioR from B. melitensis. Together, we conclude that Brucella BioR is not only a negative autoregulator but also a repressor of

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

    Directory of Open Access Journals (Sweden)

    Chang Jeong-Ho

    2006-06-01

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

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

    Science.gov (United States)

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

    2017-12-01

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

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

    Directory of Open Access Journals (Sweden)

    Thomas eNägele

    2016-03-01

    Full Text Available 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 biology 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®.

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

    Science.gov (United States)

    Röhl, Annika; Bockmayr, Alexander

    2017-01-03

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

  9. 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.

  10. 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...

  11. 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.

  12. 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.

  13. 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.

  14. 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

    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...... 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...... 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...

  15. The many forms of a pleomorphic bacterial pathogen – The developmental network of Legionella pneumophila

    Directory of Open Access Journals (Sweden)

    Peter eRobertson

    2014-12-01

    Full Text Available Legionella pneumophila is a natural intracellular bacterial parasite of free-living freshwater protozoa and an accidental human pathogen that causes Legionnaires’ disease. L. pneumophila differentiates, and does it in style. Recent experimental data on L. pneumophila’s differentiation point at the existence of a complex network that involves many developmental forms. We intend readers to: (i understand the biological relevance of L. pneumophila’s forms found in freshwater and their potential to transmit Legionnaires’ disease, and (ii learn that the common depiction of L. pneumophila’s differentiation as a biphasic developmental cycle that alternates between a replicative and a transmissive form is but an oversimplification of the actual process. Our specific objectives are to provide updates on the molecular factors that regulate L. pneumophila’s differentiation (section 2, and describe the developmental network of L. pneumophila (section 3, which for clarity’s sake we have dissected into five separate developmental cycles. Finally, since each developmental form seems to contribute differently to the human pathogenic process and the transmission of Legionnaires’ disease, readers are presented with a challenge to develop novel methods to detect the various L. pneumophila forms present in water (section 4, as a means to improve our assessment of risk and more effectively prevent legionellosis outbreaks.

  16. Field assessment of bacterial communities and total trihalomethanes: Implications for drinking water networks.

    Science.gov (United States)

    Montoya-Pachongo, Carolina; Douterelo, Isabel; Noakes, Catherine; Camargo-Valero, Miller Alonso; Sleigh, Andrew; Escobar-Rivera, Juan-Carlos; Torres-Lozada, Patricia

    2018-03-01

    Operation and maintenance (O&M) of drinking water distribution networks (DWDNs) in tropical countries simultaneously face the control of acute and chronic risks due to the presence of microorganisms and disinfection by-products, respectively. In this study, results from a detailed field characterization of microbiological, chemical and infrastructural parameters of a tropical-climate DWDN are presented. Water physicochemical parameters and the characteristics of the network were assessed to evaluate the relationship between abiotic and microbiological factors and their association with the presence of total trihalomethanes (TTHMs). Illumina sequencing of the bacterial 16s rRNA gene revealed significant differences in the composition of biofilm and planktonic communities. The highly diverse biofilm communities showed the presence of methylotrophic bacteria, which suggest the presence of methyl radicals such as THMs within this habitat. Microbiological parameters correlated with water age, pH, temperature and free residual chlorine. The results from this study are necessary to increase the awareness of O&M practices in DWDNs required to reduce biofilm formation and maintain appropriate microbiological and chemical water quality, in relation to biofilm detachment and DBP formation. Copyright © 2017 Elsevier B.V. All rights reserved.

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

    NARCIS (Netherlands)

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

    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

  18. Chronic obstructive pulmonary disease candidate gene prioritization based on metabolic networks and functional information.

    Directory of Open Access Journals (Sweden)

    Xinyan Wang

    Full Text Available Chronic obstructive pulmonary disease (COPD is a multi-factor disease, in which metabolic disturbances played important roles. In this paper, functional information was integrated into a COPD-related metabolic network to assess similarity between genes. Then a gene prioritization method was applied to the COPD-related metabolic network to prioritize COPD candidate genes. The gene prioritization method was superior to ToppGene and ToppNet in both literature validation and functional enrichment analysis. Top-ranked genes prioritized from the metabolic perspective with functional information could promote the better understanding about the molecular mechanism of this disease. Top 100 genes might be potential markers for diagnostic and effective therapies.

  19. 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. Copyright © 2016 Elsevier B.V. All rights reserved.

  20. The hypothalamic neural-glial network and the metabolic syndrome

    NARCIS (Netherlands)

    Jastroch, Martin; Morin, Silke; Tschöp, Matthias H.; Yi, Chun-Xia

    2014-01-01

    Despite numerous educational interventions and biomedical research efforts, modern society continues to suffer from obesity and its associated metabolic diseases, such as type 2 diabetes mellitus, and these diseases show little sign of abating. One reason for this is an incomplete understanding of

  1. Genome scale metabolic network reconstruction of Spirochaeta cellobiosiphila

    Directory of Open Access Journals (Sweden)

    Bharat Manna

    2017-10-01

    Full Text Available Substantial rise in the global energy demand is one of the biggest challenges in this century. Environmental pollution due to rapid depletion of the fossil fuel resources and its alarming impact on the climate change and Global Warming have motivated researchers to look for non-petroleum-based sustainable, eco-friendly, renewable, low-cost energy alternatives, such as biofuel. Lignocellulosic biomass is one of the most promising bio-resources with huge potential to contribute to this worldwide energy demand. However, the complex organization of the Cellulose, Hemicellulose and Lignin in the Lignocellulosic biomass requires extensive pre-treatment and enzymatic hydrolysis followed by fermentation, raising overall production cost of biofuel. This encourages researchers to design cost-effective approaches for the production of second generation biofuels. The products from enzymatic hydrolysis of cellulose are mostly glucose monomer or cellobiose unit that are subjected to fermentation. Spirochaeta genus is a well-known group of obligate or facultative anaerobes, living primarily on carbohydrate metabolism. Spirochaeta cellobiosiphila sp. is a facultative anaerobe under this genus, which uses a variety of monosaccharides and disaccharides as energy sources. However, most rapid growth occurs on cellobiose and fermentation yields significant amount of ethanol, acetate, CO2, H2 and small amounts of formate. It is predicted to be promising microbial machinery for industrial fermentation processes for biofuel production. The metabolic pathways that govern cellobiose metabolism in Spirochaeta cellobiosiphila are yet to be explored. The function annotation of the genome sequence of Spirochaeta cellobiosiphila is in progress. In this work we aim to map all the metabolic activities for reconstruction of genome-scale metabolic model of Spirochaeta cellobiosiphila.

  2. Detection of driver metabolites in the human liver metabolic network using structural controllability analysis

    Science.gov (United States)

    2014-01-01

    Background Abnormal states in human liver metabolism are major causes of human liver diseases ranging from hepatitis to hepatic tumor. The accumulation in relevant data makes it feasible to derive a large-scale human liver metabolic network (HLMN) and to discover important biological principles or drug-targets based on network analysis. Some studies have shown that interesting biological phenomenon and drug-targets could be discovered by applying structural controllability analysis (which is a newly prevailed concept in networks) to biological networks. The exploration on the connections between structural controllability theory and the HLMN could be used to uncover valuable information on the human liver metabolism from a fresh perspective. Results We applied structural controllability analysis to the HLMN and detected driver metabolites. The driver metabolites tend to have strong ability to influence the states of other metabolites and weak susceptibility to be influenced by the states of others. In addition, the metabolites were classified into three classes: critical, high-frequency and low-frequency driver metabolites. Among the identified 36 critical driver metabolites, 27 metabolites were found to be essential; the high-frequency driver metabolites tend to participate in different metabolic pathways, which are important in regulating the whole metabolic systems. Moreover, we explored some other possible connections between the structural controllability theory and the HLMN, and find that transport reactions and the environment play important roles in the human liver metabolism. Conclusion There are interesting connections between the structural controllability theory and the human liver metabolism: driver metabolites have essential biological functions; the crucial role of extracellular metabolites and transport reactions in controlling the HLMN highlights the importance of the environment in the health of human liver metabolism. PMID:24885538

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

    Directory of Open Access Journals (Sweden)

    Brian R Granger

    2016-04-01

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

  4. [Gene networks that regulate secondary metabolism in actinomycetes: pleiotropic regulators].

    Science.gov (United States)

    Rabyk, M V; Ostash, B O; Fedorenko, V O

    2014-01-01

    Current advances in the research and practical applications of pleiotropic regulatory genes for antibiotic production in actinomycetes are reviewed. The basic regulatory mechanisms found in these bacteria are outlined. Examples described in the review show the importance of the manipulation of regulatory systems that affect the synthesis of antibiotics for the metabolic engineering of the actinomycetes. Also, the study of these genes is the basis for the development of genetic engineering approaches towards the induction of "cryptic" part of the actinomycetes secondary metabolome, which capacity for production of biologically active compounds is much bigger than the diversity of antibiotics underpinned by traditional microbiological screening. Besides the practical problems, the study of regulatory genes for antibiotic biosynthesis will provide insights into the process of evolution of complex regulatory systems that coordinate the expression of gene operons, clusters and regulons, involved in the control of secondary metabolism and morphogenesis of actinomycetes.

  5. [Controlling arachidonic acid metabolic network: from single- to multi-target inhibitors of key enzymes].

    Science.gov (United States)

    Liu, Ying; Chen, Zheng; Shang, Er-chang; Yang, Kun; Wei, Deng-guo; Zhou, Lu; Jiang, Xiao-lu; He, Chong; Lai, Lu-hua

    2009-03-01

    Inflammatory diseases are common medical conditions seen in disorders of human immune system. There is a great demand for anti-inflammatory drugs. There are major inflammatory mediators in arachidonic acid metabolic network. Several enzymes in this network have been used as key targets for the development of anti-inflammatory drugs. However, specific single-target inhibitors can not sufficiently control the network balance and may cause side effects at the same time. Most inflammation induced diseases come from the complicated coupling of inflammatory cascades involving multiple targets. In order to treat these complicated diseases, drugs that can intervene multi-targets at the same time attracted much attention. The goal of this review is mainly focused on the key enzymes in arachidonic acid metabolic network, such as phospholipase A2, cyclooxygenase, 5-lipoxygenase and eukotriene A4 hydrolase. Advance in single target and multi-targe inhibitors is summarized.

  6. Human-Centered Development of an Online Social Network for Metabolic Syndrome Management.

    Science.gov (United States)

    Núñez-Nava, Jefersson; Orozco-Sánchez, Paola A; López, Diego M; Ceron, Jesus D; Alvarez-Rosero, Rosa E

    2016-01-01

    According to the International Diabetes Federation (IDF), a quarter of the world's population has Metabolic Syndrome (MS). To develop (and assess the users' degree of satisfaction of) an online social network for patients who suffer from Metabolic Syndrome, based on the recommendations and requirements of the Human-Centered Design. Following the recommendations of the ISO 9241-210 for Human-Centered Design (HCD), an online social network was designed to promote physical activity and healthy nutrition. In order to guarantee the active participation of the users during the development of the social network, a survey, an in-depth interview, a focal group, and usability tests were carried out with people suffering from MS. The study demonstrated how the different activities, recommendations, and requirements of the ISO 9241-210 are integrated into a traditional software development process. Early usability tests demonstrated that the user's acceptance and the effectiveness and efficiency of the social network are satisfactory.

  7. Modeling the Metabolism of Arabidopsis thaliana: Application of Network Decomposition and Network Reduction in the Context of Petri Nets

    Directory of Open Access Journals (Sweden)

    Ina Koch

    2017-06-01

    Full Text Available Motivation:Arabidopsis thaliana is a well-established model system for the analysis of the basic physiological and metabolic pathways of plants. Nevertheless, the system is not yet fully understood, although many mechanisms are described, and information for many processes exists. However, the combination and interpretation of the large amount of biological data remain a big challenge, not only because data sets for metabolic paths are still incomplete. Moreover, they are often inconsistent, because they are coming from different experiments of various scales, regarding, for example, accuracy and/or significance. Here, theoretical modeling is powerful to formulate hypotheses for pathways and the dynamics of the metabolism, even if the biological data are incomplete. To develop reliable mathematical models they have to be proven for consistency. This is still a challenging task because many verification techniques fail already for middle-sized models. Consequently, new methods, like decomposition methods or reduction approaches, are developed to circumvent this problem.Methods: We present a new semi-quantitative mathematical model of the metabolism of Arabidopsis thaliana. We used the Petri net formalism to express the complex reaction system in a mathematically unique manner. To verify the model for correctness and consistency we applied concepts of network decomposition and network reduction such as transition invariants, common transition pairs, and invariant transition pairs.Results: We formulated the core metabolism of Arabidopsis thaliana based on recent knowledge from literature, including the Calvin cycle, glycolysis and citric acid cycle, glyoxylate cycle, urea cycle, sucrose synthesis, and the starch metabolism. By applying network decomposition and reduction techniques at steady-state conditions, we suggest a straightforward mathematical modeling process. We demonstrate that potential steady-state pathways exist, which provide the

  8. 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

    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...... 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...... 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...

  9. NMR study of the 1-13C glucose colon bacterial metabolism

    International Nuclear Information System (INIS)

    Briet, F.; Flourie, B.; Pochart, P.; Rambaud, J.C.; Desjeux, J.F.; Dallery, L.; Grivet, J.P.

    1994-01-01

    The aim of the study is to examine in-vitro and by nuclear magnetic resonance the biological pathways for the fermentation of the 1- 13 C labelled glucose (99 atoms percent) by human colon bacteria. The preparation of the bacterial suspension and the glucose degradation kinetics are presented; the NMR analysis sensitivity and quantification features are discussed and results are presented. 2 figs., 1 ref

  10. Metabolic network as a progression biomarker of premanifest Huntington's disease

    NARCIS (Netherlands)

    Tang, Chris C.; Feigin, Andrew; Ma, Yilong; Habeck, Christian; Paulsen, Jane S.; Leenders, Klaus L.; Teune, Laura K.; van Oostrom, Joost C. H.; Guttman, Mark; Dhawan, Vijay; Eidelberg, David

    Background. The evaluation of effective disease-modifying therapies for neurodegenerative disorders relies on objective and accurate measures of progression in at-risk individuals. Here we used a computational approach to identify a functional brain network associated with the progression of

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

    DEFF Research Database (Denmark)

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

    2015-01-01

    are neglected by other gap-finding methods. We tested our method on the Model SEED, which is the largest repository for automatically generated genome-scale network reconstructions. In this way, we were able to identify a significant number of missing pathways in several of these reconstructions. Hence...

  12. 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

  13. Cerebral metabolic correlates of attention networks in Alzheimer's Disease: A study of the Stroop.

    Science.gov (United States)

    Melrose, Rebecca J; Young, Stephanie; Weissberger, Gali H; Natta, Laura; Harwood, Dylan; Mandelkern, Mark; Sultzer, David L

    2017-11-01

    Patients with Alzheimer's Disease (AD) show difficulties with attention. Cognitive neuroscience models posit that attention can be broken down into alerting, orienting, and executive networks. We used the Stroop Color-Word test to interrogate the neural correlates of attention deficits in AD. We hypothesized that the Word, Color, and Color-Word conditions of the Stroop would all tap into the alerting and orienting networks. The Color-Word condition would additionally tap into the executive network. A ratio of Color-Word to Color naming performance would isolate the executive network from the others. To identify the neural underpinnings of attention in AD we correlated performance on the Stroop with brain metabolic activity. Sixty-six patients with probable AD completed [ 18 F] fluorodeoxyglucose PET scanning and neuropsychological testing. Analysis was conducted with SPM12 (p<0.001 uncorrected, extent threshold 50 voxels). Performance on the Word, Color, and Color-Word conditions directly correlated with metabolic rate in right inferior parietal lobules/intraparietal sulci. The Color-Word/Color ratio revealed associations with metabolic rate in right medial prefrontal cortex and insula/operculum. Overall findings were largely consistent with the hypothesized neuroanatomical substrates of the alerting, orienting, and executive networks. As such, attention deficits in AD reflect compromise to multiple large-scale networks. Published by Elsevier Ltd.

  14. Metabolic Brain Network Analysis of Hypothyroidism Symptom Based on [18F]FDG-PET of Rats.

    Science.gov (United States)

    Wan, Hongkai; Tan, Ziyu; Zheng, Qiang; Yu, Jing

    2018-03-12

    Recent researches have demonstrated the value of using 2-deoxy-2-[ 18 F]fluoro-D-glucose ([ 18 F]FDG) positron emission tomography (PET) imaging to reveal the hypothyroidism-related damages in local brain regions. However, the influence of hypothyroidism on the entire brain network is barely studied. This study focuses on the application of graph theory on analyzing functional brain networks of the hypothyroidism symptom. For both the hypothyroidism and the control groups of Wistar rats, the functional brain networks were constructed by thresholding the glucose metabolism correlation matrices of 58 brain regions. The network topological properties (including the small-world properties and the nodal centralities) were calculated and compared between the two groups. We found that the rat brains, like human brains, have typical properties of the small-world network in both the hypothyroidism and the control groups. However, the hypothyroidism group demonstrated lower global efficiency and decreased local cliquishness of the brain network, indicating hypothyroidism-related impairment to the brain network. The hypothyroidism group also has decreased nodal centrality in the left posterior hippocampus, the right hypothalamus, pituitary, pons, and medulla. This observation accorded with the hypothyroidism-related functional disorder of hypothalamus-pituitary-thyroid (HPT) feedback regulation mechanism. Our research quantitatively confirms that hypothyroidism hampers brain cognitive function by causing impairment to the brain network of glucose metabolism. This study reveals the feasibility and validity of applying graph theory method to preclinical [ 18 F]FDG-PET images and facilitates future study on human subjects.

  15. Metabolic network topology reveals transcriptional regulatory signatures of type 2 diabetes.

    Science.gov (United States)

    Zelezniak, Aleksej; Pers, Tune H; Soares, Simão; Patti, Mary Elizabeth; Patil, Kiran Raosaheb

    2010-04-01

    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 mechanisms underlying these transcriptional changes and their impact on the cellular metabolic phenotype is a challenging task due to the complexity of transcriptional regulation and the highly interconnected nature of the metabolic network. In this study we integrate skeletal muscle gene expression datasets 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 metabolites NAD+/NADH and ATP/ADP were also identified as reporter metabolites that are potentially contributing to the widespread gene expression changes observed in T2DM. An algorithm based on the analysis of the promoter regions of the genes associated with reporter metabolites revealed a transcription factor regulatory network connecting several parts of metabolism. The identified transcription factors include members of the CREB, NRF1 and PPAR family, among others, and represent regulatory targets for further experimental analysis. Overall, our results provide a holistic picture of key metabolic and regulatory nodes potentially involved in the pathogenesis of T2DM.

  16. 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...... to exhibit a biodegradation performance superior to pure cultures, making them attractive research targets. It is believed that nutrition plays a crucial role in shaping microbial communities. Interspecies metabolite cross-feeding can confer several advantages to the community as a whole. For example, more...

  17. Rapid nanoparticle-mediated monitoring of bacterial metabolic activity and assessment of antimicrobial susceptibility in blood with magnetic relaxation.

    Directory of Open Access Journals (Sweden)

    Charalambos Kaittanis

    2008-09-01

    Full Text Available Considering the increased incidence of bacterial infections and the emergence of multidrug resistant bacteria at the global level, we designed superparamagnetic iron oxide nanoparticles as nanosensors for the assessment of antimicrobial susceptibility through magnetic relaxation. In this report, we demonstrate that iron oxide nanosensors, either dextran-coated supplemented with Con A or silica-coated conjugated directly to Con A, can be used for the fast (1 quantification of polysaccharides, (2 assessment of metabolic activity and (3 determination of antimicrobial susceptibility in blood. The use of these polysaccharide nanosensors in the determination of antimicrobial susceptibility in the clinic or the field, and the utilization of these nanoprobes in pharmaceutical R&D are anticipated.

  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. Flux Balance Analysis of Cyanobacterial Metabolism.The Metabolic Network of Synechocystis sp. PCC 6803

    Czech Academy of Sciences Publication Activity Database

    Knoop, H.; Gründel, M.; Zilliges, Y.; Lehmann, R.; Hoffmann, S.; Lockau, W.; Steuer, Ralf

    2013-01-01

    Roč. 9, č. 6 (2013), e1003081-e1003081 ISSN 1553-7358 R&D Projects: GA MŠk(CZ) EE2.3.20.0256 Institutional support: RVO:67179843 Keywords : SP STRAIN PCC-6803 * SP ATCC 51142 * photoautotrophic metabolism * anacystis-nidulans * reconstructions * pathway * plants * models * growth Subject RIV: EI - Biotechnology ; Bionics Impact factor: 4.829, year: 2013

  20. Genome-scale reconstruction of the metabolic network in Pseudomonas stutzeri A1501.

    Science.gov (United States)

    Babaei, Parizad; Marashi, Sayed-Amir; Asad, Sedigheh

    2015-11-01

    Pseudomonas stutzeri A1501 is an endophytic bacterium capable of nitrogen fixation. This strain has been isolated from the rice rhizosphere and provides the plant with fixed nitrogen and phytohormones. These interesting features encouraged us to study the metabolism of this microorganism at the systems-level. In this work, we present the first genome-scale metabolic model (iPB890) for P. stutzeri, involving 890 genes, 1135 reactions, and 813 metabolites. A combination of automatic and manual approaches was used in the reconstruction process. Briefly, using the metabolic networks of Pseudomonas aeruginosa and Pseudomonas putida as templates, a draft metabolic network of P. stutzeri was reconstructed. Then, the draft network was driven through an iterative and curative process of gap filling. In the next step, the model was evaluated using different experimental data such as specific growth rate, Biolog substrate utilization data and other experimental observations. In most of the evaluation cases, the model was successful in correctly predicting the cellular phenotypes. Thus, we posit that the iPB890 model serves as a suitable platform to explore the metabolism of P. stutzeri.

  1. 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.

  2. 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. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  3. (Im)Perfect robustness and adaptation of metabolic networks subject to metabolic and gene-expression regulation: marrying control engineering with metabolic control analysis.

    Science.gov (United States)

    He, Fei; Fromion, Vincent; Westerhoff, Hans V

    2013-11-21

    Metabolic control analysis (MCA) and supply-demand theory have led to appreciable understanding of the systems properties of metabolic networks that are subject exclusively to metabolic regulation. Supply-demand theory has not yet considered gene-expression regulation explicitly whilst a variant of MCA, i.e. Hierarchical Control Analysis (HCA), has done so. Existing analyses based on control engineering approaches have not been very explicit about whether metabolic or gene-expression regulation would be involved, but designed different ways in which regulation could be organized, with the potential of causing adaptation to be perfect. This study integrates control engineering and classical MCA augmented with supply-demand theory and HCA. Because gene-expression regulation involves time integration, it is identified as a natural instantiation of the 'integral control' (or near integral control) known in control engineering. This study then focuses on robustness against and adaptation to perturbations of process activities in the network, which could result from environmental perturbations, mutations or slow noise. It is shown however that this type of 'integral control' should rarely be expected to lead to the 'perfect adaptation': although the gene-expression regulation increases the robustness of important metabolite concentrations, it rarely makes them infinitely robust. For perfect adaptation to occur, the protein degradation reactions should be zero order in the concentration of the protein, which may be rare biologically for cells growing steadily. A proposed new framework integrating the methodologies of control engineering and metabolic and hierarchical control analysis, improves the understanding of biological systems that are regulated both metabolically and by gene expression. In particular, the new approach enables one to address the issue whether the intracellular biochemical networks that have been and are being identified by genomics and systems

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

    Science.gov (United States)

    Takemoto, Kazuhiro; Aie, Kazuki

    2017-05-25

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

  5. Random sampling of elementary flux modes in large-scale metabolic networks.

    Science.gov (United States)

    Machado, Daniel; Soons, Zita; Patil, Kiran Raosaheb; Ferreira, Eugénio C; Rocha, Isabel

    2012-09-15

    The description of a metabolic network in terms of elementary (flux) modes (EMs) provides an important framework for metabolic pathway analysis. However, their application to large networks has been hampered by the combinatorial explosion in the number of modes. In this work, we develop a method for generating random samples of EMs without computing the whole set. Our algorithm is an adaptation of the canonical basis approach, where we add an additional filtering step which, at each iteration, selects a random subset of the new combinations of modes. In order to obtain an unbiased sample, all candidates are assigned the same probability of getting selected. This approach avoids the exponential growth of the number of modes during computation, thus generating a random sample of the complete set of EMs within reasonable time. We generated samples of different sizes for a metabolic network of Escherichia coli, and observed that they preserve several properties of the full EM set. It is also shown that EM sampling can be used for rational strain design. A well distributed sample, that is representative of the complete set of EMs, should be suitable to most EM-based methods for analysis and optimization of metabolic networks. Source code for a cross-platform implementation in Python is freely available at http://code.google.com/p/emsampler. dmachado@deb.uminho.pt Supplementary data are available at Bioinformatics online.

  6. 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...

  7. Genome-scale metabolic network of Cordyceps militaris useful for comparative analysis of entomopathogenic fungi.

    Science.gov (United States)

    Vongsangnak, Wanwipa; Raethong, Nachon; Mujchariyakul, Warasinee; Nguyen, Nam Ninh; Leong, Hon Wai; Laoteng, Kobkul

    2017-08-30

    The first genome-scale metabolic network of Cordyceps militaris (iWV1170) was constructed representing its whole metabolisms, which consisted of 894 metabolites and 1,267 metabolic reactions across five compartments, including the plasma membrane, cytoplasm, mitochondria, peroxisome and extracellular space. The iWV1170 could be exploited to explain its phenotypes of growth ability, cordycepin and other metabolites production on various substrates. A high number of genes encoding extracellular enzymes for degradation of complex carbohydrates, lipids and proteins were existed in C. militaris genome. By comparative genome-scale analysis, the adenine metabolic pathway towards putative cordycepin biosynthesis was reconstructed, indicating their evolutionary relationships across eleven species of entomopathogenic fungi. The overall metabolic routes involved in the putative cordycepin biosynthesis were also identified in C. militaris, including central carbon metabolism, amino acid metabolism (glycine, l-glutamine and l-aspartate) and nucleotide metabolism (adenosine and adenine). Interestingly, a lack of the sequence coding for ribonucleotide reductase inhibitor was observed in C. militaris that might contribute to its over-production of cordycepin. Copyright © 2017. Published by Elsevier B.V.

  8. 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.

  9. 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.

  10. Community structure of the metabolically active rumen bacterial and archaeal communities of dairy cows over the transition period.

    Directory of Open Access Journals (Sweden)

    Zhigang Zhu

    Full Text Available Dairy cows experience dramatic changes in host physiology from gestation to lactation period and dietary switch from high-forage prepartum diet to high-concentrate postpartum diet over the transition period (parturition +/- three weeks. Understanding the community structure and activity of the rumen microbiota and its associative patterns over the transition period may provide insight for e.g. improving animal health and production. In the present study, rumen samples from ten primiparous Holstein dairy cows were collected over seven weeks spanning the transition period. Total RNA was extracted from the rumen samples and cDNA thereof was subsequently used for characterizing the metabolically active bacterial (16S rRNA transcript amplicon sequencing and archaeal (qPCR, T-RFLP and mcrA and 16S rRNA transcript amplicon sequencing communities. The metabolically active bacterial community was dominated by three phyla, showing significant changes in relative abundance range over the transition period: Firmicutes (from prepartum 57% to postpartum 35%, Bacteroidetes (from prepartum 22% to postpartum 18% and Proteobacteria (from prepartum 7% to postpartum 32%. For the archaea, qPCR analysis of 16S rRNA transcript number, revealed a significant prepartum to postpartum increase in Methanobacteriales, in accordance with an observed increase (from prepartum 80% to postpartum 89% in relative abundance of 16S rRNA transcript amplicons allocated to this order. On the other hand, a significant prepartum to postpartum decrease (from 15% to 2% was observed in relative abundance of Methanomassiliicoccales 16S rRNA transcripts. In contrast to qPCR analysis of the 16S rRNA transcripts, quantification of mcrA transcripts revealed no change in total abundance of metabolically active methanogens over the transition period. According to T-RFLP analysis of the mcrA transcripts, two Methanobacteriales genera, Methanobrevibacter and Methanosphaera (represented by the T

  11. 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

  12. 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

  13. 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

  14. Metabolic Network Constrains Gene Regulation of C4 Photosynthesis: The Case of Maize.

    Science.gov (United States)

    Robaina-Estévez, Semidán; Nikoloski, Zoran

    2016-05-01

    Engineering C3 plants to increase their efficiency of carbon fixation as well as of nitrogen and water use simultaneously may be facilitated by understanding the mechanisms that underpin the C4 syndrome. Existing experimental studies have indicated that the emergence of the C4 syndrome requires co-ordination between several levels of cellular organization, from gene regulation to metabolism, across two co-operating cell systems-mesophyll and bundle sheath cells. Yet, determining the extent to which the structure of the C4 plant metabolic network may constrain gene expression remains unclear, although it will provide an important consideration in engineering C4 photosynthesis in C3 plants. Here, we utilize flux coupling analysis with the second-generation maize metabolic models to investigate the correspondence between metabolic network structure and transcriptomic phenotypes along the maize leaf gradient. The examined scenarios with publically available data from independent experiments indicate that the transcriptomic programs of the two cell types are co-ordinated, quantitatively and qualitatively, due to the presence of coupled metabolic reactions in specific metabolic pathways. Taken together, our study demonstrates that precise quantitative coupling will have to be achieved in order to ensure a successfully engineered transition from C3 to C4 crops. © The Author 2016. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists.

  15. Molecular network analysis of phosphotyrosine and lipid metabolism in hepatic PTP1b deletion mice.

    Science.gov (United States)

    Miraldi, Emily R; Sharfi, Hadar; Friedline, Randall H; Johnson, Hannah; Zhang, Tejia; Lau, Ken S; Ko, Hwi Jin; Curran, Timothy G; Haigis, Kevin M; Yaffe, Michael B; Bonneau, Richard; Lauffenburger, Douglas A; Kahn, Barbara B; Kim, Jason K; Neel, Benjamin G; Saghatelian, Alan; White, Forest M

    2013-07-24

    Metabolic syndrome describes a set of obesity-related disorders that increase diabetes, cardiovascular, and mortality risk. Studies of liver-specific protein-tyrosine phosphatase 1b (PTP1b) deletion mice (L-PTP1b(-/-)) suggest that hepatic PTP1b inhibition would mitigate metabolic-syndrome through amelioration of hepatic insulin resistance, endoplasmic-reticulum stress, and whole-body lipid metabolism. However, the altered molecular-network states underlying these phenotypes are poorly understood. We used mass spectrometry to quantify protein-phosphotyrosine network changes in L-PTP1b(-/-) mouse livers relative to control mice on normal and high-fat diets. We applied a phosphosite-set-enrichment analysis to identify known and novel pathways exhibiting PTP1b- and diet-dependent phosphotyrosine regulation. Detection of a PTP1b-dependent, but functionally uncharacterized, set of phosphosites on lipid-metabolic proteins motivated global lipidomic analyses that revealed altered polyunsaturated-fatty-acid (PUFA) and triglyceride metabolism in L-PTP1b(-/-) mice. To connect phosphosites and lipid measurements in a unified model, we developed a multivariate-regression framework, which accounts for measurement noise and systematically missing proteomics data. This analysis resulted in quantitative models that predict roles for phosphoproteins involved in oxidation-reduction in altered PUFA and triglyceride metabolism.

  16. c-Myc activates multiple metabolic networks to generate substrates for cell-cycle entry.

    Energy Technology Data Exchange (ETDEWEB)

    Morrish, Fionnuala M.; Isern, Nancy; Sadilek, Martin; Jeffrey, Mark; Hockenbery, David M.

    2009-05-18

    Cell proliferation requires the coordinated activity of cytosolic and mitochondrial metabolic pathways to provide ATP and building blocks for DNA, RNA, and protein synthesis. Many metabolic pathway genes are targets of the c-myc oncogene and cell cycle regulator. However, the contribution of c-Myc to the activation of cytosolic and mitochondrial metabolic networks during cell cycle entry is unknown. Here, we report the metabolic fates of [U-13C] glucose in serum-stimulated myc-/- and myc+/+ fibroblasts by 13C isotopomer NMR analysis. We demonstrate that endogenous c-myc increased 13C-labeling of ribose sugars, purines, and amino acids, indicating partitioning of glucose carbons into C1/folate and pentose phosphate pathways, and increased tricarboxylic acid cycle turnover at the expense of anaplerotic flux. Myc expression also increased global O-linked GlcNAc protein modification, and inhibition of hexosamine biosynthesis selectively reduced growth of Myc-expressing cells, suggesting its importance in Myc-induced proliferation. These data reveal a central organizing role for the Myc oncogene in the metabolism of cycling cells. The pervasive deregulation of this oncogene in human cancers may be explained by its role in directing metabolic networks required for cell proliferation.

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

    Science.gov (United States)

    2016-03-15

    dynamics. For instance, the gene expression data from the GEO database were collected under different condi- tions related to time, temperature , medium...Microcolony formation by the opportunistic pathogen Pseudomonas aeruginosa requires pyruvate and pyruvate fermentation . Mol Microbiol. 2012 Nov; 86...reso- lution metabolic flux phenotypes and transcriptional regulation in yeast modulated by the global regula- tor Gcn4p. Proc Natl Acad Sci U S A

  18. Anthropogenic N Deposition Slows Decay by Favoring Bacterial Metabolism: Insights from Metagenomic Analyses

    Science.gov (United States)

    Freedman, Zachary B.; Upchurch, Rima A.; Zak, Donald R.; Cline, Lauren C.

    2016-01-01

    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. PMID:26973633

  19. Effect of metabolic transformation of monoterpenes on antimutagenic potential in bacterial tests

    Directory of Open Access Journals (Sweden)

    Stajković-Srbinović Olivera

    2012-01-01

    Full Text Available The effect of metabolic transformation of the monoterpenes Linalool (Lin, Myrcene (Myr and Eucalyptol (Euc was evaluated on their antimutagenic potential against t-butyl hydroperoxide (t-BOOH and 2-nitropropane (2NP in E. coli WP2 and in S. typhimurium reversion assays, respectively. Spontaneous mutagenesis was also monitored in both assays. Mammalian metabolic transformation was provided by rat liver microsomes (S9 fraction. None of the monoterpenes was mutagenic, either with or without S9. Results obtained without S9 showed the antimutagenic potential of Lin against t-BOOH, of Myr against both t-BOOH and 2NP, and of Euc against spontaneous and mutagenesis induced with both mutagens. Mammalian enzymes significantly reduced the antimutagenic effect of Lin, completely diminished the antimutagenic effect of Myr, but did not alter the antimutagenic effect of Euc. Considering the results, metabolic transformation by host enzymes could significantly influence antimutagenic potential and should be included in antimutagenicity studies in prokaryotic assays. [Acknowledgments. This research was supported by the Ministry of Education and Science of Republic of Serbia, Project No. 172058.

  20. Bacterial persistence is an active σS stress response to metabolic flux limitation.

    Science.gov (United States)

    Radzikowski, Jakub Leszek; Vedelaar, Silke; Siegel, David; Ortega, Álvaro Dario; Schmidt, Alexander; Heinemann, Matthias

    2016-09-21

    While persisters are a health threat due to their transient antibiotic tolerance, little is known about their phenotype and what actually causes persistence. Using a new method for persister generation and high-throughput methods, we comprehensively mapped the molecular phenotype of Escherichia coli during the entry and in the state of persistence in nutrient-rich conditions. The persister proteome is characterized by σ(S)-mediated stress response and a shift to catabolism, a proteome that starved cells tried to but could not reach due to absence of a carbon and energy source. Metabolism of persisters is geared toward energy production, with depleted metabolite pools. We developed and experimentally verified a model, in which persistence is established through a system-level feedback: Strong perturbations of metabolic homeostasis cause metabolic fluxes to collapse, prohibiting adjustments toward restoring homeostasis. This vicious cycle is stabilized and modulated by high ppGpp levels, toxin/anti-toxin systems, and the σ(S)-mediated stress response. Our system-level model consistently integrates past findings with our new data, thereby providing an important basis for future research on persisters. © 2016 The Authors. Published under the terms of the CC BY 4.0 license.

  1. 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.

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

    Science.gov (United States)

    Imam, Saheed; Schäuble, Sascha; Brooks, Aaron N.; Baliga, Nitin S.; Price, Nathan D.

    2015-01-01

    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. PMID:25999934

  3. 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.

  4. 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. Copyright © 2015 Elsevier Ltd. All rights reserved.

  5. 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

  6. Effects of gravel-sand mulching on soil bacterial community and metabolic capability in the semi-arid Loess Plateau, China.

    Science.gov (United States)

    Hao, Haiting; Zhao, Xia; Wang, Yun; Zhang, Yubao; Xie, Zhongkui; Guo, Zhihong; Wang, Ruoyu

    2017-11-09

    Gravel and sand mulching is an indigenous technology used for the crop yield for at least 300 years in the loess area of northwest China; however, little is known about the changes of soil bacterial community and metabolic capability under the mulching. In this study, we investigated the soil microbial community structure and metabolic functional diversity during mulching using Illumina MiSeq sequencing and Biolog ECO method. Totally, 9417 OTUs were classified at 97% similarity level for soil samples after 0 (control), 4, 7, and 10 years of mulching. Dendrogram result indicated that mulching affected the soil bacterial community; and the higher richness and diversity of bacterial community were detected in mulching samples. The average abundance of soil bacteria (such as Proteobacteria, Actinobacteria, Firmicutes and Nitrospirae) in mulching samples was higher than samples without mulching. Besides, some microbial communities (such as Rhodobacteraceae, Phenylobacterium, Pseudonocardia, Nonomuraea and Aeromicrobium) were only present in the mulched soil samples. However, the lower metabolic capability was observed in mulching samples based on Biolog method, which the main reason for the opposite result might be that the soil objects detected by the two methods are different. In conclusion, these results demonstrated that gravel and sand mulching affected the structure and metabolic capability of bacterial community and was one reason for crop yield.

  7. 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

  8. 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

  9. Bacterial communities associated with Shinkaia crosnieri from the Iheya North, Okinawa Trough: Microbial diversity and metabolic potentials

    Science.gov (United States)

    Zhang, Jian; Zeng, Zhi-gang; Chen, Shuai; Sun, Li

    2018-04-01

    Shinkaia crosnieri is a galatheid crab endemic to the deep-sea hydrothermal systems in the Okinawa Trough. In this study, we systematically analyzed and compared the diversity and metabolic potentials of the microbial communities in different tissues (setae, gill, and intestine) of S. crosnieri by high-throughput sequencing technology and quantitative real-time polymerase chain reaction. Sequence analysis based on the V3-V4 regions of the 16S rRNA gene obtained 408,079 taxon tags, which covered 15 phyla, 22 classes, 32 orders, 42 families, and 25 genera. Overall, the microbial communities in all tissues were dominated by Epsilonproteobacteria and Gammaproteobacteria, of which Epsilonproteobacteria was the largest class and accounted for 85.24% of the taxon tags. In addition, 20 classes of bacteria were discovered for the first time to be associated with S. crosnieri and no archaea were detected. Comparative analysis showed that (i) bacteria from different tissues fell into different groups by β-diversity analysis, (ii) bacterial communities in intestine were similar to that in gill and much more diverse than that in setae, and the sulfur-oxidizing genus Sulfurovum was markedly enriched in intestine and gill. Furthermore, bacteria potentially involved in methane, nitrogen, and metal metabolisms were detected in all samples. The key genes of aprA/dsrA and pmoA involved in sulfate reducing and methane oxidization, respectively, were detected in the gill and gut communities for the first time, and pmoA was significantly more abundant in gill and setae than in intestine. These results provide the first comparative and relatively complete picture of the diversity and metabolic potentials of the bacteria in different tissues of S. crosnieri. These results also indicate that the composition of the microbial communities in hydrothermal fauna changes with time, suggesting the importance of environmental influence.

  10. Hijacking CRISPR-Cas for high-throughput bacterial metabolic engineering: advances and prospects

    DEFF Research Database (Denmark)

    Mougiakos, Ioannis; Bosma, Elleke F.; Ganguly, Joyshree

    2018-01-01

    Escherichia coli and non-model organisms like Clostridia, Bacilli, Streptomycetes and cyanobacteria, opening new possibilities to use these organisms as improved cell factories. The discovery of novel Cas9-like systems from diverse microbial environments will extend the repertoire of applications and broaden...... the range of organisms in which it can be used to create novel production hosts. This review analyses the current status of prokaryotic metabolic engineering towards the production of biotechnologically relevant products, based on the exploitation of different CRISPR-related DNA/RNA endonuclease variants....

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

    DEFF Research Database (Denmark)

    Fenger, Mogens; Linneberg, Allan; Jeppesen, Jørgen

    2015-01-01

    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......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......, 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...

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

    Science.gov (United States)

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

    2017-01-01

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

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

    Directory of Open Access Journals (Sweden)

    María Camila Alvarez-Silva

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

  14. Effect of the Mycorrhizosphere on the Genotypic and Metabolic Diversity of the Bacterial Communities Involved in Mineral Weathering in a Forest Soil▿

    Science.gov (United States)

    Uroz, S.; Calvaruso, C.; Turpault, M. P.; Pierrat, J. C.; Mustin, C.; Frey-Klett, P.

    2007-01-01

    To date, several bacterial species have been described as mineral-weathering agents which improve plant nutrition and growth. However, the possible relationships between mineral-weathering potential, taxonomic identity, and metabolic ability have not been investigated thus far. In this study, we characterized a collection of 61 bacterial strains isolated from Scleroderma citrinum mycorrhizae, the mycorrhizosphere, and the adjacent bulk soil in an oak forest. The ability of bacteria to weather biotite was assessed with a new microplate bioassay that measures the pH and the quantity of iron released from this mineral. We showed that weathering bacteria occurred more frequently in the vicinity of S. citrinum than in the bulk soil. Moreover, the weathering efficacy of the mycorrhizosphere bacterial isolates was significantly greater than that of the bulk soil isolates. All the bacterial isolates were identified by partial 16S rRNA gene sequence analysis as members of the genera Burkholderia, Collimonas, Pseudomonas, and Sphingomonas, and their carbon metabolism was characterized by the BIOLOG method. The most efficient isolates belonged to the genera Burkholderia and Collimonas. Multivariate analysis resulted in identification of three metabolic groups, one of which contained mainly bacterial isolates associated with S. citrinum and exhibiting high mineral-weathering potential. Therefore, our results support the hypothesis that by its carbon metabolism this fungus selects in the bulk soil reservoir a bacterial community with high weathering potential, and they also address the question of functional complementation between mycorrhizal fungi and bacteria in the ectomycorrhizal complex for the promotion of tree nutrition. PMID:17351101

  15. 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...... ionization (ESI) modes were performed to obtain the complete information about the metabolite content. Using sparse principal component analysis (Sparse PCA), we further identified those pairs of metabolites that significantly contribute to the separation. From the list of significant metabolite pairs, we...

  16. Optimization of Bioprocess Productivity Based on Metabolic-Genetic Network Models with Bilevel Dynamic Programming.

    Science.gov (United States)

    Jabarivelisdeh, Banafsheh; Waldherr, Steffen

    2018-03-26

    One of the main goals of metabolic engineering is to obtain high levels of a microbial product through genetic modifications. To improve the productivity of such a process, the dynamic implementation of metabolic engineering strategies has been proven to be more beneficial compared to static genetic manipulations in which the gene expression is not controlled over time, by resolving the trade-off between growth and production. In this work, a bilevel optimization framework based on constraint-based models is applied to identify an optimal strategy for dynamic genetic and process level manipulations to increase productivity. The dynamic enzyme-cost flux balance analysis (deFBA) as underlying metabolic network model captures the network dynamics and enables the analysis of temporal regulation in the metabolic-genetic network. We apply our computational framework to maximize ethanol productivity in a batch process with Escherichia coli. The results highlight the importance of integrating the genetic level and enzyme production and degradation processes for obtaining optimal dynamic gene and process manipulations. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  17. 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/. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  18. Thermodynamic calculations for biochemical transport and reaction processes in metabolic networks.

    Science.gov (United States)

    Jol, Stefan J; Kümmel, Anne; Hatzimanikatis, Vassily; Beard, Daniel A; Heinemann, Matthias

    2010-11-17

    Thermodynamic analysis of metabolic networks has recently generated increasing interest for its ability to add constraints on metabolic network operation, and to combine metabolic fluxes and metabolite measurements in a mechanistic manner. Concepts for the calculation of the change in Gibbs energy of biochemical reactions have long been established. However, a concept for incorporation of cross-membrane transport in these calculations is still missing, although the theory for calculating thermodynamic properties of transport processes is long known. Here, we have developed two equivalent equations to calculate the change in Gibbs energy of combined transport and reaction processes based on two different ways of treating biochemical thermodynamics. We illustrate the need for these equations by showing that in some cases there is a significant difference between the proposed correct calculation and using an approximative method. With the developed equations, thermodynamic analysis of metabolic networks spanning over multiple physical compartments can now be correctly described. Copyright © 2010 Biophysical Society. Published by Elsevier Inc. All rights reserved.

  19. Combined metabolomic and correlation networks analyses reveal fumarase insufficiency altered amino acid metabolism.

    Science.gov (United States)

    Hou, Entai; Li, Xian; Liu, Zerong; Zhang, Fuchang; Tian, Zhongmin

    2018-04-01

    Fumarase catalyzes the interconversion of fumarate and l-malate in the tricarboxylic acid cycle. Fumarase insufficiencies were associated with increased levels of fumarate, decreased levels of malate and exacerbated salt-induced hypertension. To gain insights into the metabolism profiles induced by fumarase insufficiency and identify key regulatory metabolites, we applied a GC-MS based metabolomics platform coupled with a network approach to analyze fumarase insufficient human umbilical vein endothelial cells (HUVEC) and negative controls. A total of 24 altered metabolites involved in seven metabolic pathways were identified as significantly altered, and enriched for the biological module of amino acids metabolism. In addition, Pearson correlation network analysis revealed that fumaric acid, l-malic acid, l-aspartic acid, glycine and l-glutamic acid were hub metabolites according to Pagerank based on their three centrality indices. Alanine aminotransferase and glutamate dehydrogenase activities increased significantly in fumarase deficiency HUVEC. These results confirmed that fumarase insufficiency altered amino acid metabolism. The combination of metabolomics and network methods would provide another perspective on expounding the molecular mechanism at metabolomics level. Copyright © 2017 John Wiley & Sons, Ltd.

  20. Network Thermodynamic Curation of Human and Yeast Genome-Scale Metabolic Models

    Science.gov (United States)

    Martínez, Verónica S.; Quek, Lake-Ee; Nielsen, Lars K.

    2014-01-01

    Genome-scale models are used for an ever-widening range of applications. Although there has been much focus on specifying the stoichiometric matrix, the predictive power of genome-scale models equally depends on reaction directions. Two-thirds of reactions in the two eukaryotic reconstructions Homo sapiens Recon 1 and Yeast 5 are specified as irreversible. However, these specifications are mainly based on biochemical textbooks or on their similarity to other organisms and are rarely underpinned by detailed thermodynamic analysis. In this study, a to our knowledge new workflow combining network-embedded thermodynamic and flux variability analysis was used to evaluate existing irreversibility constraints in Recon 1 and Yeast 5 and to identify new ones. A total of 27 and 16 new irreversible reactions were identified in Recon 1 and Yeast 5, respectively, whereas only four reactions were found with directions incorrectly specified against thermodynamics (three in Yeast 5 and one in Recon 1). The workflow further identified for both models several isolated internal loops that require further curation. The framework also highlighted the need for substrate channeling (in human) and ATP hydrolysis (in yeast) for the essential reaction catalyzed by phosphoribosylaminoimidazole carboxylase in purine metabolism. Finally, the framework highlighted differences in proline metabolism between yeast (cytosolic anabolism and mitochondrial catabolism) and humans (exclusively mitochondrial metabolism). We conclude that network-embedded thermodynamics facilitates the specification and validation of irreversibility constraints in compartmentalized metabolic models, at the same time providing further insight into network properties. PMID:25028891

  1. Adenylate Kinase and AMP Signaling Networks: Metabolic Monitoring, Signal Communication and Body Energy Sensing

    Directory of Open Access Journals (Sweden)

    Andre Terzic

    2009-04-01

    Full Text Available Adenylate kinase and downstream AMP signaling is an integrated metabolic monitoring system which reads the cellular energy state in order to tune and report signals to metabolic sensors. A network of adenylate kinase isoforms (AK1-AK7 are distributed throughout intracellular compartments, interstitial space and body fluids to regulate energetic and metabolic signaling circuits, securing efficient cell energy economy, signal communication and stress response. The dynamics of adenylate kinase-catalyzed phosphotransfer regulates multiple intracellular and extracellular energy-dependent and nucleotide signaling processes, including excitation-contraction coupling, hormone secretion, cell and ciliary motility, nuclear transport, energetics of cell cycle, DNA synthesis and repair, and developmental programming. Metabolomic analyses indicate that cellular, interstitial and blood AMP levels are potential metabolic signals associated with vital functions including body energy sensing, sleep, hibernation and food intake. Either low or excess AMP signaling has been linked to human disease such as diabetes, obesity and hypertrophic cardiomyopathy. Recent studies indicate that derangements in adenylate kinase-mediated energetic signaling due to mutations in AK1, AK2 or AK7 isoforms are associated with hemolytic anemia, reticular dysgenesis and ciliary dyskinesia. Moreover, hormonal, food and antidiabetic drug actions are frequently coupled to alterations of cellular AMP levels and associated signaling. Thus, by monitoring energy state and generating and distributing AMP metabolic signals adenylate kinase represents a unique hub within the cellular homeostatic network.

  2. BAP1 inhibits the ER stress gene regulatory network and modulates metabolic stress response.

    Science.gov (United States)

    Dai, Fangyan; Lee, Hyemin; Zhang, Yilei; Zhuang, Li; Yao, Hui; Xi, Yuanxin; Xiao, Zhen-Dong; You, M James; Li, Wei; Su, Xiaoping; Gan, Boyi

    2017-03-21

    The endoplasmic reticulum (ER) is classically linked to metabolic homeostasis via the activation of unfolded protein response (UPR), which is instructed by multiple transcriptional regulatory cascades. BRCA1 associated protein 1 (BAP1) is a tumor suppressor with de-ubiquitinating enzyme activity and has been implicated in chromatin regulation of gene expression. Here we show that BAP1 inhibits cell death induced by unresolved metabolic stress. This prosurvival role of BAP1 depends on its de-ubiquitinating activity and correlates with its ability to dampen the metabolic stress-induced UPR transcriptional network. BAP1 inhibits glucose deprivation-induced reactive oxygen species and ATP depletion, two cellular events contributing to the ER stress-induced cell death. In line with this, Bap1 KO mice are more sensitive to tunicamycin-induced renal damage. Mechanically, we show that BAP1 represses metabolic stress-induced UPR and cell death through activating transcription factor 3 (ATF3) and C/EBP homologous protein (CHOP), and reveal that BAP1 binds to ATF3 and CHOP promoters and inhibits their transcription. Taken together, our results establish a previously unappreciated role of BAP1 in modulating the cellular adaptability to metabolic stress and uncover a pivotal function of BAP1 in the regulation of the ER stress gene-regulatory network. Our study may also provide new conceptual framework for further understanding BAP1 function in cancer.

  3. 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.

  4. Mycelium-Like Networks Increase Bacterial Dispersal, Growth, and Biodegradation in a Model Ecosystem at Various Water Potentials.

    Science.gov (United States)

    Worrich, Anja; König, Sara; Miltner, Anja; Banitz, Thomas; Centler, Florian; Frank, Karin; Thullner, Martin; Harms, Hauke; Kästner, Matthias; Wick, Lukas Y

    2016-05-15

    Fungal mycelia serve as effective dispersal networks for bacteria in water-unsaturated environments, thereby allowing bacteria to maintain important functions, such as biodegradation. However, poor knowledge exists on the effects of dispersal networks at various osmotic (Ψo) and matric (Ψm) potentials, which contribute to the water potential mainly in terrestrial soil environments. Here we studied the effects of artificial mycelium-like dispersal networks on bacterial dispersal dynamics and subsequent effects on growth and benzoate biodegradation at ΔΨo and ΔΨm values between 0 and -1.5 MPa. In a multiple-microcosm approach, we used a green fluorescent protein (GFP)-tagged derivative of the soil bacterium Pseudomonas putida KT2440 as a model organism and sodium benzoate as a representative of polar aromatic contaminants. We found that decreasing ΔΨo and ΔΨm values slowed bacterial dispersal in the system, leading to decelerated growth and benzoate degradation. In contrast, dispersal networks facilitated bacterial movement at ΔΨo and ΔΨm values between 0 and -0.5 MPa and thus improved the absolute biodegradation performance by up to 52 and 119% for ΔΨo and ΔΨm, respectively. This strong functional interrelationship was further emphasized by a high positive correlation between population dispersal, population growth, and degradation. We propose that dispersal networks may sustain the functionality of microbial ecosystems at low osmotic and matric potentials. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

  5. 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

    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...... or zero flux through PEP carboxykinase was estimated, indicating that the latter enzyme was not active during growth on glucose. The uptake of the amino acids in a semirich medium containing 15 of the 20 amino acids normally present in proteins was estimated using fully labeled glucose in batch...

  6. Bacterial metabolic 'toxins': a new mechanism for lactose and food intolerance, and irritable bowel syndrome.

    Science.gov (United States)

    Campbell, A K; Matthews, S B; Vassel, N; Cox, C D; Naseem, R; Chaichi, J; Holland, I B; Green, J; Wann, K T

    2010-12-30

    Lactose and food intolerance cause a wide range of gut and systemic symptoms, including gas, gut pain, diarrhoea or constipation, severe headaches, severe fatigue, loss of cognitive functions such as concentration, memory and reasoning, muscle and joint pain, heart palpitations, and a variety of allergies (Matthews and Campbell, 2000; Matthews et al., 2005; Waud et al., 2008). These can be explained by the production of toxic metabolites from gut bacteria, as a result of anaerobic digestion of carbohydrates and other foods, not absorbed in the small intestine. These metabolites include alcohols, diols such as butan 2,3 diol, ketones, acids, and aldehydes such as methylglyoxal (Campbell et al., 2005, 2009). These 'toxins' induce calcium signals in bacteria and affect their growth, thereby acting to modify the balance of microflora in the gut (Campbell et al., 2004, 2007a,b). These bacterial 'toxins' also affect signalling mechanisms in cells around the body, thereby explaining the wide range of symptoms in people with food intolerance. This new mechanism also explains the most common referral to gastroenterologists, irritable bowel syndrome (IBS), and the illness that afflicted Charles Darwin for 50 years (Campbell and Matthews, 2005a,b). We propose it will lead to a new understanding of the molecular mechanism of type 2 diabetes and some cancers. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

  7. 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)

  8. Cytotoxicity of Bacterial Metabolic Products, including Listeriolysin O, on Leukocyte Targets

    Directory of Open Access Journals (Sweden)

    R. Stachowiak

    2012-01-01

    Full Text Available Bacterial toxins can exhibit anticancer activities. Here we investigated the anticancer effects of the listeriolysin O toxin produced by Listeria monocytogenes. We found that supernatants of Listeria monocytogenes strains (wild type, 1189, and 1190 were cytotoxic to the Jurkat cell line and human peripheral blood mononuclear cells (PBMC in a concentration-dependent manner. The supernatant of strain 1044, not producing listeriolysin O, was inactive. The supernatants of Listeria strains were also cytotoxic toward B cells of chronic leukemia patients, with no significant differences in activities between strains. We also tested supernatants of Bacillus subtilis strains BR1-90, BR1-S, and BR1-89 producing listeriolysin O. BR1-S and BR1-89 were cytotoxic to PBMC and to Jurkat cells, the latter being more sensitive to the supernatants. BR1-90 was not hemolytic or cytotoxic to PBMC, but was cytotoxic to Jurkat cells in the concentration range of 10–30%, suggesting that listeriolysin O is selectively effective against T cells. Overall, the response of human peripheral blood mononuclear and human leukemia cell lines to bacteria supernatants containing listeriolysin O depended on the bacteria strain, target cell type, and supernatant concentration.

  9. Habitat variability does not generally promote metabolic network modularity in flies and mammals.

    Science.gov (United States)

    Takemoto, Kazuhiro

    2016-01-01

    The evolution of species habitat range is an important topic over a wide range of research fields. In higher organisms, habitat range evolution is generally associated with genetic events such as gene duplication. However, the specific factors that determine habitat variability remain unclear at higher levels of biological organization (e.g., biochemical networks). One widely accepted hypothesis developed from both theoretical and empirical analyses is that habitat variability promotes network modularity; however, this relationship has not yet been directly tested in higher organisms. Therefore, I investigated the relationship between habitat variability and metabolic network modularity using compound and enzymatic networks in flies and mammals. Contrary to expectation, there was no clear positive correlation between habitat variability and network modularity. As an exception, the network modularity increased with habitat variability in the enzymatic networks of flies. However, the observed association was likely an artifact, and the frequency of gene duplication appears to be the main factor contributing to network modularity. These findings raise the question of whether or not there is a general mechanism for habitat range expansion at a higher level (i.e., above the gene scale). This study suggests that the currently widely accepted hypothesis for habitat variability should be reconsidered. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  10. 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

  11. Metabolic networks: a signal-oriented approach to cellular models.

    Science.gov (United States)

    Lengeler, J W

    2000-01-01

    Complete genomes, far advanced proteomes, and even 'metabolomes' are available for at least a few organisms, e.g., Escherichia coli. Systematic functional analyses of such complete data sets will produce a wealth of information and promise an understanding of the dynamics of complex biological networks and perhaps even of entire living organisms. Such complete and holistic descriptions of biological systems, however, will increasingly require a quantitative analysis and the help of mathematical models for simulating whole systems. In particular, new procedures are required that allow a meaningful reduction of the information derived from complex systems that will consequently be used in the modeling process. In this review the biological elements of such a modeling procedure will be described. In a first step, complex living systems must be structured into well-defined and clearly delimited functional units, the elements of which have a common physiological goal, belong to a single genetic unit, and respond to the signals of a signal transduction system that senses changes in physiological states of the organism. These functional units occur at each level of complexity and more complex units originate by grouping several lower level elements into a single, more complex unit. To each complexity level corresponds a global regulator that is epistatic over lower level regulators. After its structuring into modules (functional units), a biological system is converted in a second step into mathematical submodels that by progressive combination can also be assembled into more aggregated model structures. Such a simplification of a cell (an organism) reduces its complexity to a level amenable to present modeling capacities. The universal biochemistry, however, promises a set of rules valid for modeling biological systems, from unicellular microorganisms and cells, to multicellular organisms and to populations.

  12. Lanthanide Chemistry: From Coordination in Chemical Complexes Shaping Our Technology to Coordination in Enzymes Shaping Bacterial Metabolism.

    Science.gov (United States)

    Martinez-Gomez, Norma Cecilia; Vu, Huong N; Skovran, Elizabeth

    2016-10-17

    Lanthanide chemistry has only been extensively studied for the last 2 decades, when it was recognized that these elements have unusual chemical characteristics including fluorescent and potent magnetic properties because of their unique 4f electrons.1,2 Chemists are rapidly and efficiently integrating lanthanides into numerous compounds and materials for sophisticated applications. In fact, lanthanides are often referred to as "the seeds of technology" because they are essential for many technological devices including smartphones, computers, solar cells, batteries, wind turbines, lasers, and optical glasses.3-6 However, the effect of lanthanides on biological systems has been understudied. Although displacement of Ca 2+ by lanthanides in tissues and enzymes has long been observed,7 only a few recent studies suggest a biological role for lanthanides based on their stimulatory properties toward some plants and bacteria.8,9 Also, it was not until 2011 that the first biochemical evidence for lanthanides as inherent metals in bacterial enzymes was published.10 This forum provides an overview of the classical and current aspects of lanthanide coordination chemistry employed in the development of technology along with the biological role of lanthanides in alcohol oxidation. The construction of lanthanide-organic frameworks will be described. Examples of how the luminescence field is rapidly evolving as more information about lanthanide-metal emissions is obtained will be highlighted, including biological imaging and telecommunications.11 Recent breakthroughs and observations from different exciting areas linked to the coordination chemistry of lanthanides that will be mentioned in this forum include the synthesis of (i) macrocyclic ligands, (ii) antenna molecules, (iii) coordination polymers, particularly nanoparticles, (iv) hybrid materials, and (v) lanthanide fuel cells. Further, the role of lanthanides in bacterial metabolism will be discussed, highlighting the

  13. Multilevel correlations in the biological phosphorus removal process: From bacterial enrichment to conductivity-based metabolic batch tests and polyphosphatase assays.

    Science.gov (United States)

    Weissbrodt, David G; Maillard, Julien; Brovelli, Alessandro; Chabrelie, Alexandre; May, Jonathan; Holliger, Christof

    2014-12-01

    Enhanced biological phosphorus removal (EBPR) from wastewater relies on the preferential selection of active polyphosphate-accumulating organisms (PAO) in the underlying bacterial community continuum. Efficient management of the bacterial resource requires understanding of population dynamics as well as availability of bioanalytical methods for rapid and regular assessment of relative abundances of active PAOs and their glycogen-accumulating competitors (GAO). A systems approach was adopted here toward the investigation of multilevel correlations from the EBPR bioprocess to the bacterial community, metabolic, and enzymatic levels. Two anaerobic-aerobic sequencing-batch reactors were operated to enrich activated sludge in PAOs and GAOs affiliating with "Candidati Accumulibacter and Competibacter phosphates", respectively. Bacterial selection was optimized by dynamic control of the organic loading rate and the anaerobic contact time. The distinct core bacteriomes mainly comprised populations related to the classes Betaproteobacteria, Cytophagia, and Chloroflexi in the PAO enrichment and of Gammaproteobacteria, Alphaproteobacteria, Acidobacteria, and Sphingobacteria in the GAO enrichment. An anaerobic metabolic batch test based on electrical conductivity evolution and a polyphosphatase enzymatic assay were developed for rapid and low-cost assessment of the active PAO fraction and dephosphatation potential of activated sludge. Linear correlations were obtained between the PAO fraction, biomass specific rate of conductivity increase under anaerobic conditions, and polyphosphate-hydrolyzing activity of PAO/GAO mixtures. The correlations between PAO/GAO ratios, metabolic activities, and conductivity profiles were confirmed by simulations with a mathematical model developed in the aqueous geochemistry software PHREEQC. © 2014 Wiley Periodicals, Inc.

  14. Novel Plasmodium falciparum metabolic network reconstruction identifies shifts associated with clinical antimalarial resistance.

    Science.gov (United States)

    Carey, Maureen A; Papin, Jason A; Guler, Jennifer L

    2017-07-19

    Malaria remains a major public health burden and resistance has emerged to every antimalarial on the market, including the frontline drug, artemisinin. Our limited understanding of Plasmodium biology hinders the elucidation of resistance mechanisms. In this regard, systems biology approaches can facilitate the integration of existing experimental knowledge and further understanding of these mechanisms. Here, we developed a novel genome-scale metabolic network reconstruction, iPfal17, of the asexual blood-stage P. falciparum parasite to expand our understanding of metabolic changes that support resistance. We identified 11 metabolic tasks to evaluate iPfal17 performance. Flux balance analysis and simulation of gene knockouts and enzyme inhibition predict candidate drug targets unique to resistant parasites. Moreover, integration of clinical parasite transcriptomes into the iPfal17 reconstruction reveals patterns associated with antimalarial resistance. These results predict that artemisinin sensitive and resistant parasites differentially utilize scavenging and biosynthetic pathways for multiple essential metabolites, including folate and polyamines. Our findings are consistent with experimental literature, while generating novel hypotheses about artemisinin resistance and parasite biology. We detect evidence that resistant parasites maintain greater metabolic flexibility, perhaps representing an incomplete transition to the metabolic state most appropriate for nutrient-rich blood. Using this systems biology approach, we identify metabolic shifts that arise with or in support of the resistant phenotype. This perspective allows us to more productively analyze and interpret clinical expression data for the identification of candidate drug targets for the treatment of resistant parasites.

  15. Ordinary differential equations and Boolean networks in application to modelling of 6-mercaptopurine metabolism.

    Science.gov (United States)

    Lavrova, Anastasia I; Postnikov, Eugene B; Zyubin, Andrey Yu; Babak, Svetlana V

    2017-04-01

    We consider two approaches to modelling the cell metabolism of 6-mercaptopurine, one of the important chemotherapy drugs used for treating acute lymphocytic leukaemia: kinetic ordinary differential equations, and Boolean networks supplied with one controlling node, which takes continual values. We analyse their interplay with respect to taking into account ATP concentration as a key parameter of switching between different pathways. It is shown that the Boolean networks, which allow avoiding the complexity of general kinetic modelling, preserve the possibility of reproducing the principal switching mechanism.

  16. METANNOGEN: compiling features of biochemical reactions needed for the reconstruction of metabolic networks

    Directory of Open Access Journals (Sweden)

    Holzhütter Hermann-Georg

    2007-01-01

    Full Text Available Abstract Background One central goal of computational systems biology is the mathematical modelling of complex metabolic reaction networks. The first and most time-consuming step in the development of such models consists in the stoichiometric reconstruction of the network, i. e. compilation of all metabolites, reactions and transport processes relevant to the considered network and their assignment to the various cellular compartments. Therefore an information system is required to collect and manage data from different databases and scientific literature in order to generate a metabolic network of biochemical reactions that can be subjected to further computational analyses. Results The computer program METANNOGEN facilitates the reconstruction of metabolic networks. It uses the well-known database of biochemical reactions KEGG of biochemical reactions as primary information source from which biochemical reactions relevant to the considered network can be selected, edited and stored in a separate, user-defined database. Reactions not contained in KEGG can be entered manually into the system. To aid the decision whether or not a reaction selected from KEGG belongs to the considered network METANNOGEN contains information of SWISSPROT and ENSEMBL and provides Web links to a number of important information sources like METACYC, BRENDA, NIST, and REACTOME. If a reaction is reported to occur in more than one cellular compartment, a corresponding number of reactions is generated each referring to one specific compartment. Transport processes of metabolites are entered like chemical reactions where reactants and products have different compartment attributes. The list of compartmentalized biochemical reactions and membrane transport processes compiled by means of METANNOGEN can be exported as an SBML file for further computational analysis. METANNOGEN is highly customizable with respect to the content of the SBML output file, additional data

  17. Characterizing bacterial gene expression in nitrogen cycle metabolism with RT-qPCR.

    Science.gov (United States)

    Graham, James E; Wantland, Nicholas B; Campbell, Mark; Klotz, Martin G

    2011-01-01

    Recent advances in DNA sequencing have greatly accelerated our ability to obtain the raw information needed to recognize both known and potential novel modular microbial genomic capacity for nitrogen metabolism. With PCR-based approaches to quantifying microbial mRNA expression now mainstream in most laboratories, researchers can now more efficiently propose and test hypotheses on the contributions of individual microbes to the biological accessibility of nitrogen upon which all other life depends. We review known microbial roles in these key nitrogen transformations, and describe the necessary steps in carrying out relevant gene expression studies. An example experimental design is then provided characterizing Nitrosococcus oceani mRNA expression in cultures responding to ammonia. The approach described, that of assessing microbial genome inventory and testing putative modular gene expression by mRNA quantification, is likely to remain an important tool in understanding individual microbial contributions within microbial community activities that maintain the Earth's nitrogen balance. Copyright © 2011 Elsevier Inc. All rights reserved.

  18. Characterization of metabolically active bacterial populations in subseafloor Nankai Trough sediments above, within and below the sulfate-methane transition zone

    Directory of Open Access Journals (Sweden)

    Heath J. Mills

    2012-04-01

    Full Text Available A remarkable number of microbial cells have been enumerated within subseafloor sediments, suggesting a biological impact on geochemical processes in the subseafloor habitat. However, the metabolically active fraction of these populations is largely uncharacterized. In this study, an RNA-based molecular approach was used to determine the diversity and community structure of metabolically active bacterial populations in the upper sedimentary formation of the Nankai Trough seismogenic zone. Samples used in this study were collected from the slope apron sediment overlying the accretionary prism at Site C0004 during the Integrated Ocean Drilling Program (IODP Expedition 316. The sediments represented microbial habitats above, within, and below the sulfate-methane transition zone (SMTZ, which was observed approximately 20 meters below the seafloor (mbsf. Small subunit ribosomal RNA (SSU rRNA were extracted, quantified, amplified and sequenced using high-throughput 454-pyrosequencing, indicating the occurrence of metabolically active bacterial populations to a depth of 57 mbsf. Transcript abundance and bacterial diversity decreased with increasing depth. The two communities below the SMTZ were similar at the phylum level, however only a 24% overlap was observed at the genus level. Active bacterial community composition was not confined to geochemically predicted redox stratification despite the deepest sample being more than 50 meters below the oxic/anoxic interface. Genus-level classification suggested that the metabolically active subseafloor bacterial populations had similarities to previously cultured organisms. This allowed predictions of physiological potential, expanding understanding of the subseafloor microbial ecosystem. Unique community structures suggest very diverse active populations compared to previous DNA-based diversity estimates, providing more support for enhancing community characterizations using more advanced sequencing

  19. 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.

  20. A metabolic network approach for the identification and prioritization of antimicrobial drug targets.

    Science.gov (United States)

    Chavali, Arvind K; D'Auria, Kevin M; Hewlett, Erik L; Pearson, Richard D; Papin, Jason A

    2012-03-01

    For many infectious diseases, novel treatment options are needed in order to address problems with cost, toxicity and resistance to current drugs. Systems biology tools can be used to gain valuable insight into pathogenic processes and aid in expediting drug discovery. In the past decade, constraint-based modeling of genome-scale metabolic networks has become widely used. Focusing on pathogen metabolic networks, we review in silico strategies used to identify effective drug targets and highlight recent successes as well as limitations associated with such computational analyses. We further discuss how accounting for the host environment and even targeting the host may offer new therapeutic options. These systems-level approaches are beginning to provide novel avenues for drug targeting against infectious agents. Copyright © 2011 Elsevier Ltd. All rights reserved.

  1. Dynamics of bacterial metabolic profile and community structure during the mineralization of organic carbon in intensive swine farm wastewater

    Directory of Open Access Journals (Sweden)

    Xiaoyan Ma

    2015-06-01

    Full Text Available Land application of intensive swine farm wastewater has raised serious environmental concerns due to the accumulation and microbially mediated transformation of large amounts of swine wastewater organic C (SWOC. Therefore, the study of SWOC mineralization and dynamics of wastewater microorganisms is essential to understand the environmental impacts of swine wastewater application. We measured the C mineralization of incubated swine wastewaters with high (wastewater H and low (wastewater L organic C concentrations. The dynamics of bacteria metabolic profile and community structure were also investigated. The results showed that SWOC mineralization was properly fitted by the two-simultaneous reactions model. The initial potential rate of labile C mineralization of wastewater H was 46% higher than that of wastewater L, whereas the initial potential rates of recalcitrant C mineralization of wastewaters H and L were both around 23 mg L-1 d-1. The bacterial functional and structural diversities significantly decreased for both the wastewaters during SWOC mineralization, and were all negatively correlated to specific UV absorbance (SUVA254; P < 0.01. The bacteria in the raw wastewaters exhibited functional similarity, and both metabolic profile and community structure changed with the mineralization of SWOC, mainly under the influence of SUVA254 (P < 0.001. These results suggested that SWOC mineralization was characterized by rapid mineralization of labile C and subsequent slow decomposition of recalcitrant C pool, and the quality of SWOC varied between the wastewaters with different amounts of organic C. The decreased bio-availability of dissolved organic matter affected the dynamics of wastewater bacteria during SWOC mineralization.

  2. 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 w...... 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,...

  3. 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. © The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology.

  4. PPAR? population shift produces disease-related changes in molecular networks associated with metabolic syndrome

    OpenAIRE

    Jurkowski, W; Roomp, K; Crespo, I; Schneider, J G; del Sol, A

    2011-01-01

    Peroxisome proliferator-activated receptor gamma (PPARγ) is a key regulator of adipocyte differentiation and has an important role in metabolic syndrome. Phosphorylation of the receptor's ligand-binding domain at serine 273 has been shown to change the expression of a large number of genes implicated in obesity. The difference in gene expression seen when comparing wild-type phosphorylated with mutant non-phosphorylated PPARγ may have important consequences for the cellular molecular network,...

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

    OpenAIRE

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

    2016-01-01

    Maternal metabolites and metabolic networks underlying associations between maternal glucose during pregnancy and newborn birth weight and adiposity demand fuller characterization. We performed targeted and nontargeted gas chromatography/mass spectrometry metabolomics on maternal serum collected at fasting and 1 h following glucose beverage consumption during an oral glucose tolerance test (OGTT) for 400 northern European mothers at ?28 weeks' gestation in the Hyperglycemia and Adverse Pregna...

  6. Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes

    Science.gov (United States)

    De Martino, Daniele

    2017-12-01

    In this work maximum entropy distributions in the space of steady states of metabolic networks are considered upon constraining the first and second moments of the growth rate. Coexistence of fast and slow phenotypes, with bimodal flux distributions, emerges upon considering control on the average growth (optimization) and its fluctuations (heterogeneity). This is applied to the carbon catabolic core of Escherichia coli where it quantifies the metabolic activity of slow growing phenotypes and it provides a quantitative map with metabolic fluxes, opening the possibility to detect coexistence from flux data. A preliminary analysis on data for E. coli cultures in standard conditions shows degeneracy for the inferred parameters that extend in the coexistence region.

  7. Multiobjective flux balancing using the NISE method for metabolic network analysis.

    Science.gov (United States)

    Oh, Young-Gyun; Lee, Dong-Yup; Lee, Sang Yup; Park, Sunwon

    2009-01-01

    Flux balance analysis (FBA) is well acknowledged as an analysis tool of metabolic networks in the framework of metabolic engineering. However, FBA has a limitation for solving a multiobjective optimization problem which considers multiple conflicting objectives. In this study, we propose a novel multiobjective flux balance analysis method, which adapts the noninferior set estimation (NISE) method (Solanki et al., 1993) for multiobjective linear programming (MOLP) problems. NISE method can generate an approximation of the Pareto curve for conflicting objectives without redundant iterations of single objective optimization. Furthermore, the flux distributions at each Pareto optimal solution can be obtained for understanding the internal flux changes in the metabolic network. The functionality of this approach is shown by applying it to a genome-scale in silico model of E. coli. Multiple objectives for the poly(3-hydroxybutyrate) [P(3HB)] production are considered simultaneously, and relationships among them are identified. The Pareto curve for maximizing succinic acid production vs. maximizing biomass production is used for the in silico analysis of various combinatorial knockout strains. This proposed method accelerates the strain improvement in the metabolic engineering by reducing computation time of obtaining the Pareto curve and analysis time of flux distribution at each Pareto optimal solution. (c) 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009.

  8. 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

  9. 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.

  10. Effects of photochemical Transformations of Dissolved Organic Matter on Bacterial Metabolism and Diversity in Three Contrasting Coastal Sites in the Northwestern Mediterranean Sea during Summer

    International Nuclear Information System (INIS)

    Abboudi, M.

    2010-01-01

    The effects of photo transformation of dissolved organic matter (DOM) on bacterial growth, production, respiration, growth efficiency, and diversity were investigated during summer in two lagoons and one oligo trophic coastal water samples from the Northwestern Mediterranean Sea, differing widely in DOM and chromophoric DOM concentrations. Exposure of 0.2μm filtered waters to full sun radiation for 1 d resulted in small changes in optical properties and concentrations of DOM, and no changes in nitrate, nitrite, and phosphate concentrations. After exposure to sunlight or dark (control) treatments, the water samples were inoculated with the original bacterial com community. Photo transformation of DOM had contrasting effects on bacterial production and respiration, depending on the water's origin, resulting in an increase of bacterial growth efficiency for the oligo trophic coastal water sample (120%) and a decrease for the lagoon waters (20 to 40%) relative to that observed in dark treatments. We also observed that bacterial growth on DOM irradiated by full sun resulted in changes in community structure of total and metabolically active bacterial cells for the three locations studied when compared to the bacteria growing on unirradiated DOM, and that changes were mainly caused by photo transformation of DOM by UV radiation for the eutrophic lagoon and the oligo trophic coastal water and by photosynthetically active radiation (PAR) for the meso eutrophic lagoon. These initial results indicate that photo transformation of DOM significantly alters both bacterial metabolism and community structure in surface water for a variety of coastal ecosystems in the Mediterranean Sea. Further studies will be necessary to elucidate a more detailed appreciation of potential temporal and spatial variations of the effects measured. (author)

  11. 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. PMID:23326606

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

    Science.gov (United States)

    Harrison, Paul F.; Lo, Tricia L.; Quenault, Tara; Dagley, Michael J.; Bellousoff, Matthew; Powell, David R.; Beilharz, Traude H.; Traven, Ana

    2015-01-01

    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. PMID:26474309

  13. 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.

  14. Characterizing steady states of genome-scale metabolic networks in continuous cell cultures.

    Science.gov (United States)

    Fernandez-de-Cossio-Diaz, Jorge; Leon, Kalet; Mulet, Roberto

    2017-11-01

    In the continuous mode of cell culture, a constant flow carrying fresh media replaces culture fluid, cells, nutrients and secreted metabolites. Here we present a model for continuous cell culture coupling intra-cellular metabolism to extracellular variables describing the state of the bioreactor, taking into account the growth capacity of the cell and the impact of toxic byproduct accumulation. We provide a method to determine the steady states of this system that is tractable for metabolic networks of arbitrary complexity. We demonstrate our approach in a toy model first, and then in a genome-scale metabolic network of the Chinese hamster ovary cell line, obtaining results that are in qualitative agreement with experimental observations. We derive a number of consequences from the model that are independent of parameter values. The ratio between cell density and dilution rate is an ideal control parameter to fix a steady state with desired metabolic properties. This conclusion is robust even in the presence of multi-stability, which is explained in our model by a negative feedback loop due to toxic byproduct accumulation. A complex landscape of steady states emerges from our simulations, including multiple metabolic switches, which also explain why cell-line and media benchmarks carried out in batch culture cannot be extrapolated to perfusion. On the other hand, we predict invariance laws between continuous cell cultures with different parameters. A practical consequence is that the chemostat is an ideal experimental model for large-scale high-density perfusion cultures, where the complex landscape of metabolic transitions is faithfully reproduced.

  15. Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness.

    Science.gov (United States)

    Chennu, Srivas; Annen, Jitka; Wannez, Sarah; Thibaut, Aurore; Chatelle, Camille; Cassol, Helena; Martens, Géraldine; Schnakers, Caroline; Gosseries, Olivia; Menon, David; Laureys, Steven

    2017-08-01

    Recent advances in functional neuroimaging have demonstrated novel potential for informing diagnosis and prognosis in the unresponsive wakeful syndrome and minimally conscious states. However, these technologies come with considerable expense and difficulty, limiting the possibility of wider clinical application in patients. Here, we show that high density electroencephalography, collected from 104 patients measured at rest, can provide valuable information about brain connectivity that correlates with behaviour and functional neuroimaging. Using graph theory, we visualize and quantify spectral connectivity estimated from electroencephalography as a dense brain network. Our findings demonstrate that key quantitative metrics of these networks correlate with the continuum of behavioural recovery in patients, ranging from those diagnosed as unresponsive, through those who have emerged from minimally conscious, to the fully conscious locked-in syndrome. In particular, a network metric indexing the presence of densely interconnected central hubs of connectivity discriminated behavioural consciousness with accuracy comparable to that achieved by expert assessment with positron emission tomography. We also show that this metric correlates strongly with brain metabolism. Further, with classification analysis, we predict the behavioural diagnosis, brain metabolism and 1-year clinical outcome of individual patients. Finally, we demonstrate that assessments of brain networks show robust connectivity in patients diagnosed as unresponsive by clinical consensus, but later rediagnosed as minimally conscious with the Coma Recovery Scale-Revised. Classification analysis of their brain network identified each of these misdiagnosed patients as minimally conscious, corroborating their behavioural diagnoses. If deployed at the bedside in the clinical context, such network measurements could complement systematic behavioural assessment and help reduce the high misdiagnosis rate reported

  16. 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.

  17. Metabolic patterns of bacterial communities in aerobic compost teas associated with potential biocontrol of soilborne plant diseases

    Directory of Open Access Journals (Sweden)

    Catello PANE

    2014-09-01

    Full Text Available Aerated compost teas (ACTs are organic products obtained by forced aeration of composts suspended in liquid phase. These products may be biological control tools alternative to synthetic fungicides, because ACTs contain antagonistic microorganisms. In this study, soilborne disease suppressive ability of seven water ACTs, extracted from five horticultural residue-based composts, from an animal waste anaerobic solid digestate and from a commercial municipal waste compost, was assessed using in vitro and in vivo systems. All the ACTs inhibited in vitro growth of Verticillium dahliae, Fusarium oxysporum f. sp. lycopersici, Rhizoctonia solani, Sclerotinia minor, Sclerotium rolfsii and Botrytis cinerea. Filter or thermal sterilization eliminated in vitro suppression, suggesting that microorganisms play key roles in pathogen inhibition. Drenching applications of raw ACTs have potential to reduced disease symptoms caused by R. solani on savoy cabbage, S. minor on lettuce and S. rolfsii on pepper, improved the biomass production and did not show any sign of phytotoxicity. Both in vitro and in vivo suppressiveness of ACTs may be explained by antagonistic  bacterial communities that provide general suppression activities. The metabolic BIOLOG GN and GP profiles reflected the functional potential of the numerically dominant members of the microbial communities used as inoculum. This study has demonstrated that useful resident microorganisms, including mainly Gram-positive and Gram-negative antagonistic bacteria, are likely to be responsible for biological control activity of ACTs.

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

    Science.gov (United States)

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

    2013-01-01

    Recent functional magnetic resonance imaging (fMRI) studies have emphasized the contributions of synchronized activity in distributed brain networks to cognitive processes in both health and disease. The brain's ‘functional connectivity' is typically estimated from correlations in the activity time series of anatomically remote areas, and postulated to reflect information flow between neuronal populations. Although the topological properties of functional brain networks have been studied extensively, considerably less is known regarding the neurophysiological and biochemical factors underlying the temporal coordination of large neuronal ensembles. In this review, we highlight the critical contributions of high-frequency electrical oscillations in the γ-band (30 to 100 Hz) to the emergence of functional brain networks. After describing the neurobiological substrates of γ-band dynamics, we specifically discuss the elevated energy requirements of high-frequency neural oscillations, which represent a mechanistic link between the functional connectivity of brain regions and their respective metabolic demands. Experimental evidence is presented for the high oxygen and glucose consumption, and strong mitochondrial performance required to support rhythmic cortical activity in the γ-band. Finally, the implications of mitochondrial impairments and deficits in glucose metabolism for cognition and behavior are discussed in the context of neuropsychiatric and neurodegenerative syndromes characterized by large-scale changes in the organization of functional brain networks. PMID:23756687

  19. 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.

  20. Take it of leave it : Mechanisms underlying bacterial bistable regulatory networks

    NARCIS (Netherlands)

    Siebring, Jeroen; Sorg, Robin; Herber, Martijn; Kuipers, Oscar; Filloux, Alain A.M.

    2012-01-01

    Bistable switches occur in regulatory networks that can exist in two distinct stable states. Such networks allow distinct switching of individual cells. In bacteria these switches coexist with regulatory networks that respond gradually to environmental input. Bistable switches play key roles in high

  1. Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA.

    Science.gov (United States)

    Biggs, Matthew B; Papin, Jason A

    2017-03-01

    Genome-scale metabolic network reconstructions (GENREs) are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs by representing many alternative network structures, all equally consistent with available data, and generating predictions from this ensemble. This ensemble approach is compatible with many reconstruction methods. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA). We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contributed disproportionately to the set of predicted essential reactions in a way that was unique to each Streptococcus species, leading to species-specific outcomes from small molecule interactions. Through our analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository.

  2. Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA.

    Directory of Open Access Journals (Sweden)

    Matthew B Biggs

    2017-03-01

    Full Text Available Genome-scale metabolic network reconstructions (GENREs are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs by representing many alternative network structures, all equally consistent with available data, and generating predictions from this ensemble. This ensemble approach is compatible with many reconstruction methods. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA. We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contributed disproportionately to the set of predicted essential reactions in a way that was unique to each Streptococcus species, leading to species-specific outcomes from small molecule interactions. Through our analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository.

  3. A method for estimation of elasticities in metabolic networks using steady state and dynamic metabolomics data and linlog kinetics

    NARCIS (Netherlands)

    Nikerel, I.E.; Van Winden, W.; Van Gulik, W.M.; Heijnen, J.J.

    2006-01-01

    Background: Dynamic modeling of metabolic reaction networks under in vivo conditions is a crucial step in order to obtain a better understanding of the (dis)functioning of living cells. So far dynamic metabolic models generally have been based on mechanistic rate equations which often contain so

  4. BoostGAPFILL: improving the fidelity of metabolic network reconstructions through integrated constraint and pattern-based methods.

    Science.gov (United States)

    Oyetunde, Tolutola; Zhang, Muhan; Chen, Yixin; Tang, Yinjie; Lo, Cynthia

    2017-02-15

    Metabolic network reconstructions are often incomplete. Constraint-based and pattern-based methodologies have been used for automated gap filling of these networks, each with its own strengths and weaknesses. Moreover, since validation of hypotheses made by gap filling tools require experimentation, it is challenging to benchmark performance and make improvements other than that related to speed and scalability. We present BoostGAPFILL, an open source tool that leverages both constraint-based and machine learning methodologies for hypotheses generation in gap filling and metabolic model refinement. BoostGAPFILL uses metabolite patterns in the incomplete network captured using a matrix factorization formulation to constrain the set of reactions used to fill gaps in a metabolic network. We formulate a testing framework based on the available metabolic reconstructions and demonstrate the superiority of BoostGAPFILL to state-of-the-art gap filling tools. We randomly delete a number of reactions from a metabolic network and rate the different algorithms on their ability to both predict the deleted reactions from a universal set and to fill gaps. For most metabolic network reconstructions tested, BoostGAPFILL shows above 60% precision and recall, which is more than twice that of other existing tools. MATLAB open source implementation ( https://github.com/Tolutola/BoostGAPFILL ). toyetunde@wustl.edu or muhan@wustl.edu . Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

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

    OpenAIRE

    Bordbar, Aarash; Feist, Adam M; 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...

  6. 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.

  7. Polysaccharides utilization in human gut bacterium Bacteroides thetaiotaomicron: comparative genomics reconstruction of metabolic and regulatory networks.

    Science.gov (United States)

    Ravcheev, Dmitry A; Godzik, Adam; Osterman, Andrei L; Rodionov, Dmitry A

    2013-12-12

    Bacteroides thetaiotaomicron, a predominant member of the human gut microbiota, is characterized by its ability to utilize a wide variety of polysaccharides using the extensive saccharolytic machinery that is controlled by an expanded repertoire of transcription factors (TFs). The availability of genomic sequences for multiple Bacteroides species opens an opportunity for their comparative analysis to enable characterization of their metabolic and regulatory networks. A comparative genomics approach was applied for the reconstruction and functional annotation of the carbohydrate utilization regulatory networks in 11 Bacteroides genomes. Bioinformatics analysis of promoter regions revealed putative DNA-binding motifs and regulons for 31 orthologous TFs in the Bacteroides. Among the analyzed TFs there are 4 SusR-like regulators, 16 AraC-like hybrid two-component systems (HTCSs), and 11 regulators from other families. Novel DNA motifs of HTCSs and SusR-like regulators in the Bacteroides have the common structure of direct repeats with a long spacer between two conserved sites. The inferred regulatory network in B. thetaiotaomicron contains 308 genes encoding polysaccharide and sugar catabolic enzymes, carbohydrate-binding and transport systems, and TFs. The analyzed TFs control pathways for utilization of host and dietary glycans to monosaccharides and their further interconversions to intermediates of the central metabolism. The reconstructed regulatory network allowed us to suggest and refine specific functional assignments for sugar catabolic enzymes and transporters, providing a substantial improvement to the existing metabolic models for B. thetaiotaomicron. The obtained collection of reconstructed TF regulons is available in the RegPrecise database (http://regprecise.lbl.gov).

  8. EnzDP: improved enzyme annotation for metabolic network reconstruction based on domain composition profiles.

    Science.gov (United States)

    Nguyen, Nam-Ninh; Srihari, Sriganesh; Leong, Hon Wai; Chong, Ket-Fah

    2015-10-01

    Determining the entire complement of enzymes and their enzymatic functions is a fundamental step for reconstructing the metabolic network of cells. High quality enzyme annotation helps in enhancing metabolic networks reconstructed from the genome, especially by reducing gaps and increasing the enzyme coverage. Currently, structure-based and network-based approaches can only cover a limited number of enzyme families, and the accuracy of homology-based approaches can be further improved. Bottom-up homology-based approach improves the coverage by rebuilding Hidden Markov Model (HMM) profiles for all known enzymes. However, its clustering procedure relies firmly on BLAST similarity score, ignoring protein domains/patterns, and is sensitive to changes in cut-off thresholds. Here, we use functional domain architecture to score the association between domain families and enzyme families (Domain-Enzyme Association Scoring, DEAS). The DEAS score is used to calculate the similarity between proteins, which is then used in clustering procedure, instead of using sequence similarity score. We improve the enzyme annotation protocol using a stringent classification procedure, and by choosing optimal threshold settings and checking for active sites. Our analysis shows that our stringent protocol EnzDP can cover up to 90% of enzyme families available in Swiss-Prot. It achieves a high accuracy of 94.5% based on five-fold cross-validation. EnzDP outperforms existing methods across several testing scenarios. Thus, EnzDP serves as a reliable automated tool for enzyme annotation and metabolic network reconstruction. Available at: www.comp.nus.edu.sg/~nguyennn/EnzDP .

  9. Qualitative Alterations of Bacterial Metabolome after Exposure to Metal Nanoparticles with Bactericidal Properties: A Comprehensive Workflow Based on (1)H NMR, UHPLC-HRMS, and Metabolic Databases.

    Science.gov (United States)

    Chatzimitakos, Theodoros G; Stalikas, Constantine D

    2016-09-02

    Metal nanoparticles (NPs) have proven to be more toxic than bulk analogues of the same chemical composition due to their unique physical properties. The NPs, lately, have drawn the attention of researchers because of their antibacterial and biocidal properties. In an effort to shed light on the mechanism through which the bacteria elimination is achieved and the metabolic changes they undergo, an untargeted metabolomic fingerprint study was carried out on Gram-positive (Staphylococcus aureus) and Gram-negative (Escherichia coli) bacteria species. The (1)H NMR spectroscopy, in conjunction with high resolution mass-spectrometry (HRMS) and an unsophisticated data processing workflow were implemented. The combined NMR/HRMS data, supported by an open-access metabolomic database, proved to be efficacious in the process of assigning a putative annotation to a wide range of metabolite signals and is a useful tool to appraise the metabolome alterations, as a consequence of bacterial response to NPs. Interestingly, not all the NPs diminished the intracellular metabolites; bacteria treated with iron NPs produced metabolites not present in the nonexposed bacteria sample, implying the activation of previously inactive metabolic pathways. In contrast, copper and iron-copper NPs reduced the annotated metabolites, alluding to the conclusion that the metabolic pathways (mainly alanine, aspartate, and glutamate metabolism, beta-alanine metabolism, glutathione metabolism, and arginine and proline metabolism) were hindered by the interactions of NPs with the intracellular metabolites.

  10. Transcriptomic coordination in the human metabolic network reveals links between n-3 fat intake, adipose tissue gene expression and metabolic health.

    Science.gov (United States)

    Morine, Melissa J; Tierney, Audrey C; van Ommen, Ben; Daniel, Hannelore; Toomey, Sinead; Gjelstad, Ingrid M F; Gormley, Isobel C; Pérez-Martinez, Pablo; Drevon, Christian A; López-Miranda, Jose; Roche, Helen M

    2011-11-01

    Understanding the molecular link between diet and health is a key goal in nutritional systems biology. As an alternative to pathway analysis, we have developed a joint multivariate and network-based approach to analysis of a dataset of habitual dietary records, adipose tissue transcriptomics and comprehensive plasma marker profiles from human volunteers with the Metabolic Syndrome. With this approach we identified prominent co-expressed sub-networks in the global metabolic network, which showed correlated expression with habitual n-3 PUFA intake and urinary levels of the oxidative stress marker 8-iso-PGF(2α). These sub-networks illustrated inherent cross-talk between distinct metabolic pathways, such as between triglyceride metabolism and production of lipid signalling molecules. In a parallel promoter analysis, we identified several adipogenic transcription factors as potential transcriptional regulators associated with habitual n-3 PUFA intake. Our results illustrate advantages of network-based analysis, and generate novel hypotheses on the transcriptomic link between habitual n-3 PUFA intake, adipose tissue function and oxidative stress.

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

    Science.gov (United States)

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

    2016-07-01

    Maternal metabolites and metabolic networks underlying associations between maternal glucose during pregnancy and newborn birth weight and adiposity demand fuller characterization. We performed targeted and nontargeted gas chromatography/mass spectrometry metabolomics on maternal serum collected at fasting and 1 h following glucose beverage consumption during an oral glucose tolerance test (OGTT) for 400 northern European mothers at ∼28 weeks' gestation in the Hyperglycemia and Adverse Pregnancy Outcome Study. Amino acids, fatty acids, acylcarnitines, and products of lipid metabolism decreased and triglycerides increased during the OGTT. Analyses of individual metabolites indicated limited maternal glucose associations at fasting, but broader associations, including amino acids, fatty acids, carbohydrates, and lipids, were found at 1 h. Network analyses modeling metabolite correlations provided context for individual metabolite associations and elucidated collective associations of multiple classes of metabolic fuels with newborn size and adiposity, including acylcarnitines, fatty acids, carbohydrates, and organic acids. Random forest analyses indicated an improved ability to predict newborn size outcomes by using maternal metabolomics data beyond traditional risk factors, including maternal glucose. Broad-scale association of fuel metabolites with maternal glucose is evident during pregnancy, with unique maternal metabolites potentially contributing specifically to newborn birth weight and adiposity. © 2016 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.

  12. The primary case is not enough: Variation among individuals, groups and social networks modify bacterial transmission dynamics.

    Science.gov (United States)

    Keiser, Carl N; Pinter-Wollman, Noa; Ziemba, Michael J; Kothamasu, Krishna S; Pruitt, Jonathan N

    2018-03-01

    The traits of the primary case of an infectious disease outbreak, and the circumstances for their aetiology, potentially influence the trajectory of transmission dynamics. However, these dynamics likely also depend on the traits of the individuals with whom the primary case interacts. We used the social spider Stegodyphus dumicola to test how the traits of the primary case, group phenotypic composition and group size interact to facilitate the transmission of a GFP-labelled cuticular bacterium. We also compared bacterial transmission across experimentally generated "daisy-chain" vs. "star" networks of social interactions. Finally, we compared social network structure across groups of different sizes. Groups of 10 spiders experienced more bacterial transmission events compared to groups of 30 spiders, regardless of groups' behavioural composition. Groups containing only one bold spider experienced the lowest levels of bacterial transmission regardless of group size. We found no evidence for the traits of the primary case influencing any transmission dynamics. In a second experiment, bacteria were transmitted to more individuals in experimentally induced star networks than in daisy-chains, on which transmission never exceeded three steps. In both experimental network types, transmission success depended jointly on the behavioural traits of the interacting individuals; however, the behavioural traits of the primary case were only important for transmission on star networks. Larger social groups exhibited lower interaction density (i.e. had a low ratio of observed to possible connections) and were more modular, i.e. they had more connections between nodes within a subgroup and fewer connections across subgroups. Thus, larger groups may restrict transmission by forming fewer interactions and by isolating subgroups that interacted with the primary case. These findings suggest that accounting for the traits of single exposed hosts has less power in predicting transmission

  13. 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. Copyright © 2015 Elsevier Inc. All rights reserved.

  14. PPARγ population shift produces disease-related changes in molecular networks associated with metabolic syndrome.

    Science.gov (United States)

    Jurkowski, W; Roomp, K; Crespo, I; Schneider, J G; Del Sol, A

    2011-08-11

    Peroxisome proliferator-activated receptor gamma (PPARγ) is a key regulator of adipocyte differentiation and has an important role in metabolic syndrome. Phosphorylation of the receptor's ligand-binding domain at serine 273 has been shown to change the expression of a large number of genes implicated in obesity. The difference in gene expression seen when comparing wild-type phosphorylated with mutant non-phosphorylated PPARγ may have important consequences for the cellular molecular network, the state of which can be shifted from the healthy to a stable diseased state. We found that a group of differentially expressed genes are involved in bi-stable switches and form a core network, the state of which changes with disease progression. These findings support the idea that bi-stable switches may be a mechanism for locking the core gene network into a diseased state and for efficiently propagating perturbations to more distant regions of the network. A structural analysis of the PPARγ-RXRα dimer complex supports the hypothesis of a major structural change between the two states, and this may represent an important mechanism leading to the differential expression observed in the core network.

  15. Uniform sampling of steady states in metabolic networks: heterogeneous scales and rounding.

    Directory of Open Access Journals (Sweden)

    Daniele De Martino

    Full Text Available The uniform sampling of convex polytopes is an interesting computational problem with many applications in inference from linear constraints, but the performances of sampling algorithms can be affected by ill-conditioning. This is the case of inferring the feasible steady states in models of metabolic networks, since they can show heterogeneous time scales. In this work we focus on rounding procedures based on building an ellipsoid that closely matches the sampling space, that can be used to define an efficient hit-and-run (HR Markov Chain Monte Carlo. In this way the uniformity of the sampling of the convex space of interest is rigorously guaranteed, at odds with non markovian methods. We analyze and compare three rounding methods in order to sample the feasible steady states of metabolic networks of three models of growing size up to genomic scale. The first is based on principal component analysis (PCA, the second on linear programming (LP and finally we employ the Lovazs ellipsoid method (LEM. Our results show that a rounding procedure dramatically improves the performances of the HR in these inference problems and suggest that a combination of LEM or LP with a subsequent PCA perform the best. We finally compare the distributions of the HR with that of two heuristics based on the Artificially Centered hit-and-run (ACHR, gpSampler and optGpSampler. They show a good agreement with the results of the HR for the small network, while on genome scale models present inconsistencies.

  16. 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.

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

    Science.gov (United States)

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

    2015-06-09

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

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

    Science.gov (United States)

    Ates, Ozlem; Oner, Ebru Toksoy; Arga, Kazim Y

    2011-01-21

    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. 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. 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.

  19. 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.

  20. 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.

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

    Science.gov (United States)

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

    2016-01-01

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

  2. 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.

  3. 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...... by eliminating three known negative regulators of the GAL system: Gale, Gal80, and Mig1. This led to a 41% increase in flux through the galactose utilization pathway compared with the wild-type strain. This is of significant interest within the field of biotechnology since galactose is present in many industrial...... 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....

  4. 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.

  5. Bacterial Metabolism of Arylsulfonates

    Science.gov (United States)

    Ripin, Marilyn J.; Noon, Kerry F.; Cook, Thomas M.

    1971-01-01

    Pseudomonas testosteroni H-8 utilizes as sole carbon source benzene sulfonate (BS), p-toluene sulfonate (pTS), and ethylbenzene sulfonate (EBS) but not higher homologs. Growth on BS was rapid (generation time, 3 hr) and efficient (Y = 57), and resulted in accumulation of sulfate. As the culture is acid-sensitive, the medium must be heavily buffered to permit extensive growth. The BS oxidase system is inducible. Cells grown on BS, but not glutamate, oxidized BS, pTS, or EBS without lag (QO2 = 50 to 100). Oxygen uptake on BS is temperature-dependent and sensitive to cyanide. Complete oxidation of 1 μmole of BS consumed approximately 5.7 μmoles of oxygen. PMID:5553286

  6. MetExploreViz: web component for interactive metabolic network visualization.

    Science.gov (United States)

    Chazalviel, Maxime; Frainay, Clément; Poupin, Nathalie; Vinson, Florence; Merlet, Benjamin; Gloaguen, Yoann; Cottret, Ludovic; Jourdan, Fabien

    2017-09-15

    MetExploreViz is an open source web component that can be easily embedded in any web site. It provides features dedicated to the visualization of metabolic networks and pathways and thus offers a flexible solution to analyze omics data in a biochemical context. Documentation and link to GIT code repository (GPL 3.0 license)are available at this URL: http://metexplore.toulouse.inra.fr/metexploreViz/doc /. Tutorial is available at this URL. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  7. 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

    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...

  8. 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. Copyright © 2016 Elsevier Ltd. All rights reserved.

  9. Bacterial chemotaxis: introverted or extroverted? A comparison of the advantages and disadvantages of basic forms of metabolism-based and metabolism-independent behavior using a computational model.

    Science.gov (United States)

    Egbert, Matthew D

    2013-01-01

    Using a minimal model of metabolism, we examine the limitations of behavior that is (a) solely in response to environmental phenomena or (b) solely in response to metabolic dynamics, showing that basic forms of each of these kinds of behavior are incapable of driving survival-prolonging behavior in certain situations. Inspired by experimental evidence of concurrent metabolism-based and metabolism-independent chemotactic mechanisms in Escherichia coli and Rhodobacter sphaeroides, we then investigate how metabolism-independent and metabolism-based sensitivities can be integrated into a single behavioral response, demonstrating that a simple switching mechanism can be sufficient to effectively integrate metabolism-based and metabolism-independent behaviors. Finally, we use a spatial simulation of bacteria to show that the investigated forms of behavior produce different spatio-temporal patterns that are influenced by the metabolic-history of the bacteria. We suggest that these patterns could be a way to experimentally derive insight into the relationship between metabolism and chemotaxis in real bacteria.

  10. Expression of a bacterial feedback-insensitive 3-deoxy-D-arabino-heptulosonate 7-phosphate synthase of the shikimate pathway in Arabidopsis elucidates potential metabolic bottlenecks between primary and secondary metabolism.

    Science.gov (United States)

    Tzin, Vered; Malitsky, Sergey; Ben Zvi, Michal Moyal; Bedair, Mohamed; Sumner, Lloyd; Aharoni, Asaph; Galili, Gad

    2012-04-01

    The shikimate pathway of plants mediates the conversion of primary carbon metabolites via chorismate into the three aromatic amino acids and to numerous secondary metabolites derived from them. However, the regulation of the shikimate pathway is still far from being understood. We hypothesized that 3-deoxy-d-arabino-heptulosonate 7-phosphate synthase (DAHPS) is a key enzyme regulating flux through the shikimate pathway. To test this hypothesis, we expressed a mutant bacterial AroG gene encoding a feedback-insensitive DAHPS in transgenic Arabidopsis plants. The plants were subjected to detailed analysis of primary metabolism, using GC-MS, as well as secondary metabolism, using LC-MS. Our results exposed a major effect of bacterial AroG expression on the levels of shikimate intermediate metabolites, phenylalanine, tryptophan and broad classes of secondary metabolite, such as phenylpropanoids, glucosinolates, auxin and other hormone conjugates. We propose that DAHPS is a key regulatory enzyme of the shikimate pathway. Moreover, our results shed light on additional potential metabolic bottlenecks bridging plant primary and secondary metabolism. © 2012 The Authors. New Phytologist © 2012 New Phytologist Trust.

  11. iRsp1095: A genome-scale reconstruction of the Rhodobacter sphaeroides metabolic network

    Directory of Open Access Journals (Sweden)

    Gorzalski Alexander S

    2011-07-01

    Full Text Available Abstract Background Rhodobacter sphaeroides is one of the best studied purple non-sulfur photosynthetic bacteria and serves as an excellent model for the study of photosynthesis and the metabolic capabilities of this and related facultative organisms. The ability of R. sphaeroides to produce hydrogen (H2, polyhydroxybutyrate (PHB or other hydrocarbons, as well as its ability to utilize atmospheric carbon dioxide (CO2 as a carbon source under defined conditions, make it an excellent candidate for use in a wide variety of biotechnological applications. A genome-level understanding of its metabolic capabilities should help realize this biotechnological potential. Results Here we present a genome-scale metabolic network model for R. sphaeroides strain 2.4.1, designated iRsp1095, consisting of 1,095 genes, 796 metabolites and 1158 reactions, including R. sphaeroides-specific biomass reactions developed in this study. Constraint-based analysis showed that iRsp1095 agreed well with experimental observations when modeling growth under respiratory and phototrophic conditions. Genes essential for phototrophic growth were predicted by single gene deletion analysis. During pathway-level analyses of R. sphaeroides metabolism, an alternative route for CO2 assimilation was identified. Evaluation of photoheterotrophic H2 production using iRsp1095 indicated that maximal yield would be obtained from growing cells, with this predicted maximum ~50% higher than that observed experimentally from wild type cells. Competing pathways that might prevent the achievement of this theoretical maximum were identified to guide future genetic studies. Conclusions iRsp1095 provides a robust framework for future metabolic engineering efforts to optimize the solar- and nutrient-powered production of biofuels and other valuable products by R. sphaeroides and closely related organisms.

  12. Metabolic network model guided engineering ethylmalonyl-CoA pathway to improve ascomycin production in Streptomyces hygroscopicus var. ascomyceticus.

    Science.gov (United States)

    Wang, Junhua; Wang, Cheng; Song, Kejing; Wen, Jianping

    2017-10-03

    Ascomycin is a 23-membered polyketide macrolide with high immunosuppressant and antifungal activity. As the lower production in bio-fermentation, global metabolic analysis is required to further explore its biosynthetic network and determine the key limiting steps for rationally engineering. To achieve this goal, an engineering approach guided by a metabolic network model was implemented to better understand ascomycin biosynthesis and improve its production. The metabolic conservation of Streptomyces species was first investigated by comparing the metabolic enzymes of Streptomyces coelicolor A3(2) with those of 31 Streptomyces strains, the results showed that more than 72% of the examined proteins had high sequence similarity with counterparts in every surveyed strain. And it was found that metabolic reactions are more highly conserved than the enzymes themselves because of its lower diversity of metabolic functions than that of genes. The main source of the observed metabolic differences was from the diversity of secondary metabolism. According to the high conservation of primary metabolic reactions in Streptomyces species, the metabolic network model of Streptomyces hygroscopicus var. ascomyceticus was constructed based on the latest reported metabolic model of S. coelicolor A3(2) and validated experimentally. By coupling with flux balance analysis and using minimization of metabolic adjustment algorithm, potential targets for ascomycin overproduction were predicted. Since several of the preferred targets were highly associated with ethylmalonyl-CoA biosynthesis, two target genes hcd (encoding 3-hydroxybutyryl-CoA dehydrogenase) and ccr (encoding crotonyl-CoA carboxylase/reductase) were selected for overexpression in S. hygroscopicus var. ascomyceticus FS35. Both the mutants HA-Hcd and HA-Ccr showed higher ascomycin titer, which was consistent with the model predictions. Furthermore, the combined effects of the two genes were evaluated and the strain HA

  13. RELATIONS BETWEEN BACTERIAL NITROGEN METABOLISM AND GROWTH EFFICIENCY IN AN ESTUARINE AND AN OPEN-WATER ECOSYSTEM

    Science.gov (United States)

    Bacterial uptake or release of dissolved nitrogen compounds (amino nitrogen, urea, ammonium and nitrate) were examined in 0.8 |m filtered water from an estuary (Santa Rosa Sound [SRS], northwestern Florida) and an open-water location in the Gulf of Mexico [GM]. The bacterial nutr...

  14. Quantitative time-course metabolomics in human red blood cells reveal the temperature dependence of human metabolic networks.

    Science.gov (United States)

    Yurkovich, James T; Zielinski, Daniel C; Yang, Laurence; Paglia, Giuseppe; Rolfsson, Ottar; Sigurjónsson, Ólafur E; Broddrick, Jared T; Bordbar, Aarash; Wichuk, Kristine; Brynjólfsson, Sigurður; Palsson, Sirus; Gudmundsson, Sveinn; Palsson, Bernhard O

    2017-12-01

    The temperature dependence of biological processes has been studied at the levels of individual biochemical reactions and organism physiology ( e.g. basal metabolic rates) but has not been examined at the metabolic network level. Here, we used a systems biology approach to characterize the temperature dependence of the human red blood cell (RBC) metabolic network between 4 and 37 °C through absolutely quantified exo- and endometabolomics data. We used an Arrhenius-type model ( Q 10 ) to describe how the rate of a biochemical process changes with every 10 °C change in temperature. Multivariate statistical analysis of the metabolomics data revealed that the same metabolic network-level trends previously reported for RBCs at 4 °C were conserved but accelerated with increasing temperature. We calculated a median Q 10 coefficient of 2.89 ± 1.03, within the expected range of 2-3 for biological processes, for 48 individual metabolite concentrations. We then integrated these metabolomics measurements into a cell-scale metabolic model to study pathway usage, calculating a median Q 10 coefficient of 2.73 ± 0.75 for 35 reaction fluxes. The relative fluxes through glycolysis and nucleotide metabolism pathways were consistent across the studied temperature range despite the non-uniform distributions of Q 10 coefficients of individual metabolites and reaction fluxes. Together, these results indicate that the rate of change of network-level responses to temperature differences in RBC metabolism is consistent between 4 and 37 °C. More broadly, we provide a baseline characterization of a biochemical network given no transcriptional or translational regulation that can be used to explore the temperature dependence of metabolism. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

  15. 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.

  16. In vivo metabolism of 2,2'-diaminopimelic acid from gram-positive and gram-negative bacterial cells by ruminal microorganisms and ruminants and its use as a marker of bacterial biomass

    International Nuclear Information System (INIS)

    Masson, H.A.; Denholm, A.M.; Ling, J.R.

    1991-01-01

    Cells of Bacillus megaterium GW1 and Escherichia coli W7-M5 were specifically radiolabeled with 2,2'-diamino [G- 3 H] pimelic acid ([ 3 H]DAP) as models of gram-positive and gram-negative bacteria, respectively. Two experiments were conducted to study the in vivo metabolism of 2,2'-diaminopimelic acid (DAP) in sheep. In experiment 1, cells of [ 3 H]DAP-labeled B. megaterium GW1 were infused into the rumen of one sheep and the radiolabel was traced within microbial samples, digesta, and the whole animal. Bacterially bound [ 3 H]DAP was extensively metabolized, primarily (up to 70% after 8 h) via decarboxylation to [ 3 H]lysine by both ruminal protozoa and ruminal bacteria. Recovery of infused radiolabel in urine and feces was low (42% after 96 h) and perhaps indicative of further metabolism by the host animal. In experiment 2, [ 3 H]DAP-labeled B. megaterium GW1 was infused into the rumens of three sheep and [ 3 H]DAP-labeled E. coli W7-W5 was infused into the rumen of another sheep. The radioactivity contents of these mutant bacteria were insufficient to use as tracers, but the metabolism of DAP was monitored in the total, free, and peptidyl forms. Free DAP, as a proportion of total DPA in duodenal digesta, varied from 0 to 9.5%, whereas peptidyl DAP accounted for 8.3 to 99.2%

  17. 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.

  18. Direct calculation of elementary flux modes satisfying several biological constraints in genome-scale metabolic networks.

    Science.gov (United States)

    Pey, Jon; Planes, Francisco J

    2014-08-01

    The concept of Elementary Flux Mode (EFM) has been widely used for the past 20 years. However, its application to genome-scale metabolic networks (GSMNs) is still under development because of methodological limitations. Therefore, novel approaches are demanded to extend the application of EFMs. A novel family of methods based on optimization is emerging that provides us with a subset of EFMs. Because the calculation of the whole set of EFMs goes beyond our capacity, performing a selective search is a proper strategy. Here, we present a novel mathematical approach calculating EFMs fulfilling additional linear constraints. We validated our approach based on two metabolic networks in which all the EFMs can be obtained. Finally, we analyzed the performance of our methodology in the GSMN of the yeast Saccharomyces cerevisiae by calculating EFMs producing ethanol with a given minimum carbon yield. Overall, this new approach opens new avenues for the calculation of EFMs in GSMNs. Matlab code is provided in the supplementary online materials fplanes@ceit.es. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  19. Robust detection and verification of linear relationships to generate metabolic networks using estimates of technical errors

    Directory of Open Access Journals (Sweden)

    Holschneider Matthias

    2007-05-01

    Full Text Available Abstract Background The size and magnitude of the metabolome, the ratio between individual metabolites and the response of metabolic networks is controlled by multiple cellular factors. A tight control over metabolite ratios will be reflected by a linear relationship of pairs of metabolite due to the flexibility of metabolic pathways. Hence, unbiased detection and validation of linear metabolic variance can be interpreted in terms of biological control. For robust analyses, criteria for rejecting or accepting linearities need to be developed despite technical measurement errors. The entirety of all pair wise linear metabolic relationships then yields insights into the network of cellular regulation. Results The Bayesian law was applied for detecting linearities that are validated by explaining the residues by the degree of technical measurement errors. Test statistics were developed and the algorithm was tested on simulated data using 3–150 samples and 0–100% technical error. Under the null hypothesis of the existence of a linear relationship, type I errors remained below 5% for data sets consisting of more than four samples, whereas the type II error rate quickly raised with increasing technical errors. Conversely, a filter was developed to balance the error rates in the opposite direction. A minimum of 20 biological replicates is recommended if technical errors remain below 20% relative standard deviation and if thresholds for false error rates are acceptable at less than 5%. The algorithm was proven to be robust against outliers, unlike Pearson's correlations. Conclusion The algorithm facilitates finding linear relationships in complex datasets, which is radically different from estimating linearity parameters from given linear relationships. Without filter, it provides high sensitivity and fair specificity. If the filter is activated, high specificity but only fair sensitivity is yielded. Total error rates are more favorable with

  20. Mechano-sensitization of mammalian neuronal networks through expression of the bacterial large-conductance mechanosensitive ion channel.

    Science.gov (United States)

    Soloperto, Alessandro; Boccaccio, Anna; Contestabile, Andrea; Moroni, Monica; Hallinan, Grace I; Palazzolo, Gemma; Chad, John; Deinhardt, Katrin; Carugo, Dario; Difato, Francesco

    2018-03-08

    Development of remote stimulation techniques for neuronal tissues represents a challenging goal. Among the potential methods, mechanical stimuli are the most promising vectors to convey information non-invasively into intact brain tissue. In this context, selective mechano-sensitization of neuronal circuits would pave the way to develop a new cell-type-specific stimulation approach. We report here, for the first time, the development and characterization of mechano-sensitized neuronal networks through the heterologous expression of an engineered bacterial large-conductance mechanosensitive ion channel (MscL). The neuronal functional expression of the MscL was validated through patch-clamp recordings upon application of calibrated suction pressures. Moreover, we verified the effective development of in-vitro neuronal networks expressing the engineered MscL in terms of cell survival, number of synaptic puncta and spontaneous network activity. The pure mechanosensitivity of the engineered MscL, with its wide genetic modification library, may represent a versatile tool to further develop a mechano-genetic approach.This article has an associated First Person interview with the first author of the paper. © 2018. Published by The Company of Biologists Ltd.

  1. Multiple Substrate Usage of Coxiella burnetii to Feed a Bipartite Metabolic Network

    Directory of Open Access Journals (Sweden)

    Ina Häuslein

    2017-06-01

    Full Text Available The human pathogen Coxiella burnetii causes Q-fever and is classified as a category B bio-weapon. Exploiting the development of the axenic growth medium ACCM-2, we have now used 13C-labeling experiments and isotopolog profiling to investigate the highly diverse metabolic network of C. burnetii. To this aim, C. burnetii RSA 439 NMII was cultured in ACCM-2 containing 5 mM of either [U-13C3]serine, [U-13C6]glucose, or [U-13C3]glycerol until the late-logarithmic phase. GC/MS-based isotopolog profiling of protein-derived amino acids, methanol-soluble polar metabolites, fatty acids, and cell wall components (e.g., diaminopimelate and sugars from the labeled bacteria revealed differential incorporation rates and isotopolog profiles. These data served to decipher the diverse usages of the labeled substrates and the relative carbon fluxes into the core metabolism of the pathogen. Whereas, de novo biosynthesis from any of these substrates could not be found for histidine, isoleucine, leucine, lysine, phenylalanine, proline and valine, the other amino acids and metabolites under study acquired 13C-label at specific rates depending on the nature of the tracer compound. Glucose was directly used for cell wall biosynthesis, but was also converted into pyruvate (and its downstream metabolites through the glycolytic pathway or into erythrose 4-phosphate (e.g., for the biosynthesis of tyrosine via the non-oxidative pentose phosphate pathway. Glycerol efficiently served as a gluconeogenetic substrate and could also be used via phosphoenolpyruvate and diaminopimelate as a major carbon source for cell wall biosynthesis. In contrast, exogenous serine was mainly utilized in downstream metabolic processes, e.g., via acetyl-CoA in a complete citrate cycle with fluxes in the oxidative direction and as a carbon feed for fatty acid biosynthesis. In summary, the data reflect multiple and differential substrate usages by C. burnetii in a bipartite-type metabolic network

  2. Distinct Network Interactions in Particle-Associated and Free-Living Bacterial Communities during a Microcystis aeruginosa Bloom in a Plateau Lake

    Directory of Open Access Journals (Sweden)

    Caiyun Yang

    2017-06-01

    Full Text Available Particle-associated bacteria (PAB and free-living bacteria (FLB from aquatic environments during phytoplankton blooms differ in their physical distance from algae. Both the interactions within PAB and FLB community fractions and their relationship with the surrounding environmental properties are largely unknown. Here, by using high-throughput sequencing and network-based analyses, we compared the community and network characteristics of PAB and FLB from a plateau lake during a Microcystis aeruginosa bloom. Results showed that PAB and FLB differed significantly in diversity, structure and microbial connecting network. PAB communities were characterized by highly similar bacterial community structure in different sites, tighter network connections, important topological roles for the bloom-causing M. aeruginosa and Alphaproteobacteria, especially for the potentially nitrogen-fixing (Pleomorphomonas and algicidal bacteria (Brevundimonas sp.. FLB communities were sensitive to the detected environmental factors and were characterized by significantly higher bacterial diversity, less connectivity, larger network size and marginal role of M. aeruginosa. In both networks, covariation among bacterial taxa was extensive (>88% positive connections, and bacteria potentially affiliated with biogeochemical cycling of nitrogen (i.e., denitrification, nitrogen-fixation and nitrite-oxidization were important in occupying module hubs, such as Meganema, Pleomorphomonas, and Nitrospira. These findings highlight the importance of considering microbial network interactions for the understanding of blooms.

  3. 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

  4. 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

    Synechocystis sp. PCC6803 is a model cyanobacterium capable of producing biofuels with CO2 as carbon source and with its metabolism fueled by light, for which it stands as a potential production platform of socio-economic importance. Compilation and characterization of Synechocystis genome...... networks, surrounded by a stable core of pathways leading to biomass building blocks. This analysis identified potential bottlenecks for hydrogen and ethanol production. Integration of transcriptomic data with the Synechocystis flux coupling networks lead to identification of reporter flux coupling pairs...... and reporter flux coupling groups - regulatory hot spots during metabolic shifts triggered by the availability of light. Overall, flux coupling analysis provided insight into the structural organization of Synechocystis sp. PCC6803 metabolic network toward designing of a photosynthesis-based production...

  5. 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.

  6. Toward pectin fermentation by Saccharomyces cerevisiae: expression of the first two steps of a bacterial pathway for D-galacturonate metabolism.

    Science.gov (United States)

    Huisjes, Eline H; Luttik, Marijke A H; Almering, Marinka J H; Bisschops, Markus M M; Dang, Dieu H N; Kleerebezem, Michiel; Siezen, Roland; van Maris, Antonius J A; Pronk, Jack T

    2012-12-31

    Saccharomyces cerevisiae cannot metabolize D-galacturonate, an important monomer of pectin. Use of S. cerevisiae for production of ethanol or other compounds of interest from pectin-rich feedstocks therefore requires introduction of a heterologous pathway for D-galacturonate metabolism. Bacterial D-galacturonate pathways involve D-galacturonate isomerase, D-tagaturonate reductase and three additional enzymes. This study focuses on functional expression of bacterial D-galacturonate isomerases in S. cerevisiae. After demonstrating high-level functional expression of a D-tagaturonate reductase gene (uxaB from Lactococcus lactis), the resulting yeast strain was used to screen for functional expression of six codon-optimized bacterial D-galacturonate isomerase (uxaC) genes. The L. lactis uxaC gene stood out, yielding a tenfold higher enzyme activity than the other uxaC genes. Efficient expression of D-galacturonate isomerase and D-tagaturonate reductase represents an important step toward metabolic engineering of S. cerevisiae for bioethanol production from D-galacturonate. To investigate in vivo activity of the first steps of the D-galacturonate pathway, the L. lactis uxaB and uxaC genes were expressed in a gpd1Δ gpd2Δ S. cerevisiae strain. Although D-tagaturonate reductase could, in principle, provide an alternative means for re-oxidizing cytosolic NADH, addition of D-galacturonate did not restore anaerobic growth, possibly due to absence of a functional D-altronate exporter in S. cerevisiae. Copyright © 2012 Elsevier B.V. All rights reserved.

  7. Genome-scale reconstruction and analysis of the Pseudomonas putida KT2440 metabolic network facilitates applications in biotechnology.

    Science.gov (United States)

    Puchałka, Jacek; Oberhardt, Matthew A; Godinho, Miguel; Bielecka, Agata; Regenhardt, Daniela; Timmis, Kenneth N; Papin, Jason A; Martins dos Santos, Vítor A P

    2008-10-01

    A cornerstone of biotechnology is the use of microorganisms for the efficient production of chemicals and the elimination of harmful waste. Pseudomonas putida is an archetype of such microbes due to its metabolic versatility, stress resistance, amenability to genetic modifications, and vast potential for environmental and industrial applications. To address both the elucidation of the metabolic wiring in P. putida and its uses in biocatalysis, in particular for the production of non-growth-related biochemicals, we developed and present here a genome-scale constraint-based model of the metabolism of P. putida KT2440. Network reconstruction and flux balance analysis (FBA) enabled definition of the structure of the metabolic network, identification of knowledge gaps, and pin-pointing of essential metabolic functions, facilitating thereby the refinement of gene annotations. FBA and flux variability analysis were used to analyze the properties, potential, and limits of the model. These analyses allowed identification, under various conditions, of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. The model was validated with data from continuous cell cultures, high-throughput phenotyping data, (13)C-measurement of internal flux distributions, and specifically generated knock-out mutants. Auxotrophy was correctly predicted in 75% of the cases. These systematic analyses revealed that the metabolic network structure is the main factor determining the accuracy of predictions, whereas biomass composition has negligible influence. Finally, we drew on the model to devise metabolic engineering strategies to improve production of polyhydroxyalkanoates, a class of biotechnologically useful compounds whose synthesis is not coupled to cell survival. The solidly validated model yields valuable insights into genotype-phenotype relationships and provides a sound framework to explore this versatile bacterium and to

  8. Genome-scale reconstruction and analysis of the Pseudomonas putida KT2440 metabolic network facilitates applications in biotechnology.

    Directory of Open Access Journals (Sweden)

    Jacek Puchałka

    2008-10-01

    Full Text Available A cornerstone of biotechnology is the use of microorganisms for the efficient production of chemicals and the elimination of harmful waste. Pseudomonas putida is an archetype of such microbes due to its metabolic versatility, stress resistance, amenability to genetic modifications, and vast potential for environmental and industrial applications. To address both the elucidation of the metabolic wiring in P. putida and its uses in biocatalysis, in particular for the production of non-growth-related biochemicals, we developed and present here a genome-scale constraint-based model of the metabolism of P. putida KT2440. Network reconstruction and flux balance analysis (FBA enabled definition of the structure of the metabolic network, identification of knowledge gaps, and pin-pointing of essential metabolic functions, facilitating thereby the refinement of gene annotations. FBA and flux variability analysis were used to analyze the properties, potential, and limits of the model. These analyses allowed identification, under various conditions, of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. The model was validated with data from continuous cell cultures, high-throughput phenotyping data, (13C-measurement of internal flux distributions, and specifically generated knock-out mutants. Auxotrophy was correctly predicted in 75% of the cases. These systematic analyses revealed that the metabolic network structure is the main factor determining the accuracy of predictions, whereas biomass composition has negligible influence. Finally, we drew on the model to devise metabolic engineering strategies to improve production of polyhydroxyalkanoates, a class of biotechnologically useful compounds whose synthesis is not coupled to cell survival. The solidly validated model yields valuable insights into genotype-phenotype relationships and provides a sound framework to explore this versatile

  9. 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....

  10. A systems biology approach to reconcile metabolic network models with application to Synechocystis sp. PCC 6803 for biofuel production.

    Science.gov (United States)

    Mohammadi, Reza; Fallah-Mehrabadi, Jalil; Bidkhori, Gholamreza; Zahiri, Javad; Javad Niroomand, Mohammad; Masoudi-Nejad, Ali

    2016-07-19

    Production of biofuels has been one of the promising efforts in biotechnology in the past few decades. The perspective of these efforts can be reduction of increasing demands for fossil fuels and consequently reducing environmental pollution. Nonetheless, most previous approaches did not succeed in obviating many big challenges in this way. In recent years systems biology with the help of microorganisms has been trying to overcome these challenges. Unicellular cyanobacteria are widespread phototrophic microorganisms that have capabilities such as consuming solar energy and atmospheric carbon dioxide for growth and thus can be a suitable chassis for the production of valuable organic materials such as biofuels. For the ultimate use of metabolic potential of cyanobacteria, it is necessary to understand the reactions that are taking place inside the metabolic network of these microorganisms. In this study, we developed a Java tool to reconstruct an integrated metabolic network of a cyanobacterium (Synechocystis sp. PCC 6803). We merged three existing reconstructed metabolic networks of this microorganism. Then, after modeling for biofuel production, the results from flux balance analysis (FBA) disclosed an increased yield in biofuel production for ethanol, isobutanol, 3-methyl-1-butanol, 2-methyl-1-butanol, and propanol. The numbers of blocked reactions were also decreased for 2-methyl-1-butanol production. In addition, coverage of the metabolic network in terms of the number of metabolites and reactions was increased in the new obtained model.

  11. Metabolism

    Science.gov (United States)

    ... functions: Anabolism (uh-NAB-uh-liz-um), or constructive metabolism, is all about building and storing. It ... in infants and young children. Hypothyroidism slows body processes and causes fatigue (tiredness), slow heart rate, excessive ...

  12. 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 ...

  13. A novel strategy involved in [corrected] anti-oxidative defense: the conversion of NADH into NADPH by a metabolic network.

    Directory of Open Access Journals (Sweden)

    Ranji Singh

    Full Text Available The reduced nicotinamide adenine dinucleotide phosphate (NADPH is pivotal to the cellular anti-oxidative defence strategies in most organisms. Although its production mediated by different enzyme systems has been relatively well-studied, metabolic networks dedicated to the biogenesis of NADPH have not been fully characterized. In this report, a metabolic pathway that promotes the conversion of reduced nicotinamide adenine dinucleotide (NADH, a pro-oxidant into NADPH has been uncovered in Pseudomonas fluorescens exposed to oxidative stress. Enzymes such as pyruvate carboxylase (PC, malic enzyme (ME, malate dehydrogenase (MDH, malate synthase (MS, and isocitrate lyase (ICL that are involved in disparate metabolic modules, converged to create a metabolic network aimed at the transformation of NADH into NADPH. The downregulation of phosphoenol carboxykinase (PEPCK and the upregulation of pyruvate kinase (PK ensured that this metabolic cycle fixed NADH into NADPH to combat the oxidative stress triggered by the menadione insult. This is the first demonstration of a metabolic network invoked to generate NADPH from NADH, a process that may be very effective in combating oxidative stress as the increase of an anti-oxidant is coupled to the decrease of a pro-oxidant.

  14. Genes encoding hub and bottleneck enzymes of the Arabidopsis metabolic network preferentially retain homeologs through whole genome duplication

    Directory of Open Access Journals (Sweden)

    Qi Xiaoquan

    2010-05-01

    Full Text Available Abstract Background Whole genome duplication (WGD occurs widely in angiosperm evolution. It raises the intriguing question of how interacting networks of genes cope with this dramatic evolutionary event. Results In study of the Arabidopsis metabolic network, we assigned each enzyme (node with topological centralities (in-degree, out-degree and between-ness to measure quantitatively their centralities in the network. The Arabidopsis metabolic network is highly modular and separated into 11 interconnected modules, which correspond well to the functional metabolic pathways. The enzymes with higher in-out degree and between-ness (defined as hub and bottleneck enzymes, respectively tend to be more conserved and preferentially retain homeologs after WGD. Moreover, the simultaneous retention of homeologs encoding enzymes which catalyze consecutive steps in a pathway is highly favored and easily achieved, and enzyme-enzyme interactions contribute to the retention of one-third of WGD enzymes. Conclusions Our analyses indicate that the hub and bottleneck enzymes of metabolic network obtain great benefits from WGD, and this event grants clear evolutionary advantages in adaptation to different environments.

  15. Pathway-Consensus Approach to Metabolic Network Reconstruction for Pseudomonas putida KT2440 by Systematic Comparison of Published Models.

    Science.gov (United States)

    Yuan, Qianqian; Huang, Teng; Li, Peishun; Hao, Tong; Li, Feiran; Ma, Hongwu; Wang, Zhiwen; Zhao, Xueming; Chen, Tao; Goryanin, Igor

    2017-01-01

    Over 100 genome-scale metabolic networks (GSMNs) have been published in recent years and widely used for phenotype prediction and pathway design. However, GSMNs for a specific organism reconstructed by different research groups usually produce inconsistent simulation results, which makes it difficult to use the GSMNs for precise optimal pathway design. Therefore, it is necessary to compare and identify the discrepancies among networks and build a consensus metabolic network for an organism. Here we proposed a process for systematic comparison of metabolic networks at pathway level. We compared four published GSMNs of Pseudomonas putida KT2440 and identified the discrepancies leading to inconsistent pathway calculation results. The mistakes in the models were corrected based on information from literature so that all the calculated synthesis and uptake pathways were the same. Subsequently we built a pathway-consensus model and then further updated it with the latest genome annotation information to obtain modelPpuQY1140 for P. putida KT2440, which includes 1140 genes, 1171 reactions and 1104 metabolites. We found that even small errors in a GSMN could have great impacts on the calculated optimal pathways and thus may lead to incorrect pathway design strategies. Careful investigation of the calculated pathways during the metabolic network reconstruction process is essential for building proper GSMNs for pathway design.

  16. Pathway-Consensus Approach to Metabolic Network Reconstruction for Pseudomonas putida KT2440 by Systematic Comparison of Published Models.

    Directory of Open Access Journals (Sweden)

    Qianqian Yuan

    Full Text Available Over 100 genome-scale metabolic networks (GSMNs have been published in recent years and widely used for phenotype prediction and pathway design. However, GSMNs for a specific organism reconstructed by different research groups usually produce inconsistent simulation results, which makes it difficult to use the GSMNs for precise optimal pathway design. Therefore, it is necessary to compare and identify the discrepancies among networks and build a consensus metabolic network for an organism. Here we proposed a process for systematic comparison of metabolic networks at pathway level. We compared four published GSMNs of Pseudomonas putida KT2440 and identified the discrepancies leading to inconsistent pathway calculation results. The mistakes in the models were corrected based on information from literature so that all the calculated synthesis and uptake pathways were the same. Subsequently we built a pathway-consensus model and then further updated it with the latest genome annotation information to obtain modelPpuQY1140 for P. putida KT2440, which includes 1140 genes, 1171 reactions and 1104 metabolites. We found that even small errors in a GSMN could have great impacts on the calculated optimal pathways and thus may lead to incorrect pathway design strategies. Careful investigation of the calculated pathways during the metabolic network reconstruction process is essential for building proper GSMNs for pathway design.

  17. 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."

  18. Differential sharing and distinct co-occurrence networks among spatially close bacterial microbiota of bark, mosses and lichens‬‬.

    Science.gov (United States)

    Aschenbrenner, Ines Aline; Cernava, Tomislav; Erlacher, Armin; Berg, Gabriele; Grube, Martin

    2017-05-01

    Knowledge of bacterial community host-specificity has increased greatly in recent years. However, the intermicrobiome relationships of unrelated but spatially close organisms remain little understood. Trunks of trees covered by epiphytes represent complex habitats with a mosaic of ecological niches. In this context, we investigated the structure, diversity and interactions of microbiota associated with lichens, mosses and the bare tree bark. Comparative analysis revealed significant differences in the habitat-associated community structures. Corresponding co-occurrence analysis indicated that the lichen microbial network is less complex and less densely interconnected than the moss- and bark-associated networks. Several potential generalists and specialists were identified for the selected habitats. Generalists belonged mainly to Proteobacteria, with Sphingomonas as the most abundant genus. The generalists comprise microorganisms with generally beneficial features, such as nitrogen fixation or other supporting functions, according to a metagenomic analysis. We argue that beneficial strains shared among hosts contribute to ecological stability of the host biocoenoses. © 2017 John Wiley & Sons Ltd.

  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. Exploring the metabolic network of the epidemic pathogen Burkholderia cenocepacia J2315 via genome-scale reconstruction

    Directory of Open Access Journals (Sweden)

    Panda Gurudutta

    2011-05-01

    Full Text Available Abstract Background Burkholderia cenocepacia is a threatening nosocomial epidemic pathogen in patients with cystic fibrosis (CF or a compromised immune system. Its high level of antibiotic resistance is an increasing concern in treatments against its infection. Strain B. cenocepacia J2315 is the most infectious isolate from CF patients. There is a strong demand to reconstruct a genome-scale metabolic network of B. cenocepacia J2315 to systematically analyze its metabolic capabilities and its virulence traits, and to search for potential clinical therapy targets. Results We reconstructed the genome-scale metabolic network of B. cenocepacia J2315. An iterative reconstruction process led to the establishment of a robust model, iKF1028, which accounts for 1,028 genes, 859 internal reactions, and 834 metabolites. The model iKF1028 captures important metabolic capabilities of B. cenocepacia J2315 with a particular focus on the biosyntheses of key metabolic virulence factors to assist in understanding the mechanism of disease infection and identifying potential drug targets. The model was tested through BIOLOG assays. Based on the model, the genome annotation of B. cenocepacia J2315 was refined and 24 genes were properly re-annotated. Gene and enzyme essentiality were analyzed to provide further insights into the genome function and architecture. A total of 45 essential enzymes were identified as potential therapeutic targets. Conclusions As the first genome-scale metabolic network of B. cenocepacia J2315, iKF1028 allows a systematic study of the metabolic properties of B. cenocepacia and its key metabolic virulence factors affecting the CF community. The model can be used as a discovery tool to design novel drugs against diseases caused by this notorious pathogen.

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

    Science.gov (United States)

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

    2013-01-01

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

  2. Use of metabolic inhibitors to estimate protozooplankton grazing and bacterial production in a monomictic eutrophic lake with an anaerobic hypolimnion

    International Nuclear Information System (INIS)

    Sanders, R.W.; Porter, K.G.

    1986-01-01

    Inhibitors of eucaryotes (cycloheximide and amphotericin B) and procaryotes (penicillin and chloramphenical) were used to estimate bacterivory and bacterial production in a eutrophic lake. Bacterial production appeared to be slightly greater than protozoan grazing in the aerobic waters of Lake Oglethorpe. Use of penicillin and cycloheximide yielded inconsistent results in anaerobic water and in aerobic water when bacterial production was low. Production measured by inhibiting eucaryotes with cycloheximide did not always agree with [ 3 H]thymidine estimates or differential filtration methods. Laboratory experiments showed that several common freshwater protozoans continued to swim and ingest bacterium-size latex beads in the presence of the eucaryote inhibitor. Penicillin also affected grazing rates of some ciliates. The authors recommended that caution and a corroborating method be used when estimating ecologically important parameters with specific inhibitors

  3. Regulatory network of secondary metabolism in Brassica rapa: insight into the glucosinolate pathway.

    Science.gov (United States)

    Pino Del Carpio, Dunia; Basnet, Ram Kumar; Arends, Danny; Lin, Ke; De Vos, Ric C H; Muth, Dorota; Kodde, Jan; Boutilier, Kim; Bucher, Johan; Wang, Xiaowu; Jansen, Ritsert; Bonnema, Guusje

    2014-01-01

    Brassica rapa studies towards metabolic variation have largely been focused on the profiling of the diversity of metabolic compounds in specific crop types or regional varieties, but none aimed to identify genes with regulatory function in metabolite composition. Here we followed a genetical genomics approach to identify regulatory genes for six biosynthetic pathways of health-related phytochemicals, i.e carotenoids, tocopherols, folates, glucosinolates, flavonoids and phenylpropanoids. Leaves from six weeks-old plants of a Brassica rapa doubled haploid population, consisting of 92 genotypes, were profiled for their secondary metabolite composition, using both targeted and LC-MS-based untargeted metabolomics approaches. Furthermore, the same population was profiled for transcript variation using a microarray containing EST sequences mainly derived from three Brassica species: B. napus, B. rapa and B. oleracea. The biochemical pathway analysis was based on the network analyses of both metabolite QTLs (mQTLs) and transcript QTLs (eQTLs). Co-localization of mQTLs and eQTLs lead to the identification of candidate regulatory genes involved in the biosynthesis of carotenoids, tocopherols and glucosinolates. We subsequently focused on the well-characterized glucosinolate pathway and revealed two hotspots of co-localization of eQTLs with mQTLs in linkage groups A03 and A09. Our results indicate that such a large-scale genetical genomics approach combining transcriptomics and metabolomics data can provide new insights into the genetic regulation of metabolite composition of Brassica vegetables.

  4. Regulatory network of secondary metabolism in Brassica rapa: insight into the glucosinolate pathway.

    Directory of Open Access Journals (Sweden)

    Dunia Pino Del Carpio

    Full Text Available Brassica rapa studies towards metabolic variation have largely been focused on the profiling of the diversity of metabolic compounds in specific crop types or regional varieties, but none aimed to identify genes with regulatory function in metabolite composition. Here we followed a genetical genomics approach to identify regulatory genes for six biosynthetic pathways of health-related phytochemicals, i.e carotenoids, tocopherols, folates, glucosinolates, flavonoids and phenylpropanoids. Leaves from six weeks-old plants of a Brassica rapa doubled haploid population, consisting of 92 genotypes, were profiled for their secondary metabolite composition, using both targeted and LC-MS-based untargeted metabolomics approaches. Furthermore, the same population was profiled for transcript variation using a microarray containing EST sequences mainly derived from three Brassica species: B. napus, B. rapa and B. oleracea. The biochemical pathway analysis was based on the network analyses of both metabolite QTLs (mQTLs and transcript QTLs (eQTLs. Co-localization of mQTLs and eQTLs lead to the identification of candidate regulatory genes involved in the biosynthesis of carotenoids, tocopherols and glucosinolates. We subsequently focused on the well-characterized glucosinolate pathway and revealed two hotspots of co-localization of eQTLs with mQTLs in linkage groups A03 and A09. Our results indicate that such a large-scale genetical genomics approach combining transcriptomics and metabolomics data can provide new insights into the genetic regulation of metabolite composition of Brassica vegetables.

  5. Energy metabolism and ATP balance in animal cell cultivation using a stoichiometrically based reaction network.

    Science.gov (United States)

    Xie, L; Wang, D I

    1996-12-05

    A metabolic reaction network is developed for the estimation of the stoichiometric production of adenosine triphosphate (ATP) in animal cell culture. By using the material balance data from fed-batch and batch cultures of hybridoma cells, the stoichiometric ATP productions are determined with estimated effective P/O ratios of 2 for NADH and 1.2 for FADH(2). A significant percentage of the ATP requirement (16-41%) in hybridoma cells is generated directly from free energy release without the participation of oxygen. The oxidative phosphorylation of NADH accounts for about 60% of the total ATP production in the fed-batch cultures and about 47% in the batch culture. The oxidative phosphorylation of FADH(2) accounts for less then 20% of the total ATP production in all cases.A fractional model is devised to analyze the contribution of each nutrient to the ATP production. Results show that a majority of the ATP is produced from glucose metabolism (60-76%). Less than 30% of the ATP is derived from glutamine, and less than 11% is derived from other essential amino acids. The analysis also shows that the glycolytic pathway generates more ATP in the batch (41%) than in the fed-batch (demand estimated from the dry cell weight and cell composition is significantly lower than that calculated from the maximum ATP yield, indicating that the non-growth-associated ATP demand may contain other factors than what is considered in the estimation of the biosynthetic ATP demand.

  6. Genome-scale metabolic network guided engineering of Streptomyces tsukubaensis for FK506 production improvement.

    Science.gov (United States)

    Huang, Di; Li, Shanshan; Xia, Menglei; Wen, Jianping; Jia, Xiaoqiang

    2013-05-24

    FK506 is an important immunosuppressant, which can be produced by Streptomyces tsukubaensis. However, the production capacity of the strain is very low. Hereby, a computational guided engineering approach was proposed in order to improve the intracellular precursor and cofactor availability of FK506 in S. tsukubaensis. First, a genome-scale metabolic model of S. tsukubaensis was constructed based on its annotated genome and biochemical information. Subsequently, several potential genetic targets (knockout or overexpression) that guaranteed an improved yield of FK506 were identified by the recently developed methodology. To validate the model predictions, each target gene was manipulated in the parent strain D852, respectively. All the engineered strains showed a higher FK506 production, compared with D852. Furthermore, the combined effect of the genetic modifications was evaluated. Results showed that the strain HT-ΔGDH-DAZ with gdhA-deletion and dahp-, accA2-, zwf2-overexpression enhanced FK506 concentration up to 398.9 mg/L, compared with 143.5 mg/L of the parent strain D852. Finally, fed-batch fermentations of HT-ΔGDH-DAZ were carried out, which led to the FK506 production of 435.9 mg/L, 1.47-fold higher than the parent strain D852 (158.7 mg/L). Results confirmed that the promising targets led to an increase in FK506 titer. The present work is the first attempt to engineer the primary precursor pathways to improve FK506 production in S. tsukubaensis with genome-scale metabolic network guided metabolic engineering. The relationship between model prediction and experimental results demonstrates the rationality and validity of this approach for target identification. This strategy can also be applied to the improvement of other important secondary metabolites.

  7. Reduced Metabolism in Brain 'Control Networks' Following Cocaine-Cues Exposure in Female Cocaine Abusers

    International Nuclear Information System (INIS)

    Volkow, N.D.; Tomasi, D.; Wang, G.-J.; Fowler, J.S.; Telang, F.; Goldstein, R.Z.; Alia-Klein, N.; Wong, C.T.

    2011-01-01

    Gender differences in vulnerability for cocaine addiction have been reported. Though the mechanisms are not understood, here we hypothesize that gender differences in reactivity to conditioned-cues, which contributes to relapse, are involved. To test this we compared brain metabolism (using PET and 18 FDG) between female (n = 10) and male (n = 16) active cocaine abusers when they watched a neutral video (nature scenes) versus a cocaine-cues video. Self-reports of craving increased with the cocaine-cue video but responses did not differ between genders. In contrast, changes in whole brain metabolism with cocaine-cues differed by gender (p<0.05); females significantly decreased metabolism (-8.6% ± 10) whereas males tended to increase it (+5.5% ± 18). SPM analysis (Cocaine-cues vs Neutral) in females revealed decreases in frontal, cingulate and parietal cortices, thalamus and midbrain (p<0.001) whereas males showed increases in right inferior frontal gyrus (BA 44/45) (only at p<0.005). The gender-cue interaction showed greater decrements with Cocaine-cues in females than males (p<0.001) in frontal (BA 8, 9, 10), anterior cingulate (BA 24, 32), posterior cingulate (BA 23, 31), inferior parietal (BA 40) and thalamus (dorsomedial nucleus). Females showed greater brain reactivity to cocaine-cues than males but no differences in craving, suggesting that there may be gender differences in response to cues that are not linked with craving but could affect subsequent drug use. Specifically deactivation of brain regions from 'control networks' (prefrontal, cingulate, inferior parietal, thalamus) in females could increase their vulnerability to relapse since it would interfere with executive function (cognitive inhibition). This highlights the importance of gender tailored interventions for cocaine addiction.

  8. Reduced metabolism in brain "control networks" following cocaine-cues exposure in female cocaine abusers.

    Directory of Open Access Journals (Sweden)

    Nora D Volkow

    2011-02-01

    Full Text Available Gender differences in vulnerability for cocaine addiction have been reported. Though the mechanisms are not understood, here we hypothesize that gender differences in reactivity to conditioned-cues, which contributes to relapse, are involved.To test this we compared brain metabolism (using PET and ¹⁸FDG between female (n = 10 and male (n = 16 active cocaine abusers when they watched a neutral video (nature scenes versus a cocaine-cues video.Self-reports of craving increased with the cocaine-cue video but responses did not differ between genders. In contrast, changes in whole brain metabolism with cocaine-cues differed by gender (p<0.05; females significantly decreased metabolism (-8.6%±10 whereas males tended to increase it (+5.5%±18. SPM analysis (Cocaine-cues vs Neutral in females revealed decreases in frontal, cingulate and parietal cortices, thalamus and midbrain (p<0.001 whereas males showed increases in right inferior frontal gyrus (BA 44/45 (only at p<0.005. The gender-cue interaction showed greater decrements with Cocaine-cues in females than males (p<0.001 in frontal (BA 8, 9, 10, anterior cingulate (BA 24, 32, posterior cingulate (BA 23, 31, inferior parietal (BA 40 and thalamus (dorsomedial nucleus.Females showed greater brain reactivity to cocaine-cues than males but no differences in craving, suggesting that there may be gender differences in response to cues that are not linked with craving but could affect subsequent drug use. Specifically deactivation of brain regions from "control networks" (prefrontal, cingulate, inferior parietal, thalamus in females could increase their vulnerability to relapse since it would interfere with executive function (cognitive inhibition. This highlights the importance of gender tailored interventions for cocaine addiction.

  9. 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, ...

  10. 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

    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...

  11. 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...

  12. 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

    BACKGROUND: Several protein-protein interaction studies have been performed for the yeast Saccharomyces cerevisiae using different high-throughput experimental techniques. All these results are collected in the BioGRID database and the SGD database provide detailed annotation of the different......-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...

  13. Hypoxia determines survival outcomes of bacterial infection through HIF-1α-dependent reprogramming of leukocyte metabolism

    OpenAIRE

    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.; Coelho, P.D.S.; Doherty, C.; Ryan, E.; Watts, E.; Morton, N.M.

    2017-01-01

    Hypoxia and bacterial infection frequently coexist, 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 Staphylococcus aureus and Streptococcus pneumoniae infections rapidly induced progressive neutrophil-mediated morbidity and mortality, with associated hypothermia and cardiovascular compromise. Preconditioning ...

  14. 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.

  15. Metabolic and protein interaction sub-networks controlling the proliferation rate of cancer cells and their impact on patient survival.

    Science.gov (United States)

    Feizi, Amir; Bordel, Sergio

    2013-10-24

    Cancer cells can have a broad scope of proliferation rates. Here we aim to identify the molecular mechanisms that allow some cancer cell lines to grow up to 4 times faster than other cell lines. The correlation of gene expression profiles with the growth rate in 60 different cell lines has been analyzed using several genome-scale biological networks and new algorithms. New possible regulatory feedback loops have been suggested and the known roles of several cell cycle related transcription factors have been confirmed. Over 100 growth-correlated metabolic sub-networks have been identified, suggesting a key role of simultaneous lipid synthesis and degradation in the energy supply of the cancer cells growth. Many metabolic sub-networks involved in cell line proliferation appeared also to correlate negatively with the survival expectancy of colon cancer patients.

  16. Influence of Nutrient Availability and Quorum Sensing on the Formation of Metabolically Inactive Microcolonies Within Structurally Heterogeneous Bacterial Biofilms: An Individual-Based 3D Cellular Automata Model.

    Science.gov (United States)

    Machineni, Lakshmi; Rajapantul, Anil; Nandamuri, Vandana; Pawar, Parag D

    2017-03-01

    The resistance of bacterial biofilms to antibiotic treatment has been attributed to the emergence of structurally heterogeneous microenvironments containing metabolically inactive cell populations. In this study, we use a three-dimensional individual-based cellular automata model to investigate the influence of nutrient availability and quorum sensing on microbial heterogeneity in growing biofilms. Mature biofilms exhibited at least three structurally distinct strata: a high-volume, homogeneous region sandwiched between two compact sections of high heterogeneity. Cell death occurred preferentially in layers in close proximity to the substratum, resulting in increased heterogeneity in this section of the biofilm; the thickness and heterogeneity of this lowermost layer increased with time, ultimately leading to sloughing. The model predicted the formation of metabolically dormant cellular microniches embedded within faster-growing cell clusters. Biofilms utilizing quorum sensing were more heterogeneous compared to their non-quorum sensing counterparts, and resisted sloughing, featuring a cell-devoid layer of EPS atop the substratum upon which the remainder of the biofilm developed. Overall, our study provides a computational framework to analyze metabolic diversity and heterogeneity of biofilm-associated microorganisms and may pave the way toward gaining further insights into the biophysical mechanisms of antibiotic resistance.

  17. 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.

  18. Strain Dependent Genetic Networks for Antibiotic-Sensitivity in a Bacterial Pathogen with a Large Pan-Genome.

    Directory of Open Access Journals (Sweden)

    Tim van Opijnen

    2016-09-01

    Full Text Available 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. 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.

  20. Chip-based bioassay using bacterial sensor strains immobilized in three-dimensional microfluidic network.

    Science.gov (United States)

    Tani, Hirofumi; Maehana, Koji; Kamidate, Tamio

    2004-11-15

    A whole-cell bioassay has been performed using Escherichia coli sensor strains immobilized in a chip assembly, in which a silicon substrate is placed between two poly(dimethylsiloxane) (PDMS) substrates. Microchannels fabricated on the two separate PDMS layers are connected via perforated microwells on the silicon chip, and thus, a three-dimensional microfluidic network is constructed in the assembly. Bioluminescent sensor strains mixed with agarose are injected into the channels on one of the two PDMS layers and are immobilized in the microwells by gelation. Induction of the firefly luciferase gene expression in the sensor strains can be easily carried out by filling the channels on the other layer with sample solutions containing mutagen. Bioluminescence emissions from each well are detected after injection of luciferin/ATP mixtures into the channels. In this assay format using two multichannel layers and one microwell array chip, the interactions between various types of samples and strains can be monitored at each well on one assembly in a combinatorial fashion. Using several genotypes of the sensor strains or concentrations of mitomycin C in this format, the dependence of bioluminescence on these factors was obtained simultaneously in the single screening procedure. The present method could be a promising on-chip format for high-throughput whole-cell bioassays.

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

    OpenAIRE

    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.

    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...

  2. 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.

    dm-3) with or without glucose (5 mmol dm-3). Vials were sealed with PTFE-lined crimp tops. Control vials were set up to check for chemical degradation of substrates (without bacteria) and for any production of biogenic sulfur compounds produced..., Cooper WJ (eds) Biogenic sulfur in the environment. American Chemical Society, Washington, pp 167–181 Kiene RP, Linn LJ (2000a) Distribution and turnover of dissolved DMSP and its relationship with bacterial production and dimethylsulfide in the Gulf...

  3. Articular chondrocyte network mediated by gap junctions: role in metabolic cartilage homeostasis

    Science.gov (United States)

    Mayan, Maria D; Gago-Fuentes, Raquel; Carpintero-Fernandez, Paula; Fernandez-Puente, Patricia; Filgueira-Fernandez, Purificacion; Goyanes, Noa; Valiunas, Virginijus; Brink, Peter R; Goldberg, Gary S; Blanco, Francisco J

    2017-01-01

    Objective This study investigated whether chondrocytes within the cartilage matrix have the capacity to communicate through intercellular connections mediated by voltage-gated gap junction (GJ) channels. Methods Frozen cartilage samples were used for immunofluorescence and immunohistochemistry assays. Samples were embedded in cacodylate buffer before dehydration for scanning electron microscopy. Co-immunoprecipitation experiments and mass spectrometry (MS) were performed to identify proteins that interact with the C-terminal end of Cx43. GJ communication was studied through in situ electroporation, electrophysiology and dye injection experiments. A transwell layered culture system and MS were used to identify and quantify transferred amino acids. Results Microscopic images revealed the presence of multiple cellular projections connecting chondrocytes within the matrix. These projections were between 5 and 150 μm in length. MS data analysis indicated that the C-terminus of Cx43 interacts with several cytoskeletal proteins implicated in Cx trafficking and GJ assembly, including α-tubulin and β-tubulin, actin, and vinculin. Electrophysiology experiments demonstrated that 12-mer oligonucleotides could be transferred between chondrocytes within 12 min after injection. Glucose was homogeneously distributed within 22 and 35 min. No transfer was detected when glucose was electroporated into A549 cells, which have no GJs. Transwell layered culture systems coupled with MS analysis revealed connexins can mediate the transfer of L-lysine and L-arginine between chondrocytes. Conclusions This study reveals that intercellular connections between chondrocytes contain GJs that play a key role in cell-cell communication and a metabolic function by exchange of nutrients including glucose and essential amino acids. A three-dimensional cellular network mediated through GJs might mediate metabolic and physiological homeostasis to maintain cartilage tissue. PMID:24225059

  4. A kinetic model describes metabolic response to perturbations and distribution of flux control in the benzenoid network of Petunia hybrida.

    Science.gov (United States)

    Colón, Amy Marshall; Sengupta, Neelanjan; Rhodes, David; Dudareva, Natalia; Morgan, John

    2010-04-01

    In recent years there has been much interest in the genetic enhancement of plant metabolism; however, attempts at genetic modification are often unsuccessful due to an incomplete understanding of network dynamics and their regulatory properties. Kinetic modeling of plant metabolic networks can provide predictive information on network control and response to genetic perturbations, which allow estimation of flux at any concentration of intermediate or enzyme in the system. In this research, a kinetic model of the benzenoid network was developed to simulate whole network responses to different concentrations of supplied phenylalanine (Phe) in petunia flowers and capture flux redistributions caused by genetic manipulations. Kinetic parameters were obtained by network decomposition and non-linear least squares optimization of data from petunia flowers supplied with either 75 or 150 mm(2)H(5)-Phe. A single set of kinetic parameters simultaneously accommodated labeling and pool size data obtained for all endogenous and emitted volatiles at the two concentrations of supplied (2)H(5)-Phe. The generated kinetic model was validated using flowers from transgenic petunia plants in which benzyl CoA:benzyl alcohol/phenylethanol benzoyltransferase (BPBT) was down-regulated via RNAi. The determined in vivo kinetic parameters were used for metabolic control analysis, in which flux control coefficients were calculated for fluxes around the key branch point at Phe and revealed that phenylacetaldehyde synthase activity is the primary controlling factor for the phenylacetaldehyde branch of the benzenoid network. In contrast, control of flux through the beta-oxidative and non-beta-oxidative pathways is highly distributed.

  5. AMBIENT: Active Modules for Bipartite Networks--using high-throughput transcriptomic data to dissect metabolic response.

    Science.gov (United States)

    Bryant, William A; Sternberg, Michael J E; Pinney, John W

    2013-03-25

    With the continued proliferation of high-throughput biological experiments, there is a pressing need for tools to integrate the data produced in ways that produce biologically meaningful conclusions. Many microarray studies have analysed transcriptomic data from a pathway perspective, for instance by testing for KEGG pathway enrichment in sets of upregulated genes. However, the increasing availability of species-specific metabolic models provides the opportunity to analyse these data in a more objective, system-wide manner. Here we introduce ambient (Active Modules for Bipartite Networks), a simulated annealing approach to the discovery of metabolic subnetworks (modules) that are significantly affected by a given genetic or environmental change. The metabolic modules returned by ambient are connected parts of the bipartite network that change coherently between conditions, providing a more detailed view of metabolic changes than standard approaches based on pathway enrichment. ambient is an effective and flexible tool for the analysis of high-throughput data in a metabolic context. The same approach can be applied to any system in which reactions (or metabolites) can be assigned a score based on some biological observation, without the limitation of predefined pathways. A Python implementation of ambient is available at http://www.theosysbio.bio.ic.ac.uk/ambient.

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

    Science.gov (United States)

    Levering, Jennifer; Fiedler, Tomas; Sieg, Antje; van Grinsven, Koen W A; Hering, Silvio; Veith, Nadine; Olivier, Brett G; Klett, Lara; Hugenholtz, Jeroen; Teusink, Bas; Kreikemeyer, Bernd; Kummer, Ursula

    2016-08-20

    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. Initially, we based the reconstruction on genome annotations and already existing and curated metabolic networks of Bacillus subtilis, Escherichia coli, Lactobacillus plantarum and Lactococcus lactis. This initial draft was manually curated with the final reconstruction accounting for 480 genes associated with 576 reactions and 558 metabolites. In order to constrain the model further, we performed growth experiments of wild type and arcA deletion strains of S. pyogenes M49 in a chemically defined medium and calculated nutrient uptake and production fluxes. We additionally performed amino acid auxotrophy experiments to test the consistency of the model. The established genome-scale model can be used to understand the growth requirements of the human pathogen S. pyogenes and define optimal and suboptimal conditions, but also to describe differences and similarities between S. pyogenes and related lactic acid bacteria such as L. lactis in order to find strategies to reduce the growth of the pathogen and propose drug targets. Copyright © 2016 Elsevier B.V. All rights reserved.

  7. Integration of Metabolic and Quorum Sensing Signals Governing the Decision to Cooperate in a Bacterial Social Trait

    Science.gov (United States)

    Boyle, Kerry E.; Monaco, Hilary; van Ditmarsch, Dave; Deforet, Maxime; Xavier, Joao B.

    2015-01-01

    Many unicellular organisms live in multicellular communities that rely on cooperation between cells. However, cooperative traits are vulnerable to exploitation by non-cooperators (cheaters). We expand our understanding of the molecular mechanisms that allow multicellular systems to remain robust in the face of cheating by dissecting the dynamic regulation of cooperative rhamnolipids required for swarming in Pseudomonas aeruginosa. We combine mathematical modeling and experiments to quantitatively characterize the integration of metabolic and population density signals (quorum sensing) governing expression of the rhamnolipid synthesis operon rhlAB. The combined computational/experimental analysis reveals that when nutrients are abundant, rhlAB promoter activity increases gradually in a density dependent way. When growth slows down due to nutrient limitation, rhlAB promoter activity can stop abruptly, decrease gradually or even increase depending on whether the growth-limiting nutrient is the carbon source, nitrogen source or iron. Starvation by specific nutrients drives growth on intracellular nutrient pools as well as the qualitative rhlAB promoter response, which itself is modulated by quorum sensing. Our quantitative analysis suggests a supply-driven activation that integrates metabolic prudence with quorum sensing in a non-digital manner and allows P. aeruginosa cells to invest in cooperation only when the population size is large enough (quorum sensing) and individual cells have enough metabolic resources to do so (metabolic prudence). Thus, the quantitative description of rhlAB regulatory dynamics brings a greater understating to the regulation required to make swarming cooperation stable. PMID:26102206

  8. 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. Copyright © 2013 Elsevier Inc. All rights reserved.

  9. Stepwise construction of a metabolic network in Event-B: The heat shock response.

    Science.gov (United States)

    Sanwal, Usman; Petre, Luigia; Petre, Ion

    2017-12-01

    There is a high interest in constructing large, detailed computational models for biological processes. This is often done by putting together existing submodels and adding to them extra details/knowledge. The result of such approaches is usually a model that can only answer questions on a very specific level of detail, and thus, ultimately, is of limited use. We focus instead on an approach to systematically add details to a model, with formal verification of its consistency at each step. In this way, one obtains a set of reusable models, at different levels of abstraction, to be used for different purposes depending on the question to address. We demonstrate this approach using Event-B, a computational framework introduced to develop formal specifications of distributed software systems. We first describe how to model generic metabolic networks in Event-B. Then, we apply this method for modeling the biological heat shock response in eukaryotic cells, using Event-B refinement techniques. The advantage of using Event-B consists in having refinement as an intrinsic feature; this provides as a final result not only a correct model, but a chain of models automatically linked by refinement, each of which is provably correct and reusable. This is a proof-of-concept that refinement in Event-B is suitable for biomodeling, serving for mastering biological complexity. Copyright © 2017 Elsevier Ltd. All rights reserved.</