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Sample records for metabolic networks acknowledgments

  1. Acknowledgements

    First page Back Continue Last page Overview Graphics. Acknowledgements. Graduate students-. Promita Ghosh Dastidar, Chandrama Mukherjee and Shubhra Majumder. Funds- Department of Biotechnology, Govt of India and FIRCA, NIH, USA. Kothari Centre for Gastroenterology, Kolkata for axenic cultures of E32 and ...

  2. ACKNOWLEDGEMENTS

    ACKNOWLEDGEMENTS. Journal of Earth System Science wishes to place on record the valuable assistance received from the ... Ravi Bhushan, Physical Research Laboratory, Ahmedabad. Dan Blumberg, Ben Gurion ... L Kannan, Centre of Advanced Study in Marine Biology, Parangipettai. Halil Karahan, Pamukkale ...

  3. Acknowledgement

    First page Back Continue Last page Overview Graphics. Acknowledgement. Financial support by Department of Atomic Energy, Government of India, under Young Scientist Research Award Scheme. IIT Kanpur Initiation Grant for a Young Faculty. Experimental work was carried out by: Mr. Ranjith Kumar Reddy G.

  4. Acknowledgments

    Caty Ribas Segura

    2007-01-01

    Full Text Available ACKNOWLEDGMENTS FROM THE EDITOR Coolabah 1 is a dream come true. When Dr. Susan Ballyn invited me to be the guest editor of this volume, I accepted without hesitation: I felt honoured and wanted this issue to be special. Susan explained the editing process and we started to work. During the process I had Dr Ballyn’s and Dr. Morera de la Vall´s invaluable guidance and support, which I deeply appreciate: they read, commented and improved the final version of Coolabah 1: This Foreign Country and allowed me the freedom to decide, organise and edit this publication. I would also like to thank Dr. Helen Nickas for her works, which are a constant stimulation for me and have been an inspiration whilst editing this issue. I wish to thank all contributors to Coolabah 1, former exchange students to Australia from the Universitat de Barcelona, for their fabulous pieces of writing, generous time and enthusiasm in participating in this publication. The process of working with each one of you has been a pleasure. On a personal note, I thank my mother, Mª José, my father, Rafael, my sister, Bárbara, and my friends for their constant support and love throughout all the changes in my life: they give me the freedom to be myself and the space to grow. Needless to say, the mistakes in the editing process are my entire responsibility and the copyright of each contribution remains with its author, unless otherwise stated. I hope all the contributors and readers enjoy the final version of Coolabah 1: This Foreign Country and take pleasure in reading the different approaches to this “foreign country”. Thank you.

  5. VRML metabolic network visualizer.

    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.

  6. Proper Acknowledgment?

    East, Julianne

    2005-01-01

    The concern in Australian universities about the prevalence of plagiarism has led to the development of policies about academic integrity and in turn focused attention on the need to inform students about how to avoid plagiarism and how to properly acknowledge. Teaching students how to avoid plagiarism can appear to be straightforward if based on…

  7. Faith in the net: towards the creation of digital networks of religious acknowledgement and recognition

    Luis Ignacio SIERRA GUTIÉRREZ

    2017-12-01

    Full Text Available Undoubtedly, the technological revolution in information and communications causing major changes in citizens’ social interactions. The new reticular rationality offers the possibility of shaping, developing, and strengthening social networks and virtual communities. All of them facilitate the creation of new interactive spaces, new social collectives promoting citizenship and that, from different social fields and levels of experience, articulate and streamline processes of production, circulation and appropriation of new symbolic products. Such products contribute not only to generating new sources of knowledge but, above all, to strengthening processes of citizen interaction. In such processes, the field of media, religiosities and socio-cultural processes are strategically intertwined. In this context, experiences of civic religiosity find in the potential generated by the global network, new possibilities of interaction and religious recognition. Also new forms and spaces to share plural options of faith and socio-religious practices that make sense of the existence of cybernauts. This text is divided into three parts: First, critically contextualizes the global phenomenon of social networks. Second, it makes an approximation to some experiences of digital networks of religious recognition from Latin America. Finally, raises some questions that arise from such virtual practices.

  8. Evolution of metabolic network organization

    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

  9. Flux networks in metabolic graphs

    Warren, P B; Queiros, S M Duarte; Jones, J L

    2009-01-01

    A metabolic model can be represented as a bipartite graph comprising linked reaction and metabolite nodes. Here it is shown how a network of conserved fluxes can be assigned to the edges of such a graph by combining the reaction fluxes with a conserved metabolite property such as molecular weight. A similar flux network can be constructed by combining the primal and dual solutions to the linear programming problem that typically arises in constraint-based modelling. Such constructions may help with the visualization of flux distributions in complex metabolic networks. The analysis also explains the strong correlation observed between metabolite shadow prices (the dual linear programming variables) and conserved metabolite properties. The methods were applied to recent metabolic models for Escherichia coli, Saccharomyces cerevisiae and Methanosarcina barkeri. Detailed results are reported for E. coli; similar results were found for other organisms

  10. Noise effect in metabolic networks

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

    2009-01-01

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

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

    Patil, Kiran Raosaheb; Nielsen, Jens

    2005-01-01

    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......Cellular response to genetic and environmental perturbations is often reflected and/or mediated through changes in the metabolism, because the latter plays a key role in providing Gibbs free energy and precursors for biosynthesis. Such metabolic changes are often exerted through transcriptional...... 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...

  12. Modular co-evolution of metabolic networks

    Yu Zhong-Hao

    2007-08-01

    Full Text Available Abstract Background The architecture of biological networks has been reported to exhibit high level of modularity, and to some extent, topological modules of networks overlap with known functional modules. However, how the modular topology of the molecular network affects the evolution of its member proteins remains unclear. Results In this work, the functional and evolutionary modularity of Homo sapiens (H. sapiens metabolic network were investigated from a topological point of view. Network decomposition shows that the metabolic network is organized in a highly modular core-periphery way, in which the core modules are tightly linked together and perform basic metabolism functions, whereas the periphery modules only interact with few modules and accomplish relatively independent and specialized functions. Moreover, over half of the modules exhibit co-evolutionary feature and belong to specific evolutionary ages. Peripheral modules tend to evolve more cohesively and faster than core modules do. Conclusion The correlation between functional, evolutionary and topological modularity suggests that the evolutionary history and functional requirements of metabolic systems have been imprinted in the architecture of metabolic networks. Such systems level analysis could demonstrate how the evolution of genes may be placed in a genome-scale network context, giving a novel perspective on molecular evolution.

  13. Acknowledging the back patient

    Damsgaard, Janne Brammer; Bastrup, Lene; Norlyk, Annelise

    and reluctant to speak out. Therefore, telling about experiences and perceptions is important for back patients in order to feel accepted and acknowledged. The health professionals must incorporate the patients’ narratives as an integral part of the care and treatment. Conclusions: In order to acknowledge...

  14. Hierarchical analysis of dependency in metabolic networks.

    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

  15. Control of fluxes in metabolic networks

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

    2016-01-01

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

  16. Acknowledgment - Issue 61

    Executive Editor

    2016-03-01

    • Regina Celia Baptista Belluzzo (Universidade Estadual Paulista - UNESP, Brasil In the same way our acknowledge to University Library System (University of Pittsburgh - PITTS, USA by constant technical support and advice for our journal.

  17. Network motif frequency vectors reveal evolving metabolic network organisation.

    Pearcy, Nicole; Crofts, Jonathan J; Chuzhanova, Nadia

    2015-01-01

    At the systems level many organisms of interest may be described by their patterns of interaction, and as such, are perhaps best characterised via network or graph models. Metabolic networks, in particular, are fundamental to the proper functioning of many important biological processes, and thus, have been widely studied over the past decade or so. Such investigations have revealed a number of shared topological features, such as a short characteristic path-length, large clustering coefficient and hierarchical modular structure. However, the extent to which evolutionary and functional properties of metabolism manifest via this underlying network architecture remains unclear. In this paper, we employ a novel graph embedding technique, based upon low-order network motifs, to compare metabolic network structure for 383 bacterial species categorised according to a number of biological features. In particular, we introduce a new global significance score which enables us to quantify important evolutionary relationships that exist between organisms and their physical environments. Using this new approach, we demonstrate a number of significant correlations between environmental factors, such as growth conditions and habitat variability, and network motif structure, providing evidence that organism adaptability leads to increased complexities in the resultant metabolic networks.

  18. Acknowledgment - Issue 62

    Executive Editor

    2016-03-01

    • Zapopan Martín Muela Meza (Universidad Autónoma de Nuevo León - UANL, México In the same way our acknowledge to University Library System (University of Pittsburgh - PITTS, USA by constant technical support and advice for our journal.

  19. Acknowledging the back patient

    Damsgaard, Janne Brammer; Bastrup Jørgensen, Lene; Norlyk, Annelise

    , Silkeborg Regional Hospital, Regional Hospital Central Jutland, Silkeborg, Denmark 2. Health, Section for Nursing, Aarhus University, Aarhus, Denmark 3. University of Southern Denmark & Vejle Sygehus, Vejle, Denmark keywords:Back Patient, Narrative, Biomedical, Marginalisation, Self-Identity, Ethical......#296 Acknowledging The Back Patient. A Thematic Synthesis Of Qualitative Research. A Systematic Literature Review. Janne Brammer Damsgaard1, Lene Bastrup Jørgensen1, Annelise Norlyk2, Regner Birkelund3 1. Health, Section for Nursing, Aarhus University & Research Unit, Elective Surgery Centre....... Therefore, telling about experiences and perceptions is important for back patients in order to feel accepted and acknowledged. But selfhood cannot be reduced to narrative identity since the identity of the self is only fully revealed the moment we include the ethical dimension including certain norms...

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

    Kim Hyun

    2011-12-01

    Full Text Available Abstract Background 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. Results 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. Conclusions 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.

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

    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.

  2. Acknowledgment - Issue 60

    Executive Editor

    2015-11-01

    Full Text Available Biblios wish to acknowledge the support of the following professionals who participated in the review process of submissions for our issue 60.ReviewersAndré Ancona Lopez (Universidade de Brasília - UnB, BrasilGildenir Carolino Santos (Universidade Estadual de Campinas - UNICAMP, BrasilJacira Gil Bernardes (Universidade Federal do Rio Grande do Sul - UFRGS, BrasilRodrigo Donoso Vegas (Universidad de Chile - UCHILE, ChileSaúl Hiram Souto Fuentes (Universidad de Monterrey - UDEM, MéxicoReviewers invitedAlejandro Uribe Tirado (Universidad de Antioquia - UDEA, ColombiaFernanda Passini Moreno (Universidade de Brasília - UnB, BrasilJosé Bernal Rivas Fernández (Universidad de Costa Rica - UCR, Costa RicaMarta Lígia Pomim Valentim (Universidade Estadual Paulista - UNESP, BrasilOrlando Corzo Cauracurí (Universidad Nacional Mayor de San Marcos - UNMSM, PerúTerezinha Elisabeth da Silva (Universidade Estadual de Londrina - UEL, BrasilZaira Regina Zafalon (Universidade Federal de São Carlos - UFSCAR, BrasilIn the same way our acknowledge to University Library System (University of Pittsburgh - PITTS, USA by constant technical support and advice for our journal.

  3. Acknowledgment - Issue 64

    Executive Editor

    2016-11-01

    • Saúl Hiram Souto Fuentes (Universidad de Monterrey - UDEM, México Reviewers invited • Deborah Ribeiro Carvalho (Universidade Federal do Paraná - UFPR, Brasil • Fabiano Ferreira de Castro (Universidade Federal de São Carlos - UFSCAR, Brasil • Isa Maria Freire (Universidade Federal de Paraiba - UFPB, Brasil • Luciano Antonio Digiampietri (Universidade de São Paulo - USP, Brasil • Maria Inês Tomaél (Universidade Estadual de Londrina - UEL, Brasil • Murilo Bastos da Cunha (Universidade de Brasília - UnB, Brasil • Orlando Corzo Cauracurí (Universidad Nacional Mayor de San Marcos - UNMSM, Perú • Thais Batista Zaninelli (Universidade Estadual de Londrina - UEL, Brasil In the same way our acknowledge to University Library System (University of Pittsburgh - PITTS, USA by constant technical support and advice for our journal.

  4. Acknowledgment - Issue 65

    Executive Editor

    2016-12-01

    • Andrés Vuotto (Universidad Nacional de Mar del Plata - MDP, Argentina • Pedro Jorge Dimitri (Instituto Nacional de la Administración Pública, Argentina Reviewers invited • Adilson Luiz Pinto (Universidade Federal de Santa Catarina - UFSC, Brasil • Carlos Francisco Bitencourt Jorge (Universidade Estadual Paulista Júlio de Mesquita Filho - UNESP, Brasil • Jayme Leyro Vilan Filho (Universidade de Brasília - UnB, Brasil • Murilo Bastos da Cunha (Universidade de Brasília - UnB, Brasil • Silvana Vilodre Goellner (Universidade Federal do Rio Grande do Sul - UFRGS, Brasil In the same way our acknowledge to University Library System (University of Pittsburgh - PITTS, USA by constant technical support and advice for our journal.

  5. Metabolic network prediction through pairwise rational kernels.

    Roche-Lima, Abiel; Domaratzki, Michael; Fristensky, Brian

    2014-09-26

    Metabolic networks are represented by the set of metabolic pathways. Metabolic pathways are a series of biochemical reactions, in which the product (output) from one reaction serves as the substrate (input) to another reaction. Many pathways remain incompletely characterized. One of the major challenges of computational biology is to obtain better models of metabolic pathways. Existing models are dependent on the annotation of the genes. This propagates error accumulation when the pathways are predicted by incorrectly annotated genes. Pairwise classification methods are supervised learning methods used to classify new pair of entities. Some of these classification methods, e.g., Pairwise Support Vector Machines (SVMs), use pairwise kernels. Pairwise kernels describe similarity measures between two pairs of entities. Using pairwise kernels to handle sequence data requires long processing times and large storage. Rational kernels are kernels based on weighted finite-state transducers that represent similarity measures between sequences or automata. They have been effectively used in problems that handle large amount of sequence information such as protein essentiality, natural language processing and machine translations. We create a new family of pairwise kernels using weighted finite-state transducers (called Pairwise Rational Kernel (PRK)) to predict metabolic pathways from a variety of biological data. PRKs take advantage of the simpler representations and faster algorithms of transducers. Because raw sequence data can be used, the predictor model avoids the errors introduced by incorrect gene annotations. We then developed several experiments with PRKs and Pairwise SVM to validate our methods using the metabolic network of Saccharomyces cerevisiae. As a result, when PRKs are used, our method executes faster in comparison with other pairwise kernels. Also, when we use PRKs combined with other simple kernels that include evolutionary information, the accuracy

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

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

    2003-01-01

    The metabolic network in the yeast Saccharomyces cerevisiae was reconstructed using currently available genomic, biochemical, and physiological information. The metabolic reactions were compartmentalized between the cytosol and the mitochondria, and transport steps between the compartments...

  7. Integration of metabolomics data into metabolic networks.

    Töpfer, Nadine; Kleessen, Sabrina; Nikoloski, Zoran

    2015-01-01

    Metabolite levels together with their corresponding metabolic fluxes are integrative outcomes of biochemical transformations and regulatory processes and they can be used to characterize the response of biological systems to genetic and/or environmental changes. However, while changes in transcript or to some extent protein levels can usually be traced back to one or several responsible genes, changes in fluxes and particularly changes in metabolite levels do not follow such rationale and are often the outcome of complex interactions of several components. The increasing quality and coverage of metabolomics technologies have fostered the development of computational approaches for integrating metabolic read-outs with large-scale models to predict the physiological state of a system. Constraint-based approaches, relying on the stoichiometry of the considered reactions, provide a modeling framework amenable to analyses of large-scale systems and to the integration of high-throughput data. Here we review the existing approaches that integrate metabolomics data in variants of constrained-based approaches to refine model reconstructions, to constrain flux predictions in metabolic models, and to relate network structural properties to metabolite levels. Finally, we discuss the challenges and perspectives in the developments of constraint-based modeling approaches driven by metabolomics data.

  8. From genomes to in silico cells via metabolic networks

    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 phenotypes from phenotypic arrays and in gene-deletion studies. They have also been used for gene expression analysis guided by metabolic network structure, leading to the identification of commonly regulated genes. Thus, genome-scale metabolic modeling currently stands out as one of the most promising...

  9. Astroglial Metabolic Networks Sustain Hippocampal Synaptic Transmission

    Rouach, Nathalie; Koulakoff, Annette; Abudara, Veronica; Willecke, Klaus; Giaume, Christian

    2008-12-01

    Astrocytes provide metabolic substrates to neurons in an activity-dependent manner. However, the molecular mechanisms involved in this function, as well as its role in synaptic transmission, remain unclear. Here, we show that the gap-junction subunit proteins connexin 43 and 30 allow intercellular trafficking of glucose and its metabolites through astroglial networks. This trafficking is regulated by glutamatergic synaptic activity mediated by AMPA receptors. In the absence of extracellular glucose, the delivery of glucose or lactate to astrocytes sustains glutamatergic synaptic transmission and epileptiform activity only when they are connected by gap junctions. These results indicate that astroglial gap junctions provide an activity-dependent intercellular pathway for the delivery of energetic metabolites from blood vessels to distal neurons.

  10. Astroglial metabolic networks sustain hippocampal synaptic transmission.

    Rouach, Nathalie; Koulakoff, Annette; Abudara, Veronica; Willecke, Klaus; Giaume, Christian

    2008-12-05

    Astrocytes provide metabolic substrates to neurons in an activity-dependent manner. However, the molecular mechanisms involved in this function, as well as its role in synaptic transmission, remain unclear. Here, we show that the gap-junction subunit proteins connexin 43 and 30 allow intercellular trafficking of glucose and its metabolites through astroglial networks. This trafficking is regulated by glutamatergic synaptic activity mediated by AMPA receptors. In the absence of extracellular glucose, the delivery of glucose or lactate to astrocytes sustains glutamatergic synaptic transmission and epileptiform activity only when they are connected by gap junctions. These results indicate that astroglial gap junctions provide an activity-dependent intercellular pathway for the delivery of energetic metabolites from blood vessels to distal neurons.

  11. Metabolic networks of Cucurbita maxima phloem.

    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.

  12. Environmental versatility promotes modularity in genome-scale metabolic networks.

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

    2011-08-24

    The ubiquity of modules in biological networks may result from an evolutionary benefit of a modular organization. For instance, modularity may increase the rate of adaptive evolution, because modules can be easily combined into new arrangements that may benefit their carrier. Conversely, modularity may emerge as a by-product of some trait. We here ask whether this last scenario may play a role in genome-scale metabolic networks that need to sustain life in one or more chemical environments. For such networks, we define a network module as a maximal set of reactions that are fully coupled, i.e., whose fluxes can only vary in fixed proportions. This definition overcomes limitations of purely graph based analyses of metabolism by exploiting the functional links between reactions. We call a metabolic network viable in a given chemical environment if it can synthesize all of an organism's biomass compounds from nutrients in this environment. An organism's metabolism is highly versatile if it can sustain life in many different chemical environments. We here ask whether versatility affects the modularity of metabolic networks. Using recently developed techniques to randomly sample large numbers of viable metabolic networks from a vast space of metabolic networks, we use flux balance analysis to study in silico metabolic networks that differ in their versatility. We find that highly versatile networks are also highly modular. They contain more modules and more reactions that are organized into modules. Most or all reactions in a module are associated with the same biochemical pathways. Modules that arise in highly versatile networks generally involve reactions that process nutrients or closely related chemicals. We also observe that the metabolism of E. coli is significantly more modular than even our most versatile networks. Our work shows that modularity in metabolic networks can be a by-product of functional constraints, e.g., the need to sustain life in multiple

  13. Environmental versatility promotes modularity in genome-scale metabolic networks

    Wagner Andreas

    2011-08-01

    Full Text Available Abstract Background The ubiquity of modules in biological networks may result from an evolutionary benefit of a modular organization. For instance, modularity may increase the rate of adaptive evolution, because modules can be easily combined into new arrangements that may benefit their carrier. Conversely, modularity may emerge as a by-product of some trait. We here ask whether this last scenario may play a role in genome-scale metabolic networks that need to sustain life in one or more chemical environments. For such networks, we define a network module as a maximal set of reactions that are fully coupled, i.e., whose fluxes can only vary in fixed proportions. This definition overcomes limitations of purely graph based analyses of metabolism by exploiting the functional links between reactions. We call a metabolic network viable in a given chemical environment if it can synthesize all of an organism's biomass compounds from nutrients in this environment. An organism's metabolism is highly versatile if it can sustain life in many different chemical environments. We here ask whether versatility affects the modularity of metabolic networks. Results Using recently developed techniques to randomly sample large numbers of viable metabolic networks from a vast space of metabolic networks, we use flux balance analysis to study in silico metabolic networks that differ in their versatility. We find that highly versatile networks are also highly modular. They contain more modules and more reactions that are organized into modules. Most or all reactions in a module are associated with the same biochemical pathways. Modules that arise in highly versatile networks generally involve reactions that process nutrients or closely related chemicals. We also observe that the metabolism of E. coli is significantly more modular than even our most versatile networks. Conclusions Our work shows that modularity in metabolic networks can be a by-product of functional

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

    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...

  15. Acknowledgement of manuscript reviewers, the underappreciated contributors

    Abel Lucy

    2013-02-01

    Full Text Available Contributing reviewers We and the Editorial Board acknowledge and thank all reviewers for their active participation and contribution during 2012. We greatly appreciate their dedication and behind the scenes contribution. It is largely due to their support and expertise that we have been able to publish high-standard manuscripts. We would also like to thank authors for choosing Nutrition & Metabolism and contributing their cherished work.

  16. ACKNOWLEDGEMENTS

    R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

    P K Gupta, Roorkee. Boris Gurevich, Australia. Simon Harley, Edinburgh, Scotland. Anand K Inamdar, California. A Jayaraman, Ahmedabad. K N Khattri, Dehradun. Ralf Kretz, Canada. S Krishnaswami, Ahmedabad. R K Lal, Varanasi. D C Mishra, Hyderabad. Asoke Mookherjee, Kolkata. Dhruba Mukhopadhyay, Kolkata.

  17. ACKNOWLEDGEMENTS

    Behera Mukunda, Indian Institute of Technology, Kharagpur. Bera M K, Indian Institutes ... Das Subrata, Indian Institute of Tropical Meteorology, Pune. Das Trupti ... Deb Sanjib Kumar, Space Applications Centre, Ahmedabad. Delire Christine ...

  18. Acknowledgements

    2017-01-01

    This book is the product of a seminar series funded by the UK Economic and Social Research Council entitled ‘Security in Scotland, with or without constitutional change’, which ran from 2013–2015 at the University of Edinburgh (grant reference ES/L00139X/1). The Reports from this seminar series can be found on the title page on the Open Book Publishers website, http://www.openbookpublishers.com/product/524#resources The editor and principal investigator Andrew W. Neal would like to thank his ...

  19. ACKNOWLEDGEMENTS

    ... Raha, Soumen Basak , Amitava Majumder, Mousumi Basu, Smarajit Polley, Raja Bhattacharya, and all other lab members. OUR COLLABORATORS: Prof. S. Roy, Bose Institute, Kolkata. Prof.M.S.Shaila, IISC, Bangalore. GRANTING AGENCIES: Dept. of Science and Technology. All India Council for Technical Education.

  20. ACKNOWLEDGEMENTS

    Aavudai Anandhi, City University of New York, New York City, United States. Acharyya ... Collins Waylon, National Weather Service, Corpus Christi, United States ..... Smith Anne Mhairi, Agriculture and Agri-Food Canada, Lethdbridge, Canada.

  1. ACKNOWLEDGEMENTS

    0008471

    Journal of Earth System Science wishes to place on record the valuable assistance received from ... Abhijit Basu, Indiana University, Bloomington, United States .... Manoj Jaiswal, Indian Institute of Science Education and Research, Kolkata.

  2. Acknowledgements

    2016-07-01

    The Meeting was sponsored by the Istituto Nazionale di Fisica Nucleare (INFN), the Società Italiana di Fisica (SIF), the European Physics Society (EPS), the University of Pisa and the University of Siena.

  3. ACKNOWLEDGEMENTS

    Chen Xingyuan Lorraine, University of California, Berkeley, United States ... Das Kaushik, Bengal Engineering and Science University, Kolkata de Boyer ..... Samborska Katarzyna, Institute for Ecology of Industrial Areas, Katowice, Poland.

  4. ACKNOWLEDGEMENTS

    fishes whose members appear to fill very similar ecological roles in ... ways and also provided us with a great deal of interesting information. .... lakes. Verh. into Verein. Iheor. angew. Limnol. 17: 303 - 322. FRYER, G. 1972. ... Evolulion 23:.

  5. ACKNOWLEDGEMENTS

    Khayat Saied. Hsu Shu-Lu. Huang Chenn-Jung. Huang Baoshan. Hussain R. Jahir. Ibrahim A ... Lin Tao. Liu W.K.. Liu Jiming. Liu J. Lohani Bharat. Lucey Anthony. M Venkataramana. Madhu Kumar S D. Madria Sanjay. Mahadevappa Manjunatha.

  6. Acknowledgements

    Dr. B. Rajakumar, D. Anandaraj, Janardan Kundu, Harish Chakravarty (Ph.D. students). D.S.S. Hembram, Mukesh Agrawal, Vijayanand Chandrasekharan, Naga Boopathy, Sharath (Project Assistants). Dr. Dinesh Bilehal and Dr. Amit Pathak (Postdocs). Knowledge and Wisdom (Yellow is color of wisdom: Google). Profs.

  7. ACKNOWLEDGEMENTS

    covers1

    . Dipankar Nandi, Bangalore. Vidyanand Nanjundiah, Bangalore. Sankar Narayanan, Hyderabad. Laxmi Charan Padhy, Mumbai. Gopal Pande, Hyderabad. H N Pandey, Nirjuli. Ashwani Pareek, New Delhi. Veena K Parnaik, Hyderabad.

  8. Acknowledgements

    2004-01-01

    All the team of the Théodora Foundation and the members of its Board warmly thank everyone who contributed to the collection organized after the death of our colleague and friend Hubert Muller Created 11 years ago, the Théodora Foundation's goal was to relieve the suffering of hospitalized children through laughter. Thanks to your gift of 850 CHF, hospitalized children will receive a tender and funny visit from one of the Théodora Foundation's Dream Doctors. The Foundation dedicates to everyone who participated the magic moments of joy and happiness lived by each small patient during one of these visits. We express here our deep gratitude to Hubert's family for this gesture and hope that they find comfort in knowing that Hubert's memory will live through these children, for whom this sum will help to relieve the difficulty of their every day lives. In the name of the Safety Commission

  9. ACKNOWLEDGEMENTS

    C T Achutankutty, National Institute of Oceanography, Goa. Rajesh Agnihotri, Max Planck Institute For Chemistry, Germany. S Ahmed, National Geophysical Research Institute, Hyderabad. B R Arora, Wadia Institute of Himalayan Geology, Dehradun. K Balakrishna, National Institute of Technology Karnataka, Surathkal.

  10. ACKNOWLEDGEMENTS

    Parkash B, Indian Institute of Technology, Roorkee. Parthasarathy G, National Geophysical Research Institute, Hyderabad. Parvez Imtiyaz, CSIR Centre for ... Raman Sethu, North Carolina State University, Raleigh, USA. Ramana M V, National Institute of Oceanography, Dona Paula, Goa. Ramana Muvva, Scripps Institution ...

  11. ACKNOWLEDGEMENTS

    Azamathulla Hazi Mohammad, Universiti Sains Malaysia, Nibong Tebal, Pulau Pinang, ... Bai Chao-Ying, Chang'an University, Chang'an, Xi'an, China ..... Sikdar Pradip, Indian Institute of Social Welfare and Business Management, Kolkata.

  12. ACKNOWLEDGEMENTS

    R.Narasimhan(krishtel emaging)1461 1996 Oct 15 13:05:22

    J M Bull, Southampton, UK. Chaofang Zhao, China. D Chandrasekharan, Mumbai, India. R Cullers, Kansas, USA. Danling Tang, Japan. A C Das, Ahmedabad, India. S K Dash, Delhi, India. Don Olson, USA, India. Dev Niyogi, Raleigh, NC, USA. Edward Monahan, USA. G Ekstrom, Cambridge, USA. Elgar Desa, Goa, India.

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

    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.

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

    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...

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

    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.

  16. Slave nodes and the controllability of metabolic networks

    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.

  17. Regulation of metabolic networks by small molecule metabolites

    Kanehisa Minoru

    2007-03-01

    Full Text Available Abstract Background The ability to regulate metabolism is a fundamental process in living systems. We present an analysis of one of the mechanisms by which metabolic regulation occurs: enzyme inhibition and activation by small molecules. We look at the network properties of this regulatory system and the relationship between the chemical properties of regulatory molecules. Results We find that many features of the regulatory network, such as the degree and clustering coefficient, closely match those of the underlying metabolic network. While these global features are conserved across several organisms, we do find local differences between regulation in E. coli and H. sapiens which reflect their different lifestyles. Chemical structure appears to play an important role in determining a compounds suitability for use in regulation. Chemical structure also often determines how groups of similar compounds can regulate sets of enzymes. These groups of compounds and the enzymes they regulate form modules that mirror the modules and pathways of the underlying metabolic network. We also show how knowledge of chemical structure and regulation could be used to predict regulatory interactions for drugs. Conclusion The metabolic regulatory network shares many of the global properties of the metabolic network, but often varies at the level of individual compounds. Chemical structure is a key determinant in deciding how a compound is used in regulation and for defining modules within the regulatory system.

  18. Does habitat variability really promote metabolic network modularity?

    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.

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

    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.

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

    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.

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

    Chaiboonchoe, Amphun; Dohai, Bushra Saeed; Cai, Hong; Nelson, David R. [Division of Science and Math, New York University Abu Dhabi, Abu Dhabi (United Arab Emirates); Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi (United Arab Emirates); Jijakli, Kenan [Division of Science and Math, New York University Abu Dhabi, Abu Dhabi (United Arab Emirates); Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi (United Arab Emirates); Engineering Division, Biofinery, Manhattan, KS (United States); Salehi-Ashtiani, Kourosh, E-mail: ksa3@nyu.edu [Division of Science and Math, New York University Abu Dhabi, Abu Dhabi (United Arab Emirates); Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi (United Arab Emirates)

    2014-12-10

    Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology (Biolog, Hayward, CA, USA) provides an efficient, high-throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the 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.

  2. Sirtuins as regulators of the yeast metabolic network

    Markus eRalser

    2012-03-01

    Full Text Available There is growing evidence that the metabolic network is an integral regulator of cellularphysiology. Dynamic changes in metabolite concentrations, metabolic flux, or networktopology act as reporters of biological or environmental signals, and are required for the cellto trigger an appropriate biological reaction. Changes in the metabolic network are recognizedby specific sensory macromolecules and translated into a transcriptional or translationalresponse. The protein family of sirtuins, discovered more than 30 years ago as regulators ofsilent chromatin, seems to fulfill the role of a metabolic sensor during aging and conditions ofcaloric restriction. NAD+/NADH interconverting metabolic enzymes glyceraldehyde-3-phosphate dehydrogenase and alcohol dehydrogenase, as well as enzymes involved inNAD(H, synthesis provide or deprive NAD+ in close proximity to Sir2. This influence sirtuinactivity, and facilitates a dynamic response of the metabolic network to changes inmetabolism with effects on physiology and aging. The molecular network downstream Sir2,however, is complex. In just two orders, Sir2’s metabolism-related interactions span half ofthe yeast proteome, and are connected with virtually every physiological process. Thus,although it is fundamental to analyze single molecular mechanisms, it is at the same timecrucial to consider this genome-scale complexity when correlating single molecular eventswith phenotypes such as aging, cell growth, or stress resistance.

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

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

    2006-01-01

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

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

    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

  5. Pathway discovery in metabolic networks by subgraph extraction.

    Faust, Karoline; Dupont, Pierre; Callut, Jérôme; van Helden, Jacques

    2010-05-01

    Subgraph extraction is a powerful technique to predict pathways from biological networks and a set of query items (e.g. genes, proteins, compounds, etc.). It can be applied to a variety of different data types, such as gene expression, protein levels, operons or phylogenetic profiles. In this article, we investigate different approaches to extract relevant pathways from metabolic networks. Although these approaches have been adapted to metabolic networks, they are generic enough to be adjusted to other biological networks as well. We comparatively evaluated seven sub-network extraction approaches on 71 known metabolic pathways from Saccharomyces cerevisiae and a metabolic network obtained from MetaCyc. The best performing approach is a novel hybrid strategy, which combines a random walk-based reduction of the graph with a shortest paths-based algorithm, and which recovers the reference pathways with an accuracy of approximately 77%. Most of the presented algorithms are available as part of the network analysis tool set (NeAT). The kWalks method is released under the GPL3 license.

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

    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.

  7. Preferential attachment in the evolution of metabolic networks

    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

  8. Signatures of arithmetic simplicity in metabolic network architecture.

    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.

  9. Multi-equilibrium property of metabolic networks: SSI module

    Chen Luonan

    2011-06-01

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

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

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

    2017-11-01

    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. © 2017 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

  11. Environmental versatility promotes modularity in large scale metabolic networks

    Samal A.; Wagner Andreas; Martin O.C.

    2011-01-01

    Abstract Background The ubiquity of modules in biological networks may result from an evolutionary benefit of a modular organization. For instance, modularity may increase the rate of adaptive evolution, because modules can be easily combined into new arrangements that may benefit their carrier. Conversely, modularity may emerge as a by-product of some trait. We here ask whether this last scenario may play a role in genome-scale metabolic networks that need to sustain life in one or more chem...

  12. EGFR Signal-Network Reconstruction Demonstrates Metabolic Crosstalk in EMT.

    Kumari Sonal Choudhary

    2016-06-01

    Full Text Available Epithelial to mesenchymal transition (EMT is an important event during development and cancer metastasis. There is limited understanding of the metabolic alterations that give rise to and take place during EMT. Dysregulation of signalling pathways that impact metabolism, including epidermal growth factor receptor (EGFR, are however a hallmark of EMT and metastasis. In this study, we report the investigation into EGFR signalling and metabolic crosstalk of EMT through constraint-based modelling and analysis of the breast epithelial EMT cell model D492 and its mesenchymal counterpart D492M. We built an EGFR signalling network for EMT based on stoichiometric coefficients and constrained the network with gene expression data to build epithelial (EGFR_E and mesenchymal (EGFR_M networks. Metabolic alterations arising from differential expression of EGFR genes was derived from a literature review of AKT regulated metabolic genes. Signaling flux differences between EGFR_E and EGFR_M models subsequently allowed metabolism in D492 and D492M cells to be assessed. Higher flux within AKT pathway in the D492 cells compared to D492M suggested higher glycolytic activity in D492 that we confirmed experimentally through measurements of glucose uptake and lactate secretion rates. The signaling genes from the AKT, RAS/MAPK and CaM pathways were predicted to revert D492M to D492 phenotype. Follow-up analysis of EGFR signaling metabolic crosstalk in three additional breast epithelial cell lines highlighted variability in in vitro cell models of EMT. This study shows that the metabolic phenotype may be predicted by in silico analyses of gene expression data of EGFR signaling genes, but this phenomenon is cell-specific and does not follow a simple trend.

  13. EGFR Signal-Network Reconstruction Demonstrates Metabolic Crosstalk in EMT.

    Choudhary, Kumari Sonal; Rohatgi, Neha; Halldorsson, Skarphedinn; Briem, Eirikur; Gudjonsson, Thorarinn; Gudmundsson, Steinn; Rolfsson, Ottar

    2016-06-01

    Epithelial to mesenchymal transition (EMT) is an important event during development and cancer metastasis. There is limited understanding of the metabolic alterations that give rise to and take place during EMT. Dysregulation of signalling pathways that impact metabolism, including epidermal growth factor receptor (EGFR), are however a hallmark of EMT and metastasis. In this study, we report the investigation into EGFR signalling and metabolic crosstalk of EMT through constraint-based modelling and analysis of the breast epithelial EMT cell model D492 and its mesenchymal counterpart D492M. We built an EGFR signalling network for EMT based on stoichiometric coefficients and constrained the network with gene expression data to build epithelial (EGFR_E) and mesenchymal (EGFR_M) networks. Metabolic alterations arising from differential expression of EGFR genes was derived from a literature review of AKT regulated metabolic genes. Signaling flux differences between EGFR_E and EGFR_M models subsequently allowed metabolism in D492 and D492M cells to be assessed. Higher flux within AKT pathway in the D492 cells compared to D492M suggested higher glycolytic activity in D492 that we confirmed experimentally through measurements of glucose uptake and lactate secretion rates. The signaling genes from the AKT, RAS/MAPK and CaM pathways were predicted to revert D492M to D492 phenotype. Follow-up analysis of EGFR signaling metabolic crosstalk in three additional breast epithelial cell lines highlighted variability in in vitro cell models of EMT. This study shows that the metabolic phenotype may be predicted by in silico analyses of gene expression data of EGFR signaling genes, but this phenomenon is cell-specific and does not follow a simple trend.

  14. Predicting metabolic pathways by sub-network extraction.

    Faust, Karoline; van Helden, Jacques

    2012-01-01

    Various methods result in groups of functionally related genes obtained from genomes (operons, regulons, syntheny groups, and phylogenetic profiles), transcriptomes (co-expression groups) and proteomes (modules of interacting proteins). When such groups contain two or more enzyme-coding genes, graph analysis methods can be applied to extract a metabolic pathway that interconnects them. We describe here the way to use the Pathway extraction tool available on the NeAT Web server ( http://rsat.ulb.ac.be/neat/ ) to piece together the metabolic pathway from a group of associated, enzyme-coding genes. The tool identifies the reactions that can be catalyzed by the products of the query genes (seed reactions), and applies sub-graph extraction algorithms to extract from a metabolic network a sub-network that connects the seed reactions. This sub-network represents the predicted metabolic pathway. We describe here the pathway prediction process in a step-by-step way, give hints about the main parametric choices, and illustrate how this tool can be used to extract metabolic pathways from bacterial genomes, on the basis of two study cases: the isoleucine-valine operon in Escherichia coli and a predicted operon in Cupriavidus (Ralstonia) metallidurans.

  15. Metabolic networks in epilepsy by MR spectroscopic imaging.

    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.

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

    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

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

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

    2015-01-01

    , the method we report represents a powerful tool to identify inconsistencies in large-scale metabolic networks. AVAILABILITY AND IMPLEMENTATION: The method is available as source code on http://users.minet.uni-jena.de/ approximately m3kach/ASBIG/ASBIG.zip. CONTACT: christoph.kaleta@uni-jena.de SUPPLEMENTARY...... by inherent inconsistencies and gaps. RESULTS: Here we present a novel method to validate metabolic network reconstructions based on the concept of autocatalytic sets. Autocatalytic sets correspond to collections of metabolites that, besides enzymes and a growth medium, are required to produce all biomass...... components in a metabolic model. These autocatalytic sets are well-conserved across all domains of life, and their identification in specific genome-scale reconstructions allows us to draw conclusions about potential inconsistencies in these models. The method is capable of detecting inconsistencies, which...

  18. Blueprint for antimicrobial hit discovery targeting metabolic networks.

    Shen, Y; Liu, J; Estiu, G; Isin, B; Ahn, Y-Y; Lee, D-S; Barabási, A-L; Kapatral, V; Wiest, O; Oltvai, Z N

    2010-01-19

    Advances in genome analysis, network biology, and computational chemistry have the potential to revolutionize drug discovery by combining system-level identification of drug targets with the atomistic modeling of small molecules capable of modulating their activity. To demonstrate the effectiveness of such a discovery pipeline, we deduced common antibiotic targets in Escherichia coli and Staphylococcus aureus by identifying shared tissue-specific or uniformly essential metabolic reactions in their metabolic networks. We then predicted through virtual screening dozens of potential inhibitors for several enzymes of these reactions and showed experimentally that a subset of these inhibited both enzyme activities in vitro and bacterial cell viability. This blueprint is applicable for any sequenced organism with high-quality metabolic reconstruction and suggests a general strategy for strain-specific antiinfective therapy.

  19. Optimality principles in the regulation of metabolic networks.

    Berkhout, Jan; Bruggeman, Frank J; Teusink, Bas

    2012-08-29

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

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

    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

  1. Parameter estimation in tree graph metabolic networks

    Laura Astola

    2016-09-01

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

  2. Parameter estimation in tree graph metabolic networks.

    Astola, Laura; Stigter, Hans; Gomez Roldan, Maria Victoria; van Eeuwijk, Fred; Hall, Robert D; Groenenboom, Marian; Molenaar, Jaap J

    2016-01-01

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

  3. Optimality Principles in the Regulation of Metabolic Networks

    Jan Berkhout

    2012-08-01

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

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

    CHEN Tong; XIE ZhengWei; OUYANG Qi

    2009-01-01

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

  5. Metabolite coupling in genome-scale metabolic networks

    Palsson Bernhard Ø

    2006-03-01

    Full Text Available Abstract Background Biochemically detailed stoichiometric matrices have now been reconstructed for various bacteria, yeast, and for the human cardiac mitochondrion based on genomic and proteomic data. These networks have been manually curated based on legacy data and elementally and charge balanced. Comparative analysis of these well curated networks is now possible. Pairs of metabolites often appear together in several network reactions, linking them topologically. This co-occurrence of pairs of metabolites in metabolic reactions is termed herein "metabolite coupling." These metabolite pairs can be directly computed from the stoichiometric matrix, S. Metabolite coupling is derived from the matrix ŜŜT, whose off-diagonal elements indicate the number of reactions in which any two metabolites participate together, where Ŝ is the binary form of S. Results Metabolite coupling in the studied networks was found to be dominated by a relatively small group of highly interacting pairs of metabolites. As would be expected, metabolites with high individual metabolite connectivity also tended to be those with the highest metabolite coupling, as the most connected metabolites couple more often. For metabolite pairs that are not highly coupled, we show that the number of reactions a pair of metabolites shares across a metabolic network closely approximates a line on a log-log scale. We also show that the preferential coupling of two metabolites with each other is spread across the spectrum of metabolites and is not unique to the most connected metabolites. We provide a measure for determining which metabolite pairs couple more often than would be expected based on their individual connectivity in the network and show that these metabolites often derive their principal biological functions from existing in pairs. Thus, analysis of metabolite coupling provides information beyond that which is found from studying the individual connectivity of individual

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

    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.

  7. Network analysis of metabolic enzyme evolution in Escherichia coli

    Kraulis Per

    2004-02-01

    Full Text Available Abstract Background The two most common models for the evolution of metabolism are the patchwork evolution model, where enzymes are thought to diverge from broad to narrow substrate specificity, and the retrograde evolution model, according to which enzymes evolve in response to substrate depletion. Analysis of the distribution of homologous enzyme pairs in the metabolic network can shed light on the respective importance of the two models. We here investigate the evolution of the metabolism in E. coli viewed as a single network using EcoCyc. Results Sequence comparison between all enzyme pairs was performed and the minimal path length (MPL between all enzyme pairs was determined. We find a strong over-representation of homologous enzymes at MPL 1. We show that the functionally similar and functionally undetermined enzyme pairs are responsible for most of the over-representation of homologous enzyme pairs at MPL 1. Conclusions The retrograde evolution model predicts that homologous enzymes pairs are at short metabolic distances from each other. In general agreement with previous studies we find that homologous enzymes occur close to each other in the network more often than expected by chance, which lends some support to the retrograde evolution model. However, we show that the homologous enzyme pairs which may have evolved through retrograde evolution, namely the pairs that are functionally dissimilar, show a weaker over-representation at MPL 1 than the functionally similar enzyme pairs. Our study indicates that, while the retrograde evolution model may have played a small part, the patchwork evolution model is the predominant process of metabolic enzyme evolution.

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

    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.

  9. Discovery of Boolean metabolic networks: integer linear programming based approach.

    Qiu, Yushan; Jiang, Hao; Ching, Wai-Ki; Cheng, Xiaoqing

    2018-04-11

    Traditional drug discovery methods focused on the efficacy of drugs rather than their toxicity. However, toxicity and/or lack of efficacy are produced when unintended targets are affected in metabolic networks. Thus, identification of biological targets which can be manipulated to produce the desired effect with minimum side-effects has become an important and challenging topic. Efficient computational methods are required to identify the drug targets while incurring minimal side-effects. In this paper, we propose a graph-based computational damage model that summarizes the impact of enzymes on compounds in metabolic networks. An efficient method based on Integer Linear Programming formalism is then developed to identify the optimal enzyme-combination so as to minimize the side-effects. The identified target enzymes for known successful drugs are then verified by comparing the results with those in the existing literature. Side-effects reduction plays a crucial role in the study of drug development. A graph-based computational damage model is proposed and the theoretical analysis states the captured problem is NP-completeness. The proposed approaches can therefore contribute to the discovery of drug targets. Our developed software is available at " http://hkumath.hku.hk/~wkc/APBC2018-metabolic-network.zip ".

  10. Second Law of Thermodynamics Applied to Metabolic Networks

    Nigam, R.; Liang, S.

    2003-01-01

    We present a simple algorithm based on linear programming, that combines Kirchoff's flux and potential laws and applies them to metabolic networks to predict thermodynamically feasible reaction fluxes. These law's represent mass conservation and energy feasibility that are widely used in electrical circuit analysis. Formulating the Kirchoff's potential law around a reaction loop in terms of the null space of the stoichiometric matrix leads to a simple representation of the law of entropy that can be readily incorporated into the traditional flux balance analysis without resorting to non-linear optimization. Our technique is new as it can easily check the fluxes got by applying flux balance analysis for thermodynamic feasibility and modify them if they are infeasible so that they satisfy the law of entropy. We illustrate our method by applying it to the network dealing with the central metabolism of Escherichia coli. Due to its simplicity this algorithm will be useful in studying large scale complex metabolic networks in the cell of different organisms.

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

    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. Transcriptional regulation and steady-state modeling of metabolic networks

    Zelezniak, Aleksej

    Biological systems are characterized by a high degree of complexity wherein the individual components (e.g. proteins) are inter-linked in a way that leads to emergent behaviors that are difficult to decipher. Uncovering system complexity requires, at least, answers to the following three questions......: what are the components of the systems, how are the different components interconnected and how do these networks perform the functions that make the resulting system behavior? Modern analytical technologies allow us to unravel the constituents and interactions happening in a given system; however......, the third question is the ultimate challenge for systems biology. The work of this thesis systematically addresses this question in the context of metabolic networks, which are arguably the most well characterized cellular networks in terms of their constituting components and interactions among them...

  13. Bringing metabolic networks to life: integration of kinetic, metabolic, and proteomic data

    Klipp Edda

    2006-12-01

    Full Text Available Abstract Background Translating a known metabolic network into a dynamic model requires reasonable guesses of all enzyme parameters. In Bayesian parameter estimation, model parameters are described by a posterior probability distribution, which scores the potential parameter sets, showing how well each of them agrees with the data and with the prior assumptions made. Results We compute posterior distributions of kinetic parameters within a Bayesian framework, based on integration of kinetic, thermodynamic, metabolic, and proteomic data. The structure of the metabolic system (i.e., stoichiometries and enzyme regulation needs to be known, and the reactions are modelled by convenience kinetics with thermodynamically independent parameters. The parameter posterior is computed in two separate steps: a first posterior summarises the available data on enzyme kinetic parameters; an improved second posterior is obtained by integrating metabolic fluxes, concentrations, and enzyme concentrations for one or more steady states. The data can be heterogenous, incomplete, and uncertain, and the posterior is approximated by a multivariate log-normal distribution. We apply the method to a model of the threonine synthesis pathway: the integration of metabolic data has little effect on the marginal posterior distributions of individual model parameters. Nevertheless, it leads to strong correlations between the parameters in the joint posterior distribution, which greatly improve the model predictions by the following Monte-Carlo simulations. Conclusion We present a standardised method to translate metabolic networks into dynamic models. To determine the model parameters, evidence from various experimental data is combined and weighted using Bayesian parameter estimation. The resulting posterior parameter distribution describes a statistical ensemble of parameter sets; the parameter variances and correlations can account for missing knowledge, measurement

  14. Do Orthopaedic Surgeons Acknowledge Uncertainty?

    Teunis, Teun; Janssen, Stein; Guitton, Thierry G; Ring, David; Parisien, Robert

    2016-06-01

    R(2), 0.29). The relatively low levels of uncertainty among orthopaedic surgeons and confidence bias seem inconsistent with the paucity of definitive evidence. If patients want to be informed of the areas of uncertainty and surgeon-to-surgeon variation relevant to their care, it seems possible that a low recognition of uncertainty and surgeon confidence bias might hinder adequately informing patients, informed decisions, and consent. Moreover, limited recognition of uncertainty is associated with modifiable factors such as confidence bias, trust in orthopaedic evidence base, and statistical understanding. Perhaps improved statistical teaching in residency, journal clubs to improve the critique of evidence and awareness of bias, and acknowledgment of knowledge gaps at courses and conferences might create awareness about existing uncertainties. Level 1, prognostic study.

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

    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.

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

    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

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

    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

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

    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.

  19. Integration of metabolic and gene regulatory networks modulates the C. elegans dietary response.

    Watson, Emma; MacNeil, Lesley T; Arda, H Efsun; Zhu, Lihua Julie; Walhout, Albertha J M

    2013-03-28

    Expression profiles are tailored according to dietary input. However, the networks that control dietary responses remain largely uncharacterized. Here, we combine forward and reverse genetic screens to delineate a network of 184 genes that affect the C. elegans dietary response to Comamonas DA1877 bacteria. We find that perturbation of a mitochondrial network composed of enzymes involved in amino acid metabolism and the TCA cycle affects the dietary response. In humans, mutations in the corresponding genes cause inborn diseases of amino acid metabolism, most of which are treated by dietary intervention. We identify several transcription factors (TFs) that mediate the changes in gene expression upon metabolic network perturbations. Altogether, our findings unveil a transcriptional response system that is poised to sense dietary cues and metabolic imbalances, illustrating extensive communication between metabolic networks in the mitochondria and gene regulatory networks in the nucleus. Copyright © 2013 Elsevier Inc. All rights reserved.

  20. Energetics of glucose metabolism: a phenomenological approach to metabolic network modeling.

    Diederichs, Frank

    2010-08-12

    A new formalism to describe metabolic fluxes as well as membrane transport processes was developed. The new flux equations are comparable to other phenomenological laws. Michaelis-Menten like expressions, as well as flux equations of nonequilibrium thermodynamics, can be regarded as special cases of these new equations. For metabolic network modeling, variable conductances and driving forces are required to enable pathway control and to allow a rapid response to perturbations. When applied to oxidative phosphorylation, results of simulations show that whole oxidative phosphorylation cannot be described as a two-flux-system according to nonequilibrium thermodynamics, although all coupled reactions per se fulfill the equations of this theory. Simulations show that activation of ATP-coupled load reactions plus glucose oxidation is brought about by an increase of only two different conductances: a [Ca(2+)] dependent increase of cytosolic load conductances, and an increase of phosphofructokinase conductance by [AMP], which in turn becomes increased through [ADP] generation by those load reactions. In ventricular myocytes, this feedback mechanism is sufficient to increase cellular power output and O(2) consumption several fold, without any appreciable impairment of energetic parameters. Glucose oxidation proceeds near maximal power output, since transformed input and output conductances are nearly equal, yielding an efficiency of about 0.5. This conductance matching is fulfilled also by glucose oxidation of β-cells. But, as a price for the metabolic mechanism of glucose recognition, β-cells have only a limited capability to increase their power output.

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

    Celia eMendez-Garcia

    2015-05-01

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

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

    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.

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

    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

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

    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.

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

    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

  6. The praxis of acknowledgement: from bibliometrics to influmetrics

    Cronin, B.

    1995-06-01

    Full Text Available The practice of acknowledgement in scholarly communication is widespread and growing. Acknowledgements define a variety of cognitive and social relationships between researchers within and across disciplines, and could thus be used to map networks of influence. We explore the relationship between authorship, acknowledgement and citation -the «Reward Triangle»- in the context of the academic audit process. We also consider the case for using acknowledgement data conjointly with established bibliometric indicators, such as citations, in performance assessment.

    El uso del agradecimiento está muy extendido en la comunicación académica y tiende a aumentar. El agradecimiento sirve para definir una serie de relaciones sociales y de conocimiento entre investigadores de una disciplina, así como vinculación con otras áreas del saber y, por eso, puede utilizarse para hacer un diseño de redes de influencia. En este artículo se explora la relación entre autores, agradecimientos y citas -el «triángulo del reconocimiento»- en el contexto de la evaluación académica. Los autores también consideran la posible utilización de datos procedentes del agradecimiento conjuntamente con otros indicadores bibliométricos, tales como las citas, en las evaluaciones.

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

    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...

  8. Discriminating response groups in metabolic and regulatory pathway networks.

    Van Hemert, John L; Dickerson, Julie A

    2012-04-01

    Analysis of omics experiments generates lists of entities (genes, metabolites, etc.) selected based on specific behavior, such as changes in response to stress or other signals. Functional interpretation of these lists often uses category enrichment tests using functional annotations like Gene Ontology terms and pathway membership. This approach does not consider the connected structure of biochemical pathways or the causal directionality of events. The Omics Response Group (ORG) method, described in this work, interprets omics lists in the context of metabolic pathway and regulatory networks using a statistical model for flow within the networks. Statistical results for all response groups are visualized in a novel Pathway Flow plot. The statistical tests are based on the Erlang distribution model under the assumption of independent and identically Exponential-distributed random walk flows through pathways. As a proof of concept, we applied our method to an Escherichia coli transcriptomics dataset where we confirmed common knowledge of the E.coli transcriptional response to Lipid A deprivation. The main response is related to osmotic stress, and we were also able to detect novel responses that are supported by the literature. We also applied our method to an Arabidopsis thaliana expression dataset from an abscisic acid study. In both cases, conventional pathway enrichment tests detected nothing, while our approach discovered biological processes beyond the original studies. We created a prototype for an interactive ORG web tool at http://ecoserver.vrac.iastate.edu/pathwayflow (source code is available from https://subversion.vrac.iastate.edu/Subversion/jlv/public/jlv/pathwayflow). The prototype is described along with additional figures and tables in Supplementary Material. julied@iastate.edu Supplementary data are available at Bioinformatics online.

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

    Thiele Ines

    2007-09-01

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

  10. Acknowledging the dilemmas of intrusive media

    Mathieu, David; Finger, Juliane; Dias, Patrcia

    2017-01-01

    Part of the stakeholder consultation addressed strategies that media audiences are developing to cope with pressures and intrusions in a changing media environment, characterised by digitalisation and interactive possibilities. We interviewed ten stakeholders representing interests such as content...... production, media literacy, media regulation, and activism. Consulting with these stakeholders left the impression that pressures and intrusions from media lack widespread acknowledgement, and that little is known about audiences’ strategies to cope with media. Even when intrusions are acknowledged, we find...... no consensual motivation, nor any clear avenue for action. Therefore, we have analysed different discursive positions that prevent acknowledging or taking action upon the pressures and intrusions that we presented to these stakeholders. The discursive positions are outlined below....

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

    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.

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

    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.

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

    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.

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

    Kazuhiro Takemoto

    2013-10-01

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

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

    Çakır, Tunahan, E-mail: tcakir@gyte.edu.tr [Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University (formerly known as Gebze Institute of Technology), Gebze (Turkey); Khatibipour, Mohammad Jafar [Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University (formerly known as Gebze Institute of Technology), Gebze (Turkey); Department of Chemical Engineering, Gebze Technical University (formerly known as Gebze Institute of Technology), Gebze (Turkey)

    2014-12-03

    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.

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

    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.

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

    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.

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

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

    2012-01-01

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

  19. EGFR Signal-Network Reconstruction Demonstrates Metabolic Crosstalk in EMT

    Choudhary, Kumari Sonal; Rohatgi, Neha; Halldorsson, Skarphedinn; Briem, Eirikur; Gudjonsson, Thorarinn; Gudmundsson, Steinn; Rolfsson, Ottar

    2016-01-01

    Epithelial to mesenchymal transition (EMT) is an important event during development and cancer metastasis. There is limited understanding of the metabolic alterations that give rise to and take place during EMT. Dysregulation of signalling pathways that impact metabolism, including epidermal growth factor receptor (EGFR), are however a hallmark of EMT and metastasis. In this study, we report the investigation into EGFR signalling and metabolic crosstalk of EMT through constraint-based modelli...

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

    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

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

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

    2007-04-26

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

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

    Gould John

    2007-04-01

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

  3. Acknowledging the patient with back pain

    Damsgaard, Janne Brammer; Jørgensen, Lene Bastrup; Norlyk, Annelise

    2015-01-01

    Rationale and aims: Research shows that back patients’ illness experiences affect their interaction with the healthcare system. It is important to examine the exact nature of these experiences in order to shed valuable light on how back patients perceive their illness and hospitalisation. The aim...... and translates into a certain way of perceiving and explaining illnesses and symptoms. Results: The thematic analysis shows that it is through experiences and memories that we create our identity and consciousness. Ignoring the illness experiences can therefore be seen as disregarding, the patient as a human...... of the included studies demonstrates the need for healthcare professionals to pay attention to back patients’ narratives in order to acknowledge them as human beings. This acknowledgement involves an ethical dimension and a sense of responsibility, manifested as respectful inclusion of the patient’s experiences...

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

    Morrison, Erin S; Badyaev, Alexander V

    2018-05-01

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

  5. Stoichiometric network constraints on xylose metabolism by recombinant Saccharomyces cerevisiae

    Yong-Su Jin; Thomas W. Jeffries

    2004-01-01

    Metabolic pathway engineering is constrained by the thermodynamic and stoichiometric feasibility of enzymatic activities of introduced genes. Engineering of xylose metabolism in Saccharomyces cerevisiae has focused on introducing genes for the initial xylose assimilation steps from Pichia stipitis, a xylose-fermenting yeast, into S. cerevisiae, a yeast raditionally...

  6. Optimality principles in the regulation of metabolic networks

    Berkhout, J.; Bruggeman, F.J.; Teusink, B.

    2012-01-01

    One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks

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

    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.

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

    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.

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

    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.

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

    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.

  11. Connexin 43-Mediated Astroglial Metabolic Networks Contribute to the Regulation of the Sleep-Wake Cycle.

    Clasadonte, Jerome; Scemes, Eliana; Wang, Zhongya; Boison, Detlev; Haydon, Philip G

    2017-09-13

    Astrocytes produce and supply metabolic substrates to neurons through gap junction-mediated astroglial networks. However, the role of astroglial metabolic networks in behavior is unclear. Here, we demonstrate that perturbation of astroglial networks impairs the sleep-wake cycle. Using a conditional Cre-Lox system in mice, we show that knockout of the gap junction subunit connexin 43 in astrocytes throughout the brain causes excessive sleepiness and fragmented wakefulness during the nocturnal active phase. This astrocyte-specific genetic manipulation silenced the wake-promoting orexin neurons located in the lateral hypothalamic area (LHA) by impairing glucose and lactate trafficking through astrocytic networks. This global wakefulness instability was mimicked with viral delivery of Cre recombinase to astrocytes in the LHA and rescued by in vivo injections of lactate. Our findings propose a novel regulatory mechanism critical for maintaining normal daily cycle of wakefulness and involving astrocyte-neuron metabolic interactions. Copyright © 2017 Elsevier Inc. All rights reserved.

  12. Acknowledging the results of blood tests

    Torkilsheyggi, Arnvør Martinsdottir á; Hertzum, Morten

    At the studied hospital, physicians from the Medical and Surgical Departments work some of their shifts in the Emergency Department (ED). Though icons showing the blood-test process were introduced on electronic whiteboards in the ED, these icons did not lead to increased attention to test acknow...... acknowledgement. Rather, the physicians, trans-ferred work practices from their own departments, which did not have electronic white-boards, to the ED. This finding suggests a challenge to the cross-disciplinary work and norms for how to follow up on blood-test results in the ED....

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

    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.

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

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

    2006-06-06

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

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

    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

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

    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.

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

    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.

  18. Validation of a metabolic network for Saccharomyces cerevisiae using mixed substrate studies.

    Vanrolleghem, P A; de Jong-Gubbels, P; van Gulik, W M; Pronk, J T; van Dijken, J P; Heijnen, S

    1996-01-01

    Setting up a metabolic network model for respiratory growth of Saccharomyces cerevisiae requires the estimation of only two (energetic) stoichiometric parameters: (1) the operational PO ratio and (2) a growth-related maintenance factor k. It is shown, both theoretically and practically, how chemostat cultivations with different mixtures of two substrates allow unique values to be given to these unknowns of the proposed metabolic model. For the yeast and model considered, an effective PO ratio of 1.09 mol of ATP/mol of O (95% confidence interval 1.07-1.11) and a k factor of 0.415 mol of ATP/C-mol of biomass (0.385-0.445) were obtained from biomass substrate yield data on glucose/ethanol mixtures. Symbolic manipulation software proved very valuable in this study as it supported the proof of theoretical identifiability and significantly reduced the necessary computations for parameter estimation. In the transition from 100% glucose to 100% ethanol in the feed, four metabolic regimes occur. Switching between these regimes is determined by cessation of an irreversible reaction and initiation of an alternative reaction. Metabolic network predictions of these metabolic switches compared well with activity measurements of key enzymes. As a second validation of the network, the biomass yield of S. cerevisiae on acetate was also compared to the network prediction. An excellent agreement was found for a network in which acetate transport was modeled with a proton symport, while passive diffusion of acetate gave significantly higher yield predictions.

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

    Nair, Govind; Jungreuthmayer, Christian; Zanghellini, Jürgen

    2017-02-01

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

  20. 5 CFR 1601.33 - Acknowledgment of risk.

    2010-01-01

    ... 5 Administrative Personnel 3 2010-01-01 2010-01-01 false Acknowledgment of risk. 1601.33 Section... TSP FUNDS Contribution Allocations and Interfund Transfer Requests § 1601.33 Acknowledgment of risk... acknowledgment of risk for that fund. If a required acknowledgment of risk has not been executed, no transactions...

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

    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.

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

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

    2016-01-01

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

  3. Examining the Psychological Effect of Rape Acknowledgment: The Interaction of Acknowledgment Status and Ambivalent Sexism.

    Wilson, Laura C; Miller, Katherine E; Leheney, Emma K; Ballman, Alesha D; Scarpa, Angela

    2017-07-01

    Although the majority of rape survivors do not label their experiences as rape (i.e., unacknowledged rape), the literature is mixed in terms of how this affects survivors' psychological functioning. To elucidate the discrepancies, the present study examined the interaction between rape acknowledgement and ambivalent sexism in relation to depression and posttraumatic stress disorder (PTSD) symptoms. The analyzed sample included 128 female rape survivors who were drawn from a larger college sample of 1,595 participants. The participants completed measures of sexual assault experiences, ambivalent sexism, and depression and PTSD symptoms. The results supported a significant interaction between acknowledgement status and benevolent sexism in relation to both depression and PTSD symptoms. Conversely, the present study failed to find support for an interaction between acknowledgment status and hostile sexism. The clinical implications suggest that rather than seeing acknowledging rape as essential to the recovery process, clinicians should assess for and take into account other factors that may contribute to psychological functioning. Additionally, the findings support that more complex models of trauma recovery should be investigated with the goal of working toward a more comprehensive understanding of the longitudinal process of rape acknowledgment. © 2016 Wiley Periodicals, Inc.

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

    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.

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

    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.

  6. Acknowledging and Accounting for Employee Benefits

    Florentina MOISESCU

    2009-01-01

    Full Text Available Employee benefits are all forms of counter services granted by anentity in return to services given by the employees. This category includes onlythe benefits covered by the entity, not those from the state or the employee onthe payroll. The employer counting and presenting all the benefits of theemployees, including those provided on the basis of official programs or otherofficial contracts between the entity and the individual employees, groups ofemployees or their representatives, those established on the basis of legalprovisions or by contracts at the level of activity sector, through which theentities are required to contribute to national programs, as well as thoseresulting from unofficial practices give rise to an implicit obligation.Acknowledging and especially assessing these benefits are issues demandingspecial attention.

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

    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.

  8. Integration of Plant Metabolomics Data with Metabolic Networks: Progresses and Challenges.

    Töpfer, Nadine; Seaver, Samuel M D; Aharoni, Asaph

    2018-01-01

    In the last decade, plant genome-scale modeling has developed rapidly and modeling efforts have advanced from representing metabolic behavior of plant heterotrophic cell suspensions to studying the complex interplay of cell types, tissues, and organs. A crucial driving force for such developments is the availability and integration of "omics" data (e.g., transcriptomics, proteomics, and metabolomics) which enable the reconstruction, extraction, and application of context-specific metabolic networks. In this chapter, we demonstrate a workflow to integrate gas chromatography coupled to mass spectrometry (GC-MS)-based metabolomics data of tomato fruit pericarp (flesh) tissue, at five developmental stages, with a genome-scale reconstruction of tomato metabolism. This method allows for the extraction of context-specific networks reflecting changing activities of metabolic pathways throughout fruit development and maturation.

  9. Genome scale metabolic network reconstruction of Spirochaeta cellobiosiphila

    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.

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

    Mahadevan Radhakrishnan

    2010-05-01

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

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

    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

  12. Enumeration of smallest intervention strategies in genome-scale metabolic networks.

    Axel von Kamp

    2014-01-01

    Full Text Available One ultimate goal of metabolic network modeling is the rational redesign of biochemical networks to optimize the production of certain compounds by cellular systems. Although several constraint-based optimization techniques have been developed for this purpose, methods for systematic enumeration of intervention strategies in genome-scale metabolic networks are still lacking. In principle, Minimal Cut Sets (MCSs; inclusion-minimal combinations of reaction or gene deletions that lead to the fulfilment of a given intervention goal provide an exhaustive enumeration approach. However, their disadvantage is the combinatorial explosion in larger networks and the requirement to compute first the elementary modes (EMs which itself is impractical in genome-scale networks. We present MCSEnumerator, a new method for effective enumeration of the smallest MCSs (with fewest interventions in genome-scale metabolic network models. For this we combine two approaches, namely (i the mapping of MCSs to EMs in a dual network, and (ii a modified algorithm by which shortest EMs can be effectively determined in large networks. In this way, we can identify the smallest MCSs by calculating the shortest EMs in the dual network. Realistic application examples demonstrate that our algorithm is able to list thousands of the most efficient intervention strategies in genome-scale networks for various intervention problems. For instance, for the first time we could enumerate all synthetic lethals in E.coli with combinations of up to 5 reactions. We also applied the new algorithm exemplarily to compute strain designs for growth-coupled synthesis of different products (ethanol, fumarate, serine by E.coli. We found numerous new engineering strategies partially requiring less knockouts and guaranteeing higher product yields (even without the assumption of optimal growth than reported previously. The strength of the presented approach is that smallest intervention strategies can be

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

    Reifman Jaques

    2010-11-01

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

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

    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.

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

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

    2016-04-01

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

  16. FluxVisualizer, a Software to Visualize Fluxes through Metabolic Networks

    Tim Daniel Rose

    2018-04-01

    Full Text Available FluxVisualizer (Version 1.0, 2017, freely available at https://fluxvisualizer.ibgc.cnrs.fr is a software to visualize fluxes values on a scalable vector graphic (SVG representation of a metabolic network by colouring or increasing the width of reaction arrows of the SVG file. FluxVisualizer does not aim to draw metabolic networks but to use a customer’s SVG file allowing him to exploit his representation standards with a minimum of constraints. FluxVisualizer is especially suitable for small to medium size metabolic networks, where a visual representation of the fluxes makes sense. The flux distribution can either be an elementary flux mode (EFM, a flux balance analysis (FBA result or any other flux distribution. It allows the automatic visualization of a series of pathways of the same network as is needed for a set of EFMs. The software is coded in python3 and provides a graphical user interface (GUI and an application programming interface (API. All functionalities of the program can be used from the API and the GUI and allows advanced users to add their own functionalities. The software is able to work with various formats of flux distributions (Metatool, CellNetAnalyzer, COPASI and FAME export files as well as with Excel files. This simple software can save a lot of time when evaluating fluxes simulations on a metabolic network.

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

    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.

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

    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

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

    Fenger, Mogens; Linneberg, Allan; Jeppesen, Jørgen

    2015-01-01

    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...

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

    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

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

    Tang, Chris C; Eidelberg, David

    2010-01-01

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

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

    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.

  3. Metabolic Vascular Syndrome: New Insights into a Multidimensional Network of Risk Factors and Diseases.

    Scholz, Gerhard H; Hanefeld, Markolf

    2016-10-01

    Since 1981, we have used the term metabolic syndrome to describe an association of a dysregulation in lipid metabolism (high triglycerides, low high-density lipoprotein cholesterol, disturbed glucose homeostasis (enhanced fasting and/or prandial glucose), gout, and hypertension), with android obesity being based on a common soil (overnutrition, reduced physical activity, sociocultural factors, and genetic predisposition). We hypothesized that main traits of the syndrome occur early and are tightly connected with hyperinsulinemia/insulin resistance, procoagulation, and cardiovascular diseases. To establish a close link between the traits of the metabolic vascular syndrome, we focused our literature search on recent original work and comprehensive reviews dealing with the topics metabolic syndrome, visceral obesity, fatty liver, fat tissue inflammation, insulin resistance, atherogenic dyslipidemia, arterial hypertension, and type 2 diabetes mellitus. Recent research supports the concept that the metabolic vascular syndrome is a multidimensional and interactive network of risk factors and diseases based on individual genetic susceptibility and epigenetic changes where metabolic dysregulation/metabolic inflexibility in different organs and vascular dysfunction are early interconnected. The metabolic vascular syndrome is not only a risk factor constellation but rather a life-long abnormality of a closely connected interactive cluster of developing diseases which escalate each other and should continuously attract the attention of every clinician.

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

    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.

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

    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.

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

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

    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.

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

    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.

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

    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

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

    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...... replicates. In order to analyze the dynamic mass spectrometry data, we developed the novel analysis methods that allow us to perform correlation analysis to identify metabolites that significantly correlate over time during growth on the different carbon sources. Both positive and negative electrospray...... reconstructed an interaction map that provides information of how different metabolic pathways have correlated patterns during growth on the different carbon sources....

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

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

    2011-11-01

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

  12. Identifying all moiety conservation laws in genome-scale metabolic networks.

    De Martino, Andrea; De Martino, Daniele; Mulet, Roberto; Pagnani, Andrea

    2014-01-01

    The stoichiometry of a metabolic network gives rise to a set of conservation laws for the aggregate level of specific pools of metabolites, which, on one hand, pose dynamical constraints that cross-link the variations of metabolite concentrations and, on the other, provide key insight into a cell's metabolic production capabilities. When the conserved quantity identifies with a chemical moiety, extracting all such conservation laws from the stoichiometry amounts to finding all non-negative integer solutions of a linear system, a programming problem known to be NP-hard. We present an efficient strategy to compute the complete set of integer conservation laws of a genome-scale stoichiometric matrix, also providing a certificate for correctness and maximality of the solution. Our method is deployed for the analysis of moiety conservation relationships in two large-scale reconstructions of the metabolism of the bacterium E. coli, in six tissue-specific human metabolic networks, and, finally, in the human reactome as a whole, revealing that bacterial metabolism could be evolutionarily designed to cover broader production spectra than human metabolism. Convergence to the full set of moiety conservation laws in each case is achieved in extremely reduced computing times. In addition, we uncover a scaling relation that links the size of the independent pool basis to the number of metabolites, for which we present an analytical explanation.

  13. Identifying all moiety conservation laws in genome-scale metabolic networks.

    Andrea De Martino

    Full Text Available The stoichiometry of a metabolic network gives rise to a set of conservation laws for the aggregate level of specific pools of metabolites, which, on one hand, pose dynamical constraints that cross-link the variations of metabolite concentrations and, on the other, provide key insight into a cell's metabolic production capabilities. When the conserved quantity identifies with a chemical moiety, extracting all such conservation laws from the stoichiometry amounts to finding all non-negative integer solutions of a linear system, a programming problem known to be NP-hard. We present an efficient strategy to compute the complete set of integer conservation laws of a genome-scale stoichiometric matrix, also providing a certificate for correctness and maximality of the solution. Our method is deployed for the analysis of moiety conservation relationships in two large-scale reconstructions of the metabolism of the bacterium E. coli, in six tissue-specific human metabolic networks, and, finally, in the human reactome as a whole, revealing that bacterial metabolism could be evolutionarily designed to cover broader production spectra than human metabolism. Convergence to the full set of moiety conservation laws in each case is achieved in extremely reduced computing times. In addition, we uncover a scaling relation that links the size of the independent pool basis to the number of metabolites, for which we present an analytical explanation.

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

    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

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

    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.

  16. Molecular Networking As a Drug Discovery, Drug Metabolism, and Precision Medicine Strategy.

    Quinn, Robert A; Nothias, Louis-Felix; Vining, Oliver; Meehan, Michael; Esquenazi, Eduardo; Dorrestein, Pieter C

    2017-02-01

    Molecular networking is a tandem mass spectrometry (MS/MS) data organizational approach that has been recently introduced in the drug discovery, metabolomics, and medical fields. The chemistry of molecules dictates how they will be fragmented by MS/MS in the gas phase and, therefore, two related molecules are likely to display similar fragment ion spectra. Molecular networking organizes the MS/MS data as a relational spectral network thereby mapping the chemistry that was detected in an MS/MS-based metabolomics experiment. Although the wider utility of molecular networking is just beginning to be recognized, in this review we highlight the principles behind molecular networking and its use for the discovery of therapeutic leads, monitoring drug metabolism, clinical diagnostics, and emerging applications in precision medicine. Copyright © 2016. Published by Elsevier Ltd.

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

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

    2017-01-01

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

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

    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.

  19. Abnormal metabolic network activity in REM sleep behavior disorder.

    Holtbernd, Florian; Gagnon, Jean-François; Postuma, Ron B; Ma, Yilong; Tang, Chris C; Feigin, Andrew; Dhawan, Vijay; Vendette, Mélanie; Soucy, Jean-Paul; Eidelberg, David; Montplaisir, Jacques

    2014-02-18

    To determine whether the Parkinson disease-related covariance pattern (PDRP) expression is abnormally increased in idiopathic REM sleep behavior disorder (RBD) and whether increased baseline activity is associated with greater individual risk of subsequent phenoconversion. For this cohort study, we recruited 2 groups of RBD and control subjects. Cohort 1 comprised 10 subjects with RBD (63.5 ± 9.4 years old) and 10 healthy volunteers (62.7 ± 8.6 years old) who underwent resting-state metabolic brain imaging with (18)F-fluorodeoxyglucose PET. Cohort 2 comprised 17 subjects with RBD (68.9 ± 4.8 years old) and 17 healthy volunteers (66.6 ± 6.0 years old) who underwent resting brain perfusion imaging with ethylcysteinate dimer SPECT. The latter group was followed clinically for 4.6 ± 2.5 years by investigators blinded to the imaging results. PDRP expression was measured in both RBD groups and compared with corresponding control values. PDRP expression was elevated in both groups of subjects with RBD (cohort 1: p abnormalities in subjects with idiopathic RBD are associated with a greater likelihood of subsequent phenoconversion to a progressive neurodegenerative syndrome.

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

    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.

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

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

    2014-08-12

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

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

    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

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

    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.

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

    Elena Vinay-Lara

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

  5. Differential network analysis reveals evolutionary complexity in secondary metabolism of Rauvolfia serpentina over Catharanthus roseus

    Shivalika Pathania

    2016-08-01

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

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

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

    2016-01-01

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

  7. A FDG-PET Study of Metabolic Networks in Apolipoprotein E ε4 Allele Carriers.

    Zhijun Yao

    Full Text Available Recently, some studies have applied the graph theory in brain network analysis in Alzheimer's disease (AD and Mild Cognitive Impairment (MCI. However, relatively little research has specifically explored the properties of the metabolic network in apolipoprotein E (APOE ε4 allele carriers. In our study, all the subjects, including ADs, MCIs and NCs (normal controls were divided into 165 APOE ε4 carriers and 165 APOE ε4 noncarriers. To establish the metabolic network for all brain regions except the cerebellum, cerebral glucose metabolism data obtained from FDG-PET (18F-fluorodeoxyglucose positron emission tomography were segmented into 90 areas with automated anatomical labeling (AAL template. Then, the properties of the networks were computed to explore the between-group differences. Our results suggested that both APOE ε4 carriers and noncarriers showed the small-world properties. Besides, compared with APOE ε4 noncarriers, the carriers showed a lower clustering coefficient. In addition, significant changes in 6 hub brain regions were found in between-group nodal centrality. Namely, compared with APOE ε4 noncarriers, significant decreases of the nodal centrality were found in left insula, right insula, right anterior cingulate, right paracingulate gyri, left cuneus, as well as significant increases in left paracentral lobule and left heschl gyrus in APOE ε4 carriers. Increased local short distance interregional correlations and disrupted long distance interregional correlations were found, which may support the point that the APOE ε4 carriers were more similar with AD or MCI in FDG uptake. In summary, the organization of metabolic network in APOE ε4 carriers indicated a less optimal pattern and APOE ε4 might be a risk factor for AD.

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

    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

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

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

    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.

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

    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

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

    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.

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

    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.

  14. Simultaneous Parameters Identifiability and Estimation of an E. coli Metabolic Network Model

    Kese Pontes Freitas Alberton

    2015-01-01

    Full Text Available This work proposes a procedure for simultaneous parameters identifiability and estimation in metabolic networks in order to overcome difficulties associated with lack of experimental data and large number of parameters, a common scenario in the modeling of such systems. As case study, the complex real problem of parameters identifiability of the Escherichia coli K-12 W3110 dynamic model was investigated, composed by 18 differential ordinary equations and 35 kinetic rates, containing 125 parameters. With the procedure, model fit was improved for most of the measured metabolites, achieving 58 parameters estimated, including 5 unknown initial conditions. The results indicate that simultaneous parameters identifiability and estimation approach in metabolic networks is appealing, since model fit to the most of measured metabolites was possible even when important measures of intracellular metabolites and good initial estimates of parameters are not available.

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

    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.

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

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

    2000-01-01

    Increasing the flux through central carbon metabolism is difficult because of rigidity in regulatory structures, at both the genetic and the enzymatic levels. Here we describe metabolic engineering of a regulatory network to obtain a balanced increase in the activity of all the enzymes in the pat...... 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....... 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...

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

    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,...

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

    Ingkasuwan Papapit

    2012-08-01

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

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

    Jorge Fernandez-de-Cossio-Diaz

    2017-11-01

    Full Text Available 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.

  20. Metabolism

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

  1. Legal provisions governing the acknowledgment of test results

    Strecker, A.

    1982-01-01

    The legal provisions governing the acknowledgment of test results are most frequently applied by administrative orders (design and qualification approvals or specimen testing and approval) and are thus claimable and voidable in accordance with general administrative law. The acknowledgment of test certificates requires a legal basis. Test results, however, can be acknowledged also by administrative bodies. Recently, the Federal Government began to delegate more of its legal authority in this field to private institutions, allowing test results to be acknowledged and test certificates to be issued by government controlled private institutions. (orig.) [de

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

    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

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

    Huthmacher Carola

    2010-08-01

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

  4. XenoSite: accurately predicting CYP-mediated sites of metabolism with neural networks.

    Zaretzki, Jed; Matlock, Matthew; Swamidass, S Joshua

    2013-12-23

    Understanding how xenobiotic molecules are metabolized is important because it influences the safety, efficacy, and dose of medicines and how they can be modified to improve these properties. The cytochrome P450s (CYPs) are proteins responsible for metabolizing 90% of drugs on the market, and many computational methods can predict which atomic sites of a molecule--sites of metabolism (SOMs)--are modified during CYP-mediated metabolism. This study improves on prior methods of predicting CYP-mediated SOMs by using new descriptors and machine learning based on neural networks. The new method, XenoSite, is faster to train and more accurate by as much as 4% or 5% for some isozymes. Furthermore, some "incorrect" predictions made by XenoSite were subsequently validated as correct predictions by revaluation of the source literature. Moreover, XenoSite output is interpretable as a probability, which reflects both the confidence of the model that a particular atom is metabolized and the statistical likelihood that its prediction for that atom is correct.

  5. The acknowledge project: toward improved efficiency in the knowledge acquisition process

    Marty, J.C.; Ramparany, F.

    1990-01-01

    This paper presents a general overview of the ACKnowledge Project (Acquisition of Knowledge). Knowledge Acquisition is a critical and time-consuming phase in the development of expert systems. The ACKnowledge project aims at improving the efficiency of knowledge acquisition by analyzing and evaluating knowledge acquisition techniques, and developing a Knowledge Engineering Workbench that supports the Knowledge Engineer from the early stage of knowledge aquisition up to the implementation of the knowledge base in large and complex application domains such as the diagnosis of dynamic computer networks

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

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

    2013-09-01

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

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

    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

  8. Network thermodynamic curation of human and yeast genome-scale metabolic models.

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

    2014-07-15

    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. Copyright © 2014 Biophysical Society. Published by Elsevier Inc. All rights reserved.

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

    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.

  10. An Investigation of Generic Structures of Pakistani Doctoral Thesis Acknowledgements

    Rofess, Sakander; Mahmood, Muhammad Asim

    2015-01-01

    This paper investigates Pakistani doctoral thesis acknowledgements from genre analysis perspective. A corpus of 235 PhD thesis acknowledgements written in English was taken from Pakistani doctoral theses collected from eight different disciplines. HEC Research Repository of Pakistan was used as a data sources. The theses written by Pakistani…

  11. International Adoption: Issues of Acknowledgement of Adoption and Birth Culture.

    Trolley, Barbara C.; And Others

    1995-01-01

    Families who adopt children internationally are faced with not only the acknowledgement of the adoption but also the recognition of the child's birth culture. Thirty-four families were surveyed to assess issues regarding the relevance, frequency, and means of acknowledgement of the adoption and of the birth culture. Findings suggest ways adoption…

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

    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 .

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

    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.

  14. Mimoza: web-based semantic zooming and navigation in metabolic networks.

    Zhukova, Anna; Sherman, David J

    2015-02-26

    The complexity of genome-scale metabolic models makes them quite difficult for human users to read, since they contain thousands of reactions that must be included for accurate computer simulation. Interestingly, hidden similarities between groups of reactions can be discovered, and generalized to reveal higher-level patterns. The web-based navigation system Mimoza allows a human expert to explore metabolic network models in a semantically zoomable manner: The most general view represents the compartments of the model; the next view shows the generalized versions of reactions and metabolites in each compartment; and the most detailed view represents the initial network with the generalization-based layout (where similar metabolites and reactions are placed next to each other). It allows a human expert to grasp the general structure of the network and analyze it in a top-down manner Mimoza can be installed standalone, or used on-line at http://mimoza.bordeaux.inria.fr/ , or installed in a Galaxy server for use in workflows. Mimoza views can be embedded in web pages, or downloaded as COMBINE archives.

  15. Capturing the essence of a metabolic network: a flux balance analysis approach.

    Murabito, Ettore; Simeonidis, Evangelos; Smallbone, Kieran; Swinton, Jonathan

    2009-10-07

    As genome-scale metabolic reconstructions emerge, tools to manage their size and complexity will be increasingly important. Flux balance analysis (FBA) is a constraint-based approach widely used to study the metabolic capabilities of cellular or subcellular systems. FBA problems are highly underdetermined and many different phenotypes can satisfy any set of constraints through which the metabolic system is represented. Two of the main concerns in FBA are exploring the space of solutions for a given metabolic network and finding a specific phenotype which is representative for a given task such as maximal growth rate. Here, we introduce a recursive algorithm suitable for overcoming both of these concerns. The method proposed is able to find the alternate optimal patterns of active reactions of an FBA problem and identify the minimal subnetwork able to perform a specific task as optimally as the whole. Our method represents an alternative to and an extension of other approaches conceived for exploring the space of solutions of an FBA problem. It may also be particularly helpful in defining a scaffold of reactions upon which to build up a dynamic model, when the important pathways of the system have not yet been well-defined.

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

    Sadhukhan, Priyanka P; Raghunathan, Anu

    2014-01-01

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

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

    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.

  18. Metabolism

    ... lin), which signals cells to increase their anabolic activities. Metabolism is a complicated chemical process, so it's not ... how those enzymes or hormones work. When the metabolism of body chemicals is ... Hyperthyroidism (pronounced: hi-per-THIGH-roy-dih-zum). Hyperthyroidism ...

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

    Natalie J Stanford

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

  20. Systematic Construction of Kinetic Models from Genome-Scale Metabolic Networks

    Smallbone, Kieran; Klipp, Edda; Mendes, Pedro; Liebermeister, Wolfram

    2013-01-01

    The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments. PMID:24324546

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

    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.

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

    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

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

    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...... was extensively validated against published flux data, and flux distribution values were found to correlate well between simulations and experiments. The split pathway of the lysine synthesis pathway of C. glutamicum was investigated, and it was found that the direct dehydrogenase variant gave a higher lysine...... yield than the alternative succinyl pathway at high lysine production rates. The NADPH demand of the network was not found to be critical for lysine production until lysine yields exceeded 55% (mmol lysine (mmol glucose)(-1)). The model was validated during growth on the organic acids acetate...

  4. 14 CFR 91.1013 - Operational control briefing and acknowledgment.

    2010-01-01

    ... Ownership Operations Operational Control § 91.1013 Operational control briefing and acknowledgment. (a) Upon...) Liability risk in the event of a flight-related occurrence that causes personal injury or property damage...

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

    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

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

    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

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

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

    2014-07-01

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

  8. Integration of genome-scale metabolic networks into whole-body PBPK models shows phenotype-specific cases of drug-induced metabolic perturbation.

    Cordes, Henrik; Thiel, Christoph; Baier, Vanessa; Blank, Lars M; Kuepfer, Lars

    2018-01-01

    Drug-induced perturbations of the endogenous metabolic network are a potential root cause of cellular toxicity. A mechanistic understanding of such unwanted side effects during drug therapy is therefore vital for patient safety. The comprehensive assessment of such drug-induced injuries requires the simultaneous consideration of both drug exposure at the whole-body and resulting biochemical responses at the cellular level. We here present a computational multi-scale workflow that combines whole-body physiologically based pharmacokinetic (PBPK) models and organ-specific genome-scale metabolic network (GSMN) models through shared reactions of the xenobiotic metabolism. The applicability of the proposed workflow is illustrated for isoniazid, a first-line antibacterial agent against Mycobacterium tuberculosis , which is known to cause idiosyncratic drug-induced liver injuries (DILI). We combined GSMN models of a human liver with N-acetyl transferase 2 (NAT2)-phenotype-specific PBPK models of isoniazid. The combined PBPK-GSMN models quantitatively describe isoniazid pharmacokinetics, as well as intracellular responses, and changes in the exometabolome in a human liver following isoniazid administration. Notably, intracellular and extracellular responses identified with the PBPK-GSMN models are in line with experimental and clinical findings. Moreover, the drug-induced metabolic perturbations are distributed and attenuated in the metabolic network in a phenotype-dependent manner. Our simulation results show that a simultaneous consideration of both drug pharmacokinetics at the whole-body and metabolism at the cellular level is mandatory to explain drug-induced injuries at the patient level. The proposed workflow extends our mechanistic understanding of the biochemistry underlying adverse events and may be used to prevent drug-induced injuries in the future.

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

    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

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

    Zhou, Wanding; Nakhleh, Luay

    2012-09-14

    It has been reported that the modularity of metabolic networks of bacteria is closely related to the variability of their living habitats. However, given the dependency of the modularity score on the community structure, it remains unknown whether organisms achieve certain modularity via similar or different community structures. In this work, we studied the relationship between similarities in modularity scores and similarities in community structures of the metabolic networks of 1021 species. Both similarities are then compared against the genetic distances. We revisited the association between modularity and variability of the microbial living environments and extended the analysis to other aspects of their life style such as temperature and oxygen requirements. We also tested both topological and biological intuition of the community structures identified and investigated the extent of their conservation with respect to the taxonomy. We find that similar modularities are realized by different community structures. We find that such convergent evolution of modularity is closely associated with the number of (distinct) enzymes in the organism's metabolome, a consequence of different life styles of the species. We find that the order of modularity is the same as the order of the number of the enzymes under the classification based on the temperature preference but not on the oxygen requirement. Besides, inspection of modularity-based communities reveals that these communities are graph-theoretically meaningful yet not reflective of specific biological functions. From an evolutionary perspective, we find that the community structures are conserved only at the level of kingdoms. Our results call for more investigation into the interplay between evolution and modularity: how evolution shapes modularity, and how modularity affects evolution (mainly in terms of fitness and evolvability). Further, our results call for exploring new measures of modularity and network

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

    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.

  12. Construction and simulation of the Bradyrhizobium diazoefficiens USDA110 metabolic network: a comparison between free-living and symbiotic states.

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

    2017-02-28

    Bradyrhizobium diazoefficiens is a rhizobium able to convert atmospheric nitrogen into ammonium by establishing mutualistic symbiosis with soybean. It has been recognized as an important parent strain for microbial agents and is widely applied in agricultural and environmental fields. In order to study the metabolic properties of symbiotic nitrogen fixation and the differences between a free-living cell and a symbiotic bacteroid, a genome-scale metabolic network of B. diazoefficiens USDA110 was constructed and analyzed. The metabolic network, iYY1101, contains 1031 reactions, 661 metabolites, and 1101 genes in total. Metabolic models reflecting free-living and symbiotic states were determined by defining the corresponding objective functions and substrate input sets, and were further constrained by high-throughput transcriptomic and proteomic data. Constraint-based flux analysis was used to compare the metabolic capacities and the effects on the metabolic targets of genes and reactions between the two physiological states. The results showed that a free-living rhizobium possesses a steady state flux distribution for sustaining a complex supply of biomass precursors while a symbiotic bacteroid maintains a relatively condensed one adapted to nitrogen-fixation. Our metabolic models may serve as a promising platform for better understanding the symbiotic nitrogen fixation of this species.

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

    Didic, Mira; Felician, Olivier; Gour, Natalina; Ceccaldi, Mathieu; Bernard, Rafaelle; Pecheux, Christophe; Mundler, Olivier; Guedj, Eric

    2015-01-01

    The ε4 allele of the apolipoprotein E (APO-E4) gene, a genetic risk factor for Alzheimer's disease (AD), also modulates brain metabolism and function in healthy subjects. The aim of the present study was to explore cerebral metabolism using FDG PET in healthy APO-E4 carriers by comparing cognitively normal APO-E4 carriers to noncarriers and to assess if patterns of metabolism are correlated with performance on cognitive tasks. Moreover, metabolic connectivity patterns were established in order to assess if the organization of neural networks is influenced by genetic factors. Whole-brain PET statistical analysis was performed at voxel-level using SPM8 with a threshold of p < 0.005, corrected for volume, with age, gender and level of education as nuisance variables. Significant hypometabolism between APO-E4 carriers (n = 11) and noncarriers (n = 30) was first determined. Mean metabolic values with clinical/neuropsychological data were extracted at the individual level, and correlations were searched using Spearman's rank test in the whole group. To evaluate metabolic connectivity from metabolic cluster(s) previously identified in the intergroup comparison, voxel-wise interregional correlation analysis (IRCA) was performed between groups of subjects. APO-E4 carriers had reduced metabolism within the left anterior medial temporal lobe (MTL), where neuropathological changes first appear in AD, including the entorhinal and perirhinal cortices. A correlation between metabolism in this area and performance on the DMS48 (delayed matching to sample-48 items) was found, in line with converging evidence involving the perirhinal cortex in object-based memory. Finally, a voxel-wise IRCA revealed stronger metabolic connectivity of the MTL cluster with neocortical frontoparietal regions in carriers than in noncarriers, suggesting compensatory metabolic networks. Exploring cerebral metabolism using FDG PET can contribute to a better understanding of the influence of

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

    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

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

    Sidhartha Chaudhury

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

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

    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.

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

    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.

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

    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.

  19. From Network Analysis to Functional Metabolic Modeling of the Human Gut Microbiota.

    Bauer, Eugen; Thiele, Ines

    2018-01-01

    An important hallmark of the human gut microbiota is its species diversity and complexity. Various diseases have been associated with a decreased diversity leading to reduced metabolic functionalities. Common approaches to investigate the human microbiota include high-throughput sequencing with subsequent correlative analyses. However, to understand the ecology of the human gut microbiota and consequently design novel treatments for diseases, it is important to represent the different interactions between microbes with their associated metabolites. Computational systems biology approaches can give further mechanistic insights by constructing data- or knowledge-driven networks that represent microbe interactions. In this minireview, we will discuss current approaches in systems biology to analyze the human gut microbiota, with a particular focus on constraint-based modeling. We will discuss various community modeling techniques with their advantages and differences, as well as their application to predict the metabolic mechanisms of intestinal microbial communities. Finally, we will discuss future perspectives and current challenges of simulating realistic and comprehensive models of the human gut microbiota.

  20. Novel sterol metabolic network of Trypanosoma brucei procyclic and bloodstream forms

    Nes, Craigen R.; Singha, Ujjal K.; Liu, Jialin; Ganapathy, Kulothungan; Villalta, Fernando; Waterman, Michael R.; Lepesheva, Galina I.; Chaudhuri, Minu; Nes, W. David

    2012-01-01

    Trypanosoma brucei is the protozoan parasite that causes African trypanosomiasis, a neglected disease of people and animals. Co-metabolite analysis, labelling studies using [methyl-2H3]-methionine and substrate/product specificities of the cloned 24-SMT (sterol C24-methyltransferase) and 14-SDM (sterol C14-demethylase) from T. brucei afforded an uncommon sterol metabolic network that proceeds from lanosterol and 31-norlanosterol to ETO [ergosta-5,7,25(27)-trien-3β-ol], 24-DTO [dimethyl ergosta-5,7,25(27)-trienol] and ergosterol [ergosta-5,7,22(23)-trienol]. To assess the possible carbon sources of ergosterol biosynthesis, specifically 13C-labelled specimens of lanosterol, acetate, leucine and glucose were administered to T. brucei and the 13C distributions found were in accord with the operation of the acetate–mevalonate pathway, with leucine as an alternative precursor, to ergostenols in either the insect or bloodstream form. In searching for metabolic signatures of procyclic cells, we observed that the 13C-labelling treatments induce fluctuations between the acetyl-CoA (mitochondrial) and sterol (cytosolic) synthetic pathways detected by the progressive increase in 13C-ergosterol production (control sterol synthesis that is further fluctuated in the cytosol, yielding distinct sterol profiles in relation to cell demands on growth. PMID:22176028

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

    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.

  2. Capturing Qualitative Data: Northwestern University Special Libraries' Acknowledgments Database

    Stigberg, Sara; Guittar, Michelle; Morse, Geoffrey

    2015-01-01

    Assessment and supporting data have become of increasing interest in librarianship. In this paper, we describe the development and implementation of the Northwestern University Library Acknowledgments Database tool, which gathers and documents qualitative data, as well as its component reporting function. This collaborative project and resulting…

  3. Listener Responses According to Stuttering Self-Acknowledgment and Modification

    Lee, Kyungjae; Manning, Walter H.

    2010-01-01

    Given the well-documented understanding that stuttering behavior elicits stereotypically negative responses from listeners, two experiments explored the equivocal results of earlier investigations concerning the potential for self-acknowledgment and modification of stuttering to elicit positive responses from naive (unfamiliar with stuttering)…

  4. Reframing Assessment: Reconceptualising Relationships and Acknowledging Emotional Labour

    Cooper, Maria

    2017-01-01

    Implementing relationships-based pedagogies in infant and toddler settings might assume that teachers' experiences of emotional labour will be acknowledged. This assumption may be complicated by assessment practices that both rely on and detract from relationship-building opportunities with infants and toddlers. Assessment also relies on…

  5. Insights on the evolution of metabolic networks of unicellular translationally biased organisms from transcriptomic data and sequence analysis.

    Carbone, Alessandra; Madden, Richard

    2005-10-01

    Codon bias is related to metabolic functions in translationally biased organisms, and two facts are argued about. First, genes with high codon bias describe in meaningful ways the metabolic characteristics of the organism; important metabolic pathways corresponding to crucial characteristics of the lifestyle of an organism, such as photosynthesis, nitrification, anaerobic versus aerobic respiration, sulfate reduction, methanogenesis, and others, happen to involve especially biased genes. Second, gene transcriptional levels of sets of experiments representing a significant variation of biological conditions strikingly confirm, in the case of Saccharomyces cerevisiae, that metabolic preferences are detectable by purely statistical analysis: the high metabolic activity of yeast during fermentation is encoded in the high bias of enzymes involved in the associated pathways, suggesting that this genome was affected by a strong evolutionary pressure that favored a predominantly fermentative metabolism of yeast in the wild. The ensemble of metabolic pathways involving enzymes with high codon bias is rather well defined and remains consistent across many species, even those that have not been considered as translationally biased, such as Helicobacter pylori, for instance, reveal some weak form of translational bias for this genome. We provide numerical evidence, supported by experimental data, of these facts and conclude that the metabolic networks of translationally biased genomes, observable today as projections of eons of evolutionary pressure, can be analyzed numerically and predictions of the role of specific pathways during evolution can be derived. The new concepts of Comparative Pathway Index, used to compare organisms with respect to their metabolic networks, and Evolutionary Pathway Index, used to detect evolutionarily meaningful bias in the genetic code from transcriptional data, are introduced.

  6. Acknowledging patient heterogeneity in economic evaluation : a systematic literature review.

    Grutters, Janneke P C; Sculpher, Mark; Briggs, Andrew H; Severens, Johan L; Candel, Math J; Stahl, James E; De Ruysscher, Dirk; Boer, Albert; Ramaekers, Bram L T; Joore, Manuela A

    2013-02-01

    Patient heterogeneity is the part of variability that can be explained by certain patient characteristics (e.g. age, disease stage). Population reimbursement decisions that acknowledge patient heterogeneity could potentially save money and increase population health. To date, however, economic evaluations pay only limited attention to patient heterogeneity. The objective of the present paper is to provide a comprehensive overview of the current knowledge regarding patient heterogeneity within economic evaluation of healthcare programmes. A systematic literature review was performed to identify methodological papers on the topic of patient heterogeneity in economic evaluation. Data were obtained using a keyword search of the PubMed database and manual searches. Handbooks were also included. Relevant data were extracted regarding potential sources of patient heterogeneity, in which of the input parameters of an economic evaluation these occur, methods to acknowledge patient heterogeneity and specific concerns associated with this acknowledgement. A total of 20 articles and five handbooks were included. The relevant sources of patient heterogeneity (demographics, preferences and clinical characteristics) and the input parameters where they occurred (baseline risk, treatment effect, health state utility and resource utilization) were combined in a framework. Methods were derived for the design, analysis and presentation phases of an economic evaluation. Concerns related mainly to the danger of false-positive results and equity issues. By systematically reviewing current knowledge regarding patient heterogeneity within economic evaluations of healthcare programmes, we provide guidance for future economic evaluations. Guidance is provided on which sources of patient heterogeneity to consider, how to acknowledge them in economic evaluation and potential concerns. The improved acknowledgement of patient heterogeneity in future economic evaluations may well improve the

  7. Canadian University Acknowledgment of Indigenous Lands, Treaties, and Peoples.

    Wilkes, Rima; Duong, Aaron; Kesler, Linc; Ramos, Howard

    2017-02-01

    At many Canadian universities it is now common to publicly acknowledge Indigenous lands, treaties, and peoples. Yet, this practice has yet to be considered as a subject of scholarly inquiry. How does this practice vary and why? In this paper we describe the content and practice of acknowledgment, linking this content to treaty relationships (or lack thereof). We show that acknowledgment tends to be one of five general types: of land and title (British Columbia), of specific treaties and political relationships (Prairies), of multiculturalism and heterogeneity (Ontario), of no practice (most of Quebec), and of people, territory, and openness to doing more (Atlantic). Based on these results, we conclude that the fluidity of acknowledgment as a practice, including changing meanings depending on the positionality of the acknowledger, need to be taken into account. Plusieurs universités Canadien pratique une reconnaissance des territoires, des traités, et des peoples autochtone en publique. Cette pratique, cependant, n'a jamais été considérée comme une enquête savante. Dans ce projet nous regardons comment les reconnaissances varie par institution et pourquoi. Nous trouvons qu'il y a un lien entre le contenu des reconnaissances et les relations traité. On démontre cinq forme des reconnaissances: territoire et titre (Colombie britannique); traité spécifique and les relations politiques (Prairies); multiculturalisme et hétérogénéité (Ontario); l'absence (la majorité du Québec); et des peoples, territoire et volonté a plus faire (Atlantique). Nous concluons que la fluidité de la reconnaissance, comme pratique, est fluide et doit prendre en considération la position de la personne qui le fait. © 2017 Canadian Sociological Association/La Société canadienne de sociologie.

  8. Bringing metabolic networks to life: convenience rate law and thermodynamic constraints

    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.

  9. LmSmdB: an integrated database for metabolic and gene regulatory network in Leishmania major and Schistosoma mansoni

    Priyanka Patel

    2016-03-01

    Full Text Available A database that integrates all the information required for biological processing is essential to be stored in one platform. We have attempted to create one such integrated database that can be a one stop shop for the essential features required to fetch valuable result. LmSmdB (L. major and S. mansoni database is an integrated database that accounts for the biological networks and regulatory pathways computationally determined by integrating the knowledge of the genome sequences of the mentioned organisms. It is the first database of its kind that has together with the network designing showed the simulation pattern of the product. This database intends to create a comprehensive canopy for the regulation of lipid metabolism reaction in the parasite by integrating the transcription factors, regulatory genes and the protein products controlled by the transcription factors and hence operating the metabolism at genetic level. Keywords: L.major, S.mansoni, Regulatory networks, Transcription factors, Database

  10. Improving the satellite communication efficiency of the accumulative acknowledgement strategies

    Duarte, Otto Carlos M. B.; de Lima, Heliomar Medeiros

    The performances of two finite buffer error recovery strategies are analyzed. In both strategies the retransmission request decision between selective repeat and continuous retransmission is based on an imminent buffer overflow condition. These are accumulative acknowledgment schemes, but in the second strategy the selective-repeat control frame is uniquely an individual negative acknowledgment. The two strategies take advantage of the availability of a greater buffer capacity, making the most of the selective repeat, postponing the sending of a continuous retransmission request. Numerical results show a better performance very close to the ideal, but it does not integrally conform to the high-level data link control (HDLC) procedures. It is shown that these strategies are well suited for high-speed data transfer in the high-error-rate satellite environment.

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

    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

  12. Linking metabolic QTLs with network and cis-eQTLs controlling biosynthetic pathways.

    Adam M Wentzell

    2007-09-01

    Full Text Available Phenotypic variation between individuals of a species is often under quantitative genetic control. Genomic analysis of gene expression polymorphisms between individuals is rapidly gaining popularity as a way to query the underlying mechanistic causes of variation between individuals. However, there is little direct evidence of a linkage between global gene expression polymorphisms and phenotypic consequences. In this report, we have mapped quantitative trait loci (QTLs-controlling glucosinolate content in a population of 403 Arabidopsis Bay x Sha recombinant inbred lines, 211 of which were previously used to identify expression QTLs controlling the transcript levels of biosynthetic genes. In a comparative study, we have directly tested two plant biosynthetic pathways for association between polymorphisms controlling biosynthetic gene transcripts and the resulting metabolites within the Arabidopsis Bay x Sha recombinant inbred line population. In this analysis, all loci controlling expression variation also affected the accumulation of the resulting metabolites. In addition, epistasis was detected more frequently for metabolic traits compared to transcript traits, even when both traits showed similar distributions. An analysis of candidate genes for QTL-controlling networks of transcripts and metabolites suggested that the controlling factors are a mix of enzymes and regulatory factors. This analysis showed that regulatory connections can feedback from metabolism to transcripts. Surprisingly, the most likely major regulator of both transcript level for nearly the entire pathway and aliphatic glucosinolate accumulation is variation in the last enzyme in the biosynthetic pathway, AOP2. This suggests that natural variation in transcripts may significantly impact phenotypic variation, but that natural variation in metabolites or their enzymatic loci can feed back to affect the transcripts.

  13. ABORt: Acknowledgement-Based Opportunistic Routing Protocol for High Data Rate Multichannel WSNs

    Hamadoun Tall

    2017-10-01

    Full Text Available The ease of deployment and the auto-configuration capabilities of Wireless Sensor Networks (WSNs make them very attractive in different domains like environmental, home automation or heath care applications. The use of multichannel communications in WSNs helps to improve the overall performance of the network. However, in heavy traffic scenarios, routing protocols should be adapted to allow load balancing and to avoid losing data packets due to congestion and queue overflow. In this paper, we present an Acknowledgement-Based Opportunistic Routing (ABORt protocol designed for high data rate multichannel WSNs. It is a low overhead protocol that does not rely on synchronization for control traffic exchange during the operational phase of the network. ABORt is an opportunistic protocol that relies on link layer acknowledgements to disseminate routing metrics, which helps to reduce overhead. The performance of ABORt is evaluated using the Cooja simulator and the obtained results show that ABORt has a high packet delivery ratio with reduced packet end-to-end delay compared to two single channel routing protocols and two multichannel routing protocols that use number of hops and expected transmission count as routing metrics.

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

    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.

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

    Yuan, H.; Cheung, C.Y. Maurice; Poolman, M.G.; Hilbers, P.A.J.; van Riel, N.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

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

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

    2016-09-15

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

  17. MOST-visualization: software for producing automated textbook-style maps of genome-scale metabolic networks.

    Kelley, James J; Maor, Shay; Kim, Min Kyung; Lane, Anatoliy; Lun, Desmond S

    2017-08-15

    Visualization of metabolites, reactions and pathways in genome-scale metabolic networks (GEMs) can assist in understanding cellular metabolism. Three attributes are desirable in software used for visualizing GEMs: (i) automation, since GEMs can be quite large; (ii) production of understandable maps that provide ease in identification of pathways, reactions and metabolites; and (iii) visualization of the entire network to show how pathways are interconnected. No software currently exists for visualizing GEMs that satisfies all three characteristics, but MOST-Visualization, an extension of the software package MOST (Metabolic Optimization and Simulation Tool), satisfies (i), and by using a pre-drawn overview map of metabolism based on the Roche map satisfies (ii) and comes close to satisfying (iii). MOST is distributed for free on the GNU General Public License. The software and full documentation are available at http://most.ccib.rutgers.edu/. dslun@rutgers.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

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

    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

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

    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

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

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

    2015-02-01

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

  1. [The birth of acknowledgement: Michel Foucault and Werner Leibbrand].

    Mildenberger, Florian

    2006-01-01

    In 1964, Werner Leibbrand (1896-1974) was the first German medical historian to present, in Sudhoffs Archiv, a review of the work of Michel Foucault (1926-1984). This paper examines some of the reasons leading to the fact that Leibbrand's own generation refused to acknowledge the importance of Foucault's ideas, while, later on, younger German medical historians, although impressed with Foucault's writings, failed to acknowledge, first, the close relationship between Leibbrand's and Foucault's world views, and, second, Leibbrand's attempts at introducing Foucault to German medical historians. Leibbrand with his Jewish wife had survived the Nazi period partly in hiding. His attempts at clearing post-war German psychiatry and medical historiography of NS-sympathizers isolated him among his colleagues, many of whom had begun their career during the Third Reich. Leibbrand enjoyed the support by the Swiss medical historian and avowed Communist Erwin Ackerknecht (1906-1988), but later turned against him, possibly because Acknerknecht had called Leibbrand's writings "unscientific". Leibbrand was unable to overcome his antagonisms with his contemporaries. At the same time, opposition to Ackerknecht made him appear a respresentative of the past in the eyes of the younger generation. Thus, when Foucault was accepted by the latter, they were not prepared to examine the work of Leibbrand and realize how close some of the ideas developed by Leibbrand and Foucault had been.

  2. Acknowledging sexual bereavement: a path out of disenfranchised grief.

    Radosh, Alice; Simkin, Linda

    2016-11-01

    Despite increasing awareness of the importance of sexuality for older adults, research and popular literature rarely acknowledge what we term "sexual bereavement" - mourning the loss of sexual intimacy when predeceased. The reluctance to acknowledge sexual bereavement may create "disenfranchised grief" leaving the bereaved unsupported in coping with this aspect of mourning. This preliminary study focuses on women in the United States and sought to determine whether they anticipate missing sex if predeceased, whether they would want to talk about this loss, and identified factors associated with communicating about sexual bereavement. Findings from our survey of 104 women, 55 years and older, most of whom were heterosexual, revealed that a large majority (72%) anticipates missing sex with their partner and 67% would want to initiate a discussion about this. An even higher percentage would want friends to initiate the topic. Yet, 57% of participants report it would not occur to them to initiate a discussion with a widowed friend about the friend's loss. Disenfranchised grief can have negative emotional and physical consequences. This paper suggests a role for friends and professionals in addressing this neglected issue. Copyright © 2016 Elsevier Inc. All rights reserved.

  3. Abnormal metabolic brain network associated with Parkinson's disease: replication on a new European sample

    Tomse, Petra; Jensterle, Luka; Grmek, Marko; Zaletel, Katja; Pirtosek, Zvezdan; Trost, Maja; Dhawan, Vijay; Peng, Shichun; Eidelberg, David; Ma, Yilong

    2017-01-01

    The purpose of this study was to identify the specific metabolic brain pattern characteristic for Parkinson's disease (PD): Parkinson's disease-related pattern (PDRP), using network analysis of [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) brain images in a cohort of Slovenian PD patients. Twenty PD patients (age 70.1 ± 7.8 years, Movement Disorder Society Unified Parkinson's Disease Motor Rating Scale (MDS-UPDRS-III) 38.3 ± 12.2; disease duration 4.3 ± 4.1 years) and 20 age-matched normal controls (NCs) underwent FDG-PET brain imaging. An automatic voxel-based scaled subprofile model/principal component analysis (SSM/PCA) was applied to these scans for PDRP-Slovenia identification. The pattern was characterized by relative hypermetabolism in pallidum, putamen, thalamus, brain stem, and cerebellum associated with hypometabolism in sensorimotor cortex, posterior parietal, occipital, and frontal cortices. The expression of PDRP-Slovenia discriminated PD patients from NCs (p < 0.0001) and correlated positively with patients' clinical score (MDS-UPDRS-III, p = 0.03). Additionally, its topography agrees well with the original PDRP (p < 0.001) identified in American cohort of PD patients. We validated the PDRP-Slovenia expression on additional FDG-PET scans of 20 PD patients, 20 NCs, and 25 patients with atypical parkinsonism (AP). We confirmed that the expression of PDRP-Slovenia manifests good diagnostic accuracy with specificity and sensitivity of 85-90% at optimal pattern expression cutoff for discrimination of PD patients and NCs and is not expressed in AP. PDRP-Slovenia proves to be a robust and reproducible functional imaging biomarker independent of patient population. It accurately differentiates PD patients from NCs and AP and correlates well with the clinical measure of PD progression. (orig.)

  4. Robust Regression Analysis of GCMS Data Reveals Differential Rewiring of Metabolic Networks in Hepatitis B and C Patients

    Cedric Simillion

    2017-10-01

    Full Text Available About one in 15 of the world’s population is chronically infected with either hepatitis virus B (HBV or C (HCV, with enormous public health consequences. The metabolic alterations caused by these infections have never been directly compared and contrasted. We investigated groups of HBV-positive, HCV-positive, and uninfected healthy controls using gas chromatography-mass spectrometry analyses of their plasma and urine. A robust regression analysis of the metabolite data was conducted to reveal correlations between metabolite pairs. Ten metabolite correlations appeared for HBV plasma and urine, with 18 for HCV plasma and urine, none of which were present in the controls. Metabolic perturbation networks were constructed, which permitted a differential view of the HBV- and HCV-infected liver. HBV hepatitis was consistent with enhanced glucose uptake, glycolysis, and pentose phosphate pathway metabolism, the latter using xylitol and producing threonic acid, which may also be imported by glucose transporters. HCV hepatitis was consistent with impaired glucose uptake, glycolysis, and pentose phosphate pathway metabolism, with the tricarboxylic acid pathway fueled by branched-chain amino acids feeding gluconeogenesis and the hepatocellular loss of glucose, which most probably contributed to hyperglycemia. It is concluded that robust regression analyses can uncover metabolic rewiring in disease states.

  5. Thermodynamic analysis of computed pathways integrated into the metabolic networks of E. coli and Synechocystis reveals contrasting expansion potential.

    Asplund-Samuelsson, Johannes; Janasch, Markus; Hudson, Elton P

    2018-01-01

    Introducing biosynthetic pathways into an organism is both reliant on and challenged by endogenous biochemistry. Here we compared the expansion potential of the metabolic network in the photoautotroph Synechocystis with that of the heterotroph E. coli using the novel workflow POPPY (Prospecting Optimal Pathways with PYthon). First, E. coli and Synechocystis metabolomic and fluxomic data were combined with metabolic models to identify thermodynamic constraints on metabolite concentrations (NET analysis). Then, thousands of automatically constructed pathways were placed within each network and subjected to a network-embedded variant of the max-min driving force analysis (NEM). We found that the networks had different capabilities for imparting thermodynamic driving forces toward certain compounds. Key metabolites were constrained differently in Synechocystis due to opposing flux directions in glycolysis and carbon fixation, the forked tri-carboxylic acid cycle, and photorespiration. Furthermore, the lysine biosynthesis pathway in Synechocystis was identified as thermodynamically constrained, impacting both endogenous and heterologous reactions through low 2-oxoglutarate levels. Our study also identified important yet poorly covered areas in existing metabolomics data and provides a reference for future thermodynamics-based engineering in Synechocystis and beyond. The POPPY methodology represents a step in making optimal pathway-host matches, which is likely to become important as the practical range of host organisms is diversified. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  6. Respiratory and metabolic acidosis differentially affect the respiratory neuronal network in the ventral medulla of neonatal rats.

    Okada, Yasumasa; Masumiya, Haruko; Tamura, Yoshiyasu; Oku, Yoshitaka

    2007-11-01

    Two respiratory-related areas, the para-facial respiratory group/retrotrapezoid nucleus (pFRG/RTN) and the pre-Bötzinger complex/ventral respiratory group (preBötC/VRG), are thought to play key roles in respiratory rhythm. Because respiratory output patterns in response to respiratory and metabolic acidosis differ, we hypothesized that the responses of the medullary respiratory neuronal network to respiratory and metabolic acidosis are different. To test these hypotheses, we analysed respiratory-related activity in the pFRG/RTN and preBötC/VRG of the neonatal rat brainstem-spinal cord in vitro by optical imaging using a voltage-sensitive dye, and compared the effects of respiratory and metabolic acidosis on these two populations. We found that the spatiotemporal responses of respiratory-related regional activities to respiratory and metabolic acidosis are fundamentally different, although both acidosis similarly augmented respiratory output by increasing respiratory frequency. PreBötC/VRG activity, which is mainly inspiratory, was augmented by respiratory acidosis. Respiratory-modulated pixels increased in the preBötC/VRG area in response to respiratory acidosis. Metabolic acidosis shifted the respiratory phase in the pFRG/RTN; the pre-inspiratory dominant pattern shifted to inspiratory dominant. The responses of the pFRG/RTN activity to respiratory and metabolic acidosis are complex, and involve either augmentation or reduction in the size of respiratory-related areas. Furthermore, the activation pattern in the pFRG/RTN switched bi-directionally between pre-inspiratory/inspiratory and post-inspiratory. Electrophysiological study supported the results of our optical imaging study. We conclude that respiratory and metabolic acidosis differentially affect activities of the pFRG/RTN and preBötC/VRG, inducing switching and shifts of the respiratory phase. We suggest that they differently influence the coupling states between the pFRG/RTN and preBötC/VRG.

  7. Coexpression network analysis in abdominal and gluteal adipose tissue reveals regulatory genetic loci for metabolic syndrome and related phenotypes

    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...... and 51 (0.6%) in GLU only. Differential eigengene network analysis of 8,256 shared probesets detected 22 shared modules with high preservation across adipose depots (D(ABD-GLU) = 0.89), seven of which were associated with MetS (FDR P100,000 individuals; rs10282458, affecting expression of RARRES2...... and their interactions influence complex traits such as MetS, integrated analysis of genotypes and coexpression networks across multiple tissues relevant to clinical traits is an efficient strategy to identify novel associations....

  8. The origin of modern metabolic networks inferred from phylogenomic analysis of protein architecture.

    Caetano-Anollés, Gustavo; Kim, Hee Shin; Mittenthal, Jay E

    2007-05-29

    Metabolism represents a complex collection of enzymatic reactions and transport processes that convert metabolites into molecules capable of supporting cellular life. Here we explore the origins and evolution of modern metabolism. Using phylogenomic information linked to the structure of metabolic enzymes, we sort out recruitment processes and discover that most enzymatic activities were associated with the nine most ancient and widely distributed protein fold architectures. An analysis of newly discovered functions showed enzymatic diversification occurred early, during the onset of the modern protein world. Most importantly, phylogenetic reconstruction exercises and other evidence suggest strongly that metabolism originated in enzymes with the P-loop hydrolase fold in nucleotide metabolism, probably in pathways linked to the purine metabolic subnetwork. Consequently, the first enzymatic takeover of an ancient biochemistry or prebiotic chemistry was related to the synthesis of nucleotides for the RNA world.

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

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

    2017-09-01

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

  10. Genome-scale reconstruction of the metabolic network in Yersinia pestis CO92

    Navid, Ali; Almaas, Eivind

    2007-03-01

    The gram-negative bacterium Yersinia pestis is the causative agent of bubonic plague. Using publicly available genomic, biochemical and physiological data, we have developed a constraint-based flux balance model of metabolism in the CO92 strain (biovar Orientalis) of this organism. The metabolic reactions were appropriately compartmentalized, and the model accounts for the exchange of metabolites, as well as the import of nutrients and export of waste products. We have characterized the metabolic capabilities and phenotypes of this organism, after comparing the model predictions with available experimental observations to evaluate accuracy and completeness. We have also begun preliminary studies into how cellular metabolism affects virulence.

  11. Addressing unknown constants and metabolic network behaviors through petascale computing: understanding H2 production in green algae

    Chang, Christopher; Alber, David; Graf, Peter; Kim, Kwiseon; Seibert, Michael

    2007-01-01

    The Genomics Revolution has resulted in a massive and growing quantity of whole-genome DNA sequences, which encode the metabolic catalysts necessary for life. However, gene annotations can rarely be complete, and measurement of the kinetic constants associated with the encoded enzymes can not possibly keep pace, necessitating the use of careful modeling to explore plausible network behaviors. Key challenges are (1) quantitatively formulating kinetic laws governing each transformation in a fixed model network; (2) characterizing the stable solution (if any) of the associated ordinary differential equations (ODEs); (3) fitting the latter to metabolomics data as it becomes available; and (4) optimizing a model output against the possible space of kinetic parameters, with respect to properties such as robustness of network response, or maximum consumption/production. This SciDAC-2 project addresses this large-scale uncertainty in the genome-scale metabolic network of the water-splitting, H 2 -producing green alga Chlamydomonas reinhardtii. Each metabolic transformation is formulated as an irreversible steady-state process, such that the vast literature on known enzyme mechanisms may be incorporated directly. To start, glycolysis, the tricarboxylic acid cycle, and basic fermentation pathways have been encoded in Systems Biology Markup Language (SBML) with careful annotation and consistency with the KEGG database, yielding a model with 3 compartments, 95 species, 38 reactions, and 109 kinetic constants. To study and optimize such models with a view toward larger models, we have developed a system which takes as input an SBML model, and automatically produces C code that when compiled and executed optimizes the model's kinetic parameters according to test criteria. We describe the system and present numerical results. Further development, including overlaying of a parallel multistart algorithm, will allow optimization of thousands of parameters on high-performance systems

  12. Addressing unknown constants and metabolic network behaviors through petascale computing: understanding H{sub 2} production in green algae

    Chang, Christopher; Alber, David; Graf, Peter; Kim, Kwiseon; Seibert, Michael [National Renewable Energy Laboratory (NREL), Golden, CO 80401 (United States)

    2007-07-15

    The Genomics Revolution has resulted in a massive and growing quantity of whole-genome DNA sequences, which encode the metabolic catalysts necessary for life. However, gene annotations can rarely be complete, and measurement of the kinetic constants associated with the encoded enzymes can not possibly keep pace, necessitating the use of careful modeling to explore plausible network behaviors. Key challenges are (1) quantitatively formulating kinetic laws governing each transformation in a fixed model network; (2) characterizing the stable solution (if any) of the associated ordinary differential equations (ODEs); (3) fitting the latter to metabolomics data as it becomes available; and (4) optimizing a model output against the possible space of kinetic parameters, with respect to properties such as robustness of network response, or maximum consumption/production. This SciDAC-2 project addresses this large-scale uncertainty in the genome-scale metabolic network of the water-splitting, H{sub 2}-producing green alga Chlamydomonas reinhardtii. Each metabolic transformation is formulated as an irreversible steady-state process, such that the vast literature on known enzyme mechanisms may be incorporated directly. To start, glycolysis, the tricarboxylic acid cycle, and basic fermentation pathways have been encoded in Systems Biology Markup Language (SBML) with careful annotation and consistency with the KEGG database, yielding a model with 3 compartments, 95 species, 38 reactions, and 109 kinetic constants. To study and optimize such models with a view toward larger models, we have developed a system which takes as input an SBML model, and automatically produces C code that when compiled and executed optimizes the model's kinetic parameters according to test criteria. We describe the system and present numerical results. Further development, including overlaying of a parallel multistart algorithm, will allow optimization of thousands of parameters on high

  13. In Silico Genome-Scale Reconstruction and Validation of the Staphylococcus aureus Metabolic Network

    Heinemann, Matthias; Kümmel, Anne; Ruinatscha, Reto; Panke, Sven

    2005-01-01

    A genome-scale metabolic model of the Gram-positive, facultative anaerobic opportunistic pathogen Staphylococcus aureus N315 was constructed based on current genomic data, literature, and physiological information. The model comprises 774 metabolic processes representing approximately 23% of all

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

    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

  15. Loss of variation of state detected in soybean metabolic and human myelomonocytic leukaemia cell transcriptional networks under external stimuli

    Sakata, Katsumi

    2016-10-24

    Soybean (Glycine max) is sensitive to flooding stress, and flood damage at the seedling stage is a barrier to growth. We constructed two mathematical models of the soybean metabolic network, a control model and a flooded model, from metabolic profiles in soybean plants. We simulated the metabolic profiles with perturbations before and after the flooding stimulus using the two models. We measured the variation of state that the system could maintain from a state–space description of the simulated profiles. The results showed a loss of variation of state during the flooding response in the soybean plants. Loss of variation of state was also observed in a human myelomonocytic leukaemia cell transcriptional network in response to a phorbol-ester stimulus. Thus, we detected a loss of variation of state under external stimuli in two biological systems, regardless of the regulation and stimulus types. Our results suggest that a loss of robustness may occur concurrently with the loss of variation of state in biological systems. We describe the possible applications of the quantity of variation of state in plant genetic engineering and cell biology. Finally, we present a hypothetical “external stimulus-induced information loss” model of biological systems.

  16. Loss of variation of state detected in soybean metabolic and human myelomonocytic leukaemia cell transcriptional networks under external stimuli

    Sakata, Katsumi; Saito, Toshiyuki; Ohyanagi, Hajime; Okumura, Jun; Ishige, Kentaro; Suzuki, Harukazu; Nakamura, Takuji; Komatsu, Setsuko

    2016-01-01

    Soybean (Glycine max) is sensitive to flooding stress, and flood damage at the seedling stage is a barrier to growth. We constructed two mathematical models of the soybean metabolic network, a control model and a flooded model, from metabolic profiles in soybean plants. We simulated the metabolic profiles with perturbations before and after the flooding stimulus using the two models. We measured the variation of state that the system could maintain from a state–space description of the simulated profiles. The results showed a loss of variation of state during the flooding response in the soybean plants. Loss of variation of state was also observed in a human myelomonocytic leukaemia cell transcriptional network in response to a phorbol-ester stimulus. Thus, we detected a loss of variation of state under external stimuli in two biological systems, regardless of the regulation and stimulus types. Our results suggest that a loss of robustness may occur concurrently with the loss of variation of state in biological systems. We describe the possible applications of the quantity of variation of state in plant genetic engineering and cell biology. Finally, we present a hypothetical “external stimulus-induced information loss” model of biological systems.

  17. Using a genome-scale metabolic network model to elucidate the mechanism of chloroquine action in Plasmodium falciparum

    Shivendra G. Tewari

    2017-08-01

    . falciparum from the host system. Keywords: Plasmodium, Chloroquine, Metabolic network modeling, Redox metabolism, Carbon fixation

  18. Using isotopic tracers to assess the impact of tillage and straw management on the microbial metabolic network in soil

    Van Groenigen, K.; Forristal, D.; Jones, M. B.; Schwartz, E.; Hungate, B. A.; Dijkstra, P.

    2013-12-01

    By decomposing soil organic matter, microbes gain energy and building blocks for biosynthesis and release CO2 to the atmosphere. Therefore, insight into the effect of management practices on microbial metabolic pathways and C use efficiency (CUE; microbial C produced per substrate C utilized) may help to predict long term changes in soil C stocks. We studied the effects of reduced (RT) and conventional tillage (CT) on the microbial central C metabolic network, using soil samples from a 12-year-old field experiment in an Irish winter wheat cropping system. Each year after harvest, straw was removed from half of the RT and CT plots or incorporated into the soil in the other half, resulting in four treatment combinations. We added 1-13C and 2,3-13C pyruvate and 1-13C and U-13C glucose as metabolic tracer isotopomers to composite soil samples taken at two depths (0-15 cm and 15-30 cm) from each treatment and used the rate of position-specific respired 13CO2 to parameterize a metabolic model. Model outcomes were then used to calculate CUE of the microbial community. We found that the composite samples differed in CUE, but the changes were small, with values ranging between 0.757-0.783 across treatments and soil depth. Increases in CUE were associated with a decrease in tricarboxylic acid cycle and reductive pentose phosphate pathway activity and increased consumption of metabolic intermediates for biosynthesis. Our results indicate that RT and straw incorporation promote soil C storage without substantially changing CUE or any of the microbial metabolic pathways. This suggests that at our site, RT and straw incorporation promote soil C storage mostly through direct effects such as increased soil C input and physical protection from decomposition, rather than by feedback responses of the microbial community.

  19. Cholesteryl ester transfer protein alters liver and plasma triglyceride metabolism through two liver networks in female mice[S

    Palmisano, Brian T.; Le, Thao D.; Zhu, Lin; Lee, Yoon Kwang; Stafford, John M.

    2016-01-01

    Elevated plasma TGs increase risk of cardiovascular disease in women. Estrogen treatment raises plasma TGs in women, but molecular mechanisms remain poorly understood. Here we explore the role of cholesteryl ester transfer protein (CETP) in the regulation of TG metabolism in female mice, which naturally lack CETP. In transgenic CETP females, acute estrogen treatment raised plasma TGs 50%, increased TG production, and increased expression of genes involved in VLDL synthesis, but not in nontransgenic littermate females. In CETP females, estrogen enhanced expression of small heterodimer partner (SHP), a nuclear receptor regulating VLDL production. Deletion of liver SHP prevented increases in TG production and expression of genes involved in VLDL synthesis in CETP mice with estrogen treatment. We also examined whether CETP expression had effects on TG metabolism independent of estrogen treatment. CETP increased liver β-oxidation and reduced liver TG content by 60%. Liver estrogen receptor α (ERα) was required for CETP expression to enhance β-oxidation and reduce liver TG content. Thus, CETP alters at least two networks governing TG metabolism, one involving SHP to increase VLDL-TG production in response to estrogen, and another involving ERα to enhance β-oxidation and lower liver TG content. These findings demonstrate a novel role for CETP in estrogen-mediated increases in TG production and a broader role for CETP in TG metabolism. PMID:27354419

  20. Cholesteryl ester transfer protein alters liver and plasma triglyceride metabolism through two liver networks in female mice.

    Palmisano, Brian T; Le, Thao D; Zhu, Lin; Lee, Yoon Kwang; Stafford, John M

    2016-08-01

    Elevated plasma TGs increase risk of cardiovascular disease in women. Estrogen treatment raises plasma TGs in women, but molecular mechanisms remain poorly understood. Here we explore the role of cholesteryl ester transfer protein (CETP) in the regulation of TG metabolism in female mice, which naturally lack CETP. In transgenic CETP females, acute estrogen treatment raised plasma TGs 50%, increased TG production, and increased expression of genes involved in VLDL synthesis, but not in nontransgenic littermate females. In CETP females, estrogen enhanced expression of small heterodimer partner (SHP), a nuclear receptor regulating VLDL production. Deletion of liver SHP prevented increases in TG production and expression of genes involved in VLDL synthesis in CETP mice with estrogen treatment. We also examined whether CETP expression had effects on TG metabolism independent of estrogen treatment. CETP increased liver β-oxidation and reduced liver TG content by 60%. Liver estrogen receptor α (ERα) was required for CETP expression to enhance β-oxidation and reduce liver TG content. Thus, CETP alters at least two networks governing TG metabolism, one involving SHP to increase VLDL-TG production in response to estrogen, and another involving ERα to enhance β-oxidation and lower liver TG content. These findings demonstrate a novel role for CETP in estrogen-mediated increases in TG production and a broader role for CETP in TG metabolism. Copyright © 2016 by the American Society for Biochemistry and Molecular Biology, Inc.

  1. Integrated in silico Analyses of Regulatory and Metabolic Networks of Synechococcus sp. PCC 7002 Reveal Relationships between Gene Centrality and Essentiality

    Hyun-Seob Song

    2015-03-01

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

  2. Complications: acknowledging, managing, and coping with human error.

    Helo, Sevann; Moulton, Carol-Anne E

    2017-08-01

    Errors are inherent in medicine due to the imperfectness of human nature. Health care providers may have a difficult time accepting their fallibility, acknowledging mistakes, and disclosing errors. Fear of litigation, shame, blame, and concern about reputation are just some of the barriers preventing physicians from being more candid with their patients, despite the supporting body of evidence that patients cite poor communication and lack of transparency as primary drivers to file a lawsuit in the wake of a medical complication. Proper error disclosure includes a timely explanation of what happened, who was involved, why the error occurred, and how it will be prevented in the future. Medical mistakes afford the opportunity for individuals and institutions to be candid about their weaknesses while improving patient care processes. When a physician takes the Hippocratic Oath they take on a tremendous sense of responsibility for the care of their patients, and often bear the burden of their mistakes in isolation. Physicians may struggle with guilt, shame, and a crisis of confidence, which may thwart efforts to identify areas for improvement that can lead to meaningful change. Coping strategies for providers include discussing the event with others, seeking professional counseling, and implementing quality improvement projects. Physicians and health care organizations need to find adaptive ways to deal with complications that will benefit patients, providers, and their institutions.

  3. Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering

    He, F.; Murabito, E.; Westerhoff, H.V.

    2016-01-01

    Metabolic pathways can be engineered to maximize the synthesis of various products of interest. With the advent of computational systems biology, this endeavour is usually carried out throughin silicotheoretical studies with the aim to guide and complement furtherin vitroandin vivoexperimental

  4. Investigating genotype-phenotype relationships in Saccharomyces cerevisiae metabolic network through stoichiometric modeling

    Brochado, Ana Rita

    processes. Metabolism is an extensively studied and characterised subcellular system, for which several modeling approaches have been proposed over the last 20 years. Nowadays, stoichiometric modeling of metabolism is done at the genome scale and it has diverse applications, many of them for helping....... This chapter aims at providing the reader with relevant state-of-the-art information concerning Systems Biology, Genome-Scale Metabolic Modeling and Metabolic Engineering. Particular attention is given to the yeast Saccharomyces cerevisiae, the eukaryotic model organism used thought the thesis.......A holistic view of the cell is fundamental for gaining insights into genotype to phenotype relationships. Systems Biology is a discipline within Biology, which uses such holistic approach by focusing on the development and application of tools for studying the structure and dynamics of cellular...

  5. A systems approach to predict oncometabolites via context-specific genome-scale metabolic networks.

    Hojung Nam

    2014-09-01

    Full Text Available Altered metabolism in cancer cells has been viewed as a passive response required for a malignant transformation. However, this view has changed through the recently described metabolic oncogenic factors: mutated isocitrate dehydrogenases (IDH, succinate dehydrogenase (SDH, and fumarate hydratase (FH that produce oncometabolites that competitively inhibit epigenetic regulation. In this study, we demonstrate in silico predictions of oncometabolites that have the potential to dysregulate epigenetic controls in nine types of cancer by incorporating massive scale genetic mutation information (collected from more than 1,700 cancer genomes, expression profiling data, and deploying Recon 2 to reconstruct context-specific genome-scale metabolic models. Our analysis predicted 15 compounds and 24 substructures of potential oncometabolites that could result from the loss-of-function and gain-of-function mutations of metabolic enzymes, respectively. These results suggest a substantial potential for discovering unidentified oncometabolites in various forms of cancers.

  6. Hybrid dynamic modeling of Escherichia coli central metabolic network combining Michaelis–Menten and approximate kinetic equations

    Costa, Rafael S.; Machado, Daniel; Rocha, Isabel

    2010-01-01

    , represent nowadays the limiting factor in the construction of such models. In this study, we compare four alternative modeling approaches based on Michaelis–Menten kinetics for the bi-molecular reactions and different types of simplified rate equations for the remaining reactions (generalized mass action......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...

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

    Huthmacher, Carola; Hoppe, Andreas; Bulik, Sascha; Holzh?tter, Hermann-Georg

    2010-01-01

    Abstract Background Despite enormous efforts to combat malaria the disease still afflicts up to half a billion people each year of which more than one million die. Currently no approved vaccine is available and resistances to antimalarials are widely spread. Hence, new antimalarial drugs are urgently needed. Results Here, we present a computational analysis of the metabolism of Plasmodium falciparum, the deadliest malaria pathogen. We assembled a compartmentalized metabolic model and predicte...

  8. PPARγ isoforms differentially regulate metabolic networks to mediate mouse prostatic epithelial differentiation.

    Strand, D W; Jiang, M; Murphy, T A; Yi, Y; Konvinse, K C; Franco, O E; Wang, Y; Young, J D; Hayward, S W

    2012-08-09

    Recent observations indicate prostatic diseases are comorbidities of systemic metabolic dysfunction. These discoveries revealed fundamental questions regarding the nature of prostate metabolism. We previously showed that prostate-specific ablation of PPARγ in mice resulted in tumorigenesis and active autophagy. Here, we demonstrate control of overlapping and distinct aspects of prostate epithelial metabolism by ectopic expression of individual PPARγ isoforms in PPARγ knockout prostate epithelial cells. Expression and activation of either PPARγ 1 or 2 reduced de novo lipogenesis and oxidative stress and mediated a switch from glucose to fatty acid oxidation through regulation of genes including Pdk4, Fabp4, Lpl, Acot1 and Cd36. Differential effects of PPARγ isoforms included decreased basal cell differentiation, Scd1 expression and triglyceride fatty acid desaturation and increased tumorigenicity by PPARγ1. In contrast, PPARγ2 expression significantly increased basal cell differentiation, Scd1 expression and AR expression and responsiveness. Finally, in confirmation of in vitro data, a PPARγ agonist versus high-fat diet (HFD) regimen in vivo confirmed that PPARγ agonization increased prostatic differentiation markers, whereas HFD downregulated PPARγ-regulated genes and decreased prostate differentiation. These data provide a rationale for pursuing a fundamental metabolic understanding of changes to glucose and fatty acid metabolism in benign and malignant prostatic diseases associated with systemic metabolic stress.

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

    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. SolCyc: a database hub at the Sol Genomics Network (SGN) for the manual curation of metabolic networks in Solanum and Nicotiana specific databases

    Foerster, Hartmut; Bombarely, Aureliano; Battey, James N D; Sierro, Nicolas; Ivanov, Nikolai V; Mueller, Lukas A

    2018-01-01

    Abstract SolCyc is the entry portal to pathway/genome databases (PGDBs) for major species of the Solanaceae family hosted at the Sol Genomics Network. Currently, SolCyc comprises six organism-specific PGDBs for tomato, potato, pepper, petunia, tobacco and one Rubiaceae, coffee. The metabolic networks of those PGDBs have been computationally predicted by the pathologic component of the pathway tools software using the manually curated multi-domain database MetaCyc (http://www.metacyc.org/) as reference. SolCyc has been recently extended by taxon-specific databases, i.e. the family-specific SolanaCyc database, containing only curated data pertinent to species of the nightshade family, and NicotianaCyc, a genus-specific database that stores all relevant metabolic data of the Nicotiana genus. Through manual curation of the published literature, new metabolic pathways have been created in those databases, which are complemented by the continuously updated, relevant species-specific pathways from MetaCyc. At present, SolanaCyc comprises 199 pathways and 29 superpathways and NicotianaCyc accounts for 72 pathways and 13 superpathways. Curator-maintained, taxon-specific databases such as SolanaCyc and NicotianaCyc are characterized by an enrichment of data specific to these taxa and free of falsely predicted pathways. Both databases have been used to update recently created Nicotiana-specific databases for Nicotiana tabacum, Nicotiana benthamiana, Nicotiana sylvestris and Nicotiana tomentosiformis by propagating verifiable data into those PGDBs. In addition, in-depth curation of the pathways in N.tabacum has been carried out which resulted in the elimination of 156 pathways from the 569 pathways predicted by pathway tools. Together, in-depth curation of the predicted pathway network and the supplementation with curated data from taxon-specific databases has substantially improved the curation status of the species–specific N.tabacum PGDB. The implementation of this

  11. Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming

    Song, Hyun-Seob; Goldberg, Noam; Mahajan, Ashutosh; Ramkrishna, Doraiswami

    2017-03-27

    Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. To alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Results: Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs.

  12. Metal availability and the expanding network of microbial metabolisms in the Archaean eon

    Moore, Eli K.; Jelen, Benjamin I.; Giovannelli, Donato; Raanan, Hagai; Falkowski, Paul G.

    2017-09-01

    Life is based on energy gained by electron-transfer processes; these processes rely on oxidoreductase enzymes, which often contain transition metals in their structures. The availability of different metals and substrates has changed over the course of Earth's history as a result of secular changes in redox conditions, particularly global oxygenation. New metabolic pathways using different transition metals co-evolved alongside changing redox conditions. Sulfur reduction, sulfate reduction, methanogenesis and anoxygenic photosynthesis appeared between about 3.8 and 3.4 billion years ago. The oxidoreductases responsible for these metabolisms incorporated metals that were readily available in Archaean oceans, chiefly iron and iron-sulfur clusters. Oxygenic photosynthesis appeared between 3.2 and 2.5 billion years ago, as did methane oxidation, nitrogen fixation, nitrification and denitrification. These metabolisms rely on an expanded range of transition metals presumably made available by the build-up of molecular oxygen in soil crusts and marine microbial mats. The appropriation of copper in enzymes before the Great Oxidation Event is particularly important, as copper is key to nitrogen and methane cycling and was later incorporated into numerous aerobic metabolisms. We find that the diversity of metals used in oxidoreductases has increased through time, suggesting that surface redox potential and metal incorporation influenced the evolution of metabolism, biological electron transfer and microbial ecology.

  13. ReacKnock: identifying reaction deletion strategies for microbial strain optimization based on genome-scale metabolic network.

    Zixiang Xu

    Full Text Available Gene knockout has been used as a common strategy to improve microbial strains for producing chemicals. Several algorithms are available to predict the target reactions to be deleted. Most of them apply mixed integer bi-level linear programming (MIBLP based on metabolic networks, and use duality theory to transform bi-level optimization problem of large-scale MIBLP to single-level programming. However, the validity of the transformation was not proved. Solution of MIBLP depends on the structure of inner problem. If the inner problem is continuous, Karush-Kuhn-Tucker (KKT method can be used to reformulate the MIBLP to a single-level one. We adopt KKT technique in our algorithm ReacKnock to attack the intractable problem of the solution of MIBLP, demonstrated with the genome-scale metabolic network model of E. coli for producing various chemicals such as succinate, ethanol, threonine and etc. Compared to the previous methods, our algorithm is fast, stable and reliable to find the optimal solutions for all the chemical products tested, and able to provide all the alternative deletion strategies which lead to the same industrial objective.

  14. Deletion of the Mitochondrial Chaperone TRAP-1 Uncovers Global Reprogramming of Metabolic Networks

    Sofia Lisanti

    2014-08-01

    Full Text Available Reprogramming of metabolic pathways contributes to human disease, especially cancer, but the regulators of this process are unknown. Here, we have generated a mouse knockout for the mitochondrial chaperone TRAP-1, a regulator of bioenergetics in tumors. TRAP-1−/− mice are viable and showed reduced incidence of age-associated pathologies, including obesity, inflammatory tissue degeneration, dysplasia, and spontaneous tumor formation. This was accompanied by global upregulation of oxidative phosphorylation and glycolysis transcriptomes, causing deregulated mitochondrial respiration, oxidative stress, impaired cell proliferation, and a switch to glycolytic metabolism in vivo. These data identify TRAP-1 as a central regulator of mitochondrial bioenergetics, and this pathway could contribute to metabolic rewiring in tumors.

  15. Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics

    Bordbar, Aarash; Yurkovich, James T.; Paglia, Giuseppe

    2017-01-01

    The increasing availability of metabolomics data necessitates novel methods for deeper data analysis and interpretation. We present a flux balance analysis method that allows for the computation of dynamic intracellular metabolic changes at the cellular scale through integration of time-course ab......The increasing availability of metabolomics data necessitates novel methods for deeper data analysis and interpretation. We present a flux balance analysis method that allows for the computation of dynamic intracellular metabolic changes at the cellular scale through integration of time...

  16. Predicting Rape Victim Empathy Based on Rape Victimization and Acknowledgment Labeling.

    Osman, Suzanne L

    2016-06-01

    Two studies examined rape victim empathy based on personal rape victimization and acknowledgment labeling. Female undergraduates (Study 1, n = 267; Study 2, n = 381) from a Northeast U.S. midsize public university completed the Rape-Victim Empathy Scale and Sexual Experiences Survey. As predicted, both studies found that acknowledged "rape" victims reported greater empathy than unacknowledged victims and nonvictims. Unexpectedly, these latter two groups did not differ. Study 1 also found that acknowledged "rape" victims reported greater empathy than victims who acknowledged being "sexually victimized." Findings suggest that being raped and acknowledging "rape" together may facilitate rape victim empathy. © The Author(s) 2015.

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

    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...

  18. Enzyme allocation problems in kinetic metabolic networks: Optimal solutions are elementary flux modes

    Müller, Stefan; Regensburger, G.; Steuer, Ralf

    2014-01-01

    Roč. 347, APR 2014 (2014), s. 182-190 ISSN 0022-5193 R&D Projects: GA MŠk(CZ) EE2.3.20.0256 Institutional support: RVO:67179843 Keywords : metabolic optimization * enzyme kinetics * oriented matroid * elementary vector * conformal sum Subject RIV: EI - Biotechnology ; Bionics Impact factor: 2.116, year: 2014

  19. Lack of evolvability in self-sustaining autocatalytic networks constraints metabolism-first scenarios for the origin of life.

    Vasas, Vera; Szathmáry, Eörs; Santos, Mauro

    2010-01-26

    A basic property of life is its capacity to experience Darwinian evolution. The replicator concept is at the core of genetics-first theories of the origin of life, which suggest that self-replicating oligonucleotides or their similar ancestors may have been the first "living" systems and may have led to the evolution of an RNA world. But problems with the nonenzymatic synthesis of biopolymers and the origin of template replication have spurred the alternative metabolism-first scenario, where self-reproducing and evolving proto-metabolic networks are assumed to have predated self-replicating genes. Recent theoretical work shows that "compositional genomes" (i.e., the counts of different molecular species in an assembly) are able to propagate compositional information and can provide a setup on which natural selection acts. Accordingly, if we stick to the notion of replicator as an entity that passes on its structure largely intact in successive replications, those macromolecular aggregates could be dubbed "ensemble replicators" (composomes) and quite different from the more familiar genes and memes. In sharp contrast with template-dependent replication dynamics, we demonstrate here that replication of compositional information is so inaccurate that fitter compositional genomes cannot be maintained by selection and, therefore, the system lacks evolvability (i.e., it cannot substantially depart from the asymptotic steady-state solution already built-in in the dynamical equations). We conclude that this fundamental limitation of ensemble replicators cautions against metabolism-first theories of the origin of life, although ancient metabolic systems could have provided a stable habitat within which polymer replicators later evolved.

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

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

    2009-01-01

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

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

    Sylvain Sené

    2009-10-01

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

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

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

    2017-12-11

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

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

    Väremo, Leif; Scheele, Camilla; Broholm, Christa

    2015-01-01

    -analysis of six studies comparing muscle transcription in T2D versus healthy subjects. Transcriptional changes were mapped on the myocyte GEM, revealing extensive transcriptional regulation in T2D, particularly around pyruvate oxidation, branched-chain amino acid catabolism, and tetrahydrofolate metabolism......Skeletal myocytes are metabolically active and susceptible to insulin resistance and are thus implicated in type 2 diabetes (T2D). This complex disease involves systemic metabolic changes, and their elucidation at the systems level requires genome-wide data and biological networks. Genome...

  4. Conservation of lipid metabolic gene transcriptional regulatory networks in fish and mammals.

    Carmona-Antoñanzas, Greta; Tocher, Douglas R; Martinez-Rubio, Laura; Leaver, Michael J

    2014-01-15

    Lipid content and composition in aquafeeds have changed rapidly as a result of the recent drive to replace ecologically limited marine ingredients, fishmeal and fish oil (FO). Terrestrial plant products are the most economic and sustainable alternative; however, plant meals and oils are devoid of physiologically important cholesterol and long-chain polyunsaturated fatty acids (LC-PUFA), eicosapentaenoic (EPA), docosahexaenoic (DHA) and arachidonic (ARA) acids. Although replacement of dietary FO with vegetable oil (VO) has little effect on growth in Atlantic salmon (Salmo salar), several studies have shown major effects on the activity and expression of genes involved in lipid homeostasis. In vertebrates, sterols and LC-PUFA play crucial roles in lipid metabolism by direct interaction with lipid-sensing transcription factors (TFs) and consequent regulation of target genes. The primary aim of the present study was to elucidate the role of key TFs in the transcriptional regulation of lipid metabolism in fish by transfection and overexpression of TFs. The results show that the expression of genes of LC-PUFA biosynthesis (elovl and fads2) and cholesterol metabolism (abca1) are regulated by Lxr and Srebp TFs in salmon, indicating highly conserved regulatory mechanism across vertebrates. In addition, srebp1 and srebp2 mRNA respond to replacement of dietary FO with VO. Thus, Atlantic salmon adjust lipid metabolism in response to dietary lipid composition through the transcriptional regulation of gene expression. It may be possible to further increase efficient and effective use of sustainable alternatives to marine products in aquaculture by considering these important molecular interactions when formulating diets. © 2013.

  5. Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering.

    He, Fei; Murabito, Ettore; Westerhoff, Hans V

    2016-04-01

    Metabolic pathways can be engineered to maximize the synthesis of various products of interest. With the advent of computational systems biology, this endeavour is usually carried out through in silico theoretical studies with the aim to guide and complement further in vitro and in vivo experimental efforts. Clearly, what counts is the result in vivo, not only in terms of maximal productivity but also robustness against environmental perturbations. Engineering an organism towards an increased production flux, however, often compromises that robustness. In this contribution, we review and investigate how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed by systems and control engineering. While trade-offs between production optimality and cellular robustness have already been studied diagnostically and statically, the dynamics also matter. Integration of the dynamic design aspects of control engineering with the more diagnostic aspects of metabolic, hierarchical control and regulation analysis is leading to the new, conceptual and operational framework required for the design of robust and productive dynamic pathways. © 2016 The Author(s).

  6. Flavonoids: a metabolic network mediating plants adaptation to their real estate.

    Mouradov, Aidyn; Spangenberg, German

    2014-01-01

    From an evolutionary perspective, the emergence of the sophisticated chemical scaffolds of flavonoid molecules represents a key step in the colonization of Earth's terrestrial environment by vascular plants nearly 500 million years ago. The subsequent evolution of flavonoids through recruitment and modification of ancestors involved in primary metabolism has allowed vascular plants to cope with pathogen invasion and damaging UV light. The functional properties of flavonoids as a unique combination of different classes of compounds vary significantly depending on the demands of their local real estate. Apart from geographical location, the composition of flavonoids is largely dependent on the plant species, their developmental stage, tissue type, subcellular localization, and key ecological influences of both biotic and abiotic origin. Molecular and metabolic cross-talk between flavonoid and other pathways as a result of the re-direction of intermediate molecules have been well investigated. This metabolic plasticity is a key factor in plant adaptive strength and is of paramount importance for early land plants adaptation to their local ecosystems. In human and animal health the biological and pharmacological activities of flavonoids have been investigated in great depth and have shown a wide range of anti-inflammatory, anti-oxidant, anti-microbial, and anti-cancer properties. In this paper we review the application of advanced gene technologies for targeted reprogramming of the flavonoid pathway in plants to understand its molecular functions and explore opportunities for major improvements in forage plants enhancing animal health and production.

  7. Flavonoids: A Metabolic Network Mediating Plants Adaptation to Their Real Estate

    Aidyn eMouradov

    2014-11-01

    Full Text Available From an evolutionary perspective, the emergence of the sophisticated chemical scaffolds of flavonoid molecules represents a key step in the colonization of Earth’s terrestrial environment by vascular plants nearly 500 million years ago. The subsequent evolution of flavonoids through recruitment and modification of ancestors involved in primary metabolism has allowed vascular plants to cope with pathogen invasion and damaging UV light. The functional properties of flavonoids as a unique combination of different classes of compounds vary significantly depending on the demands of their local real estate. Apart from geographical location, the composition of flavonoids is largely dependent on the plant species, their developmental stage, tissue type, subcellular localization, and key ecological influences of both biotic and abiotic origin. Molecular and metabolic cross-talk between flavonoid and other pathways as a result of the re-direction of intermediate molecules have been well investigated. This metabolic plasticity is a key factor in plant adaptive strength and is of paramount importance for early land plants adaptation to their local ecosystems. In human and animal health the biological and pharmacological activities of flavonoids have been investigated in great depth and have shown a wide range of anti-inflammatory, anti-oxidant, anti-microbial and anti-cancer properties. In this paper we review the application of advanced gene technologies for targeted reprogramming of the flavonoid pathway in plants to understand its molecular functions and explore opportunities for major improvements in forage plants enhancing animal health and production.

  8. Enhancing Carbon Fixation by Metabolic Engineering: A Model System of Complex Network Modulation

    Dr. Gregory Stephanopoulos

    2008-04-10

    In the first two years of this research we focused on the development of a DNA microarray for transcriptional studies in the photosynthetic organism Synechocystis and the elucidation of the metabolic pathway for biopolymer synthesis in this organism. In addition we also advanced the molecular biological tools for metabolic engineering of biopolymer synthesis in Synechocystis and initiated a series of physiological studies for the elucidation of the carbon fixing pathways and basic central carbon metabolism of these organisms. During the last two-year period we focused our attention on the continuation and completion of the last task, namely, the development of tools for basic investigations of the physiology of these cells through, primarily, the determination of their metabolic fluxes. The reason for this decision lies in the importance of fluxes as key indicators of physiology and the high level of information content they carry in terms of identifying rate limiting steps in a metabolic pathway. While flux determination is a well-advanced subject for heterotrophic organisms, for the case of autotrophic bacteria, like Synechocystis, some special challenges had to be overcome. These challenges stem mostly from the fact that if one uses {sup 13}C labeled CO{sub 2} for flux determination, the {sup 13}C label will mark, at steady state, all carbon atoms of all cellular metabolites, thus eliminating the necessary differentiation required for flux determination. This peculiarity of autotrophic organisms makes it imperative to carry out flux determination under transient conditions, something that had not been accomplished before. We are pleased to report that we have solved this problem and we are now able to determine fluxes in photosynthetic organisms from stable isotope labeling experiments followed by measurements of label enrichment in cellular metabolites using Gas Chromatography-Mass Spectrometry. We have conducted extensive simulations to test the method and

  9. Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming.

    Song, Hyun-Seob; Goldberg, Noam; Mahajan, Ashutosh; Ramkrishna, Doraiswami

    2017-08-01

    Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. To alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs. The software is implemented in Matlab, and is provided as supplementary information . hyunseob.song@pnnl.gov. Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2017. This work is written by US Government employees and are in the public domain in the US.

  10. Modules in the metabolic network of E.coli with regulatory interactions

    Geryk, J.; Slanina, František

    2013-01-01

    Roč. 8, č. 2 (2013), s. 188-202 ISSN 1748-5673 R&D Projects: GA MŠk OC09078 Institutional support: RVO:68378271 Keywords : networks * modularity * biophysics Subject RIV: BO - Biophysics Impact factor: 0.655, year: 2013 http://www.inderscience.com/info/inarticle.php?artid=55500

  11. The Sexual Advantage of Looking, Smelling, and Tasting Good: The Metabolic Network that Produces Signals for Pollinators.

    Borghi, Monica; Fernie, Alisdair R; Schiestl, Florian P; Bouwmeester, Harro J

    2017-04-01

    A striking feature of the angiosperms that use animals as pollen carriers to sexually reproduce is the great diversity of their flowers with regard to morphology and traits such as color, odor, and nectar. These traits are underpinned by the synthesis of secondary metabolites such as pigments and volatiles, as well as carbohydrates and amino acids, which are used by plants to lure and reward animal pollinators. We review here the knowledge of the metabolic network that supports the biosynthesis of these compounds and the behavioral responses that these molecules elicit in the animal pollinators. Such knowledge provides us with a deeper insight into the ecology and evolution of plant-pollinator interactions, and should help us to better manage these ecologically essential interactions in agricultural ecosystems. Copyright © 2016 Elsevier Ltd. All rights reserved.

  12. Comprehensive reconstruction and in silico analysis of Aspergillus niger genome-scale metabolic network model that accounts for 1210 ORFs.

    Lu, Hongzhong; Cao, Weiqiang; Ouyang, Liming; Xia, Jianye; Huang, Mingzhi; Chu, Ju; Zhuang, Yingping; Zhang, Siliang; Noorman, Henk

    2017-03-01

    Aspergillus niger is one of the most important cell factories for industrial enzymes and organic acids production. A comprehensive genome-scale metabolic network model (GSMM) with high quality is crucial for efficient strain improvement and process optimization. The lack of accurate reaction equations and gene-protein-reaction associations (GPRs) in the current best model of A. niger named GSMM iMA871, however, limits its application scope. To overcome these limitations, we updated the A. niger GSMM by combining the latest genome annotation and literature mining technology. Compared with iMA871, the number of reactions in iHL1210 was increased from 1,380 to 1,764, and the number of unique ORFs from 871 to 1,210. With the aid of our transcriptomics analysis, the existence of 63% ORFs and 68% reactions in iHL1210 can be verified when glucose was used as the only carbon source. Physiological data from chemostat cultivations, 13 C-labeled and molecular experiments from the published literature were further used to check the performance of iHL1210. The average correlation coefficients between the predicted fluxes and estimated fluxes from 13 C-labeling data were sufficiently high (above 0.89) and the prediction of cell growth on most of the reported carbon and nitrogen sources was consistent. Using the updated genome-scale model, we evaluated gene essentiality on synthetic and yeast extract medium, as well as the effects of NADPH supply on glucoamylase production in A. niger. In summary, the new A. niger GSMM iHL1210 contains significant improvements with respect to the metabolic coverage and prediction performance, which paves the way for systematic metabolic engineering of A. niger. Biotechnol. Bioeng. 2017;114: 685-695. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

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

    van Gulik Walter M

    2006-12-01

    Full Text Available Abstract Background Dynamic modeling of metabolic reaction networks under in vivo conditions is a crucial step in order to obtain a better understanding of the (disfunctioning of living cells. So far dynamic metabolic models generally have been based on mechanistic rate equations which often contain so many parameters that their identifiability from experimental data forms a serious problem. Recently, approximative rate equations, based on the linear logarithmic (linlog format have been proposed as a suitable alternative with fewer parameters. Results In this paper we present a method for estimation of the kinetic model parameters, which are equal to the elasticities defined in Metabolic Control Analysis, from metabolite data obtained from dynamic as well as steady state perturbations, using the linlog kinetic format. Additionally, we address the question of parameter identifiability from dynamic perturbation data in the presence of noise. The method is illustrated using metabolite data generated with a dynamic model of the glycolytic pathway of Saccharomyces cerevisiae based on mechanistic rate equations. Elasticities are estimated from the generated data, which define the complete linlog kinetic model of the glycolysis. The effect of data noise on the accuracy of the estimated elasticities is presented. Finally, identifiable subset of parameters is determined using information on the standard deviations of the estimated elasticities through Monte Carlo (MC simulations. Conclusion The parameter estimation within the linlog kinetic framework as presented here allows the determination of the elasticities directly from experimental data from typical dynamic and/or steady state experiments. These elasticities allow the reconstruction of the full kinetic model of Saccharomyces cerevisiae, and the determination of the control coefficients. MC simulations revealed that certain elasticities are potentially unidentifiable from dynamic data only

  14. Regulatory and metabolic networks for the adaptation of Pseudomonas aeruginosa biofilms to urinary tract-like conditions.

    Petra Tielen

    Full Text Available Biofilms of the Gram-negative bacterium Pseudomonas aeruginosa are one of the major causes of complicated urinary tract infections with detrimental outcome. To develop novel therapeutic strategies the molecular adaption strategies of P. aeruginosa biofilms to the conditions of the urinary tract were investigated thoroughly at the systems level using transcriptome, proteome, metabolome and enzyme activity analyses. For this purpose biofilms were grown anaerobically in artificial urine medium (AUM. Obtained data were integrated bioinformatically into gene regulatory and metabolic networks. The dominating response at the transcriptome and proteome level was the adaptation to iron limitation via the broad Fur regulon including 19 sigma factors and up to 80 regulated target genes or operons. In agreement, reduction of the iron cofactor-dependent nitrate respiratory metabolism was detected. An adaptation of the central metabolism to lactate, citrate and amino acid as carbon sources with the induction of the glyoxylate bypass was observed, while other components of AUM like urea and creatinine were not used. Amino acid utilization pathways were found induced, while fatty acid biosynthesis was reduced. The high amounts of phosphate found in AUM explain the reduction of phosphate assimilation systems. Increased quorum sensing activity with the parallel reduction of chemotaxis and flagellum assembly underscored the importance of the biofilm life style. However, reduced formation of the extracellular polysaccharide alginate, typical for P. aeruginosa biofilms in lungs, indicated a different biofilm type for urinary tract infections. Furthermore, the obtained quorum sensing response results in an increased production of virulence factors like the extracellular lipase LipA and protease LasB and AprA explaining the harmful cause of these infections.

  15. Regulatory and Metabolic Networks for the Adaptation of Pseudomonas aeruginosa Biofilms to Urinary Tract-Like Conditions

    Dohnt, Katrin; Haddad, Isam; Jänsch, Lothar; Klein, Johannes; Narten, Maike; Pommerenke, Claudia; Scheer, Maurice; Schobert, Max; Schomburg, Dietmar; Thielen, Bernhard; Jahn, Dieter

    2013-01-01

    Biofilms of the Gram-negative bacterium Pseudomonas aeruginosa are one of the major causes of complicated urinary tract infections with detrimental outcome. To develop novel therapeutic strategies the molecular adaption strategies of P. aeruginosa biofilms to the conditions of the urinary tract were investigated thoroughly at the systems level using transcriptome, proteome, metabolome and enzyme activity analyses. For this purpose biofilms were grown anaerobically in artificial urine medium (AUM). Obtained data were integrated bioinformatically into gene regulatory and metabolic networks. The dominating response at the transcriptome and proteome level was the adaptation to iron limitation via the broad Fur regulon including 19 sigma factors and up to 80 regulated target genes or operons. In agreement, reduction of the iron cofactor-dependent nitrate respiratory metabolism was detected. An adaptation of the central metabolism to lactate, citrate and amino acid as carbon sources with the induction of the glyoxylate bypass was observed, while other components of AUM like urea and creatinine were not used. Amino acid utilization pathways were found induced, while fatty acid biosynthesis was reduced. The high amounts of phosphate found in AUM explain the reduction of phosphate assimilation systems. Increased quorum sensing activity with the parallel reduction of chemotaxis and flagellum assembly underscored the importance of the biofilm life style. However, reduced formation of the extracellular polysaccharide alginate, typical for P. aeruginosa biofilms in lungs, indicated a different biofilm type for urinary tract infections. Furthermore, the obtained quorum sensing response results in an increased production of virulence factors like the extracellular lipase LipA and protease LasB and AprA explaining the harmful cause of these infections. PMID:23967252

  16. Regulatory and metabolic networks for the adaptation of Pseudomonas aeruginosa biofilms to urinary tract-like conditions.

    Tielen, Petra; Rosin, Nathalie; Meyer, Ann-Kathrin; Dohnt, Katrin; Haddad, Isam; Jänsch, Lothar; Klein, Johannes; Narten, Maike; Pommerenke, Claudia; Scheer, Maurice; Schobert, Max; Schomburg, Dietmar; Thielen, Bernhard; Jahn, Dieter

    2013-01-01

    Biofilms of the Gram-negative bacterium Pseudomonas aeruginosa are one of the major causes of complicated urinary tract infections with detrimental outcome. To develop novel therapeutic strategies the molecular adaption strategies of P. aeruginosa biofilms to the conditions of the urinary tract were investigated thoroughly at the systems level using transcriptome, proteome, metabolome and enzyme activity analyses. For this purpose biofilms were grown anaerobically in artificial urine medium (AUM). Obtained data were integrated bioinformatically into gene regulatory and metabolic networks. The dominating response at the transcriptome and proteome level was the adaptation to iron limitation via the broad Fur regulon including 19 sigma factors and up to 80 regulated target genes or operons. In agreement, reduction of the iron cofactor-dependent nitrate respiratory metabolism was detected. An adaptation of the central metabolism to lactate, citrate and amino acid as carbon sources with the induction of the glyoxylate bypass was observed, while other components of AUM like urea and creatinine were not used. Amino acid utilization pathways were found induced, while fatty acid biosynthesis was reduced. The high amounts of phosphate found in AUM explain the reduction of phosphate assimilation systems. Increased quorum sensing activity with the parallel reduction of chemotaxis and flagellum assembly underscored the importance of the biofilm life style. However, reduced formation of the extracellular polysaccharide alginate, typical for P. aeruginosa biofilms in lungs, indicated a different biofilm type for urinary tract infections. Furthermore, the obtained quorum sensing response results in an increased production of virulence factors like the extracellular lipase LipA and protease LasB and AprA explaining the harmful cause of these infections.

  17. Genome-scale reconstruction of the metabolic network in Yersinia pestis, strain 91001

    Navid, A; Almaas, E

    2009-01-13

    The gram-negative bacterium Yersinia pestis, the aetiological agent of bubonic plague, is one the deadliest pathogens known to man. Despite its historical reputation, plague is a modern disease which annually afflicts thousands of people. Public safety considerations greatly limit clinical experimentation on this organism and thus development of theoretical tools to analyze the capabilities of this pathogen is of utmost importance. Here, we report the first genome-scale metabolic model of Yersinia pestis biovar Mediaevalis based both on its recently annotated genome, and physiological and biochemical data from literature. Our model demonstrates excellent agreement with Y. pestis known metabolic needs and capabilities. Since Y. pestis is a meiotrophic organism, we have developed CryptFind, a systematic approach to identify all candidate cryptic genes responsible for known and theoretical meiotrophic phenomena. In addition to uncovering every known cryptic gene for Y. pestis, our analysis of the rhamnose fermentation pathway suggests that betB is the responsible cryptic gene. Despite all of our medical advances, we still do not have a vaccine for bubonic plague. Recent discoveries of antibiotic resistant strains of Yersinia pestis coupled with the threat of plague being used as a bioterrorism weapon compel us to develop new tools for studying the physiology of this deadly pathogen. Using our theoretical model, we can study the cell's phenotypic behavior under different circumstances and identify metabolic weaknesses which may be harnessed for the development of therapeutics. Additionally, the automatic identification of cryptic genes expands the usage of genomic data for pharmaceutical purposes.

  18. Effects of chlorinated drinking water on the xenobiotic metabolism in Cyprinus carpio treated with samples from two Italian municipal networks.

    Cirillo, Silvia; Canistro, Donatella; Vivarelli, Fabio; Paolini, Moreno

    2016-09-01

    Drinking water (DW) disinfection represents a milestone of the past century, thanks to its efficacy in the reduction of risks of epidemic forms by water micro-organisms. Nevertheless, such process generates disinfection by-products (DBPs), some of which are genotoxic both in animals and in humans and carcinogenic in animals. At present, chlorination is one of the most employed strategies but the toxicological effects of several classes of DBPs are unknown. In this investigation, a multidisciplinary approach foreseeing the chemical analysis of chlorinated DW samples and the study of its effects on mixed function oxidases (MFOs) belonging to the superfamily of cytochrome P450-linked monooxygenases of Cyprinus carpio hepatopancreas, was employed. The experimental samples derived from aquifers of two Italian towns (plant 1, river water and plant 2, spring water) were obtained immediately after the disinfection (A) and along the network (R1). Animals treated with plant 1 DW-processed fractions showed a general CYP-associated MFO induction. By contrast, in plant 2, a complex modulation pattern was achieved, with a general up-regulation for the point A and a marked MFO inactivation in the R1 group, particularly for the testosterone metabolism. Together, the toxicity and co-carcinogenicity (i.e. unremitting over-generation of free radicals and increased bioactivation capability) of DW linked to the recorded metabolic manipulation, suggests that a prolonged exposure to chlorine-derived disinfectants may produce adverse health effects.

  19. The impact of rape acknowledgment on survivor outcomes: The moderating effects of rape myth acceptance.

    Wilson, Laura C; Newins, Amie R; White, Susan W

    2017-11-13

    Little is known about how rape acknowledgment relates to posttrauma functioning; recent research suggests the effect may depend on additional factors. In the current study, the moderating effect of rape myth acceptance (RMA) on the relationships between rape acknowledgment and mental health outcomes was examined. A sample of 181 female rape survivors recruited from a university completed an online survey assessing RMA, rape acknowledgment, depression symptoms, and alcohol use. Generally, the results supported that RMA moderated the influence of rape acknowledgment on depression symptoms and average quantity per drinking episode, but not frequency of alcohol use. The findings demonstrated that when individuals endorsed high levels of RMA, acknowledged rape survivors reported worse outcomes than unacknowledged rape survivors. Among individuals low on RMA, unacknowledged rape survivors reported worse outcomes than acknowledged rape survivors. It is recommended that clinicians recognize the role of survivor beliefs, such as RMA, in the relationship between labeling sexual assault experiences and mental health consequences. © 2017 Wiley Periodicals, Inc.

  20. Identifying genes and gene networks involved in chromium metabolism and detoxification in Crambe abyssinica

    Zulfiqar, Asma, E-mail: asmazulfiqar08@yahoo.com [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States); Paulose, Bibin, E-mail: bpaulose@psis.umass.edu [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States); Chhikara, Sudesh, E-mail: sudesh@psis.umass.edu [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States); Dhankher, Om Parkash, E-mail: parkash@psis.umass.edu [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States)

    2011-10-15

    Chromium pollution is a serious environmental problem with few cost-effective remediation strategies available. Crambe abyssinica (a member of Brassicaseae), a non-food, fast growing high biomass crop, is an ideal candidate for phytoremediation of heavy metals contaminated soils. The present study used a PCR-Select Suppression Subtraction Hybridization approach in C. abyssinica to isolate differentially expressed genes in response to Cr exposure. A total of 72 differentially expressed subtracted cDNAs were sequenced and found to represent 43 genes. The subtracted cDNAs suggest that Cr stress significantly affects pathways related to stress/defense, ion transporters, sulfur assimilation, cell signaling, protein degradation, photosynthesis and cell metabolism. The regulation of these genes in response to Cr exposure was further confirmed by semi-quantitative RT-PCR. Characterization of these differentially expressed genes may enable the engineering of non-food, high-biomass plants, including C. abyssinica, for phytoremediation of Cr-contaminated soils and sediments. - Highlights: > Molecular mechanism of Cr uptake and detoxification in plants is not well known. > We identified differentially regulated genes upon Cr exposure in Crambe abyssinica. > 72 Cr-induced subtracted cDNAs were sequenced and found to represent 43 genes. > Pathways linked to stress, ion transport, and sulfur assimilation were affected. > This is the first Cr transcriptome study in a crop with phytoremediation potential. - This study describes the identification and isolation of differentially expressed genes involved in chromium metabolism and detoxification in a non-food industrial oil crop Crambe abyssinica.

  1. Correlation-based network analysis of metabolite and enzyme profiles reveals a role of citrate biosynthesis in modulating N and C metabolism in zea mays

    To investigate the natural variability of leaf metabolism and enzymatic activity in a maize inbred population, statistical and network analyses were employed on metabolite and enzyme profiles. The test of coefficient of variation showed that sugars and amino acids displayed opposite trends in their ...

  2. Genealogy profiling through strain improvement by using metabolic network analysis: metabolic flux genealogy of several generations of lysine-producing corynebacteria.

    Wittmann, Christoph; Heinzle, Elmar

    2002-12-01

    A comprehensive approach of metabolite balancing, (13)C tracer studies, gas chromatography-mass spectrometry, matrix-assisted laser desorption ionization-time of flight mass spectrometry, and isotopomer modeling was applied for comparative metabolic network analysis of a genealogy of five successive generations of lysine-producing Corynebacterium glutamicum. The five strains examined (C. glutamicum ATCC 13032, 13287, 21253, 21526, and 21543) were previously obtained by random mutagenesis and selection. Throughout the genealogy, the lysine yield in batch cultures increased markedly from 1.2 to 24.9% relative to the glucose uptake flux. Strain optimization was accompanied by significant changes in intracellular flux distributions. The relative pentose phosphate pathway (PPP) flux successively increased, clearly corresponding to the product yield. Moreover, the anaplerotic net flux increased almost twofold as a consequence of concerted regulation of C(3) carboxylation and C(4) decarboxylation fluxes to cover the increased demand for lysine formation; thus, the overall increase was a consequence of concerted regulation of C(3) carboxylation and C(4) decarboxylation fluxes. The relative flux through isocitrate dehydrogenase dropped from 82.7% in the wild type to 59.9% in the lysine-producing mutants. In contrast to the NADPH demand, which increased from 109 to 172% due to the increasing lysine yield, the overall NADPH supply remained constant between 185 and 196%, resulting in a decrease in the apparent NADPH excess through strain optimization. Extrapolated to industrial lysine producers, the NADPH supply might become a limiting factor. The relative contributions of PPP and the tricarboxylic acid cycle to NADPH generation changed markedly, indicating that C. glutamicum is able to maintain a constant supply of NADPH under completely different flux conditions. Statistical analysis by a Monte Carlo approach revealed high precision for the estimated fluxes, underlining the

  3. Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival.

    Megchelenbrink, Wout; Katzir, Rotem; Lu, Xiaowen; Ruppin, Eytan; Notebaart, Richard A

    2015-09-29

    Synthetic dosage lethality (SDL) denotes a genetic interaction between two genes whereby the underexpression of gene A combined with the overexpression of gene B is lethal. SDLs offer a promising way to kill cancer cells by inhibiting the activity of SDL partners of activated oncogenes in tumors, which are often difficult to target directly. As experimental genome-wide SDL screens are still scarce, here we introduce a network-level computational modeling framework that quantitatively predicts human SDLs in metabolism. For each enzyme pair (A, B) we systematically knock out the flux through A combined with a stepwise flux increase through B and search for pairs that reduce cellular growth more than when either enzyme is perturbed individually. The predictive signal of the emerging network of 12,000 SDLs is demonstrated in five different ways. (i) It can be successfully used to predict gene essentiality in shRNA cancer cell line screens. Moving to clinical tumors, we show that (ii) SDLs are significantly underrepresented in tumors. Furthermore, breast cancer tumors with SDLs active (iii) have smaller sizes and (iv) result in increased patient survival, indicating that activation of SDLs increases cancer vulnerability. Finally, (v) patient survival improves when multiple SDLs are present, pointing to a cumulative effect. This study lays the basis for quantitative identification of cancer SDLs in a model-based mechanistic manner. The approach presented can be used to identify SDLs in species and cell types in which "omics" data necessary for data-driven identification are missing.

  4. Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors.

    Duardo-Sánchez, Aliuska; Munteanu, Cristian R; Riera-Fernández, Pablo; López-Díaz, Antonio; Pazos, Alejandro; González-Díaz, Humberto

    2014-01-27

    The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein interactions, or metabolic networks. However, the applications are not restricted to the world of molecules and can be extended to the study of macroscopic nonliving systems, organisms, or even legal or social networks. On the other hand, the development of the field of Artificial Intelligence has led to the formulation of computational algorithms whose design is based on the structure and functioning of networks of biological neurons. These algorithms, called Artificial Neural Networks (ANNs), can be useful for the study of complex networks, since the numerical parameters that encode information of the network (for example centralities/node descriptors) can be used as inputs for the ANNs. The Wiener index (W) is a graph invariant widely used in chemoinformatics to quantify the molecular structure of drugs and to study complex networks. In this work, we explore for the first time the possibility of using Markov chains to calculate analogues of node distance numbers/W to describe complex networks from the point of view of their nodes. These parameters are called Markov-Wiener node descriptors of order k(th) (W(k)). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the W(k)(i) values for a very high number of nodes (>100,000) in more than 100 different complex networks using the software MI-NODES. These networks were grouped according to the field of application. Molecular networks include the Metabolic Reaction Networks (MRNs) of 40 different organisms. In addition, we analyzed other biological and legal and social networks. These include the Interaction Web Database Biological Networks (IWDBNs), with 75 food webs or ecological systems and the Spanish Financial Law Network (SFLN). The calculated W

  5. Identifying genes and gene networks involved in chromium metabolism and detoxification in Crambe abyssinica

    Zulfiqar, Asma; Paulose, Bibin; Chhikara, Sudesh; Dhankher, Om Parkash

    2011-01-01

    Chromium pollution is a serious environmental problem with few cost-effective remediation strategies available. Crambe abyssinica (a member of Brassicaseae), a non-food, fast growing high biomass crop, is an ideal candidate for phytoremediation of heavy metals contaminated soils. The present study used a PCR-Select Suppression Subtraction Hybridization approach in C. abyssinica to isolate differentially expressed genes in response to Cr exposure. A total of 72 differentially expressed subtracted cDNAs were sequenced and found to represent 43 genes. The subtracted cDNAs suggest that Cr stress significantly affects pathways related to stress/defense, ion transporters, sulfur assimilation, cell signaling, protein degradation, photosynthesis and cell metabolism. The regulation of these genes in response to Cr exposure was further confirmed by semi-quantitative RT-PCR. Characterization of these differentially expressed genes may enable the engineering of non-food, high-biomass plants, including C. abyssinica, for phytoremediation of Cr-contaminated soils and sediments. - Highlights: → Molecular mechanism of Cr uptake and detoxification in plants is not well known. → We identified differentially regulated genes upon Cr exposure in Crambe abyssinica. → 72 Cr-induced subtracted cDNAs were sequenced and found to represent 43 genes. → Pathways linked to stress, ion transport, and sulfur assimilation were affected. → This is the first Cr transcriptome study in a crop with phytoremediation potential. - This study describes the identification and isolation of differentially expressed genes involved in chromium metabolism and detoxification in a non-food industrial oil crop Crambe abyssinica.

  6. Equal opportunity for low-degree network nodes: a PageRank-based method for protein target identification in metabolic graphs.

    Dániel Bánky

    Full Text Available Biological network data, such as metabolic-, signaling- or physical interaction graphs of proteins are increasingly available in public repositories for important species. Tools for the quantitative analysis of these networks are being developed today. Protein network-based drug target identification methods usually return protein hubs with large degrees in the networks as potentially important targets. Some known, important protein targets, however, are not hubs at all, and perturbing protein hubs in these networks may have several unwanted physiological effects, due to their interaction with numerous partners. Here, we show a novel method applicable in networks with directed edges (such as metabolic networks that compensates for the low degree (non-hub vertices in the network, and identifies important nodes, regardless of their hub properties. Our method computes the PageRank for the nodes of the network, and divides the PageRank by the in-degree (i.e., the number of incoming edges of the node. This quotient is the same in all nodes in an undirected graph (even for large- and low-degree nodes, that is, for hubs and non-hubs as well, but may differ significantly from node to node in directed graphs. We suggest to assign importance to non-hub nodes with large PageRank/in-degree quotient. Consequently, our method gives high scores to nodes with large PageRank, relative to their degrees: therefore non-hub important nodes can easily be identified in large networks. We demonstrate that these relatively high PageRank scores have biological relevance: the method correctly finds numerous already validated drug targets in distinct organisms (Mycobacterium tuberculosis, Plasmodium falciparum and MRSA Staphylococcus aureus, and consequently, it may suggest new possible protein targets as well. Additionally, our scoring method was not chosen arbitrarily: its value for all nodes of all undirected graphs is constant; therefore its high value captures

  7. Equal opportunity for low-degree network nodes: a PageRank-based method for protein target identification in metabolic graphs.

    Bánky, Dániel; Iván, Gábor; Grolmusz, Vince

    2013-01-01

    Biological network data, such as metabolic-, signaling- or physical interaction graphs of proteins are increasingly available in public repositories for important species. Tools for the quantitative analysis of these networks are being developed today. Protein network-based drug target identification methods usually return protein hubs with large degrees in the networks as potentially important targets. Some known, important protein targets, however, are not hubs at all, and perturbing protein hubs in these networks may have several unwanted physiological effects, due to their interaction with numerous partners. Here, we show a novel method applicable in networks with directed edges (such as metabolic networks) that compensates for the low degree (non-hub) vertices in the network, and identifies important nodes, regardless of their hub properties. Our method computes the PageRank for the nodes of the network, and divides the PageRank by the in-degree (i.e., the number of incoming edges) of the node. This quotient is the same in all nodes in an undirected graph (even for large- and low-degree nodes, that is, for hubs and non-hubs as well), but may differ significantly from node to node in directed graphs. We suggest to assign importance to non-hub nodes with large PageRank/in-degree quotient. Consequently, our method gives high scores to nodes with large PageRank, relative to their degrees: therefore non-hub important nodes can easily be identified in large networks. We demonstrate that these relatively high PageRank scores have biological relevance: the method correctly finds numerous already validated drug targets in distinct organisms (Mycobacterium tuberculosis, Plasmodium falciparum and MRSA Staphylococcus aureus), and consequently, it may suggest new possible protein targets as well. Additionally, our scoring method was not chosen arbitrarily: its value for all nodes of all undirected graphs is constant; therefore its high value captures importance in the

  8. 25 CFR 16.5 - Acceptance and acknowledgement of service of process.

    2010-04-01

    ... 25 Indians 1 2010-04-01 2010-04-01 false Acceptance and acknowledgement of service of process. 16... INDIANS OF THE FIVE CIVILIZED TRIBES § 16.5 Acceptance and acknowledgement of service of process. Service by the Field Solicitor or any other person of any process or notice, pursuant to any Federal statute...

  9. Reconstruction of a metabolic regulatory network in Escherichia coli for purposeful switching from cell growth mode to production mode in direct GABA fermentation from glucose.

    Soma, Yuki; Fujiwara, Yuri; Nakagawa, Takuya; Tsuruno, Keigo; Hanai, Taizo

    2017-09-01

    γ-aminobutyric acid (GABA) is a drug and functional food additive and is used as a monomer for producing the biodegradable plastic, polyamide 4. Recently, direct GABA fermentation from glucose has been developed as an alternative to glutamate-based whole cell bioconversion. Although total productivity in fermentation is determined by the specific productivity and cell amount responsible for GABA production, the optimal metabolic state for GABA production conflicts with that for bacterial cell growth. Herein, we demonstrated metabolic state switching from the cell growth mode based on the metabolic pathways of the wild type strain to a GABA production mode based on a synthetic metabolic pathway in Escherichia coli through rewriting of the metabolic regulatory network and pathway engineering. The GABA production mode was achieved by multiple strategies such as conditional interruption of the TCA and glyoxylate cycles, engineering of GABA production pathway including a bypass for precursor metabolite supply, and upregulation of GABA transporter. As a result, we achieved 3-fold improvement in total GABA production titer and yield (4.8g/L, 49.2% (mol/mol glucose)) in batch fermentation compared to the case without metabolic state switching (1.6g/L, 16.4% (mol/mol glucose)). This study reports the highest GABA production performance among previous reports on GABA fermentation from glucose using engineered E. coli. Copyright © 2017 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

  10. Beyond funding: What can acknowledgements reveal about credit distribution in science?

    Paul-Hus, A.; DiazFaes, A.; Desrochers, N.; Costas, R.; Sainte-Marie, M.; Macaluso, B.; Lariviere, V.

    2016-07-01

    Funding acknowledgements found in scientific publications have been used for decades to study the role of funding in science production. However, beyond funding information, acknowledgements convey the indebtedness of authors to individuals, institutions and organizations that contributed, in some way, to the research that lead to publication. The objective of this paper is to explore the different types of contributions acknowledged in WoS funding acknowledgement (FA) texts. The Correspondence Analysis performed in this study reveals that FAs offer a unique window on research and collaborative practices, credit distribution, and how these vary across disciplines. FAs thus contribute to make the traditionally “invisible contributions” visible to the scientific community. Results presented in this study go further in demonstrating that acknowledgements are not confined to credit attribution, as they include disclosures of conflict of interest. (Author)

  11. Genome-scale metabolic network validation of Shewanella oneidensis using transposon insertion frequency analysis.

    Hong Yang

    2014-09-01

    Full Text Available Transposon mutagenesis, in combination with parallel sequencing, is becoming a powerful tool for en-masse mutant analysis. A probability generating function was used to explain observed miniHimar transposon insertion patterns, and gene essentiality calls were made by transposon insertion frequency analysis (TIFA. TIFA incorporated the observed genome and sequence motif bias of the miniHimar transposon. The gene essentiality calls were compared to: 1 previous genome-wide direct gene-essentiality assignments; and, 2 flux balance analysis (FBA predictions from an existing genome-scale metabolic model of Shewanella oneidensis MR-1. A three-way comparison between FBA, TIFA, and the direct essentiality calls was made to validate the TIFA approach. The refinement in the interpretation of observed transposon insertions demonstrated that genes without insertions are not necessarily essential, and that genes that contain insertions are not always nonessential. The TIFA calls were in reasonable agreement with direct essentiality calls for S. oneidensis, but agreed more closely with E. coli essentiality calls for orthologs. The TIFA gene essentiality calls were in good agreement with the MR-1 FBA essentiality predictions, and the agreement between TIFA and FBA predictions was substantially better than between the FBA and the direct gene essentiality predictions.

  12. Integrative microRNA and proteomic approaches identify novel osteoarthritis genes and their collaborative metabolic and inflammatory networks.

    Dimitrios Iliopoulos

    Full Text Available BACKGROUND: Osteoarthritis is a multifactorial disease characterized by destruction of the articular cartilage due to genetic, mechanical and environmental components affecting more than 100 million individuals all over the world. Despite the high prevalence of the disease, the absence of large-scale molecular studies limits our ability to understand the molecular pathobiology of osteoathritis and identify targets for drug development. METHODOLOGY/PRINCIPAL FINDINGS: In this study we integrated genetic, bioinformatic and proteomic approaches in order to identify new genes and their collaborative networks involved in osteoarthritis pathogenesis. MicroRNA profiling of patient-derived osteoarthritic cartilage in comparison to normal cartilage, revealed a 16 microRNA osteoarthritis gene signature. Using reverse-phase protein arrays in the same tissues we detected 76 differentially expressed proteins between osteoarthritic and normal chondrocytes. Proteins such as SOX11, FGF23, KLF6, WWOX and GDF15 not implicated previously in the genesis of osteoarthritis were identified. Integration of microRNA and proteomic data with microRNA gene-target prediction algorithms, generated a potential "interactome" network consisting of 11 microRNAs and 58 proteins linked by 414 potential functional associations. Comparison of the molecular and clinical data, revealed specific microRNAs (miR-22, miR-103 and proteins (PPARA, BMP7, IL1B to be highly correlated with Body Mass Index (BMI. Experimental validation revealed that miR-22 regulated PPARA and BMP7 expression and its inhibition blocked inflammatory and catabolic changes in osteoarthritic chondrocytes. CONCLUSIONS/SIGNIFICANCE: Our findings indicate that obesity and inflammation are related to osteoarthritis, a metabolic disease affected by microRNA deregulation. Gene network approaches provide new insights for elucidating the complexity of diseases such as osteoarthritis. The integration of microRNA, proteomic

  13. Characterization of the periplasmic redox network that sustains the versatile anaerobic metabolism of Shewanella oneidensis MR-1

    Mónica N. Alves

    2015-06-01

    Full Text Available The versatile anaerobic metabolism of the Gram-negative bacterium Shewanella oneidensis MR-1 (SOMR-1 relies on a multitude of redox proteins found in its periplasm. Most are multiheme cytochromes that carry electrons to terminal reductases of insoluble electron acceptors located at the cell surface, or bona fide terminal reductases of soluble electron acceptors. In this study, the interaction network of several multiheme cytochromes was explored by a combination of NMR spectroscopy, activity assays followed by UV-visible spectroscopy and comparison of surface electrostatic potentials. From these data the small tetraheme cytochrome (STC emerges as the main periplasmic redox shuttle in SOMR-1. It accepts electrons from CymA and distributes them to a number of terminal oxidoreductases involved in the respiration of various compounds. STC is also involved in the electron transfer pathway to reduce nitrite by interaction with the octaheme tetrathionate reductase (OTR, but not with cytochrome c nitrite reductase (ccNiR. In the main pathway leading the metal respiration STC pairs with flavocytochrome c (FccA, the other major periplasmic cytochrome, which provides redundancy in this important pathway. The data reveals that the two proteins compete for the binding site at the surface of MtrA, the decaheme cytochrome inserted on the periplasmic side of the MtrCAB-OmcA outer-membrane complex. However, this is not observed for the MtrA homologues. Indeed, neither STC nor FccA interact with MtrD, the best replacement for MtrA, and only STC is able to interact with the decaheme cytochrome DmsE of the outer-membrane complex DmsEFABGH. Overall, these results shown that STC plays a central role in the anaerobic respiratory metabolism of SOMR-1. Nonetheless, the trans-periplasmic electron transfer chain is functionally resilient as a consequence of redundancies that arise from the presence of alternative pathways that bypass/compete with STC.

  14. Expression profiling of Crambe abyssinica under arsenate stress identifies genes and gene networks involved in arsenic metabolism and detoxification

    Kandasamy Suganthi

    2010-06-01

    Full Text Available Abstract Background Arsenic contamination is widespread throughout the world and this toxic metalloid is known to cause cancers of organs such as liver, kidney, skin, and lung in human. In spite of a recent surge in arsenic related studies, we are still far from a comprehensive understanding of arsenic uptake, detoxification, and sequestration in plants. Crambe abyssinica, commonly known as 'abyssinian mustard', is a non-food, high biomass oil seed crop that is naturally tolerant to heavy metals. Moreover, it accumulates significantly higher levels of arsenic as compared to other species of the Brassicaceae family. Thus, C. abyssinica has great potential to be utilized as an ideal inedible crop for phytoremediation of heavy metals and metalloids. However, the mechanism of arsenic metabolism in higher plants, including C. abyssinica, remains elusive. Results To identify the differentially expressed transcripts and the pathways involved in arsenic metabolism and detoxification, C. abyssinica plants were subjected to arsenate stress and a PCR-Select Suppression Subtraction Hybridization (SSH approach was employed. A total of 105 differentially expressed subtracted cDNAs were sequenced which were found to represent 38 genes. Those genes encode proteins functioning as antioxidants, metal transporters, reductases, enzymes involved in the protein degradation pathway, and several novel uncharacterized proteins. The transcripts corresponding to the subtracted cDNAs showed strong upregulation by arsenate stress as confirmed by the semi-quantitative RT-PCR. Conclusions Our study revealed novel insights into the plant defense mechanisms and the regulation of genes and gene networks in response to arsenate toxicity. The differential expression of transcripts encoding glutathione-S-transferases, antioxidants, sulfur metabolism, heat-shock proteins, metal transporters, and enzymes in the ubiquitination pathway of protein degradation as well as several unknown

  15. Expression profiling of Crambe abyssinica under arsenate stress identifies genes and gene networks involved in arsenic metabolism and detoxification

    2010-01-01

    Background Arsenic contamination is widespread throughout the world and this toxic metalloid is known to cause cancers of organs such as liver, kidney, skin, and lung in human. In spite of a recent surge in arsenic related studies, we are still far from a comprehensive understanding of arsenic uptake, detoxification, and sequestration in plants. Crambe abyssinica, commonly known as 'abyssinian mustard', is a non-food, high biomass oil seed crop that is naturally tolerant to heavy metals. Moreover, it accumulates significantly higher levels of arsenic as compared to other species of the Brassicaceae family. Thus, C. abyssinica has great potential to be utilized as an ideal inedible crop for phytoremediation of heavy metals and metalloids. However, the mechanism of arsenic metabolism in higher plants, including C. abyssinica, remains elusive. Results To identify the differentially expressed transcripts and the pathways involved in arsenic metabolism and detoxification, C. abyssinica plants were subjected to arsenate stress and a PCR-Select Suppression Subtraction Hybridization (SSH) approach was employed. A total of 105 differentially expressed subtracted cDNAs were sequenced which were found to represent 38 genes. Those genes encode proteins functioning as antioxidants, metal transporters, reductases, enzymes involved in the protein degradation pathway, and several novel uncharacterized proteins. The transcripts corresponding to the subtracted cDNAs showed strong upregulation by arsenate stress as confirmed by the semi-quantitative RT-PCR. Conclusions Our study revealed novel insights into the plant defense mechanisms and the regulation of genes and gene networks in response to arsenate toxicity. The differential expression of transcripts encoding glutathione-S-transferases, antioxidants, sulfur metabolism, heat-shock proteins, metal transporters, and enzymes in the ubiquitination pathway of protein degradation as well as several unknown novel proteins serve as

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

    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

  17. Acknowledgements; Preface

    Patrick Valkenburg

    2005-05-01

    Full Text Available Approximately 180 people attended the 10th North American Caribou Workshop which was held immedi¬ately following the annual meeting of the Northwest Section of The Wildlife Society. Participants came from Canada, Greenland, Finland, Norway, Russia, Sweden, and the United States (primarily Alaska. Over 80 papers and posters were presented and about 25% of those are published here in this special edition of Rangifer. Many of the papers, including the keynote address by Dr. Gunter Weller, concerned climate change and global warming.

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

    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

  19. Extraction of elementary rate constants from global network analysis of E. coli central metabolism

    Broderick Gordon

    2008-05-01

    Full Text Available Abstract Background As computational performance steadily increases, so does interest in extending one-particle-per-molecule models to larger physiological problems. Such models however require elementary rate constants to calculate time-dependent rate coefficients under physiological conditions. Unfortunately, even when in vivo kinetic data is available, it is often in the form of aggregated rate laws (ARL that do not specify the required elementary rate constants corresponding to mass-action rate laws (MRL. There is therefore a need to develop a method which is capable of automatically transforming ARL kinetic information into more detailed MRL rate constants. Results By incorporating proteomic data related to enzyme abundance into an MRL modelling framework, here we present an efficient method operating at a global network level for extracting elementary rate constants from experiment-based aggregated rate law (ARL models. The method combines two techniques that can be used to overcome the difficult properties in parameterization. The first, a hybrid MRL/ARL modelling technique, is used to divide the parameter estimation problem into sub-problems, so that the parameters of the mass action rate laws for each enzyme are estimated in separate steps. This reduces the number of parameters that have to be optimized simultaneously. The second, a hybrid algebraic-numerical simulation and optimization approach, is used to render some rate constants identifiable, as well as to greatly narrow the bounds of the other rate constants that remain unidentifiable. This is done by incorporating equality constraints derived from the King-Altman and Cleland method into the simulated annealing algorithm. We apply these two techniques to estimate the rate constants of a model of E. coli glycolytic pathways. The simulation and statistical results show that our innovative method performs well in dealing with the issues of high computation cost, stiffness, local

  20. Extraction of elementary rate constants from global network analysis of E. coli central metabolism

    Zhao, Jiao; Ridgway, Douglas; Broderick, Gordon; Kovalenko, Andriy; Ellison, Michael

    2008-01-01

    Background As computational performance steadily increases, so does interest in extending one-particle-per-molecule models to larger physiological problems. Such models however require elementary rate constants to calculate time-dependent rate coefficients under physiological conditions. Unfortunately, even when in vivo kinetic data is available, it is often in the form of aggregated rate laws (ARL) that do not specify the required elementary rate constants corresponding to mass-action rate laws (MRL). There is therefore a need to develop a method which is capable of automatically transforming ARL kinetic information into more detailed MRL rate constants. Results By incorporating proteomic data related to enzyme abundance into an MRL modelling framework, here we present an efficient method operating at a global network level for extracting elementary rate constants from experiment-based aggregated rate law (ARL) models. The method combines two techniques that can be used to overcome the difficult properties in parameterization. The first, a hybrid MRL/ARL modelling technique, is used to divide the parameter estimation problem into sub-problems, so that the parameters of the mass action rate laws for each enzyme are estimated in separate steps. This reduces the number of parameters that have to be optimized simultaneously. The second, a hybrid algebraic-numerical simulation and optimization approach, is used to render some rate constants identifiable, as well as to greatly narrow the bounds of the other rate constants that remain unidentifiable. This is done by incorporating equality constraints derived from the King-Altman and Cleland method into the simulated annealing algorithm. We apply these two techniques to estimate the rate constants of a model of E. coli glycolytic pathways. The simulation and statistical results show that our innovative method performs well in dealing with the issues of high computation cost, stiffness, local minima and uncertainty

  1. Acorn: A grid computing system for constraint based modeling and visualization of the genome scale metabolic reaction networks via a web interface

    Bushell Michael E

    2011-05-01

    Full Text Available Abstract Background Constraint-based approaches facilitate the prediction of cellular metabolic capabilities, based, in turn on predictions of the repertoire of enzymes encoded in the genome. Recently, genome annotations have been used to reconstruct genome scale metabolic reaction networks for numerous species, including Homo sapiens, which allow simulations that provide valuable insights into topics, including predictions of gene essentiality of pathogens, interpretation of genetic polymorphism in metabolic disease syndromes and suggestions for novel approaches to microbial metabolic engineering. These constraint-based simulations are being integrated with the functional genomics portals, an activity that requires efficient implementation of the constraint-based simulations in the web-based environment. Results Here, we present Acorn, an open source (GNU GPL grid computing system for constraint-based simulations of genome scale metabolic reaction networks within an interactive web environment. The grid-based architecture allows efficient execution of computationally intensive, iterative protocols such as Flux Variability Analysis, which can be readily scaled up as the numbers of models (and users increase. The web interface uses AJAX, which facilitates efficient model browsing and other search functions, and intuitive implementation of appropriate simulation conditions. Research groups can install Acorn locally and create user accounts. Users can also import models in the familiar SBML format and link reaction formulas to major functional genomics portals of choice. Selected models and simulation results can be shared between different users and made publically available. Users can construct pathway map layouts and import them into the server using a desktop editor integrated within the system. Pathway maps are then used to visualise numerical results within the web environment. To illustrate these features we have deployed Acorn and created a

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

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

    2017-11-01

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

  3. 36 CFR 1206.90 - Must I acknowledge NHPRC grant support?

    2010-07-01

    ....90 Must I acknowledge NHPRC grant support? Yes, grantee institutions, grant project directors, or grant staff personnel may publish results of any work supported by an NHPRC grant without review by the...

  4. Modular design of metabolic network for robust production of n-butanol from galactose-glucose mixtures.

    Lim, Hyun Gyu; Lim, Jae Hyung; Jung, Gyoo Yeol

    2015-01-01

    Refactoring microorganisms for efficient production of advanced biofuel such as n-butanol from a mixture of sugars in the cheap feedstock is a prerequisite to achieve economic feasibility in biorefinery. However, production of biofuel from inedible and cheap feedstock is highly challenging due to the slower utilization of biomass-driven sugars, arising from complex assimilation pathway, difficulties in amplification of biosynthetic pathways for heterologous metabolite, and redox imbalance caused by consuming intracellular reducing power to produce quite reduced biofuel. Even with these problems, the microorganisms should show robust production of biofuel to obtain industrial feasibility. Thus, refactoring microorganisms for efficient conversion is highly desirable in biofuel production. In this study, we engineered robust Escherichia coli to accomplish high production of n-butanol from galactose-glucose mixtures via the design of modular pathway, an efficient and systematic way, to reconstruct the entire metabolic pathway with many target genes. Three modular pathways designed using the predictable genetic elements were assembled for efficient galactose utilization, n-butanol production, and redox re-balancing to robustly produce n-butanol from a sugar mixture of galactose and glucose. Specifically, the engineered strain showed dramatically increased n-butanol production (3.3-fold increased to 6.2 g/L after 48-h fermentation) compared to the parental strain (1.9 g/L) in galactose-supplemented medium. Moreover, fermentation with mixtures of galactose and glucose at various ratios from 2:1 to 1:2 confirmed that our engineered strain was able to robustly produce n-butanol regardless of sugar composition with simultaneous utilization of galactose and glucose. Collectively, modular pathway engineering of metabolic network can be an effective approach in strain development for optimal biofuel production with cost-effective fermentable sugars. To the best of our

  5. Rape myth acceptance and rape acknowledgment: The mediating role of sexual refusal assertiveness.

    Newins, Amie R; Wilson, Laura C; White, Susan W

    2018-05-01

    Unacknowledged rape, defined as when an individual experiences an event that meets a legal or empirical definition of rape but the individual does not label it as such, is prevalent. Research examining predictors of rape acknowledgment is needed. Sexual assertiveness may be an important variable to consider, as an individual's typical behavior during sexual situations may influence rape acknowledgment. To assess the indirect effect of rape myth acceptance on rape acknowledgment through sexual refusal assertiveness, an online survey of 181 female rape survivors was conducted. The indirect effects of two types of rape myths (He didn't mean to and Rape is a deviant event) were significant and positive. Specifically, acceptance of these two rape myths was negatively related to sexual refusal assertiveness, which was negatively associated with likelihood of rape acknowledgment. The results of this study indicate that sexual refusal assertiveness is associated with lower likelihood of rape acknowledgment among rape survivors. As a result, it appears that, under certain circumstances, women high in rape myth acceptance may be more likely to acknowledge rape when it results in decreased sexual refusal assertiveness. Copyright © 2018 Elsevier B.V. All rights reserved.

  6. Abnormal metabolic brain network associated with Parkinson's disease: replication on a new European sample

    Tomse, Petra; Jensterle, Luka; Grmek, Marko; Zaletel, Katja [University Medical Centre Ljubljana, Department of Nuclear Medicine, Ljubljana (Slovenia); Pirtosek, Zvezdan; Trost, Maja [University Medical Centre Ljubljana, Department of Neurology, 1000 Ljubljana (Slovenia); Dhawan, Vijay; Peng, Shichun; Eidelberg, David; Ma, Yilong [The Feinstein Institute for Medical Research, Center for Neurosciences, Manhasset, NY (United States)

    2017-05-15

    The purpose of this study was to identify the specific metabolic brain pattern characteristic for Parkinson's disease (PD): Parkinson's disease-related pattern (PDRP), using network analysis of [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) brain images in a cohort of Slovenian PD patients. Twenty PD patients (age 70.1 ± 7.8 years, Movement Disorder Society Unified Parkinson's Disease Motor Rating Scale (MDS-UPDRS-III) 38.3 ± 12.2; disease duration 4.3 ± 4.1 years) and 20 age-matched normal controls (NCs) underwent FDG-PET brain imaging. An automatic voxel-based scaled subprofile model/principal component analysis (SSM/PCA) was applied to these scans for PDRP-Slovenia identification. The pattern was characterized by relative hypermetabolism in pallidum, putamen, thalamus, brain stem, and cerebellum associated with hypometabolism in sensorimotor cortex, posterior parietal, occipital, and frontal cortices. The expression of PDRP-Slovenia discriminated PD patients from NCs (p < 0.0001) and correlated positively with patients' clinical score (MDS-UPDRS-III, p = 0.03). Additionally, its topography agrees well with the original PDRP (p < 0.001) identified in American cohort of PD patients. We validated the PDRP-Slovenia expression on additional FDG-PET scans of 20 PD patients, 20 NCs, and 25 patients with atypical parkinsonism (AP). We confirmed that the expression of PDRP-Slovenia manifests good diagnostic accuracy with specificity and sensitivity of 85-90% at optimal pattern expression cutoff for discrimination of PD patients and NCs and is not expressed in AP. PDRP-Slovenia proves to be a robust and reproducible functional imaging biomarker independent of patient population. It accurately differentiates PD patients from NCs and AP and correlates well with the clinical measure of PD progression. (orig.)

  7. Differential RNA-seq, Multi-Network Analysis and Metabolic Regulation Analysis of Kluyveromyces marxianus Reveals a Compartmentalised Response to Xylose.

    Du Toit W P Schabort

    Full Text Available We investigated the transcriptomic response of a new strain of the yeast Kluyveromyces marxianus, in glucose and xylose media using RNA-seq. The data were explored in a number of innovative ways using a variety of networks types, pathway maps, enrichment statistics, reporter metabolites and a flux simulation model, revealing different aspects of the genome-scale response in an integrative systems biology manner. The importance of the subcellular localisation in the transcriptomic response is emphasised here, revealing new insights. As was previously reported by others using a rich medium, we show that peroxisomal fatty acid catabolism was dramatically up-regulated in a defined xylose mineral medium without fatty acids, along with mechanisms to activate fatty acids and transfer products of β-oxidation to the mitochondria. Notably, we observed a strong up-regulation of the 2-methylcitrate pathway, supporting capacity for odd-chain fatty acid catabolism. Next we asked which pathways would respond to the additional requirement for NADPH for xylose utilisation, and rationalised the unexpected results using simulations with Flux Balance Analysis. On a fundamental level, we investigated the contribution of the hierarchical and metabolic regulation levels to the regulation of metabolic fluxes. Metabolic regulation analysis suggested that genetic level regulation plays a major role in regulating metabolic fluxes in adaptation to xylose, even for the high capacity reactions, which is unexpected. In addition, isozyme switching may play an important role in re-routing of metabolic fluxes in subcellular compartments in K. marxianus.

  8. Comparison of methods of alert acknowledgement by critical care clinicians in the ICU setting

    Andrew M. Harrison

    2017-03-01

    Full Text Available Background Electronic Health Record (EHR-based sepsis alert systems have failed to demonstrate improvements in clinically meaningful endpoints. However, the effect of implementation barriers on the success of new sepsis alert systems is rarely explored. Objective To test the hypothesis time to severe sepsis alert acknowledgement by critical care clinicians in the ICU setting would be reduced using an EHR-based alert acknowledgement system compared to a text paging-based system. Study Design In one arm of this simulation study, real alerts for patients in the medical ICU were delivered to critical care clinicians through the EHR. In the other arm, simulated alerts were delivered through text paging. The primary outcome was time to alert acknowledgement. The secondary outcomes were a structured, mixed quantitative/qualitative survey and informal group interview. Results The alert acknowledgement rate from the severe sepsis alert system was 3% (N = 148 and 51% (N = 156 from simulated severe sepsis alerts through traditional text paging. Time to alert acknowledgement from the severe sepsis alert system was median 274 min (N = 5 and median 2 min (N = 80 from text paging. The response rate from the EHR-based alert system was insufficient to compare primary measures. However, secondary measures revealed important barriers. Conclusion Alert fatigue, interruption, human error, and information overload are barriers to alert and simulation studies in the ICU setting.

  9. The unique associations between rape acknowledgment and the DSM-5 PTSD symptom clusters.

    Wilson, Laura C; Scarpa, Angela

    2017-11-01

    It is well documented in the sexual assault literature that more than half of rape survivors do not label their experience as rape. This is called unacknowledged rape. Although this phenomenon is common and undoubtedly has huge implications for psychotherapy, the impact of acknowledgment status on psychological adjustment is unclear. The present study aimed to delineate the unique impact of rape acknowledgment on psychopathology by examining PTSD symptoms at the cluster level. To examine this, 178 female college students who reported rape completed an online survey, including an assessment of PTSD symptoms in the past month. The results suggested that, after accounting for several covariates, acknowledged rape survivors reported significantly greater levels of intrusion and avoidance symptoms compared to unacknowledged rape survivors. The findings suggest that examining PTSD symptoms at the cluster level may provide more insight into the process of recovery following rape and therefore may better inform treatment decisions. Copyright © 2017 Elsevier B.V. All rights reserved.

  10. Affirmation, acknowledgment of in-group responsibility, group-based guilt, and support for reparative measures.

    Cehajić-Clancy, Sabina; Effron, Daniel A; Halperin, Eran; Liberman, Varda; Ross, Lee D

    2011-08-01

    Three studies, 2 conducted in Israel and 1 conducted in Bosnia and Herzegovina, demonstrated that affirming a positive aspect of the self can increase one's willingness to acknowledge in-group responsibility for wrongdoing against others, express feelings of group-based guilt, and consequently provide greater support for reparation policies. By contrast, affirming one's group, although similarly boosting feelings of pride, failed to increase willingness to acknowledge and redress in-group wrongdoing. Studies 2 and 3 demonstrated the mediating role of group-based guilt. That is, increased acknowledgment of in-group responsibility for out-group victimization produced increased feelings of guilt, which in turn increased support for reparation policies to the victimized group. Theoretical and applied implications are discussed.

  11. Ecological network analysis for carbon metabolism of eco-industrial parks: a case study of a typical eco-industrial park in Beijing.

    Lu, Yi; Chen, Bin; Feng, Kuishuang; Hubacek, Klaus

    2015-06-16

    Energy production and industrial processes are crucial economic sectors accounting for about 62% of greenhouse gas (GHG) emissions globally in 2012. Eco-industrial parks are practical attempts to mitigate GHG emissions through cooperation among businesses and the local community in order to reduce waste and pollution, efficiently share resources, and help with the pursuit of sustainable development. This work developed a framework based on ecological network analysis to trace carbon metabolic processes in eco-industrial parks and applied it to a typical eco-industrial park in Beijing. Our findings show that the entire metabolic system is dominated by supply of primary goods from the external environment and final demand. The more carbon flows through a sector, the more influence it would exert upon the whole system. External environment and energy providers are the most active and dominating part of the carbon metabolic system, which should be the first target to mitigate emissions by increasing efficiencies. The carbon metabolism of the eco-industrial park can be seen as an evolutionary system with high levels of efficiency, but this may come at the expense of larger levels of resilience. This work may provide a useful modeling framework for low-carbon design and management of industrial parks.

  12. J. Astrophys. Astr. (2014) 35, 745–746 Acknowledgments Journal of ...

    BetiCiciJoan

    Vidal, V., Chile von Fay-Siebenburgen, Robertus, Sheffield, United Kingdom. Wadadekar Yogesh, Pune, India. Wei Hao, China. Wickramasinghe Thulsi, New Jersey, United States. Wiita, Paul, J., New Jersey, United States. Yadav, Ramakant, Nainital, India. Yashiro Seiji, Washington, United States. 746. Acknowledgements.

  13. 47 CFR 80.1117 - Procedure for receipt and acknowledgement of distress alerts.

    2010-10-01

    ..._locations.html. The ITU-R Recommendation can be purchased from the International Telecommunication Union... identification of the station sending the distress message, spoken three times; (3) The words THIS IS (or DE... station acknowledging receipt, spoken three times; (5) The word RECEIVED (or RRR spoken as ROMEO ROMEO...

  14. 76 FR 3859 - Trade Acknowledgment and Verification of Security-Based Swap Transactions

    2011-01-21

    ... automation and the purported assignment of positions by transferring parties to third parties without notice... over-the- counter derivatives market and improve operational performance, by increasing automation... acknowledgments to their counterparties? 2. What are the current market practices with respect to confirming SBS...

  15. Acknowledging the patient with back pain: A systematic review based on thematic synthesis

    Damsgaard, Janne Brammer; Bastrup, Lene; Norlyk, Annelise

    are ignored by the health professionals, who are often concerned about identifying the cause. This can result in patients feeling mistrusted, marginalised and reluctant to speak out. Therefore, telling about experiences and perceptions is important for back patients in order to feel accepted and acknowledged...

  16. 48 CFR 252.235-7010 - Acknowledgment of support and disclaimer.

    2010-10-01

    ...) contract number(s)). (b) All material, except scientific articles or papers published in scientific... 48 Federal Acquisition Regulations System 3 2010-10-01 2010-10-01 false Acknowledgment of support... ACQUISITION REGULATIONS SYSTEM, DEPARTMENT OF DEFENSE CLAUSES AND FORMS SOLICITATION PROVISIONS AND CONTRACT...

  17. Hippocampus and serum metabolomic studies to explore the regulation of Chaihu-Shu-Gan-San on metabolic network disturbances of rats exposed to chronic variable stress.

    Su, Zhi-heng; Jia, Hong-mei; Zhang, Hong-wu; Feng, Yu-Fei; An, Lei; Zou, Zhong-mei

    2014-03-04

    Chaihu-Shu-Gan-San (CSGS), a traditional Chinese medicine formula, has been effectively used for the treatment of depression. However, studies of its anti-depressive mechanism are challenging, due to the complex pathophysiology of depression, and complexity of CSGS with multiple constituents acting on different receptors. In the present work, metabolomic studies of biochemical changes in the hippocampus and serum of chronic variable stress (CVS)-induced depression rats after treatment with CSGS were performed using ultra performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS). Partial least squares-discriminate analysis indicated that the metabolic perturbation induced by CVS was reduced by treatment with CSGS. A total of twenty-six metabolites (16 from the hippocampus and 10 from serum) were considered as potential biomarkers involved in the development of depression. Among them, 11 were first reported to have potential relevance in the pathogenesis of depression, and 25 may correlate to the regulation of CSGS treatment on depression. The results combined with a previous study indicated that CSGS mediated synergistically abnormalities of the metabolic network, composed of energy metabolism, synthesis of neurotransmitters, tryptophan, phospholipids, fatty acid and bile acid metabolism, bone loss and liver detoxification, which may be helpful for understanding its mechanism of action. Furthermore, the extracellular signal-regulated kinase (ERK) signal pathway, involved in the neuronal protective mechanism of depression related to energy metabolism, was investigated by western blot analysis. The results showed that CSGS reversed disruptions of BDNF, ERK1/2 and pERK1/2 in CVS rats, which provides the first evidence that the ERK signal system may be one of the targets related to the antidepressant action of CSGS.

  18. Correlation-based network analysis of metabolite and enzyme profiles reveals a role of citrate biosynthesis in modulating N and C metabolism in Zea mays

    David Toubiana

    2016-07-01

    Full Text Available To investigate the natural variability of leaf metabolism and enzymatic activity in a maize inbred population, statistical and network analyses were employed on metabolite and enzyme profiles. The test of coefficient of variation showed that sugars and amino acids displayed opposite trends in their variance within the population, consistently with their related enzymes. The overall higher CV values for metabolites as compared to the tested enzymes are indicative for their greater phenotypic plasticity. H2 tests revealed galactinol (1 and asparagine (0.91 as the highest scorers among metabolites and nitrate reductase (0.73, NAD-glutamate dehydrogenase (0.52, and phosphoglucomutase (0.51 among enzymes. The overall low H2 scores for metabolites and enzymes are suggestive for a great environmental impact or gene-environment interaction. Correlation-based network generation followed by community detection analysis, partitioned the network into three main communities and one dyad, (i reflecting the different levels of phenotypic plasticity of the two molecular classes as observed for the CV values and (ii highlighting the concerted changes between classes of chemically related metabolites. Community 1 is composed mainly of enzymes and specialized metabolites, community 2’ is enriched in N-containing compounds and phosphorylated-intermediates. The third community contains mainly organic acids and sugars. Cross-community linkages are supported by aspartate, by the photorespiration amino acids glycine and serine, by the metabolically related GABA and putrescine, and by citrate. The latter displayed the strongest node-betweenness value (185.25 of all nodes highlighting its fundamental structural role in the connectivity of the network by linking between different communities and to the also strongly connected enzyme aldolase.

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

    Montagud, Arnau; Zelezniak, Aleksej; Navarro, Emilio

    2011-01-01

    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...

  20. Magnetic resonance diffusion and relaxation characterization of water in the unfrozen vein network in polycrystalline ice and its response to microbial metabolic products

    Brown, Jennifer R.; Brox, Timothy I.; Vogt, Sarah J.; Seymour, Joseph D.; Skidmore, Mark L.; Codd, Sarah L.

    2012-12-01

    Polycrystalline ice, as found in glaciers and the ice sheets of Antarctica, is a low porosity porous media consisting of a complicated and dynamic pore structure of liquid-filled intercrystalline veins within a solid ice matrix. In this work, Nuclear Magnetic Resonance measurements of relaxation rates and molecular diffusion, useful for probing pore structure and transport dynamics in porous systems, were used to physically characterize the unfrozen vein network structure in ice and its response to the presence of metabolic products produced by V3519-10, a cold tolerant microorganism isolated from the Vostok ice core. Recent research has found microorganisms that can remain viable and even metabolically active within icy environments at sub-zero temperatures. One potential mechanism of survival for V3519-10 is secretion of an extracellular ice binding protein that binds to the prism face of ice crystals and inhibits ice recrystallization, a coarsening process resulting in crystal growth with ice aging. Understanding the impact of ice binding activity on the bulk vein network structure in ice is important to modeling of frozen geophysical systems and in development of ice interacting proteins for biotechnology applications, such as cryopreservation of cell lines, and manufacturing processes in food sciences. Here, we present the first observations of recrystallization inhibition in low porosity ice containing V3519-10 extracellular protein extract as measured with Nuclear Magnetic Resonance and Magnetic Resonance Imaging.

  1. Networking

    Rauno Lindholm, Daniel; Boisen Devantier, Lykke; Nyborg, Karoline Lykke; Høgsbro, Andreas; Fries, de; Skovlund, Louise

    2016-01-01

    The purpose of this project was to examine what influencing factor that has had an impact on the presumed increasement of the use of networking among academics on the labour market and how it is expressed. On the basis of the influence from globalization on the labour market it can be concluded that the globalization has transformed the labour market into a market based on the organization of networks. In this new organization there is a greater emphasis on employees having social qualificati...

  2. An in silico assessment of gene function and organization of the phenylpropanoid pathway metabolic networks in Arabidopsis thaliana and limitations thereof

    Costa, Michael A.; Collins, R. Eric; Anterola, Aldwin M.; Cochrane, Fiona C.; Davin, Laurence B.; Lewis, Norman G.

    2003-01-01

    The Arabidopsis genome sequencing in 2000 gave to science the first blueprint of a vascular plant. Its successful completion also prompted the US National Science Foundation to launch the Arabidopsis 2010 initiative, the goal of which is to identify the function of each gene by 2010. In this study, an exhaustive analysis of The Institute for Genomic Research (TIGR) and The Arabidopsis Information Resource (TAIR) databases, together with all currently compiled EST sequence data, was carried out in order to determine to what extent the various metabolic networks from phenylalanine ammonia lyase (PAL) to the monolignols were organized and/or could be predicted. In these databases, there are some 65 genes which have been annotated as encoding putative enzymatic steps in monolignol biosynthesis, although many of them have only very low homology to monolignol pathway genes of known function in other plant systems. Our detailed analysis revealed that presently only 13 genes (two PALs, a cinnamate-4-hydroxylase, a p-coumarate-3-hydroxylase, a ferulate-5-hydroxylase, three 4-coumarate-CoA ligases, a cinnamic acid O-methyl transferase, two cinnamoyl-CoA reductases) and two cinnamyl alcohol dehydrogenases can be classified as having a bona fide (definitive) function; the remaining 52 genes currently have undetermined physiological roles. The EST database entries for this particular set of genes also provided little new insight into how the monolignol pathway was organized in the different tissues and organs, this being perhaps a consequence of both limitations in how tissue samples were collected and in the incomplete nature of the EST collections. This analysis thus underscores the fact that even with genomic sequencing, presumed to provide the entire suite of putative genes in the monolignol-forming pathway, a very large effort needs to be conducted to establish actual catalytic roles (including enzyme versatility), as well as the physiological function(s) for each member

  3. Isoliquiritigenin induces growth inhibition and apoptosis through downregulating arachidonic acid metabolic network and the deactivation of PI3K/Akt in human breast cancer

    Li, Ying; Zhao, Haixia; Wang, Yuzhong; Zheng, Hao; Yu, Wei; Chai, Hongyan; Zhang, Jing; Falck, John R.; Guo, Austin M.; Yue, Jiang; Peng, Renxiu; Yang, Jing

    2013-01-01

    Arachidonic acid (AA)-derived eicosanoids and its downstream pathways have been demonstrated to play crucial roles in growth control of breast cancer. Here, we demonstrate that isoliquiritigenin, a flavonoid phytoestrogen from licorice, induces growth inhibition and apoptosis through downregulating multiple key enzymes in AA metabolic network and the deactivation of PI3K/Akt in human breast cancer. Isoliquiritigenin diminished cell viability, 5-bromo-2′-deoxyuridine (BrdU) incorporation, and clonogenic ability in both MCF-7 and MDA-MB-231cells, and induced apoptosis as evidenced by an analysis of cytoplasmic histone-associated DNA fragmentation, flow cytometry and hoechst staining. Furthermore, isoliquiritigenin inhibited mRNA expression of multiple forms of AA-metabolizing enzymes, including phospholipase A2 (PLA2), cyclooxygenases (COX)-2 and cytochrome P450 (CYP) 4A, and decreased secretion of their products, including prostaglandin E 2 (PGE 2 ) and 20-hydroxyeicosatetraenoic acid (20-HETE), without affecting COX-1, 5-lipoxygenase (5-LOX), 5-lipoxygenase activating protein (FLAP), and leukotriene B 4 (LTB 4 ). In addition, it downregulated the levels of phospho-PI3K, phospho-PDK (Ser 241 ), phospho-Akt (Thr 308 ), phospho-Bad (Ser 136 ), and Bcl-x L expression, thereby activating caspase cascades and eventually cleaving poly(ADP-ribose) polymerase (PARP). Conversely, the addition of exogenous eicosanoids, including PGE 2 , LTB 4 and a 20-HETE analog (WIT003), and caspase inhibitors, or overexpression of constitutively active Akt reversed isoliquiritigenin-induced apoptosis. Notably, isoliquiritigenin induced growth inhibition and apoptosis of MDA-MB-231 human breast cancer xenografts in nude mice, together with decreased intratumoral levels of eicosanoids and phospho-Akt (Thr 308 ). Collectively, these data suggest that isoliquiritigenin induces growth inhibition and apoptosis through downregulating AA metabolic network and the deactivation of PI3K/Akt in

  4. Short communication: Use of genomic and metabolic information as well as milk performance records for prediction of subclinical ketosis risk via artificial neural networks.

    Ehret, A; Hochstuhl, D; Krattenmacher, N; Tetens, J; Klein, M S; Gronwald, W; Thaller, G

    2015-01-01

    Subclinical ketosis is one of the most prevalent metabolic disorders in high-producing dairy cows during early lactation. This renders its early detection and prevention important for both economical and animal-welfare reasons. Construction of reliable predictive models is challenging, because traits like ketosis are commonly affected by multiple factors. In this context, machine learning methods offer great advantages because of their universal learning ability and flexibility in integrating various sorts of data. Here, an artificial-neural-network approach was applied to investigate the utility of metabolic, genetic, and milk performance data for the prediction of milk levels of β-hydroxybutyrate within and across consecutive weeks postpartum. Data were collected from 218 dairy cows during their first 5wk in milk. All animals were genotyped with a 50,000 SNP panel, and weekly information on the concentrations of the milk metabolites glycerophosphocholine and phosphocholine as well as milk composition data (milk yield, fat and protein percentage) was available. The concentration of β-hydroxybutyric acid in milk was used as target variable in all prediction models. Average correlations between observed and predicted target values up to 0.643 could be obtained, if milk metabolite and routine milk recording data were combined for prediction at the same day within weeks. Predictive performance of metabolic as well as milk performance-based models was higher than that of models based on genetic information. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

  5. Funding Acknowledgements in the Web of Science: inconsistencies in data collection and standardization of funding organizations

    Van Honk, J.; Calero-Medina, C.; Costas, R.

    2016-07-01

    Funding acknowledgements (FAs), as provided by the Web of Science, are a recent inclusion in the bibliometric toolset. They are starting to be used to study the presence, relationships and effects of funding and funders in the production of new scientific research. The incorporation of this new source of information comes with the need to understand how these data are collected and indexed in the database. This paper discusses important inconsistencies related to the method by which the data for FA and funders are selected, extracted and indexed by WoS, thereby highlighting the need to construct a thesaurus for the data. Problematic areas are found to be the quality of the input data and the conceptualization of what does and does not constitute a funding acknowledgement. Based on this critical analysis of the data and the identification of the main issues, we provide several recommendations for researchers, funders, WoS and other users of the data. (Author)

  6. TRAILER Project Overview. Tagging, Recognition and Acknowledgment of Informal Learning ExpeRiences

    García-Peñalvo, Francisco J.; Zangrando, Valentina; García Holgado, Alicia; Conde González, Miguel Ángel; Seoane Pardo, Antonio; Alier Forment, Marc; Janssen, José; Griffiths, Dai; Mykowska, Aleksandra; Ribeiro Alves, Gustavo; Minovic, Miroslav

    2012-01-01

    García-Peñalvo, F. J., Zangrando, V., García Holgado, A., Conde González, M. Á., Seoane Pardo, A. M., Alier Forment, M., Janssen, J., et al. (2012). TRAILER Project Overview. Tagging, Recognition and Acknowledgment of Informal Learning ExpeRiences. In F. J. García, L. Vicent, M. Ribó, A. Climent, J.

  7. COACHES' PERCEPTIONS OF COMPETENCE AND ACKNOWLEDGEMENT OF TRAINING NEEDS RELATED TO PROFESSIONAL COMPETENCES

    Sofia Santos

    2010-03-01

    Full Text Available The purpose of the present study was to examine coaches' perceptions of competence and acknowledgement of training needs related to professional competences according to the professional experience and academic education. The participants were 343 coaches from several sports, who answered to a questionnaire that includes a scale focused on perceptions of competence and another scale on acknowledgment of training needs. An exploratory factor analysis with Maximum Likelihood Factoring was used with Oblimin rotation for the identification of emergent factors. Comparison on coaches' perceptions in function of coaching experience and coaches' academic background were made applying One-way ANOVA and Tukey's post hoc multiple comparisons. Factor analysis on coaches' perceptions of competence and acknowledgement of training needs made apparent three main areas of competences, i.e. competences related to annual and multi-annual planning; competences related to orientation towards practice and competition; and personal and coaching education competences. Coaches' perceptions were influenced by their experience, as low experienced coaches rated themselves at lower levels of competence and with more training needs; also coaches with high education, in Physical Education or others, perceived themselves as more competent than coaches with no higher education. Finally, the majority of the coaches perceived themselves to be competent but, nevertheless, they indicated to have training needs, which brings an important feedback to coach education. This suggests that coaches are interested in increasing their knowledge and competence in a broad range of areas which should be considered in future coach education programs

  8. Using a Genome-Scale Metabolic Network Model to Elucidate the Mechanism of Chloroquine Action in Plasmodium falciparum

    2017-03-22

    The transcriptome data of P. falciparum obtained under different stress conditions (e.g., drug exposure, genetic mutation , etc.) contain information...Linking high-resolution metabolic flux phenotypes and transcriptional regulation in yeast modulated by the global regulator Gcn4p. Proc. Natl. Acad

  9. Probing the metabolic network in bloodstream-form Trypanosoma brucei using untargeted metabolomics with stable isotope labelled glucose.

    Darren J Creek

    2015-03-01

    Full Text Available Metabolomics coupled with heavy-atom isotope-labelled glucose has been used to probe the metabolic pathways active in cultured bloodstream form trypomastigotes of Trypanosoma brucei, a parasite responsible for human African trypanosomiasis. Glucose enters many branches of metabolism beyond glycolysis, which has been widely held to be the sole route of glucose metabolism. Whilst pyruvate is the major end-product of glucose catabolism, its transamination product, alanine, is also produced in significant quantities. The oxidative branch of the pentose phosphate pathway is operative, although the non-oxidative branch is not. Ribose 5-phosphate generated through this pathway distributes widely into nucleotide synthesis and other branches of metabolism. Acetate, derived from glucose, is found associated with a range of acetylated amino acids and, to a lesser extent, fatty acids; while labelled glycerol is found in many glycerophospholipids. Glucose also enters inositol and several sugar nucleotides that serve as precursors to macromolecule biosynthesis. Although a Krebs cycle is not operative, malate, fumarate and succinate, primarily labelled in three carbons, were present, indicating an origin from phosphoenolpyruvate via oxaloacetate. Interestingly, the enzyme responsible for conversion of phosphoenolpyruvate to oxaloacetate, phosphoenolpyruvate carboxykinase, was shown to be essential to the bloodstream form trypanosomes, as demonstrated by the lethal phenotype induced by RNAi-mediated downregulation of its expression. In addition, glucose derivatives enter pyrimidine biosynthesis via oxaloacetate as a precursor to aspartate and orotate.

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

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

    2018-06-01

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

  11. Extracellular magnesium enhances the damage to locomotor networks produced by metabolic perturbation mimicking spinal injury in the neonatal rat spinal cord in vitro.

    Margaryan, G; Mladinic, M; Mattioli, C; Nistri, A

    2009-10-06

    An acute injury to brain or spinal cord produces profound metabolic perturbation that extends and exacerbates tissue damage. Recent clinical interventions to treat this condition with i.v. Mg(2+) to stabilize its extracellular concentration provided disappointing results. The present study used an in vitro spinal cord model from the neonatal rat to investigate the role of extracellular Mg(2+) in the lesion evoked by a pathological medium mimicking the metabolic perturbation (hypoxia, aglycemia, oxidative stress, and acid pH) occurring in vivo. Damage was measured by taking as outcome locomotor network activity for up to 24 h after the primary insult. Pathological medium in 1 mM Mg(2+) solution (1 h) largely depressed spinal reflexes and suppressed fictive locomotion on the same and the following day. Conversely, pathological medium in either Mg(2+)-free or 5 mM Mg(2+) solution evoked temporary network depression and enabled fictive locomotion the day after. While global cell death was similar regardless of extracellular Mg(2+) solution, white matter was particularly affected. In ventral horn the number of surviving neurons was the highest in Mg(2+) free solution and the lowest in 1 mM Mg(2+), while motoneurons were unaffected. Although the excitotoxic damage elicited by kainate was insensitive to extracellular Mg(2+), 1 mM Mg(2+) potentiated the effect of combining pathological medium with kainate at low concentrations. These results indicate that preserving Mg(2+) homeostasis rendered experimental spinal injury more severe. Furthermore, analyzing ventral horn neuron numbers in relation to fictive locomotion expression might provide a first estimate of the minimal size of the functional locomotor network.

  12. System-Level and Granger Network Analysis of Integrated Proteomic and Metabolomic Dynamics Identifies Key Points of Grape Berry Development at the Interface of Primary and Secondary Metabolism

    Lei Wang

    2017-06-01

    Full Text Available Grapevine is a fruit crop with worldwide economic importance. The grape berry undergoes complex biochemical changes from fruit set until ripening. This ripening process and production processes define the wine quality. Thus, a thorough understanding of berry ripening is crucial for the prediction of wine quality. For a systemic analysis of grape berry development we applied mass spectrometry based platforms to analyse the metabolome and proteome of Early Campbell at 12 stages covering major developmental phases. Primary metabolites involved in central carbon metabolism, such as sugars, organic acids and amino acids together with various bioactive secondary metabolites like flavonols, flavan-3-ols and anthocyanins were annotated and quantified. At the same time, the proteomic analysis revealed the protein dynamics of the developing grape berries. Multivariate statistical analysis of the integrated metabolomic and proteomic dataset revealed the growth trajectory and corresponding metabolites and proteins contributing most to the specific developmental process. K-means clustering analysis revealed 12 highly specific clusters of co-regulated metabolites and proteins. Granger causality network analysis allowed for the identification of time-shift correlations between metabolite-metabolite, protein- protein and protein-metabolite pairs which is especially interesting for the understanding of developmental processes. The integration of metabolite and protein dynamics with their corresponding biochemical pathways revealed an energy-linked metabolism before veraison with high abundances of amino acids and accumulation of organic acids, followed by protein and secondary metabolite synthesis. Anthocyanins were strongly accumulated after veraison whereas other flavonoids were in higher abundance at early developmental stages and decreased during the grape berry developmental processes. A comparison of the anthocyanin profile of Early Campbell to other

  13. System-Level and Granger Network Analysis of Integrated Proteomic and Metabolomic Dynamics Identifies Key Points of Grape Berry Development at the Interface of Primary and Secondary Metabolism.

    Wang, Lei; Sun, Xiaoliang; Weiszmann, Jakob; Weckwerth, Wolfram

    2017-01-01

    Grapevine is a fruit crop with worldwide economic importance. The grape berry undergoes complex biochemical changes from fruit set until ripening. This ripening process and production processes define the wine quality. Thus, a thorough understanding of berry ripening is crucial for the prediction of wine quality. For a systemic analysis of grape berry development we applied mass spectrometry based platforms to analyse the metabolome and proteome of Early Campbell at 12 stages covering major developmental phases. Primary metabolites involved in central carbon metabolism, such as sugars, organic acids and amino acids together with various bioactive secondary metabolites like flavonols, flavan-3-ols and anthocyanins were annotated and quantified. At the same time, the proteomic analysis revealed the protein dynamics of the developing grape berries. Multivariate statistical analysis of the integrated metabolomic and proteomic dataset revealed the growth trajectory and corresponding metabolites and proteins contributing most to the specific developmental process. K-means clustering analysis revealed 12 highly specific clusters of co-regulated metabolites and proteins. Granger causality network analysis allowed for the identification of time-shift correlations between metabolite-metabolite, protein- protein and protein-metabolite pairs which is especially interesting for the understanding of developmental processes. The integration of metabolite and protein dynamics with their corresponding biochemical pathways revealed an energy-linked metabolism before veraison with high abundances of amino acids and accumulation of organic acids, followed by protein and secondary metabolite synthesis. Anthocyanins were strongly accumulated after veraison whereas other flavonoids were in higher abundance at early developmental stages and decreased during the grape berry developmental processes. A comparison of the anthocyanin profile of Early Campbell to other cultivars revealed

  14. Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling.

    Vijayakumar, Supreeta; Conway, Max; Lió, Pietro; Angione, Claudio

    2017-05-30

    Metabolic modelling has entered a mature phase with dozens of methods and software implementations available to the practitioner and the theoretician. It is not easy for a modeller to be able to see the wood (or the forest) for the trees. Driven by this analogy, we here present a 'forest' of principal methods used for constraint-based modelling in systems biology. This provides a tree-based view of methods available to prospective modellers, also available in interactive version at http://modellingmetabolism.net, where it will be kept updated with new methods after the publication of the present manuscript. Our updated classification of existing methods and tools highlights the most promising in the different branches, with the aim to develop a vision of how existing methods could hybridize and become more complex. We then provide the first hands-on tutorial for multi-objective optimization of metabolic models in R. We finally discuss the implementation of multi-view machine learning approaches in poly-omic integration. Throughout this work, we demonstrate the optimization of trade-offs between multiple metabolic objectives, with a focus on omic data integration through machine learning. We anticipate that the combination of a survey, a perspective on multi-view machine learning and a step-by-step R tutorial should be of interest for both the beginner and the advanced user. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  15. Heterogeneity in a Suburban River Network: Understanding the Impact of Fluvial Wetlands on Dissolved Oxygen and Metabolism in Headwater Streams

    Cain, J. S.; Wollheim, W. M.; Sheehan, K.; Lightbody, A.

    2014-12-01

    Low dissolved oxygen content in rivers threatens fish populations, aquatic organisms, and the health of entire ecosystems. River systems with high fluvial wetland abundance and organic matter, may result in high metabolism that in conjunction with low re-aeration rates, lead to low oxygen conditions. Increasing abundance of beaver ponds in many areas may exacerbate this phenomenon. This research aims to understand the impact of fluvial wetlands, including beaver ponds, on dissolved oxygen (D.O.) and metabolism throughout the headwaters of the Ipswich R. watershed, MA, USA. In several fluvial wetland dominated systems, we measured diel D.O. and metabolism in the upstream inflow, the surface water transient storage zones of fluvial wetland sidepools, and at the outflow to understand how the wetlands modify dissolved oxygen. D.O. was also measured longitudinally along entire surface water flow paths (x-y km long) to determine how low levels of D.O. propagate downstream. Nutrient samples were also collected to understand how their behavior was related to D.O. behavior. Results show that D.O. in fluvial wetlands has large swings with periods of very low D.O. at night. D.O. swings were also seen in downstream outflow, though lagged and somewhat attenuated. Flow conditions affect the level of inundation and the subsequent effects of fluvial wetlands on main channel D.O.. Understanding the D.O. behavior throughout river systems has important implications for the ability of river systems to remove anthropogenic nitrogen.

  16. Simulation of Escherichia coli Dynamics in Biofilms and Submerged Colonies with an Individual-Based Model Including Metabolic Network Information.

    Tack, Ignace L M M; Nimmegeers, Philippe; Akkermans, Simen; Hashem, Ihab; Van Impe, Jan F M

    2017-01-01

    Clustered microbial communities are omnipresent in the food industry, e.g., as colonies of microbial pathogens in/on food media or as biofilms on food processing surfaces. These clustered communities are often characterized by metabolic differentiation among their constituting cells as a result of heterogeneous environmental conditions in the cellular surroundings. This paper focuses on the role of metabolic differentiation due to oxygen gradients in the development of Escherichia coli cell communities, whereby low local oxygen concentrations lead to cellular secretion of weak acid products. For this reason, a metabolic model has been developed for the facultative anaerobe E. coli covering the range of aerobic, microaerobic, and anaerobic environmental conditions. This metabolic model is expressed as a multiparametric programming problem, in which the influence of low extracellular pH values and the presence of undissociated acid cell products in the environment has been taken into account. Furthermore, the developed metabolic model is incorporated in MICRODIMS, an in-house developed individual-based modeling framework to simulate microbial colony and biofilm dynamics. Two case studies have been elaborated using the MICRODIMS simulator: (i) biofilm growth on a substratum surface and (ii) submerged colony growth in a semi-solid mixed food product. In the first case study, the acidification of the biofilm environment and the emergence of typical biofilm morphologies have been observed, such as the mushroom-shaped structure of mature biofilms and the formation of cellular chains at the exterior surface of the biofilm. The simulations show that these morphological phenomena are respectively dependent on the initial affinity of pioneer cells for the substratum surface and the cell detachment process at the outer surface of the biofilm. In the second case study, a no-growth zone emerges in the colony center due to a local decline of the environmental pH. As a result

  17. Simulation of Escherichia coli Dynamics in Biofilms and Submerged Colonies with an Individual-Based Model Including Metabolic Network Information

    Ignace L. M. M. Tack

    2017-12-01

    Full Text Available Clustered microbial communities are omnipresent in the food industry, e.g., as colonies of microbial pathogens in/on food media or as biofilms on food processing surfaces. These clustered communities are often characterized by metabolic differentiation among their constituting cells as a result of heterogeneous environmental conditions in the cellular surroundings. This paper focuses on the role of metabolic differentiation due to oxygen gradients in the development of Escherichia coli cell communities, whereby low local oxygen concentrations lead to cellular secretion of weak acid products. For this reason, a metabolic model has been developed for the facultative anaerobe E. coli covering the range of aerobic, microaerobic, and anaerobic environmental conditions. This metabolic model is expressed as a multiparametric programming problem, in which the influence of low extracellular pH values and the presence of undissociated acid cell products in the environment has been taken into account. Furthermore, the developed metabolic model is incorporated in MICRODIMS, an in-house developed individual-based modeling framework to simulate microbial colony and biofilm dynamics. Two case studies have been elaborated using the MICRODIMS simulator: (i biofilm growth on a substratum surface and (ii submerged colony growth in a semi-solid mixed food product. In the first case study, the acidification of the biofilm environment and the emergence of typical biofilm morphologies have been observed, such as the mushroom-shaped structure of mature biofilms and the formation of cellular chains at the exterior surface of the biofilm. The simulations show that these morphological phenomena are respectively dependent on the initial affinity of pioneer cells for the substratum surface and the cell detachment process at the outer surface of the biofilm. In the second case study, a no-growth zone emerges in the colony center due to a local decline of the environmental p

  18. Mathematical modeling of cancer metabolism.

    Medina, Miguel Ángel

    2018-04-01

    Systemic approaches are needed and useful for the study of the very complex issue of cancer. Modeling has a central position in these systemic approaches. Metabolic reprogramming is nowadays acknowledged as an essential hallmark of cancer. Mathematical modeling could contribute to a better understanding of cancer metabolic reprogramming and to identify new potential ways of therapeutic intervention. Herein, I review several alternative approaches to metabolic modeling and their current and future impact in oncology. Copyright © 2018 Elsevier B.V. All rights reserved.

  19. On results of monitoring of environmental radiation around Rokkasho reprocessing facilities (fiscal year 1995) (acknowledgement)

    1997-01-01

    The Nuclear Safety Commission received the report on this matter from the expert committee on environmental radiation monitoring central evaluation on August 30, 1996, and acknowledged it after the deliberation. The report was recognized as appropriate. The monitoring was carried out according to the monitoring plan for environmental radiation around Rokkasho reprocessing facilities. The monitoring plan is outlined. At the time of the deliberation, the propriety of the techniques of monitoring and technical level carried out by Japan Nuclear Fuel Service Co. was examined. As the result, it was confirmed that the techniques of monitoring and technical level were appropriate. The results of monitoring are reported. (K.I.)

  20. The transcriptional regulatory network of Corynebacterium jeikeium K411 and its interaction with metabolic routes contributing to human body odor formation.

    Barzantny, Helena; Schröder, Jasmin; Strotmeier, Jasmin; Fredrich, Eugenie; Brune, Iris; Tauch, Andreas

    2012-06-15

    Lipophilic corynebacteria are involved in the generation of volatile odorous products in the process of human body odor formation by degrading skin lipids and specific odor precursors. Therefore, these bacteria represent appropriate model systems for the cosmetic industry to examine axillary malodor formation on the molecular level. To understand the transcriptional control of metabolic pathways involved in this process, the transcriptional regulatory network of the lipophilic axilla isolate Corynebacterium jeikeium K411 was reconstructed from the complete genome sequence. This bioinformatic approach detected a gene-regulatory repertoire of 83 candidate proteins, including 56 DNA-binding transcriptional regulators, nine two-component systems, nine sigma factors, and nine regulators with diverse physiological functions. Furthermore, a cross-genome comparison among selected corynebacterial species of the taxonomic cluster 3 revealed a common gene-regulatory repertoire of 44 transcriptional regulators, including the MarR-like regulator Jk0257, which is exclusively encoded in the genomes of this taxonomical subline. The current network reconstruction comprises 48 transcriptional regulators and 674 gene-regulatory interactions that were assigned to five interconnected functional modules. Most genes involved in lipid degradation are under the combined control of the global cAMP-sensing transcriptional regulator GlxR and the LuxR-family regulator RamA, probably reflecting the essential role of lipid degradation in C. jeikeium. This study provides the first genome-scale in silico analysis of the transcriptional regulation of metabolism in a lipophilic bacterium involved in the formation of human body odor. Copyright © 2012 Elsevier B.V. All rights reserved.

  1. Pathways of Lipid Metabolism in Marine Algae, Co-Expression Network, Bottlenecks and Candidate Genes for Enhanced Production of EPA and DHA in Species of Chromista

    Alice Mühlroth

    2013-11-01

    Full Text Available The importance of n-3 long chain polyunsaturated fatty acids (LC-PUFAs for human health has received more focus the last decades, and the global consumption of n-3 LC-PUFA has increased. Seafood, the natural n-3 LC-PUFA source, is harvested beyond a sustainable capacity, and it is therefore imperative to develop alternative n-3 LC-PUFA sources for both eicosapentaenoic acid (EPA, 20:5n-3 and docosahexaenoic acid (DHA, 22:6n-3. Genera of algae such as Nannochloropsis, Schizochytrium, Isochrysis and Phaedactylum within the kingdom Chromista have received attention due to their ability to produce n-3 LC-PUFAs. Knowledge of LC-PUFA synthesis and its regulation in algae at the molecular level is fragmentary and represents a bottleneck for attempts to enhance the n-3 LC-PUFA levels for industrial production. In the present review, Phaeodactylum tricornutum has been used to exemplify the synthesis and compartmentalization of n-3 LC-PUFAs. Based on recent transcriptome data a co-expression network of 106 genes involved in lipid metabolism has been created. Together with recent molecular biological and metabolic studies, a model pathway for n-3 LC-PUFA synthesis in P. tricornutum has been proposed, and is compared to industrialized species of Chromista. Limitations of the n-3 LC-PUFA synthesis by enzymes such as thioesterases, elongases, acyl-CoA synthetases and acyltransferases are discussed and metabolic bottlenecks are hypothesized such as the supply of the acetyl-CoA and NADPH. A future industrialization will depend on optimization of chemical compositions and increased biomass production, which can be achieved by exploitation of the physiological potential, by selective breeding and by genetic engineering.

  2. Aroclor 1254, a developmental neurotoxicant, alters energy metabolism- and intracellular signaling-associated protein networks in rat cerebellum and hippocampus

    Kodavanti, Prasada Rao S.; Osorio, Cristina; Royland, Joyce E.; Ramabhadran, Ram; Alzate, Oscar

    2011-01-01

    The vast literature on the mode of action of polychlorinated biphenyls (PCBs) indicates that PCBs are a unique model for understanding the mechanisms of toxicity of environmental mixtures of persistent chemicals. PCBs have been shown to adversely affect psychomotor function and learning and memory in humans. Although the molecular mechanisms for PCB effects are unclear, several studies indicate that the disruption of Ca 2+ -mediated signal transduction plays significant roles in PCB-induced developmental neurotoxicity. Culminating events in signal transduction pathways include the regulation of gene and protein expression, which affects the growth and function of the nervous system. Our previous studies showed changes in gene expression related to signal transduction and neuronal growth. In this study, protein expression following developmental exposure to PCB is examined. Pregnant rats (Long Evans) were dosed with 0.0 or 6.0 mg/kg/day of Aroclor-1254 from gestation day 6 through postnatal day (PND) 21, and the cerebellum and hippocampus from PND14 animals were analyzed to determine Aroclor 1254-induced differential protein expression. Two proteins were found to be differentially expressed in the cerebellum following PCB exposure while 18 proteins were differentially expressed in the hippocampus. These proteins are related to energy metabolism in mitochondria (ATP synthase, sub unit β (ATP5B), creatine kinase, and malate dehydrogenase), calcium signaling (voltage-dependent anion-selective channel protein 1 (VDAC1) and ryanodine receptor type II (RyR2)), and growth of the nervous system (dihydropyrimidinase-related protein 4 (DPYSL4), valosin-containing protein (VCP)). Results suggest that Aroclor 1254-like persistent chemicals may alter energy metabolism and intracellular signaling, which might result in developmental neurotoxicity. -- Highlights: ► We performed brain proteomic analysis of rats exposed to the neurotoxicant, Aroclor 1254. ► Cerebellum and

  3. Genome and metabolic network of Candidatus Phaeomarinobacter ectocarpi Ec32, a new candidate genus of Alphaproteobacteria frequently associated with brown algae

    Simon M Dittami

    2014-07-01

    Full Text Available Rhizobiales and related orders of Alphaproteobacteria comprise several genera of nodule-inducing symbiotic bacteria associated with plant roots. Here we describe the genome and the metabolic network of Candidatus Phaeomarinobacter ectocarpi Ec32, a member of a new candidate genus closely related to Rhizobiales and found in association with cultures of the filamentous brown algal model Ectocarpus. The Ca. P. ectocarpi genome encodes numerous metabolic pathways that may be relevant for this bacterium to interact with algae. Notably, it possesses a large set of glycoside hydrolases and transporters, which may serve to process and assimilate algal metabolites. It also harbors several proteins likely to be involved in the synthesis of algal hormones such as auxins and cytokinins, as well as the vitamins pyridoxine, biotin, and thiamine. As of today, Ca. P. ectocarpi has not been successfully cultured, and identical 16S rDNA sequences have been found exclusively associated with Ectocarpus. However, related sequences (≥ 97% identity have also been detected free-living and in a Fucus vesiculosus microbiome barcoding project, indicating that the candidate genus Phaeomarinobacter may comprise several species, which may colonize different niches.

  4. Integration of liver gene co-expression networks and eGWAs analyses highlighted candidate regulators implicated in lipid metabolism in pigs.

    Ballester, Maria; Ramayo-Caldas, Yuliaxis; Revilla, Manuel; Corominas, Jordi; Castelló, Anna; Estellé, Jordi; Fernández, Ana I; Folch, Josep M

    2017-04-19

    In the present study, liver co-expression networks and expression Genome Wide Association Study (eGWAS) were performed to identify DNA variants and molecular pathways implicated in the functional regulatory mechanisms of meat quality traits in pigs. With this purpose, the liver mRNA expression of 44 candidates genes related with lipid metabolism was analysed in 111 Iberian x Landrace backcross animals. The eGWAS identified 92 eSNPs located in seven chromosomal regions and associated with eight genes: CROT, CYP2U1, DGAT1, EGF, FABP1, FABP5, PLA2G12A, and PPARA. Remarkably, cis-eSNPs associated with FABP1 gene expression which may be determining the C18:2(n-6)/C18:3(n-3) ratio in backfat through the multiple interaction of DNA variants and genes were identified. Furthermore, a hotspot on SSC8 associated with the gene expression of eight genes was identified and the TBCK gene was pointed out as candidate gene regulating it. Our results also suggested that the PI3K-Akt-mTOR pathway plays an important role in the control of the analysed genes highlighting nuclear receptors as the NR3C1 or PPARA. Finally, sex-dimorphism associated with hepatic lipid metabolism was identified with over-representation of female-biased genes. These results increase our knowledge of the genetic architecture underlying fat composition traits.

  5. Values in environmental research: Citizens’ views of scientists who acknowledge values

    McCright, Aaron M.; Allen, Summer; Dietz, Thomas

    2017-01-01

    Scientists who perform environmental research on policy-relevant topics face challenges when communicating about how values may have influenced their research. This study examines how citizens view scientists who publicly acknowledge values. Specifically, we investigate whether it matters: if citizens share or oppose a scientist’s values, if a scientist’s conclusions seem contrary to or consistent with the scientist’s values, and if a scientist is assessing the state of the science or making a policy recommendation. We conducted two 3x2 factorial design online experiments. Experiment 1 featured a hypothetical scientist assessing the state of the science on the public-health effects of exposure to Bisphenol A (BPA), and Experiment 2 featured a scientist making a policy recommendation on use of BPA. We manipulated whether or not the scientist expressed values and whether the scientist’s conclusion appeared contrary to or consistent with the scientist’s values, and we accounted for whether or not subjects’ values aligned with the scientist’s values. We analyzed our data with ordinary least squares (OLS) regression techniques. Our results provide at least preliminary evidence that acknowledging values may reduce the perceived credibility of scientists within the general public, but this effect differs depending on whether scientists and citizens share values, whether scientists draw conclusions that run contrary to their values, and whether scientists make policy recommendations. PMID:29069087

  6. Metabolic network reconstruction and genome-scale model of butanol-producing strain Clostridium beijerinckii NCIMB 8052

    Kim Pan-Jun

    2011-08-01

    Full Text Available Abstract Background Solventogenic clostridia offer a sustainable alternative to petroleum-based production of butanol--an important chemical feedstock and potential fuel additive or replacement. C. beijerinckii is an attractive microorganism for strain design to improve butanol production because it (i naturally produces the highest recorded butanol concentrations as a byproduct of fermentation; and (ii can co-ferment pentose and hexose sugars (the primary products from lignocellulosic hydrolysis. Interrogating C. beijerinckii metabolism from a systems viewpoint using constraint-based modeling allows for simulation of the global effect of genetic modifications. Results We present the first genome-scale metabolic model (iCM925 for C. beijerinckii, containing 925 genes, 938 reactions, and 881 metabolites. To build the model we employed a semi-automated procedure that integrated genome annotation information from KEGG, BioCyc, and The SEED, and utilized computational algorithms with manual curation to improve model completeness. Interestingly, we found only a 34% overlap in reactions collected from the three databases--highlighting the importance of evaluating the predictive accuracy of the resulting genome-scale model. To validate iCM925, we conducted fermentation experiments using the NCIMB 8052 strain, and evaluated the ability of the model to simulate measured substrate uptake and product production rates. Experimentally observed fermentation profiles were found to lie within the solution space of the model; however, under an optimal growth objective, additional constraints were needed to reproduce the observed profiles--suggesting the existence of selective pressures other than optimal growth. Notably, a significantly enriched fraction of actively utilized reactions in simulations--constrained to reflect experimental rates--originated from the set of reactions that overlapped between all three databases (P = 3.52 × 10-9, Fisher's exact test

  7. Aroclor 1254, a developmental neurotoxicant, alters energy metabolism- and intracellular signaling-associated protein networks in rat cerebellum and hippocampus

    Kodavanti, Prasada Rao S., E-mail: kodavanti.prasada@epa.gov [Neurotoxicology Branch, NHEERL, ORD, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina (United States); Osorio, Cristina [Systems Proteomics Center, University of North Carolina at Chapel Hill, North Carolina (United States); Program on Molecular Biology and Biotechnology, University of North Carolina at Chapel Hill, North Carolina (United States); Royland, Joyce E.; Ramabhadran, Ram [Genetic and Cellular Toxicology Branch, NHEERL, ORD, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina (United States); Alzate, Oscar [Department of Cellular and Developmental Biology, University of North Carolina at Chapel Hill, North Carolina (United States); Systems Proteomics Center, University of North Carolina at Chapel Hill, North Carolina (United States); Program on Molecular Biology and Biotechnology, University of North Carolina at Chapel Hill, North Carolina (United States)

    2011-11-15

    The vast literature on the mode of action of polychlorinated biphenyls (PCBs) indicates that PCBs are a unique model for understanding the mechanisms of toxicity of environmental mixtures of persistent chemicals. PCBs have been shown to adversely affect psychomotor function and learning and memory in humans. Although the molecular mechanisms for PCB effects are unclear, several studies indicate that the disruption of Ca{sup 2+}-mediated signal transduction plays significant roles in PCB-induced developmental neurotoxicity. Culminating events in signal transduction pathways include the regulation of gene and protein expression, which affects the growth and function of the nervous system. Our previous studies showed changes in gene expression related to signal transduction and neuronal growth. In this study, protein expression following developmental exposure to PCB is examined. Pregnant rats (Long Evans) were dosed with 0.0 or 6.0 mg/kg/day of Aroclor-1254 from gestation day 6 through postnatal day (PND) 21, and the cerebellum and hippocampus from PND14 animals were analyzed to determine Aroclor 1254-induced differential protein expression. Two proteins were found to be differentially expressed in the cerebellum following PCB exposure while 18 proteins were differentially expressed in the hippocampus. These proteins are related to energy metabolism in mitochondria (ATP synthase, sub unit {beta} (ATP5B), creatine kinase, and malate dehydrogenase), calcium signaling (voltage-dependent anion-selective channel protein 1 (VDAC1) and ryanodine receptor type II (RyR2)), and growth of the nervous system (dihydropyrimidinase-related protein 4 (DPYSL4), valosin-containing protein (VCP)). Results suggest that Aroclor 1254-like persistent chemicals may alter energy metabolism and intracellular signaling, which might result in developmental neurotoxicity. -- Highlights: Black-Right-Pointing-Pointer We performed brain proteomic analysis of rats exposed to the neurotoxicant

  8. Reconstruction of the yeast protein-protein interaction network involved in nutrient sensing and global metabolic regulation

    Nandy, Subir Kumar; Jouhten, Paula; Nielsen, Jens

    2010-01-01

    proteins. Despite the value of BioGRID for studying protein-protein interactions, there is a need for manual curation of these interactions in order to remove false positives. RESULTS: Here we describe an annotated reconstruction of the protein-protein interactions around four key nutrient......) and for all the interactions between them (edges). The annotated information is readily available utilizing the functionalities of network modelling tools such as Cytoscape and CellDesigner. CONCLUSIONS: The reported fully annotated interaction model serves as a platform for integrated systems biology studies...

  9. The Growing Acknowledgement of Wrongful Conviction – The Australian Response Within an International Context

    Lynne Weathered

    2013-12-01

    Full Text Available The wrongful conviction of innocent people is being acknowledged in an increasing number of countries around the world. The problem of wrongful conviction is now framed as an international human rights issue. More attention is being given to whether criminal justice systems are providing sufficient measures for the effective review and rectification of wrongful convictions and whether international obligations in that regard are being met. England, Wales, Northern Ireland, Scotland, Norway, Canada and the United States have substantial new mechanisms in place to better identify and correct wrongful convictions but Australia has been slower to respond. While some Australian states have introduced reforms such as DNA innocence testing and a new appeal avenue, many issues remain as highlighted in recent debates as to whether Australia should establish a Criminal Cases Review Commission.

  10. A clinical classification acknowledging neuropsychiatric and cognitive impairment in Huntingtons disease

    Vinther-Jensen, Tua; Larsen, Ida U; Hjermind, Lena E

    2014-01-01

    based on the presence of involuntary movements and a positive genetic test for the HD CAG repeat expansion. After investigating the frequencies of the triad manifestations in a large outpatient clinical cohort of HD gene-expansion carriers, we propose a new clinical classification.MethodsIn this cross...... medication, and cognitive impairment.ResultsAmong the motor manifest HD gene-expansion carriers, 51.8% presented with the full symptom triad, 25.0% were defined as cognitively impaired in addition to motor symptoms, and 14.3% had neuropsychiatric symptoms along with motor symptoms. Only 8.9% had isolated...... terms, suggesting that the current clinical classification is neither necessarily suitable nor helpful for this patient group. Some premanifest gene-expansion carriers may have psychiatric and/or cognitive symptoms caused by reactive stress or other pathology than HD. Acknowledging this fact we, however...

  11. iCN718, an Updated and Improved Genome-Scale Metabolic Network Reconstruction of Acinetobacter baumannii AYE.

    Norsigian, Charles J; Kavvas, Erol; Seif, Yara; Palsson, Bernhard O; Monk, Jonathan M

    2018-01-01

    Acinetobacter baumannii has become an urgent clinical threat due to the recent emergence of multi-drug resistant strains. There is thus a significant need to discover new therapeutic targets in this organism. One means for doing so is through the use of high-quality genome-scale reconstructions. Well-curated and accurate genome-scale models (GEMs) of A. baumannii would be useful for improving treatment options. We present an updated and improved genome-scale reconstruction of A. baumannii AYE, named iCN718, that improves and standardizes previous A. baumannii AYE reconstructions. iCN718 has 80% accuracy for predicting gene essentiality data and additionally can predict large-scale phenotypic data with as much as 89% accuracy, a new capability for an A. baumannii reconstruction. We further demonstrate that iCN718 can be used to analyze conserved metabolic functions in the A. baumannii core genome and to build strain-specific GEMs of 74 other A. baumannii strains from genome sequence alone. iCN718 will serve as a resource to integrate and synthesize new experimental data being generated for this urgent threat pathogen.

  12. GC-MS metabolic profiling of Cabernet Sauvignon and Merlot cultivars during grapevine berry development and network analysis reveals a stage- and cultivar-dependent connectivity of primary metabolites.

    Cuadros-Inostroza, Alvaro; Ruíz-Lara, Simón; González, Enrique; Eckardt, Aenne; Willmitzer, Lothar; Peña-Cortés, Hugo

    Information about the total chemical composition of primary metabolites during grape berry development is scarce, as are comparative studies trying to understand to what extent metabolite modifications differ between cultivars during ripening. Thus, correlating the metabolic profiles with the changes occurring in berry development and ripening processes is essential to progress in their comprehension as well in the development of new approaches to improve fruit attributes. Here, the developmental metabolic profiling analysis across six stages from flowering to fully mature berries of two cultivars, Cabernet Sauvignon and Merlot, is reported at metabolite level. Based on a gas chromatography-mass spectrometry untargeted approach, 115 metabolites were identified and relative quantified in both cultivars. Sugars and amino acids levels show an opposite behaviour in both cultivars undergoing a highly coordinated shift of metabolite associated to primary metabolism during the stages involved in growth, development and ripening of berries. The changes are characteristic for each stage, the most pronounced ones occuring at fruit setting and pre-Veraison. They are associated to a reduction of the levels of metabolites present in the earlier corresponding stage, revealing a required catabolic activity of primary metabolites for grape berry developmental process. Network analysis revealed that the network connectivity of primary metabolites is stage- and cultivar-dependent, suggesting differences in metabolism regulation between both cultivars as the maturity process progresses. Furthermore, network analysis may represent an appropriate method to display the association between primary metabolites during berry developmental processes among different grapevine cultivars and for identifying potential biologically relevant metabolites.

  13. Filling Knowledge Gaps in Biological Networks: integrating global approaches to understand H2 metabolism in Chlamydomonas reinhardtii - Final Report

    Posewitz, Matthew C

    2011-06-30

    The green alga Chlamydomonas reinhardtii (Chlamydomonas) has numerous genes encoding enzymes that function in fermentative pathways. Among these genes, are the [FeFe]-hydrogenases, pyruvate formate lyase, pyruvate ferredoxin oxidoreductase, acetate kinase, and phosphotransacetylase. We have systematically undertaken a series of targeted mutagenesis approaches to disrupt each of these key genes and omics techniques to characterize alterations in metabolic flux. Funds from DE-FG02-07ER64423 were specifically leveraged to generate mutants with disruptions in the genes encoding the [FeFe]-hydrogenases HYDA1 and HYDA2, pyruvate formate lyase (PFL1), and in bifunctional alcohol/aldehyde alcohol dehydrogenase (ADH1). Additionally funds were used to conduct global transcript profiling experiments of wildtype Chlamydomonas cells, as well as of the hydEF-1 mutant, which is unable to make H2 due to a lesion in the [FeFe]-hydrogenase biosynthetic pathway. In the wildtype cells, formate, acetate and ethanol are the dominant fermentation products with traces of CO2 and H2 also being produced. In the hydEF-1 mutant, succinate production is increased to offset the loss of protons as a terminal electron acceptor. In the pfl-1 mutant, lactate offsets the loss of formate production, and in the adh1-1 mutant glycerol is made instead of ethanol. To further probe the system, we generated a double mutant (pfl1-1 adh1) that is unable to synthesize both formate and ethanol. This strain, like the pfl1 mutants, secreted lactate, but also exhibited a significant increase in the levels of extracellular glycerol, acetate, and intracellular reduced sugars, and a decline in dark, fermentative H2 production. Whereas wild-type Chlamydomonas fermentation primarily produces formate and ethanol, the double mutant performs a complete rerouting of the glycolytic carbon to lactate and glycerol. Lastly, transcriptome data have been analysed for both the wildtype and hydEF-1, that correlate with our

  14. For the acknowledgement of a social value of carbon in the Climate agreement

    Finon, Dominique

    2015-01-01

    After having outlined that it might be difficult to define a unique price for carbon emissions, the author states that acknowledging a reference price in the Climate agreement would help to guide investment decisions and new financing modes. He proposes that the agreement could include the following article: 'The social and economic value of mitigation actions and their co-benefits to adaptation, health and sustainable development should be recognized and formalized as such. It will help to orient the investment of firms towards low carbon options, to mobilize public funding and to develop financial vehicles to allow scaling up of private investments to support development of low carbon projects and countries' transitions to low-carbon economies. This reference of carbon value will be established by consensus between parties: it should be referred to abatement action costs to reach a global emissions cap as well as the co-benefits of the actions. It will be regularly revised.' The author then discusses how to fill the gap between private and social value of low carbon investments, and how to determine this reference value. He outlines the benefit of a reference value of carbon with respect to a constraining price to reach a credible agreement

  15. The grief experience of same-sex couples within an Irish context: tacit acknowledgement.

    Glackin, Michelle; Higgins, Agnes

    2008-06-01

    This study sought to explore the grief experience of same sex couples. To date, the majority of research in this area has focused on the bereavement experience of individuals whose partner has died from an AIDS/HIV-related illness. The research design used was descriptive exploratory. A multi-pronged sampling strategy was employed to generate participants. Seven people underwent in-depth interviews once the study had received ethical approval. Data were analysed by coding, comparing, and merging codes into higher order themes. Five themes subsequently emerged that captured the essence of the bereavement experience, namely:'tacit acknowledgement'; 'sculpting the distress'; 'multiple losses'; 'seeking support'; and 'journeying anew.' While not all bereaved gay or lesbian partners experience 'disenfranchized grief', particularly if their relationship with the deceased was not hidden, it is clear from the findings of this study that many of the participants did (Doka, 1989;Wallbank, 1998). Health care professionals need to consider their approach to people who identify themselves as gay or lesbian, if they are to provide support structures (formal and informal) to meet their unique needs.

  16. Mens Rea and Methamphetamine: High Time for a Modern Doctrine Acknowledging the Neuroscience of Addiction.

    Cusick, Meredith

    2017-04-01

    In American criminal law, actus non facit reum, nisi mens sit rea, "an act does not make one guilty, without a guilty mind." Both actus reus and mens rea are required to justify criminal liability. The Model Penal Code's (MPC) section on culpability has been especially influential on mens rea analysis. An issue of increasing importance in this realm arises when an offensive act is committed while the actor is under the influence of drugs. Several legal doctrines address the effect of intoxication on mental state, including the MPC, limiting or eliminating its relevance to the mens rea analysis. Yet these doctrines do not differentiate between intoxication and addiction. Neuroscience research reveals that drug addiction results in catastrophic damage to the brain resulting in cognitive and behavioral deficits. Methamphetamine addiction is of particular interest to criminal law because it causes extensive neural destruction and is associated with impulsive behavior, violent crime, and psychosis. Furthermore, research has revealed important distinctions between the effects of acute intoxication and addiction. These findings have implications for the broader doctrine of mens rea and, specifically, the intoxication doctrines. This Note argues for the adoption of an addiction doctrine that acknowledges the effect of addiction on mens rea that is distinct from doctrines of intoxication.

  17. Modeling metabolic networks in C. glutamicum: a comparison of rate laws in combination with various parameter optimization strategies

    Oldiges Marco

    2009-01-01

    Full Text Available Abstract Background To understand the dynamic behavior of cellular systems, mathematical modeling is often necessary and comprises three steps: (1 experimental measurement of participating molecules, (2 assignment of rate laws to each reaction, and (3 parameter calibration with respect to the measurements. In each of these steps the modeler is confronted with a plethora of alternative approaches, e. g., the selection of approximative rate laws in step two as specific equations are often unknown, or the choice of an estimation procedure with its specific settings in step three. This overall process with its numerous choices and the mutual influence between them makes it hard to single out the best modeling approach for a given problem. Results We investigate the modeling process using multiple kinetic equations together with various parameter optimization methods for a well-characterized example network, the biosynthesis of valine and leucine in C. glutamicum. For this purpose, we derive seven dynamic models based on generalized mass action, Michaelis-Menten and convenience kinetics as well as the stochastic Langevin equation. In addition, we introduce two modeling approaches for feedback inhibition to the mass action kinetics. The parameters of each model are estimated using eight optimization strategies. To determine the most promising modeling approaches together with the best optimization algorithms, we carry out a two-step benchmark: (1 coarse-grained comparison of the algorithms on all models and (2 fine-grained tuning of the best optimization algorithms and models. To analyze the space of the best parameters found for each model, we apply clustering, variance, and correlation analysis. Conclusion A mixed model based on the convenience rate law and the Michaelis-Menten equation, in which all reactions are assumed to be reversible, is the most suitable deterministic modeling approach followed by a reversible generalized mass action kinetics

  18. On the Creation, Utility and Sustaining of Rare Diseases Research Networks: Lessons learned from the Urea Cycle Disorders Consortium, the Japanese Urea Cycle Disorders Consortium and the European Registry and Network for Intoxication Type Metabolic Diseases.

    Summar, Marshall L; Endo, Fumio; Kölker, Stefan

    2014-01-01

    The past two decades has seen a rapid expansion in the scientific and public interest in rare diseases and their treatment. One consequence of this has been the formation of registries/longitudinal natural history studies for these disorders. Given the expense and effort needed to develop and maintain such programs, we describe our experience with three linked registries on the same disease group, urea cycle disorders. The Urea Cycle Disorders Consortium (UCDC) was formed in the U.S. in 2003 in response to a request for application from the National Institutes of Health (NIH); the European Registry and Network for Intoxication Type Metabolic Diseases (E-IMD) was formed in 2011 in response to a request for applications from the Directorate-General for Health and Consumers (DG SANCO) of the EU; and the Japanese Urea Cycle Disorders Consortium (JUCDC) was founded in 2012 as a sister organization to the UCDC and E-IMD. The functions of these groups are to collect natural history data, educate the professional and lay population, develop and test new treatments, and establish networks of excellence for the care for these disorders. The UCDC and JUCDC focus exclusively on urea cycle disorders while the E-IMD includes patients with urea cycle disorders and organic acidurias. More than 1400 patients have been enrolled in the three consortia, and numerous projects have been developed and joint meetings held including an international UCDC/E-IMD/JUCDC Urea Cycle meeting in Barcelona in 2013. This article summarizes some of the experiences from the three groups regarding formation, funding, and models for sustainability. Copyright © 2014 Elsevier Inc. All rights reserved.

  19. Welcome to cultural competency: surgery's efforts to acknowledge diversity in residency training.

    Ly, Catherine L; Chun, Maria B J

    2013-01-01

    Although cultural competency is not a new concept in healthcare, it has only recently been formally embraced as important in the field of surgery. All physicians, including and especially surgeons, must acknowledge the potential influence of culture in order to provide effective and equitable care for patients of all backgrounds. The Accreditation Council for Graduate Medical Education (ACGME) recognizes cultural competency as a component of "patient care," "professionalism," and "interpersonal and communication skills." A systematic literature search was conducted using the MEDLINE, EBSCOhost, Web of Science, and Google Scholar databases. All publications focusing on surgical residents and the assessment of patient care, professionalism, interpersonal and communication skills, or specifically cultural competency and/or were considered. This initial search resulted in 12 articles. To further refine the review, publications discussing curricula in residencies other than surgery, the assessment of technical, or clinical skills and/or without any explicit focus on cultural competency were excluded. Based on the specified inclusion and exclusion criteria, 5 articles were selected. These studies utilized various methods to improve surgical residents' cultural competency, including lectures, Objective Structural Clinical Examinations (OSCE), and written exercises and evaluations. A number of surgical residency programs have made promising strides in training culturally competent surgeons. Ultimately, in order to maximize our collective efforts to improve the quality of health care, the development of cultural competency curricula must be made a priority and such training should be a requirement for all trainees in surgical residency programs. Copyright © 2013 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

  20. Assessment of nitric oxide (NO) redox reactions contribution to nitrous oxide (N2 O) formation during nitrification using a multispecies metabolic network model.

    Perez-Garcia, Octavio; Chandran, Kartik; Villas-Boas, Silas G; Singhal, Naresh

    2016-05-01

    Over the coming decades nitrous oxide (N2O) is expected to become a dominant greenhouse gas and atmospheric ozone depleting substance. In wastewater treatment systems, N2O is majorly produced by nitrifying microbes through biochemical reduction of nitrite (NO2(-)) and nitric oxide (NO). However it is unknown if the amount of N2O formed is affected by alternative NO redox reactions catalyzed by oxidative nitrite oxidoreductase (NirK), cytochromes (i.e., P460 [CytP460] and 554 [Cyt554 ]) and flavohemoglobins (Hmp) in ammonia- and nitrite-oxidizing bacteria (AOB and NOB, respectively). In this study, a mathematical model is developed to assess how N2O formation is affected by such alternative nitrogen redox transformations. The developed multispecies metabolic network model captures the nitrogen respiratory pathways inferred from genomes of eight AOB and NOB species. The performance of model variants, obtained as different combinations of active NO redox reactions, was assessed against nine experimental datasets for nitrifying cultures producing N2O at different concentration of electron donor and acceptor. Model predicted metabolic fluxes show that only variants that included NO oxidation to NO2(-) by CytP460 and Hmp in AOB gave statistically similar estimates to observed production rates of N2O, NO, NO2(-) and nitrate (NO3(-)), together with fractions of AOB and NOB species in biomass. Simulations showed that NO oxidation to NO2(-) decreased N2O formation by 60% without changing culture's NO2(-) production rate. Model variants including NO reduction to N2O by Cyt554 and cNor in NOB did not improve the accuracy of experimental datasets estimates, suggesting null N2O production by NOB during nitrification. Finally, the analysis shows that in nitrifying cultures transitioning from dissolved oxygen levels above 3.8 ± 0.38 to <1.5 ± 0.8 mg/L, NOB cells can oxidize the NO produced by AOB through reactions catalyzed by oxidative NirK. © 2015 Wiley Periodicals, Inc.

  1. A Transdiagnostic Network Approach to Psychosis

    Wigman, Johanna T. W.; de Vos, Stijn; Wichers, Marieke; van Os, Jim; Bartels-Velthuis, Agna A.

    Our ability to accurately predict development and outcome of early expression of psychosis is limited. To elucidate the mechanisms underlying psychopathology, a broader, transdiagnostic approach that acknowledges the complexity of mental illness is required. The upcoming network paradigm may be

  2. Is there a relationship between research sponsorship and publication impact? An analysis of funding acknowledgments in nanotechnology papers.

    Jue Wang

    Full Text Available This study analyzes funding acknowledgments in scientific papers to investigate relationships between research sponsorship and publication impacts. We identify acknowledgments to research sponsors for nanotechnology papers published in the Web of Science during a one-year sample period. We examine the citations accrued by these papers and the journal impact factors of their publication titles. The results show that publications from grant sponsored research exhibit higher impacts in terms of both journal ranking and citation counts than research that is not grant sponsored. We discuss the method and models used, and the insights provided by this approach as well as it limitations.

  3. The metabolic network of Clostridium acetobutylicum: Comparison of the approximate Bayesian computation via sequential Monte Carlo (ABC-SMC) and profile likelihood estimation (PLE) methods for determinability analysis.

    Thorn, Graeme J; King, John R

    2016-01-01

    The Gram-positive bacterium Clostridium acetobutylicum is an anaerobic endospore-forming species which produces acetone, butanol and ethanol via the acetone-butanol (AB) fermentation process, leading to biofuels including butanol. In previous work we looked to estimate the parameters in an ordinary differential equation model of the glucose metabolism network using data from pH-controlled continuous culture experiments. Here we combine two approaches, namely the approximate Bayesian computation via an existing sequential Monte Carlo (ABC-SMC) method (to compute credible intervals for the parameters), and the profile likelihood estimation (PLE) (to improve the calculation of confidence intervals for the same parameters), the parameters in both cases being derived from experimental data from forward shift experiments. We also apply the ABC-SMC method to investigate which of the models introduced previously (one non-sporulation and four sporulation models) have the greatest strength of evidence. We find that the joint approximate posterior distribution of the parameters determines the same parameters as previously, including all of the basal and increased enzyme production rates and enzyme reaction activity parameters, as well as the Michaelis-Menten kinetic parameters for glucose ingestion, while other parameters are not as well-determined, particularly those connected with the internal metabolites acetyl-CoA, acetoacetyl-CoA and butyryl-CoA. We also find that the approximate posterior is strongly non-Gaussian, indicating that our previous assumption of elliptical contours of the distribution is not valid, which has the effect of reducing the numbers of pairs of parameters that are (linearly) correlated with each other. Calculations of confidence intervals using the PLE method back this up. Finally, we find that all five of our models are equally likely, given the data available at present. Copyright © 2015 Elsevier Inc. All rights reserved.

  4. The effect of 18F-FDG-PET image reconstruction algorithms on the expression of characteristic metabolic brain network in Parkinson's disease.

    Tomše, Petra; Jensterle, Luka; Rep, Sebastijan; Grmek, Marko; Zaletel, Katja; Eidelberg, David; Dhawan, Vijay; Ma, Yilong; Trošt, Maja

    2017-09-01

    To evaluate the reproducibility of the expression of Parkinson's Disease Related Pattern (PDRP) across multiple sets of 18F-FDG-PET brain images reconstructed with different reconstruction algorithms. 18F-FDG-PET brain imaging was performed in two independent cohorts of Parkinson's disease (PD) patients and normal controls (NC). Slovenian cohort (20 PD patients, 20 NC) was scanned with Siemens Biograph mCT camera and reconstructed using FBP, FBP+TOF, OSEM, OSEM+TOF, OSEM+PSF and OSEM+PSF+TOF. American Cohort (20 PD patients, 7 NC) was scanned with GE Advance camera and reconstructed using 3DRP, FORE-FBP and FORE-Iterative. Expressions of two previously-validated PDRP patterns (PDRP-Slovenia and PDRP-USA) were calculated. We compared the ability of PDRP to discriminate PD patients from NC, differences and correlation between the corresponding subject scores and ROC analysis results across the different reconstruction algorithms. The expression of PDRP-Slovenia and PDRP-USA networks was significantly elevated in PD patients compared to NC (palgorithms. PDRP expression strongly correlated between all studied algorithms and the reference algorithm (r⩾0.993, palgorithms varied within 0.73 and 0.08 of the reference value for PDRP-Slovenia and PDRP-USA, respectively. ROC analysis confirmed high similarity in sensitivity, specificity and AUC among all studied reconstruction algorithms. These results show that the expression of PDRP is reproducible across a variety of reconstruction algorithms of 18F-FDG-PET brain images. PDRP is capable of providing a robust metabolic biomarker of PD for multicenter 18F-FDG-PET images acquired in the context of differential diagnosis or clinical trials. Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

  5. Final Report: Filling Knowledge Gaps in Biological Networks: Integrated Global Approaches to Understand H{sub 2} Metabolism in Chlamydomonas Reinhardtii

    Grossman, Arthur

    2012-05-01

    The major goal of our part of this project has been to generate mutants in fermentation metabolism and begin to decipher how lesions in the pathways associated with fermentation metabolism impact both H{sub 2} production and the production of other metabolites that accumulate as cells become anoxic. We are also trying to understand how metabolic pathways are regulated as O{sub 2} in the environment becomes depleted.

  6. Code-Switching to Know a TL Equivalent of an L1 Word: Request-Provision-Acknowledgement (RPA) Sequence

    Lucero, Edgar

    2011-01-01

    This article focuses on the learner's use of Code-switching to learn the TL (Target Language) equivalent of an L1 word. The interactional pattern that this situation creates defines the Request-Provision-Acknowledgement (RPA) sequence. The article explains each of the turns of the sequence under the combination of the Ethnomethodological…

  7. 47 CFR 80.1121 - Receipt and acknowledgement of distress alerts by ship stations and ship earth stations.

    2010-10-01

    ... 47 Telecommunication 5 2010-10-01 2010-10-01 false Receipt and acknowledgement of distress alerts by ship stations and ship earth stations. 80.1121 Section 80.1121 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) SAFETY AND SPECIAL RADIO SERVICES STATIONS IN THE MARITIME SERVICES Global Maritime Distress and Safety System (GMDSS)...

  8. Acknowledging individual responsibility while emphasizing social determinants in narratives to promote obesity-reducing public policy: a randomized experiment.

    Niederdeppe, Jeff; Roh, Sungjong; Shapiro, Michael A

    2015-01-01

    This study tests whether policy narratives designed to increase support for obesity-reducing public policies should explicitly acknowledge individual responsibility while emphasizing social, physical, and economic (social) determinants of obesity. We use a web-based, randomized experiment with a nationally representative sample of American adults (n = 718) to test hypotheses derived from theory and research on narrative persuasion. Respondents exposed to narratives that acknowledged individual responsibility while emphasizing obesity's social determinants were less likely to engage in counterargument and felt more empathy for the story's main character than those exposed to a message that did not acknowledge individual responsibility. Counterarguing and affective empathy fully mediated the relationship between message condition and support for policies to reduce rates of obesity. Failure to acknowledge individual responsibility in narratives emphasizing social determinants of obesity may undermine the persuasiveness of policy narratives. Omitting information about individual responsibility, a strongly-held American value, invites the public to engage in counterargument about the narratives and reduces feelings of empathy for a character that experiences the challenges and benefits of social determinants of obesity.

  9. Acknowledging individual responsibility while emphasizing social determinants in narratives to promote obesity-reducing public policy: a randomized experiment.

    Jeff Niederdeppe

    Full Text Available This study tests whether policy narratives designed to increase support for obesity-reducing public policies should explicitly acknowledge individual responsibility while emphasizing social, physical, and economic (social determinants of obesity. We use a web-based, randomized experiment with a nationally representative sample of American adults (n = 718 to test hypotheses derived from theory and research on narrative persuasion. Respondents exposed to narratives that acknowledged individual responsibility while emphasizing obesity's social determinants were less likely to engage in counterargument and felt more empathy for the story's main character than those exposed to a message that did not acknowledge individual responsibility. Counterarguing and affective empathy fully mediated the relationship between message condition and support for policies to reduce rates of obesity. Failure to acknowledge individual responsibility in narratives emphasizing social determinants of obesity may undermine the persuasiveness of policy narratives. Omitting information about individual responsibility, a strongly-held American value, invites the public to engage in counterargument about the narratives and reduces feelings of empathy for a character that experiences the challenges and benefits of social determinants of obesity.

  10. Posttraumatic growth, social acknowledgment as survivors, and sense of coherence in former German child soldiers of World War II.

    Forstmeier, Simon; Kuwert, Philipp; Spitzer, Carsten; Freyberger, Harald J; Maercker, Andreas

    2009-12-01

    To examine posttraumatic growth (PTG) and its predictors social acknowledgment as survivors, sense of coherence (SOC), trauma severity, and further factors in former child soldiers more than 60 years after deployment. Cross-sectional. University-based geropsychiatric center in Germany. One hundred three former German child soldiers of World War II, mean age 78 years in which 96% experienced at least one war trauma. Subjects completed the Posttraumatic Growth Inventory, Social Acknowledgment Questionnaire (SAQ), and SOC Scale. Trauma exposure and posttraumatic stress disorder (PTSD) symptoms were assessed by the Posttraumatic Diagnostic Scale. Depression, anxiety, and somatization were assessed using the Brief Symptom Inventory. Number of traumas, recognition by significant others, and general disapproval as facets of social acknowledgment as a survivor, and meaningfulness as a dimension of SOC correlated significantly with PTG. In a multiple hierarchical regression analysis, recognition as a survivor by significant others (SAQ) and meaningfulness (SOC) remained the only significant predictors of PTG. Social acknowledgment as a survivor by significant others and the belief that the world is meaningful are among the most important factors contributing to PTG. Further research should investigate whether treatments of PTSD in people who experienced war traumas recently or many years ago might benefit from a focus on the belief system and the role of family and social support.

  11. Easy and low-cost identification of metabolic syndrome in patients treated with second-generation antipsychotics: artificial neural network and logistic regression models.

    Lin, Chao-Cheng; Bai, Ya-Mei; Chen, Jen-Yeu; Hwang, Tzung-Jeng; Chen, Tzu-Ting; Chiu, Hung-Wen; Li, Yu-Chuan

    2010-03-01

    Metabolic syndrome (MetS) is an important side effect of second-generation antipsychotics (SGAs). However, many SGA-treated patients with MetS remain undetected. In this study, we trained and validated artificial neural network (ANN) and multiple logistic regression models without biochemical parameters to rapidly identify MetS in patients with SGA treatment. A total of 383 patients with a diagnosis of schizophrenia or schizoaffective disorder (DSM-IV criteria) with SGA treatment for more than 6 months were investigated to determine whether they met the MetS criteria according to the International Diabetes Federation. The data for these patients were collected between March 2005 and September 2005. The input variables of ANN and logistic regression were limited to demographic and anthropometric data only. All models were trained by randomly selecting two-thirds of the patient data and were internally validated with the remaining one-third of the data. The models were then externally validated with data from 69 patients from another hospital, collected between March 2008 and June 2008. The area under the receiver operating characteristic curve (AUC) was used to measure the performance of all models. Both the final ANN and logistic regression models had high accuracy (88.3% vs 83.6%), sensitivity (93.1% vs 86.2%), and specificity (86.9% vs 83.8%) to identify MetS in the internal validation set. The mean +/- SD AUC was high for both the ANN and logistic regression models (0.934 +/- 0.033 vs 0.922 +/- 0.035, P = .63). During external validation, high AUC was still obtained for both models. Waist circumference and diastolic blood pressure were the common variables that were left in the final ANN and logistic regression models. Our study developed accurate ANN and logistic regression models to detect MetS in patients with SGA treatment. The models are likely to provide a noninvasive tool for large-scale screening of MetS in this group of patients. (c) 2010 Physicians

  12. Networks in Cell Biology

    Buchanan, Mark; Caldarelli, Guido; De Los Rios, Paolo; Rao, Francesco; Vendruscolo, Michele

    2010-05-01

    Introduction; 1. Network views of the cell Paolo De Los Rios and Michele Vendruscolo; 2. Transcriptional regulatory networks Sarath Chandra Janga and M. Madan Babu; 3. Transcription factors and gene regulatory networks Matteo Brilli, Elissa Calistri and Pietro Lió; 4. Experimental methods for protein interaction identification Peter Uetz, Björn Titz, Seesandra V. Rajagopala and Gerard Cagney; 5. Modeling protein interaction networks Francesco Rao; 6. Dynamics and evolution of metabolic networks Daniel Segré; 7. Hierarchical modularity in biological networks: the case of metabolic networks Erzsébet Ravasz Regan; 8. Signalling networks Gian Paolo Rossini; Appendix 1. Complex networks: from local to global properties D. Garlaschelli and G. Caldarelli; Appendix 2. Modelling the local structure of networks D. Garlaschelli and G. Caldarelli; Appendix 3. Higher-order topological properties S. Ahnert, T. Fink and G. Caldarelli; Appendix 4. Elementary mathematical concepts A. Gabrielli and G. Caldarelli; References.

  13. The acknowledgement of the Schneeberg lung disease as occupational disease in the first decree of occupational diseases from 1925

    Schuettmann, W.

    1987-01-01

    The Schneeberg lung disease is the lung cancer, conditioned by radiation which is caused by the influence of radon and of its subsequent products. It has gained a great importance after World War II as a consequence of the intensified mining of uranium ore. From the history of the disease, lasting some centuries, the period of the twenties and thirties of this century is represented in which on one side the conception of the causal importance of radon has made its way little by little, and on the other side the disease was acknowledged as occupational disease within the first decree of occupational diseases in the former German Reich. Evaluating materials from Saxon archives it is described how the legislative preparations to the acknowledgement of the Schneeberg lung disease as occupational disease and the simultaneous research to the elucidation of nature and cause of the disease have penetrated and influenced each other. (author)

  14. Posttraumatic growth, social acknowledgment as survivors, and sense of coherence in former German child soldiers of World War II

    Forstmeier, Simon; Kuwert, P; Spitzer, C; Freyberger, H J; Maercker, Andreas

    2009-01-01

    Objectives: To examine posttraumatic growth and its predictors social acknowledgment as survivors, sense of coherence, trauma severity, and further factors in former child soldiers more than 60 years after deployment. Design: Cross-sectional. Setting: University-based geropsychiatric center in Germany. Participants: 103 former German child soldiers of World War II, mean age 78 years, 96% experienced at least one war trauma. Measurement: Subjects completed the Posttraumatic Growth ...

  15. Modeling of Zymomonas mobilis central metabolism for novel metabolic engineering strategies.

    Kalnenieks, Uldis; Pentjuss, Agris; Rutkis, Reinis; Stalidzans, Egils; Fell, David A

    2014-01-01

    Mathematical modeling of metabolism is essential for rational metabolic engineering. The present work focuses on several types of modeling approach to quantitative understanding of central metabolic network and energetics in the bioethanol-producing bacterium Zymomonas mobilis. Combined use of Flux Balance, Elementary Flux Mode, and thermodynamic analysis of its central metabolism, together with dynamic modeling of the core catabolic pathways, can help to design novel substrate and product pathways by systematically analyzing the solution space for metabolic engineering, and yields insights into the function of metabolic network, hardly achievable without applying modeling tools.

  16. Understanding Regulation of Metabolism through Feasibility Analysis

    Nikerel, I.E.; Berkhout, J.; Hu, F.; Teusink, B.; Reinders, M.J.T.; De Ridder, D.

    2012-01-01

    Understanding cellular regulation of metabolism is a major challenge in systems biology. Thus far, the main assumption was that enzyme levels are key regulators in metabolic networks. However, regulation analysis recently showed that metabolism is rarely controlled via enzyme levels only, but

  17. Metabolic Syndrome

    Metabolic syndrome is a group of conditions that put you at risk for heart disease and diabetes. These conditions ... agree on the definition or cause of metabolic syndrome. The cause might be insulin resistance. Insulin is ...

  18. Unacknowledged Cities: Modernity and Acknowledgement in China Miéville's The City & The City and Marc Isaacs' All White in Barking

    Martin, N.

    2013-01-01

    Acknowledgement names a peculiar, and frequently uneasy, relation to knowledge. For example, when one acknowledges as one’s own an act or deed with which one wishes to dissociate oneself, one is simultaneously owning and disowning that act. Through accounts of China Miéville’s novel The City & the

  19. Globalization and the diffusion of ideas: why we should acknowledge the roots of mainstream ideas in global health.

    Robert, Emilie; Hajizadeh, Mohammad; El-Bialy, Rowan; Bidisha, Sayema Haque

    2014-06-01

    Although globalization has created ample opportunities and spaces to share experiences and information, the diffusion of ideas, especially in global health, is primarily influenced by the unequal distribution of economic, political and scientific powers around the world. These ideas in global health are generally rooted in High-Income Countries (HICs), and then reach Low- and Middle-Income Countries (LMICs). We argue that acknowledging and addressing this invisible trend would contribute to a greater degree of open discussions in global health. This is expected to favor innovative, alternative, and culturally sound solutions for persistent health problems and reducing inequities.

  20. Attenuation of insulin-evoked responses in brain networks controlling appetite and reward in insulin resistance: the cerebral basis for impaired control of food intake in metabolic syndrome?

    Anthony, Karen; Reed, Laurence J; Dunn, Joel T; Bingham, Emma; Hopkins, David; Marsden, Paul K; Amiel, Stephanie A

    2006-11-01

    The rising prevalence of obesity and type 2 diabetes is a global challenge. A possible mechanism linking insulin resistance and weight gain would be attenuation of insulin-evoked responses in brain areas relevant to eating in systemic insulin resistance. We measured brain glucose metabolism, using [(18)F]fluorodeoxyglucose positron emission tomography, in seven insulin-sensitive (homeostasis model assessment of insulin resistance [HOMA-IR] = 1.3) and seven insulin-resistant (HOMA-IR = 6.3) men, during suppression of endogenous insulin by somatostatin, with and without an insulin infusion that elevated insulin to 24.6 +/- 5.2 and 23.2 +/- 5.8 mU/l (P = 0.76), concentrations similar to fasting levels of the resistant subjects and approximately threefold above those of the insulin-sensitive subjects. Insulin-evoked change in global cerebral metabolic rate for glucose was reduced in insulin resistance (+7 vs. +17.4%, P = 0.033). Insulin was associated with increased metabolism in ventral striatum and prefrontal cortex and with decreased metabolism in right amygdala/hippocampus and cerebellar vermis (P reward. Diminishing the link be-tween control of food intake and energy balance may contribute to development of obesity in insulin resistance.

  1. Jasmonoyl-l-Isoleucine Coordinates Metabolic Networks Required for Anthesis and Floral Attractant Emission in Wild Tobacco (Nicotiana attenuata)[C][W][OPEN

    Stitz, Michael; Hartl, Markus; Baldwin, Ian T.; Gaquerel, Emmanuel

    2014-01-01

    Jasmonic acid and its derivatives (jasmonates [JAs]) play central roles in floral development and maturation. The binding of jasmonoyl-l-isoleucine (JA-Ile) to the F-box of CORONATINE INSENSITIVE1 (COI1) is required for many JA-dependent physiological responses, but its role in anthesis and pollinator attraction traits remains largely unexplored. Here, we used the wild tobacco Nicotiana attenuata, which develops sympetalous flowers with complex pollination biology, to examine the coordinating function of JA homeostasis in the distinct metabolic processes that underlie flower maturation, opening, and advertisement to pollinators. From combined transcriptomic, targeted metabolic, and allometric analyses of transgenic N. attenuata plants for which signaling deficiencies were complemented with methyl jasmonate, JA-Ile, and its functional homolog, coronatine (COR), we demonstrate that (1) JA-Ile/COR-based signaling regulates corolla limb opening and a JA-negative feedback loop; (2) production of floral volatiles (night emissions of benzylacetone) and nectar requires JA-Ile/COR perception through COI1; and (3) limb expansion involves JA-Ile-induced changes in limb fresh mass and carbohydrate metabolism. These findings demonstrate a master regulatory function of the JA-Ile/COI1 duet for the main function of a sympetalous corolla, that of advertising for and rewarding pollinator services. Flower opening, by contrast, requires JA-Ile signaling-dependent changes in primary metabolism, which are not compromised in the COI1-silenced RNA interference line used in this study. PMID:25326292

  2. Impact of a Metabolic Screening Bundle on Rates of Screening for Metabolic Syndrome in a Psychiatry Resident Outpatient Clinic

    Wiechers, Ilse R.; Viron, Mark; Stoklosa, Joseph; Freudenreich, Oliver; Henderson, David C.; Weiss, Anthony

    2012-01-01

    Objective: Although it is widely acknowledged that second-generation antipsychotics are associated with cardiometabolic side effects, rates of metabolic screening have remained low. The authors created a quality-improvement (QI) intervention in an academic medical center outpatient psychiatry resident clinic with the aim of improving rates of…

  3. Acknowledging the back patient

    Damsgaard, Janne Brammer; Bastrup, Lene; Norlyk, Annelise

    the back patient the narrative must be complemented by a different perspective that includes the issue of ethical responsibility. It is therefore also a question of adopting certain norms as binding; to be bound by obligation or loyalty. Thus, the literature review argues for a more process......-oriented patient approach that incorporates patients' narratives as an integral and ethical part of the care and treatment....

  4. BMRAT reviewer acknowledgement 2015

    Lili Hami

    2016-01-01

    Contributing reviewers The editors of Biomedical Research and Therapy would like to thank all our reviewers who have contributed to the journal in Volume 2 (2015). [Biomed Res Ther 2016; 3(2.000): 528-532

  5. Reviewer acknowledgement 2013

    Stengel, Dirk; Luckmann, Annette

    2014-01-01

    Contributing reviewers The editors of the Journal of Trauma Management and Outcomes and BioMed Central would like to show our appreciation for the following reviewers for their time, hard work and support by reviewing manuscripts for the journal in 2013.

  6. The Regretful Acknowledgement

    Villadsen, Lisa Storm

    2013-01-01

    This chapter offers a close rhetorical reading of an open hearing in the Danish Parliament’s Commission on Social Affairs concerning the Minister of Social Affair’s refusal to apologize in response to a report documenting mistreatment of children in state-supervised orphanages. The minister uses...

  7. Cluster emergence and network evolution A longitudinal analysis of the inventor network in Sophia-Antipolis

    Anne L. J. ter Wal

    2008-01-01

    Abstract It is increasingly acknowledged that clusters do not necessarily exhibit networks of local collective learning. This paper addresses the question under which conditions this is the case. Through a longitudinal case study of the business park Sophia-Antipolis it investigates how networks of collective learning emerged throughout the growth of the cluster. Network reconstruction with patent data shows that an innovation network emerged only in Information Technology, in whic...

  8. Genome-scale modeling for metabolic engineering.

    Simeonidis, Evangelos; Price, Nathan D

    2015-03-01

    We focus on the application of constraint-based methodologies and, more specifically, flux balance analysis in the field of metabolic engineering, and enumerate recent developments and successes of the field. We also review computational frameworks that have been developed with the express purpose of automatically selecting optimal gene deletions for achieving improved production of a chemical of interest. The application of flux balance analysis methods in rational metabolic engineering requires a metabolic network reconstruction and a corresponding in silico metabolic model for the microorganism in question. For this reason, we additionally present a brief overview of automated reconstruction techniques. Finally, we emphasize the importance of integrating metabolic networks with regulatory information-an area which we expect will become increasingly important for metabolic engineering-and present recent developments in the field of metabolic and regulatory integration.

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

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

    2015-01-01

    By mapping translated metagenomic reads to a microbial metabolic network, we show that ruminal ecosystems that are rather dissimilar in their taxonomy can be considerably more similar at the metabolic network level. Using a new network bi-partition approach for linking the microbial network to a bovine metabolic network, we observe that these ruminal metabolic networks exhibit properties consistent with distinct metabolic communities producing similar outputs from common inputs. For instance,...

  10. What is Metabolic Syndrome?

    ... Intramural Research Home / Metabolic Syndrome Metabolic Syndrome Also known as What Is Metabolic syndrome ... metabolic risk factors to be diagnosed with metabolic syndrome. Metabolic Risk Factors A Large Waistline Having a large ...

  11. STUDY ABOUT THE ACKNOWLEDGEMENT IN ACCOUNTING FOR THE SOFTWARE PRODUCTS – ON THEIR PRODUCTION AND SALE FLUX

    Violeta Isai

    2013-12-01

    Full Text Available The modern economy can’t be conceived without the IT industry contribution, the engine of lasting economic development. In order to help the increase of software industry the countries create national strategies of information implementation, which will assure in time: the increase of the production, added value, productivity and international economic exchanges. In this area, Romania elaborates its own implementation strategy, according to the EU policy. On the company level, the informatics implementation raises problems regarding: the acknowledgement of software products in accounting as intangible assets; the registering of their reversible and irreversible devaluation, but also fiscal aspects about the modality of classification for the software supply – as goods delivery or as services performance, in the country and abroad.

  12. Toward Synthetic Biology Strategies for Adipic Acid Production: An in Silico Tool for Combined Thermodynamics and Stoichiometric Analysis of Metabolic Networks

    Averesch, Nils J. H.; Martínez, Verónica S.; Nielsen, Lars K.

    2018-01-01

    Adipic acid, a nylon-6,6 precursor, has recently gained popularity in synthetic biology. Here, 16 different production routes to adipic acid were evaluated using a novel tool for network-embedded thermodynamic analysis of elementary flux modes. The tool distinguishes between thermodynamically...

  13. Analysis of metabolic networks of Streptomyces leeuwenhoekii C34 by means of a genome scale model: Prediction of modifications that enhance the production of specialized metabolites.

    Razmilic, Valeria; Castro, Jean F; Andrews, Barbara; Asenjo, Juan A

    2018-07-01

    The first genome scale model (GSM) for Streptomyces leeuwenhoekii C34 was developed to study the biosynthesis pathways of specialized metabolites and to find metabolic engineering targets for enhancing their production. The model, iVR1007, consists of 1,722 reactions, 1,463 metabolites, and 1,007 genes, it includes the biosynthesis pathways of chaxamycins, chaxalactins, desferrioxamines, ectoine, and other specialized metabolites. iVR1007 was validated using experimental information of growth on 166 different sources of carbon, nitrogen and phosphorous, showing an 83.7% accuracy. The model was used to predict metabolic engineering targets for enhancing the biosynthesis of chaxamycins and chaxalactins. Gene knockouts, such as sle03600 (L-homoserine O-acetyltransferase), and sle39090 (trehalose-phosphate synthase), that enhance the production of the specialized metabolites by increasing the pool of precursors were identified. Using the algorithm of flux scanning based on enforced objective flux (FSEOF) implemented in python, 35 and 25 over-expression targets for increasing the production of chaxamycin A and chaxalactin A, respectively, that were not directly associated with their biosynthesis routes were identified. Nineteen over-expression targets that were common to the two specialized metabolites studied, like the over-expression of the acetyl carboxylase complex (sle47660 (accA) and any of the following genes: sle44630 (accA_1) or sle39830 (accA_2) or sle27560 (bccA) or sle59710) were identified. The predicted knockouts and over-expression targets will be used to perform metabolic engineering of S. leeuwenhoekii C34 and obtain overproducer strains. © 2018 Wiley Periodicals, Inc.

  14. [Metabolic acidosis].

    Regolisti, Giuseppe; Fani, Filippo; Antoniotti, Riccardo; Castellano, Giuseppe; Cremaschi, Elena; Greco, Paolo; Parenti, Elisabetta; Morabito, Santo; Sabatino, Alice; Fiaccadori, Enrico

    2016-01-01

    Metabolic acidosis is frequently observed in clinical practice, especially among critically ill patients and/or in the course of renal failure. Complex mechanisms are involved, in most cases identifiable by medical history, pathophysiology-based diagnostic reasoning and measure of some key acid-base parameters that are easily available or calculable. On this basis the bedside differential diagnosis of metabolic acidosis should be started from the identification of the two main subtypes of metabolic acidosis: the high anion gap metabolic acidosis and the normal anion gap (or hyperchloremic) metabolic acidosis. Metabolic acidosis, especially in its acute forms with elevated anion gap such as is the case of lactic acidosis, diabetic and acute intoxications, may significantly affect metabolic body homeostasis and patients hemodynamic status, setting the stage for true medical emergencies. The therapeutic approach should be first aimed at early correction of concurrent clinical problems (e.g. fluids and hemodynamic optimization in case of shock, mechanical ventilation in case of concomitant respiratory failure, hemodialysis for acute intoxications etc.), in parallel to the formulation of a diagnosis. In case of severe acidosis, the administration of alkalizing agents should be carefully evaluated, taking into account the risk of side effects, as well as the potential need of renal replacement therapy.

  15. Drug Metabolism

    Home; Journals; Resonance – Journal of Science Education; Volume 19; Issue 3. Drug Metabolism: A Fascinating Link Between Chemistry and Biology. Nikhil Taxak Prasad V Bharatam. General Article Volume 19 Issue 3 March 2014 pp 259-282 ...

  16. Drug Metabolism

    IAS Admin

    behind metabolic reactions, importance, and consequences with several ... required for drug action. ... lism, which is catalyzed by enzymes present in the above-men- ... catalyze the transfer of one atom of oxygen to a substrate produc-.

  17. Adipogenic and energy metabolism gene networks in longissimus lumborum during rapid post-weaning growth in Angus and Angus x Simmental cattle fed high-starch or low-starch diets.

    Graugnard, Daniel E; Piantoni, Paola; Bionaz, Massimo; Berger, Larry L; Faulkner, Dan B; Loor, Juan J

    2009-03-31

    Transcriptional networks coordinate adipocyte differentiation and energy metabolism in rodents. The level of fiber and starch in diets with adequate energy content fed to young cattle has the potential to alter intramuscular adipose tissue development in skeletal muscle. Post-weaning alterations in gene expression networks driving adipogenesis, lipid filling, and intracellular energy metabolism provide a means to evaluate long-term effects of nutrition on longissimus muscle development across cattle types. Longissimus lumborum (LL) from Angus (n = 6) and Angus x Simmental (A x S; n = 6) steer calves (155 +/- 10 days age) fed isonitrogenous high-starch (HiS; 1.43 Mcal/kg diet dry matter; n = 6) or low-starch (LoS; 1.19 Mcal/kg diet dry matter; n = 6) diets was biopsied at 0, 56, and 112 days of feeding for transcript profiling of 31 genes associated with aspects of adipogenesis and energy metabolism. Intake of dietary energy (9.44 +/- 0.57 Mcal/d) across groups during the study did not differ but feed efficiency (weight gain/feed intake) during the first 56 days was greater for steers fed HiS. Expression of PPARG increased ca. 2-fold by day 56 primarily due to HiS in A x S steers. Several potential PPARG-target genes (e.g., ACACA, FASN, FABP4, SCD) increased 2.5-to-25-fold by day 56 across all groups, with responses (e.g., FASN, FABP4) being less pronounced in A x S steers fed LoS. This latter group of steers had markedly greater blood plasma glucose (0.99 vs. 0.79 g/L) and insulin (2.95 vs. 1.17 microg/L) by day 112, all of which were suggestive of insulin resistance. Interactions were observed for FABP4, FASN, GPAM, SCD, and DGAT2, such that feeding A x S steers high-starch and Angus steers low-starch resulted in greater fold-changes by day 56 or 112 (GPAM). Marked up-regulation of INSIG1 (4-to-8-fold) occurred throughout the study across all groups. SREBF1 expression, however, was only greater on day 112 namely due to LoS in A x S steers. The lipogenic

  18. Adipogenic and energy metabolism gene networks in longissimus lumborum during rapid post-weaning growth in Angus and Angus × Simmental cattle fed high-starch or low-starch diets

    Graugnard Daniel E

    2009-03-01

    Full Text Available Abstract Background Transcriptional networks coordinate adipocyte differentiation and energy metabolism in rodents. The level of fiber and starch in diets with adequate energy content fed to young cattle has the potential to alter intramuscular adipose tissue development in skeletal muscle. Post-weaning alterations in gene expression networks driving adipogenesis, lipid filling, and intracellular energy metabolism provide a means to evaluate long-term effects of nutrition on longissimus muscle development across cattle types. Results Longissimus lumborum (LL from Angus (n = 6 and Angus × Simmental (A × S; n = 6 steer calves (155 ± 10 days age fed isonitrogenous high-starch (HiS; 1.43 Mcal/kg diet dry matter; n = 6 or low-starch (LoS; 1.19 Mcal/kg diet dry matter; n = 6 diets was biopsied at 0, 56, and 112 days of feeding for transcript profiling of 31 genes associated with aspects of adipogenesis and energy metabolism. Intake of dietary energy (9.44 ± 0.57 Mcal/d across groups during the study did not differ but feed efficiency (weight gain/feed intake during the first 56 days was greater for steers fed HiS. Expression of PPARG increased ca. 2-fold by day 56 primarily due to HiS in A × S steers. Several potential PPARG-target genes (e.g., ACACA, FASN, FABP4, SCD increased 2.5-to-25-fold by day 56 across all groups, with responses (e.g., FASN, FABP4 being less pronounced in A × S steers fed LoS. This latter group of steers had markedly greater blood plasma glucose (0.99 vs. 0.79 g/L and insulin (2.95 vs. 1.17 μg/L by day 112, all of which were suggestive of insulin resistance. Interactions were observed for FABP4, FASN, GPAM, SCD, and DGAT2, such that feeding A × S steers high-starch and Angus steers low-starch resulted in greater fold-changes by day 56 or 112 (GPAM. Marked up-regulation of INSIG1 (4-to-8-fold occurred throughout the study across all groups. SREBF1 expression, however, was only greater on day 112 namely due to LoS in A

  19. Adipogenic and energy metabolism gene networks in longissimus lumborum during rapid post-weaning growth in Angus and Angus × Simmental cattle fed high-starch or low-starch diets

    Graugnard, Daniel E; Piantoni, Paola; Bionaz, Massimo; Berger, Larry L; Faulkner, Dan B; Loor, Juan J

    2009-01-01

    Background Transcriptional networks coordinate adipocyte differentiation and energy metabolism in rodents. The level of fiber and starch in diets with adequate energy content fed to young cattle has the potential to alter intramuscular adipose tissue development in skeletal muscle. Post-weaning alterations in gene expression networks driving adipogenesis, lipid filling, and intracellular energy metabolism provide a means to evaluate long-term effects of nutrition on longissimus muscle development across cattle types. Results Longissimus lumborum (LL) from Angus (n = 6) and Angus × Simmental (A × S; n = 6) steer calves (155 ± 10 days age) fed isonitrogenous high-starch (HiS; 1.43 Mcal/kg diet dry matter; n = 6) or low-starch (LoS; 1.19 Mcal/kg diet dry matter; n = 6) diets was biopsied at 0, 56, and 112 days of feeding for transcript profiling of 31 genes associated with aspects of adipogenesis and energy metabolism. Intake of dietary energy (9.44 ± 0.57 Mcal/d) across groups during the study did not differ but feed efficiency (weight gain/feed intake) during the first 56 days was greater for steers fed HiS. Expression of PPARG increased ca. 2-fold by day 56 primarily due to HiS in A × S steers. Several potential PPARG-target genes (e.g., ACACA, FASN, FABP4, SCD) increased 2.5-to-25-fold by day 56 across all groups, with responses (e.g., FASN, FABP4) being less pronounced in A × S steers fed LoS. This latter group of steers had markedly greater blood plasma glucose (0.99 vs. 0.79 g/L) and insulin (2.95 vs. 1.17 μg/L) by day 112, all of which were suggestive of insulin resistance. Interactions were observed for FABP4, FASN, GPAM, SCD, and DGAT2, such that feeding A × S steers high-starch and Angus steers low-starch resulted in greater fold-changes by day 56 or 112 (GPAM). Marked up-regulation of INSIG1 (4-to-8-fold) occurred throughout the study across all groups. SREBF1 expression, however, was only greater on day 112 namely due to LoS in A × S steers. The

  20. Metabolic Myopathies.

    Tarnopolsky, Mark A

    2016-12-01

    Metabolic myopathies are genetic disorders that impair intermediary metabolism in skeletal muscle. Impairments in glycolysis/glycogenolysis (glycogen-storage disease), fatty acid transport and oxidation (fatty acid oxidation defects), and the mitochondrial respiratory chain (mitochondrial myopathies) represent the majority of known defects. The purpose of this review is to develop a diagnostic and treatment algorithm for the metabolic myopathies. The metabolic myopathies can present in the neonatal and infant period as part of more systemic involvement with hypotonia, hypoglycemia, and encephalopathy; however, most cases present in childhood or in adulthood with exercise intolerance (often with rhabdomyolysis) and weakness. The glycogen-storage diseases present during brief bouts of high-intensity exercise, whereas fatty acid oxidation defects and mitochondrial myopathies present during a long-duration/low-intensity endurance-type activity or during fasting or another metabolically stressful event (eg, surgery, fever). The clinical examination is often normal between acute events, and evaluation involves exercise testing, blood testing (creatine kinase, acylcarnitine profile, lactate, amino acids), urine organic acids (ketones, dicarboxylic acids, 3-methylglutaconic acid), muscle biopsy (histology, ultrastructure, enzyme testing), MRI/spectroscopy, and targeted or untargeted genetic testing. Accurate and early identification of metabolic myopathies can lead to therapeutic interventions with lifestyle and nutritional modification, cofactor treatment, and rapid treatment of rhabdomyolysis.

  1. Animal metabolism

    Walburg, H.E.

    1977-01-01

    Studies on placental transport included the following: clearance of tritiated water as a baseline measurement for transport of materials across perfused placentas; transport of organic and inorganic mercury across the perfused placenta of the guinea pig in late gestation; and transport of cadmium across the perfused placenta of the guinea pig in late gestation. Studies on cadmium absorption and metabolism included the following: intestinal absorption and retention of cadmium in neonatal rats; uptake and distribution of an oral dose of cadmium in postweanling male and female, iron-deficient and normal rats; postnatal viability and growth in rat pups after oral cadmium administration during gestation; and the effect of calcium and phosphorus on the absorption and toxicity of cadmium. Studies on gastrointestinal absorption and mineral metabolism included: uptake and distribution of orally administered plutonium complex compounds in male mice; gastrointestinal absorption of 144 Ce in the newborn mouse, rat, and pig; and gastrointestinal absorption of 95 Nb by rats of different ages. Studies on iodine metabolism included the following: influence of thyroid status and thiocyanate on iodine metabolism in the bovine; effects of simulated fallout radiation on iodine metabolism in dairy cattle; and effects of feeding iodine binding agents on iodine metabolism in the calf

  2. Insights from the cold transcriptome and metabolome of Dendrobium officinale: global reprogramming of metabolic and gene regulation networks during cold acclimation

    Zhi-Gang Wu

    2016-11-01

    Full Text Available Plant cold acclimation (CA is a genetically complex phenomenon involving gene regulation and expression. Little is known about the cascading pattern of gene regulatroy network and the link between genes and metabolites during CA. Dendrobium officinale (DOKM is an important medicinal and ornamental plant and hypersensitive to low temperature. Here, we used the large scale metabolomic and transcriptomic technologies to reveal the response to CA in DOKM seedlings based on the physiological profile analyses. Lowering temperature from 4 oC to -2 oC resulted in significant increase(P<0.01)in antioxidant activities and electrolyte leakage during 24 h. The fitness CA piont of 0 oC and control (20 oC during 20 h were firstly obtained according to physiological analyses. Subsequently, massive transcriptome and metabolome reprogramming occurred during CA. The gene to metabolite network demonstrated that the CA associated processes are highly energy demanding through activating hydrolysis of sugars, amino acids catabolism and citrate cycle. The expression levels of 2,767 genes were significantly affected by CA, including 153-fold upregulation of CBF transcription factor, 56-fold upregulation of MAPKKK16 protein kinase. Moreover, the gene interaction and regulation network analysis revealed that the CA as an active process, was regulated at the transcriptional, post-transcriptional, translational and post-translational levels. Our findings highligted a comprehensive regulatory mechanism including cold signal transduction, transcriptional regulation and gene expression, which contributes a deeper understanding of the highly complex regulatory program during CA in DOKM. Some marker genes identified in DOKM seedlings will allow us to understand the role of each individual during CA by further functional analyses.

  3. The Stakeholder Model of voice research: Acknowledging barriers to human rights of all stakeholders in a communicative exchange.

    Madill, Catherine; Warhurst, Samantha; McCabe, Patricia

    2018-02-01

    The act of communication is a complex, transient and often abstract phenomenon that involves many stakeholders, each of whom has their own perspective: the speaker, the listener, the observer and the researcher. Current research practices in voice disorder are frequently framed through a single lens - that of the researcher/clinician or their participant/patient. This single lens approach risks overlooking significant barriers to the basic human right of freedom of expression for those with a voice disorder as it omits consideration of the impact of voice disorder on the listener, and consideration of the wider impact of the voice in the occupational context. Recent research in the area of voice has developed a multiple lens and subsequent Stakeholder Model that acknowledges the experience and reality of multiple stakeholders viewing the same phenomenon, the voice. This research paradigm is built on Article 19 of the Universal Declaration of Human Rights as it considers the realities of all stakeholders in forming a deeper understanding of the causality, impact and aspects of communication disorder. The Stakeholder Model will be presented as a suggestion for future investigations of communication disorders more widely.

  4. Sequences of emotional distress expressed by clients and acknowledged by therapists: are they associated more with some therapists than others?

    Viney, L L

    1994-11-01

    When clients come to psychotherapy they are distressed, this distress usually being expressed in the form of anxiety, hostility, depression and helplessness. This study explored the sequences of emotional distress expressed by clients and acknowledged by therapists, and examined their associations with other factors. The transcripts of five therapists (two single sessions each) were content-analysed: they used personal construct, client centered, rational-emotive, Gestalt and transactional analysis therapy. Log-linear analyses of appropriate contingency table cell frequencies were conducted to test associations between identified sequences and the two variables of therapist and timing of completion of the sequence. Therapist-client sequences of Anxiety-Anxiety, Anxiety-Hostility and Helplessness-Hostility were found to be associated more with the personal construct and client centred therapists than with the rational-emotive therapist. Client-therapist sequences of Anxiety-Anxiety, Helplessness-Anxiety and Helplessness-Helplessness were more often found with the client centred therapist than the other therapists. For most of these sequences timing had an effect, yet timing rarely interacted with the therapist variable. The findings are discussed in terms of their relevance to the theoretical positions represented, the shortcomings of the research and the value of this methodology in studies linking therapy process with outcome.

  5. AP2/EREBP transcription factors are part of gene regulatory networks and integrate metabolic, hormonal and environmental signals in stress acclimation and retrograde signalling.

    Dietz, Karl-Josef; Vogel, Marc Oliver; Viehhauser, Andrea

    2010-09-01

    To optimize acclimation responses to environmental growth conditions, plants integrate and weigh a diversity of input signals. Signal integration within the signalling networks occurs at different sites including the level of transcription factor activation. Accumulating evidence assigns a major and diversified role in environmental signal integration to the family of APETALA 2/ethylene response element binding protein (AP2/EREBP) transcription factors. Presently, the Plant Transcription Factor Database 3.0 assigns 147 gene loci to this family in Arabidopsis thaliana, 200 in Populus trichocarpa and 163 in Oryza sativa subsp. japonica as compared to 13 to 14 in unicellular algae ( http://plntfdb.bio.uni-potsdam.de/v3.0/ ). AP2/EREBP transcription factors have been implicated in hormone, sugar and redox signalling in context of abiotic stresses such as cold and drought. This review exemplarily addresses present-day knowledge of selected AP2/EREBP with focus on a function in stress signal integration and retrograde signalling and defines AP2/EREBP-linked gene networks from transcriptional profiling-based graphical Gaussian models. The latter approach suggests highly interlinked functions of AP2/EREBPs in retrograde and stress signalling.

  6. [Metabolic myopathies].

    Papazian, Óscar; Rivas-Chacón, Rafael

    2013-09-06

    To review the metabolic myopathies manifested only by crisis of myalgias, cramps and rigidity of the muscles with decreased voluntary contractions and normal inter crisis neurologic examination in children and adolescents. These metabolic myopathies are autosomic recessive inherited enzymatic deficiencies of the carbohydrates and lipids metabolisms. The end result is a reduction of intra muscle adenosine triphosphate, mainly through mitochondrial oxidative phosphorylation, with decrease of available energy for muscle contraction. The one secondary to carbohydrates intra muscle metabolism disorders are triggered by high intensity brief (fatty acids metabolism disorders are triggered by low intensity prolonged (> 10 min) exercises. The conditions in the first group in order of decreasing frequency are the deficiencies of myophosforilase (GSD V), muscle phosphofructokinase (GSD VII), phosphoglycerate mutase 1 (GSD X) and beta enolase (GSD XIII). The conditions in the second group in order of decreasing frequency are the deficiencies of carnitine palmitoyl transferase II and very long chain acyl CoA dehydrogenase. The differential characteristics of patients in each group and within each group will allow to make the initial presumptive clinical diagnosis in the majority and then to order only the necessary tests to achieve the final diagnosis. Treatment during the crisis includes hydration, glucose and alkalinization of urine if myoglobin in blood and urine are elevated. Prevention includes avoiding exercise which may induce the crisis and fasting. The prognosis is good with the exception of rare cases of acute renal failure due to hipermyoglobinemia because of severe rabdomyolisis.

  7. Optimization of a blueprint for in vitro glycolysis by metabolic real-time analysis

    Bujara, Matthias; Schümperli, Michael; Pellaux, René; Heinemann, Matthias; Panke, Sven

    Recruiting complex metabolic reaction networks for chemical synthesis has attracted considerable attention but frequently requires optimization of network composition and dynamics to reach sufficient productivity. As a design framework to predict optimal levels for all enzymes in the network is

  8. Bio-crude transcriptomics: Gene discovery and metabolic network reconstruction for the biosynthesis of the terpenome of the hydrocarbon oil-producing green alga, Botryococcus braunii race B (Showa*

    Molnár István

    2012-10-01

    Full Text Available Abstract Background Microalgae hold promise for yielding a biofuel feedstock that is sustainable, carbon-neutral, distributed, and only minimally disruptive for the production of food and feed by traditional agriculture. Amongst oleaginous eukaryotic algae, the B race of Botryococcus braunii is unique in that it produces large amounts of liquid hydrocarbons of terpenoid origin. These are comparable to fossil crude oil, and are sequestered outside the cells in a communal extracellular polymeric matrix material. Biosynthetic engineering of terpenoid bio-crude production requires identification of genes and reconstruction of metabolic pathways responsible for production of both hydrocarbons and other metabolites of the alga that compete for photosynthetic carbon and energy. Results A de novo assembly of 1,334,609 next-generation pyrosequencing reads form the Showa strain of the B race of B. braunii yielded a transcriptomic database of 46,422 contigs with an average length of 756 bp. Contigs were annotated with pathway, ontology, and protein domain identifiers. Manual curation allowed the reconstruction of pathways that produce terpenoid liquid hydrocarbons from primary metabolites, and pathways that divert photosynthetic carbon into tetraterpenoid carotenoids, diterpenoids, and the prenyl chains of meroterpenoid quinones and chlorophyll. Inventories of machine-assembled contigs are also presented for reconstructed pathways for the biosynthesis of competing storage compounds including triacylglycerol and starch. Regeneration of S-adenosylmethionine, and the extracellular localization of the hydrocarbon oils by active transport and possibly autophagy are also investigated. Conclusions The construction of an annotated transcriptomic database, publicly available in a web-based data depository and annotation tool, provides a foundation for metabolic pathway and network reconstruction, and facilitates further omics studies in the absence of a genome

  9. [Mental Health and Political Violence. Care of Psychiatric Patient or Acknowledge of the Micropolitics of the Subject].

    Arias López, Beatriz Elena

    2013-09-01

    Political violence is a global phenomenon, especially in low- to middle-income countries. This phenomenon increasingly involves civilians. This situation is a priority in collective health, as it produces multiple and complex effects on physical and mental health, and human and social ecosystems. The objective of this article is to present the main tendencies that coexist in research and practice on the understanding of the effects of political violence on mental health. The biomedical approach of psychiatric trauma and the wider perspective of social sciences, which incorporate the collective dimension of these effects, are also taken into account. Review of research determines the relationship with political violence / collective violence and mental health in international databases and national documentation centers, academics and NGOs within the last decade of the twentieth century, and the first of this century under the headings of trauma, war, armed conflict and political violence. The limitations of general explanations of psychiatric trauma in understanding the complex effects of political violence on mental health are shown. The constructs that incorporate social and collective dimensions increase this comprehension of these effects and knowledge of mental health, both conceptually as methodologically. In a political violence context it urgent to change attitudes about mental health. It is a way to overcome the biomedical, individualistic, and short term epidemiology, and to remove medication from mental health. This means acknowledging that people who experience the effects of political violence effects are not sick. They are powerful people who can transform and produce the life they dream of. Copyright © 2013 Asociación Colombiana de Psiquiatría. Publicado por Elsevier España. All rights reserved.

  10. Acknowledging and allocating responsibility for clinical inertia in the management of Type 2 diabetes in primary care: a qualitative study.

    Zafar, A; Stone, M A; Davies, M J; Khunti, K

    2015-03-01

    Failure to intensify treatment in patients with Type 2 diabetes with suboptimal blood glucose control has been termed clinical inertia and has been shown to contribute to poorer patient outcomes. We aimed to identify and explore perceptions about clinical inertia from the perspective of primary healthcare providers. A qualitative study was conducted in Leicestershire and Northamptonshire, UK. Purposive sampling was based on healthcare providers working in primary care settings with 'higher' and 'lower' target achievement based on routine data. Twenty semi-structured interviews were conducted, face-to-face or by telephone. Thematic analysis was informed by the constant comparative approach. An important broad theme that emerged during the analysis was related to attribution and explanation of responsibility for clinical inertia. This included general willingness to accept a degree of responsibility for clinical inertia. In some cases, however, participants had inaccurate perceptions about levels of target achievement in their primary care centres, as indicated by routine data. Participants sought to lessen their own sense of accountability by highlighting patient-level barriers such as comorbidities and human fallibility, and also system-level barriers, particularly time constraints. Perceptions about ways of addressing the problem of clinical inertia were not seen as straightforward, further emphasizing a complex and cumulative pattern of barriers. In order to understand and address the problem of clinical inertia, provider, patient- and system-level barriers should be considered together rather than as separate issues. Acknowledgement of responsibility should be regarded positively as a motivator for change. © 2014 The Authors. Diabetic Medicine © 2014 Diabetes UK.

  11. Digital Cushion Fatty Acid Composition and Lipid Metabolism Gene Network Expression in Holstein Dairy Cows Fed a High-Energy Diet.

    Zeeshan Muhammad Iqbal

    Full Text Available The hoof digital cushion is a complex structure composed of adipose tissue beneath the distal phalanx, i.e. axial, middle and abaxial fat pad. The major role of these fat depots is dampening compression of the corium underneath the cushion. The study aimed to determine expression of target genes and fatty acid profiles in the hoof of non-pregnant dry Holstein cows fed low (CON or high-energy (OVE diets. The middle fat pad of the hoof digital cushion was collected soon after slaughter. Despite the lack of effect on expression of the transcription regulators SREBF1 and PPARG, the expression of the lipogenic enzymes ACACA, FASN, SCD, and DGAT2 was upregulated with OVE. Along with the upregulation of G6PD and IDH1, important for NADPH synthesis during lipogenesis, and the basal glucose transporter SLC2A1, these data indicated a pro-lipogenic response in the digital cushion with OVE. The expression of the lipid droplet-associated protein PLIN2 was upregulated while expression of lipolytic enzymes (ATGL, ABDH5, and LIPE only tended to be upregulated with OVE. Therefore, OVE induced lipogenesis, lipid droplet formation, and lipolysis, albeit to different extents. Although concentration of monounsaturated fatty acids (MUFA did not differ, among the polyunsaturated fatty acids (PUFA, the concentration of 20:5n3 was lower with OVE. Among the saturated fatty acids, 20:0 concentration was greater with OVE. Although data indicated that the hoof digital cushion metabolic transcriptome is responsive to higher-energy diets, this did not translate into marked differences in the fatty acid composition. The decrease in concentration of PUFA, which could contribute to synthesis of inflammatory molecules, in OVE-fed cows indicated that feeding higher-energy diets might be detrimental for the mediation of inflammation in digital cushion. This effect could be further exacerbated by physiologic and endocrine changes during the peripartal period that favor inflammation.

  12. Using logic programming for modeling the one-carbon metabolism network to study the impact of folate deficiency on methylation processes.

    Gnimpieba, Etienne Z; Eveillard, Damien; Guéant, Jean-Louis; Chango, Abalo

    2011-08-01

    Dynamical modeling is an accurate tool for describing the dynamic regulation of one-carbon metabolism (1CM) with emphasis on the alteration of DNA methylation and/or dUMP methylation into dTMP. Using logic programming we present a comprehensive and adaptative mathematical model to study the impact of folate deficiency, including folate transport and enzymes activities. 5-Methyltetrahydrofolate (5mTHF) uptake and DNA and dUMP methylation were studied by simulating nutritional 5mTHF deficiency and methylenetetrahydrofolate reductase (MTHFR) gene defects. Both conditions had distinct effects on 1CM metabolite synthesis. Simulating severe 5mTHF deficiency (25% of normal levels) modulated 11 metabolites. However, simulating a severe decrease in MTHFR activity (25% of normal activity) modulated another set of metabolites. Two oscillations of varying amplitude were observed at the steady state for DNA methylation with severe 5mTHF deficiency, and the dUMP/dTMP ratio reached a steady state after 2 h, compared to 2.5 h for 100% 5mTHF. MTHFR activity with 25% of V(max) resulted in an increased methylated DNA pool after half an hour. We observed a deviation earlier in the profile compared to 50% and 100% V(max). For dUMP methylation, the highest level was observed with 25%, suggesting a low rate of dUMP methylation into dTMP with 25% of MTHFR activity. In conclusion, using logic programming we were able to construct the 1CM for analyzing the dynamic system behavior. This model may be used to refine biological interpretations of data or as a tool that can provide new hypotheses for pathogenesis.

  13. Artificial neural network-based exploration of gene-nutrient interactions in folate and xenobiotic metabolic pathways that modulate susceptibility to breast cancer.

    Naushad, Shaik Mohammad; Ramaiah, M Janaki; Pavithrakumari, Manickam; Jayapriya, Jaganathan; Hussain, Tajamul; Alrokayan, Salman A; Gottumukkala, Suryanarayana Raju; Digumarti, Raghunadharao; Kutala, Vijay Kumar

    2016-04-15

    In the current study, an artificial neural network (ANN)-based breast cancer prediction model was developed from the data of folate and xenobiotic pathway genetic polymorphisms along with the nutritional and demographic variables to investigate how micronutrients modulate susceptibility to breast cancer. The developed ANN model explained 94.2% variability in breast cancer prediction. Fixed effect models of folate (400 μg/day) and B12 (6 μg/day) showed 33.3% and 11.3% risk reduction, respectively. Multifactor dimensionality reduction analysis showed the following interactions in responders to folate: RFC1 G80A × MTHFR C677T (primary), COMT H108L × CYP1A1 m2 (secondary), MTR A2756G (tertiary). The interactions among responders to B12 were RFC1G80A × cSHMT C1420T and CYP1A1 m2 × CYP1A1 m4. ANN simulations revealed that increased folate might restore ER and PR expression and reduce the promoter CpG island methylation of extra cellular superoxide dismutase and BRCA1. Dietary intake of folate appears to confer protection against breast cancer through its modulating effects on ER and PR expression and methylation of EC-SOD and BRCA1. Copyright © 2016 Elsevier B.V. All rights reserved.

  14. Urban metabolism: A review of research methodologies

    Zhang, Yan

    2013-01-01

    Urban metabolism analysis has become an important tool for the study of urban ecosystems. The problems of large metabolic throughput, low metabolic efficiency, and disordered metabolic processes are a major cause of unhealthy urban systems. In this paper, I summarize the international research on urban metabolism, and describe the progress that has been made in terms of research methodologies. I also review the methods used in accounting for and evaluating material and energy flows in urban metabolic processes, simulation of these flows using a network model, and practical applications of these methods. Based on this review of the literature, I propose directions for future research, and particularly the need to study the urban carbon metabolism because of the modern context of global climate change. Moreover, I recommend more research on the optimal regulation of urban metabolic systems. Highlights: •Urban metabolic processes can be analyzed by regarding cities as superorganisms. •Urban metabolism methods include accounting, assessment, modeling, and regulation. •Research methodologies have improved greatly since this field began in 1965. •Future research should focus on carbon metabolism and optimal regulation. -- The author reviews research progress in the field of urban metabolism, and based on her literature review, proposes directions for future research

  15. Nucleotide Metabolism

    Martinussen, Jan; Willemoës, M.; Kilstrup, Mogens

    2011-01-01

    Metabolic pathways are connected through their utilization of nucleotides as supplier of energy, allosteric effectors, and their role in activation of intermediates. Therefore, any attempt to exploit a given living organism in a biotechnological process will have an impact on nucleotide metabolis...

  16. Study on the Effect of Wing Bud Chitin Metabolism and Its Developmental Network Genes in the Brown Planthopper, Nilaparvata lugens, by Knockdown of TRE Gene

    Lu Zhang

    2017-09-01

    Full Text Available The brown planthopper, Nilaparvata lugens is one of the most serious pests of rice, and there is so far no effective way to manage this pest. However, RNA interference not only can be used to study gene function, but also provide potential opportunities for novel pest management. The development of wing plays a key role in insect physiological activities and mainly involves chitin. Hence, the regulating role of trehalase (TRE genes on wing bud formation has been studied by RNAi. In this paper, the activity levels of TRE and the contents of the two sugars trehalose and glucose were negatively correlated indicating the potential role of TRE in the molting process. In addition, NlTRE1-1 and NlTRE2 were expressed at higher levels in wing bud tissue than in other tissues, and abnormal molting and wing deformity or curling were noted 48 h after the insect was injected with any double-stranded TRE (dsTRE, even though different TREs have compensatory functions. The expression levels of NlCHS1b, NlCht1, NlCht2, NlCht6, NlCht7, NlCht8, NlCht10, NlIDGF, and NlENGase decreased significantly 48 h after the insect was injected with a mixture of three kinds of dsTREs. Similarly, the TRE inhibitor validamycin can inhibit NlCHS1 and NlCht gene expression. However, the wing deformity was the result of the NlIDGF, NlENGase, NlAP, and NlTSH genes being inhibited when a single dsTRE was injected. These results demonstrate that silencing of TRE gene expression can lead to wing deformities due to the down-regulation of the AP and TSH genes involved in wing development and that the TRE inhibitor validamycin can co-regulate chitin metabolism and the expression of wing development-related genes in wing bud tissue. The results provide a new approach for the prevention and management of N. lugens.

  17. Study on the Effect of Wing Bud Chitin Metabolism and Its Developmental Network Genes in the Brown Planthopper, Nilaparvata lugens, by Knockdown of TRE Gene.

    Zhang, Lu; Qiu, Ling-Yu; Yang, Hui-Li; Wang, Hui-Juan; Zhou, Min; Wang, Shi-Gui; Tang, Bin

    2017-01-01

    The brown planthopper, Nilaparvata lugens is one of the most serious pests of rice, and there is so far no effective way to manage this pest. However, RNA interference not only can be used to study gene function, but also provide potential opportunities for novel pest management. The development of wing plays a key role in insect physiological activities and mainly involves chitin. Hence, the regulating role of trehalase (TRE) genes on wing bud formation has been studied by RNAi. In this paper, the activity levels of TRE and the contents of the two sugars trehalose and glucose were negatively correlated indicating the potential role of TRE in the molting process. In addition, NlTRE1-1 and NlTRE2 were expressed at higher levels in wing bud tissue than in other tissues, and abnormal molting and wing deformity or curling were noted 48 h after the insect was injected with any double-stranded TRE ( dsTRE ), even though different TREs have compensatory functions. The expression levels of NlCHS1b, NlCht1, NlCht2, NlCht6, NlCht7, NlCht8, NlCht10, NlIDGF , and NlENGase decreased significantly 48 h after the insect was injected with a mixture of three kinds of dsTREs . Similarly, the TRE inhibitor validamycin can inhibit NlCHS1 and NlCht gene expression. However, the wing deformity was the result of the NlIDGF, NlENGase, NlAP , and NlTSH genes being inhibited when a single dsTRE was injected. These results demonstrate that silencing of TRE gene expression can lead to wing deformities due to the down-regulation of the AP and TSH genes involved in wing development and that the TRE inhibitor validamycin can co-regulate chitin metabolism and the expression of wing development-related genes in wing bud tissue. The results provide a new approach for the prevention and management of N. lugens .

  18. Sodium glucose co-transporter inhibitors for the management of diabetes mellitus: an opinion paper from the Endocrine and Metabolism Practice and Research Network of the American College of Clinical Pharmacy.

    Clements, Jennifer N; Whitley, Heather P; D'Souza, Jennifer J; Gross, Benjamin; Hess, Rick; Reece, Sara; Gentry, Chad; Shealy, Kayce

    2015-01-01

    Type 2 diabetes mellitus (T2DM) carries a high prevalence in the United States and worldwide. Therefore, the number of medication classes being developed and studied has grown. The individualized management of diabetes is accomplished by evaluating a medication's efficacy, safety, and cost, along with the patient's preference and tolerance to the medication. Sodium glucose co-transporter 2 inhibitors are a new therapeutic class indicated for the treatment of diabetes and have a unique mechanism of action, independent of beta-cell function. The first agent approved by the Food and Drug Administration (FDA) was canagliflozin in March 2013. Two agents - dapagliflozin and empagliflozin - were FDA-approved in January and July 2014, respectively. A clear understanding of the new class is needed to identify its appropriate use in clinical practice. Members of the American College of Clinical Pharmacy Endocrine and Metabolism Practice and Research Network reviewed available literature regarding this therapeutic class. The article addresses the advantages, disadvantages, emerging role, and patient education for sodium glucose co-transporter 2 inhibitors. Key limitations for this article include limited access to clinical trial data not published by the pharmaceutical company and limited data on products produced outside the United States.

  19. Circadian physiology of metabolism.

    Panda, Satchidananda

    2016-11-25

    A majority of mammalian genes exhibit daily fluctuations in expression levels, making circadian expression rhythms the largest known regulatory network in normal physiology. Cell-autonomous circadian clocks interact with daily light-dark and feeding-fasting cycles to generate approximately 24-hour oscillations in the function of thousands of genes. Circadian expression of secreted molecules and signaling components transmits timing information between cells and tissues. Such intra- and intercellular daily rhythms optimize physiology both by managing energy use and by temporally segregating incompatible processes. Experimental animal models and epidemiological data indicate that chronic circadian rhythm disruption increases the risk of metabolic diseases. Conversely, time-restricted feeding, which imposes daily cycles of feeding and fasting without caloric reduction, sustains robust diurnal rhythms and can alleviate metabolic diseases. These findings highlight an integrative role of circadian rhythms in physiology and offer a new perspective for treating chronic diseases in which metabolic disruption is a hallmark. Copyright © 2016, American Association for the Advancement of Science.

  20. Comparison of Gratitude across Context Variations: A Generic Analysis of Dissertation Acknowledgements Written by Taiwanese Authors in EFL and ESL Contexts

    Wenhsien Yang

    2012-09-01

    Full Text Available Research on generic structures of acknowledgements in dissertations has gradually drawn attention in various contexts. However, there is relatively scant research on the ways in which acknowledgements are written by authors with mutually similar cultural backgrounds but in two different academic environments and language contexts. To fill this gap, this study compared 60 PhD dissertation acknowledgements written by Taiwanese postgraduates in Taiwan, an EFL context, with another 60 written by Taiwanese scholars who obtained their doctorates in the United States, an ESL context. The focus was on the generic structures and linguistic features of the writing styles of the two groups. The study aimed to investigate whether divergences existed in the two different academic and language settings, but with the writers sharing the same cultural and language background. If such divergences did exist, the likely causes would be explored. The results revealed that firstly, the participants in both contexts generally followed a three-tier structure when writing their dissertation acknowledgements, namely, reflecting, thanking, and announcing moves. However, academic conventions, institutional preferences and the language context, together with socio-cultural factors, affected their construction of moves/steps and their choice of linguistic elements. It was found that the rhetorical language in both corpora was relatively direct, emotional and precise.

  1. TRAILER project (Tagging, recognition, acknowledgment of informal learning experiences). A Methodology to make learners’ informal learning activities visible to the institutions

    García-Peñalvo, Francisco J.; Conde, Miguel Á.; Zangrando, Valentina; García-Holgado, Alicia; Seoane, Antón M.; Alier, Marc; Galanis, Nikolas; Brouns, Francis; Vogten, Hubert; Griffiths, Dai; Mykowska, Aleksandra; Alves, Gustavo R.; Minović, Miroslav

    2013-01-01

    García-Peñalvo, F. J., Conde, M. Á, Zangrando, V., García-Holgado, A., Seoane, A. M., Alier, M., Galanis, N., Brouns, F., Vogten, H., Griffiths, D., Mykowska, A., Ribeiro-Alves, G., & Minović, M. (2013). TRAILER project (Tagging, recognition, acknowledgment of informal learning experiences). A

  2. Cancer metabolism meets systems biology: Pyruvate kinase isoform PKM2 is a metabolic master regulator

    Fabian V Filipp

    2013-01-01

    Pyruvate kinase activity is controlled by a tightly woven regulatory network. The oncofetal isoform of pyruvate kinase (PKM2) is a master regulator of cancer metabolism. PKM2 engages in parallel, feed-forward, positive and negative feedback control contributing to cancer progression. Besides its metabolic role, non-metabolic functions of PKM2 as protein kinase and transcriptional coactivator for c-MYC and hypoxia-inducible factor 1-alpha are essential for epidermal growth factor receptor acti...

  3. Metabolic Surgery

    Pareek, Manan; Schauer, Philip R; Kaplan, Lee M

    2018-01-01

    The alarming rise in the worldwide prevalence of obesity is paralleled by an increasing burden of type 2 diabetes mellitus. Metabolic surgery is the most effective means of obtaining substantial and durable weight loss in individuals with obesity. Randomized trials have recently shown...... the superiority of surgery over medical treatment alone in achieving improved glycemic control, as well as a reduction in cardiovascular risk factors. The mechanisms seem to extend beyond the magnitude of weight loss alone and include improvements in incretin profiles, insulin secretion, and insulin sensitivity....... Moreover, observational data suggest that the reduction in cardiovascular risk factors translates to better patient outcomes. This review describes commonly used metabolic surgical procedures and their current indications and summarizes the evidence related to weight loss and glycemic outcomes. It further...

  4. Metabolic Syndrome

    Sevil Ikinci

    2010-10-01

    Full Text Available Metabolic Syndrome is a combination of risk factors including common etiopathogenesis. These risk factors play different roles in occurence of atherosclerotic diseases, type 2 diabetes, and cancers. Although a compromise can not be achieved on differential diagnosis for MS, the existence of any three criterias enable to diagnose MS. These are abdominal obesity, dislipidemia (hypertrigliceridemia, hypercholesterolemia, and reduced high density lipoprotein hypertension, and elevated fasting blood glucose. According to the results of Metabolic Syndrome Research (METSAR, the overall prevalence of MS in Turkey is 34%; in females 40%, and in males it is 28%. As a result of “Western” diet, and increased frequency of obesity, MS is observed in children and in adolescents both in the world and in Turkey. Resulting in chronic diseases, it is thought that the syndrome can be prevented by healthy lifestyle behaviours. [TAF Prev Med Bull 2010; 9(5.000: 535-540

  5. A workflow for mathematical modeling of subcellular metabolic pathways in leaf metabolism of Arabidopsis thaliana

    Thomas eNägele

    2013-12-01

    Full Text Available During the last decade genome sequencing has experienced a rapid technological development resulting in numerous sequencing projects and applications in life science. In plant molecular biology, the availability of sequence data on whole genomes has enabled the reconstruction of metabolic networks. Enzymatic reactions are predicted by the sequence information. Pathways arise due to the participation of chemical compounds as substrates and products in these reactions. Although several of these comprehensive networks have been reconstructed for the genetic model plant Arabidopsis thaliana, the integration of experimental data is still challenging. Particularly the analysis of subcellular organization of plant cells limits the understanding of regulatory instances in these metabolic networks in vivo. In this study, we develop an approach for the functional integration of experimental high-throughput data into such large-scale networks. We present a subcellular metabolic network model comprising 524 metabolic intermediates and 548 metabolic interactions derived from a total of 2769 reactions. We demonstrate how to link the metabolite covariance matrix of different Arabidopsis thaliana accessions with the subcellular metabolic network model for the inverse calculation of the biochemical Jacobian, finally resulting in the calculation of a matrix which satisfies a Lyaponov equation involving a covariance matrix. In this way, differential strategies of metabolite compartmentation and involved reactions were identified in the accessions when exposed to low temperature.

  6. Cellular metabolism

    Hildebrand, C.E.; Walters, R.A.

    1977-01-01

    Progress is reported on the following research projects: chromatin structure; the use of circular synthetic polydeoxynucleotides as substrates for the study of DNA repair enzymes; human cellular kinetic response following exposure to DNA-interactive compounds; histone phosphorylation and chromatin structure in cell proliferation; photoaddition products induced in chromatin by uv light; pollutants and genetic information transfer; altered RNA metabolism as a function of cadmium accumulation and intracellular distribution in cultured cells; and thymidylate chromophore destruction by water free radicals

  7. Network reconfiguration and neuronal plasticity in rhythm-generating networks.

    Koch, Henner; Garcia, Alfredo J; Ramirez, Jan-Marino

    2011-12-01

    Neuronal networks are highly plastic and reconfigure in a state-dependent manner. The plasticity at the network level emerges through multiple intrinsic and synaptic membrane properties that imbue neurons and their interactions with numerous nonlinear properties. These properties are continuously regulated by neuromodulators and homeostatic mechanisms that are critical to maintain not only network stability and also adapt networks in a short- and long-term manner to changes in behavioral, developmental, metabolic, and environmental conditions. This review provides concrete examples from neuronal networks in invertebrates and vertebrates, and illustrates that the concepts and rules that govern neuronal networks and behaviors are universal.

  8. Psychosocial risk factors for the metabolic syndrome

    Pedersen, Jolene Masters; Lund, Rikke; Andersen, Ingelise

    2016-01-01

    Background/Objectives: Metabolic deregulations and development of metabolic syndrome may be an important pathway underlying the relationship between stress and cardiovascular disease. We aim to estimate the effect of a comprehensive range of psychosocial factors on the risk of developing metabolic.......11) to be risk factors for developing the metabolic syndrome in women, while vital exhaustion (OR 2.09, 95% CI 0.95 to 4.59) and intake of sleep medications (OR 2.54, 95% CI 0.92 to 5.96) may play a more important role in men. Conclusions: Experiencing major life events in work and adult life and....../or dysfunctional social networks is a risk factor for metabolic syndrome in women, and stress reactions such as vital exhaustion and intake of sleep medications may play a more important role in the development of metabolic syndrome men....

  9. Determinism and Contingency Shape Metabolic Complementation in an Endosymbiotic Consortium.

    Ponce-de-Leon, Miguel; Tamarit, Daniel; Calle-Espinosa, Jorge; Mori, Matteo; Latorre, Amparo; Montero, Francisco; Pereto, Juli

    2017-01-01

    Bacterial endosymbionts and their insect hosts establish an intimate metabolic relationship. Bacteria offer a variety of essential nutrients to their hosts, whereas insect cells provide the necessary sources of matter and energy to their tiny metabolic allies. These nutritional complementations sustain themselves on a diversity of metabolite exchanges between the cell host and the reduced yet highly specialized bacterial metabolism-which, for instance, overproduces a small set of essential amino acids and vitamins. A well-known case of metabolic complementation is provided by the cedar aphid Cinara cedri that harbors two co-primary endosymbionts, Buchnera aphidicola BCc and Ca . Serratia symbiotica SCc, and in which some metabolic pathways are partitioned between different partners. Here we present a genome-scale metabolic network (GEM) for the bacterial consortium from the cedar aphid i BSCc. The analysis of this GEM allows us the confirmation of cases of metabolic complementation previously described by genome analysis (i.e., tryptophan and biotin biosynthesis) and the redefinition of an event of metabolic pathway sharing between the two endosymbionts, namely the biosynthesis of tetrahydrofolate. In silico knock-out experiments with i BSCc showed that the consortium metabolism is a highly integrated yet fragile network. We also have explored the evolutionary pathways leading to the emergence of metabolic complementation between reduced metabolisms starting from individual, complete networks. Our results suggest that, during the establishment of metabolic complementation in endosymbionts, adaptive evolution is significant in the case of tryptophan biosynthesis, whereas vitamin production pathways seem to adopt suboptimal solutions.

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

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

    2003-11-01

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

  11. Acknowledgements [3. international conference on small angle neutron scattering dedicated to the 80 anniversary of Yu.M. Ostanevich, Dubna (Russian Federation), 6-9 June 2016

    2017-01-01

    The Organizers of the III International Conference on Small Angle Neutron Scattering dedicated to the 80-th anniversary of Yu. M. Ostanevich, acknowledge the financial support from the Grants of the Governmental Plenipotentiary Representatives of Romania, Slovakia, Czech Republic, scientific projects of the Cooperation Programmes JINR-Romania. Special gratitude to the JINR and FLNP administration and staff is expressed. The conference is also dedicated to the 60th Anniversary of the Joint Institute for Nuclear Research, Dubna. (paper)

  12. Exploring network operations for data and information networks

    Yao, Bing; Su, Jing; Ma, Fei; Wang, Xiaomin; Zhao, Xiyang; Yao, Ming

    2017-01-01

    Barabási and Albert, in 1999, formulated scale-free models based on some real networks: World-Wide Web, Internet, metabolic and protein networks, language or sexual networks. Scale-free networks not only appear around us, but also have high qualities in the world. As known, high quality information networks can transfer feasibly and efficiently data, clearly, their topological structures are very important for data safety. We build up network operations for constructing large scale of dynamic networks from smaller scale of network models having good property and high quality. We focus on the simplest operators to formulate complex operations, and are interesting on the closeness of operations to desired network properties.

  13. Longitudinal study of respiratory function and symptoms in a non-smoking group of long-term officially-acknowledged victims of pollution-related illness.

    Tanaka, Takako; Asai, Masaharu; Yanagita, Yorihide; Nishinakagawa, Tsuyoshi; Miyamoto, Naomi; Kotaki, Kenji; Yano, Yudai; Kozu, Ryo; Honda, Sumihisa; Senjyu, Hideaki

    2013-08-17

    Air pollution is known to be a leading cause of respiratory symptoms. Many cross-sectional studies reported that air pollution caused respiratory disease in Japanese individuals in the 1960s. Japan has laws regulating air pollution levels and providing compensation for victims of pollution-related respiratory disease. However, long-term changes in respiratory function and symptoms in individuals who were exposed to air pollution in the 1960s have not been well studied. This study aimed to investigate longitudinal respiratory function and symptoms in older, non-smoking, long-term officially-acknowledged victims of pollution-related illness. The study included 563 officially-acknowledged victims of pollution-related illness living in Kurashiki, Okayama who were aged ≥ 65 years in 2009. Data were retrospectively collected from yearly respiratory symptom questionnaires and spirometry examinations conducted from 2000 to 2009. Respiratory function declined significantly from 2000 to 2009 (p pollutants around 1970 resulted in a decrease in respiratory function and an increase in respiratory symptoms in the study population. From 2000 to 2009, the mean annual changes in respiratory function were within the normal range, even though the severity of dyspnea worsened. The changes in respiratory function and symptoms over the study period were probably due to aging. The laws governing air pollution levels and providing compensation for officially-acknowledged victims of pollution-related illness in Japan may be effective for respiratory disease cause by pollution.

  14. Formal Women-only Networks

    Villesèche, Florence; Josserand, Emmanuel

    2017-01-01

    /organisations and the wider social group of women in business. Research limitations/implications: The authors focus on the distinction between external and internal formal women-only networks while also acknowledging the broader diversity that can characterise such networks. Their review provides the reader with an insight...... member level, the authors suggest that such networks can be of value for organisations and the wider social group of women in management and leadership positions.......Purpose: The purpose of this paper is to review the emerging literature on formal women-only business networks and outline propositions to develop this under-theorised area of knowledge and stimulate future research. Design/methodology/approach: The authors review the existing literature on formal...

  15. Saccharomyces cerevisiae metabolism in ecological context

    Jouhten, Paula; Ponomarova, Olga; González García, Ramón; Patil, Kiran R.

    2016-01-01

    The architecture and regulation of Saccharomyces cerevisiae metabolic network are among the best studied owing to its widespread use in both basic research and industry. Yet, several recent studies have revealed notable limitations in explaining genotype?metabolic phenotype relations in this yeast, especially when concerning multiple genetic/environmental perturbations. Apparently unexpected genotype?phenotype relations may originate in the evolutionarily shaped cellular operating principles ...

  16. Parameter estimation in tree graph metabolic networks

    Astola, Laura; Stigter, Hans; Gomez Roldan, Maria Victoria; Eeuwijk, van Fred; Hall, Robert D.; Groenenboom, Marian; Molenaar, Jaap J.

    2016-01-01

    We study the glycosylation processes that convert initially toxic substrates to nu- tritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum) seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs) to model the enzyme

  17. Regulation of Mammalian Metabolism by Facilitated Transport Across the Inner Mitochondrial Membrane

    Vacanti, Nathaniel Martin

    2015-01-01

    The enzymes and reactions of the metabolic network provide cells with a means to utilize the energy stored in substrate chemical bonds and to rearrange those bonds to form biosynthetic building blocks. The chapters of this dissertation are all independent bodies of work exploring how the metabolic network influences and regulates cellular function or dysfunction. Chapter 1, titled "Exploring Metabolic Pathways that Contribute to the Stem Cell Phenotype", is a case study on how the metabolic n...

  18. Learning Networks, Networked Learning

    Sloep, Peter; Berlanga, Adriana

    2010-01-01

    Sloep, P. B., & Berlanga, A. J. (2011). Learning Networks, Networked Learning [Redes de Aprendizaje, Aprendizaje en Red]. Comunicar, XIX(37), 55-63. Retrieved from http://dx.doi.org/10.3916/C37-2011-02-05

  19. Bile Acid Metabolism in Liver Pathobiology

    Chiang, John Y. L.; Ferrell, Jessica M.

    2018-01-01

    Bile acids facilitate intestinal nutrient absorption and biliary cholesterol secretion to maintain bile acid homeostasis, which is essential for protecting liver and other tissues and cells from cholesterol and bile acid toxicity. Bile acid metabolism is tightly regulated by bile acid synthesis in the liver and bile acid biotransformation in the intestine. Bile acids are endogenous ligands that activate a complex network of nuclear receptor farnesoid X receptor and membrane G protein-coupled bile acid receptor-1 to regulate hepatic lipid and glucose metabolic homeostasis and energy metabolism. The gut-to-liver axis plays a critical role in the regulation of enterohepatic circulation of bile acids, bile acid pool size, and bile acid composition. Bile acids control gut bacteria overgrowth, and gut bacteria metabolize bile acids to regulate host metabolism. Alteration of bile acid metabolism by high-fat diets, sleep disruption, alcohol, and drugs reshapes gut microbiome and causes dysbiosis, obesity, and metabolic disorders. Gender differences in bile acid metabolism, FXR signaling, and gut microbiota have been linked to higher prevalence of fatty liver disease and hepatocellular carcinoma in males. Alteration of bile acid homeostasis contributes to cholestatic liver diseases, inflammatory diseases in the digestive system, obesity, and diabetes. Bile acid-activated receptors are potential therapeutic targets for developing drugs to treat metabolic disorders. PMID:29325602

  20. Carbohydrate Metabolism Disorders

    ... metabolic disorder, something goes wrong with this process. Carbohydrate metabolism disorders are a group of metabolic disorders. Normally your enzymes break carbohydrates down into glucose (a type of sugar). If ...

  1. Comprehensive metabolic panel

    Metabolic panel - comprehensive; Chem-20; SMA20; Sequential multi-channel analysis with computer-20; SMAC20; Metabolic panel 20 ... Chernecky CC, Berger BJ. Comprehensive metabolic panel (CMP) - blood. In: ... Tests and Diagnostic Procedures . 6th ed. St Louis, MO: ...

  2. Inferring general relations between network characteristics from specific network ensembles.

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

    2012-01-01

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

  3. Sampling networks with prescribed degree correlations

    Del Genio, Charo; Bassler, Kevin; Erdos, Péter; Miklos, István; Toroczkai, Zoltán

    2014-03-01

    A feature of a network known to affect its structural and dynamical properties is the presence of correlations amongst the node degrees. Degree correlations are a measure of how much the connectivity of a node influences the connectivity of its neighbours, and they are fundamental in the study of processes such as the spreading of information or epidemics, the cascading failures of damaged systems and the evolution of social relations. We introduce a method, based on novel mathematical results, that allows the exact sampling of networks where the number of connections between nodes of any given connectivity is specified. Our algorithm provides a weight associated to each sample, thereby allowing network observables to be measured according to any desired distribution, and it is guaranteed to always terminate successfully in polynomial time. Thus, our new approach provides a preferred tool for scientists to model complex systems of current relevance, and enables researchers to precisely study correlated networks with broad societal importance. CIDG acknowledges support by the European Commission's FP7 through grant No. 288021. KEB acknowledges support from the NSF through grant DMR?1206839. KEB, PE, IM and ZT acknowledge support from AFSOR and DARPA through grant FA?9550-12-1-0405.

  4. Relationships, networks and innovation - squaring the circle?

    Goduscheit, René Chester

    2009-01-01

    energy sector, two specific aspects are addressed: 1) the trade-off between explorative innovation and network efficiency, and 2) the effect of innovating in a network, in which the organisations have established relationships. The case study illustrates that a network based on existing relationships......The virtue of interorganisational networks as the locus of innovation is widely acknowledged. However, it is often neglected that interorganisational collaboration does not automatically lead to idea generation and the exploration of new possibilities. On the basis of a case study from the Danish...... was working efficiently but was lacking the ability to explore new possibilities. On the other hand, a comprehensive network consisting of ‘new' partners did not succeed in getting an outcome from the network....

  5. Kidney cancer progression linked to shifts in tumor metabolism

    Investigators in The Cancer Genome Atlas Research Network have uncovered a connection between how tumor cells use energy from metabolic processes and the aggressiveness of the most common form of kidney cancer, clear cell renal cell carcinoma.

  6. Synergy between 13C-metabolic flux analysis and flux balance analysis for understanding metabolic adaption to anaerobiosis in e. coli

    Genome-based Flux Balance Analysis (FBA, constraints based flux analysis) and steady state isotopic-labeling-based Metabolic Flux Analysis (MFA) are complimentary approaches to predicting and measuring the operation and regulation of metabolic networks. Here a genome-derived model of E. coli metabol...

  7. Icariin reverses corticosterone-induced depression-like behavior, decrease in hippocampal brain-derived neurotrophic factor (BDNF) and metabolic network disturbances revealed by NMR-based metabonomics in rats.

    Gong, Meng-Juan; Han, Bin; Wang, Shu-mei; Liang, Sheng-wang; Zou, Zhong-jie

    2016-05-10

    Previously published reports have revealed the antidepressant-like effects of icariin in a chronic mild stress model of depression and in a social defeat stress model in mice. However, the therapeutic effect of icariin in an animal model of glucocorticoid-induced depression remains unclear. This study aimed to investigate antidepressant-like effect and the possible mechanisms of icariin in a rat model of corticosterone (CORT)-induced depression by using a combination of behavioral and biochemical assessments and NMR-based metabonomics. The depression model was established by subcutaneous injections of CORT for 21 consecutive days in rats, as evidenced by reduced sucrose intake and hippocampal brain-derived neurotrophic factor (BDNF) levels, together with an increase in immobility time in a forced swim test (FST). Icariin significantly increased sucrose intake and hippocampal BDNF level and decreased the immobility time in FST in CORT-induced depressive rats, suggesting its potent antidepressant activity. Moreover, metabonomic analysis identified eight, five and three potential biomarkers associated with depression in serum, urine and brain tissue extract, respectively. These biomarkers are primarily involved in energy metabolism, lipid metabolism, amino acid metabolism and gut microbe metabolism. Icariin reversed the pathological process of CORT-induced depression, partially via regulation of the disturbed metabolic pathways. These results provide important mechanistic insights into the protective effects of icariin against CORT-induced depression and metabolic dysfunction. Copyright © 2016 Elsevier B.V. All rights reserved.

  8. Recent Changes to ABR Maintenance of Certification Part 4 (PQI): Acknowledgment of Radiologists' Activities to Improve Quality and Safety.

    Donnelly, Lane F; Mathews, Vincent P; Laszakovits, David J; Jackson, Valerie P; Guiberteau, Milton J

    2016-02-01

    The ABR has recently reviewed and revised its policy establishing how ABR diplomates may comply with requirements for Maintenance of Certification Part 4: Practice Quality Improvement (PQI). The changes were deemed necessary by the Board of Trustees to acknowledge and credit the numerous ways in which radiology professionals contribute to improving patient care through existing and evolving activities available to them within the radiology community. In addition to meeting requirements by completing a traditional PQI project, the policy revision now allows diplomates to meet criteria by completing one of a number of activities in an expanded spectrum of PQI options recognized by the ABR. The new policy also acknowledges the maturing state of quality improvement science by permitting PQI projects to use "any standard quality improvement methodology," such as Six Sigma, Lean, the Institute for Healthcare Improvement's Model for Improvement, and others in addition to the previously prescribed three-phase plan-do-study-act format. Copyright © 2016 American College of Radiology. Published by Elsevier Inc. All rights reserved.

  9. Lactate rescues neuronal sodium homeostasis during impaired energy metabolism

    Karus, Claudia; Ziemens, Daniel; Rose, Christine R

    2015-01-01

    Recently, we established that recurrent activity evokes network sodium oscillations in neurons and astrocytes in hippocampal tissue slices. Interestingly, metabolic integrity of astrocytes was essential for the neurons' capacity to maintain low sodium and to recover from sodium loads, indicating an intimate metabolic coupling between the 2 cell types. Here, we studied if lactate can support neuronal sodium homeostasis during impaired energy metabolism by analyzing whether glucose removal, pha...

  10. Next-generation genome-scale models for metabolic engineering

    King, Zachary A.; Lloyd, Colton J.; Feist, Adam M.

    2015-01-01

    Constraint-based reconstruction and analysis (COBRA) methods have become widely used tools for metabolic engineering in both academic and industrial laboratories. By employing a genome-scale in silico representation of the metabolic network of a host organism, COBRA methods can be used to predict...... examples of applying COBRA methods to strain optimization are presented and discussed. Then, an outlook is provided on the next generation of COBRA models and the new types of predictions they will enable for systems metabolic engineering....

  11. The role of social networks in financing technology-based ventures: an empirical exploration

    Heuven, J.M.J.; Groen, Arend J.

    2012-01-01

    The focus of this study is on the role of networks in both identifying and accessing financial resource providers by technology-based ventures. We explore the role of networks by taking into account several specifications. We (1) acknowledge that new ventures can access financial resource providers

  12. Adaptive Evolution of Phosphorus Metabolism in Prochlorococcus

    Casey, John R; Mardinoglu, Adil; Nielsen, Jens

    2016-01-01

    Inorganic phosphorus is scarce in the eastern Mediterranean Sea, where the high-light-adapted ecotype HLI of the marine picocyanobacterium Prochlorococcus marinus thrives. Physiological and regulatory control of phosphorus acquisition and partitioning has been observed in HLI both in culture...... and in the field; however, the optimization of phosphorus metabolism and associated gains for its phosphorus-limited-growth (PLG) phenotype have not been studied. Here, we reconstructed a genome-scale metabolic network of the HLI axenic strain MED4 (iJC568), consisting of 568 metabolic genes in relation to 794...... through drastic depletion of phosphorus-containing biomass components but also through network-wide reductions in phosphate-reaction participation and the loss of a key enzyme, succinate dehydrogenase. These alterations occur despite the stringency of having relatively few pathway redundancies...

  13. Computational Modeling of Lipid Metabolism in Yeast

    Vera Schützhold

    2016-09-01

    Full Text Available Lipid metabolism is essential for all major cell functions and has recently gained increasing attention in research and health studies. However, mathematical modeling by means of classical approaches such as stoichiometric networks and ordinary differential equation systems has not yet provided satisfactory insights, due to the complexity of lipid metabolism characterized by many different species with only slight differences and by promiscuous multifunctional enzymes.Here, we present a object-oriented stochastic model approach as a way to cope with the complex lipid metabolic network. While all lipid species are treated objects in the model, they can be modified by the respective converting reactions based on reaction rules, a hybrid method that integrates benefits of agent-based and classical stochastic simulation. This approach allows to follow the dynamics of all lipid species with different fatty acids, different degrees of saturation and different headgroups over time and to analyze the effect of parameter changes, potential mutations in the catalyzing enzymes or provision of different precursors. Applied to yeast metabolism during one cell cycle period, we could analyze the distribution of all lipids to the various membranes in time-dependent manner.The presented approach allows to efficiently treat the complexity of cellular lipid metabolism and to derive conclusions on the time- and location-dependent distributions of lipid species and their properties such as saturation. It is widely applicable, easily extendable and will provide further insights in healthy and diseased states of cell metabolism.

  14. Multilevel Complex Networks and Systems

    Caldarelli, Guido

    2014-03-01

    Network theory has been a powerful tool to model isolated complex systems. However, the classical approach does not take into account the interactions often present among different systems. Hence, the scientific community is nowadays concentrating the efforts on the foundations of new mathematical tools for understanding what happens when multiple networks interact. The case of economic and financial networks represents a paramount example of multilevel networks. In the case of trade, trade among countries the different levels can be described by the different granularity of the trading relations. Indeed, we have now data from the scale of consumers to that of the country level. In the case of financial institutions, we have a variety of levels at the same scale. For example one bank can appear in the interbank networks, ownership network and cds networks in which the same institution can take place. In both cases the systemically important vertices need to be determined by different procedures of centrality definition and community detection. In this talk I will present some specific cases of study related to these topics and present the regularities found. Acknowledged support from EU FET Project ``Multiplex'' 317532.

  15. Acknowledging Materiality as Agential Literacy

    Hojer, Maja; Hasse, Cathrine

    2008-01-01

    The articles in this book share a dedication to broadening and stretching the scholarly field of feminist citizenship studies and invite the reader to reflect on the many different ways citizenship is formed in contemporary Europe. They do so by stretching the concept of citizenship itself, going...

  16. Do Orthopaedic Surgeons Acknowledge Uncertainty?

    Teunis, Teun; Janssen, Stein; Guitton, Thierry G.; Ring, David; Parisien, Robert

    2016-01-01

    Much of the decision-making in orthopaedics rests on uncertain evidence. Uncertainty is therefore part of our normal daily practice, and yet physician uncertainty regarding treatment could diminish patients' health. It is not known if physician uncertainty is a function of the evidence alone or if

  17. Interconnectivity of human cellular metabolism and disease prevalence

    Lee, Deok-Sun

    2010-01-01

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

  18. Interconnectivity of human cellular metabolism and disease prevalence

    Lee, Deok-Sun

    2010-12-01

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