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Sample records for genome-scale metabolic reconstructions

  1. Accelerating the reconstruction of genome-scale metabolic networks

    PubMed Central

    Notebaart, Richard A; van Enckevort, Frank HJ; Francke, Christof; Siezen, Roland J; Teusink, Bas

    2006-01-01

    Background The genomic information of a species allows for the genome-scale reconstruction of its metabolic capacity. Such a metabolic reconstruction gives support to metabolic engineering, but also to integrative bioinformatics and visualization. Sequence-based automatic reconstructions require extensive manual curation, which can be very time-consuming. Therefore, we present a method to accelerate the time-consuming process of network reconstruction for a query species. The method exploits the availability of well-curated metabolic networks and uses high-resolution predictions of gene equivalency between species, allowing the transfer of gene-reaction associations from curated networks. Results We have evaluated the method using Lactococcus lactis IL1403, for which a genome-scale metabolic network was published recently. We recovered most of the gene-reaction associations (i.e. 74 – 85%) which are incorporated in the published network. Moreover, we predicted over 200 additional genes to be associated to reactions, including genes with unknown function, genes for transporters and genes with specific metabolic reactions, which are good candidates for an extension to the previously published network. In a comparison of our developed method with the well-established approach Pathologic, we predicted 186 additional genes to be associated to reactions. We also predicted a relatively high number of complete conserved protein complexes, which are derived from curated metabolic networks, illustrating the potential predictive power of our method for protein complexes. Conclusion We show that our methodology can be applied to accelerate the reconstruction of genome-scale metabolic networks by taking optimal advantage of existing, manually curated networks. As orthology detection is the first step in the method, only the translated open reading frames (ORFs) of a newly sequenced genome are necessary to reconstruct a metabolic network. When more manually curated metabolic

  2. Genome-scale models of bacterial metabolism: reconstruction and applications

    PubMed Central

    Durot, Maxime; Bourguignon, Pierre-Yves; Schachter, Vincent

    2009-01-01

    Genome-scale metabolic models bridge the gap between genome-derived biochemical information and metabolic phenotypes in a principled manner, providing a solid interpretative framework for experimental data related to metabolic states, and enabling simple in silico experiments with whole-cell metabolism. Models have been reconstructed for almost 20 bacterial species, so far mainly through expert curation efforts integrating information from the literature with genome annotation. A wide variety of computational methods exploiting metabolic models have been developed and applied to bacteria, yielding valuable insights into bacterial metabolism and evolution, and providing a sound basis for computer-assisted design in metabolic engineering. Recent advances in computational systems biology and high-throughput experimental technologies pave the way for the systematic reconstruction of metabolic models from genomes of new species, and a corresponding expansion of the scope of their applications. In this review, we provide an introduction to the key ideas of metabolic modeling, survey the methods, and resources that enable model reconstruction and refinement, and chart applications to the investigation of global properties of metabolic systems, the interpretation of experimental results, and the re-engineering of their biochemical capabilities. PMID:19067749

  3. Flux Coupling Analysis of Genome-Scale Metabolic Network Reconstructions

    PubMed Central

    Burgard, Anthony P.; Nikolaev, Evgeni V.; Schilling, Christophe H.; Maranas, Costas D.

    2004-01-01

    In this paper, we introduce the Flux Coupling Finder (FCF) framework for elucidating the topological and flux connectivity features of genome-scale metabolic networks. The framework is demonstrated on genome-scale metabolic reconstructions of Helicobacter pylori, Escherichia coli, and Saccharomyces cerevisiae. The analysis allows one to determine whether any two metabolic fluxes, v1 and v2, are (1) directionally coupled, if a non-zero flux for v1 implies a non-zero flux for v2 but not necessarily the reverse; (2) partially coupled, if a non-zero flux for v1 implies a non-zero, though variable, flux for v2 and vice versa; or (3) fully coupled, if a non-zero flux for v1 implies not only a non-zero but also a fixed flux for v2 and vice versa. Flux coupling analysis also enables the global identification of blocked reactions, which are all reactions incapable of carrying flux under a certain condition; equivalent knockouts, defined as the set of all possible reactions whose deletion forces the flux through a particular reaction to zero; and sets of affected reactions denoting all reactions whose fluxes are forced to zero if a particular reaction is deleted. The FCF approach thus provides a novel and versatile tool for aiding metabolic reconstructions and guiding genetic manipulations. PMID:14718379

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

    PubMed

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

    2016-08-01

    A compartmentalized genome scale metabolic network was reconstructed for Chlorella variabilis to offer insight into various metabolic potentials from this alga. The model, iAJ526, was reconstructed with 1455 reactions, 1236 metabolites and 526 genes. 21% of the reactions were transport reactions and about 81% of the total reactions were associated with enzymes. Along with gap filling reactions, 2 major sub-pathways were added to the model, chitosan synthesis and rhamnose metabolism. The reconstructed model had reaction participation of 4.3 metabolites per reaction and average lethality fraction of 0.21. The model was effective in capturing the growth of C. variabilis under three light conditions (white, red and red+blue light) with fair agreement. This reconstructed metabolic network will serve an important role in systems biology for further exploration of metabolism for specific target metabolites and enable improved characteristics in the strain through metabolic engineering. PMID:26995318

  5. A protocol for generating a high-quality genome-scale metabolic reconstruction

    PubMed Central

    Thiele, Ines; Palsson, Bernhard Ø.

    2011-01-01

    Network reconstructions are a common denominator in systems biology. Bottom-up metabolic network reconstructions have developed over the past 10 years. These reconstructions represent structured knowledge-bases that abstract pertinent information on the biochemical transformations taking place within specific target organisms. The conversion of a reconstruction into a mathematical format facilitates myriad computational biological studies including evaluation of network content, hypothesis testing and generation, analysis of phenotypic characteristics, and metabolic engineering. To date, genome-scale metabolic reconstructions for more than 30 organisms have been published and this number is expected to increase rapidly. However, these reconstructions differ in quality and coverage that may minimize their predictive potential and use as knowledge-bases. Here, we present a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction as well as common trials and tribulations. Therefore, this protocol provides a helpful manual for all stages of the reconstruction process. PMID:20057383

  6. Genome Scale Reconstruction of a Salmonella Metabolic Model

    PubMed Central

    AbuOun, Manal; Suthers, Patrick F.; Jones, Gareth I.; Carter, Ben R.; Saunders, Mark P.; Maranas, Costas D.; Woodward, Martin J.; Anjum, Muna F.

    2009-01-01

    Salmonella are closely related to commensal Escherichia coli but have gained virulence factors enabling them to behave as enteric pathogens. Less well studied are the similarities and differences that exist between the metabolic properties of these organisms that may contribute toward niche adaptation of Salmonella pathogens. To address this, we have constructed a genome scale Salmonella metabolic model (iMA945). The model comprises 945 open reading frames or genes, 1964 reactions, and 1036 metabolites. There was significant overlap with genes present in E. coli MG1655 model iAF1260. In silico growth predictions were simulated using the model on different carbon, nitrogen, phosphorous, and sulfur sources. These were compared with substrate utilization data gathered from high throughput phenotyping microarrays revealing good agreement. Of the compounds tested, the majority were utilizable by both Salmonella and E. coli. Nevertheless a number of differences were identified both between Salmonella and E. coli and also within the Salmonella strains included. These differences provide valuable insight into differences between a commensal and a closely related pathogen and within different pathogenic strains opening new avenues for future explorations. PMID:19690172

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

    PubMed Central

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

    2014-01-01

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

  8. Reconstruction and analysis of the genome-scale metabolic model of Lactobacillus casei LC2W.

    PubMed

    Xu, Nan; Liu, Jie; Ai, Lianzhong; Liu, Liming

    2015-01-10

    Lactobacillus casei LC2W is a recently isolated probiotic lactic acid bacterial strain, which is widely used in the dairy and pharmaceutical industries and in clinical medicine. The first genome-scale metabolic model for L. casei, composed of 846 genes, 969 metabolic reactions, and 785 metabolites, was reconstructed using both manual genome annotation and an automatic SEED model. Then, the iJL846 model was validated by simulating cell growth on 15 reported carbon sources. The iJL846 model explored the metabolism of L. casei on a genome scale: (1) explanation of the genetic codes-metabolic functions of 342 genes were reannotated in this model; (2) characterization of the physiology-10 amino acids and 7 vitamins were identified to be essential nutrients for L. casei LC2W growth; (3) analyses of metabolic pathways-the transport and metabolism of the 17 essential nutrients and exopolysaccharide (EPS) biosynthesis-were performed; (4) exploration of metabolic capacity was conducted-for lactate, the importance of genes in its biosynthetic pathways was evaluated, and the requirements of amino acids were predicted for mixed acid fermentation; for flavor compounds, the effects of oxygen were analyzed, and three new knockout targets were selected for acetoin production; for EPS, 11 types of nutrients in the rich medium and important reactions in the biosynthetic pathway were identified that enhanced EPS production. In conclusion, the iJL846 model serves as a useful tool for understanding and engineering the metabolism of this probiotic strain. PMID:25452194

  9. An extended bioreaction database that significantly improves reconstruction and analysis of genome-scale metabolic networks.

    PubMed

    Stelzer, Michael; Sun, Jibin; Kamphans, Tom; Fekete, Sándor P; Zeng, An-Ping

    2011-11-01

    The bioreaction database established by Ma and Zeng (Bioinformatics, 2003, 19, 270-277) for in silico reconstruction of genome-scale metabolic networks has been widely used. Based on more recent information in the reference databases KEGG LIGAND and Brenda, we upgrade the bioreaction database in this work by almost doubling the number of reactions from 3565 to 6851. Over 70% of the reactions have been manually updated/revised in terms of reversibility, reactant pairs, currency metabolites and error correction. For the first time, 41 spontaneous sugar mutarotation reactions are introduced into the biochemical database. The upgrade significantly improves the reconstruction of genome scale metabolic networks. Many gaps or missing biochemical links can be recovered, as exemplified with three model organisms Homo sapiens, Aspergillus niger, and Escherichia coli. The topological parameters of the constructed networks were also largely affected, however, the overall network structure remains scale-free. Furthermore, we consider the problem of computing biologically feasible shortest paths in reconstructed metabolic networks. We show that these paths are hard to compute and present solutions to find such paths in networks of small and medium size. PMID:21952610

  10. An experimentally-supported genome-scale metabolic network reconstruction for Yersinia pestis CO92

    PubMed Central

    2011-01-01

    Background Yersinia pestis is a gram-negative bacterium that causes plague, a disease linked historically to the Black Death in Europe during the Middle Ages and to several outbreaks during the modern era. Metabolism in Y. pestis displays remarkable flexibility and robustness, allowing the bacterium to proliferate in both warm-blooded mammalian hosts and cold-blooded insect vectors such as fleas. Results Here we report a genome-scale reconstruction and mathematical model of metabolism for Y. pestis CO92 and supporting experimental growth and metabolite measurements. The model contains 815 genes, 678 proteins, 963 unique metabolites and 1678 reactions, accurately simulates growth on a range of carbon sources both qualitatively and quantitatively, and identifies gaps in several key biosynthetic pathways and suggests how those gaps might be filled. Furthermore, our model presents hypotheses to explain certain known nutritional requirements characteristic of this strain. Conclusions Y. pestis continues to be a dangerous threat to human health during modern times. The Y. pestis genome-scale metabolic reconstruction presented here, which has been benchmarked against experimental data and correctly reproduces known phenotypes, provides an in silico platform with which to investigate the metabolism of this important human pathogen. PMID:21995956

  11. An Experimentally-Supported Genome-Scale Metabolic Network Reconstruction for Yersinia pestis CO92

    SciTech Connect

    Charusanti, Pep; Chauhan, Sadhana; Mcateer, Kathleen; Lerman, Joshua A.; Hyduke, Daniel R.; Motin, Vladimir L.; Ansong, Charles; Adkins, Joshua N.; Palsson, Bernhard O.

    2011-10-13

    Yersinia pestis is a gram-negative bacterium that causes plague, a disease linked historically to the Black Death in Europe during the Middle Ages and to several outbreaks during the modern era. Metabolism in Y. pestis displays remarkable flexibility and robustness, allowing the bacterium to proliferate in both warm-blooded mammalian hosts and cold-blooded insect vectors such as fleas. Here we report a genome-scale reconstruction and mathematical model of metabolism for Y. pestis CO92 and supporting experimental growth and metabolite measurements. The model contains 815 genes, 678 proteins, 963 unique metabolites and 1678 reactions, accurately simulates growth on a range of carbon sources both qualitatively and quantitatively, and identifies gaps in several key biosynthetic pathways and suggests how those gaps might be filled. Furthermore, our model presents hypotheses to explain certain known nutritional requirements characteristic of this strain. Y. pestis continues to be a dangerous threat to human health during modern times. The Y. pestis genome-scale metabolic reconstruction presented here, which has been benchmarked against experimental data and correctly reproduces known phenotypes, thus provides an in silico platform with which to investigate the metabolism of this important human pathogen.

  12. Genome-scale reconstruction of Salinispora tropica CNB-440 metabolism to study strain-specific adaptation.

    PubMed

    Contador, C A; Rodríguez, V; Andrews, B A; Asenjo, J A

    2015-11-01

    The first manually curated genome-scale metabolic model for Salinispora tropica strain CNB-440 was constructed. The reconstruction enables characterization of the metabolic capabilities for understanding and modeling the cellular physiology of this actinobacterium. The iCC908 model was based on physiological and biochemical information of primary and specialised metabolism pathways. The reconstructed stoichiometric matrix consists of 1169 biochemical conversions, 204 transport reactions and 1317 metabolites. A total of 908 structural open reading frames (ORFs) were included in the reconstructed network. The number of gene functions included in the reconstructed network corresponds to 20% of all characterized ORFs in the S. tropica genome. The genome-scale metabolic model was used to study strain-specific capabilities in defined minimal media. iCC908 was used to analyze growth capabilities in 41 different minimal growth-supporting environments. These nutrient sources were evaluated experimentally to assess the accuracy of in silico growth simulations. The model predicted no auxotrophies for essential amino acids, which was corroborated experimentally. The strain is able to use 21 different carbon sources, 8 nitrogen sources and 4 sulfur sources from the nutrient sources tested. Experimental observation suggests that the cells may be able to store sulfur. False predictions provided opportunities to gain new insights into the physiology of this species, and to gap fill the missing knowledge. The incorporation of modifications led to increased accuracy in predicting the outcome of growth/no growth experiments from 76 to 93%. iCC908 can thus be used to define the metabolic capabilities of S. tropica and guide and enhance the production of specialised metabolites. PMID:26459337

  13. Elucidation and Structural Analysis of Conserved Pools for Genome-Scale Metabolic Reconstructions

    PubMed Central

    Nikolaev, Evgeni V.; Burgard, Anthony P.; Maranas, Costas D.

    2005-01-01

    In this article, we introduce metabolite concentration coupling analysis (MCCA) to study conservation relationships for metabolite concentrations in genome-scale metabolic networks. The analysis allows the global identification of subsets of metabolites whose concentrations are always coupled within common conserved pools. Also, the minimal conserved pool identification (MCPI) procedure is developed for elucidating conserved pools for targeted metabolites without computing the entire basis conservation relationships. The approaches are demonstrated on genome-scale metabolic reconstructions of Helicobacter pylori, Escherichia coli, and Saccharomyces cerevisiae. Despite significant differences in the size and complexity of the examined organism's models, we find that the concentrations of nearly all metabolites are coupled within a relatively small number of subsets. These correspond to the overall exchange of carbon molecules into and out of the networks, interconversion of energy and redox cofactors, and the transfer of nitrogen, sulfur, phosphate, coenzyme A, and acyl carrier protein moieties among metabolites. The presence of large conserved pools can be viewed as global biophysical barriers protecting cellular systems from stresses, maintaining coordinated interconversions between key metabolites, and providing an additional mode of global metabolic regulation. The developed approaches thus provide novel and versatile tools for elucidating coupling relationships between metabolite concentrations with implications in biotechnological and medical applications. PMID:15489308

  14. Characterizing acetogenic metabolism using a genome-scale metabolic reconstruction of Clostridium ljungdahlii

    PubMed Central

    2013-01-01

    Background The metabolic capabilities of acetogens to ferment a wide range of sugars, to grow autotrophically on H2/CO2, and more importantly on synthesis gas (H2/CO/CO2) make them very attractive candidates as production hosts for biofuels and biocommodities. Acetogenic metabolism is considered one of the earliest modes of bacterial metabolism. A thorough understanding of various factors governing the metabolism, in particular energy conservation mechanisms, is critical for metabolic engineering of acetogens for targeted production of desired chemicals. Results Here, we present the genome-scale metabolic network of Clostridium ljungdahlii, the first such model for an acetogen. This genome-scale model (iHN637) consisting of 637 genes, 785 reactions, and 698 metabolites captures all the major central metabolic and biosynthetic pathways, in particular pathways involved in carbon fixation and energy conservation. A combination of metabolic modeling, with physiological and transcriptomic data provided insights into autotrophic metabolism as well as aided the characterization of a nitrate reduction pathway in C. ljungdahlii. Analysis of the iHN637 metabolic model revealed that flavin based electron bifurcation played a key role in energy conservation during autotrophic growth and helped identify genes for some of the critical steps in this mechanism. Conclusions iHN637 represents a predictive model that recapitulates experimental data, and provides valuable insights into the metabolic response of C. ljungdahlii to genetic perturbations under various growth conditions. Thus, the model will be instrumental in guiding metabolic engineering of C. ljungdahlii for the industrial production of biocommodities and biofuels. PMID:24274140

  15. Characterizing acetogenic metabolism using a genome-scale metabolic reconstruction of Clostridium ljungdahlii

    SciTech Connect

    Nagarajan, H; Sahin, M; Nogales, J; Latif, H; Lovley, DR; Ebrahim, A; Zengler, K

    2013-11-25

    Background: The metabolic capabilities of acetogens to ferment a wide range of sugars, to grow autotrophically on H-2/CO2, and more importantly on synthesis gas (H-2/CO/CO2) make them very attractive candidates as production hosts for biofuels and biocommodities. Acetogenic metabolism is considered one of the earliest modes of bacterial metabolism. A thorough understanding of various factors governing the metabolism, in particular energy conservation mechanisms, is critical for metabolic engineering of acetogens for targeted production of desired chemicals. Results: Here, we present the genome-scale metabolic network of Clostridium ljungdahlii, the first such model for an acetogen. This genome-scale model (iHN637) consisting of 637 genes, 785 reactions, and 698 metabolites captures all the major central metabolic and biosynthetic pathways, in particular pathways involved in carbon fixation and energy conservation. A combination of metabolic modeling, with physiological and transcriptomic data provided insights into autotrophic metabolism as well as aided the characterization of a nitrate reduction pathway in C. ljungdahlii. Analysis of the iHN637 metabolic model revealed that flavin based electron bifurcation played a key role in energy conservation during autotrophic growth and helped identify genes for some of the critical steps in this mechanism. Conclusions: iHN637 represents a predictive model that recapitulates experimental data, and provides valuable insights into the metabolic response of C. ljungdahlii to genetic perturbations under various growth conditions. Thus, the model will be instrumental in guiding metabolic engineering of C. ljungdahlii for the industrial production of biocommodities and biofuels.

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

    PubMed Central

    2011-01-01

    Background Chromohalobacter salexigens (formerly Halomonas elongata DSM 3043) is a halophilic extremophile with a very broad salinity range and is used as a model organism to elucidate prokaryotic osmoadaptation due to its strong euryhaline phenotype. Results C. salexigens DSM 3043's metabolism was reconstructed based on genomic, biochemical and physiological information via a non-automated but iterative process. This manually-curated reconstruction accounts for 584 genes, 1386 reactions, and 1411 metabolites. By using flux balance analysis, the model was extensively validated against literature data on the C. salexigens phenotypic features, the transport and use of different substrates for growth as well as against experimental observations on the uptake and accumulation of industrially important organic osmolytes, ectoine, betaine, and its precursor choline, which play important roles in the adaptive response to osmotic stress. Conclusions This work presents the first comprehensive genome-scale metabolic model of a halophilic bacterium. Being a useful guide for identification and filling of knowledge gaps, the reconstructed metabolic network iOA584 will accelerate the research on halophilic bacteria towards application of systems biology approaches and design of metabolic engineering strategies. PMID:21251315

  17. Reconstruction and analysis of a genome-scale metabolic network of Corynebacterium glutamicum S9114.

    PubMed

    Mei, Jie; Xu, Nan; Ye, Chao; Liu, Liming; Wu, Jianrong

    2016-01-10

    Corynebacterium glutamicum S9114 is commonly used for industrial glutamate production. Therefore, a comprehensive understanding of the physiological and metabolic characteristics of C. glutamicum is important for developing its potential for industrial production. A genome-scale metabolic model, iJM658, was reconstructed based on genome annotation and literature mining. The model consists of 658 genes, 984 metabolites and 1065 reactions. The model quantitatively predicted C. glutamicum growth on different carbon and nitrogen sources and determined 129 genes to be essential for cell growth. The iJM658 model predicted that C. glutamicum had two glutamate biosynthesis pathways and lacked eight key genes in biotin synthesis. Robustness analysis indicated a relative low oxygen level (1.21mmol/gDW/h) would improve glutamate production rate. Potential metabolic engineering targets for improving γ-aminobutyrate and isoleucine production rate were predicted by in silico deletion or overexpression of some genes. The iJM658 model is a useful tool for understanding and optimizing the metabolism of C. glutamicum and a valuable resource for future metabolic and physiological research. PMID:26392034

  18. Genome-scale metabolic network reconstruction of Saccharopolyspora spinosa for Spinosad Production improvement

    PubMed Central

    2014-01-01

    Background Spinosad is a macrolide antibiotic produced by Saccharopolyspora spinosa with aerobic fermentation. However, the wild strain has a low productivity. In this article, a computational guided engineering approach was adopted in order to improve the yield of spinosad in S. spinosa. Results Firstly, a genome-scale metabolic network reconstruction (GSMR) for S.spinosa based on its genome information, literature data and experimental data was extablished. The model was consists of 1,577 reactions, 1,726 metabolites, and 733 enzymes after manually refined. Then, amino acids supplying experiments were performed in order to test the capabilities of the model, and the results showed a high consistency. Subsequently, transhydrogenase (PntAB, EC 1.6.1.2) was chosen as the potential target for spinosad yield improvement based on the in silico metabolic network models. Furthermore, the target gene was manipulated in the parent strain in order to validate the model predictions. At last, shake flask fermentation was carried out which led to spinosad production of 75.32 mg/L, 86.5% higher than the parent strain (40.39 mg/L). Conclusions Results confirmed the model had a high potential in engineering S. spinosa for spinosad production. It is the first GSMM for S.spinosa, it has significance for a better understanding of the comprehensive metabolism and guiding strain designing of Saccharopolyspora spinosa in the future. PMID:24628959

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

    PubMed

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

    2010-08-01

    With the advent of modern omics technologies, it has become feasible to reconstruct (quasi-) whole-cell metabolic networks and characterize them in more and more detail. Computer simulations of the dynamic behavior of such networks are difficult due to a lack of kinetic data and to computational limitations. In contrast, network analysis based on appropriate constraints such as the steady-state condition (constraint-based analysis) is feasible and allows one to derive conclusions about the system's metabolic capabilities. Here, we review methods for the reconstruction of metabolic networks, modeling techniques such as flux balance analysis and elementary flux modes and current progress in their development and applications. Game-theoretical methods for studying metabolic networks are discussed as well. PMID:20692823

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

    PubMed

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

    2016-01-01

    Tomato (Solanum lycopersicum L.) has been studied extensively due to its high economic value in the market, and high content in health-promoting antioxidant compounds. Tomato is also considered as an excellent model organism for studying the development and metabolism of fleshy fruits. However, the growth, yield and fruit quality of tomatoes can be affected by drought stress, a common abiotic stress for tomato. To investigate the potential metabolic response of tomato plants to drought, we reconstructed iHY3410, a genome-scale metabolic model of tomato leaf, and used this metabolic network to simulate tomato leaf metabolism. The resulting model includes 3410 genes and 2143 biochemical and transport reactions distributed across five intracellular organelles including cytosol, plastid, mitochondrion, peroxisome and vacuole. The model successfully described the known metabolic behaviour of tomato leaf under heterotrophic and phototrophic conditions. The in silico investigation of the metabolic characteristics for photorespiration and other relevant metabolic processes under drought stress suggested that: (i) the flux distributions through the mevalonate (MVA) pathway under drought were distinct from that under normal conditions; and (ii) the changes in fluxes through core metabolic pathways with varying flux ratio of RubisCO carboxylase to oxygenase may contribute to the adaptive stress response of plants. In addition, we improved on previous studies of reaction essentiality analysis for leaf metabolism by including potential alternative routes for compensating reaction knockouts. Altogether, the genome-scale model provides a sound framework for investigating tomato metabolism and gives valuable insights into the functional consequences of abiotic stresses. PMID:26576489

  1. A genome-scale metabolic reconstruction of Pseudomonas putida KT2440: iJN746 as a cell factory

    PubMed Central

    Nogales, Juan; Palsson, Bernhard Ø; Thiele, Ines

    2008-01-01

    Background Pseudomonas putida is the best studied pollutant degradative bacteria and is harnessed by industrial biotechnology to synthesize fine chemicals. Since the publication of P. putida KT2440's genome, some in silico analyses of its metabolic and biotechnology capacities have been published. However, global understanding of the capabilities of P. putida KT2440 requires the construction of a metabolic model that enables the integration of classical experimental data along with genomic and high-throughput data. The constraint-based reconstruction and analysis (COBRA) approach has been successfully used to build and analyze in silico genome-scale metabolic reconstructions. Results We present a genome-scale reconstruction of P. putida KT2440's metabolism, iJN746, which was constructed based on genomic, biochemical, and physiological information. This manually-curated reconstruction accounts for 746 genes, 950 reactions, and 911 metabolites. iJN746 captures biotechnologically relevant pathways, including polyhydroxyalkanoate synthesis and catabolic pathways of aromatic compounds (e.g., toluene, benzoate, phenylacetate, nicotinate), not described in other metabolic reconstructions or biochemical databases. The predictive potential of iJN746 was validated using experimental data including growth performance and gene deletion studies. Furthermore, in silico growth on toluene was found to be oxygen-limited, suggesting the existence of oxygen-efficient pathways not yet annotated in P. putida's genome. Moreover, we evaluated the production efficiency of polyhydroxyalkanoates from various carbon sources and found fatty acids as the most prominent candidates, as expected. Conclusion Here we presented the first genome-scale reconstruction of P. putida, a biotechnologically interesting all-surrounder. Taken together, this work illustrates the utility of iJN746 as i) a knowledge-base, ii) a discovery tool, and iii) an engineering platform to explore P. putida's potential in

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

    PubMed

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

    2014-01-01

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

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

    PubMed

    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. PMID:26970054

  4. A Genome-Scale Database and Reconstruction of Caenorhabditis elegans Metabolism.

    PubMed

    Gebauer, Juliane; Gentsch, Christoph; Mansfeld, Johannes; Schmeißer, Kathrin; Waschina, Silvio; Brandes, Susanne; Klimmasch, Lukas; Zamboni, Nicola; Zarse, Kim; Schuster, Stefan; Ristow, Michael; Schäuble, Sascha; Kaleta, Christoph

    2016-05-25

    We present a genome-scale model of Caenorhabditis elegans metabolism along with the public database ElegCyc (http://elegcyc.bioinf.uni-jena.de:1100), which represents a reference for metabolic pathways in the worm and allows for the visualization as well as analysis of omics datasets. Our model reflects the metabolic peculiarities of C. elegans that make it distinct from other higher eukaryotes and mammals, including mice and humans. We experimentally verify one of these peculiarities by showing that the lifespan-extending effect of L-tryptophan supplementation is dose dependent (hormetic). Finally, we show the utility of our model for analyzing omics datasets through predicting changes in amino acid concentrations after genetic perturbations and analyzing metabolic changes during normal aging as well as during two distinct, reactive oxygen species (ROS)-related lifespan-extending treatments. Our analyses reveal a notable similarity in metabolic adaptation between distinct lifespan-extending interventions and point to key pathways affecting lifespan in nematodes. PMID:27211858

  5. Genome-scale metabolic network reconstruction and in silico flux analysis of the thermophilic bacterium Thermus thermophilus HB27

    PubMed Central

    2014-01-01

    Background Thermus thermophilus, an extremely thermophilic bacterium, has been widely recognized as a model organism for studying how microbes can survive and adapt under high temperature environment. However, the thermotolerant mechanisms and cellular metabolism still remains mostly unravelled. Thus, it is highly required to consider systems biological approaches where T. thermophilus metabolic network model can be employed together with high throughput experimental data for elucidating its physiological characteristics under such harsh conditions. Results We reconstructed a genome-scale metabolic model of T. thermophilus, iTT548, the first ever large-scale network of a thermophilic bacterium, accounting for 548 unique genes, 796 reactions and 635 unique metabolites. Our initial comparative analysis of the model with Escherichia coli has revealed several distinctive metabolic reactions, mainly in amino acid metabolism and carotenoid biosynthesis, producing relevant compounds to retain the cellular membrane for withstanding high temperature. Constraints-based flux analysis was, then, applied to simulate the metabolic state in glucose minimal and amino acid rich media. Remarkably, resulting growth predictions were highly consistent with the experimental observations. The subsequent comparative flux analysis under different environmental conditions highlighted that the cells consumed branched chain amino acids preferably and utilized them directly in the relevant anabolic pathways for the fatty acid synthesis. Finally, gene essentiality study was also conducted via single gene deletion analysis, to identify the conditional essential genes in glucose minimal and complex media. Conclusions The reconstructed genome-scale metabolic model elucidates the phenotypes of T. thermophilus, thus allowing us to gain valuable insights into its cellular metabolism through in silico simulations. The information obtained from such analysis would not only shed light on the

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

    SciTech Connect

    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.

  7. Genome-Scale Reconstruction and Analysis of the Pseudomonas putida KT2440 Metabolic Network Facilitates Applications in Biotechnology

    PubMed Central

    Godinho, Miguel; Bielecka, Agata; Regenhardt, Daniela; Timmis, Kenneth N.

    2008-01-01

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

  8. Genome-scale metabolic reconstructions of Pichia stipitis and Pichia pastoris and in silico evaluation of their potentials

    PubMed Central

    2012-01-01

    Background Pichia stipitis and Pichia pastoris have long been investigated due to their native abilities to metabolize every sugar from lignocellulose and to modulate methanol consumption, respectively. The latter has been driving the production of several recombinant proteins. As a result, significant advances in their biochemical knowledge, as well as in genetic engineering and fermentation methods have been generated. The release of their genome sequences has allowed systems level research. Results In this work, genome-scale metabolic models (GEMs) of P. stipitis (iSS884) and P. pastoris (iLC915) were reconstructed. iSS884 includes 1332 reactions, 922 metabolites, and 4 compartments. iLC915 contains 1423 reactions, 899 metabolites, and 7 compartments. Compared with the previous GEMs of P. pastoris, PpaMBEL1254 and iPP668, iLC915 contains more genes and metabolic functions, as well as improved predictive capabilities. Simulations of physiological responses for the growth of both yeasts on selected carbon sources using iSS884 and iLC915 closely reproduced the experimental data. Additionally, the iSS884 model was used to predict ethanol production from xylose at different oxygen uptake rates. Simulations with iLC915 closely reproduced the effect of oxygen uptake rate on physiological states of P. pastoris expressing a recombinant protein. The potential of P. stipitis for the conversion of xylose and glucose into ethanol using reactors in series, and of P. pastoris to produce recombinant proteins using mixtures of methanol and glycerol or sorbitol are also discussed. Conclusions In conclusion the first GEM of P. stipitis (iSS884) was reconstructed and validated. The expanded version of the P. pastoris GEM, iLC915, is more complete and has improved capabilities over the existing models. Both GEMs are useful frameworks to explore the versatility of these yeasts and to capitalize on their biotechnological potentials. PMID:22472172

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

    NASA Astrophysics Data System (ADS)

    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.

  10. Reconstruction and analysis of genome-scale metabolic model of a photosynthetic bacterium

    PubMed Central

    2010-01-01

    Background Synechocystis sp. PCC6803 is a cyanobacterium considered as a candidate photo-biological production platform - an attractive cell factory capable of using CO2 and light as carbon and energy source, respectively. In order to enable efficient use of metabolic potential of Synechocystis sp. PCC6803, it is of importance to develop tools for uncovering stoichiometric and regulatory principles in the Synechocystis metabolic network. Results We report the most comprehensive metabolic model of Synechocystis sp. PCC6803 available, iSyn669, which includes 882 reactions, associated with 669 genes, and 790 metabolites. The model includes a detailed biomass equation which encompasses elementary building blocks that are needed for cell growth, as well as a detailed stoichiometric representation of photosynthesis. We demonstrate applicability of iSyn669 for stoichiometric analysis by simulating three physiologically relevant growth conditions of Synechocystis sp. PCC6803, and through in silico metabolic engineering simulations that allowed identification of a set of gene knock-out candidates towards enhanced succinate production. Gene essentiality and hydrogen production potential have also been assessed. Furthermore, iSyn669 was used as a transcriptomic data integration scaffold and thereby we found metabolic hot-spots around which gene regulation is dominant during light-shifting growth regimes. Conclusions iSyn669 provides a platform for facilitating the development of cyanobacteria as microbial cell factories. PMID:21083885

  11. Characterizing the optimal flux space of genome-scale metabolic reconstructions through modified latin-hypercube sampling.

    PubMed

    Chaudhary, Neha; Tøndel, Kristin; Bhatnagar, Rakesh; dos Santos, Vítor A P Martins; Puchałka, Jacek

    2016-03-01

    Genome-Scale Metabolic Reconstructions (GSMRs), along with optimization-based methods, predominantly Flux Balance Analysis (FBA) and its derivatives, are widely applied for assessing and predicting the behavior of metabolic networks upon perturbation, thereby enabling identification of potential novel drug targets and biotechnologically relevant pathways. The abundance of alternate flux profiles has led to the evolution of methods to explore the complete solution space aiming to increase the accuracy of predictions. Herein we present a novel, generic algorithm to characterize the entire flux space of GSMR upon application of FBA, leading to the optimal value of the objective (the optimal flux space). Our method employs Modified Latin-Hypercube Sampling (LHS) to effectively border the optimal space, followed by Principal Component Analysis (PCA) to identify and explain the major sources of variability within it. The approach was validated with the elementary mode analysis of a smaller network of Saccharomyces cerevisiae and applied to the GSMR of Pseudomonas aeruginosa PAO1 (iMO1086). It is shown to surpass the commonly used Monte Carlo Sampling (MCS) in providing a more uniform coverage for a much larger network in less number of samples. Results show that although many fluxes are identified as variable upon fixing the objective value, majority of the variability can be reduced to several main patterns arising from a few alternative pathways. In iMO1086, initial variability of 211 reactions could almost entirely be explained by 7 alternative pathway groups. These findings imply that the possibilities to reroute greater portions of flux may be limited within metabolic networks of bacteria. Furthermore, the optimal flux space is subject to change with environmental conditions. Our method may be a useful device to validate the predictions made by FBA-based tools, by describing the optimal flux space associated with these predictions, thus to improve them. PMID

  12. A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information

    PubMed Central

    Feist, Adam M; Henry, Christopher S; Reed, Jennifer L; Krummenacker, Markus; Joyce, Andrew R; Karp, Peter D; Broadbelt, Linda J; Hatzimanikatis, Vassily; Palsson, Bernhard Ø

    2007-01-01

    An updated genome-scale reconstruction of the metabolic network in Escherichia coli K-12 MG1655 is presented. This updated metabolic reconstruction includes: (1) an alignment with the latest genome annotation and the metabolic content of EcoCyc leading to the inclusion of the activities of 1260 ORFs, (2) characterization and quantification of the biomass components and maintenance requirements associated with growth of E. coli and (3) thermodynamic information for the included chemical reactions. The conversion of this metabolic network reconstruction into an in silico model is detailed. A new step in the metabolic reconstruction process, termed thermodynamic consistency analysis, is introduced, in which reactions were checked for consistency with thermodynamic reversibility estimates. Applications demonstrating the capabilities of the genome-scale metabolic model to predict high-throughput experimental growth and gene deletion phenotypic screens are presented. The increased scope and computational capability using this new reconstruction is expected to broaden the spectrum of both basic biology and applied systems biology studies of E. coli metabolism. PMID:17593909

  13. Reconstruction of Genome-Scale Active Metabolic Networks for 69 Human Cell Types and 16 Cancer Types Using INIT

    PubMed Central

    Mardinoglu, Adil; Pornputtapong, Natapol; Nookaew, Intawat; Nielsen, Jens

    2012-01-01

    Development of high throughput analytical methods has given physicians the potential access to extensive and patient-specific data sets, such as gene sequences, gene expression profiles or metabolite footprints. This opens for a new approach in health care, which is both personalized and based on system-level analysis. Genome-scale metabolic networks provide a mechanistic description of the relationships between different genes, which is valuable for the analysis and interpretation of large experimental data-sets. Here we describe the generation of genome-scale active metabolic networks for 69 different cell types and 16 cancer types using the INIT (Integrative Network Inference for Tissues) algorithm. The INIT algorithm uses cell type specific information about protein abundances contained in the Human Proteome Atlas as the main source of evidence. The generated models constitute the first step towards establishing a Human Metabolic Atlas, which will be a comprehensive description (accessible online) of the metabolism of different human cell types, and will allow for tissue-level and organism-level simulations in order to achieve a better understanding of complex diseases. A comparative analysis between the active metabolic networks of cancer types and healthy cell types allowed for identification of cancer-specific metabolic features that constitute generic potential drug targets for cancer treatment. PMID:22615553

  14. Genome-scale comparison and constraint-based metabolic reconstruction of the facultative anaerobic Fe(III)-reducer Rhodoferax ferrireducens

    PubMed Central

    Risso, Carla; Sun, Jun; Zhuang, Kai; Mahadevan, Radhakrishnan; DeBoy, Robert; Ismail, Wael; Shrivastava, Susmita; Huot, Heather; Kothari, Sagar; Daugherty, Sean; Bui, Olivia; Schilling, Christophe H; Lovley, Derek R; Methé, Barbara A

    2009-01-01

    Background Rhodoferax ferrireducens is a metabolically versatile, Fe(III)-reducing, subsurface microorganism that is likely to play an important role in the carbon and metal cycles in the subsurface. It also has the unique ability to convert sugars to electricity, oxidizing the sugars to carbon dioxide with quantitative electron transfer to graphite electrodes in microbial fuel cells. In order to expand our limited knowledge about R. ferrireducens, the complete genome sequence of this organism was further annotated and then the physiology of R. ferrireducens was investigated with a constraint-based, genome-scale in silico metabolic model and laboratory studies. Results The iterative modeling and experimental approach unveiled exciting, previously unknown physiological features, including an expanded range of substrates that support growth, such as cellobiose and citrate, and provided additional insights into important features such as the stoichiometry of the electron transport chain and the ability to grow via fumarate dismutation. Further analysis explained why R. ferrireducens is unable to grow via photosynthesis or fermentation of sugars like other members of this genus and uncovered novel genes for benzoate metabolism. The genome also revealed that R. ferrireducens is well-adapted for growth in the subsurface because it appears to be capable of dealing with a number of environmental insults, including heavy metals, aromatic compounds, nutrient limitation and oxidative stress. Conclusion This study demonstrates that combining genome-scale modeling with the annotation of a new genome sequence can guide experimental studies and accelerate the understanding of the physiology of under-studied yet environmentally relevant microorganisms. PMID:19772637

  15. Reconstruction of a charge balanced genome-scale metabolic model to study the energy-uncoupled growth of Zymomonas mobilis ZM1.

    PubMed

    Motamedian, E; Saeidi, M; Shojaosadati, S A

    2016-04-01

    Zymomonas mobilis is an ethanologenic bacterium and is known to be an example microorganism with energy-uncoupled growth. A genome-scale metabolic model could be applicable for understanding the characteristics of Z. mobilis with rapid catabolism and inefficient energy conversion. In this study, a charge balanced genome-scale metabolic model (iEM439) of Z. mobilis ATCC 10988 (ZM1) including 439 genes, 692 metabolic reactions and 658 metabolites was reconstructed based on genome annotation and previously published information. The model presents a much better prediction for biomass and ethanol concentrations in a batch culture by using dynamic flux balance analysis compared with the two previous genome-scale metabolic models. Furthermore, intracellular flux distribution obtained from the model was consistent with the fluxes for glucose fermentation determined by (13)C NMR. The model predicts that there is no difference in growth rates of Z. mobilis under aerobic and anaerobic conditions whereas ethanol production is decreased and production of other metabolites including acetate and acetoin is increased under aerobic conditions. Experimental data confirm the predicted differences between the aerobic and anaerobic growth of Z. mobilis. Finally, the model was used to study the energy-uncoupled growth of Z. mobilis and to predict its effect on flux distribution in the central metabolism. Flux distribution obtained from the model indicates that coupling growth and energy reduces ethanol secretion and changes the flux distribution to produce more biomass. This coupling is also associated with a significant increase in the proton uptake rate based on the prediction of the charge balanced model. Hence, resistance to intracellular pH reduction could be the main reason for uncoupled growth and Z. mobilis uses ATPase to pump out the proton. Experimental observations are in accordance with the predicted relationship between growth, ATP dissipation and proton exchange. PMID

  16. Reconstruction and in silico analysis of an Actinoplanes sp. SE50/110 genome-scale metabolic model for acarbose production

    PubMed Central

    Wang, Yali; Xu, Nan; Ye, Chao; Liu, Liming; Shi, Zhongping; Wu, Jing

    2015-01-01

    Actinoplanes sp. SE50/110 produces the α-glucosidase inhibitor acarbose, which is used to treat type 2 diabetes mellitus. To obtain a comprehensive understanding of its cellular metabolism, a genome-scale metabolic model of strain SE50/110, iYLW1028, was reconstructed on the bases of the genome annotation, biochemical databases, and extensive literature mining. Model iYLW1028 comprises 1028 genes, 1128 metabolites, and 1219 reactions. One hundred and twenty-two and eighty one genes were essential for cell growth on acarbose synthesis and sucrose media, respectively, and the acarbose biosynthetic pathway in SE50/110 was expounded completely. Based on model predictions, the addition of arginine and histidine to the media increased acarbose production by 78 and 59%, respectively. Additionally, dissolved oxygen has a great effect on acarbose production based on model predictions. Furthermore, genes to be overexpressed for the overproduction of acarbose were identified, and the deletion of treY eliminated the formation of by-product component C. Model iYLW1028 is a useful platform for optimizing and systems metabolic engineering for acarbose production in Actinoplanes sp. SE50/110. PMID:26161077

  17. Modeling cancer metabolism on a genome scale

    PubMed Central

    Yizhak, Keren; Chaneton, Barbara; Gottlieb, Eyal; Ruppin, Eytan

    2015-01-01

    Cancer cells have fundamentally altered cellular metabolism that is associated with their tumorigenicity and malignancy. In addition to the widely studied Warburg effect, several new key metabolic alterations in cancer have been established over the last decade, leading to the recognition that altered tumor metabolism is one of the hallmarks of cancer. Deciphering the full scope and functional implications of the dysregulated metabolism in cancer requires both the advancement of a variety of omics measurements and the advancement of computational approaches for the analysis and contextualization of the accumulated data. Encouragingly, while the metabolic network is highly interconnected and complex, it is at the same time probably the best characterized cellular network. Following, this review discusses the challenges that genome-scale modeling of cancer metabolism has been facing. We survey several recent studies demonstrating the first strides that have been done, testifying to the value of this approach in portraying a network-level view of the cancer metabolism and in identifying novel drug targets and biomarkers. Finally, we outline a few new steps that may further advance this field. PMID:26130389

  18. Genome-Scale Metabolic Reconstructions and Theoretical Investigation of Methane Conversion in Methylomicrobium buryatense Strain 5G(B1)

    SciTech Connect

    de la Torre, Andrea; Metivier, Aisha; Chu, Frances; Laurens, Lieve M. L.; Beck, David A. C.; Pienkos, Philip T.; Lidstrom, Mary E.; Kalyuzhnaya, Marina G.

    2015-11-25

    Methane-utilizing bacteria (methanotrophs) are capable of growth on methane and are attractive systems for bio-catalysis. However, the application of natural methanotrophic strains to large-scale production of value-added chemicals/biofuels requires a number of physiological and genetic alterations. An accurate metabolic model coupled with flux balance analysis can provide a solid interpretative framework for experimental data analyses and integration.

  19. Genome-scale modeling for metabolic engineering

    SciTech Connect

    Simeonidis, E; Price, ND

    2015-01-13

    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.

  20. Linking genome-scale metabolic modeling and genome annotation

    PubMed Central

    Blais, Edik M.; Chavali, Arvind K.; Papin, Jason A.

    2014-01-01

    Summary Genome-scale metabolic network reconstructions, assembled from annotated genomes, serve as a platform for integrating data from heterogeneous sources and generating hypotheses for further experimental validation. Implementing constraint-based modeling techniques such as Flux Balance Analysis (FBA) on network reconstructions allow for interrogating metabolism at a systems-level, which aids in identifying and rectifying gaps in knowledge. With genome sequences for various organisms from prokaryotes to eukaryotes becoming increasingly available, a significant bottleneck lies in the structural and functional annotation of these sequences. Using topologically-based and biologically-inspired metabolic network refinement, we can better characterize enzymatic functions present in an organism and link annotation of these functions to candidate transcripts, both steps that can be experimentally validated. PMID:23417799

  1. Next-generation genome-scale models for metabolic engineering.

    PubMed

    King, Zachary A; Lloyd, Colton J; Feist, Adam M; Palsson, Bernhard O

    2015-12-01

    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 optimal genetic modifications that improve the rate and yield of chemical production. A new generation of COBRA models and methods is now being developed--encompassing many biological processes and simulation strategies-and next-generation models enable new types of predictions. Here, three key 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. PMID:25575024

  2. Genome scale metabolic modeling of the riboflavin overproducer Ashbya gossypii.

    PubMed

    Ledesma-Amaro, Rodrigo; Kerkhoven, Eduard J; Revuelta, José Luis; Nielsen, Jens

    2014-06-01

    Ashbya gossypii is a filamentous fungus that naturally overproduces riboflavin, or vitamin B2. Advances in genetic and metabolic engineering of A. gossypii have permitted the switch from industrial chemical synthesis to the current biotechnological production of this vitamin. Additionally, A. gossypii is a model organism with one of the smallest eukaryote genomes being phylogenetically close to Saccharomyces cerevisiae. It has therefore been used to study evolutionary aspects of bakers' yeast. We here reconstructed the first genome scale metabolic model of A. gossypii, iRL766. The model was validated by biomass growth, riboflavin production and substrate utilization predictions. Gene essentiality analysis of the A. gossypii model in comparison with the S. cerevisiae model demonstrated how the whole-genome duplication event that separates the two species has led to an even spread of paralogs among all metabolic pathways. Additionally, iRL766 was used to integrate transcriptomics data from two different growth stages of A. gossypii, comparing exponential growth to riboflavin production stages. Both reporter metabolite analysis and in silico identification of transcriptionally regulated enzymes demonstrated the important involvement of beta-oxidation and the glyoxylate cycle in riboflavin production. PMID:24374726

  3. High-throughput generation, optimization and analysis of genome-scale metabolic models.

    SciTech Connect

    Henry, C. S.; DeJongh, M.; Best, A. A.; Frybarger, P. M.; Linsay, B.; Stevens, R. L.

    2010-09-01

    Genome-scale metabolic models have proven to be valuable for predicting organism phenotypes from genotypes. Yet efforts to develop new models are failing to keep pace with genome sequencing. To address this problem, we introduce the Model SEED, a web-based resource for high-throughput generation, optimization and analysis of genome-scale metabolic models. The Model SEED integrates existing methods and introduces techniques to automate nearly every step of this process, taking {approx}48 h to reconstruct a metabolic model from an assembled genome sequence. We apply this resource to generate 130 genome-scale metabolic models representing a taxonomically diverse set of bacteria. Twenty-two of the models were validated against available gene essentiality and Biolog data, with the average model accuracy determined to be 66% before optimization and 87% after optimization.

  4. Development and experimental verification of a genome-scale metabolic model for Corynebacterium glutamicum

    PubMed Central

    Shinfuku, Yohei; Sorpitiporn, Natee; Sono, Masahiro; Furusawa, Chikara; Hirasawa, Takashi; Shimizu, Hiroshi

    2009-01-01

    Background In silico genome-scale metabolic models enable the analysis of the characteristics of metabolic systems of organisms. In this study, we reconstructed a genome-scale metabolic model of Corynebacterium glutamicum on the basis of genome sequence annotation and physiological data. The metabolic characteristics were analyzed using flux balance analysis (FBA), and the results of FBA were validated using data from culture experiments performed at different oxygen uptake rates. Results The reconstructed genome-scale metabolic model of C. glutamicum contains 502 reactions and 423 metabolites. We collected the reactions and biomass components from the database and literatures, and made the model available for the flux balance analysis by filling gaps in the reaction networks and removing inadequate loop reactions. Using the framework of FBA and our genome-scale metabolic model, we first simulated the changes in the metabolic flux profiles that occur on changing the oxygen uptake rate. The predicted production yields of carbon dioxide and organic acids agreed well with the experimental data. The metabolic profiles of amino acid production phases were also investigated. A comprehensive gene deletion study was performed in which the effects of gene deletions on metabolic fluxes were simulated; this helped in the identification of several genes whose deletion resulted in an improvement in organic acid production. Conclusion The genome-scale metabolic model provides useful information for the evaluation of the metabolic capabilities and prediction of the metabolic characteristics of C. glutamicum. This can form a basis for the in silico design of C. glutamicum metabolic networks for improved bioproduction of desirable metabolites. PMID:19646286

  5. Generation and Evaluation of a Genome-Scale Metabolic Network Model of Synechococcus elongatus PCC7942

    PubMed Central

    Triana, Julián; Montagud†, Arnau; Siurana, Maria; Fuente, David; Urchueguía, Arantxa; Gamermann, Daniel; Torres, Javier; Tena, Jose; de Córdoba, Pedro Fernández; Urchueguía, Javier F.

    2014-01-01

    The reconstruction of genome-scale metabolic models and their applications represent a great advantage of systems biology. Through their use as metabolic flux simulation models, production of industrially-interesting metabolites can be predicted. Due to the growing number of studies of metabolic models driven by the increasing genomic sequencing projects, it is important to conceptualize steps of reconstruction and analysis. We have focused our work in the cyanobacterium Synechococcus elongatus PCC7942, for which several analyses and insights are unveiled. A comprehensive approach has been used, which can be of interest to lead the process of manual curation and genome-scale metabolic analysis. The final model, iSyf715 includes 851 reactions and 838 metabolites. A biomass equation, which encompasses elementary building blocks to allow cell growth, is also included. The applicability of the model is finally demonstrated by simulating autotrophic growth conditions of Synechococcus elongatus PCC7942. PMID:25141288

  6. 13C metabolic flux analysis at a genome-scale.

    PubMed

    Gopalakrishnan, Saratram; Maranas, Costas D

    2015-11-01

    Metabolic models used in 13C metabolic flux analysis generally include a limited number of reactions primarily from central metabolism. They typically omit degradation pathways, complete cofactor balances, and atom transition contributions for reactions outside central metabolism. This study addresses the impact on prediction fidelity of scaling-up mapping models to a genome-scale. The core mapping model employed in this study accounts for (75 reactions and 65 metabolites) primarily from central metabolism. The genome-scale metabolic mapping model (GSMM) (697 reaction and 595 metabolites) is constructed using as a basis the iAF1260 model upon eliminating reactions guaranteed not to carry flux based on growth and fermentation data for a minimal glucose growth medium. Labeling data for 17 amino acid fragments obtained from cells fed with glucose labeled at the second carbon was used to obtain fluxes and ranges. Metabolic fluxes and confidence intervals are estimated, for both core and genome-scale mapping models, by minimizing the sum of square of differences between predicted and experimentally measured labeling patterns using the EMU decomposition algorithm. Overall, we find that both topology and estimated values of the metabolic fluxes remain largely consistent between core and GSM model. Stepping up to a genome-scale mapping model leads to wider flux inference ranges for 20 key reactions present in the core model. The glycolysis flux range doubles due to the possibility of active gluconeogenesis, the TCA flux range expanded by 80% due to the availability of a bypass through arginine consistent with labeling data, and the transhydrogenase reaction flux was essentially unresolved due to the presence of as many as five routes for the inter-conversion of NADPH to NADH afforded by the genome-scale model. By globally accounting for ATP demands in the GSMM model the unused ATP decreased drastically with the lower bound matching the maintenance ATP requirement. A non

  7. MEMOSys: Bioinformatics platform for genome-scale metabolic models

    PubMed Central

    2011-01-01

    Background Recent advances in genomic sequencing have enabled the use of genome sequencing in standard biological and biotechnological research projects. The challenge is how to integrate the large amount of data in order to gain novel biological insights. One way to leverage sequence data is to use genome-scale metabolic models. We have therefore designed and implemented a bioinformatics platform which supports the development of such metabolic models. Results MEMOSys (MEtabolic MOdel research and development System) is a versatile platform for the management, storage, and development of genome-scale metabolic models. It supports the development of new models by providing a built-in version control system which offers access to the complete developmental history. Moreover, the integrated web board, the authorization system, and the definition of user roles allow collaborations across departments and institutions. Research on existing models is facilitated by a search system, references to external databases, and a feature-rich comparison mechanism. MEMOSys provides customizable data exchange mechanisms using the SBML format to enable analysis in external tools. The web application is based on the Java EE framework and offers an intuitive user interface. It currently contains six annotated microbial metabolic models. Conclusions We have developed a web-based system designed to provide researchers a novel application facilitating the management and development of metabolic models. The system is freely available at http://www.icbi.at/MEMOSys. PMID:21276275

  8. Genome-scale thermodynamic analysis of Escherichia coli metabolism.

    PubMed

    Henry, Christopher S; Jankowski, Matthew D; Broadbelt, Linda J; Hatzimanikatis, Vassily

    2006-02-15

    Genome-scale metabolic models are an invaluable tool for analyzing metabolic systems as they provide a more complete picture of the processes of metabolism. We have constructed a genome-scale metabolic model of Escherichia coli based on the iJR904 model developed by the Palsson Laboratory at the University of California at San Diego. Group contribution methods were utilized to estimate the standard Gibbs free energy change of every reaction in the constructed model. Reactions in the model were classified based on the activity of the reactions during optimal growth on glucose in aerobic media. The most thermodynamically unfavorable reactions involved in the production of biomass in E. coli were identified as ATP phosphoribosyltransferase, ATP synthase, methylene-tetra-hydrofolate dehydrogenase, and tryptophanase. The effect of a knockout of these reactions on the production of biomass and the production of individual biomass precursors was analyzed. Changes in the distribution of fluxes in the cell after knockout of these unfavorable reactions were also studied. The methodologies and results discussed can be used to facilitate the refinement of the feasible ranges for cellular parameters such as species concentrations and reaction rate constants. PMID:16299075

  9. Investigating Moorella thermoacetica metabolism with a genome-scale constraint-based metabolic model.

    PubMed

    Islam, M Ahsanul; Zengler, Karsten; Edwards, Elizabeth A; Mahadevan, Radhakrishnan; Stephanopoulos, Gregory

    2015-08-01

    Moorella thermoacetica is a strictly anaerobic, endospore-forming, and metabolically versatile acetogenic bacterium capable of conserving energy by both autotrophic (acetogenesis) and heterotrophic (homoacetogenesis) modes of metabolism. Its metabolic diversity and the ability to efficiently convert a wide range of compounds, including syngas (CO + H2) into acetyl-CoA have made this thermophilic bacterium a promising host for industrial biotechnology applications. However, lack of detailed information on M. thermoacetica's metabolism is a major impediment to its use as a microbial cell factory. In order to overcome this issue, a genome-scale constraint-based metabolic model of Moorella thermoacetica, iAI558, has been developed using its genome sequence and physiological data from published literature. The reconstructed metabolic network of M. thermoacetica comprises 558 metabolic genes, 705 biochemical reactions, and 698 metabolites. Of the total 705 model reactions, 680 are gene-associated while the rest are non-gene associated reactions. The model, in addition to simulating both autotrophic and heterotrophic growth of M. thermoacetica, revealed degeneracy in its TCA-cycle, a common characteristic of anaerobic metabolism. Furthermore, the model helped elucidate the poorly understood energy conservation mechanism of M. thermoacetica during autotrophy. Thus, in addition to generating experimentally testable hypotheses regarding its physiology, such a detailed model will facilitate rapid strain designing and metabolic engineering of M. thermoacetica for industrial applications. PMID:25994252

  10. Membrane transporters in a human genome-scale metabolic knowledgebase and their implications for disease

    PubMed Central

    Sahoo, Swagatika; Aurich, Maike K.; Jonsson, Jon J.; Thiele, Ines

    2014-01-01

    Membrane transporters enable efficient cellular metabolism, aid in nutrient sensing, and have been associated with various diseases, such as obesity and cancer. Genome-scale metabolic network reconstructions capture genomic, physiological, and biochemical knowledge of a target organism, along with a detailed representation of the cellular metabolite transport mechanisms. Since the first reconstruction of human metabolism, Recon 1, published in 2007, progress has been made in the field of metabolite transport. Recently, we published an updated reconstruction, Recon 2, which significantly improved the metabolic coverage and functionality. Human metabolic reconstructions have been used to investigate the role of metabolism in disease and to predict biomarkers and drug targets. Given the importance of cellular transport systems in understanding human metabolism in health and disease, we analyzed the coverage of transport systems for various metabolite classes in Recon 2. We will review the current knowledge on transporters (i.e., their preferred substrates, transport mechanisms, metabolic relevance, and disease association for each metabolite class). We will assess missing coverage and propose modifications and additions through a transport module that is functional when combined with Recon 2. This information will be valuable for further refinements. These data will also provide starting points for further experiments by highlighting areas of incomplete knowledge. This review represents the first comprehensive overview of the transporters involved in central metabolism and their transport mechanisms, thus serving as a compendium of metabolite transporters specific for human metabolic reconstructions. PMID:24653705

  11. The evolution of genome-scale models of cancer metabolism

    PubMed Central

    Lewis, Nathan E.; Abdel-Haleem, Alyaa M.

    2013-01-01

    The importance of metabolism in cancer is becoming increasingly apparent with the identification of metabolic enzyme mutations and the growing awareness of the influence of metabolism on signaling, epigenetic markers, and transcription. However, the complexity of these processes has challenged our ability to make sense of the metabolic changes in cancer. Fortunately, constraint-based modeling, a systems biology approach, now enables one to study the entirety of cancer metabolism and simulate basic phenotypes. With the newness of this field, there has been a rapid evolution of both the scope of these models and their applications. Here we review the various constraint-based models built for cancer metabolism and how their predictions are shedding new light on basic cancer phenotypes, elucidating pathway differences between tumors, and dicovering putative anti-cancer targets. As the field continues to evolve, the scope of these genome-scale cancer models must expand beyond central metabolism to address questions related to the diverse processes contributing to tumor development and metastasis. PMID:24027532

  12. From DNA to FBA: How to Build Your Own Genome-Scale Metabolic Model.

    PubMed

    Cuevas, Daniel A; Edirisinghe, Janaka; Henry, Chris S; Overbeek, Ross; O'Connell, Taylor G; Edwards, Robert A

    2016-01-01

    Microbiological studies are increasingly relying on in silico methods to perform exploration and rapid analysis of genomic data, and functional genomics studies are supplemented by the new perspectives that genome-scale metabolic models offer. A mathematical model consisting of a microbe's entire metabolic map can be rapidly determined from whole-genome sequencing and annotating the genomic material encoded in its DNA. Flux-balance analysis (FBA), a linear programming technique that uses metabolic models to predict the phenotypic responses imposed by environmental elements and factors, is the leading method to simulate and manipulate cellular growth in silico. However, the process of creating an accurate model to use in FBA consists of a series of steps involving a multitude of connections between bioinformatics databases, enzyme resources, and metabolic pathways. We present the methodology and procedure to obtain a metabolic model using PyFBA, an extensible Python-based open-source software package aimed to provide a platform where functional annotations are used to build metabolic models (http://linsalrob.github.io/PyFBA). Backed by the Model SEED biochemistry database, PyFBA contains methods to reconstruct a microbe's metabolic map, run FBA upon different media conditions, and gap-fill its metabolism. The extensibility of PyFBA facilitates novel techniques in creating accurate genome-scale metabolic models. PMID:27379044

  13. From DNA to FBA: How to Build Your Own Genome-Scale Metabolic Model

    PubMed Central

    Cuevas, Daniel A.; Edirisinghe, Janaka; Henry, Chris S.; Overbeek, Ross; O’Connell, Taylor G.; Edwards, Robert A.

    2016-01-01

    Microbiological studies are increasingly relying on in silico methods to perform exploration and rapid analysis of genomic data, and functional genomics studies are supplemented by the new perspectives that genome-scale metabolic models offer. A mathematical model consisting of a microbe’s entire metabolic map can be rapidly determined from whole-genome sequencing and annotating the genomic material encoded in its DNA. Flux-balance analysis (FBA), a linear programming technique that uses metabolic models to predict the phenotypic responses imposed by environmental elements and factors, is the leading method to simulate and manipulate cellular growth in silico. However, the process of creating an accurate model to use in FBA consists of a series of steps involving a multitude of connections between bioinformatics databases, enzyme resources, and metabolic pathways. We present the methodology and procedure to obtain a metabolic model using PyFBA, an extensible Python-based open-source software package aimed to provide a platform where functional annotations are used to build metabolic models (http://linsalrob.github.io/PyFBA). Backed by the Model SEED biochemistry database, PyFBA contains methods to reconstruct a microbe’s metabolic map, run FBA upon different media conditions, and gap-fill its metabolism. The extensibility of PyFBA facilitates novel techniques in creating accurate genome-scale metabolic models. PMID:27379044

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

    PubMed Central

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

    2015-01-01

    Genome-scale metabolic models usually contain inconsistencies that manifest as blocked reactions and gap metabolites. With the purpose to detect recurrent inconsistencies in metabolic models, a large-scale analysis was performed using a previously published dataset of 130 genome-scale models. The results showed that a large number of reactions (~22%) are blocked in all the models where they are present. To unravel the nature of such inconsistencies a metamodel was construed by joining the 130 models in a single network. This metamodel was manually curated using the unconnected modules approach, and then, it was used as a reference network to perform a gap-filling on each individual genome-scale model. Finally, a set of 36 models that had not been considered during the construction of the metamodel was used, as a proof of concept, to extend the metamodel with new biochemical information, and to assess its impact on gap-filling results. The analysis performed on the metamodel allowed to conclude: 1) the recurrent inconsistencies found in the models were already present in the metabolic database used during the reconstructions process; 2) the presence of inconsistencies in a metabolic database can be propagated to the reconstructed models; 3) there are reactions not manifested as blocked which are active as a consequence of some classes of artifacts, and; 4) the results of an automatic gap-filling are highly dependent on the consistency and completeness of the metamodel or metabolic database used as the reference network. In conclusion the consistency analysis should be applied to metabolic databases in order to detect and fill gaps as well as to detect and remove artifacts and redundant information. PMID:26629901

  15. Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm

    DOE PAGESBeta

    Seaver, Samuel M.D.; Bradbury, Louis M.T.; Frelin, Océane; Zarecki, Raphy; Ruppin, Eytan; Hanson, Andrew D.; Henry, Christopher S.

    2015-03-10

    There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions andmore » possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes.« less

  16. Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm

    SciTech Connect

    Seaver, Samuel M.D.; Bradbury, Louis M.T.; Frelin, Océane; Zarecki, Raphy; Ruppin, Eytan; Hanson, Andrew D.; Henry, Christopher S.

    2015-03-10

    There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes.

  17. Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm

    PubMed Central

    Seaver, Samuel M. D.; Bradbury, Louis M. T.; Frelin, Océane; Zarecki, Raphy; Ruppin, Eytan; Hanson, Andrew D.; Henry, Christopher S.

    2015-01-01

    There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes. PMID:25806041

  18. Genome-scale model reveals metabolic basis of biomass partitioning in a model diatom

    DOE PAGESBeta

    Levering, Jennifer; Broddrick, Jared; Dupont, Christopher L.; Peers, Graham; Beeri, Karen; Mayers, Joshua; Gallina, Alessandra A.; Allen, Andrew E.; Palsson, Bernhard O.; Zengler, Karsten; et al

    2016-05-06

    Diatoms are eukaryotic microalgae that contain genes from various sources, including bacteria and the secondary endosymbiotic host. Due to this unique combination of genes, diatoms are taxonomically and functionally distinct from other algae and vascular plants and confer novel metabolic capabilities. Based on the genome annotation, we performed a genome-scale metabolic network reconstruction for the marine diatom Phaeodactylum tricornutum. Due to their endosymbiotic origin, diatoms possess a complex chloroplast structure which complicates the prediction of subcellular protein localization. Based on previous work we implemented a pipeline that exploits a series of bioinformatics tools to predict protein localization. The manually curatedmore » reconstructed metabolic network iLB1027_lipid accounts for 1,027 genes associated with 4,456 reactions and 2,172 metabolites distributed across six compartments. To constrain the genome-scale model, we determined the organism specific biomass composition in terms of lipids, carbohydrates, and proteins using Fourier transform infrared spectrometry. Our simulations indicate the presence of a yet unknown glutamine-ornithine shunt that could be used to transfer reducing equivalents generated by photosynthesis to the mitochondria. Furthermore, the model reflects the known biochemical composition of P. tricornutum in defined culture conditions and enables metabolic engineering strategies to improve the use of P. tricornutum for biotechnological applications.« less

  19. Genome-Scale Model Reveals Metabolic Basis of Biomass Partitioning in a Model Diatom.

    PubMed

    Levering, Jennifer; Broddrick, Jared; Dupont, Christopher L; Peers, Graham; Beeri, Karen; Mayers, Joshua; Gallina, Alessandra A; Allen, Andrew E; Palsson, Bernhard O; Zengler, Karsten

    2016-01-01

    Diatoms are eukaryotic microalgae that contain genes from various sources, including bacteria and the secondary endosymbiotic host. Due to this unique combination of genes, diatoms are taxonomically and functionally distinct from other algae and vascular plants and confer novel metabolic capabilities. Based on the genome annotation, we performed a genome-scale metabolic network reconstruction for the marine diatom Phaeodactylum tricornutum. Due to their endosymbiotic origin, diatoms possess a complex chloroplast structure which complicates the prediction of subcellular protein localization. Based on previous work we implemented a pipeline that exploits a series of bioinformatics tools to predict protein localization. The manually curated reconstructed metabolic network iLB1027_lipid accounts for 1,027 genes associated with 4,456 reactions and 2,172 metabolites distributed across six compartments. To constrain the genome-scale model, we determined the organism specific biomass composition in terms of lipids, carbohydrates, and proteins using Fourier transform infrared spectrometry. Our simulations indicate the presence of a yet unknown glutamine-ornithine shunt that could be used to transfer reducing equivalents generated by photosynthesis to the mitochondria. The model reflects the known biochemical composition of P. tricornutum in defined culture conditions and enables metabolic engineering strategies to improve the use of P. tricornutum for biotechnological applications. PMID:27152931

  20. Genome-Scale Model Reveals Metabolic Basis of Biomass Partitioning in a Model Diatom

    PubMed Central

    Broddrick, Jared; Dupont, Christopher L.; Peers, Graham; Beeri, Karen; Mayers, Joshua; Gallina, Alessandra A.; Allen, Andrew E.; Palsson, Bernhard O.; Zengler, Karsten

    2016-01-01

    Diatoms are eukaryotic microalgae that contain genes from various sources, including bacteria and the secondary endosymbiotic host. Due to this unique combination of genes, diatoms are taxonomically and functionally distinct from other algae and vascular plants and confer novel metabolic capabilities. Based on the genome annotation, we performed a genome-scale metabolic network reconstruction for the marine diatom Phaeodactylum tricornutum. Due to their endosymbiotic origin, diatoms possess a complex chloroplast structure which complicates the prediction of subcellular protein localization. Based on previous work we implemented a pipeline that exploits a series of bioinformatics tools to predict protein localization. The manually curated reconstructed metabolic network iLB1027_lipid accounts for 1,027 genes associated with 4,456 reactions and 2,172 metabolites distributed across six compartments. To constrain the genome-scale model, we determined the organism specific biomass composition in terms of lipids, carbohydrates, and proteins using Fourier transform infrared spectrometry. Our simulations indicate the presence of a yet unknown glutamine-ornithine shunt that could be used to transfer reducing equivalents generated by photosynthesis to the mitochondria. The model reflects the known biochemical composition of P. tricornutum in defined culture conditions and enables metabolic engineering strategies to improve the use of P. tricornutum for biotechnological applications. PMID:27152931

  1. A Systems Approach to Predict Oncometabolites via Context-Specific Genome-Scale Metabolic Networks

    PubMed Central

    Nam, Hojung; Campodonico, Miguel; Bordbar, Aarash; Hyduke, Daniel R.; Kim, Sangwoo; Zielinski, Daniel C.; Palsson, Bernhard O.

    2014-01-01

    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. PMID:25232952

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

    PubMed

    Sadhukhan, Priyanka P; Raghunathan, Anu

    2014-01-01

    Genome Scale Metabolic Modeling methods represent one way to compute whole cell function starting from the genome sequence of an organism and contribute towards understanding and predicting the genotype-phenotype relationship. About 80 models spanning all the kingdoms of life from archaea to eukaryotes have been built till date and used to interrogate cell phenotype under varying conditions. These models have been used to not only understand the flux distribution in evolutionary conserved pathways like glycolysis and the Krebs cycle but also in applications ranging from value added product formation in Escherichia coli to predicting inborn errors of Homo sapiens metabolism. This chapter describes a protocol that delineates the process of genome scale metabolic modeling for analysing host-pathogen behavior and interaction using flux balance analysis (FBA). The steps discussed in the process include (1) reconstruction of a metabolic network from the genome sequence, (2) its representation in a precise mathematical framework, (3) its translation to a model, and (4) the analysis using linear algebra and optimization. The methods for biological interpretations of computed cell phenotypes in the context of individual host and pathogen models and their integration are also discussed. PMID:25048144

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

    PubMed Central

    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

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

    PubMed Central

    2012-01-01

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

  5. A Caenorhabditis elegans Genome-Scale Metabolic Network Model.

    PubMed

    Yilmaz, L Safak; Walhout, Albertha J M

    2016-05-25

    Caenorhabditis elegans is a powerful model to study metabolism and how it relates to nutrition, gene expression, and life history traits. However, while numerous experimental techniques that enable perturbation of its diet and gene function are available, a high-quality metabolic network model has been lacking. Here, we reconstruct an initial version of the C. elegans metabolic network. This network model contains 1,273 genes, 623 enzymes, and 1,985 metabolic reactions and is referred to as iCEL1273. Using flux balance analysis, we show that iCEL1273 is capable of representing the conversion of bacterial biomass into C. elegans biomass during growth and enables the predictions of gene essentiality and other phenotypes. In addition, we demonstrate that gene expression data can be integrated with the model by comparing metabolic rewiring in dauer animals versus growing larvae. iCEL1273 is available at a dedicated website (wormflux.umassmed.edu) and will enable the unraveling of the mechanisms by which different macro- and micronutrients contribute to the animal's physiology. PMID:27211857

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

    PubMed

    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. PMID:24988199

  7. Identifying All Moiety Conservation Laws in Genome-Scale Metabolic Networks

    PubMed Central

    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. PMID:24988199

  8. MetExplore: a web server to link metabolomic experiments and genome-scale metabolic networks.

    PubMed

    Cottret, Ludovic; Wildridge, David; Vinson, Florence; Barrett, Michael P; Charles, Hubert; Sagot, Marie-France; Jourdan, Fabien

    2010-07-01

    High-throughput metabolomic experiments aim at identifying and ultimately quantifying all metabolites present in biological systems. The metabolites are interconnected through metabolic reactions, generally grouped into metabolic pathways. Classical metabolic maps provide a relational context to help interpret metabolomics experiments and a wide range of tools have been developed to help place metabolites within metabolic pathways. However, the representation of metabolites within separate disconnected pathways overlooks most of the connectivity of the metabolome. By definition, reference pathways cannot integrate novel pathways nor show relationships between metabolites that may be linked by common neighbours without being considered as joint members of a classical biochemical pathway. MetExplore is a web server that offers the possibility to link metabolites identified in untargeted metabolomics experiments within the context of genome-scale reconstructed metabolic networks. The analysis pipeline comprises mapping metabolomics data onto the specific metabolic network of an organism, then applying graph-based methods and advanced visualization tools to enhance data analysis. The MetExplore web server is freely accessible at http://metexplore.toulouse.inra.fr. PMID:20444866

  9. Quantitative Assessment of Thermodynamic Constraints on the Solution Space of Genome-Scale Metabolic Models

    PubMed Central

    Hamilton, Joshua J.; Dwivedi, Vivek; Reed, Jennifer L.

    2013-01-01

    Constraint-based methods provide powerful computational techniques to allow understanding and prediction of cellular behavior. These methods rely on physiochemical constraints to eliminate infeasible behaviors from the space of available behaviors. One such constraint is thermodynamic feasibility, the requirement that intracellular flux distributions obey the laws of thermodynamics. The past decade has seen several constraint-based methods that interpret this constraint in different ways, including those that are limited to small networks, rely on predefined reaction directions, and/or neglect the relationship between reaction free energies and metabolite concentrations. In this work, we utilize one such approach, thermodynamics-based metabolic flux analysis (TMFA), to make genome-scale, quantitative predictions about metabolite concentrations and reaction free energies in the absence of prior knowledge of reaction directions, while accounting for uncertainties in thermodynamic estimates. We applied TMFA to a genome-scale network reconstruction of Escherichia coli and examined the effect of thermodynamic constraints on the flux space. We also assessed the predictive performance of TMFA against gene essentiality and quantitative metabolomics data, under both aerobic and anaerobic, and optimal and suboptimal growth conditions. Based on these results, we propose that TMFA is a useful tool for validating phenotypes and generating hypotheses, and that additional types of data and constraints can improve predictions of metabolite concentrations. PMID:23870272

  10. The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum

    PubMed Central

    Agren, Rasmus; Liu, Liming; Shoaie, Saeed; Vongsangnak, Wanwipa; Nookaew, Intawat; Nielsen, Jens

    2013-01-01

    We present the RAVEN (Reconstruction, Analysis and Visualization of Metabolic Networks) Toolbox: a software suite that allows for semi-automated reconstruction of genome-scale models. It makes use of published models and/or the KEGG database, coupled with extensive gap-filling and quality control features. The software suite also contains methods for visualizing simulation results and omics data, as well as a range of methods for performing simulations and analyzing the results. The software is a useful tool for system-wide data analysis in a metabolic context and for streamlined reconstruction of metabolic networks based on protein homology. The RAVEN Toolbox workflow was applied in order to reconstruct a genome-scale metabolic model for the important microbial cell factory Penicillium chrysogenum Wisconsin54-1255. The model was validated in a bibliomic study of in total 440 references, and it comprises 1471 unique biochemical reactions and 1006 ORFs. It was then used to study the roles of ATP and NADPH in the biosynthesis of penicillin, and to identify potential metabolic engineering targets for maximization of penicillin production. PMID:23555215

  11. Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling.

    PubMed

    Agren, Rasmus; Mardinoglu, Adil; Asplund, Anna; Kampf, Caroline; Uhlen, Mathias; Nielsen, Jens

    2014-01-01

    Genome-scale metabolic models (GEMs) have proven useful as scaffolds for the integration of omics data for understanding the genotype-phenotype relationship in a mechanistic manner. Here, we evaluated the presence/absence of proteins encoded by 15,841 genes in 27 hepatocellular carcinoma (HCC) patients using immunohistochemistry. We used this information to reconstruct personalized GEMs for six HCC patients based on the proteomics data, HMR 2.0, and a task-driven model reconstruction algorithm (tINIT). The personalized GEMs were employed to identify anticancer drugs using the concept of antimetabolites; i.e., drugs that are structural analogs to metabolites. The toxicity of each antimetabolite was predicted by assessing the in silico functionality of 83 healthy cell type-specific GEMs, which were also reconstructed with the tINIT algorithm. We predicted 101 antimetabolites that could be effective in preventing tumor growth in all HCC patients, and 46 antimetabolites which were specific to individual patients. Twenty-two of the 101 predicted antimetabolites have already been used in different cancer treatment strategies, while the remaining antimetabolites represent new potential drugs. Finally, one of the identified targets was validated experimentally, and it was confirmed to attenuate growth of the HepG2 cell line. PMID:24646661

  12. Identifying anti-growth factors for human cancer cell lines through genome-scale metabolic modeling

    PubMed Central

    Ghaffari, Pouyan; Mardinoglu, Adil; Asplund, Anna; Shoaie, Saeed; Kampf, Caroline; Uhlen, Mathias; Nielsen, Jens

    2015-01-01

    Human cancer cell lines are used as important model systems to study molecular mechanisms associated with tumor growth, hereunder how genomic and biological heterogeneity found in primary tumors affect cellular phenotypes. We reconstructed Genome scale metabolic models (GEMs) for eleven cell lines based on RNA-Seq data and validated the functionality of these models with data from metabolite profiling. We used cell line-specific GEMs to analyze the differences in the metabolism of cancer cell lines, and to explore the heterogeneous expression of the metabolic subsystems. Furthermore, we predicted 85 antimetabolites that can inhibit growth of, or even kill, any of the cell lines, while at the same time not being toxic for 83 different healthy human cell types. 60 of these antimetabolites were found to inhibit growth in all cell lines. Finally, we experimentally validated one of the predicted antimetabolites using two cell lines with different phenotypic origins, and found that it is effective in inhibiting the growth of these cell lines. Using immunohistochemistry, we also showed high or moderate expression levels of proteins targeted by the validated antimetabolite. Identified anti-growth factors for inhibition of cell growth may provide leads for the development of efficient cancer treatment strategies. PMID:25640694

  13. Identifying anti-growth factors for human cancer cell lines through genome-scale metabolic modeling.

    PubMed

    Ghaffari, Pouyan; Mardinoglu, Adil; Asplund, Anna; Shoaie, Saeed; Kampf, Caroline; Uhlen, Mathias; Nielsen, Jens

    2015-01-01

    Human cancer cell lines are used as important model systems to study molecular mechanisms associated with tumor growth, hereunder how genomic and biological heterogeneity found in primary tumors affect cellular phenotypes. We reconstructed Genome scale metabolic models (GEMs) for eleven cell lines based on RNA-Seq data and validated the functionality of these models with data from metabolite profiling. We used cell line-specific GEMs to analyze the differences in the metabolism of cancer cell lines, and to explore the heterogeneous expression of the metabolic subsystems. Furthermore, we predicted 85 antimetabolites that can inhibit growth of, or even kill, any of the cell lines, while at the same time not being toxic for 83 different healthy human cell types. 60 of these antimetabolites were found to inhibit growth in all cell lines. Finally, we experimentally validated one of the predicted antimetabolites using two cell lines with different phenotypic origins, and found that it is effective in inhibiting the growth of these cell lines. Using immunohistochemistry, we also showed high or moderate expression levels of proteins targeted by the validated antimetabolite. Identified anti-growth factors for inhibition of cell growth may provide leads for the development of efficient cancer treatment strategies. PMID:25640694

  14. MapMaker and PathTracer for tracking carbon in genome-scale metabolic models.

    PubMed

    Tervo, Christopher J; Reed, Jennifer L

    2016-05-01

    Constraint-based reconstruction and analysis (COBRA) modeling results can be difficult to interpret given the large numbers of reactions in genome-scale models. While paths in metabolic networks can be found, existing methods are not easily combined with constraint-based approaches. To address this limitation, two tools (MapMaker and PathTracer) were developed to find paths (including cycles) between metabolites, where each step transfers carbon from reactant to product. MapMaker predicts carbon transfer maps (CTMs) between metabolites using only information on molecular formulae and reaction stoichiometry, effectively determining which reactants and products share carbon atoms. MapMaker correctly assigned CTMs for over 97% of the 2,251 reactions in an Escherichia coli metabolic model (iJO1366). Using CTMs as inputs, PathTracer finds paths between two metabolites. PathTracer was applied to iJO1366 to investigate the importance of using CTMs and COBRA constraints when enumerating paths, to find active and high flux paths in flux balance analysis (FBA) solutions, to identify paths for putrescine utilization, and to elucidate a potential CO2 fixation pathway in E. coli. These results illustrate how MapMaker and PathTracer can be used in combination with constraint-based models to identify feasible, active, and high flux paths between metabolites. PMID:26771089

  15. Metabolic stasis in an ancient symbiosis: genome-scale metabolic networks from two Blattabacterium cuenoti strains, primary endosymbionts of cockroaches

    PubMed Central

    2012-01-01

    Background Cockroaches are terrestrial insects that strikingly eliminate waste nitrogen as ammonia instead of uric acid. Blattabacterium cuenoti (Mercier 1906) strains Bge and Pam are the obligate primary endosymbionts of the cockroaches Blattella germanica and Periplaneta americana, respectively. The genomes of both bacterial endosymbionts have recently been sequenced, making possible a genome-scale constraint-based reconstruction of their metabolic networks. The mathematical expression of a metabolic network and the subsequent quantitative studies of phenotypic features by Flux Balance Analysis (FBA) represent an efficient functional approach to these uncultivable bacteria. Results We report the metabolic models of Blattabacterium strains Bge (iCG238) and Pam (iCG230), comprising 296 and 289 biochemical reactions, associated with 238 and 230 genes, and 364 and 358 metabolites, respectively. Both models reflect both the striking similarities and the singularities of these microorganisms. FBA was used to analyze the properties, potential and limits of the models, assuming some environmental constraints such as aerobic conditions and the net production of ammonia from these bacterial systems, as has been experimentally observed. In addition, in silico simulations with the iCG238 model have enabled a set of carbon and nitrogen sources to be defined, which would also support a viable phenotype in terms of biomass production in the strain Pam, which lacks the first three steps of the tricarboxylic acid cycle. FBA reveals a metabolic condition that renders these enzymatic steps dispensable, thus offering a possible evolutionary explanation for their elimination. We also confirm, by computational simulations, the fragility of the metabolic networks and their host dependence. Conclusions The minimized Blattabacterium metabolic networks are surprisingly similar in strains Bge and Pam, after 140 million years of evolution of these endosymbionts in separate cockroach

  16. Determining the control circuitry of redox metabolism at the genome-scale.

    PubMed

    Federowicz, Stephen; Kim, Donghyuk; Ebrahim, Ali; Lerman, Joshua; Nagarajan, Harish; Cho, Byung-kwan; Zengler, Karsten; Palsson, Bernhard

    2014-04-01

    Determining how facultative anaerobic organisms sense and direct cellular responses to electron acceptor availability has been a subject of intense study. However, even in the model organism Escherichia coli, established mechanisms only explain a small fraction of the hundreds of genes that are regulated during electron acceptor shifts. Here we propose a qualitative model that accounts for the full breadth of regulated genes by detailing how two global transcription factors (TFs), ArcA and Fnr of E. coli, sense key metabolic redox ratios and act on a genome-wide basis to regulate anabolic, catabolic, and energy generation pathways. We first fill gaps in our knowledge of this transcriptional regulatory network by carrying out ChIP-chip and gene expression experiments to identify 463 regulatory events. We then interfaced this reconstructed regulatory network with a highly curated genome-scale metabolic model to show that ArcA and Fnr regulate >80% of total metabolic flux and 96% of differential gene expression across fermentative and nitrate respiratory conditions. Based on the data, we propose a feedforward with feedback trim regulatory scheme, given the extensive repression of catabolic genes by ArcA and extensive activation of chemiosmotic genes by Fnr. We further corroborated this regulatory scheme by showing a 0.71 r(2) (p<1e-6) correlation between changes in metabolic flux and changes in regulatory activity across fermentative and nitrate respiratory conditions. Finally, we are able to relate the proposed model to a wealth of previously generated data by contextualizing the existing transcriptional regulatory network. PMID:24699140

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

    PubMed Central

    2011-01-01

    Background Genome-scale metabolic reconstructions provide a biologically meaningful mechanistic basis for the genotype-phenotype relationship. The global human metabolic network, termed Recon 1, has recently been reconstructed allowing the systems analysis of human metabolic physiology and pathology. Utilizing high-throughput data, Recon 1 has recently been tailored to different cells and tissues, including the liver, kidney, brain, and alveolar macrophage. These models have shown utility in the study of systems medicine. However, no integrated analysis between human tissues has been done. Results To describe tissue-specific functions, Recon 1 was tailored to describe metabolism in three human cells: adipocytes, hepatocytes, and myocytes. These cell-specific networks were manually curated and validated based on known cellular metabolic functions. To study intercellular interactions, a novel multi-tissue type modeling approach was developed to integrate the metabolic functions for the three cell types, and subsequently used to simulate known integrated metabolic cycles. In addition, the multi-tissue model was used to study diabetes: a pathology with systemic properties. High-throughput data was integrated with the network to determine differential metabolic activity between obese and type II obese gastric bypass patients in a whole-body context. Conclusion The multi-tissue type modeling approach presented provides a platform to study integrated metabolic states. As more cell and tissue-specific models are released, it is critical to develop a framework in which to study their interdependencies. PMID:22041191

  18. Genome-scale reconstruction of the sigma factor network in Escherichia coli: topology and functional states

    PubMed Central

    2014-01-01

    Background At the beginning of the transcription process, the RNA polymerase (RNAP) core enzyme requires a σ-factor to recognize the genomic location at which the process initiates. Although the crucial role of σ-factors has long been appreciated and characterized for many individual promoters, we do not yet have a genome-scale assessment of their function. Results Using multiple genome-scale measurements, we elucidated the network of σ-factor and promoter interactions in Escherichia coli. The reconstructed network includes 4,724 σ-factor-specific promoters corresponding to transcription units (TUs), representing an increase of more than 300% over what has been previously reported. The reconstructed network was used to investigate competition between alternative σ-factors (the σ70 and σ38 regulons), confirming the competition model of σ substitution and negative regulation by alternative σ-factors. Comparison with σ-factor binding in Klebsiella pneumoniae showed that transcriptional regulation of conserved genes in closely related species is unexpectedly divergent. Conclusions The reconstructed network reveals the regulatory complexity of the promoter architecture in prokaryotic genomes, and opens a path to the direct determination of the systems biology of their transcriptional regulatory networks. PMID:24461193

  19. Achieving Metabolic Flux Analysis for S. cerevisiae at a Genome-Scale: Challenges, Requirements, and Considerations

    PubMed Central

    Gopalakrishnan, Saratram; Maranas, Costas D.

    2015-01-01

    Recent advances in 13C-Metabolic flux analysis (13C-MFA) have increased its capability to accurately resolve fluxes using a genome-scale model with narrow confidence intervals without pre-judging the activity or inactivity of alternate metabolic pathways. However, the necessary precautions, computational challenges, and minimum data requirements for successful analysis remain poorly established. This review aims to establish the necessary guidelines for performing 13C-MFA at the genome-scale for a compartmentalized eukaryotic system such as yeast in terms of model and data requirements, while addressing key issues such as statistical analysis and network complexity. We describe the various approaches used to simplify the genome-scale model in the absence of sufficient experimental flux measurements, the availability and generation of reaction atom mapping information, and the experimental flux and metabolite labeling distribution measurements to ensure statistical validity of the obtained flux distribution. Organism-specific challenges such as the impact of compartmentalization of metabolism, variability of biomass composition, and the cell-cycle dependence of metabolism are discussed. Identification of errors arising from incorrect gene annotation and suggested alternate routes using MFA are also highlighted. PMID:26393660

  20. A multi-tissue genome-scale metabolic modeling framework for the analysis of whole plant systems

    PubMed Central

    Gomes de Oliveira Dal'Molin, Cristiana; Quek, Lake-Ee; Saa, Pedro A.; Nielsen, Lars K.

    2015-01-01

    Genome scale metabolic modeling has traditionally been used to explore metabolism of individual cells or tissues. In higher organisms, the metabolism of individual tissues and organs is coordinated for the overall growth and well-being of the organism. Understanding the dependencies and rationale for multicellular metabolism is far from trivial. Here, we have advanced the use of AraGEM (a genome-scale reconstruction of Arabidopsis metabolism) in a multi-tissue context to understand how plants grow utilizing their leaf, stem and root systems across the day-night (diurnal) cycle. Six tissue compartments were created, each with their own distinct set of metabolic capabilities, and hence a reliance on other compartments for support. We used the multi-tissue framework to explore differences in the “division-of-labor” between the sources and sink tissues in response to: (a) the energy demand for the translocation of C and N species in between tissues; and (b) the use of two distinct nitrogen sources (NO−3 or NH+4). The “division-of-labor” between compartments was investigated using a minimum energy (photon) objective function. Random sampling of the solution space was used to explore the flux distributions under different scenarios as well as to identify highly coupled reaction sets in different tissues and organelles. Efficient identification of these sets was achieved by casting this problem as a maximum clique enumeration problem. The framework also enabled assessing the impact of energetic constraints in resource (redox and ATP) allocation between leaf, stem, and root tissues required for efficient carbon and nitrogen assimilation, including the diurnal cycle constraint forcing the plant to set aside resources during the day and defer metabolic processes that are more efficiently performed at night. This study is a first step toward autonomous modeling of whole plant metabolism. PMID:25657653

  1. GSMN-TB: a web-based genome-scale network model of Mycobacterium tuberculosis metabolism

    PubMed Central

    Beste, Dany JV; Hooper, Tracy; Stewart, Graham; Bonde, Bhushan; Avignone-Rossa, Claudio; Bushell, Michael E; Wheeler, Paul; Klamt, Steffen; Kierzek, Andrzej M; McFadden, Johnjoe

    2007-01-01

    Background An impediment to the rational development of novel drugs against tuberculosis (TB) is a general paucity of knowledge concerning the metabolism of Mycobacterium tuberculosis, particularly during infection. Constraint-based modeling provides a novel approach to investigating microbial metabolism but has not yet been applied to genome-scale modeling of M. tuberculosis. Results GSMN-TB, a genome-scale metabolic model of M. tuberculosis, was constructed, consisting of 849 unique reactions and 739 metabolites, and involving 726 genes. The model was calibrated by growing Mycobacterium bovis bacille Calmette Guérin in continuous culture and steady-state growth parameters were measured. Flux balance analysis was used to calculate substrate consumption rates, which were shown to correspond closely to experimentally determined values. Predictions of gene essentiality were also made by flux balance analysis simulation and were compared with global mutagenesis data for M. tuberculosis grown in vitro. A prediction accuracy of 78% was achieved. Known drug targets were predicted to be essential by the model. The model demonstrated a potential role for the enzyme isocitrate lyase during the slow growth of mycobacteria, and this hypothesis was experimentally verified. An interactive web-based version of the model is available. Conclusion The GSMN-TB model successfully simulated many of the growth properties of M. tuberculosis. The model provides a means to examine the metabolic flexibility of bacteria and predict the phenotype of mutants, and it highlights previously unexplored features of M. tuberculosis metabolism. PMID:17521419

  2. Genome-scale metabolic modeling of Mucor circinelloides and comparative analysis with other oleaginous species.

    PubMed

    Vongsangnak, Wanwipa; Klanchui, Amornpan; Tawornsamretkit, Iyarest; Tatiyaborwornchai, Witthawin; Laoteng, Kobkul; Meechai, Asawin

    2016-06-01

    We present a novel genome-scale metabolic model iWV1213 of Mucor circinelloides, which is an oleaginous fungus for industrial applications. The model contains 1213 genes, 1413 metabolites and 1326 metabolic reactions across different compartments. We demonstrate that iWV1213 is able to accurately predict the growth rates of M. circinelloides on various nutrient sources and culture conditions using Flux Balance Analysis and Phenotypic Phase Plane analysis. Comparative analysis of three oleaginous genome-scale models, including M. circinelloides (iWV1213), Mortierella alpina (iCY1106) and Yarrowia lipolytica (iYL619_PCP) revealed that iWV1213 possesses a higher number of genes involved in carbohydrate, amino acid, and lipid metabolisms that might contribute to its versatility in nutrient utilization. Moreover, the identification of unique and common active reactions among the Zygomycetes oleaginous models using Flux Variability Analysis unveiled a set of gene/enzyme candidates as metabolic engineering targets for cellular improvement. Thus, iWV1213 offers a powerful metabolic engineering tool for multi-level omics analysis, enabling strain optimization as a cell factory platform of lipid-based production. PMID:26911256

  3. Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease.

    PubMed

    Mardinoglu, Adil; Agren, Rasmus; Kampf, Caroline; Asplund, Anna; Uhlen, Mathias; Nielsen, Jens

    2014-01-01

    Several liver disorders result from perturbations in the metabolism of hepatocytes, and their underlying mechanisms can be outlined through the use of genome-scale metabolic models (GEMs). Here we reconstruct a consensus GEM for hepatocytes, which we call iHepatocytes2322, that extends previous models by including an extensive description of lipid metabolism. We build iHepatocytes2322 using Human Metabolic Reaction 2.0 database and proteomics data in Human Protein Atlas, which experimentally validates the incorporated reactions. The reconstruction process enables improved annotation of the proteomics data using the network centric view of iHepatocytes2322. We then use iHepatocytes2322 to analyse transcriptomics data obtained from patients with non-alcoholic fatty liver disease. We show that blood concentrations of chondroitin and heparan sulphates are suitable for diagnosing non-alcoholic steatohepatitis and for the staging of non-alcoholic fatty liver disease. Furthermore, we observe serine deficiency in patients with NASH and identify PSPH, SHMT1 and BCAT1 as potential therapeutic targets for the treatment of non-alcoholic steatohepatitis. PMID:24419221

  4. Using a Genome-Scale Metabolic Model of Enterococcus faecalis V583 To Assess Amino Acid Uptake and Its Impact on Central Metabolism

    PubMed Central

    Solheim, Margrete; van Grinsven, Koen W. A.; Olivier, Brett G.; Levering, Jennifer; Grosseholz, Ruth; Hugenholtz, Jeroen; Holo, Helge; Nes, Ingolf; Teusink, Bas; Kummer, Ursula

    2014-01-01

    Increasing antibiotic resistance in pathogenic bacteria necessitates the development of new medication strategies. Interfering with the metabolic network of the pathogen can provide novel drug targets but simultaneously requires a deeper and more detailed organism-specific understanding of the metabolism, which is often surprisingly sparse. In light of this, we reconstructed a genome-scale metabolic model of the pathogen Enterococcus faecalis V583. The manually curated metabolic network comprises 642 metabolites and 706 reactions. We experimentally determined metabolic profiles of E. faecalis grown in chemically defined medium in an anaerobic chemostat setup at different dilution rates and calculated the net uptake and product fluxes to constrain the model. We computed growth-associated energy and maintenance parameters and studied flux distributions through the metabolic network. Amino acid auxotrophies were identified experimentally for model validation and revealed seven essential amino acids. In addition, the important metabolic hub of glutamine/glutamate was altered by constructing a glutamine synthetase knockout mutant. The metabolic profile showed a slight shift in the fermentation pattern toward ethanol production and increased uptake rates of multiple amino acids, especially l-glutamine and l-glutamate. The model was used to understand the altered flux distributions in the mutant and provided an explanation for the experimentally observed redirection of the metabolic flux. We further highlighted the importance of gene-regulatory effects on the redirection of the metabolic fluxes upon perturbation. The genome-scale metabolic model presented here includes gene-protein-reaction associations, allowing a further use for biotechnological applications, for studying essential genes, proteins, or reactions, and the search for novel drug targets. PMID:25527553

  5. Genome-scale metabolic flux analysis of Streptomyces lividans growing on a complex medium.

    PubMed

    D'Huys, Pieter-Jan; Lule, Ivan; Vercammen, Dominique; Anné, Jozef; Van Impe, Jan F; Bernaerts, Kristel

    2012-09-15

    Constraint-based metabolic modeling comprises various excellent tools to assess experimentally observed phenotypic behavior of micro-organisms in terms of intracellular metabolic fluxes. In combination with genome-scale metabolic networks, micro-organisms can be investigated in much more detail and under more complex environmental conditions. Although complex media are ubiquitously applied in industrial fermentations and are often a prerequisite for high protein secretion yields, such multi-component conditions are seldom investigated using genome-scale flux analysis. In this paper, a systematic and integrative approach is presented to determine metabolic fluxes in Streptomyces lividans TK24 grown on a nutritious and complex medium. Genome-scale flux balance analysis and randomized sampling of the solution space are combined to extract maximum information from exometabolome profiles. It is shown that biomass maximization cannot predict the observed metabolite production pattern as such. Although this cellular objective commonly applies to batch fermentation data, both input and output constraints are required to reproduce the measured biomass production rate. Rich media hence not necessarily lead to maximum biomass growth. To eventually identify a unique intracellular flux vector, a hierarchical optimization of cellular objectives is adopted. Out of various tested secondary objectives, maximization of the ATP yield per flux unit returns the closest agreement with the maximum frequency in flux histograms. This unique flux estimation is hence considered as a reasonable approximation for the biological fluxes. Flux maps for different growth phases show no active oxidative part of the pentose phosphate pathway, but NADPH generation in the TCA cycle and NADPH transdehydrogenase activity are most important in fulfilling the NADPH balance. Amino acids contribute to biomass growth by augmenting the pool of available amino acids and by boosting the TCA cycle, particularly

  6. MultiMetEval: Comparative and Multi-Objective Analysis of Genome-Scale Metabolic Models

    PubMed Central

    Gevorgyan, Albert; Kierzek, Andrzej M.; Breitling, Rainer; Takano, Eriko

    2012-01-01

    Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEval/downloads. PMID:23272111

  7. Applications of Genome-Scale Metabolic Models in Biotechnology and Systems Medicine

    PubMed Central

    Zhang, Cheng; Hua, Qiang

    2016-01-01

    Genome-scale metabolic models (GEMs) have become a popular tool for systems biology, and they have been used in many fields such as industrial biotechnology and systems medicine. Since more and more studies are being conducted using GEMs, they have recently received considerable attention. In this review, we introduce the basic concept of GEMs and provide an overview of their applications in biotechnology, systems medicine, and some other fields. In addition, we describe the general principle of the applications and analyses built on GEMs. The purpose of this review is to introduce the application of GEMs in biological analysis and to promote its wider use by biologists. PMID:26779040

  8. Genome-scale Metabolic Reaction Modeling: a New Approach to Geomicrobial Kinetics

    NASA Astrophysics Data System (ADS)

    McKernan, S. E.; Shapiro, B.; Jin, Q.

    2014-12-01

    Geomicrobial rates, rates of microbial metabolism in natural environments, are a key parameter of theoretical and practical problems in geobiology and biogeochemistry. Both laboratory- and field-based approaches have been applied to study rates of geomicrobial processes. Laboratory-based approaches analyze geomicrobial kinetics by incubating environmental samples under controlled laboratory conditions. Field methods quantify geomicrobial rates by observing the progress of geomicrobial processes. To take advantage of recent development in biogeochemical modeling and genome-scale metabolic modeling, we suggest that geomicrobial rates can also be predicted by simulating metabolic reaction networks of microbes. To predict geomicrobial rates, we developed a genome-scale metabolic model that describes enzyme reaction networks of microbial metabolism, and simulated the network model by accounting for the kinetics and thermodynamics of enzyme reactions. The model is simulated numerically to solve cellular enzyme abundance and hence metabolic rates under the constraints of cellular physiology. The new modeling approach differs from flux balance analysis of system biology in that it accounts for the thermodynamics and kinetics of enzymatic reactions. It builds on subcellular metabolic reaction networks, and hence also differs from classical biogeochemical reaction modeling. We applied the new approach to Methanosarcina acetivorans, an anaerobic, marine methanogen capable of disproportionating acetate to carbon dioxide and methane. The input of the new model includes (1) enzyme reaction network of acetoclastic methanogenesis, and (2) representative geochemical conditions of freshwater sedimentary environments. The output of the simulation includes the proteomics, metabolomics, and energy and matter fluxes of M. acetivorans. Our simulation results demonstrate the predictive power of the new modeling approach. Specifically, the results illustrate how methanogenesis rates vary

  9. Exact quantification of cellular robustness in genome-scale metabolic networks

    PubMed Central

    Gerstl, Matthias P.; Klamt, Steffen; Jungreuthmayer, Christian; Zanghellini, Jürgen

    2016-01-01

    Motivation: Robustness, the ability of biological networks to uphold their functionality in spite of perturbations, is a key characteristic of all living systems. Although several theoretical approaches have been developed to formalize robustness, it still eludes an exact quantification. Here, we present a rigorous and quantitative approach for the structural robustness of metabolic networks by measuring their ability to tolerate random reaction (or gene) knockouts. Results: In analogy to reliability theory, based on an explicit consideration of all possible knockout sets, we exactly quantify the probability of failure for a given network function (e.g. growth). This measure can be computed if the network’s minimal cut sets (MSCs) are known. We show that even in genome-scale metabolic networks the probability of (network) failure can be reliably estimated from MSCs with lowest cardinalities. We demonstrate the applicability of our theory by analyzing the structural robustness of multiple Enterobacteriaceae and Blattibacteriaceae and show a dramatically low structural robustness for the latter. We find that structural robustness develops from the ability to proliferate in multiple growth environments consistent with experimentally found knowledge. Conclusion: The probability of (network) failure provides thus a reliable and easily computable measure of structural robustness and redundancy in (genome-scale) metabolic networks. Availability and implementation: Source code is available under the GNU General Public License at https://github.com/mpgerstl/networkRobustnessToolbox. Contact: juergen.zanghellini@boku.ac.at Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26543173

  10. Genome-scale metabolic model of Pichia pastoris with native and humanized glycosylation of recombinant proteins.

    PubMed

    Irani, Zahra Azimzadeh; Kerkhoven, Eduard J; Shojaosadati, Seyed Abbas; Nielsen, Jens

    2016-05-01

    Pichia pastoris is used for commercial production of human therapeutic proteins, and genome-scale models of P. pastoris metabolism have been generated in the past to study the metabolism and associated protein production by this yeast. A major challenge with clinical usage of recombinant proteins produced by P. pastoris is the difference in N-glycosylation of proteins produced by humans and this yeast. However, through metabolic engineering, a P. pastoris strain capable of producing humanized N-glycosylated proteins was constructed. The current genome-scale models of P. pastoris do not address native nor humanized N-glycosylation, and we therefore developed ihGlycopastoris, an extension to the iLC915 model with both native and humanized N-glycosylation for recombinant protein production, but also an estimation of N-glycosylation of P. pastoris native proteins. This new model gives a better prediction of protein yield, demonstrates the effect of the different types of N-glycosylation of protein yield, and can be used to predict potential targets for strain improvement. The model represents a step towards a more complete description of protein production in P. pastoris, which is required for using these models to understand and optimize protein production processes. Biotechnol. Bioeng. 2016;113: 961-969. © 2015 Wiley Periodicals, Inc. PMID:26480251

  11. Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions.

    PubMed

    Zuñiga, Cristal; Li, Chien-Ting; Huelsman, Tyler; Levering, Jennifer; Zielinski, Daniel C; McConnell, Brian O; Long, Christopher P; Knoshaug, Eric P; Guarnieri, Michael T; Antoniewicz, Maciek R; Betenbaugh, Michael J; Zengler, Karsten

    2016-09-01

    The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine. PMID:27372244

  12. Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis

    NASA Astrophysics Data System (ADS)

    De Martino, D.

    2016-02-01

    In this article the notion of metabolic turnover is revisited in the light of recent results of out-of-equilibrium thermodynamics. By means of Monte Carlo methods we perform an exact sampling of the enzymatic fluxes in a genome scale metabolic network of E. Coli in stationary growth conditions from which we infer the metabolites turnover times. However the latter are inferred from net fluxes, and we argue that this approximation is not valid for enzymes working nearby thermodynamic equilibrium. We recalculate turnover times from total fluxes by performing an energy balance analysis of the network and recurring to the fluctuation theorem. We find in many cases values one of order of magnitude lower, implying a faster picture of intermediate metabolism.

  13. Likelihood-based gene annotations for gap filling and quality assessment in genome-scale metabolic models

    DOE PAGESBeta

    Benedict, Matthew N.; Mundy, Michael B.; Henry, Christopher S.; Chia, Nicholas; Price, Nathan D.; Maranas, Costas D.

    2014-10-16

    Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genesmore » and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary

  14. Likelihood-based gene annotations for gap filling and quality assessment in genome-scale metabolic models

    SciTech Connect

    Benedict, Matthew N.; Mundy, Michael B.; Henry, Christopher S.; Chia, Nicholas; Price, Nathan D.; Maranas, Costas D.

    2014-10-16

    Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genes and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary to

  15. Construction of a Genome-Scale Metabolic Model of Arthrospira platensis NIES-39 and Metabolic Design for Cyanobacterial Bioproduction

    PubMed Central

    Yoshikawa, Katsunori; Aikawa, Shimpei; Kojima, Yuta; Toya, Yoshihiro; Furusawa, Chikara; Kondo, Akihiko; Shimizu, Hiroshi

    2015-01-01

    Arthrospira (Spirulina) platensis is a promising feedstock and host strain for bioproduction because of its high accumulation of glycogen and superior characteristics for industrial production. Metabolic simulation using a genome-scale metabolic model and flux balance analysis is a powerful method that can be used to design metabolic engineering strategies for the improvement of target molecule production. In this study, we constructed a genome-scale metabolic model of A. platensis NIES-39 including 746 metabolic reactions and 673 metabolites, and developed novel strategies to improve the production of valuable metabolites, such as glycogen and ethanol. The simulation results obtained using the metabolic model showed high consistency with experimental results for growth rates under several trophic conditions and growth capabilities on various organic substrates. The metabolic model was further applied to design a metabolic network to improve the autotrophic production of glycogen and ethanol. Decreased flux of reactions related to the TCA cycle and phosphoenolpyruvate reaction were found to improve glycogen production. Furthermore, in silico knockout simulation indicated that deletion of genes related to the respiratory chain, such as NAD(P)H dehydrogenase and cytochrome-c oxidase, could enhance ethanol production by using ammonium as a nitrogen source. PMID:26640947

  16. Construction of a Genome-Scale Metabolic Model of Arthrospira platensis NIES-39 and Metabolic Design for Cyanobacterial Bioproduction.

    PubMed

    Yoshikawa, Katsunori; Aikawa, Shimpei; Kojima, Yuta; Toya, Yoshihiro; Furusawa, Chikara; Kondo, Akihiko; Shimizu, Hiroshi

    2015-01-01

    Arthrospira (Spirulina) platensis is a promising feedstock and host strain for bioproduction because of its high accumulation of glycogen and superior characteristics for industrial production. Metabolic simulation using a genome-scale metabolic model and flux balance analysis is a powerful method that can be used to design metabolic engineering strategies for the improvement of target molecule production. In this study, we constructed a genome-scale metabolic model of A. platensis NIES-39 including 746 metabolic reactions and 673 metabolites, and developed novel strategies to improve the production of valuable metabolites, such as glycogen and ethanol. The simulation results obtained using the metabolic model showed high consistency with experimental results for growth rates under several trophic conditions and growth capabilities on various organic substrates. The metabolic model was further applied to design a metabolic network to improve the autotrophic production of glycogen and ethanol. Decreased flux of reactions related to the TCA cycle and phosphoenolpyruvate reaction were found to improve glycogen production. Furthermore, in silico knockout simulation indicated that deletion of genes related to the respiratory chain, such as NAD(P)H dehydrogenase and cytochrome-c oxidase, could enhance ethanol production by using ammonium as a nitrogen source. PMID:26640947

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2016-01-01

    This article describes a generic programmatic method for mapping chemical compound libraries on organism-specific metabolic networks from various databases (KEGG, BioCyc) and flat file formats (SBML and Matlab files). We show how this pipeline was successfully applied to decipher the coverage of chemical libraries set up by two metabolomics facilities MetaboHub (French National infrastructure for metabolomics and fluxomics) and Glasgow Polyomics (GP) on the metabolic networks available in the MetExplore web server. The present generic protocol is designed to formalize and reduce the volume of information transfer between the library and the network database. Matching of metabolites between libraries and metabolic networks is based on InChIs or InChIKeys and therefore requires that these identifiers are specified in both libraries and networks. In addition to providing covering statistics, this pipeline also allows the visualization of mapping results in the context of metabolic networks. In order to achieve this goal, we tackled issues on programmatic interaction between two servers, improvement of metabolite annotation in metabolic networks and automatic loading of a mapping in genome scale metabolic network analysis tool MetExplore. It is important to note that this mapping can also be performed on a single or a selection of organisms of interest and is thus not limited to large facilities. PMID:26909353

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

    PubMed Central

    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. PMID:26909353

  20. Genome-scale metabolic modeling and in silico analysis of lipid accumulating yeast Candida tropicalis for dicarboxylic acid production.

    PubMed

    Mishra, Pranjul; Park, Gyu-Yeon; Lakshmanan, Meiyappan; Lee, Hee-Seok; Lee, Hongweon; Chang, Matthew Wook; Ching, Chi Bun; Ahn, Jungoh; Lee, Dong-Yup

    2016-09-01

    Recently, the bio-production of α,ω-dicarboxylic acids (DCAs) has gained significant attention, which potentially leads to the replacement of the conventional petroleum-based products. In this regard, the lipid accumulating yeast Candida tropicalis, has been recognized as a promising microbial host for DCA biosynthesis: it possess the unique ω-oxidation pathway where the terminal carbon of α-fatty acids is oxidized to form DCAs with varying chain lengths. However, despite such industrial importance, its cellular physiology and lipid accumulation capability remain largely uncharacterized. Thus, it is imperative to better understand the metabolic behavior of this lipogenic yeast, which could be achieved by a systems biological approach. To this end, herein, we reconstructed the genome-scale metabolic model of C. tropicalis, iCT646, accounting for 646 unique genes, 945 metabolic reactions, and 712 metabolites. Initially, the comparative network analysis of iCT646 with other yeasts revealed several distinctive metabolic reactions, mainly within the amino acid and lipid metabolism including the ω-oxidation pathway. Constraints-based flux analysis was, then, employed to predict the in silico growth rates of C. tropicalis which are highly consistent with the cellular phenotype observed in glucose and xylose minimal media chemostat cultures. Subsequently, the lipid accumulation capability of C. tropicalis was explored in comparison with Saccharomyces cerevisiae, indicating that the formation of "citrate pyruvate cycle" is essential to the lipid accumulation in oleaginous yeasts. The in silico flux analysis also highlighted the enhanced ability of pentose phosphate pathway as NADPH source rather than malic enzyme during lipogenesis. Finally, iCT646 was successfully utilized to highlight the key directions of C. tropicalis strain design for the whole cell biotransformation application to produce long-chain DCAs from alkanes. Biotechnol. Bioeng. 2016;113: 1993-2004.

  1. Evaluation of a Genome-Scale In Silico Metabolic Model for Geobacter metallireducens Using Proteomic Data from a Field Biostimulation Experiment

    SciTech Connect

    Fang, Yilin; Wilkins, Michael J.; Yabusaki, Steven B.; Lipton, Mary S.; Long, Philip E.

    2012-12-12

    Biomass and shotgun global proteomics data that reflected relative protein abundances from samples collected during the 2008 experiment at the U.S. Department of Energy Integrated Field-Scale Subsurface Research Challenge site in Rifle, Colorado, provided an unprecedented opportunity to validate a genome-scale metabolic model of Geobacter metallireducens and assess its performance with respect to prediction of metal reduction, biomass yield, and growth rate under dynamic field conditions. Reconstructed from annotated genomic sequence, biochemical, and physiological data, the constraint-based in silico model of G. metallireducens relates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637 G. metallireducens proteins detected during the 2008 experiment were associated with specific metabolic reactions in the in silico model. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through the in silico model reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low fluxes through amino acid transport and metabolism, revealed pathways or flux constraints in the in silico model that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.

  2. Understanding the Causes and Implications of Endothelial Metabolic Variation in Cardiovascular Disease through Genome-Scale Metabolic Modeling.

    PubMed

    McGarrity, Sarah; Halldórsson, Haraldur; Palsson, Sirus; Johansson, Pär I; Rolfsson, Óttar

    2016-01-01

    High-throughput biochemical profiling has led to a requirement for advanced data interpretation techniques capable of integrating the analysis of gene, protein, and metabolic profiles to shed light on genotype-phenotype relationships. Herein, we consider the current state of knowledge of endothelial cell (EC) metabolism and its connections to cardiovascular disease (CVD) and explore the use of genome-scale metabolic models (GEMs) for integrating metabolic and genomic data. GEMs combine gene expression and metabolic data acting as frameworks for their analysis and, ultimately, afford mechanistic understanding of how genetic variation impacts metabolism. We demonstrate how GEMs can be used to investigate CVD-related genetic variation, drug resistance mechanisms, and novel metabolic pathways in ECs. The application of GEMs in personalized medicine is also highlighted. Particularly, we focus on the potential of GEMs to identify metabolic biomarkers of endothelial dysfunction and to discover methods of stratifying treatments for CVDs based on individual genetic markers. Recent advances in systems biology methodology, and how these methodologies can be applied to understand EC metabolism in both health and disease, are thus highlighted. PMID:27148541

  3. Understanding the Causes and Implications of Endothelial Metabolic Variation in Cardiovascular Disease through Genome-Scale Metabolic Modeling

    PubMed Central

    McGarrity, Sarah; Halldórsson, Haraldur; Palsson, Sirus; Johansson, Pär I.; Rolfsson, Óttar

    2016-01-01

    High-throughput biochemical profiling has led to a requirement for advanced data interpretation techniques capable of integrating the analysis of gene, protein, and metabolic profiles to shed light on genotype–phenotype relationships. Herein, we consider the current state of knowledge of endothelial cell (EC) metabolism and its connections to cardiovascular disease (CVD) and explore the use of genome-scale metabolic models (GEMs) for integrating metabolic and genomic data. GEMs combine gene expression and metabolic data acting as frameworks for their analysis and, ultimately, afford mechanistic understanding of how genetic variation impacts metabolism. We demonstrate how GEMs can be used to investigate CVD-related genetic variation, drug resistance mechanisms, and novel metabolic pathways in ECs. The application of GEMs in personalized medicine is also highlighted. Particularly, we focus on the potential of GEMs to identify metabolic biomarkers of endothelial dysfunction and to discover methods of stratifying treatments for CVDs based on individual genetic markers. Recent advances in systems biology methodology, and how these methodologies can be applied to understand EC metabolism in both health and disease, are thus highlighted. PMID:27148541

  4. Integration and Validation of the Genome-Scale Metabolic Models of Pichia pastoris: A Comprehensive Update of Protein Glycosylation Pathways, Lipid and Energy Metabolism

    PubMed Central

    Tomàs-Gamisans, Màrius; Ferrer, Pau; Albiol, Joan

    2016-01-01

    Motivation Genome-scale metabolic models (GEMs) are tools that allow predicting a phenotype from a genotype under certain environmental conditions. GEMs have been developed in the last ten years for a broad range of organisms, and are used for multiple purposes such as discovering new properties of metabolic networks, predicting new targets for metabolic engineering, as well as optimizing the cultivation conditions for biochemicals or recombinant protein production. Pichia pastoris is one of the most widely used organisms for heterologous protein expression. There are different GEMs for this methylotrophic yeast of which the most relevant and complete in the published literature are iPP668, PpaMBEL1254 and iLC915. However, these three models differ regarding certain pathways, terminology for metabolites and reactions and annotations. Moreover, GEMs for some species are typically built based on the reconstructed models of related model organisms. In these cases, some organism-specific pathways could be missing or misrepresented. Results In order to provide an updated and more comprehensive GEM for P. pastoris, we have reconstructed and validated a consensus model integrating and merging all three existing models. In this step a comprehensive review and integration of the metabolic pathways included in each one of these three versions was performed. In addition, the resulting iMT1026 model includes a new description of some metabolic processes. Particularly new information described in recently published literature is included, mainly related to fatty acid and sphingolipid metabolism, glycosylation and cell energetics. Finally the reconstructed model was tested and validated, by comparing the results of the simulations with available empirical physiological datasets results obtained from a wide range of experimental conditions, such as different carbon sources, distinct oxygen availability conditions, as well as producing of two different recombinant proteins. In

  5. Revisiting the chlorophyll biosynthesis pathway using genome scale metabolic model of Oryza sativa japonica

    PubMed Central

    Chatterjee, Ankita; Kundu, Sudip

    2015-01-01

    Chlorophyll is one of the most important pigments present in green plants and rice is one of the major food crops consumed worldwide. We curated the existing genome scale metabolic model (GSM) of rice leaf by incorporating new compartment, reactions and transporters. We used this modified GSM to elucidate how the chlorophyll is synthesized in a leaf through a series of bio-chemical reactions spanned over different organelles using inorganic macronutrients and light energy. We predicted the essential reactions and the associated genes of chlorophyll synthesis and validated against the existing experimental evidences. Further, ammonia is known to be the preferred source of nitrogen in rice paddy fields. The ammonia entering into the plant is assimilated in the root and leaf. The focus of the present work is centered on rice leaf metabolism. We studied the relative importance of ammonia transporters through the chloroplast and the cytosol and their interlink with other intracellular transporters. Ammonia assimilation in the leaves takes place by the enzyme glutamine synthetase (GS) which is present in the cytosol (GS1) and chloroplast (GS2). Our results provided possible explanation why GS2 mutants show normal growth under minimum photorespiration and appear chlorotic when exposed to air. PMID:26443104

  6. Revisiting the chlorophyll biosynthesis pathway using genome scale metabolic model of Oryza sativa japonica.

    PubMed

    Chatterjee, Ankita; Kundu, Sudip

    2015-01-01

    Chlorophyll is one of the most important pigments present in green plants and rice is one of the major food crops consumed worldwide. We curated the existing genome scale metabolic model (GSM) of rice leaf by incorporating new compartment, reactions and transporters. We used this modified GSM to elucidate how the chlorophyll is synthesized in a leaf through a series of bio-chemical reactions spanned over different organelles using inorganic macronutrients and light energy. We predicted the essential reactions and the associated genes of chlorophyll synthesis and validated against the existing experimental evidences. Further, ammonia is known to be the preferred source of nitrogen in rice paddy fields. The ammonia entering into the plant is assimilated in the root and leaf. The focus of the present work is centered on rice leaf metabolism. We studied the relative importance of ammonia transporters through the chloroplast and the cytosol and their interlink with other intracellular transporters. Ammonia assimilation in the leaves takes place by the enzyme glutamine synthetase (GS) which is present in the cytosol (GS1) and chloroplast (GS2). Our results provided possible explanation why GS2 mutants show normal growth under minimum photorespiration and appear chlorotic when exposed to air. PMID:26443104

  7. Multiscale Metabolic Modeling of C4 Plants: Connecting Nonlinear Genome-Scale Models to Leaf-Scale Metabolism in Developing Maize Leaves

    PubMed Central

    Bogart, Eli; Myers, Christopher R.

    2016-01-01

    C4 plants, such as maize, concentrate carbon dioxide in a specialized compartment surrounding the veins of their leaves to improve the efficiency of carbon dioxide assimilation. Nonlinear relationships between carbon dioxide and oxygen levels and reaction rates are key to their physiology but cannot be handled with standard techniques of constraint-based metabolic modeling. We demonstrate that incorporating these relationships as constraints on reaction rates and solving the resulting nonlinear optimization problem yields realistic predictions of the response of C4 systems to environmental and biochemical perturbations. Using a new genome-scale reconstruction of maize metabolism, we build an 18000-reaction, nonlinearly constrained model describing mesophyll and bundle sheath cells in 15 segments of the developing maize leaf, interacting via metabolite exchange, and use RNA-seq and enzyme activity measurements to predict spatial variation in metabolic state by a novel method that optimizes correlation between fluxes and expression data. Though such correlations are known to be weak in general, we suggest that developmental gradients may be particularly suited to the inference of metabolic fluxes from expression data, and we demonstrate that our method predicts fluxes that achieve high correlation with the data, successfully capture the experimentally observed base-to-tip transition between carbon-importing tissue and carbon-exporting tissue, and include a nonzero growth rate, in contrast to prior results from similar methods in other systems. PMID:26990967

  8. Genome-Scale Metabolic Modeling in the Simulation of Field-Scale Uranium Bioremediation

    NASA Astrophysics Data System (ADS)

    Yabusaki, S.; Wilkins, M.; Fang, Y.; Williams, K. H.; Waichler, S.; Long, P. E.

    2015-12-01

    Coupled variably saturated flow and biogeochemical reactive transport modeling is used to improve understanding of the processes, properties, and conditions controlling uranium bio-immobilization in a field experiment where uranium-contaminated groundwater was amended with acetate and bicarbonate. The acetate stimulates indigenous microorganisms that catalyze metal reduction, including the conversion of aqueous U(VI) to solid-phase U(IV), which effectively removes uranium from solution. The initiation of the bicarbonate amendment prior to biostimulation was designed to promote U(VI) desorption that would increase the aqueous U(VI) available for bioreduction. The three-dimensional simulations were able to largely reproduce the timing and magnitude of the physical, chemical and biological responses to the acetate and bicarbonate amendment in the context of changing water table elevation and gradient. A time series of groundwater proteomic samples exhibited correlations between the most abundant Geobacter metallireducens proteins and the genome-scale metabolic model-predicted fluxes of intra-cellular reactions associated with each of those proteins. The desorption of U(VI) induced by the bicarbonate amendment led to initially higher rates of bioreduction compared to locations with minimal bicarbonate exposure. After bicarbonate amendment ceased, bioreduction continued at these locations whereas U(VI) sorption was the dominant removal mechanism at the bicarbonate-impacted sites.

  9. Genome-scale study reveals reduced metabolic adaptability in patients with non-alcoholic fatty liver disease.

    PubMed

    Hyötyläinen, Tuulia; Jerby, Livnat; Petäjä, Elina M; Mattila, Ismo; Jäntti, Sirkku; Auvinen, Petri; Gastaldelli, Amalia; Yki-Järvinen, Hannele; Ruppin, Eytan; Orešič, Matej

    2016-01-01

    Non-alcoholic fatty liver disease (NAFLD) is a major risk factor leading to chronic liver disease and type 2 diabetes. Here we chart liver metabolic activity and functionality in NAFLD by integrating global transcriptomic data, from human liver biopsies, and metabolic flux data, measured across the human splanchnic vascular bed, within a genome-scale model of human metabolism. We show that an increased amount of liver fat induces mitochondrial metabolism, lipolysis, glyceroneogenesis and a switch from lactate to glycerol as substrate for gluconeogenesis, indicating an intricate balance of exacerbated opposite metabolic processes in glycemic regulation. These changes were associated with reduced metabolic adaptability on a network level in the sense that liver fat accumulation puts increasing demands on the liver to adaptively regulate metabolic responses to maintain basic liver functions. We propose that failure to meet excessive metabolic challenges coupled with reduced metabolic adaptability may lead to a vicious pathogenic cycle leading to the co-morbidities of NAFLD. PMID:26839171

  10. Genome-scale study reveals reduced metabolic adaptability in patients with non-alcoholic fatty liver disease

    PubMed Central

    Hyötyläinen, Tuulia; Jerby, Livnat; Petäjä, Elina M.; Mattila, Ismo; Jäntti, Sirkku; Auvinen, Petri; Gastaldelli, Amalia; Yki-Järvinen, Hannele; Ruppin, Eytan; Orešič, Matej

    2016-01-01

    Non-alcoholic fatty liver disease (NAFLD) is a major risk factor leading to chronic liver disease and type 2 diabetes. Here we chart liver metabolic activity and functionality in NAFLD by integrating global transcriptomic data, from human liver biopsies, and metabolic flux data, measured across the human splanchnic vascular bed, within a genome-scale model of human metabolism. We show that an increased amount of liver fat induces mitochondrial metabolism, lipolysis, glyceroneogenesis and a switch from lactate to glycerol as substrate for gluconeogenesis, indicating an intricate balance of exacerbated opposite metabolic processes in glycemic regulation. These changes were associated with reduced metabolic adaptability on a network level in the sense that liver fat accumulation puts increasing demands on the liver to adaptively regulate metabolic responses to maintain basic liver functions. We propose that failure to meet excessive metabolic challenges coupled with reduced metabolic adaptability may lead to a vicious pathogenic cycle leading to the co-morbidities of NAFLD. PMID:26839171

  11. Genome-Scale Reconstruction of Escherichia coli's Transcriptional and Translational Machinery: A Knowledge Base, Its Mathematical Formulation, and Its Functional Characterization

    PubMed Central

    Thiele, Ines; Jamshidi, Neema; Fleming, Ronan M. T.; Palsson, Bernhard Ø.

    2009-01-01

    Metabolic network reconstructions represent valuable scaffolds for ‘-omics’ data integration and are used to computationally interrogate network properties. However, they do not explicitly account for the synthesis of macromolecules (i.e., proteins and RNA). Here, we present the first genome-scale, fine-grained reconstruction of Escherichia coli's transcriptional and translational machinery, which produces 423 functional gene products in a sequence-specific manner and accounts for all necessary chemical transformations. Legacy data from over 500 publications and three databases were reviewed, and many pathways were considered, including stable RNA maturation and modification, protein complex formation, and iron–sulfur cluster biogenesis. This reconstruction represents the most comprehensive knowledge base for these important cellular functions in E. coli and is unique in its scope. Furthermore, it was converted into a mathematical model and used to: (1) quantitatively integrate gene expression data as reaction constraints and (2) compute functional network states, which were compared to reported experimental data. For example, the model predicted accurately the ribosome production, without any parameterization. Also, in silico rRNA operon deletion suggested that a high RNA polymerase density on the remaining rRNA operons is needed to reproduce the reported experimental ribosome numbers. Moreover, functional protein modules were determined, and many were found to contain gene products from multiple subsystems, highlighting the functional interaction of these proteins. This genome-scale reconstruction of E. coli's transcriptional and translational machinery presents a milestone in systems biology because it will enable quantitative integration of ‘-omics’ datasets and thus the study of the mechanistic principles underlying the genotype–phenotype relationship. PMID:19282977

  12. Capturing the response of Clostridium acetobutylicum to chemical stressors using a regulated genome-scale metabolic model

    DOE PAGESBeta

    Dash, Satyakam; Mueller, Thomas J.; Venkataramanan, Keerthi P.; Papoutsakis, Eleftherios T.; Maranas, Costas D.

    2014-10-14

    Clostridia are anaerobic Gram-positive Firmicutes containing broad and flexible systems for substrate utilization, which have been used successfully to produce a range of industrial compounds. Clostridium acetobutylicum has been used to produce butanol on an industrial scale through acetone-butanol-ethanol (ABE) fermentation. A genome-scale metabolic (GSM) model is a powerful tool for understanding the metabolic capacities of an organism and developing metabolic engineering strategies for strain development. The integration of stress related specific transcriptomics information with the GSM model provides opportunities for elucidating the focal points of regulation.

  13. Reconstruction of Tissue-Specific Metabolic Networks Using CORDA

    PubMed Central

    Schultz, André; Qutub, Amina A.

    2016-01-01

    Human metabolism involves thousands of reactions and metabolites. To interpret this complexity, computational modeling becomes an essential experimental tool. One of the most popular techniques to study human metabolism as a whole is genome scale modeling. A key challenge to applying genome scale modeling is identifying critical metabolic reactions across diverse human tissues. Here we introduce a novel algorithm called Cost Optimization Reaction Dependency Assessment (CORDA) to build genome scale models in a tissue-specific manner. CORDA performs more efficiently computationally, shows better agreement to experimental data, and displays better model functionality and capacity when compared to previous algorithms. CORDA also returns reaction associations that can greatly assist in any manual curation to be performed following the automated reconstruction process. Using CORDA, we developed a library of 76 healthy and 20 cancer tissue-specific reconstructions. These reconstructions identified which metabolic pathways are shared across diverse human tissues. Moreover, we identified changes in reactions and pathways that are differentially included and present different capacity profiles in cancer compared to healthy tissues, including up-regulation of folate metabolism, the down-regulation of thiamine metabolism, and tight regulation of oxidative phosphorylation. PMID:26942765

  14. Metabolic Reconstruction and Modeling of Nitrogen Fixation in Rhizobium etli

    PubMed Central

    Resendis-Antonio, Osbaldo; Reed, Jennifer L; Encarnación, Sergio; Collado-Vides, Julio; Palsson, Bernhard Ø

    2007-01-01

    Rhizobiaceas are bacteria that fix nitrogen during symbiosis with plants. This symbiotic relationship is crucial for the nitrogen cycle, and understanding symbiotic mechanisms is a scientific challenge with direct applications in agronomy and plant development. Rhizobium etli is a bacteria which provides legumes with ammonia (among other chemical compounds), thereby stimulating plant growth. A genome-scale approach, integrating the biochemical information available for R. etli, constitutes an important step toward understanding the symbiotic relationship and its possible improvement. In this work we present a genome-scale metabolic reconstruction (iOR363) for R. etli CFN42, which includes 387 metabolic and transport reactions across 26 metabolic pathways. This model was used to analyze the physiological capabilities of R. etli during stages of nitrogen fixation. To study the physiological capacities in silico, an objective function was formulated to simulate symbiotic nitrogen fixation. Flux balance analysis (FBA) was performed, and the predicted active metabolic pathways agreed qualitatively with experimental observations. In addition, predictions for the effects of gene deletions during nitrogen fixation in Rhizobia in silico also agreed with reported experimental data. Overall, we present some evidence supporting that FBA of the reconstructed metabolic network for R. etli provides results that are in agreement with physiological observations. Thus, as for other organisms, the reconstructed genome-scale metabolic network provides an important framework which allows us to compare model predictions with experimental measurements and eventually generate hypotheses on ways to improve nitrogen fixation. PMID:17922569

  15. Reconstructed Metabolic Network Models Predict Flux-Level Metabolic Reprogramming in Glioblastoma.

    PubMed

    Özcan, Emrah; Çakır, Tunahan

    2016-01-01

    Developments in genome scale metabolic modeling techniques and omics technologies have enabled the reconstruction of context-specific metabolic models. In this study, glioblastoma multiforme (GBM), one of the most common and aggressive malignant brain tumors, is investigated by mapping GBM gene expression data on the growth-implemented brain specific genome-scale metabolic network, and GBM-specific models are generated. The models are used to calculate metabolic flux distributions in the tumor cells. Metabolic phenotypes predicted by the GBM-specific metabolic models reconstructed in this work reflect the general metabolic reprogramming of GBM, reported both in in-vitro and in-vivo experiments. The computed flux profiles quantitatively predict that major sources of the acetyl-CoA and oxaloacetic acid pool used in TCA cycle are pyruvate dehydrogenase from glycolysis and anaplerotic flux from glutaminolysis, respectively. Also, our results, in accordance with recent studies, predict a contribution of oxidative phosphorylation to ATP pool via a slightly active TCA cycle in addition to the major contributor aerobic glycolysis. We verified our results by using different computational methods that incorporate transcriptome data with genome-scale models and by using different transcriptome datasets. Correct predictions of flux distributions in glycolysis, glutaminolysis, TCA cycle and lipid precursor metabolism validate the reconstructed models for further use in future to simulate more specific metabolic patterns for GBM. PMID:27147948

  16. Reconstructed Metabolic Network Models Predict Flux-Level Metabolic Reprogramming in Glioblastoma

    PubMed Central

    Özcan, Emrah; Çakır, Tunahan

    2016-01-01

    Developments in genome scale metabolic modeling techniques and omics technologies have enabled the reconstruction of context-specific metabolic models. In this study, glioblastoma multiforme (GBM), one of the most common and aggressive malignant brain tumors, is investigated by mapping GBM gene expression data on the growth-implemented brain specific genome-scale metabolic network, and GBM-specific models are generated. The models are used to calculate metabolic flux distributions in the tumor cells. Metabolic phenotypes predicted by the GBM-specific metabolic models reconstructed in this work reflect the general metabolic reprogramming of GBM, reported both in in-vitro and in-vivo experiments. The computed flux profiles quantitatively predict that major sources of the acetyl-CoA and oxaloacetic acid pool used in TCA cycle are pyruvate dehydrogenase from glycolysis and anaplerotic flux from glutaminolysis, respectively. Also, our results, in accordance with recent studies, predict a contribution of oxidative phosphorylation to ATP pool via a slightly active TCA cycle in addition to the major contributor aerobic glycolysis. We verified our results by using different computational methods that incorporate transcriptome data with genome-scale models and by using different transcriptome datasets. Correct predictions of flux distributions in glycolysis, glutaminolysis, TCA cycle and lipid precursor metabolism validate the reconstructed models for further use in future to simulate more specific metabolic patterns for GBM. PMID:27147948

  17. Patterns of metabolite changes identified from large-scale gene perturbations in Arabidopsis using a genome-scale metabolic network.

    PubMed

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

    2015-04-01

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

  18. SeqFold: genome-scale reconstruction of RNA secondary structure integrating high-throughput sequencing data.

    PubMed

    Ouyang, Zhengqing; Snyder, Michael P; Chang, Howard Y

    2013-02-01

    We present an integrative approach, SeqFold, that combines high-throughput RNA structure profiling data with computational prediction for genome-scale reconstruction of RNA secondary structures. SeqFold transforms experimental RNA structure information into a structure preference profile (SPP) and uses it to select stable RNA structure candidates representing the structure ensemble. Under a high-dimensional classification framework, SeqFold efficiently matches a given SPP to the most likely cluster of structures sampled from the Boltzmann-weighted ensemble. SeqFold is able to incorporate diverse types of RNA structure profiling data, including parallel analysis of RNA structure (PARS), selective 2'-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq), fragmentation sequencing (FragSeq) data generated by deep sequencing, and conventional SHAPE data. Using the known structures of a wide range of mRNAs and noncoding RNAs as benchmarks, we demonstrate that SeqFold outperforms or matches existing approaches in accuracy and is more robust to noise in experimental data. Application of SeqFold to reconstruct the secondary structures of the yeast transcriptome reveals the diverse impact of RNA secondary structure on gene regulation, including translation efficiency, transcription initiation, and protein-RNA interactions. SeqFold can be easily adapted to incorporate any new types of high-throughput RNA structure profiling data and is widely applicable to analyze RNA structures in any transcriptome. PMID:23064747

  19. Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicity

    PubMed Central

    Bosi, Emanuele; Monk, Jonathan M.; Aziz, Ramy K.; Fondi, Marco; Nizet, Victor; Palsson, Bernhard Ø.

    2016-01-01

    Staphylococcus aureus is a preeminent bacterial pathogen capable of colonizing diverse ecological niches within its human host. We describe here the pangenome of S. aureus based on analysis of genome sequences from 64 strains of S. aureus spanning a range of ecological niches, host types, and antibiotic resistance profiles. Based on this set, S. aureus is expected to have an open pangenome composed of 7,411 genes and a core genome composed of 1,441 genes. Metabolism was highly conserved in this core genome; however, differences were identified in amino acid and nucleotide biosynthesis pathways between the strains. Genome-scale models (GEMs) of metabolism were constructed for the 64 strains of S. aureus. These GEMs enabled a systems approach to characterizing the core metabolic and panmetabolic capabilities of the S. aureus species. All models were predicted to be auxotrophic for the vitamins niacin (vitamin B3) and thiamin (vitamin B1), whereas strain-specific auxotrophies were predicted for riboflavin (vitamin B2), guanosine, leucine, methionine, and cysteine, among others. GEMs were used to systematically analyze growth capabilities in more than 300 different growth-supporting environments. The results identified metabolic capabilities linked to pathogenic traits and virulence acquisitions. Such traits can be used to differentiate strains responsible for mild vs. severe infections and preference for hosts (e.g., animals vs. humans). Genome-scale analysis of multiple strains of a species can thus be used to identify metabolic determinants of virulence and increase our understanding of why certain strains of this deadly pathogen have spread rapidly throughout the world. PMID:27286824

  20. Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicity.

    PubMed

    Bosi, Emanuele; Monk, Jonathan M; Aziz, Ramy K; Fondi, Marco; Nizet, Victor; Palsson, Bernhard Ø

    2016-06-28

    Staphylococcus aureus is a preeminent bacterial pathogen capable of colonizing diverse ecological niches within its human host. We describe here the pangenome of S. aureus based on analysis of genome sequences from 64 strains of S. aureus spanning a range of ecological niches, host types, and antibiotic resistance profiles. Based on this set, S. aureus is expected to have an open pangenome composed of 7,411 genes and a core genome composed of 1,441 genes. Metabolism was highly conserved in this core genome; however, differences were identified in amino acid and nucleotide biosynthesis pathways between the strains. Genome-scale models (GEMs) of metabolism were constructed for the 64 strains of S. aureus These GEMs enabled a systems approach to characterizing the core metabolic and panmetabolic capabilities of the S. aureus species. All models were predicted to be auxotrophic for the vitamins niacin (vitamin B3) and thiamin (vitamin B1), whereas strain-specific auxotrophies were predicted for riboflavin (vitamin B2), guanosine, leucine, methionine, and cysteine, among others. GEMs were used to systematically analyze growth capabilities in more than 300 different growth-supporting environments. The results identified metabolic capabilities linked to pathogenic traits and virulence acquisitions. Such traits can be used to differentiate strains responsible for mild vs. severe infections and preference for hosts (e.g., animals vs. humans). Genome-scale analysis of multiple strains of a species can thus be used to identify metabolic determinants of virulence and increase our understanding of why certain strains of this deadly pathogen have spread rapidly throughout the world. PMID:27286824

  1. Integrating Kinetic Model of E. coli with Genome Scale Metabolic Fluxes Overcomes Its Open System Problem and Reveals Bistability in Central Metabolism

    PubMed Central

    Mannan, Ahmad A.; Toya, Yoshihiro; Shimizu, Kazuyuki; McFadden, Johnjoe; Kierzek, Andrzej M.; Rocco, Andrea

    2015-01-01

    An understanding of the dynamics of the metabolic profile of a bacterial cell is sought from a dynamical systems analysis of kinetic models. This modelling formalism relies on a deterministic mathematical description of enzyme kinetics and their metabolite regulation. However, it is severely impeded by the lack of available kinetic information, limiting the size of the system that can be modelled. Furthermore, the subsystem of the metabolic network whose dynamics can be modelled is faced with three problems: how to parameterize the model with mostly incomplete steady state data, how to close what is now an inherently open system, and how to account for the impact on growth. In this study we address these challenges of kinetic modelling by capitalizing on multi-‘omics’ steady state data and a genome-scale metabolic network model. We use these to generate parameters that integrate knowledge embedded in the genome-scale metabolic network model, into the most comprehensive kinetic model of the central carbon metabolism of E. coli realized to date. As an application, we performed a dynamical systems analysis of the resulting enriched model. This revealed bistability of the central carbon metabolism and thus its potential to express two distinct metabolic states. Furthermore, since our model-informing technique ensures both stable states are constrained by the same thermodynamically feasible steady state growth rate, the ensuing bistability represents a temporal coexistence of the two states, and by extension, reveals the emergence of a phenotypically heterogeneous population. PMID:26469081

  2. An integrated approach to reconstructing genome-scale transcriptional regulatory networks

    SciTech Connect

    Imam, Saheed; Noguera, Daniel R.; Donohue, Timothy J.; Leslie, Christina

    2015-02-27

    Transcriptional regulatory networks (TRNs) program cells to dynamically alter their gene expression in response to changing internal or environmental conditions. In this study, we develop a novel workflow for generating large-scale TRN models that integrates comparative genomics data, global gene expression analyses, and intrinsic properties of transcription factors (TFs). An assessment of this workflow using benchmark datasets for the well-studied γ-proteobacterium Escherichia coli showed that it outperforms expression-based inference approaches, having a significantly larger area under the precision-recall curve. Further analysis indicated that this integrated workflow captures different aspects of the E. coli TRN than expression-based approaches, potentially making them highly complementary. We leveraged this new workflow and observations to build a large-scale TRN model for the α-Proteobacterium Rhodobacter sphaeroides that comprises 120 gene clusters, 1211 genes (including 93 TFs), 1858 predicted protein-DNA interactions and 76 DNA binding motifs. We found that ~67% of the predicted gene clusters in this TRN are enriched for functions ranging from photosynthesis or central carbon metabolism to environmental stress responses. We also found that members of many of the predicted gene clusters were consistent with prior knowledge in R. sphaeroides and/or other bacteria. Experimental validation of predictions from this R. sphaeroides TRN model showed that high precision and recall was also obtained for TFs involved in photosynthesis (PpsR), carbon metabolism (RSP_0489) and iron homeostasis (RSP_3341). In addition, this integrative approach enabled generation of TRNs with increased information content relative to R. sphaeroides TRN models built via other approaches. We also show how this approach can be used to simultaneously produce TRN models for each related organism used in the comparative genomics analysis. Our results highlight the advantages of integrating

  3. An integrated approach to reconstructing genome-scale transcriptional regulatory networks

    DOE PAGESBeta

    Imam, Saheed; Noguera, Daniel R.; Donohue, Timothy J.; Leslie, Christina

    2015-02-27

    Transcriptional regulatory networks (TRNs) program cells to dynamically alter their gene expression in response to changing internal or environmental conditions. In this study, we develop a novel workflow for generating large-scale TRN models that integrates comparative genomics data, global gene expression analyses, and intrinsic properties of transcription factors (TFs). An assessment of this workflow using benchmark datasets for the well-studied γ-proteobacterium Escherichia coli showed that it outperforms expression-based inference approaches, having a significantly larger area under the precision-recall curve. Further analysis indicated that this integrated workflow captures different aspects of the E. coli TRN than expression-based approaches, potentially making themmore » highly complementary. We leveraged this new workflow and observations to build a large-scale TRN model for the α-Proteobacterium Rhodobacter sphaeroides that comprises 120 gene clusters, 1211 genes (including 93 TFs), 1858 predicted protein-DNA interactions and 76 DNA binding motifs. We found that ~67% of the predicted gene clusters in this TRN are enriched for functions ranging from photosynthesis or central carbon metabolism to environmental stress responses. We also found that members of many of the predicted gene clusters were consistent with prior knowledge in R. sphaeroides and/or other bacteria. Experimental validation of predictions from this R. sphaeroides TRN model showed that high precision and recall was also obtained for TFs involved in photosynthesis (PpsR), carbon metabolism (RSP_0489) and iron homeostasis (RSP_3341). In addition, this integrative approach enabled generation of TRNs with increased information content relative to R. sphaeroides TRN models built via other approaches. We also show how this approach can be used to simultaneously produce TRN models for each related organism used in the comparative genomics analysis. Our results highlight the advantages of

  4. An Integrated Approach to Reconstructing Genome-Scale Transcriptional Regulatory Networks

    PubMed Central

    Imam, Saheed; Noguera, Daniel R.; Donohue, Timothy J.

    2015-01-01

    Transcriptional regulatory networks (TRNs) program cells to dynamically alter their gene expression in response to changing internal or environmental conditions. In this study, we develop a novel workflow for generating large-scale TRN models that integrates comparative genomics data, global gene expression analyses, and intrinsic properties of transcription factors (TFs). An assessment of this workflow using benchmark datasets for the well-studied γ-proteobacterium Escherichia coli showed that it outperforms expression-based inference approaches, having a significantly larger area under the precision-recall curve. Further analysis indicated that this integrated workflow captures different aspects of the E. coli TRN than expression-based approaches, potentially making them highly complementary. We leveraged this new workflow and observations to build a large-scale TRN model for the α-Proteobacterium Rhodobacter sphaeroides that comprises 120 gene clusters, 1211 genes (including 93 TFs), 1858 predicted protein-DNA interactions and 76 DNA binding motifs. We found that ~67% of the predicted gene clusters in this TRN are enriched for functions ranging from photosynthesis or central carbon metabolism to environmental stress responses. We also found that members of many of the predicted gene clusters were consistent with prior knowledge in R. sphaeroides and/or other bacteria. Experimental validation of predictions from this R. sphaeroides TRN model showed that high precision and recall was also obtained for TFs involved in photosynthesis (PpsR), carbon metabolism (RSP_0489) and iron homeostasis (RSP_3341). In addition, this integrative approach enabled generation of TRNs with increased information content relative to R. sphaeroides TRN models built via other approaches. We also show how this approach can be used to simultaneously produce TRN models for each related organism used in the comparative genomics analysis. Our results highlight the advantages of integrating

  5. Modeling and Simulation of Optimal Resource Management during the Diurnal Cycle in Emiliania huxleyi by Genome-Scale Reconstruction and an Extended Flux Balance Analysis Approach

    PubMed Central

    Knies, David; Wittmüß, Philipp; Appel, Sebastian; Sawodny, Oliver; Ederer, Michael; Feuer, Ronny

    2015-01-01

    The coccolithophorid unicellular alga Emiliania huxleyi is known to form large blooms, which have a strong effect on the marine carbon cycle. As a photosynthetic organism, it is subjected to a circadian rhythm due to the changing light conditions throughout the day. For a better understanding of the metabolic processes under these periodically-changing environmental conditions, a genome-scale model based on a genome reconstruction of the E. huxleyi strain CCMP 1516 was created. It comprises 410 reactions and 363 metabolites. Biomass composition is variable based on the differentiation into functional biomass components and storage metabolites. The model is analyzed with a flux balance analysis approach called diurnal flux balance analysis (diuFBA) that was designed for organisms with a circadian rhythm. It allows storage metabolites to accumulate or be consumed over the diurnal cycle, while keeping the structure of a classical FBA problem. A feature of this approach is that the production and consumption of storage metabolites is not defined externally via the biomass composition, but the result of optimal resource management adapted to the diurnally-changing environmental conditions. The model in combination with this approach is able to simulate the variable biomass composition during the diurnal cycle in proximity to literature data. PMID:26516924

  6. Analysis of Genetic Variation and Potential Applications in Genome-Scale Metabolic Modeling

    PubMed Central

    Cardoso, João G. R.; Andersen, Mikael Rørdam; Herrgård, Markus J.; Sonnenschein, Nikolaus

    2015-01-01

    Genetic variation is the motor of evolution and allows organisms to overcome the environmental challenges they encounter. It can be both beneficial and harmful in the process of engineering cell factories for the production of proteins and chemicals. Throughout the history of biotechnology, there have been efforts to exploit genetic variation in our favor to create strains with favorable phenotypes. Genetic variation can either be present in natural populations or it can be artificially created by mutagenesis and selection or adaptive laboratory evolution. On the other hand, unintended genetic variation during a long term production process may lead to significant economic losses and it is important to understand how to control this type of variation. With the emergence of next-generation sequencing technologies, genetic variation in microbial strains can now be determined on an unprecedented scale and resolution by re-sequencing thousands of strains systematically. In this article, we review challenges in the integration and analysis of large-scale re-sequencing data, present an extensive overview of bioinformatics methods for predicting the effects of genetic variants on protein function, and discuss approaches for interfacing existing bioinformatics approaches with genome-scale models of cellular processes in order to predict effects of sequence variation on cellular phenotypes. PMID:25763369

  7. A genome-scale metabolic flux model of Escherichia coli K–12 derived from the EcoCyc database

    PubMed Central

    2014-01-01

    Background Constraint-based models of Escherichia coli metabolic flux have played a key role in computational studies of cellular metabolism at the genome scale. We sought to develop a next-generation constraint-based E. coli model that achieved improved phenotypic prediction accuracy while being frequently updated and easy to use. We also sought to compare model predictions with experimental data to highlight open questions in E. coli biology. Results We present EcoCyc–18.0–GEM, a genome-scale model of the E. coli K–12 MG1655 metabolic network. The model is automatically generated from the current state of EcoCyc using the MetaFlux software, enabling the release of multiple model updates per year. EcoCyc–18.0–GEM encompasses 1445 genes, 2286 unique metabolic reactions, and 1453 unique metabolites. We demonstrate a three-part validation of the model that breaks new ground in breadth and accuracy: (i) Comparison of simulated growth in aerobic and anaerobic glucose culture with experimental results from chemostat culture and simulation results from the E. coli modeling literature. (ii) Essentiality prediction for the 1445 genes represented in the model, in which EcoCyc–18.0–GEM achieves an improved accuracy of 95.2% in predicting the growth phenotype of experimental gene knockouts. (iii) Nutrient utilization predictions under 431 different media conditions, for which the model achieves an overall accuracy of 80.7%. The model’s derivation from EcoCyc enables query and visualization via the EcoCyc website, facilitating model reuse and validation by inspection. We present an extensive investigation of disagreements between EcoCyc–18.0–GEM predictions and experimental data to highlight areas of interest to E. coli modelers and experimentalists, including 70 incorrect predictions of gene essentiality on glucose, 80 incorrect predictions of gene essentiality on glycerol, and 83 incorrect predictions of nutrient utilization. Conclusion Significant

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

    PubMed

    Vitkin, Edward; Shlomi, Tomer

    2012-01-01

    Genome-scale metabolic network reconstructions are considered a key step in quantifying the genotype-phenotype relationship. We present a novel gap-filling approach, MetabolIc Reconstruction via functionAl GEnomics (MIRAGE), which identifies missing network reactions by integrating metabolic flux analysis and functional genomics data. MIRAGE's performance is demonstrated on the reconstruction of metabolic network models of E. coli and Synechocystis sp. and validated via existing networks for these species. Then, it is applied to reconstruct genome-scale metabolic network models for 36 sequenced cyanobacteria amenable for constraint-based modeling analysis and specifically for metabolic engineering. The reconstructed network models are supplied via standard SBML files. PMID:23194418

  9. A kidney-specific genome-scale metabolic network model for analyzing focal segmental glomerulosclerosis.

    PubMed

    Sohrabi-Jahromi, Salma; Marashi, Sayed-Amir; Kalantari, Shiva

    2016-04-01

    Focal Segmental Glomerulosclerosis (FSGS) is a type of nephrotic syndrome which accounts for 20 and 40 % of such cases in children and adults, respectively. The high prevalence of FSGS makes it the most common primary glomerular disorder causing end-stage renal disease. Although the pathogenesis of this disorder has been widely investigated, the exact mechanism underlying this disease is still to be discovered. Current therapies seek to stop the progression of FSGS and often fail to cure the patients since progression to end-stage renal failure is usually inevitable. In the present work, we use a kidney-specific metabolic network model to study FSGS. The model was obtained by merging two previously published kidney-specific metabolic network models. The validity of the new model was checked by comparing the inactivating reaction genes identified in silico to the list of kidney disease implicated genes. To model the disease state, we used a complete list of FSGS metabolic biomarkers extracted from transcriptome and proteome profiling of patients as well as genetic deficiencies known to cause FSGS. We observed that some specific pathways including chondroitin sulfate degradation, eicosanoid metabolism, keratan sulfate biosynthesis, vitamin B6 metabolism, and amino acid metabolism tend to show variations in FSGS model compared to healthy kidney. Furthermore, we computationally searched for the potential drug targets that can revert the diseased metabolic state to the healthy state. Interestingly, only one drug target, N-acetylgalactosaminidase, was found whose inhibition could alter cellular metabolism towards healthy state. PMID:26923795

  10. In silico method for modelling metabolism and gene product expression at genome scale

    SciTech Connect

    Lerman, Joshua A.; Hyduke, Daniel R.; Latif, Haythem; Portnoy, Vasiliy A.; Lewis, Nathan E.; Orth, Jeffrey D.; Rutledge, Alexandra C.; Smith, Richard D.; Adkins, Joshua N.; Zengler, Karsten; Palsson, Bernard O.

    2012-07-03

    Transcription and translation use raw materials and energy generated metabolically to create the macromolecular machinery responsible for all cellular functions, including metabolism. A biochemically accurate model of molecular biology and metabolism will facilitate comprehensive and quantitative computations of an organism's molecular constitution as a function of genetic and environmental parameters. Here we formulate a model of metabolism and macromolecular expression. Prototyping it using the simple microorganism Thermotoga maritima, we show our model accurately simulates variations in cellular composition and gene expression. Moreover, through in silico comparative transcriptomics, the model allows the discovery of new regulons and improving the genome and transcription unit annotations. Our method presents a framework for investigating molecular biology and cellular physiology in silico and may allow quantitative interpretation of multi-omics data sets in the context of an integrated biochemical description of an organism.

  11. In silico method for modelling metabolism and gene product expression at genome scale

    PubMed Central

    Lerman, Joshua A.; Hyduke, Daniel R.; Latif, Haythem; Portnoy, Vasiliy A.; Lewis, nathan E.; Orth, Jeffrey D.; Schrimpe-Rutledge, Alexandra C.; Smith, Richard D.; Adkins, Joshua n.; Zengler, Karsten; Palsson, Bernhard O.

    2013-01-01

    Transcription and translation use raw materials and energy generated metabolically to create the macromolecular machinery responsible for all cellular functions, including metabolism. A biochemically accurate model of molecular biology and metabolism will facilitate comprehensive and quantitative computations of an organism's molecular constitution as a function of genetic and environmental parameters. Here we formulate a model of metabolism and macromolecular expression. Prototyping it using the simple microorganism Thermotoga maritima, we show our model accurately simulates variations in cellular composition and gene expression. Moreover, through in silico comparative transcriptomics, the model allows the discovery of new regulons and improving the genome and transcription unit annotations. Our method presents a framework for investigating molecular biology and cellular physiology in silico and may allow quantitative interpretation of multi-omics data sets in the context of an integrated biochemical description of an organism. PMID:22760628

  12. Genome-scale analysis of anaerobic benzoate and phenol metabolism in the hyperthermophilic archaeon Ferroglobus placidus

    PubMed Central

    Holmes, Dawn E; Risso, Carla; Smith, Jessica A; Lovley, Derek R

    2012-01-01

    Insight into the mechanisms for the anaerobic metabolism of aromatic compounds by the hyperthermophilic archaeon Ferroglobus placidus is expected to improve understanding of the degradation of aromatics in hot (>80° C) environments and to identify enzymes that might have biotechnological applications. Analysis of the F. placidus genome revealed genes predicted to encode enzymes homologous to those previously identified as having a role in benzoate and phenol metabolism in mesophilic bacteria. Surprisingly, F. placidus lacks genes for an ATP-independent class II benzoyl-CoA (coenzyme A) reductase (BCR) found in all strictly anaerobic bacteria, but has instead genes coding for a bzd-type ATP-consuming class I BCR, similar to those found in facultative bacteria. The lower portion of the benzoate degradation pathway appears to be more similar to that found in the phototroph Rhodopseudomonas palustris, than the pathway reported for all heterotrophic anaerobic benzoate degraders. Many of the genes predicted to be involved in benzoate metabolism were found in one of two gene clusters. Genes for phenol carboxylation proceeding through a phenylphosphate intermediate were identified in a single gene cluster. Analysis of transcript abundance with a whole-genome microarray and quantitative reverse transcriptase polymerase chain reaction demonstrated that most of the genes predicted to be involved in benzoate or phenol metabolism had higher transcript abundance during growth on those substrates vs growth on acetate. These results suggest that the general strategies for benzoate and phenol metabolism are highly conserved between microorganisms living in moderate and hot environments, and that anaerobic metabolism of aromatic compounds might be analyzed in a wide range of environments with similar molecular targets. PMID:21776029

  13. ReacKnock: Identifying Reaction Deletion Strategies for Microbial Strain Optimization Based on Genome-Scale Metabolic Network

    PubMed Central

    Xu, Zixiang; Zheng, Ping; Sun, Jibin; Ma, Yanhe

    2013-01-01

    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. PMID:24348984

  14. Responses to Light Intensity in a Genome-Scale Model of Rice Metabolism1[C][W][OA

    PubMed Central

    Poolman, Mark G.; Kundu, Sudip; Shaw, Rahul; Fell, David A.

    2013-01-01

    We describe the construction and analysis of a genome-scale metabolic model representing a developing leaf cell of rice (Oryza sativa) primarily derived from the annotations in the RiceCyc database. We used flux balance analysis to determine that the model represents a network capable of producing biomass precursors (amino acids, nucleotides, lipid, starch, cellulose, and lignin) in experimentally reported proportions, using carbon dioxide as the sole carbon source. We then repeated the analysis over a range of photon flux values to examine responses in the solutions. The resulting flux distributions show that (1) redox shuttles between the chloroplast, cytosol, and mitochondrion may play a significant role at low light levels, (2) photorespiration can act to dissipate excess energy at high light levels, and (3) the role of mitochondrial metabolism is likely to vary considerably according to the balance between energy demand and availability. It is notable that these organelle interactions, consistent with many experimental observations, arise solely as a result of the need for mass and energy balancing without any explicit assumptions concerning kinetic or other regulatory mechanisms. PMID:23640755

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

    PubMed

    Stanford, Natalie J; Lubitz, Timo; 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

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

    SciTech Connect

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

    2015-05-05

    Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription, and signaling) have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or 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. Lastly, in this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system.

  17. MetaboTools: A Comprehensive Toolbox for Analysis of Genome-Scale Metabolic Models

    PubMed Central

    Aurich, Maike K.; Fleming, Ronan M. T.; Thiele, Ines

    2016-01-01

    Metabolomic data sets provide a direct read-out of cellular phenotypes and are increasingly generated to study biological questions. Previous work, by us and others, revealed the potential of analyzing extracellular metabolomic data in the context of the metabolic model using constraint-based modeling. With the MetaboTools, we make our methods available to the broader scientific community. The MetaboTools consist of a protocol, a toolbox, and tutorials of two use cases. The protocol describes, in a step-wise manner, the workflow of data integration, and computational analysis. The MetaboTools comprise the Matlab code required to complete the workflow described in the protocol. Tutorials explain the computational steps for integration of two different data sets and demonstrate a comprehensive set of methods for the computational analysis of metabolic models and stratification thereof into different phenotypes. The presented workflow supports integrative analysis of multiple omics data sets. Importantly, all analysis tools can be applied to metabolic models without performing the entire workflow. Taken together, the MetaboTools constitute a comprehensive guide to the intra-model analysis of extracellular metabolomic data from microbial, plant, or human cells. This computational modeling resource offers a broad set of computational analysis tools for a wide biomedical and non-biomedical research community. PMID:27536246

  18. MetaboTools: A Comprehensive Toolbox for Analysis of Genome-Scale Metabolic Models.

    PubMed

    Aurich, Maike K; Fleming, Ronan M T; Thiele, Ines

    2016-01-01

    Metabolomic data sets provide a direct read-out of cellular phenotypes and are increasingly generated to study biological questions. Previous work, by us and others, revealed the potential of analyzing extracellular metabolomic data in the context of the metabolic model using constraint-based modeling. With the MetaboTools, we make our methods available to the broader scientific community. The MetaboTools consist of a protocol, a toolbox, and tutorials of two use cases. The protocol describes, in a step-wise manner, the workflow of data integration, and computational analysis. The MetaboTools comprise the Matlab code required to complete the workflow described in the protocol. Tutorials explain the computational steps for integration of two different data sets and demonstrate a comprehensive set of methods for the computational analysis of metabolic models and stratification thereof into different phenotypes. The presented workflow supports integrative analysis of multiple omics data sets. Importantly, all analysis tools can be applied to metabolic models without performing the entire workflow. Taken together, the MetaboTools constitute a comprehensive guide to the intra-model analysis of extracellular metabolomic data from microbial, plant, or human cells. This computational modeling resource offers a broad set of computational analysis tools for a wide biomedical and non-biomedical research community. PMID:27536246

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

    SciTech Connect

    Imam, S; Schauble, S; Brooks, AN; Baliga, NS; Price, ND

    2015-05-05

    Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription, and signaling) have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or 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.

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

    DOE PAGESBeta

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

    2015-05-05

    Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription, and signaling) have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or moremore » network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. Lastly, in this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system.« less

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

    PubMed Central

    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

  2. Global Metabolic Reconstruction and Metabolic Gene Evolution in the Cattle Genome

    PubMed Central

    Kim, Woonsu; Park, Hyesun; Seo, Seongwon

    2016-01-01

    The sequence of cattle genome provided a valuable opportunity to systematically link genetic and metabolic traits of cattle. The objectives of this study were 1) to reconstruct genome-scale cattle-specific metabolic pathways based on the most recent and updated cattle genome build and 2) to identify duplicated metabolic genes in the cattle genome for better understanding of metabolic adaptations in cattle. A bioinformatic pipeline of an organism for amalgamating genomic annotations from multiple sources was updated. Using this, an amalgamated cattle genome database based on UMD_3.1, was created. The amalgamated cattle genome database is composed of a total of 33,292 genes: 19,123 consensus genes between NCBI and Ensembl databases, 8,410 and 5,493 genes only found in NCBI or Ensembl, respectively, and 266 genes from NCBI scaffolds. A metabolic reconstruction of the cattle genome and cattle pathway genome database (PGDB) was also developed using Pathway Tools, followed by an intensive manual curation. The manual curation filled or revised 68 pathway holes, deleted 36 metabolic pathways, and added 23 metabolic pathways. Consequently, the curated cattle PGDB contains 304 metabolic pathways, 2,460 reactions including 2,371 enzymatic reactions, and 4,012 enzymes. Furthermore, this study identified eight duplicated genes in 12 metabolic pathways in the cattle genome compared to human and mouse. Some of these duplicated genes are related with specific hormone biosynthesis and detoxifications. The updated genome-scale metabolic reconstruction is a useful tool for understanding biology and metabolic characteristics in cattle. There has been significant improvements in the quality of cattle genome annotations and the MetaCyc database. The duplicated metabolic genes in the cattle genome compared to human and mouse implies evolutionary changes in the cattle genome and provides a useful information for further research on understanding metabolic adaptations of cattle. PMID

  3. Reconstruction and applications of consensus yeast metabolic network based on RNA sequencing.

    PubMed

    Zhao, Yuqi; Wang, Yanjie; Zou, Lei; Huang, Jingfei

    2016-04-01

    One practical application of genome-scale metabolic reconstructions is to interrogate multispecies relationships. Here, we report a consensus metabolic model in four yeast species (Saccharomyces cerevisiae, S. paradoxus, S. mikatae, and S. bayanus) by integrating metabolic network simulations with RNA sequencing (RNA-seq) datasets. We generated high-resolution transcriptome maps of four yeast species through de novo assembly and genome-guided approaches. The transcriptomes were annotated and applied to build the consensus metabolic network, which was verified using independent RNA-seq experiments. The expression profiles reveal that the genes involved in amino acid and lipid metabolism are highly coexpressed. The diverse phenotypic characteristics, such as cellular growth and gene deletions, can be simulated using the metabolic model. We also explored the applications of the consensus model in metabolic engineering using yeast-specific reactions and biofuel production as examples. Similar strategies will benefit communities studying genome-scale metabolic networks of other organisms. PMID:27239440

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

    PubMed Central

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

    2011-01-01

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

  5. A genome-scale metabolic model of Methanococcus maripaludis S2 for CO2 capture and conversion to methane.

    PubMed

    Goyal, Nishu; Widiastuti, Hanifah; Karimi, I A; Zhou, Zhi

    2014-05-01

    Methane is a major energy source for heating and electricity. Its production by methanogenic bacteria is widely known in nature. M. maripaludis S2 is a fully sequenced hydrogenotrophic methanogen and an excellent laboratory strain with robust genetic tools. However, a quantitative systems biology model to complement these tools is absent in the literature. To understand and enhance its methanogenesis from CO2, this work presents the first constraint-based genome-scale metabolic model (iMM518). It comprises 570 reactions, 556 distinct metabolites, and 518 genes along with gene-protein-reaction (GPR) associations, and covers 30% of open reading frames (ORFs). The model was validated using biomass growth data and experimental phenotypic studies from the literature. Its comparison with the in silico models of Methanosarcina barkeri, Methanosarcina acetivorans, and Sulfolobus solfataricus P2 shows M. maripaludis S2 to be a better organism for producing methane. Using the model, genes essential for growth were identified, and the efficacies of alternative carbon, hydrogen and nitrogen sources were studied. The model can predict the effects of reengineering M. maripaludis S2 to guide or expedite experimental efforts. PMID:24553424

  6. Pore-scale simulation of microbial growth using a genome-scale metabolic model: Implications for Darcy-scale reactive transport

    SciTech Connect

    Tartakovsky, Guzel D.; Tartakovsky, Alexandre M.; Scheibe, Timothy D.; Fang, Yilin; Mahadevan, Radhakrishnan; Lovley, Derek R.

    2013-09-07

    Recent advances in microbiology have enabled the quantitative simulation of microbial metabolism and growth based on genome-scale characterization of metabolic pathways and fluxes. We have incorporated a genome-scale metabolic model of the iron-reducing bacteria Geobacter sulfurreducens into a pore-scale simulation of microbial growth based on coupling of iron reduction to oxidation of a soluble electron donor (acetate). In our model, fluid flow and solute transport is governed by a combination of the Navier-Stokes and advection-diffusion-reaction equations. Microbial growth occurs only on the surface of soil grains where solid-phase mineral iron oxides are available. Mass fluxes of chemical species associated with microbial growth are described by the genome-scale microbial model, implemented using a constraint-based metabolic model, and provide the Robin-type boundary condition for the advection-diffusion equation at soil grain surfaces. Conventional models of microbially-mediated subsurface reactions use a lumped reaction model that does not consider individual microbial reaction pathways, and describe reactions rates using empirically-derived rate formulations such as the Monod-type kinetics. We have used our pore-scale model to explore the relationship between genome-scale metabolic models and Monod-type formulations, and to assess the manifestation of pore-scale variability (microenvironments) in terms of apparent Darcy-scale microbial reaction rates. The genome-scale model predicted lower biomass yield, and different stoichiometry for iron consumption, in comparisonto prior Monod formulations based on energetics considerations. We were able to fit an equivalent Monod model, by modifying the reaction stoichiometry and biomass yield coefficient, that could effectively match results of the genome-scale simulation of microbial behaviors under excess nutrient conditions, but predictions of the fitted Monod model deviated from those of the genome-scale model under

  7. Pore-scale simulation of microbial growth using a genome-scale metabolic model: Implications for Darcy-scale reactive transport

    NASA Astrophysics Data System (ADS)

    Scheibe, T. D.; Tartakovsky, G.; Tartakovsky, A. M.; Fang, Y.; Mahadevan, R.; Lovley, D. R.

    2012-12-01

    Recent advances in microbiology have enabled the quantitative simulation of microbial metabolism and growth based on genome-scale characterization of metabolic pathways and fluxes. We have incorporated a genome-scale metabolic model of the iron-reducing bacteria Geobacter sulfurreducens into a pore-scale simulation of microbial growth based on coupling of iron reduction to oxidation of a soluble electron donor (acetate). In our model, fluid flow and solute transport is governed by a combination of the Navier-Stokes and advection-diffusion-reaction equations. Microbial growth occurs only on the surface of soil grains where solid-phase mineral iron oxides are available. Mass fluxes of chemical species associated with microbial growth are described by the genome-scale microbial model, implemented using a constraint-based metabolic model, and provide the Robin-type boundary condition for the advection-diffusion equation at soil grain surfaces. Conventional models of microbially-mediated subsurface reactions use a lumped reaction model that does not consider individual microbial reaction pathways, and describe reactions rates using empirically-derived rate formulations such as the Monod-type kinetics. We have used our pore-scale model to explore the relationship between genome-scale metabolic models and Monod-type formulations, and to assess the manifestation of pore-scale variability (microenvironments) in terms of apparent Darcy-scale microbial reaction rates. The genome-scale model predicted lower biomass yield, and different stoichiometry for iron consumption, in comparison to prior Monod formulations based on energetics considerations. We were able to fit an equivalent Monod model, by modifying the reaction stoichiometry and biomass yield coefficient, that could effectively match results of the genome-scale simulation of microbial behaviors under excess nutrient conditions, but predictions of the fitted Monod model deviated from those of the genome-scale model

  8. Genome-Scale NAD(H/+) Availability Patterns as a Differentiating Feature between Saccharomyces cerevisiae and Scheffersomyces stipitis in Relation to Fermentative Metabolism

    PubMed Central

    Acevedo, Alejandro; Aroca, German; Conejeros, Raul

    2014-01-01

    Scheffersomyces stipitis is a yeast able to ferment pentoses to ethanol, unlike Saccharomyces cerevisiae, it does not present the so-called overflow phenomenon. Metabolic features characterizing the presence or not of this phenomenon have not been fully elucidated. This work proposes that genome-scale metabolic response to variations in NAD(H/+) availability characterizes fermentative behavior in both yeasts. Thus, differentiating features in S. stipitis and S. cerevisiae were determined analyzing growth sensitivity response to changes in available reducing capacity in relation to ethanol production capacity and overall metabolic flux span. Using genome-scale constraint-based metabolic models, phenotypic phase planes and shadow price analyses, an excess of available reducing capacity for growth was found in S. cerevisiae at every metabolic phenotype where growth is limited by oxygen uptake, while in S. stipitis this was observed only for a subset of those phenotypes. Moreover, by using flux variability analysis, an increased metabolic flux span was found in S. cerevisiae at growth limited by oxygen uptake, while in S. stipitis flux span was invariant. Therefore, each yeast can be characterized by a significantly different metabolic response and flux span when growth is limited by oxygen uptake, both features suggesting a higher metabolic flexibility in S. cerevisiae. By applying an optimization-based approach on the genome-scale models, three single reaction deletions were found to generate in S. stipitis the reducing capacity availability pattern found in S. cerevisiae, two of them correspond to reactions involved in the overflow phenomenon. These results show a close relationship between the growth sensitivity response given by the metabolic network and fermentative behavior. PMID:24489927

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

    PubMed Central

    Chi, Baofang; Tao, Shiheng; Liu, Yanlin

    2015-01-01

    Sampling the solution space of genome-scale models is generally conducted to determine the feasible region for metabolic flux distribution. Because the region for actual metabolic states resides only in a small fraction of the entire space, it is necessary to shrink the solution space to improve the predictive power of a model. A common strategy is to constrain models by integrating extra datasets such as high-throughput datasets and C13-labeled flux datasets. However, studies refining these approaches by performing a meta-analysis of massive experimental metabolic flux measurements, which are closely linked to cellular phenotypes, are limited. In the present study, experimentally identified metabolic flux data from 96 published reports were systematically reviewed. Several strong associations among metabolic flux phenotypes were observed. These phenotype-phenotype associations at the flux level were quantified and integrated into a Saccharomyces cerevisiae genome-scale model as extra physiological constraints. By sampling the shrunken solution space of the model, the metabolic flux fluctuation level, which is an intrinsic trait of metabolic reactions determined by the network, was estimated and utilized to explore its relationship to gene expression noise. Although no correlation was observed in all enzyme-coding genes, a relationship between metabolic flux fluctuation and expression noise of genes associated with enzyme-dosage sensitive reactions was detected, suggesting that the metabolic network plays a role in shaping gene expression noise. Such correlation was mainly attributed to the genes corresponding to non-essential reactions, rather than essential ones. This was at least partially, due to regulations underlying the flux phenotype-phenotype associations. Altogether, this study proposes a new approach in shrinking the solution space of a genome-scale model, of which sampling provides new insights into gene expression noise. PMID:26448560

  10. Using Genome-Scale Models to Predict Biological Capabilities

    PubMed Central

    O’Brien, Edward J.; Monk, Jonathan M.; Palsson, Bernhard O.

    2015-01-01

    Constraint-based reconstruction and analysis (COBRA) methods at the genome-scale have been under development since the first whole genome sequences appeared in the mid-1990s. A few years ago this approach began to demonstrate the ability to predict a range of cellular functions including cellular growth capabilities on various substrates and the effect of gene knockouts at the genome-scale. Thus, much interest has developed in understanding and applying these methods to areas such as metabolic engineering, antibiotic design, and organismal and enzyme evolution. This primer will get you started. PMID:26000478

  11. Capturing the response of Clostridium acetobutylicum to chemical stressors using a regulated genome-scale metabolic model

    SciTech Connect

    Dash, Satyakam; Mueller, Thomas J.; Venkataramanan, Keerthi P.; Papoutsakis, Eleftherios T.; Maranas, Costas D.

    2014-10-14

    Clostridia are anaerobic Gram-positive Firmicutes containing broad and flexible systems for substrate utilization, which have been used successfully to produce a range of industrial compounds. Clostridium acetobutylicum has been used to produce butanol on an industrial scale through acetone-butanol-ethanol (ABE) fermentation. A genome-scale metabolic (GSM) model is a powerful tool for understanding the metabolic capacities of an organism and developing metabolic engineering strategies for strain development. The integration of stress related specific transcriptomics information with the GSM model provides opportunities for elucidating the focal points of regulation.

  12. Integration of Genome-Scale Metabolic Nodels of Iron-Reducing Bacteria With Subsurface Flow and Geochemical Reactive Transport Models

    NASA Astrophysics Data System (ADS)

    Scheibe, T. D.; Mahadevan, R.; Fang, Y.; Garg, S.; Long, P. E.; Lovley, D. M.

    2008-12-01

    Several field and laboratory experiments have demonstrated that the growth and activity of iron-reducing bacteria can be stimulated in many subsurface environments by amendment of groundwater with a soluble electron donor. Under strong iron-reducing conditions, these organisms mediate reactions that can impact a wide range of subsurface contaminants including chlorinated hydrocarbons, metals, and radionuclides. Therefore there is strong interest in in-situ bioremediation as a potential technology for cleanup of contaminated aquifers. To evaluate and design bioremediation systems, as well as to evaluate the viability of monitored natural attenuation as an alternative, quantitative models of biogeochemically reactive transport are needed. To date, most such models represent microbial activity in terms of kinetic rate (e.g., Monod- type) formulations. Such models do not account for fundamental changes in microbial functionality (such as utilization of alternative respiratory pathways) that occur as the result of spatial and temporal variations in the geochemical environment experienced by microorganisms. Constraint-based genome-scale in silico models of microbial metabolism present an alternative to simplified rate formulations that provide flexibility to account for changes in microbial function in response to local geochemical conditions. We have developed and applied a methodology for coupling a constraint-based in silico model of Geobacter sulfurreducens with a conventional model of groundwater flow, transport, and geochemical reaction. Two uses of the in silico model are tested: 1) incorporation of modified microbial growth yield coefficients based on the in silico model, and 2) variation of reaction rates in a reactive transport model based on in silico modeling of a range of local geochemical conditions. Preliminary results from this integrated model will be presented.

  13. Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions.

    PubMed

    Bordbar, Aarash; Lewis, Nathan E; Schellenberger, Jan; Palsson, Bernhard Ø; Jamshidi, Neema

    2010-10-19

    Metabolic coupling of Mycobacterium tuberculosis to its host is foundational to its pathogenesis. Computational genome-scale metabolic models have shown utility in integrating -omic as well as physiologic data for systemic, mechanistic analysis of metabolism. To date, integrative analysis of host-pathogen interactions using in silico mass-balanced, genome-scale models has not been performed. We, therefore, constructed a cell-specific alveolar macrophage model, iAB-AMØ-1410, from the global human metabolic reconstruction, Recon 1. The model successfully predicted experimentally verified ATP and nitric oxide production rates in macrophages. This model was then integrated with an M. tuberculosis H37Rv model, iNJ661, to build an integrated host-pathogen genome-scale reconstruction, iAB-AMØ-1410-Mt-661. The integrated host-pathogen network enables simulation of the metabolic changes during infection. The resulting reaction activity and gene essentiality targets of the integrated model represent an altered infectious state. High-throughput data from infected macrophages were mapped onto the host-pathogen network and were able to describe three distinct pathological states. Integrated host-pathogen reconstructions thus form a foundation upon which understanding the biology and pathophysiology of infections can be developed. PMID:20959820

  14. HSA: integrating multi-track Hi-C data for genome-scale reconstruction of 3D chromatin structure.

    PubMed

    Zou, Chenchen; Zhang, Yuping; Ouyang, Zhengqing

    2016-01-01

    Genome-wide 3C technologies (Hi-C) are being increasingly employed to study three-dimensional (3D) genome conformations. Existing computational approaches are unable to integrate accumulating data to facilitate studying 3D chromatin structure and function. We present HSA ( http://ouyanglab.jax.org/hsa/ ), a flexible tool that jointly analyzes multiple contact maps to infer 3D chromatin structure at the genome scale. HSA globally searches the latent structure underlying different cleavage footprints. Its robustness and accuracy outperform or rival existing tools on extensive simulations and orthogonal experiment validations. Applying HSA to recent in situ Hi-C data, we found the 3D chromatin structures are highly conserved across various human cell types. PMID:26936376

  15. Evaluation of a Genome-Scale In Silico Metabolic Model for Geobacter metallireducens by Using Proteomic Data from a Field Biostimulation Experiment

    PubMed Central

    Fang, Yilin; Yabusaki, Steven B.; Lipton, Mary S.; Long, Philip E.

    2012-01-01

    Accurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within an in silico model using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model of Geobacter metallireducens—specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-based in silico model of G. metallireducens relates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637 G. metallireducens proteins detected during the 2008 experiment were associated with specific metabolic reactions in the in silico model. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through the in silico model reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low abundances of proteins associated with amino acid transport and metabolism, revealed pathways or flux constraints in the in silico model that could be updated to more accurately predict metabolic processes that occur in the subsurface environment. PMID:23042184

  16. Global reconstruction of the human metabolic network based on genomic and bibliomic data

    PubMed Central

    Duarte, Natalie C.; Becker, Scott A.; Jamshidi, Neema; Thiele, Ines; Mo, Monica L.; Vo, Thuy D.; Srivas, Rohith; Palsson, Bernhard Ø.

    2007-01-01

    Metabolism is a vital cellular process, and its malfunction is a major contributor to human disease. Metabolic networks are complex and highly interconnected, and thus systems-level computational approaches are required to elucidate and understand metabolic genotype–phenotype relationships. We have manually reconstructed the global human metabolic network based on Build 35 of the genome annotation and a comprehensive evaluation of >50 years of legacy data (i.e., bibliomic data). Herein we describe the reconstruction process and demonstrate how the resulting genome-scale (or global) network can be used (i) for the discovery of missing information, (ii) for the formulation of an in silico model, and (iii) as a structured context for analyzing high-throughput biological data sets. Our comprehensive evaluation of the literature revealed many gaps in the current understanding of human metabolism that require future experimental investigation. Mathematical analysis of network structure elucidated the implications of intracellular compartmentalization and the potential use of correlated reaction sets for alternative drug target identification. Integrated analysis of high-throughput data sets within the context of the reconstruction enabled a global assessment of functional metabolic states. These results highlight some of the applications enabled by the reconstructed human metabolic network. The establishment of this network represents an important step toward genome-scale human systems biology. PMID:17267599

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed Central

    Heavner, Benjamin D.; Price, Nathan D.

    2015-01-01

    We have compared 12 genome-scale models of the Saccharomyces cerevisiae metabolic network published since 2003 to evaluate progress in reconstruction of the yeast metabolic network. We compared the genomic coverage, overlap of annotated metabolites, predictive ability for single gene essentiality with a selection of model parameters, and biomass production predictions in simulated nutrient-limited conditions. We have also compared pairwise gene knockout essentiality predictions for 10 of these models. We found that varying approaches to model scope and annotation reflected the involvement of multiple research groups in model development; that single-gene essentiality predictions were affected by simulated medium, objective function, and the reference list of essential genes; and that predictive ability for single-gene essentiality did not correlate well with predictive ability for our reference list of synthetic lethal gene interactions (R = 0.159). We conclude that the reconstruction of the yeast metabolic network is indeed gradually improving through the iterative process of model development, and there remains great opportunity for advancing our understanding of biology through continued efforts to reconstruct the full biochemical reaction network that constitutes yeast metabolism. Additionally, we suggest that there is opportunity for refining the process of deriving a metabolic model from a metabolic network reconstruction to facilitate mechanistic investigation and discovery. This comparative study lays the groundwork for developing improved tools and formalized methods to quantitatively assess metabolic network reconstructions independently of any particular model application, which will facilitate ongoing efforts to advance our understanding of the relationship between genotype and cellular phenotype. PMID:26566239

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

    PubMed

    Heavner, Benjamin D; Price, Nathan D

    2015-11-01

    We have compared 12 genome-scale models of the Saccharomyces cerevisiae metabolic network published since 2003 to evaluate progress in reconstruction of the yeast metabolic network. We compared the genomic coverage, overlap of annotated metabolites, predictive ability for single gene essentiality with a selection of model parameters, and biomass production predictions in simulated nutrient-limited conditions. We have also compared pairwise gene knockout essentiality predictions for 10 of these models. We found that varying approaches to model scope and annotation reflected the involvement of multiple research groups in model development; that single-gene essentiality predictions were affected by simulated medium, objective function, and the reference list of essential genes; and that predictive ability for single-gene essentiality did not correlate well with predictive ability for our reference list of synthetic lethal gene interactions (R = 0.159). We conclude that the reconstruction of the yeast metabolic network is indeed gradually improving through the iterative process of model development, and there remains great opportunity for advancing our understanding of biology through continued efforts to reconstruct the full biochemical reaction network that constitutes yeast metabolism. Additionally, we suggest that there is opportunity for refining the process of deriving a metabolic model from a metabolic network reconstruction to facilitate mechanistic investigation and discovery. This comparative study lays the groundwork for developing improved tools and formalized methods to quantitatively assess metabolic network reconstructions independently of any particular model application, which will facilitate ongoing efforts to advance our understanding of the relationship between genotype and cellular phenotype. PMID:26566239

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  1. Coupling a Genome-Scale Metabolic Model with a Reactive Transport Model to Describe In Situ Uranium Bioremediation

    SciTech Connect

    Scheibe, Timothy D.; Mahadevan, Radhakrishnan; Fang, Yilin; Garg, Srinath; Long, Philip E.; Lovley, Derek R.

    2009-03-01

    Quantitative numerical simulation codes known as reactive transport models are widely used for simulating the hydrologic transport and geochemical speciation of dissolved constituents in the subsurface (Steefel et al., 2005). Because the activity of microorganisms strongly influences the fate of many constituents, both organic and inorganic, such models often include microbially-mediated reactions in their reaction networks (Hunter et al., 1998; Burgos et al., 2002; Fang et al., 2006; Scheibe et al., 2006; Yabusaki et al., 2007). However, the canonical form and stoichiometry of microbial reactions, reaction rate formulations and parameters, and biomass growth yield coefficients are prescribed a priori and applied over the entire range of simulated conditions. This approach does not account for the fact that fundamental microbial functions vary in response to local variations in environmental conditions(Stewart and Franklin, 2008). Multiple alternative reaction pathways are encoded in microbial genomes; specific pathways become active or inactive in response to, for example, nutrient limitation. Recent advances in genomic analysis allow us to define cellular metabolic networks, and accurate predictions of active pathways and reaction fluxes have been made using constraint-based metabolic models (Mahadevan et al., 2002; Price et al., 2003; Reed and Palsson, 2003; Mahadevan et al., 2006). Here, we demonstrate for the first time a methodology of coupling constraint-based metabolic models with reactive transport models. Our approach integrates advanced microbiological characterization, hydrology, and geochemistry in a powerful manner that will significantly improve subsurface reactive transport models.

  2. Development of an Extensible Computational Framework for Centralized Storage and Distributed Curation and Analysis of Genomic Data Genome-scale Metabolic Models

    SciTech Connect

    Stevens, Rick

    2010-08-01

    The DOE funded KBase project of the Stevens group at the University of Chicago was focused on four high-level goals: (i) improve extensibility, accessibility, and scalability of the SEED framework for genome annotation, curation, and analysis; (ii) extend the SEED infrastructure to support transcription regulatory network reconstructions (2.1), metabolic model reconstruction and analysis (2.2), assertions linked to data (2.3), eukaryotic annotation (2.4), and growth phenotype prediction (2.5); (iii) develop a web-API for programmatic remote access to SEED data and services; and (iv) application of all tools to bioenergy-related genomes and organisms. In response to these goals, we enhanced and improved the ModelSEED resource within the SEED to enable new modeling analyses, including improved model reconstruction and phenotype simulation. We also constructed a new website and web-API for the ModelSEED. Further, we constructed a comprehensive web-API for the SEED as a whole. We also made significant strides in building infrastructure in the SEED to support the reconstruction of transcriptional regulatory networks by developing a pipeline to identify sets of consistently expressed genes based on gene expression data. We applied this pipeline to 29 organisms, computing regulons which were subsequently stored in the SEED database and made available on the SEED website (http://pubseed.theseed.org). We developed a new pipeline and database for the use of kmers, or short 8-residue oligomer sequences, to annotate genomes at high speed. Finally, we developed the PlantSEED, or a new pipeline for annotating primary metabolism in plant genomes. All of the work performed within this project formed the early building blocks for the current DOE Knowledgebase system, and the kmer annotation pipeline, plant annotation pipeline, and modeling tools are all still in use in KBase today.

  3. Reconstruction and In Silico Analysis of Metabolic Network for an Oleaginous Yeast, Yarrowia lipolytica

    PubMed Central

    Pan, Pengcheng; Hua, Qiang

    2012-01-01

    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. PMID:23236514

  4. A Method to Constrain Genome-Scale Models with 13C Labeling Data

    PubMed Central

    García Martín, Héctor; Kumar, Vinay Satish; Weaver, Daniel; Ghosh, Amit; Chubukov, Victor; Mukhopadhyay, Aindrila; Arkin, Adam; Keasling, Jay D.

    2015-01-01

    Current limitations in quantitatively predicting biological behavior hinder our efforts to engineer biological systems to produce biofuels and other desired chemicals. Here, we present a new method for calculating metabolic fluxes, key targets in metabolic engineering, that incorporates data from 13C labeling experiments and genome-scale models. The data from 13C labeling experiments provide strong flux constraints that eliminate the need to assume an evolutionary optimization principle such as the growth rate optimization assumption used in Flux Balance Analysis (FBA). This effective constraining is achieved by making the simple but biologically relevant assumption that flux flows from core to peripheral metabolism and does not flow back. The new method is significantly more robust than FBA with respect to errors in genome-scale model reconstruction. Furthermore, it can provide a comprehensive picture of metabolite balancing and predictions for unmeasured extracellular fluxes as constrained by 13C labeling data. A comparison shows that the results of this new method are similar to those found through 13C Metabolic Flux Analysis (13C MFA) for central carbon metabolism but, additionally, it provides flux estimates for peripheral metabolism. The extra validation gained by matching 48 relative labeling measurements is used to identify where and why several existing COnstraint Based Reconstruction and Analysis (COBRA) flux prediction algorithms fail. We demonstrate how to use this knowledge to refine these methods and improve their predictive capabilities. This method provides a reliable base upon which to improve the design of biological systems. PMID:26379153

  5. Efficient Reconstruction of Predictive Consensus Metabolic Network Models

    PubMed Central

    Martins dos Santos, Vitor A. P.; Stelling, Joerg

    2016-01-01

    Understanding cellular function requires accurate, comprehensive representations of metabolism. Genome-scale, constraint-based metabolic models (GSMs) provide such representations, but their usability is often hampered by inconsistencies at various levels, in particular for concurrent models. COMMGEN, our tool for COnsensus Metabolic Model GENeration, automatically identifies inconsistencies between concurrent models and semi-automatically resolves them, thereby contributing to consolidate knowledge of metabolic function. Tests of COMMGEN for four organisms showed that automatically generated consensus models were predictive and that they substantially increased coherence of knowledge representation. COMMGEN ought to be particularly useful for complex scenarios in which manual curation does not scale, such as for eukaryotic organisms, microbial communities, and host-pathogen interactions. PMID:27563720

  6. Efficient Reconstruction of Predictive Consensus Metabolic Network Models.

    PubMed

    van Heck, Ruben G A; Ganter, Mathias; Martins Dos Santos, Vitor A P; Stelling, Joerg

    2016-08-01

    Understanding cellular function requires accurate, comprehensive representations of metabolism. Genome-scale, constraint-based metabolic models (GSMs) provide such representations, but their usability is often hampered by inconsistencies at various levels, in particular for concurrent models. COMMGEN, our tool for COnsensus Metabolic Model GENeration, automatically identifies inconsistencies between concurrent models and semi-automatically resolves them, thereby contributing to consolidate knowledge of metabolic function. Tests of COMMGEN for four organisms showed that automatically generated consensus models were predictive and that they substantially increased coherence of knowledge representation. COMMGEN ought to be particularly useful for complex scenarios in which manual curation does not scale, such as for eukaryotic organisms, microbial communities, and host-pathogen interactions. PMID:27563720

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

    SciTech Connect

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

    2011-06-15

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

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

    PubMed Central

    2011-01-01

    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. oneidensis MR-1. Analysis of

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

    PubMed

    Islam, M Ahsanul; Waller, Alison S; Hug, Laura A; Provart, Nicholas J; Edwards, Elizabeth A; Mahadevan, Radhakrishnan

    2014-01-01

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

  10. New Insights into Dehalococcoides mccartyi Metabolism from a Reconstructed Metabolic Network-Based Systems-Level Analysis of D. mccartyi Transcriptomes

    PubMed Central

    Islam, M. Ahsanul; Waller, Alison S.; Hug, Laura A.; Provart, Nicholas J.; Edwards, Elizabeth A.; Mahadevan, Radhakrishnan

    2014-01-01

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

  11. Systems biology study of mucopolysaccharidosis using a human metabolic reconstruction network.

    PubMed

    Salazar, Diego A; Rodríguez-López, Alexander; Herreño, Angélica; Barbosa, Hector; Herrera, Juliana; Ardila, Andrea; Barreto, George E; González, Janneth; Alméciga-Díaz, Carlos J

    2016-02-01

    Mucopolysaccharidosis (MPS) is a group of lysosomal storage diseases (LSD), characterized by the deficiency of a lysosomal enzyme responsible for the degradation of glycosaminoglycans (GAG). This deficiency leads to the lysosomal accumulation of partially degraded GAG. Nevertheless, deficiency of a single lysosomal enzyme has been associated with impairment in other cell mechanism, such as apoptosis and redox balance. Although GAG analysis represents the main biomarker for MPS diagnosis, it has several limitations that can lead to a misdiagnosis, whereby the identification of new biomarkers represents an important issue for MPS. In this study, we used a system biology approach, through the use of a genome-scale human metabolic reconstruction to understand the effect of metabolism alterations in cell homeostasis and to identify potential new biomarkers in MPS. In-silico MPS models were generated by silencing of MPS-related enzymes, and were analyzed through a flux balance and variability analysis. We found that MPS models used approximately 2286 reactions to satisfy the objective function. Impaired reactions were mainly involved in cellular respiration, mitochondrial process, amino acid and lipid metabolism, and ion exchange. Metabolic changes were similar for MPS I and II, and MPS III A to C; while the remaining MPS showed unique metabolic profiles. Eight and thirteen potential high-confidence biomarkers were identified for MPS IVB and VII, respectively, which were associated with the secondary pathologic process of LSD. In vivo evaluation of predicted intermediate confidence biomarkers (β-hexosaminidase and β-glucoronidase) for MPS IVA and VI correlated with the in-silico prediction. These results show the potential of a computational human metabolic reconstruction to understand the molecular mechanisms this group of diseases, which can be used to identify new biomarkers for MPS. PMID:26276570

  12. A taxonomic framework for emerging groups of ecologically important marine gammaproteobacteria based on the reconstruction of evolutionary relationships using genome-scale data.

    PubMed

    Spring, Stefan; Scheuner, Carmen; Göker, Markus; Klenk, Hans-Peter

    2015-01-01

    In recent years a large number of isolates were obtained from saline environments that are phylogenetically related to distinct clades of oligotrophic marine gammaproteobacteria, which were originally identified in seawater samples using cultivation independent methods and are characterized by high seasonal abundances in coastal environments. To date a sound taxonomic framework for the classification of these ecologically important isolates and related species in accordance with their evolutionary relationships is missing. In this study we demonstrate that a reliable allocation of members of the oligotrophic marine gammaproteobacteria (OMG) group and related species to higher taxonomic ranks is possible by phylogenetic analyses of whole proteomes but also of the RNA polymerase beta subunit, whereas phylogenetic reconstructions based on 16S rRNA genes alone resulted in unstable tree topologies with only insignificant bootstrap support. The identified clades could be correlated with distinct phenotypic traits illustrating an adaptation to common environmental factors in their evolutionary history. Genome wide gene-content analyses revealed the existence of two distinct ecological guilds within the analyzed lineage of marine gammaproteobacteria which can be distinguished by their trophic strategies. Based on our results a novel order within the class Gammaproteobacteria is proposed, which is designated Cellvibrionales ord. nov. and comprises the five novel families Cellvibrionaceae fam. nov., Halieaceae fam. nov., Microbulbiferaceae fam. nov., Porticoccaceae fam. nov., and Spongiibacteraceae fam. nov. PMID:25914684

  13. A taxonomic framework for emerging groups of ecologically important marine gammaproteobacteria based on the reconstruction of evolutionary relationships using genome-scale data

    PubMed Central

    Spring, Stefan; Scheuner, Carmen; Göker, Markus; Klenk, Hans-Peter

    2015-01-01

    In recent years a large number of isolates were obtained from saline environments that are phylogenetically related to distinct clades of oligotrophic marine gammaproteobacteria, which were originally identified in seawater samples using cultivation independent methods and are characterized by high seasonal abundances in coastal environments. To date a sound taxonomic framework for the classification of these ecologically important isolates and related species in accordance with their evolutionary relationships is missing. In this study we demonstrate that a reliable allocation of members of the oligotrophic marine gammaproteobacteria (OMG) group and related species to higher taxonomic ranks is possible by phylogenetic analyses of whole proteomes but also of the RNA polymerase beta subunit, whereas phylogenetic reconstructions based on 16S rRNA genes alone resulted in unstable tree topologies with only insignificant bootstrap support. The identified clades could be correlated with distinct phenotypic traits illustrating an adaptation to common environmental factors in their evolutionary history. Genome wide gene-content analyses revealed the existence of two distinct ecological guilds within the analyzed lineage of marine gammaproteobacteria which can be distinguished by their trophic strategies. Based on our results a novel order within the class Gammaproteobacteria is proposed, which is designated Cellvibrionales ord. nov. and comprises the five novel families Cellvibrionaceae fam. nov., Halieaceae fam. nov., Microbulbiferaceae fam. nov., Porticoccaceae fam. nov., and Spongiibacteraceae fam. nov. PMID:25914684

  14. iSCHRUNK--In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks.

    PubMed

    Andreozzi, Stefano; Miskovic, Ljubisa; Hatzimanikatis, Vassily

    2016-01-01

    Accurate determination of physiological states of cellular metabolism requires detailed information about metabolic fluxes, metabolite concentrations and distribution of enzyme states. Integration of fluxomics and metabolomics data, and thermodynamics-based metabolic flux analysis contribute to improved understanding of steady-state properties of metabolism. However, knowledge about kinetics and enzyme activities though essential for quantitative understanding of metabolic dynamics remains scarce and involves uncertainty. Here, we present a computational methodology that allow us to determine and quantify the kinetic parameters that correspond to a certain physiology as it is described by a given metabolic flux profile and a given metabolite concentration vector. Though we initially determine kinetic parameters that involve a high degree of uncertainty, through the use of kinetic modeling and machine learning principles we are able to obtain more accurate ranges of kinetic parameters, and hence we are able to reduce the uncertainty in the model analysis. We computed the distribution of kinetic parameters for glucose-fed E. coli producing 1,4-butanediol and we discovered that the observed physiological state corresponds to a narrow range of kinetic parameters of only a few enzymes, whereas the kinetic parameters of other enzymes can vary widely. Furthermore, this analysis suggests which are the enzymes that should be manipulated in order to engineer the reference state of the cell in a desired way. The proposed approach also sets up the foundations of a novel type of approaches for efficient, non-asymptotic, uniform sampling of solution spaces. PMID:26474788

  15. A community-driven global reconstruction of human metabolism.

    PubMed

    Thiele, Ines; Swainston, Neil; Fleming, Ronan M T; Hoppe, Andreas; Sahoo, Swagatika; Aurich, Maike K; Haraldsdottir, Hulda; Mo, Monica L; Rolfsson, Ottar; Stobbe, Miranda D; Thorleifsson, Stefan G; Agren, Rasmus; Bölling, Christian; Bordel, Sergio; Chavali, Arvind K; Dobson, Paul; Dunn, Warwick B; Endler, Lukas; Hala, David; Hucka, Michael; Hull, Duncan; Jameson, Daniel; Jamshidi, Neema; Jonsson, Jon J; Juty, Nick; Keating, Sarah; Nookaew, Intawat; Le Novère, Nicolas; Malys, Naglis; Mazein, Alexander; Papin, Jason A; Price, Nathan D; Selkov, Evgeni; Sigurdsson, Martin I; Simeonidis, Evangelos; Sonnenschein, Nikolaus; Smallbone, Kieran; Sorokin, Anatoly; van Beek, Johannes H G M; Weichart, Dieter; Goryanin, Igor; Nielsen, Jens; Westerhoff, Hans V; Kell, Douglas B; Mendes, Pedro; Palsson, Bernhard Ø

    2013-05-01

    Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. PMID:23455439

  16. The OME Framework for genome-scale systems biology

    SciTech Connect

    Palsson, Bernhard O.; Ebrahim, Ali; Federowicz, Steve

    2014-12-19

    The life sciences are undergoing continuous and accelerating integration with computational and engineering sciences. The biology that many in the field have been trained on may be hardly recognizable in ten to twenty years. One of the major drivers for this transformation is the blistering pace of advancements in DNA sequencing and synthesis. These advances have resulted in unprecedented amounts of new data, information, and knowledge. Many software tools have been developed to deal with aspects of this transformation and each is sorely needed [1-3]. However, few of these tools have been forced to deal with the full complexity of genome-scale models along with high throughput genome- scale data. This particular situation represents a unique challenge, as it is simultaneously necessary to deal with the vast breadth of genome-scale models and the dizzying depth of high-throughput datasets. It has been observed time and again that as the pace of data generation continues to accelerate, the pace of analysis significantly lags behind [4]. It is also evident that, given the plethora of databases and software efforts [5-12], it is still a significant challenge to work with genome-scale metabolic models, let alone next-generation whole cell models [13-15]. We work at the forefront of model creation and systems scale data generation [16-18]. The OME Framework was borne out of a practical need to enable genome-scale modeling and data analysis under a unified framework to drive the next generation of genome-scale biological models. Here we present the OME Framework. It exists as a set of Python classes. However, we want to emphasize the importance of the underlying design as an addition to the discussions on specifications of a digital cell. A great deal of work and valuable progress has been made by a number of communities [13, 19-24] towards interchange formats and implementations designed to achieve similar goals. While many software tools exist for handling genome-scale

  17. Reconstruction of Danio rerio Metabolic Model Accounting for Subcellular Compartmentalisation

    PubMed Central

    Bekaert, Michaël

    2012-01-01

    Plant and microbial metabolic engineering is commonly used in the production of functional foods and quality trait improvement. Computational model-based approaches have been used in this important endeavour. However, to date, fish metabolic models have only been scarcely and partially developed, in marked contrast to their prominent success in metabolic engineering. In this study we present the reconstruction of fully compartmentalised models of the Danio rerio (zebrafish) on a global scale. This reconstruction involves extraction of known biochemical reactions in D. rerio for both primary and secondary metabolism and the implementation of methods for determining subcellular localisation and assignment of enzymes. The reconstructed model (ZebraGEM) is amenable for constraint-based modelling analysis, and accounts for 4,988 genes coding for 2,406 gene-associated reactions and only 418 non-gene-associated reactions. A set of computational validations (i.e., simulations of known metabolic functionalities and experimental data) strongly testifies to the predictive ability of the model. Overall, the reconstructed model is expected to lay down the foundations for computational-based rational design of fish metabolic engineering in aquaculture. PMID:23166792

  18. Fast Reconstruction of Compact Context-Specific Metabolic Network Models

    PubMed Central

    Sauter, Thomas

    2014-01-01

    Systemic approaches to the study of a biological cell or tissue rely increasingly on the use of context-specific metabolic network models. The reconstruction of such a model from high-throughput data can routinely involve large numbers of tests under different conditions and extensive parameter tuning, which calls for fast algorithms. We present fastcore, a generic algorithm for reconstructing context-specific metabolic network models from global genome-wide metabolic network models such as Recon X. fastcore takes as input a core set of reactions that are known to be active in the context of interest (e.g., cell or tissue), and it searches for a flux consistent subnetwork of the global network that contains all reactions from the core set and a minimal set of additional reactions. Our key observation is that a minimal consistent reconstruction can be defined via a set of sparse modes of the global network, and fastcore iteratively computes such a set via a series of linear programs. Experiments on liver data demonstrate speedups of several orders of magnitude, and significantly more compact reconstructions, over a rival method. Given its simplicity and its excellent performance, fastcore can form the backbone of many future metabolic network reconstruction algorithms. PMID:24453953

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

    PubMed

    Vlassis, Nikos; Pacheco, Maria Pires; Sauter, Thomas

    2014-01-01

    Systemic approaches to the study of a biological cell or tissue rely increasingly on the use of context-specific metabolic network models. The reconstruction of such a model from high-throughput data can routinely involve large numbers of tests under different conditions and extensive parameter tuning, which calls for fast algorithms. We present fastcore, a generic algorithm for reconstructing context-specific metabolic network models from global genome-wide metabolic network models such as Recon X. fastcore takes as input a core set of reactions that are known to be active in the context of interest (e.g., cell or tissue), and it searches for a flux consistent subnetwork of the global network that contains all reactions from the core set and a minimal set of additional reactions. Our key observation is that a minimal consistent reconstruction can be defined via a set of sparse modes of the global network, and fastcore iteratively computes such a set via a series of linear programs. Experiments on liver data demonstrate speedups of several orders of magnitude, and significantly more compact reconstructions, over a rival method. Given its simplicity and its excellent performance, fastcore can form the backbone of many future metabolic network reconstruction algorithms. PMID:24453953

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

    PubMed

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

    2016-01-01

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

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

    PubMed Central

    Doungpan, Narumol; Meechai, Asawin; Shen, Bairong

    2016-01-01

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

  2. Phylogenomic reconstruction of archaeal fatty acid metabolism

    PubMed Central

    Dibrova, Daria V.; Galperin, Michael Y.; Mulkidjanian, Armen Y.

    2014-01-01

    While certain archaea appear to synthesize and/or metabolize fatty acids, the respective pathways still remain obscure. By analyzing the genomic distribution of the key lipid-related enzymes, we were able to identify the likely components of the archaeal pathway of fatty acid metabolism, namely, a combination of the enzymes of bacterial-type β-oxidation of fatty acids (acyl-CoA-dehydrogenase, enoyl-CoA hydratase, and 3-hydroxyacyl-CoA dehydrogenase) with paralogs of the archaeal acetyl-CoA C-acetyltransferase, an enzyme of the mevalonate biosynthesis pathway. These three β-oxidation enzymes working in the reverse direction could potentially catalyze biosynthesis of fatty acids, with paralogs of acetyl-CoA C-acetyltransferase performing addition of C2 fragments. The presence in archaea of the genes for energy-transducing membrane enzyme complexes, such as cytochrome bc complex, cytochrome c oxidase, and diverse rhodopsins, was found to correlate with the presence of the proposed system of fatty acid biosynthesis. We speculate that because these membrane complexes functionally depend on fatty acid chains, their genes could have been acquired via lateral gene transfer from bacteria only by those archaea that already possessed a system of fatty acid biosynthesis. The proposed pathway of archaeal fatty acid metabolism operates in extreme conditions and therefore might be of interest in the context of biofuel production and other industrial applications. PMID:24818264

  3. The Edinburgh human metabolic network reconstruction and its functional analysis

    PubMed Central

    Ma, Hongwu; Sorokin, Anatoly; Mazein, Alexander; Selkov, Alex; Selkov, Evgeni; Demin, Oleg; Goryanin, Igor

    2007-01-01

    A better understanding of human metabolism and its relationship with diseases is an important task in human systems biology studies. In this paper, we present a high-quality human metabolic network manually reconstructed by integrating genome annotation information from different databases and metabolic reaction information from literature. The network contains nearly 3000 metabolic reactions, which were reorganized into about 70 human-specific metabolic pathways according to their functional relationships. By analysis of the functional connectivity of the metabolites in the network, the bow-tie structure, which was found previously by structure analysis, is reconfirmed. Furthermore, the distribution of the disease related genes in the network suggests that the IN (substrates) subset of the bow-tie structure has more flexibility than other parts. PMID:17882155

  4. Genome-scale constraint-based modeling of Geobacter metallireducens

    PubMed Central

    Sun, Jun; Sayyar, Bahareh; Butler, Jessica E; Pharkya, Priti; Fahland, Tom R; Famili, Iman; Schilling, Christophe H; Lovley, Derek R; Mahadevan, Radhakrishnan

    2009-01-01

    Background Geobacter metallireducens was the first organism that can be grown in pure culture to completely oxidize organic compounds with Fe(III) oxide serving as electron acceptor. Geobacter species, including G. sulfurreducens and G. metallireducens, are used for bioremediation and electricity generation from waste organic matter and renewable biomass. The constraint-based modeling approach enables the development of genome-scale in silico models that can predict the behavior of complex biological systems and their responses to the environments. Such a modeling approach was applied to provide physiological and ecological insights on the metabolism of G. metallireducens. Results The genome-scale metabolic model of G. metallireducens was constructed to include 747 genes and 697 reactions. Compared to the G. sulfurreducens model, the G. metallireducens metabolic model contains 118 unique reactions that reflect many of G. metallireducens' specific metabolic capabilities. Detailed examination of the G. metallireducens model suggests that its central metabolism contains several energy-inefficient reactions that are not present in the G. sulfurreducens model. Experimental biomass yield of G. metallireducens growing on pyruvate was lower than the predicted optimal biomass yield. Microarray data of G. metallireducens growing with benzoate and acetate indicated that genes encoding these energy-inefficient reactions were up-regulated by benzoate. These results suggested that the energy-inefficient reactions were likely turned off during G. metallireducens growth with acetate for optimal biomass yield, but were up-regulated during growth with complex electron donors such as benzoate for rapid energy generation. Furthermore, several computational modeling approaches were applied to accelerate G. metallireducens research. For example, growth of G. metallireducens with different electron donors and electron acceptors were studied using the genome-scale metabolic model, which

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

    NASA Astrophysics Data System (ADS)

    Almaas, Eivind

    2008-03-01

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

  6. Large-scale reconstruction and phylogenetic analysis of metabolic environments

    PubMed Central

    Borenstein, Elhanan; Kupiec, Martin; Feldman, Marcus W.; Ruppin, Eytan

    2008-01-01

    The topology of metabolic networks may provide important insights not only into the metabolic capacity of species, but also into the habitats in which they evolved. Here we introduce the concept of a metabolic network's “seed set”—the set of compounds that, based on the network topology, are exogenously acquired—and provide a methodological framework to computationally infer the seed set of a given network. Such seed sets form ecological “interfaces” between metabolic networks and their surroundings, approximating the effective biochemical environment of each species. Analyzing the metabolic networks of 478 species and identifying the seed set of each species, we present a comprehensive large-scale reconstruction of such predicted metabolic environments. The seed sets' composition significantly correlates with several basic properties characterizing the species' environments and agrees with biological observations concerning major adaptations. Species whose environments are highly predictable (e.g., obligate parasites) tend to have smaller seed sets than species living in variable environments. Phylogenetic analysis of the seed sets reveals the complex dynamics governing gain and loss of seeds across the phylogenetic tree and the process of transition between seed and non-seed compounds. Our findings suggest that the seed state is transient and that seeds tend either to be dropped completely from the network or to become non-seed compounds relatively fast. The seed sets also permit a successful reconstruction of a phylogenetic tree of life. The “reverse ecology” approach presented lays the foundations for studying the evolutionary interplay between organisms and their habitats on a large scale. PMID:18787117

  7. Genome-scale modeling of light-driven reductant partitioning and carbon fluxes in diazotrophic unicellular cyanobacterium Cyanothece sp. ATCC 51142

    SciTech Connect

    Vu, Trang; Stolyar, Sergey; Pinchuk, Grigoriy E.; Hill, Eric A.; Kucek, Leo A.; Brown, Roslyn N.; Lipton, Mary S.; Osterman, Andrei L.; Fredrickson, Jim K.; Konopka, Allan; Beliaev, Alex S.; Reed, Jennifer L.

    2012-04-05

    Genome-scale metabolic models have proven useful for answering fundamental questions about metabolic capabilities of a variety of microorganisms, as well as informing their metabolic engineering. However, only a few models are available for oxygenic photosynthetic microorganisms, particularly in cyanobacteria in which photosynthetic and respiratory electron transport chains (ETC) share components. We addressed the complexity of cyanobacterial ETC by developing a genome-scale model for the diazotrophic cyanobacterium, Cyanothece sp. ATCC 51142. The resulting metabolic reconstruction, iCce806, consists of 806 genes associated with 667 metabolic reactions and includes a detailed representation of the ETC and a biomass equation based on experimental measurements. Both computational and experimental approaches were used to investigate light-driven metabolism in Cyanothece sp. ATCC 51142, with a particular focus on reductant production and partitioning within the ETC. The simulation results suggest that growth and metabolic flux distributions are substantially impacted by the relative amounts of light going into the individual photosystems. When photosystem II flux is high, terminal oxidases of respiratory electron transport are predicted to be an important mechanism for removing excess electrons. When photosystem I flux is high cyclic electron transport becomes important. Model predictions of growth rates were in good quantitative agreement with measured growth rates, and predictions of reaction usage were qualitatively consistent with protein and mRNA expression data, when these latter datasets were used to constrain the model.

  8. Genome-Scale Modeling of Light-Driven Reductant Partitioning and Carbon Fluxes in Diazotrophic Unicellular Cyanobacterium Cyanothece sp. ATCC 51142

    SciTech Connect

    Vu, Trang; Stolyar, Sergey; Pinchuk, Grigoriy E.; Hill, Eric A.; Kucek, Leo A.; Brown, Roslyn N.; Lipton, Mary S.; Osterman, Andrei L.; Fredrickson, Jim K.; Konopka, Allan; Beliaev, Alex S.; Reed, Jennifer L.

    2012-04-05

    Genome-scale metabolic models have proven useful for answering fundamental questions about metabolic capabilities of a variety of microorganisms, as well as informing their metabolic engineering. However, only a few models are available for oxygenic photosynthetic microorganisms, particularly in cyanobacteria in which photosynthetic and respiratory electron transport chains (ETC) share components. We addressed the complexity of cyanobacterial ETC by developing a genome-scale model for the diazotrophic cyanobacterium, Cyanothece sp. ATCC 51142. The resulting metabolic reconstruction, iCce806, consists of 806 genes associated with 667 metabolic reactions and includes a detailed representation of the ETC and a biomass equation based on experimental measurements. Both computational and experimental approaches were used to investigate light-driven metabolism in Cyanothece sp. ATCC 51142, with a particular focus on reductant production and partitioning within the ETC. The simulation results suggest that growth and metabolic flux distributions are substantially impacted by the relative amounts of light going into the individual photosystems. When photosystem II flux is high, terminal oxidases of respiratory electron transport are predicted to be an important mechanism for removing excess electrons. When photosystem I flux is high cyclic electron transport becomes important. Model predictions of growth rates were in good quantitative agreement with measured growth rates, and predictions of reaction usage were ualitatively consistent with protein and mRNA expression data, when these latter datasets were used to constrain the model.

  9. Current state of genome-scale modeling in filamentous fungi.

    PubMed

    Brandl, Julian; Andersen, Mikael R

    2015-06-01

    The group of filamentous fungi contains important species used in industrial biotechnology for acid, antibiotics and enzyme production. Their unique lifestyle turns these organisms into a valuable genetic reservoir of new natural products and biomass degrading enzymes that has not been used to full capacity. One of the major bottlenecks in the development of new strains into viable industrial hosts is the alteration of the metabolism towards optimal production. Genome-scale models promise a reduction in the time needed for metabolic engineering by predicting the most potent targets in silico before testing them in vivo. The increasing availability of high quality models and molecular biological tools for manipulating filamentous fungi renders the model-guided engineering of these fungal factories possible with comprehensive metabolic networks. A typical fungal model contains on average 1138 unique metabolic reactions and 1050 ORFs, making them a vast knowledge-base of fungal metabolism. In the present review we focus on the current state as well as potential future applications of genome-scale models in filamentous fungi. PMID:25700817

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

    NASA Astrophysics Data System (ADS)

    Thomas, Alex; Rahmanian, Sorena; Bordbar, Aarash; Palsson, Bernhard Ø.; Jamshidi, Neema

    2014-01-01

    Recently there has not been a systematic, objective assessment of the metabolic capabilities of the human platelet. A manually curated, functionally tested, and validated biochemical reaction network of platelet metabolism, iAT-PLT-636, was reconstructed using 33 proteomic datasets and 354 literature references. The network contains enzymes mapping to 403 diseases and 231 FDA approved drugs, alluding to an expansive scope of biochemical transformations that may affect or be affected by disease processes in multiple organ systems. The effect of aspirin (ASA) resistance on platelet metabolism was evaluated using constraint-based modeling, which revealed a redirection of glycolytic, fatty acid, and nucleotide metabolism reaction fluxes in order to accommodate eicosanoid synthesis and reactive oxygen species stress. These results were confirmed with independent proteomic data. The construction and availability of iAT-PLT-636 should stimulate further data-driven, systems analysis of platelet metabolism towards the understanding of pathophysiological conditions including, but not strictly limited to, coagulopathies.

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

    PubMed Central

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

    2014-01-01

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

  12. Metabolic Reconstruction of Setaria italica: A Systems Biology Approach for Integrating Tissue-Specific Omics and Pathway Analysis of Bioenergy Grasses

    PubMed Central

    de Oliveira Dal'Molin, Cristiana G.; Orellana, Camila; Gebbie, Leigh; Steen, Jennifer; Hodson, Mark P.; Chrysanthopoulos, Panagiotis; Plan, Manuel R.; McQualter, Richard; Palfreyman, Robin W.; Nielsen, Lars K.

    2016-01-01

    The urgent need for major gains in industrial crops productivity and in biofuel production from bioenergy grasses have reinforced attention on understanding C4 photosynthesis. Systems biology studies of C4 model plants may reveal important features of C4 metabolism. Here we chose foxtail millet (Setaria italica), as a C4 model plant and developed protocols to perform systems biology studies. As part of the systems approach, we have developed and used a genome-scale metabolic reconstruction in combination with the use of multi-omics technologies to gain more insights into the metabolism of S. italica. mRNA, protein, and metabolite abundances, were measured in mature and immature stem/leaf phytomers, and the multi-omics data were integrated into the metabolic reconstruction framework to capture key metabolic features in different developmental stages of the plant. RNA-Seq reads were mapped to the S. italica resulting for 83% coverage of the protein coding genes of S. italica. Besides revealing similarities and differences in central metabolism of mature and immature tissues, transcriptome analysis indicates significant gene expression of two malic enzyme isoforms (NADP- ME and NAD-ME). Although much greater expression levels of NADP-ME genes are observed and confirmed by the correspondent protein abundances in the samples, the expression of multiple genes combined to the significant abundance of metabolites that participates in C4 metabolism of NAD-ME and NADP-ME subtypes suggest that S. italica may use mixed decarboxylation modes of C4 photosynthetic pathways under different plant developmental stages. The overall analysis also indicates different levels of regulation in mature and immature tissues in carbon fixation, glycolysis, TCA cycle, amino acids, fatty acids, lignin, and cellulose syntheses. Altogether, the multi-omics analysis reveals different biological entities and their interrelation and regulation over plant development. With this study, we demonstrated

  13. Metabolic Reconstruction of Setaria italica: A Systems Biology Approach for Integrating Tissue-Specific Omics and Pathway Analysis of Bioenergy Grasses.

    PubMed

    de Oliveira Dal'Molin, Cristiana G; Orellana, Camila; Gebbie, Leigh; Steen, Jennifer; Hodson, Mark P; Chrysanthopoulos, Panagiotis; Plan, Manuel R; McQualter, Richard; Palfreyman, Robin W; Nielsen, Lars K

    2016-01-01

    The urgent need for major gains in industrial crops productivity and in biofuel production from bioenergy grasses have reinforced attention on understanding C4 photosynthesis. Systems biology studies of C4 model plants may reveal important features of C4 metabolism. Here we chose foxtail millet (Setaria italica), as a C4 model plant and developed protocols to perform systems biology studies. As part of the systems approach, we have developed and used a genome-scale metabolic reconstruction in combination with the use of multi-omics technologies to gain more insights into the metabolism of S. italica. mRNA, protein, and metabolite abundances, were measured in mature and immature stem/leaf phytomers, and the multi-omics data were integrated into the metabolic reconstruction framework to capture key metabolic features in different developmental stages of the plant. RNA-Seq reads were mapped to the S. italica resulting for 83% coverage of the protein coding genes of S. italica. Besides revealing similarities and differences in central metabolism of mature and immature tissues, transcriptome analysis indicates significant gene expression of two malic enzyme isoforms (NADP- ME and NAD-ME). Although much greater expression levels of NADP-ME genes are observed and confirmed by the correspondent protein abundances in the samples, the expression of multiple genes combined to the significant abundance of metabolites that participates in C4 metabolism of NAD-ME and NADP-ME subtypes suggest that S. italica may use mixed decarboxylation modes of C4 photosynthetic pathways under different plant developmental stages. The overall analysis also indicates different levels of regulation in mature and immature tissues in carbon fixation, glycolysis, TCA cycle, amino acids, fatty acids, lignin, and cellulose syntheses. Altogether, the multi-omics analysis reveals different biological entities and their interrelation and regulation over plant development. With this study, we demonstrated

  14. Iterative reconstruction of a global metabolic model of Acinetobacter baylyi ADP1 using high-throughput growth phenotype and gene essentiality data

    PubMed Central

    Durot, Maxime; Le Fèvre, François; de Berardinis, Véronique; Kreimeyer, Annett; Vallenet, David; Combe, Cyril; Smidtas, Serge; Salanoubat, Marcel; Weissenbach, Jean; Schachter, Vincent

    2008-01-01

    Background Genome-scale metabolic models are powerful tools to study global properties of metabolic networks. They provide a way to integrate various types of biological information in a single framework, providing a structured representation of available knowledge on the metabolism of the respective species. Results We reconstructed a constraint-based metabolic model of Acinetobacter baylyi ADP1, a soil bacterium of interest for environmental and biotechnological applications with large-spectrum biodegradation capabilities. Following initial reconstruction from genome annotation and the literature, we iteratively refined the model by comparing its predictions with the results of large-scale experiments: (1) high-throughput growth phenotypes of the wild-type strain on 190 distinct environments, (2) genome-wide gene essentialities from a knockout mutant library, and (3) large-scale growth phenotypes of all mutant strains on 8 minimal media. Out of 1412 predictions, 1262 were initially consistent with our experimental observations. Inconsistencies were systematically examined, leading in 65 cases to model corrections. The predictions of the final version of the model, which included three rounds of refinements, are consistent with the experimental results for (1) 91% of the wild-type growth phenotypes, (2) 94% of the gene essentiality results, and (3) 94% of the mutant growth phenotypes. To facilitate the exploitation of the metabolic model, we provide a web interface allowing online predictions and visualization of results on metabolic maps. Conclusion The iterative reconstruction procedure led to significant model improvements, showing that genome-wide mutant phenotypes on several media can significantly facilitate the transition from genome annotation to a high-quality model. PMID:18840283

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

    PubMed

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

    2015-05-12

    Skeletal myocytes are metabolically active and susceptible to insulin resistance and are thus implicated in type 2 diabetes (T2D). This complex disease involves systemic metabolic changes, and their elucidation at the systems level requires genome-wide data and biological networks. Genome-scale metabolic models (GEMs) provide a network context for the integration of high-throughput data. We generated myocyte-specific RNA-sequencing data and investigated their correlation with proteome data. These data were then used to reconstruct a comprehensive myocyte GEM. Next, we performed a meta-analysis of six studies comparing muscle transcription in T2D versus healthy subjects. Transcriptional changes were mapped on the myocyte GEM, revealing extensive transcriptional regulation in T2D, particularly around pyruvate oxidation, branched-chain amino acid catabolism, and tetrahydrofolate metabolism, connected through the downregulated dihydrolipoamide dehydrogenase. Strikingly, the gene signature underlying this metabolic regulation successfully classifies the disease state of individual samples, suggesting that regulation of these pathways is a ubiquitous feature of myocytes in response to T2D. PMID:25937284

  16. Genome Scale Transcriptomics of Baculovirus-Insect Interactions

    PubMed Central

    Nguyen, Quan; Nielsen, Lars K.; Reid, Steven

    2013-01-01

    Baculovirus-insect cell technologies are applied in the production of complex proteins, veterinary and human vaccines, gene delivery vectors‚ and biopesticides. Better understanding of how baculoviruses and insect cells interact would facilitate baculovirus-based production. While complete genomic sequences are available for over 58 baculovirus species, little insect genomic information is known. The release of the Bombyx mori and Plutella xylostella genomes, the accumulation of EST sequences for several Lepidopteran species, and especially the availability of two genome-scale analysis tools, namely oligonucleotide microarrays and next generation sequencing (NGS), have facilitated expression studies to generate a rich picture of insect gene responses to baculovirus infections. This review presents current knowledge on the interaction dynamics of the baculovirus-insect system‚ which is relatively well studied in relation to nucleocapsid transportation, apoptosis, and heat shock responses, but is still poorly understood regarding responses involved in pro-survival pathways, DNA damage pathways, protein degradation, translation, signaling pathways, RNAi pathways, and importantly metabolic pathways for energy, nucleotide and amino acid production. We discuss how the two genome-scale transcriptomic tools can be applied for studying such pathways and suggest that proteomics and metabolomics can produce complementary findings to transcriptomic studies. PMID:24226166

  17. Variations in metabolic pathways create challenges for automated metabolic reconstructions: Examples from the tetrahydrofolate synthesis pathway

    PubMed Central

    de Crécy-Lagard, Valérie

    2014-01-01

    The availability of thousands of sequenced genomes has revealed the diversity of biochemical solutions to similar chemical problems. Even for molecules at the heart of metabolism, such as cofactors, the pathway enzymes first discovered in model organisms like Escherichia coli or Saccharomyces cerevisiae are often not universally conserved. Tetrahydrofolate (THF) (or its close relative tetrahydromethanopterin) is a universal and essential C1-carrier that most microbes and plants synthesize de novo. The THF biosynthesis pathway and enzymes are, however, not universal and alternate solutions are found for most steps, making this pathway a challenge to annotate automatically in many genomes. Comparing THF pathway reconstructions and functional annotations of a chosen set of folate synthesis genes in specific prokaryotes revealed the strengths and weaknesses of different microbial annotation platforms. This analysis revealed that most current platforms fail in metabolic reconstruction of variant pathways. However, all the pieces are in place to quickly correct these deficiencies if the different databases were built on each other's strengths. PMID:25210598

  18. Metabolic Network Modeling of Microbial Communities

    PubMed Central

    Biggs, Matthew B.; Medlock, Gregory L.; Kolling, Glynis L.

    2015-01-01

    Genome-scale metabolic network reconstructions and constraint-based analysis are powerful methods that have the potential to make functional predictions about microbial communities. Current use of genome-scale metabolic networks to characterize the metabolic functions of microbial communities includes species compartmentalization, separating species-level and community-level objectives, dynamic analysis, the “enzyme-soup” approach, multi-scale modeling, and others. There are many challenges inherent to the field, including a need for tools that accurately assign high-level omics signals to individual community members, new automated reconstruction methods that rival manual curation, and novel algorithms for integrating omics data and engineering communities. As technologies and modeling frameworks improve, we expect that there will be proportional advances in the fields of ecology, health science, and microbial community engineering. PMID:26109480

  19. Genome-scale neurogenetics: methodology and meaning

    PubMed Central

    McCarroll, Steven A; Feng, Guoping; Hyman, Steven E

    2016-01-01

    Genetic analysis is currently offering glimpses into molecular mechanisms underlying such neuropsychiatric disorders as schizophrenia, bipolar disorder and autism. After years of frustration, success in identifying disease-associated DNA sequence variation has followed from new genomic technologies, new genome data resources, and global collaborations that could achieve the scale necessary to find the genes underlying highly polygenic disorders. Here we describe early results from genome-scale studies of large numbers of subjects and the emerging significance of these results for neurobiology. PMID:24866041

  20. Generalized framework for context-specific metabolic model extraction methods

    PubMed Central

    Robaina Estévez, Semidán; Nikoloski, Zoran

    2014-01-01

    Genome-scale metabolic models (GEMs) are increasingly applied to investigate the physiology not only of simple prokaryotes, but also eukaryotes, such as plants, characterized with compartmentalized cells of multiple types. While genome-scale models aim at including the entirety of known metabolic reactions, mounting evidence has indicated that only a subset of these reactions is active in a given context, including: developmental stage, cell type, or environment. As a result, several methods have been proposed to reconstruct context-specific models from existing genome-scale models by integrating various types of high-throughput data. Here we present a mathematical framework that puts all existing methods under one umbrella and provides the means to better understand their functioning, highlight similarities and differences, and to help users in selecting a most suitable method for an application. PMID:25285097

  1. Inference of Network Dynamics and Metabolic Interactions in the Gut Microbiome

    PubMed Central

    Loughran, Thomas P.; Papin, Jason A.; Albert, Reka

    2015-01-01

    We present a novel methodology to construct a Boolean dynamic model from time series metagenomic information and integrate this modeling with genome-scale metabolic network reconstructions to identify metabolic underpinnings for microbial interactions. We apply this in the context of a critical health issue: clindamycin antibiotic treatment and opportunistic Clostridium difficile infection. Our model recapitulates known dynamics of clindamycin antibiotic treatment and C. difficile infection and predicts therapeutic probiotic interventions to suppress C. difficile infection. Genome-scale metabolic network reconstructions reveal metabolic differences between community members and are used to explore the role of metabolism in the observed microbial interactions. In vitro experimental data validate a key result of our computational model, that B. intestinihominis can in fact slow C. difficile growth. PMID:26102287

  2. BiGG Models: A platform for integrating, standardizing and sharing genome-scale models

    PubMed Central

    King, Zachary A.; Lu, Justin; Dräger, Andreas; Miller, Philip; Federowicz, Stephen; Lerman, Joshua A.; Ebrahim, Ali; Palsson, Bernhard O.; Lewis, Nathan E.

    2016-01-01

    Genome-scale metabolic models are mathematically-structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scale metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases. Reaction and metabolite identifiers have been standardized across models to conform to community standards and enable rapid comparison across models. Furthermore, BiGG Models provides a comprehensive application programming interface for accessing BiGG Models with modeling and analysis tools. As a resource for highly curated, standardized and accessible models of metabolism, BiGG Models will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data. PMID:26476456

  3. BiGG Models: A platform for integrating, standardizing and sharing genome-scale models.

    PubMed

    King, Zachary A; Lu, Justin; Dräger, Andreas; Miller, Philip; Federowicz, Stephen; Lerman, Joshua A; Ebrahim, Ali; Palsson, Bernhard O; Lewis, Nathan E

    2016-01-01

    Genome-scale metabolic models are mathematically-structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scale metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases. Reaction and metabolite identifiers have been standardized across models to conform to community standards and enable rapid comparison across models. Furthermore, BiGG Models provides a comprehensive application programming interface for accessing BiGG Models with modeling and analysis tools. As a resource for highly curated, standardized and accessible models of metabolism, BiGG Models will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data. PMID:26476456

  4. BiGG Models: A platform for integrating, standardizing and sharing genome-scale models

    SciTech Connect

    King, Zachary A.; Lu, Justin; Drager, Andreas; Miller, Philip; Federowicz, Stephen; Lerman, Joshua A.; Ebrahim, Ali; Palsson, Bernhard O.; Lewis, Nathan E.

    2015-10-17

    In this study, genome-scale metabolic models are mathematically structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scale metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases. Reaction and metabolite identifiers have been standardized across models to conform to community standards and enable rapid comparison across models. Furthermore, BiGG Models provides a comprehensive application programming interface for accessing BiGG Models with modeling and analysis tools. As a resource for highly curated, standardized and accessible models of metabolism, BiGG Models will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data.

  5. Redirector: Designing Cell Factories by Reconstructing the Metabolic Objective

    PubMed Central

    Church, George M.

    2013-01-01

    Advances in computational metabolic optimization are required to realize the full potential of new in vivo metabolic engineering technologies by bridging the gap between computational design and strain development. We present Redirector, a new Flux Balance Analysis-based framework for identifying engineering targets to optimize metabolite production in complex pathways. Previous optimization frameworks have modeled metabolic alterations as directly controlling fluxes by setting particular flux bounds. Redirector develops a more biologically relevant approach, modeling metabolic alterations as changes in the balance of metabolic objectives in the system. This framework iteratively selects enzyme targets, adds the associated reaction fluxes to the metabolic objective, thereby incentivizing flux towards the production of a metabolite of interest. These adjustments to the objective act in competition with cellular growth and represent up-regulation and down-regulation of enzyme mediated reactions. Using the iAF1260 E. coli metabolic network model for optimization of fatty acid production as a test case, Redirector generates designs with as many as 39 simultaneous and 111 unique engineering targets. These designs discover proven in vivo targets, novel supporting pathways and relevant interdependencies, many of which cannot be predicted by other methods. Redirector is available as open and free software, scalable to computational resources, and powerful enough to find all known enzyme targets for fatty acid production. PMID:23341769

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

    PubMed Central

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

    2016-01-01

    Epithelial to mesenchymal transition (EMT) is an important event during development and cancer metastasis. There is limited understanding of the metabolic alterations that give rise to and take place during EMT. Dysregulation of signalling pathways that impact metabolism, including epidermal growth factor receptor (EGFR), are however a hallmark of EMT and metastasis. In this study, we report the investigation into EGFR signalling and metabolic crosstalk of EMT through constraint-based modelling and analysis of the breast epithelial EMT cell model D492 and its mesenchymal counterpart D492M. We built an EGFR signalling network for EMT based on stoichiometric coefficients and constrained the network with gene expression data to build epithelial (EGFR_E) and mesenchymal (EGFR_M) networks. Metabolic alterations arising from differential expression of EGFR genes was derived from a literature review of AKT regulated metabolic genes. Signaling flux differences between EGFR_E and EGFR_M models subsequently allowed metabolism in D492 and D492M cells to be assessed. Higher flux within AKT pathway in the D492 cells compared to D492M suggested higher glycolytic activity in D492 that we confirmed experimentally through measurements of glucose uptake and lactate secretion rates. The signaling genes from the AKT, RAS/MAPK and CaM pathways were predicted to revert D492M to D492 phenotype. Follow-up analysis of EGFR signaling metabolic crosstalk in three additional breast epithelial cell lines highlighted variability in in vitro cell models of EMT. This study shows that the metabolic phenotype may be predicted by in silico analyses of gene expression data of EGFR signaling genes, but this phenomenon is cell-specific and does not follow a simple trend. PMID:27253373

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

    PubMed

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

    2016-06-01

    Epithelial to mesenchymal transition (EMT) is an important event during development and cancer metastasis. There is limited understanding of the metabolic alterations that give rise to and take place during EMT. Dysregulation of signalling pathways that impact metabolism, including epidermal growth factor receptor (EGFR), are however a hallmark of EMT and metastasis. In this study, we report the investigation into EGFR signalling and metabolic crosstalk of EMT through constraint-based modelling and analysis of the breast epithelial EMT cell model D492 and its mesenchymal counterpart D492M. We built an EGFR signalling network for EMT based on stoichiometric coefficients and constrained the network with gene expression data to build epithelial (EGFR_E) and mesenchymal (EGFR_M) networks. Metabolic alterations arising from differential expression of EGFR genes was derived from a literature review of AKT regulated metabolic genes. Signaling flux differences between EGFR_E and EGFR_M models subsequently allowed metabolism in D492 and D492M cells to be assessed. Higher flux within AKT pathway in the D492 cells compared to D492M suggested higher glycolytic activity in D492 that we confirmed experimentally through measurements of glucose uptake and lactate secretion rates. The signaling genes from the AKT, RAS/MAPK and CaM pathways were predicted to revert D492M to D492 phenotype. Follow-up analysis of EGFR signaling metabolic crosstalk in three additional breast epithelial cell lines highlighted variability in in vitro cell models of EMT. This study shows that the metabolic phenotype may be predicted by in silico analyses of gene expression data of EGFR signaling genes, but this phenomenon is cell-specific and does not follow a simple trend. PMID:27253373

  8. Refining metabolic models and accounting for regulatory effects.

    PubMed

    Kim, Joonhoon; Reed, Jennifer L

    2014-10-01

    Advances in genome-scale metabolic modeling allow us to investigate and engineer metabolism at a systems level. Metabolic network reconstructions have been made for many organisms and computational approaches have been developed to convert these reconstructions into predictive models. However, due to incomplete knowledge these reconstructions often have missing or extraneous components and interactions, which can be identified by reconciling model predictions with experimental data. Recent studies have provided methods to further improve metabolic model predictions by incorporating transcriptional regulatory interactions and high-throughput omics data to yield context-specific metabolic models. Here we discuss recent approaches for resolving model-data discrepancies and building context-specific metabolic models. Once developed highly accurate metabolic models can be used in a variety of biotechnology applications. PMID:24632483

  9. Genome-Scale Variation of Tubeworm Symbionts

    NASA Astrophysics Data System (ADS)

    Robidart, J.; Felbeck, H.

    2005-12-01

    Hydrothermal vent tubeworms are completely dependent on their bacterial symbionts for nutrition. Despite this dependency, many studies have concluded that bacterial symbionts are acquired anew from the environment, every generation rather than the more reliable mode of symbiont transmission from parent directly to offspring. Ribosomal 16S sequences have shown little variation of symbiont phylogeny from worm to worm, but higher resolution genome-scale analyses have found that there is genomic heterogeneity between symbionts from worms in different environments. What genes can be "spared," while resulting in an intact symbiosis? Have symbionts from one environment gained physiological capabilities that make them more fit in that environment? In order to answer these questions, subtractive hybridization was used on symbionts of Riftia pachyptila tubeworms from different environments to gain insight into which genes are present in one symbiont and absent in the other. Many genes were found to be unique to each symbiont and these results will be presented. This technique will be applied to answer many fundamental questions regarding microbial symbiont evolution to a specific physico-chemical environment, to a different host species, and more.

  10. Modeling Method for Increased Precision and Scope of Directly Measurable Fluxes at a Genome-Scale.

    PubMed

    McCloskey, Douglas; Young, Jamey D; Xu, Sibei; Palsson, Bernhard O; Feist, Adam M

    2016-04-01

    Metabolic flux analysis (MFA) is considered to be the gold standard for determining the intracellular flux distribution of biological systems. The majority of work using MFA has been limited to core models of metabolism due to challenges in implementing genome-scale MFA and the undesirable trade-off between increased scope and decreased precision in flux estimations. This work presents a tunable workflow for expanding the scope of MFA to the genome-scale without trade-offs in flux precision. The genome-scale MFA model presented here, iDM2014, accounts for 537 net reactions, which includes the core pathways of traditional MFA models and also covers the additional pathways of purine, pyrimidine, isoprenoid, methionine, riboflavin, coenzyme A, and folate, as well as other biosynthetic pathways. When evaluating the iDM2014 using a set of measured intracellular intermediate and cofactor mass isotopomer distributions (MIDs),1 it was found that a total of 232 net fluxes of central and peripheral metabolism could be resolved in the E. coli network. The increase in scope was shown to cover the full biosynthetic route to an expanded set of bioproduction pathways, which should facilitate applications such as the design of more complex bioprocessing strains and aid in identifying new antimicrobials. Importantly, it was found that there was no loss in precision of core fluxes when compared to a traditional core model, and additionally there was an overall increase in precision when considering all observable reactions. PMID:26981784

  11. Microbial Community Metabolic Modeling: A Community Data-Driven Network Reconstruction.

    PubMed

    Henry, Christopher S; Bernstein, Hans C; Weisenhorn, Pamela; Taylor, Ronald C; Lee, Joon-Yong; Zucker, Jeremy; Song, Hyun-Seob

    2016-11-01

    Metabolic network modeling of microbial communities provides an in-depth understanding of community-wide metabolic and regulatory processes. Compared to single organism analyses, community metabolic network modeling is more complex because it needs to account for interspecies interactions. To date, most approaches focus on reconstruction of high-quality individual networks so that, when combined, they can predict community behaviors as a result of interspecies interactions. However, this conventional method becomes ineffective for communities whose members are not well characterized and cannot be experimentally interrogated in isolation. Here, we tested a new approach that uses community-level data as a critical input for the network reconstruction process. This method focuses on directly predicting interspecies metabolic interactions in a community, when axenic information is insufficient. We validated our method through the case study of a bacterial photoautotroph-heterotroph consortium that was used to provide data needed for a community-level metabolic network reconstruction. Resulting simulations provided experimentally validated predictions of how a photoautotrophic cyanobacterium supports the growth of an obligate heterotrophic species by providing organic carbon and nitrogen sources. J. Cell. Physiol. 231: 2339-2345, 2016. © 2016 Wiley Periodicals, Inc. PMID:27186840

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

    PubMed Central

    Hamilton, Joshua J.; Reed, Jennifer L.

    2012-01-01

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

  13. Database constraints applied to metabolic pathway reconstruction tools.

    PubMed

    Vilaplana, Jordi; Solsona, Francesc; Teixido, Ivan; Usié, Anabel; Karathia, Hiren; Alves, Rui; Mateo, Jordi

    2014-01-01

    Our group developed two biological applications, Biblio-MetReS and Homol-MetReS, accessing the same database of organisms with annotated genes. Biblio-MetReS is a data-mining application that facilitates the reconstruction of molecular networks based on automated text-mining analysis of published scientific literature. Homol-MetReS allows functional (re)annotation of proteomes, to properly identify both the individual proteins involved in the process(es) of interest and their function. It also enables the sets of proteins involved in the process(es) in different organisms to be compared directly. The efficiency of these biological applications is directly related to the design of the shared database. We classified and analyzed the different kinds of access to the database. Based on this study, we tried to adjust and tune the configurable parameters of the database server to reach the best performance of the communication data link to/from the database system. Different database technologies were analyzed. We started the study with a public relational SQL database, MySQL. Then, the same database was implemented by a MapReduce-based database named HBase. The results indicated that the standard configuration of MySQL gives an acceptable performance for low or medium size databases. Nevertheless, tuning database parameters can greatly improve the performance and lead to very competitive runtimes. PMID:25202745

  14. Database Constraints Applied to Metabolic Pathway Reconstruction Tools

    PubMed Central

    Vilaplana, Jordi; Solsona, Francesc; Teixido, Ivan; Usié, Anabel; Karathia, Hiren; Alves, Rui; Mateo, Jordi

    2014-01-01

    Our group developed two biological applications, Biblio-MetReS and Homol-MetReS, accessing the same database of organisms with annotated genes. Biblio-MetReS is a data-mining application that facilitates the reconstruction of molecular networks based on automated text-mining analysis of published scientific literature. Homol-MetReS allows functional (re)annotation of proteomes, to properly identify both the individual proteins involved in the process(es) of interest and their function. It also enables the sets of proteins involved in the process(es) in different organisms to be compared directly. The efficiency of these biological applications is directly related to the design of the shared database. We classified and analyzed the different kinds of access to the database. Based on this study, we tried to adjust and tune the configurable parameters of the database server to reach the best performance of the communication data link to/from the database system. Different database technologies were analyzed. We started the study with a public relational SQL database, MySQL. Then, the same database was implemented by a MapReduce-based database named HBase. The results indicated that the standard configuration of MySQL gives an acceptable performance for low or medium size databases. Nevertheless, tuning database parameters can greatly improve the performance and lead to very competitive runtimes. PMID:25202745

  15. Reconstruction Of Ancient Microbial Biodiversity And Metabolism From Fossil Travertine

    NASA Astrophysics Data System (ADS)

    Asangba, A. E.; Dong, Y.; Lindo, J.; Malhi, R. S.; Foubert, A.; Swennen, R.; Ozkul, M.; Fouke, B. W.

    2013-12-01

    Abstract: A virtually unexplored frontier in the study of life in extreme environments is the extraction of genetic and environmental information directly from microbes entombed in calcium carbonate (CaCO3) crystals in the geological record. An environmental metagenomic study has been initiated to systematically track the fate of microbial gene sequences, lipids and other 'biomarkers' during the fossilization and diagenesis of Pleistocene terrestrial hot-spring travertine in Yellowstone National Park and the Pamukkale region of Turkey. The Mammoth Hot Springs corridor of Yellowstone contains thermal springs (73oC) that are actively and rapidly (mm's/day) precipitating travertine, as well as a complete time-series of travertine deposits that extend back to the Pleistocene (~33 ka). Comparative samples have been collected from quarries in the Denizli Basin (700-900 ka). The goal is to quantitatively track the preservation of these biomolecules through geological time, across specific sequences of down-flow travertine depositional facies and use this information to accurately reconstruct the identity, activity and ecology of the ancient microbes and their hot-spring environments. Analyses are being conducted of biomarkers extracted from bulk rock (cm's in diameter) as well as micro-drilled samples (μm in diameter). Each travertine sample is first being quantitatively screened (optically and geochemically) to determine the extent and fabric of water-rock alteration. Biomass has been successfully extracted from 10 μm-diameter fluid inclusions in primary crystals, as well as inter-crystalline deposits, and is undergoing metagenomic sequencing.

  16. Diagnostics for Stochastic Genome-Scale Modeling via Model Slicing and Debugging

    PubMed Central

    Tsai, Kevin J.; Chang, Chuan-Hsiung

    2014-01-01

    Modeling of biological behavior has evolved from simple gene expression plots represented by mathematical equations to genome-scale systems biology networks. However, due to obstacles in complexity and scalability of creating genome-scale models, several biological modelers have turned to programming or scripting languages and away from modeling fundamentals. In doing so, they have traded the ability to have exchangeable, standardized model representation formats, while those that remain true to standardized model representation are faced with challenges in model complexity and analysis. We have developed a model diagnostic methodology inspired by program slicing and debugging and demonstrate the effectiveness of the methodology on a genome-scale metabolic network model published in the BioModels database. The computer-aided identification revealed specific points of interest such as reversibility of reactions, initialization of species amounts, and parameter estimation that improved a candidate cell's adenosine triphosphate production. We then compared the advantages of our methodology over other modeling techniques such as model checking and model reduction. A software application that implements the methodology is available at http://gel.ym.edu.tw/gcs/. PMID:25368989

  17. BiGG Models: A platform for integrating, standardizing and sharing genome-scale models

    DOE PAGESBeta

    King, Zachary A.; Lu, Justin; Drager, Andreas; Miller, Philip; Federowicz, Stephen; Lerman, Joshua A.; Ebrahim, Ali; Palsson, Bernhard O.; Lewis, Nathan E.

    2015-10-17

    In this study, genome-scale metabolic models are mathematically structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scalemore » metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases. Reaction and metabolite identifiers have been standardized across models to conform to community standards and enable rapid comparison across models. Furthermore, BiGG Models provides a comprehensive application programming interface for accessing BiGG Models with modeling and analysis tools. As a resource for highly curated, standardized and accessible models of metabolism, BiGG Models will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data.« less

  18. Reconstruction and Comparison of the Metabolic Potential of Cyanobacteria Cyanothece sp. ATCC 51142 and Synechocystis sp. PCC 6803

    PubMed Central

    Saha, Rajib; Verseput, Alex T.; Berla, Bertram M.; Mueller, Thomas J.; Pakrasi, Himadri B.; Maranas, Costas D.

    2012-01-01

    Cyanobacteria are an important group of photoautotrophic organisms that can synthesize valuable bio-products by harnessing solar energy. They are endowed with high photosynthetic efficiencies and diverse metabolic capabilities that confer the ability to convert solar energy into a variety of biofuels and their precursors. However, less well studied are the similarities and differences in metabolism of different species of cyanobacteria as they pertain to their suitability as microbial production chassis. Here we assemble, update and compare genome-scale models (iCyt773 and iSyn731) for two phylogenetically related cyanobacterial species, namely Cyanothece sp. ATCC 51142 and Synechocystis sp. PCC 6803. All reactions are elementally and charge balanced and localized into four different intracellular compartments (i.e., periplasm, cytosol, carboxysome and thylakoid lumen) and biomass descriptions are derived based on experimental measurements. Newly added reactions absent in earlier models (266 and 322, respectively) span most metabolic pathways with an emphasis on lipid biosynthesis. All thermodynamically infeasible loops are identified and eliminated from both models. Comparisons of model predictions against gene essentiality data reveal a specificity of 0.94 (94/100) and a sensitivity of 1 (19/19) for the Synechocystis iSyn731 model. The diurnal rhythm of Cyanothece 51142 metabolism is modeled by constructing separate (light/dark) biomass equations and introducing regulatory restrictions over light and dark phases. Specific metabolic pathway differences between the two cyanobacteria alluding to different bio-production potentials are reflected in both models. PMID:23133581

  19. Simultaneous prediction of enzyme orthologs from chemical transformation patterns for de novo metabolic pathway reconstruction

    PubMed Central

    Tabei, Yasuo; Yamanishi, Yoshihiro; Kotera, Masaaki

    2016-01-01

    Motivation: Metabolic pathways are an important class of molecular networks consisting of compounds, enzymes and their interactions. The understanding of global metabolic pathways is extremely important for various applications in ecology and pharmacology. However, large parts of metabolic pathways remain unknown, and most organism-specific pathways contain many missing enzymes. Results: In this study we propose a novel method to predict the enzyme orthologs that catalyze the putative reactions to facilitate the de novo reconstruction of metabolic pathways from metabolome-scale compound sets. The algorithm detects the chemical transformation patterns of substrate–product pairs using chemical graph alignments, and constructs a set of enzyme-specific classifiers to simultaneously predict all the enzyme orthologs that could catalyze the putative reactions of the substrate–product pairs in the joint learning framework. The originality of the method lies in its ability to make predictions for thousands of enzyme orthologs simultaneously, as well as its extraction of enzyme-specific chemical transformation patterns of substrate–product pairs. We demonstrate the usefulness of the proposed method by applying it to some ten thousands of metabolic compounds, and analyze the extracted chemical transformation patterns that provide insights into the characteristics and specificities of enzymes. The proposed method will open the door to both primary (central) and secondary metabolism in genomics research, increasing research productivity to tackle a wide variety of environmental and public health matters. Availability and Implementation: Contact: maskot@bio.titech.ac.jp PMID:27307627

  20. Computational Modeling of Human Metabolism and Its Application to Systems Biomedicine.

    PubMed

    Aurich, Maike K; Thiele, Ines

    2016-01-01

    Modern high-throughput techniques offer immense opportunities to investigate whole-systems behavior, such as those underlying human diseases. However, the complexity of the data presents challenges in interpretation, and new avenues are needed to address the complexity of both diseases and data. Constraint-based modeling is one formalism applied in systems biology. It relies on a genome-scale reconstruction that captures extensive biochemical knowledge regarding an organism. The human genome-scale metabolic reconstruction is increasingly used to understand normal cellular and disease states because metabolism is an important factor in many human diseases. The application of human genome-scale reconstruction ranges from mere querying of the model as a knowledge base to studies that take advantage of the model's topology and, most notably, to functional predictions based on cell- and condition-specific metabolic models built based on omics data.An increasing number and diversity of biomedical questions are being addressed using constraint-based modeling and metabolic models. One of the most successful biomedical applications to date is cancer metabolism, but constraint-based modeling also holds great potential for inborn errors of metabolism or obesity. In addition, it offers great prospects for individualized approaches to diagnostics and the design of disease prevention and intervention strategies. Metabolic models support this endeavor by providing easy access to complex high-throughput datasets. Personalized metabolic models have been introduced. Finally, constraint-based modeling can be used to model whole-body metabolism, which will enable the elucidation of metabolic interactions between organs and disturbances of these interactions as either causes or consequence of metabolic diseases. This chapter introduces constraint-based modeling and describes some of its contributions to systems biomedicine. PMID:26677187

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

    PubMed

    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 . PMID:26542446

  2. Systems biology of cancer: moving toward the integrative study of the metabolic alterations in cancer cells

    PubMed Central

    Hernández Patiño, Claudia E.; Jaime-Muñoz, Gustavo; Resendis-Antonio, Osbaldo

    2013-01-01

    One of the main objectives in systems biology is to understand the biological mechanisms that give rise to the phenotype of a microorganism by using high-throughput technologies (HTs) and genome-scale mathematical modeling. The computational modeling of genome-scale metabolic reconstructions is one systemic and quantitative strategy for characterizing the metabolic phenotype associated with human diseases and potentially for designing drugs with optimal clinical effects. The purpose of this short review is to describe how computational modeling, including the specific case of constraint-based modeling, can be used to explore, characterize, and predict the metabolic capacities that distinguish the metabolic phenotype of cancer cell lines. As we show herein, this computational framework is far from a pure theoretical description, and to ensure proper biological interpretation, it is necessary to integrate high-throughput data and generate predictions for later experimental assessment. Hence, genome-scale modeling serves as a platform for the following: (1) the integration of data from HTs, (2) the assessment of how metabolic activity is related to phenotype in cancer cell lines, and (3) the design of new experiments to evaluate the outcomes of the in silico analysis. By combining the functions described above, we show that computational modeling is a useful methodology to construct an integrative, systemic, and quantitative scheme for understanding the metabolic profiles of cancer cell lines, a first step to determine the metabolic mechanism by which cancer cells maintain and support their malignant phenotype in human tissues. PMID:23316163

  3. Genome scale models of yeast: towards standardized evaluation and consistent omic integration.

    PubMed

    Sánchez, Benjamín J; Nielsen, Jens

    2015-08-01

    Genome scale models (GEMs) have enabled remarkable advances in systems biology, acting as functional databases of metabolism, and as scaffolds for the contextualization of high-throughput data. In the case of Saccharomyces cerevisiae (budding yeast), several GEMs have been published and are currently used for metabolic engineering and elucidating biological interactions. Here we review the history of yeast's GEMs, focusing on recent developments. We study how these models are typically evaluated, using both descriptive and predictive metrics. Additionally, we analyze the different ways in which all levels of omics data (from gene expression to flux) have been integrated in yeast GEMs. Relevant conclusions and current challenges for both GEM evaluation and omic integration are highlighted. PMID:26079294

  4. Gap Detection for Genome-Scale Constraint-Based Models

    PubMed Central

    Brooks, J. Paul; Burns, William P.; Fong, Stephen S.; Gowen, Chris M.; Roberts, Seth B.

    2012-01-01

    Constraint-based metabolic models are currently the most comprehensive system-wide models of cellular metabolism. Several challenges arise when building an in silico constraint-based model of an organism that need to be addressed before flux balance analysis (FBA) can be applied for simulations. An algorithm called FBA-Gap is presented here that aids the construction of a working model based on plausible modifications to a given list of reactions that are known to occur in the organism. When applied to a working model, the algorithm gives a hypothesis concerning a minimal medium for sustaining the cell in culture. The utility of the algorithm is demonstrated in creating a new model organism and is applied to four existing working models for generating hypotheses about culture media. In modifying a partial metabolic reconstruction so that biomass may be produced using FBA, the proposed method is more efficient than a previously proposed method in that fewer new reactions are added to complete the model. The proposed method is also more accurate than other approaches in that only biologically plausible reactions and exchange reactions are used. PMID:22997515

  5. [Predicting genetic modification targets based on metabolic network analysis--a review].

    PubMed

    Li, Peishun; Ma, Hongwu; Zhao, Xueming; Chen, Tao

    2016-01-01

    Construction of artificial cell factory to produce specific compounds of interest needs wild strain to be genetically engineered. In recent years, with the reconstruction of many genome-scale metabolic networks, a number of methods have been proposed based on metabolic network analysis for predicting genetic modification targets that lead to overproduction of compounds of interest. These approaches use constraints of stoichiometry and reaction reversibility in genome-scale models of metabolism and adopt different mathematical algorithms to predict modification targets, and thus can discover new targets that are difficult to find through traditional intuitive methods. In this review, we introduce the principle, merit, demerit and application of various strain optimization methods in detail. The main problems in existing methods and perspectives on this emerging research field are also discussed, aiming to provide guidance to choose the appropriate methods according to different types of products and the reliability of the predicted results. PMID:27363195

  6. A genome-scale map of expression for a mouse brain section obtained using voxelation

    SciTech Connect

    Chin, Mark H.; Geng, Alex B.; Khan, Arshad H.; Qian, Weijun; Petyuk, Vladislav A.; Boline, Jyl; Levy, Shawn; Toga, Arthur W.; Smith, Richard D.; Leahy, Richard M.; Smith, Desmond J.

    2007-08-20

    Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological diseases. We have reconstructed 2- dimensional images of gene expression for 20,000 genes in a coronal slice of the mouse brain at the level of the striatum by using microarrays in combination with voxelation at a resolution of 1 mm3. Good reliability of the microarray results were confirmed using multiple replicates, subsequent quantitative RT-PCR voxelation, mass spectrometry voxelation and publicly available in situ hybridization data. Known and novel genes were identified with expression patterns localized to defined substructures within the brain. In addition, genes with unexpected patterns were identified and cluster analysis identified a set of genes with a gradient of dorsal/ventral expression not restricted to known anatomical boundaries. The genome-scale maps of gene expression obtained using voxelation will be a valuable tool for the neuroscience community.

  7. Metabolic reconstruction of sulfur assimilation in the extremophile Acidithiobacillus ferrooxidans based on genome analysis

    PubMed Central

    Valdés, Jorge; Veloso, Felipe; Jedlicki, Eugenia; Holmes, David

    2003-01-01

    Background Acidithiobacillus ferrooxidans is a gamma-proteobacterium that lives at pH2 and obtains energy by the oxidation of sulfur and iron. It is used in the biomining industry for the recovery of metals and is one of the causative agents of acid mine drainage. Effective tools for the study of its genetics and physiology are not in widespread use and, despite considerable effort, an understanding of its unusual physiology remains at a rudimentary level. Nearly complete genome sequences of A. ferrooxidans are available from two public sources and we have exploited this information to reconstruct aspects of its sulfur metabolism. Results Two candidate mechanisms for sulfate uptake from the environment were detected but both belong to large paralogous families of membrane transporters and their identification remains tentative. Prospective genes, pathways and regulatory mechanisms were identified that are likely to be involved in the assimilation of sulfate into cysteine and in the formation of Fe-S centers. Genes and regulatory networks were also uncovered that may link sulfur assimilation with nitrogen fixation, hydrogen utilization and sulfur reduction. Potential pathways were identified for sulfation of extracellular metabolites that may possibly be involved in cellular attachment to pyrite, sulfur and other solid substrates. Conclusions A bioinformatic analysis of the genome sequence of A. ferrooxidans has revealed candidate genes, metabolic process and control mechanisms potentially involved in aspects of sulfur metabolism. Metabolic modeling provides an important preliminary step in understanding the unusual physiology of this extremophile especially given the severe difficulties involved in its genetic manipulation and biochemical analysis. PMID:14675496

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

    PubMed Central

    Sertbaş, Mustafa; Ülgen, Kutlu; Çakır, Tunahan

    2014-01-01

    Network-oriented analysis is essential to identify those parts of a cell affected by a given perturbation. The effect of neurodegenerative perturbations in the form of diseases of brain metabolism was investigated by using a newly reconstructed brain-specific metabolic network. The developed stoichiometric model correctly represents healthy brain metabolism, and includes 630 metabolic reactions in and between astrocytes and neurons, which are controlled by 570 genes. The integration of transcriptome data of six neurodegenerative diseases (Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis, multiple sclerosis, schizophrenia) with the model was performed to identify reporter features specific and common for these diseases, which revealed metabolites and pathways around which the most significant changes occur. The identified metabolites are potential biomarkers for the pathology of the related diseases. Our model indicated perturbations in oxidative stress, energy metabolism including TCA cycle and lipid metabolism as well as several amino acid related pathways, in agreement with the role of these pathways in the studied diseases. The computational prediction of transcription factors that commonly regulate the reporter metabolites was achieved through binding-site analysis. Literature support for the identified transcription factors such as USF1, SP1 and those from FOX families are known from the literature to have regulatory roles in the identified reporter metabolic pathways as well as in the neurodegenerative diseases. In essence, the reconstructed brain model enables the elucidation of effects of a perturbation on brain metabolism and the illumination of possible machineries in which a specific metabolite or pathway acts as a regulatory spot for cellular reorganization. PMID:25061554

  9. An online system for metabolic network analysis

    PubMed Central

    Cicek, Abdullah Ercument; Qi, Xinjian; Cakmak, Ali; Johnson, Stephen R.; Han, Xu; Alshalwi, Sami; Ozsoyoglu, Zehra Meral; Ozsoyoglu, Gultekin

    2014-01-01

    Metabolic networks have become one of the centers of attention in life sciences research with the advancements in the metabolomics field. A vast array of studies analyzes metabolites and their interrelations to seek explanations for various biological questions, and numerous genome-scale metabolic networks have been assembled to serve for this purpose. The increasing focus on this topic comes with the need for software systems that store, query, browse, analyze and visualize metabolic networks. PathCase Metabolomics Analysis Workbench (PathCaseMAW) is built, released and runs on a manually created generic mammalian metabolic network. The PathCaseMAW system provides a database-enabled framework and Web-based computational tools for browsing, querying, analyzing and visualizing stored metabolic networks. PathCaseMAW editor, with its user-friendly interface, can be used to create a new metabolic network and/or update an existing metabolic network. The network can also be created from an existing genome-scale reconstructed network using the PathCaseMAW SBML parser. The metabolic network can be accessed through a Web interface or an iPad application. For metabolomics analysis, steady-state metabolic network dynamics analysis (SMDA) algorithm is implemented and integrated with the system. SMDA tool is accessible through both the Web-based interface and the iPad application for metabolomics analysis based on a metabolic profile. PathCaseMAW is a comprehensive system with various data input and data access subsystems. It is easy to work with by design, and is a promising tool for metabolomics research and for educational purposes. Database URL: http://nashua.case.edu/PathwaysMAW/Web PMID:25267793

  10. Genome-Scale Studies of Aging: Challenges and Opportunities

    PubMed Central

    McCormick, Mark A; Kennedy, Brian K

    2012-01-01

    Whole-genome studies involving a phenotype of interest are increasingly prevalent, in part due to a dramatic increase in speed at which many high throughput technologies can be performed coupled to simultaneous decreases in cost. This type of genome-scale methodology has been applied to the phenotype of lifespan, as well as to whole-transcriptome changes during the aging process or in mutants affecting aging. The value of high throughput discovery-based science in this field is clearly evident, but will it yield a true systems-level understanding of the aging process? Here we review some of this work to date, focusing on recent findings and the unanswered puzzles to which they point. In this context, we also discuss recent technological advances and some of the likely future directions that they portend. PMID:23633910

  11. Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods.

    PubMed

    Lewis, Nathan E; Nagarajan, Harish; Palsson, Bernhard O

    2012-04-01

    Reconstructed microbial metabolic networks facilitate a mechanistic description of the genotype-phenotype relationship through the deployment of constraint-based reconstruction and analysis (COBRA) methods. As reconstructed networks leverage genomic data for insight and phenotype prediction, the development of COBRA methods has accelerated following the advent of whole-genome sequencing. Here, we describe a phylogeny of COBRA methods that has rapidly evolved from the few early methods, such as flux balance analysis and elementary flux mode analysis, into a repertoire of more than 100 methods. These methods have enabled genome-scale analysis of microbial metabolism for numerous basic and applied uses, including antibiotic discovery, metabolic engineering and modelling of microbial community behaviour. PMID:22367118

  12. In vitro reconstruction and analysis of evolutionary variation of the tomato acylsucrose metabolic network.

    PubMed

    Fan, Pengxiang; Miller, Abigail M; Schilmiller, Anthony L; Liu, Xiaoxiao; Ofner, Itai; Jones, A Daniel; Zamir, Dani; Last, Robert L

    2016-01-12

    Plant glandular secreting trichomes are epidermal protuberances that produce structurally diverse specialized metabolites, including medically important compounds. Trichomes of many plants in the nightshade family (Solanaceae) produce O-acylsugars, and in cultivated and wild tomatoes these are mixtures of aliphatic esters of sucrose and glucose of varying structures and quantities documented to contribute to insect defense. We characterized the first two enzymes of acylsucrose biosynthesis in the cultivated tomato Solanum lycopersicum. These are type I/IV trichome-expressed BAHD acyltransferases encoded by Solyc12g006330--or S. lycopersicum acylsucrose acyltransferase 1 (Sl-ASAT1)--and Solyc04g012020 (Sl-ASAT2). These enzymes were used--in concert with two previously identified BAHD acyltransferases--to reconstruct the entire cultivated tomato acylsucrose biosynthetic pathway in vitro using sucrose and acyl-CoA substrates. Comparative genomics and biochemical analysis of ASAT enzymes were combined with in vitro mutagenesis to identify amino acids that influence CoA ester substrate specificity and contribute to differences in types of acylsucroses that accumulate in cultivated and wild tomato species. This work demonstrates the feasibility of the metabolic engineering of these insecticidal metabolites in plants and microbes. PMID:26715757

  13. Reconstructing metabolic pathways of hydrocarbon-degrading bacteria from the Deepwater Horizon oil spill.

    PubMed

    Dombrowski, Nina; Donaho, John A; Gutierrez, Tony; Seitz, Kiley W; Teske, Andreas P; Baker, Brett J

    2016-01-01

    The Deepwater Horizon blowout in the Gulf of Mexico in 2010, one of the largest marine oil spills(1), changed bacterial communities in the water column and sediment as they responded to complex hydrocarbon mixtures(2-4). Shifts in community composition have been correlated to the microbial degradation and use of hydrocarbons(2,5,6), but the full genetic potential and taxon-specific metabolisms of bacterial hydrocarbon degraders remain unresolved. Here, we have reconstructed draft genomes of marine bacteria enriched from sea surface and deep plume waters of the spill that assimilate alkane and polycyclic aromatic hydrocarbons during stable-isotope probing experiments, and we identify genes of hydrocarbon degradation pathways. Alkane degradation genes were ubiquitous in the assembled genomes. Marinobacter was enriched with n-hexadecane, and uncultured Alpha- and Gammaproteobacteria populations were enriched in the polycyclic-aromatic-hydrocarbon-degrading communities and contained a broad gene set for degrading phenanthrene and naphthalene. The repertoire of polycyclic aromatic hydrocarbon use varied among different bacterial taxa and the combined capabilities of the microbial community exceeded those of its individual components, indicating that the degradation of complex hydrocarbon mixtures requires the non-redundant capabilities of a complex oil-degrading community. PMID:27572965

  14. In vitro reconstruction and analysis of evolutionary variation of the tomato acylsucrose metabolic network

    PubMed Central

    Fan, Pengxiang; Miller, Abigail M.; Schilmiller, Anthony L.; Liu, Xiaoxiao; Ofner, Itai; Jones, A. Daniel; Zamir, Dani; Last, Robert L.

    2016-01-01

    Plant glandular secreting trichomes are epidermal protuberances that produce structurally diverse specialized metabolites, including medically important compounds. Trichomes of many plants in the nightshade family (Solanaceae) produce O-acylsugars, and in cultivated and wild tomatoes these are mixtures of aliphatic esters of sucrose and glucose of varying structures and quantities documented to contribute to insect defense. We characterized the first two enzymes of acylsucrose biosynthesis in the cultivated tomato Solanum lycopersicum. These are type I/IV trichome-expressed BAHD acyltransferases encoded by Solyc12g006330─or S. lycopersicum acylsucrose acyltransferase 1 (Sl-ASAT1)─and Solyc04g012020 (Sl-ASAT2). These enzymes were used—in concert with two previously identified BAHD acyltransferases—to reconstruct the entire cultivated tomato acylsucrose biosynthetic pathway in vitro using sucrose and acyl-CoA substrates. Comparative genomics and biochemical analysis of ASAT enzymes were combined with in vitro mutagenesis to identify amino acids that influence CoA ester substrate specificity and contribute to differences in types of acylsucroses that accumulate in cultivated and wild tomato species. This work demonstrates the feasibility of the metabolic engineering of these insecticidal metabolites in plants and microbes. PMID:26715757

  15. Constraint-based modeling analysis of the metabolism of two Pelobacter species

    PubMed Central

    2010-01-01

    Background Pelobacter species are commonly found in a number of subsurface environments, and are unique members of the Geobacteraceae family. They are phylogenetically intertwined with both Geobacter and Desulfuromonas species. Pelobacter species likely play important roles in the fermentative degradation of unusual organic matters and syntrophic metabolism in the natural environments, and are of interest for applications in bioremediation and microbial fuel cells. Results In order to better understand the physiology of Pelobacter species, genome-scale metabolic models for Pelobacter carbinolicus and Pelobacter propionicus were developed. Model development was greatly aided by the availability of models of the closely related Geobacter sulfurreducens and G. metallireducens. The reconstructed P. carbinolicus model contains 741 genes and 708 reactions, whereas the reconstructed P. propionicus model contains 661 genes and 650 reactions. A total of 470 reactions are shared among the two Pelobacter models and the two Geobacter models. The different reactions between the Pelobacter and Geobacter models reflect some unique metabolic capabilities such as fermentative growth for both Pelobacter species. The reconstructed Pelobacter models were validated by simulating published growth conditions including fermentations, hydrogen production in syntrophic co-culture conditions, hydrogen utilization, and Fe(III) reduction. Simulation results matched well with experimental data and indicated the accuracy of the models. Conclusions We have developed genome-scale metabolic models of P. carbinolicus and P. propionicus. These models of Pelobacter metabolism can now be incorporated into the growing repertoire of genome scale models of the Geobacteraceae family to aid in describing the growth and activity of these organisms in anoxic environments and in the study of their roles and interactions in the subsurface microbial community. PMID:21182788

  16. Genome-scale modeling of the evolutionary path to C4 photosynthesis

    NASA Astrophysics Data System (ADS)

    Myers, Christopher R.; Bogart, Eli

    In C4 photosynthesis, plants maintain a high carbon dioxide level in specialized bundle sheath cells surrounding leaf veins and restrict CO2 assimilation to those cells, favoring CO2 over O2 in competition for Rubisco active sites. In C3 plants, which do not possess such a carbon concentrating mechanism, CO2 fixation is reduced due to this competition. Despite the complexity of the C4 system, it has evolved convergently from more than 60 independent origins in diverse families of plants around the world over the last 30 million years. We study the evolution of the C4 system in a genome-scale model of plant metabolism that describes interacting mesophyll and bundle sheath cells and enforces key nonlinear kinetic relationships. Adapting the zero-temperature string method for simulating transition paths in physics and chemistry, we find the highest-fitness paths connecting C3 and C4 positions in the model's high-dimensional parameter space, and show that they reproduce known aspects of the C3-C4 transition while making additional predictions about metabolic changes along the path. We explore the relationship between evolutionary history and C4 biochemical subtype, and the effects of atmospheric carbon dioxide levels.

  17. New paradigms for metabolic modeling of human cells.

    PubMed

    Mardinoglu, Adil; Nielsen, Jens

    2015-08-01

    Abnormalities in cellular functions are associated with the progression of human diseases, often resulting in metabolic reprogramming. GEnome-scale metabolic Models (GEMs) have enabled studying global metabolic reprogramming in connection with disease development in a systematic manner. Here we review recent work on reconstruction of GEMs for human cell/tissue types and cancer, and the use of GEMs for identification of metabolic changes occurring in response to disease development. We further discuss how GEMs can be used for the development of efficient therapeutic strategies. Finally, challenges in integration of cell/tissue models for simulation of whole body functions as well as integration of GEMs with other biological networks for generating complete cell/tissue models are presented. PMID:25559199

  18. Accomplishments in genome-scale in silico modeling for industrial and medical biotechnology

    PubMed Central

    Milne, Caroline B.; Kim, Pan-Jun; Eddy, James A.; Price, Nathan D.

    2011-01-01

    Driven by advancements in high-throughput biological technologies and the growing number of sequenced genomes, the construction of in silico models at the genome scale has provided powerful tools to investigate a vast array of biological systems and applications. Here, we review comprehensively the uses of such models in industrial and medical biotechnology, including biofuel generation, food production, and drug development. While the use of in silico models is still in its early stages for delivering to industry, significant initial successes have been achieved. For the cases presented here, genome-scale models predict engineering strategies to enhance properties of interest in an organism or to inhibit harmful mechanisms of pathogens or in disease. Going forward, genome-scale in silico models promise to extend their application and analysis scope to become a transformative tool in biotechnology. As such, genome-scale models can provide a basis for rational genome-scale engineering and synthetic biology. PMID:19946878

  19. HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology

    PubMed Central

    Gille, Christoph; Bölling, Christian; Hoppe, Andreas; Bulik, Sascha; Hoffmann, Sabrina; Hübner, Katrin; Karlstädt, Anja; Ganeshan, Ramanan; König, Matthias; Rother, Kristian; Weidlich, Michael; Behre, Jörn; Holzhütter, Herrmann-Georg

    2010-01-01

    We present HepatoNet1, the first reconstruction of a comprehensive metabolic network of the human hepatocyte that is shown to accomplish a large canon of known metabolic liver functions. The network comprises 777 metabolites in six intracellular and two extracellular compartments and 2539 reactions, including 1466 transport reactions. It is based on the manual evaluation of >1500 original scientific research publications to warrant a high-quality evidence-based model. The final network is the result of an iterative process of data compilation and rigorous computational testing of network functionality by means of constraint-based modeling techniques. Taking the hepatic detoxification of ammonia as an example, we show how the availability of nutrients and oxygen may modulate the interplay of various metabolic pathways to allow an efficient response of the liver to perturbations of the homeostasis of blood compounds. PMID:20823849

  20. Metabolic network reconstruction and flux variability analysis of storage synthesis in developing oilseed rape (Brassica napus L.) embryos

    SciTech Connect

    Hay, J.; Schwender, J.

    2011-08-01

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

  1. Genome-scale phylodynamics and evolution analysis of global H7N7 influenza viruses.

    PubMed

    Wei, Kaifa; Tang, Xiaoping; Li, Yuhan

    2016-09-25

    Previous studies lacked of comprehensive analysis about the evolutionary history and phylogeography of global H7N7 viruses. In this study, it is essential to undertake a genome-scale analysis to investigate the evolutionary processes in a global perspective. There was local phylogenetic divergence among eight trees based on individual segments of 132 strains. We detected four reassortments between four distinct groups of viruses divided by HA gene, suggesting intrasubtype reassortment could accelerate the emergence of highly pathogenic virus. The molecular clock estimated that H7N7 virus evolved at a slower evolutionary rate ranged from 1.03E-03 to 2.81E-03subs/site/year. And we also showed that all gene segments of the virus were under strong purifying selection. A total of 11 positively selected sites were detected by at least two out of three methods. We reconstructed the population dynamics of global H7N7 viruses spanning over a century, revealing that temporal trends of the effective population size were consistent with the major epidemics previously reported. Our study adopt a Bayesian phylogeographic approach to investigate the geographic spread of H7N7 viruses, which combined with temporal and spatial information of all sequences. We have confirmed several migration events between different geographic locations supported by higher values of Bayes factor. The diffusion patterns of H7N7 viruses reveal that the virus is more likely to evolve to expand their host ranges even cross the species. PMID:27599934

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

    PubMed Central

    Knoop, Henning; Gründel, Marianne; Zilliges, Yvonne; Lehmann, Robert; Hoffmann, Sabrina; Lockau, Wolfgang; Steuer, Ralf

    2013-01-01

    Cyanobacteria are versatile unicellular phototrophic microorganisms that are highly abundant in many environments. Owing to their capability to utilize solar energy and atmospheric carbon dioxide for growth, cyanobacteria are increasingly recognized as a prolific resource for the synthesis of valuable chemicals and various biofuels. To fully harness the metabolic capabilities of cyanobacteria necessitates an in-depth understanding of the metabolic interconversions taking place during phototrophic growth, as provided by genome-scale reconstructions of microbial organisms. Here we present an extended reconstruction and analysis of the metabolic network of the unicellular cyanobacterium Synechocystis sp. PCC 6803. Building upon several recent reconstructions of cyanobacterial metabolism, unclear reaction steps are experimentally validated and the functional consequences of unknown or dissenting pathway topologies are discussed. The updated model integrates novel results with respect to the cyanobacterial TCA cycle, an alleged glyoxylate shunt, and the role of photorespiration in cellular growth. Going beyond conventional flux-balance analysis, we extend the computational analysis to diurnal light/dark cycles of cyanobacterial metabolism. PMID:23843751

  3. A systematic comparison of genome-scale clustering algorithms

    PubMed Central

    2012-01-01

    Background A wealth of clustering algorithms has been applied to gene co-expression experiments. These algorithms cover a broad range of approaches, from conventional techniques such as k-means and hierarchical clustering, to graphical approaches such as k-clique communities, weighted gene co-expression networks (WGCNA) and paraclique. Comparison of these methods to evaluate their relative effectiveness provides guidance to algorithm selection, development and implementation. Most prior work on comparative clustering evaluation has focused on parametric methods. Graph theoretical methods are recent additions to the tool set for the global analysis and decomposition of microarray co-expression matrices that have not generally been included in earlier methodological comparisons. In the present study, a variety of parametric and graph theoretical clustering algorithms are compared using well-characterized transcriptomic data at a genome scale from Saccharomyces cerevisiae. Methods For each clustering method under study, a variety of parameters were tested. Jaccard similarity was used to measure each cluster's agreement with every GO and KEGG annotation set, and the highest Jaccard score was assigned to the cluster. Clusters were grouped into small, medium, and large bins, and the Jaccard score of the top five scoring clusters in each bin were averaged and reported as the best average top 5 (BAT5) score for the particular method. Results Clusters produced by each method were evaluated based upon the positive match to known pathways. This produces a readily interpretable ranking of the relative effectiveness of clustering on the genes. Methods were also tested to determine whether they were able to identify clusters consistent with those identified by other clustering methods. Conclusions Validation of clusters against known gene classifications demonstrate that for this data, graph-based techniques outperform conventional clustering approaches, suggesting that further

  4. Comparative metabolic systems analysis of pathogenic Burkholderia.

    PubMed

    Bartell, Jennifer A; Yen, Phillip; Varga, John J; Goldberg, Joanna B; Papin, Jason A

    2014-01-01

    Burkholderia cenocepacia and Burkholderia multivorans are opportunistic drug-resistant pathogens that account for the majority of Burkholderia cepacia complex infections in cystic fibrosis patients and also infect other immunocompromised individuals. While they share similar genetic compositions, B. cenocepacia and B. multivorans exhibit important differences in pathogenesis. We have developed reconciled genome-scale metabolic network reconstructions of B. cenocepacia J2315 and B. multivorans ATCC 17616 in parallel (designated iPY1537 and iJB1411, respectively) to compare metabolic abilities and contextualize genetic differences between species. The reconstructions capture the metabolic functions of the two species and give insight into similarities and differences in their virulence and growth capabilities. The two reconstructions have 1,437 reactions in common, and iPY1537 and iJB1411 have 67 and 36 metabolic reactions unique to each, respectively. After curating the extensive reservoir of metabolic genes in Burkholderia, we identified 6 genes essential to growth that are unique to iPY1513 and 13 genes uniquely essential to iJB1411. The reconstructions were refined and validated by comparing in silico growth predictions to in vitro growth capabilities of B. cenocepacia J2315, B. cenocepacia K56-2, and B. multivorans ATCC 17616 on 104 carbon sources. Overall, we identified functional pathways that indicate B. cenocepacia can produce a wider array of virulence factors compared to B. multivorans, which supports the clinical observation that B. cenocepacia is more virulent than B. multivorans. The reconciled reconstructions provide a framework for generating and testing hypotheses on the metabolic and virulence capabilities of these two related emerging pathogens. PMID:24163337

  5. Comparative Metabolic Systems Analysis of Pathogenic Burkholderia

    PubMed Central

    Bartell, Jennifer A.; Yen, Phillip; Varga, John J.; Goldberg, Joanna B.

    2014-01-01

    Burkholderia cenocepacia and Burkholderia multivorans are opportunistic drug-resistant pathogens that account for the majority of Burkholderia cepacia complex infections in cystic fibrosis patients and also infect other immunocompromised individuals. While they share similar genetic compositions, B. cenocepacia and B. multivorans exhibit important differences in pathogenesis. We have developed reconciled genome-scale metabolic network reconstructions of B. cenocepacia J2315 and B. multivorans ATCC 17616 in parallel (designated iPY1537 and iJB1411, respectively) to compare metabolic abilities and contextualize genetic differences between species. The reconstructions capture the metabolic functions of the two species and give insight into similarities and differences in their virulence and growth capabilities. The two reconstructions have 1,437 reactions in common, and iPY1537 and iJB1411 have 67 and 36 metabolic reactions unique to each, respectively. After curating the extensive reservoir of metabolic genes in Burkholderia, we identified 6 genes essential to growth that are unique to iPY1513 and 13 genes uniquely essential to iJB1411. The reconstructions were refined and validated by comparing in silico growth predictions to in vitro growth capabilities of B. cenocepacia J2315, B. cenocepacia K56-2, and B. multivorans ATCC 17616 on 104 carbon sources. Overall, we identified functional pathways that indicate B. cenocepacia can produce a wider array of virulence factors compared to B. multivorans, which supports the clinical observation that B. cenocepacia is more virulent than B. multivorans. The reconciled reconstructions provide a framework for generating and testing hypotheses on the metabolic and virulence capabilities of these two related emerging pathogens. PMID:24163337

  6. Global probabilistic annotation of metabolic networks enables enzyme discovery

    PubMed Central

    Plata, Germán; Fuhrer, Tobias; Hsiao, Tzu-Lin; Sauer, Uwe; Vitkup, Dennis

    2013-01-01

    Annotation of organism-specific metabolic networks is one of the main challenges of systems biology. Importantly, due to inherent uncertainty of computational annotations, predictions of biochemical function need to be treated probabilistically. We present a global probabilistic approach to annotate genome-scale metabolic networks that integrates sequence homology and context-based correlations under a single principled framework. The developed method for Global Biochemical reconstruction Using Sampling (GLOBUS) not only provides annotation probabilities for each functional assignment, but also suggests likely alternative functions. GLOBUS is based on statistical Gibbs sampling of probable metabolic annotations and is able to make accurate functional assignments even in cases of remote sequence identity to known enzymes. We apply GLOBUS to genomes of Bacillus subtilis and Staphylococcus aureus, and validate the method predictions by experimentally demonstrating the 6-phosphogluconolactonase activity of ykgB and the role of the sps pathway for rhamnose biosynthesis in B. subtilis. PMID:22960854

  7. Development of Computational Tools for Metabolic Model Curation, Flux Elucidation and Strain Design

    SciTech Connect

    Maranas, Costas D

    2012-05-21

    An overarching goal of the Department of Energy mission is the efficient deployment and engineering of microbial and plant systems to enable biomass conversion in pursuit of high energy density liquid biofuels. This has spurred the pace at which new organisms are sequenced and annotated. This torrent of genomic information has opened the door to understanding metabolism in not just skeletal pathways and a handful of microorganisms but for truly genome-scale reconstructions derived for hundreds of microbes and plants. Understanding and redirecting metabolism is crucial because metabolic fluxes are unique descriptors of cellular physiology that directly assess the current cellular state and quantify the effect of genetic engineering interventions. At the same time, however, trying to keep pace with the rate of genomic data generation has ushered in a number of modeling and computational challenges related to (i) the automated assembly, testing and correction of genome-scale metabolic models, (ii) metabolic flux elucidation using labeled isotopes, and (iii) comprehensive identification of engineering interventions leading to the desired metabolism redirection.

  8. Xenobiotic metabolizing enzymes in human skin and SkinEthic reconstructed human skin models.

    PubMed

    Eilstein, Joan; Léreaux, Guillaume; Arbey, Eric; Daronnat, Edwige; Wilkinson, Simon; Duché, Daniel

    2015-07-01

    Skin metabolism is becoming a major consideration in the development of new cosmetic ingredients, skin being the first organ exposed to them. In order to replace limited samples of Excised human skin (EHS), in vitro engineered human skins have been developed. 3D models are daily used to develop and evaluate new cosmetic ingredients and have to be characterized and compared with EHS in terms of metabolic capabilities. This work presents the determination of apparent catalytic parameters (apparent Vmax, Km and the ratio Vmax/Km) in 3D models compared with EHS for cytochrome P450 dependent monooxygenase isoforms involved in drug metabolism, esterases, alcohol dehydrogenases, aldehyde dehydrogenases, peroxidases, glutathione S-transferases, N-acetyl transferases, uridinyl diphosphate glucuronyl transferases and sulfotransferases. Results show that all these enzymes involved in the metabolism of xenobiotics are expressed and functional in the EHS and 3D models. Also, the Vmax/Km ratios (estimating the intrinsic metabolic clearances) show that the metabolic abilities are the most often comparable between the skin models and EHS. These results indicate that the 3D models can substitute themselves for EHS to select cosmetic ingredients on the basis of their metabolism, efficacy or/and safety. PMID:25808006

  9. Deep Genomic-Scale Analyses of the Metazoa Reject Coelomata: Evidence from Single- and Multigene Families Analyzed Under a Supertree and Supermatrix Paradigm

    PubMed Central

    Holton, Thérèse A.; Pisani, Davide

    2010-01-01

    Solving the phylogeny of the animals with bilateral symmetry has proven difficult. Morphological studies have suggested a variety of alternative hypotheses, of which, Hyman’s Coelomata hypothesis has become the most established. Studies based on 18S rRNA have failed to endorse Coelomata, supporting instead the rearrangement of the protostomes into two new clades: the Lophotrochozoa (including, e.g., the molluscs and the annelids) and the Ecdysozoa (including the Panarthropoda and most pseudocoelomates, such as the nematodes and priapulids). Support for this new animal phylogeny has been attained from expressed sequence tag studies, although these generally have a limited gene sampling. In contrast, deep genomic-scale analyses have often supported Coelomata. However, these studies are problematic due to their limited taxonomic sampling, which could exacerbate tree reconstruction artifacts. Here, we address both of these sampling limitations; we study the effect of long-branch attraction (LBA) in deep genomic-scale analyses and provide convincing evidence, using both single- and multigene families, that Coelomata is an artifact. We show that optimal outgroup selection is key in avoiding LBA and identify the use of inadequate outgroups as the reason previous deep genomic-scale analyses found strong support for Coelomata. PMID:20624736

  10. The role of integrated databases in microbial genome sequence analysis and metabolic reconstruction

    SciTech Connect

    Gaasterland, T., Maltsev, N., Overbeek, R.

    1997-02-01

    This paper provides an overview of the PUMA system which provides access to data about metabolic pathways, enzymes, compounds, organisms, encoded activity, and assay condition information for enzymes in particular organisms and multiple sequence alignments.

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

    PubMed Central

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

    2014-01-01

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

  12. A new method to reconstruct recombination events at a genomic scale.

    PubMed

    Melé, Marta; Javed, Asif; Pybus, Marc; Calafell, Francesc; Parida, Laxmi; Bertranpetit, Jaume

    2010-01-01

    Recombination is one of the main forces shaping genome diversity, but the information it generates is often overlooked. A recombination event creates a junction between two parental sequences that may be transmitted to the subsequent generations. Just like mutations, these junctions carry evidence of the shared past of the sequences. We present the IRiS algorithm, which detects past recombination events from extant sequences and specifies the place of each recombination and which are the recombinants sequences. We have validated and calibrated IRiS for the human genome using coalescent simulations replicating standard human demographic history and a variable recombination rate model, and we have fine-tuned IRiS parameters to simultaneously optimize for false discovery rate, sensitivity, and accuracy in placing the recombination events in the sequence. Newer recombinations overwrite traces of past ones and our results indicate more recent recombinations are detected by IRiS with greater sensitivity. IRiS analysis of the MS32 region, previously studied using sperm typing, showed good concordance with estimated recombination rates. We also applied IRiS to haplotypes for 18 X-chromosome regions in HapMap Phase 3 populations. Recombination events detected for each individual were recoded as binary allelic states and combined into recotypes. Principal component analysis and multidimensional scaling based on recotypes reproduced the relationships between the eleven HapMap Phase III populations that can be expected from known human population history, thus further validating IRiS. We believe that our new method will contribute to the study of the distribution of recombination events across the genomes and, for the first time, it will allow the use of recombination as genetic marker to study human genetic variation. PMID:21124860

  13. In Situ Metabolism of Cinnamyl Alcohol in Reconstructed Human Epidermis: New Insights into the Activation of This Fragrance Skin Sensitizer.

    PubMed

    Moss, Eric; Debeuckelaere, Camille; Berl, Valérie; Elbayed, Karim; Moussallieh, François-Marie; Namer, Izzie-Jacques; Lepoittevin, J-P

    2016-07-18

    Chemical modification of epidermal proteins by skin sensitizers is the molecular event which initiates the induction of contact allergy. However, not all chemical skin allergens react directly as haptens with epidermal proteins but need either a chemical (prehaptens) or metabolic (prohaptens) activation step to become reactive. Cinnamyl alcohol has been considered a model prohapten, as this skin sensitizer has no intrinsic reactivity. Therefore, the prevailing theory is that cinnamyl alcohol is enzymatically oxidized into the protein-reactive cinnamaldehyde, which is the sensitizing agent. Knowing that reconstructed human epidermis (RHE) models have been demonstrated to be quite similar to the normal human epidermis in terms of metabolic enzymes, use of RHE may be useful to investigate the in situ metabolism/activation of cinnamyl alcohol, particularly when coupled with high-resolution magic angle spinning nuclear magnetic resonance. Incubation of carbon-13 substituted cinnamyl derivatives with RHE did not result in the formation of cinnamaldehyde. The metabolites formed suggest the formation of an epoxy-alcohol and an allylic sulfate as potential electrophiles. These data suggest that cinnamyl alcohol is inducing skin sensitization through a route independent of the one involving cinnamaldehyde and should therefore be considered as a skin sensitizer on its own. PMID:27281158

  14. Reconstruction of metabolic networks in a fluoranthene-degrading enrichments from polycyclic aromatic hydrocarbon polluted soil.

    PubMed

    Zhao, Jian-Kang; Li, Xiao-Ming; Ai, Guo-Min; Deng, Ye; Liu, Shuang-Jiang; Jiang, Cheng-Ying

    2016-11-15

    Microbial degradation of polycyclic aromatic hydrocarbons (PAHs) is the primary process of removing PAHs from environments. The metabolic pathway of PAHs in pure cultures has been intensively studied, but cooperative metabolisms at community-level remained to be explored. In this study, we determined the dynamic composition of a microbial community and its metabolic intermediates during fluoranthene degradation using high-throughput metagenomics and gas chromatography-mass spectrometry (GC-MS), respectively. Subsequently, a cooperative metabolic network for fluoranthene degradation was constructed. The network shows that Mycobacterium contributed the majority of ring-hydroxylating and -cleavage dioxygenases, while Diaphorobacter contributed most of the dehydrogenases. Hyphomicrobium, Agrobacterium, and Sphingopyxis contributed to genes encoding enzymes involved in downstream reactions of fluoranthene degradation. The contributions of various microbial groups were calculated with the PICRUSt program. The contributions of Hyphomicrobium to alcohol dehydrogenases were 62.4% in stage 1 (i.e., when fluoranthene was rapidly removed) and 76.8% in stage 3 (i.e., when fluoranthene was not detectable), respectively; the contribution of Pseudomonas were 6.6% in stage 1 and decreased to 1.2% in subsequent stages. To the best of the author's knowledge, this report describes the first cooperative metabolic network to predict the contributions of various microbial groups during PAH-degradation at community-level. PMID:27415596

  15. Reconstruction of the lipid metabolism for the microalga Monoraphidium neglectum from its genome sequence reveals characteristics suitable for biofuel production

    PubMed Central

    2013-01-01

    Background Microalgae are gaining importance as sustainable production hosts in the fields of biotechnology and bioenergy. A robust biomass accumulating strain of the genus Monoraphidium (SAG 48.87) was investigated in this work as a potential feedstock for biofuel production. The genome was sequenced, annotated, and key enzymes for triacylglycerol formation were elucidated. Results Monoraphidium neglectum was identified as an oleaginous species with favourable growth characteristics as well as a high potential for crude oil production, based on neutral lipid contents of approximately 21% (dry weight) under nitrogen starvation, composed of predominantly C18:1 and C16:0 fatty acids. Further characterization revealed growth in a relatively wide pH range and salt concentrations of up to 1.0% NaCl, in which the cells exhibited larger structures. This first full genome sequencing of a member of the Selenastraceae revealed a diploid, approximately 68 Mbp genome with a G + C content of 64.7%. The circular chloroplast genome was assembled to a 135,362 bp single contig, containing 67 protein-coding genes. The assembly of the mitochondrial genome resulted in two contigs with an approximate total size of 94 kb, the largest known mitochondrial genome within algae. 16,761 protein-coding genes were assigned to the nuclear genome. Comparison of gene sets with respect to functional categories revealed a higher gene number assigned to the category “carbohydrate metabolic process” and in “fatty acid biosynthetic process” in M. neglectum when compared to Chlamydomonas reinhardtii and Nannochloropsis gaditana, indicating a higher metabolic diversity for applications in carbohydrate conversions of biotechnological relevance. Conclusions The genome of M. neglectum, as well as the metabolic reconstruction of crucial lipid pathways, provides new insights into the diversity of the lipid metabolism in microalgae. The results of this work provide a platform to encourage the

  16. Metagenome-Based Metabolic Reconstruction Reveals the Ecophysiological Function of Epsilonproteobacteria in a Hydrocarbon-Contaminated Sulfidic Aquifer.

    PubMed

    Keller, Andreas H; Schleinitz, Kathleen M; Starke, Robert; Bertilsson, Stefan; Vogt, Carsten; Kleinsteuber, Sabine

    2015-01-01

    The population genome of an uncultured bacterium assigned to the Campylobacterales (Epsilonproteobacteria) was reconstructed from a metagenome dataset obtained by whole-genome shotgun pyrosequencing. Genomic DNA was extracted from a sulfate-reducing, m-xylene-mineralizing enrichment culture isolated from groundwater of a benzene-contaminated sulfidic aquifer. The identical epsilonproteobacterial phylotype has previously been detected in toluene- or benzene-mineralizing, sulfate-reducing consortia enriched from the same site. Previous stable isotope probing (SIP) experiments with (13)C6-labeled benzene suggested that this phylotype assimilates benzene-derived carbon in a syntrophic benzene-mineralizing consortium that uses sulfate as terminal electron acceptor. However, the type of energy metabolism and the ecophysiological function of this epsilonproteobacterium within aromatic hydrocarbon-degrading consortia and in the sulfidic aquifer are poorly understood. Annotation of the epsilonproteobacterial population genome suggests that the bacterium plays a key role in sulfur cycling as indicated by the presence of an sqr gene encoding a sulfide quinone oxidoreductase and psr genes encoding a polysulfide reductase. It may gain energy by using sulfide or hydrogen/formate as electron donors. Polysulfide, fumarate, as well as oxygen are potential electron acceptors. Auto- or mixotrophic carbon metabolism seems plausible since a complete reductive citric acid cycle was detected. Thus the bacterium can thrive in pristine groundwater as well as in hydrocarbon-contaminated aquifers. In hydrocarbon-contaminated sulfidic habitats, the epsilonproteobacterium may generate energy by coupling the oxidation of hydrogen or formate and highly abundant sulfide with the reduction of fumarate and/or polysulfide, accompanied by efficient assimilation of acetate produced during fermentation or incomplete oxidation of hydrocarbons. The highly efficient assimilation of acetate was recently

  17. Metagenome-Based Metabolic Reconstruction Reveals the Ecophysiological Function of Epsilonproteobacteria in a Hydrocarbon-Contaminated Sulfidic Aquifer

    PubMed Central

    Keller, Andreas H.; Schleinitz, Kathleen M.; Starke, Robert; Bertilsson, Stefan; Vogt, Carsten; Kleinsteuber, Sabine

    2015-01-01

    The population genome of an uncultured bacterium assigned to the Campylobacterales (Epsilonproteobacteria) was reconstructed from a metagenome dataset obtained by whole-genome shotgun pyrosequencing. Genomic DNA was extracted from a sulfate-reducing, m-xylene-mineralizing enrichment culture isolated from groundwater of a benzene-contaminated sulfidic aquifer. The identical epsilonproteobacterial phylotype has previously been detected in toluene- or benzene-mineralizing, sulfate-reducing consortia enriched from the same site. Previous stable isotope probing (SIP) experiments with 13C6-labeled benzene suggested that this phylotype assimilates benzene-derived carbon in a syntrophic benzene-mineralizing consortium that uses sulfate as terminal electron acceptor. However, the type of energy metabolism and the ecophysiological function of this epsilonproteobacterium within aromatic hydrocarbon-degrading consortia and in the sulfidic aquifer are poorly understood. Annotation of the epsilonproteobacterial population genome suggests that the bacterium plays a key role in sulfur cycling as indicated by the presence of an sqr gene encoding a sulfide quinone oxidoreductase and psr genes encoding a polysulfide reductase. It may gain energy by using sulfide or hydrogen/formate as electron donors. Polysulfide, fumarate, as well as oxygen are potential electron acceptors. Auto- or mixotrophic carbon metabolism seems plausible since a complete reductive citric acid cycle was detected. Thus the bacterium can thrive in pristine groundwater as well as in hydrocarbon-contaminated aquifers. In hydrocarbon-contaminated sulfidic habitats, the epsilonproteobacterium may generate energy by coupling the oxidation of hydrogen or formate and highly abundant sulfide with the reduction of fumarate and/or polysulfide, accompanied by efficient assimilation of acetate produced during fermentation or incomplete oxidation of hydrocarbons. The highly efficient assimilation of acetate was recently

  18. Global insights into energetic and metabolic networks in Rhodobacter sphaeroides

    PubMed Central

    2013-01-01

    Background Improving our understanding of processes at the core of cellular lifestyles can be aided by combining information from genetic analyses, high-throughput experiments and computational predictions. Results We combined data and predictions derived from phenotypic, physiological, genetic and computational analyses to dissect the metabolic and energetic networks of the facultative photosynthetic bacterium Rhodobacter sphaeroides. We focused our analysis on pathways crucial to the production and recycling of pyridine nucleotides during aerobic respiratory and anaerobic photosynthetic growth in the presence of an organic electron donor. In particular, we assessed the requirement for NADH/NADPH transhydrogenase enzyme, PntAB during respiratory and photosynthetic growth. Using high-throughput phenotype microarrays (PMs), we found that PntAB is essential for photosynthetic growth in the presence of many organic electron donors, particularly those predicted to require its activity to produce NADPH. Utilizing the genome-scale metabolic model iRsp1095, we predicted alternative routes of NADPH synthesis and used gene expression analyses to show that transcripts from a subset of the corresponding genes were conditionally increased in a ΔpntAB mutant. We then used a combination of metabolic flux predictions and mutational analysis to identify flux redistribution patterns utilized in the ΔpntAB mutant to compensate for the loss of this enzyme. Data generated from metabolic and phenotypic analyses of wild type and mutant cells were used to develop iRsp1140, an expanded genome-scale metabolic reconstruction for R. sphaeroides with improved ability to analyze and predict pathways associated with photosynthesis and other metabolic processes. Conclusions These analyses increased our understanding of key aspects of the photosynthetic lifestyle, highlighting the added importance of NADPH production under these conditions. It also led to a significant improvement in the

  19. Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome

    PubMed Central

    Abubucker, Sahar; Segata, Nicola; Goll, Johannes; Schubert, Alyxandria M.; Izard, Jacques; Cantarel, Brandi L.; Rodriguez-Mueller, Beltran; Zucker, Jeremy; Thiagarajan, Mathangi; Henrissat, Bernard; White, Owen; Kelley, Scott T.; Methé, Barbara; Schloss, Patrick D.; Gevers, Dirk; Mitreva, Makedonka; Huttenhower, Curtis

    2012-01-01

    Microbial communities carry out the majority of the biochemical activity on the planet, and they play integral roles in processes including metabolism and immune homeostasis in the human microbiome. Shotgun sequencing of such communities' metagenomes provides information complementary to organismal abundances from taxonomic markers, but the resulting data typically comprise short reads from hundreds of different organisms and are at best challenging to assemble comparably to single-organism genomes. Here, we describe an alternative approach to infer the functional and metabolic potential of a microbial community metagenome. We determined the gene families and pathways present or absent within a community, as well as their relative abundances, directly from short sequence reads. We validated this methodology using a collection of synthetic metagenomes, recovering the presence and abundance both of large pathways and of small functional modules with high accuracy. We subsequently applied this method, HUMAnN, to the microbial communities of 649 metagenomes drawn from seven primary body sites on 102 individuals as part of the Human Microbiome Project (HMP). This provided a means to compare functional diversity and organismal ecology in the human microbiome, and we determined a core of 24 ubiquitously present modules. Core pathways were often implemented by different enzyme families within different body sites, and 168 functional modules and 196 metabolic pathways varied in metagenomic abundance specifically to one or more niches within the microbiome. These included glycosaminoglycan degradation in the gut, as well as phosphate and amino acid transport linked to host phenotype (vaginal pH) in the posterior fornix. An implementation of our methodology is available at http://huttenhower.sph.harvard.edu/humann. This provides a means to accurately and efficiently characterize microbial metabolic pathways and functional modules directly from high-throughput sequencing reads

  20. Quantitative Proteogenomics and the Reconstruction of the Metabolic Pathway in Lactobacillus mucosae LM1.

    PubMed

    Pajarillo, Edward Alain B; Kim, Sang Hoon; Lee, Ji-Yoon; Valeriano, Valerie Diane V; Kang, Dae-Kyung

    2015-01-01

    Lactobacillus mucosae is a natural resident of the gastrointestinal tract of humans and animals and a potential probiotic bacterium. To understand the global protein expression profile and metabolic features of L. mucosae LM1 in the early stationary phase, the QExactive(TM) Hybrid Quadrupole-Orbitrap Mass Spectrometer was used. Characterization of the intracellular proteome identified 842 proteins, accounting for approximately 35% of the 2,404 protein-coding sequences in the complete genome of L. mucosae LM1. Proteome quantification using QExactive(TM) Orbitrap MS detected 19 highly abundant proteins (> 1.0% of the intracellular proteome), including CysK (cysteine synthase, 5.41%) and EF-Tu (elongation factor Tu, 4.91%), which are involved in cell survival against environmental stresses. Metabolic pathway annotation of LM1 proteome using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database showed that half of the proteins expressed are important for basic metabolic and biosynthetic processes, and the other half might be structurally important or involved in basic cellular processes. In addition, glycogen biosynthesis was activated in the early stationary phase, which is important for energy storage and maintenance. The proteogenomic data presented in this study provide a suitable reference to understand the protein expression pattern of lactobacilli in standard conditions. PMID:26761899

  1. Metabolic reconstruction identifies strain-specific regulation of virulence in Toxoplasma gondii

    PubMed Central

    Song, Carl; Chiasson, Melissa A; Nursimulu, Nirvana; Hung, Stacy S; Wasmuth, James; Grigg, Michael E; Parkinson, John

    2013-01-01

    Increasingly, metabolic potential is proving to be a critical determinant governing a pathogen's virulence as well as its capacity to expand its host range. To understand the potential contribution of metabolism to strain-specific infectivity differences, we present a constraint-based metabolic model of the opportunistic parasite, Toxoplasma gondii. Dominated by three clonal strains (Type I, II, and III demonstrating distinct virulence profiles), T. gondii exhibits a remarkably broad host range. Integrating functional genomic data, our model (which we term as iCS382) reveals that observed strain-specific differences in growth rates are driven by altered capacities for energy production. We further predict strain-specific differences in drug susceptibilities and validate one of these predictions in a drug-based assay, with a Type I strain demonstrating resistance to inhibitors that are effective against a Type II strain. We propose that these observed differences reflect an evolutionary strategy that allows the parasite to extend its host range, as well as result in a subsequent partitioning into discrete strains that display altered virulence profiles across different hosts, different organs, and even cell types. PMID:24247825

  2. Quantitative Proteogenomics and the Reconstruction of the Metabolic Pathway in Lactobacillus mucosae LM1

    PubMed Central

    Lee, Ji-Yoon

    2015-01-01

    Lactobacillus mucosae is a natural resident of the gastrointestinal tract of humans and animals and a potential probiotic bacterium. To understand the global protein expression profile and metabolic features of L. mucosae LM1 in the early stationary phase, the QExactiveTM Hybrid Quadrupole-Orbitrap Mass Spectrometer was used. Characterization of the intracellular proteome identified 842 proteins, accounting for approximately 35% of the 2,404 protein-coding sequences in the complete genome of L. mucosae LM1. Proteome quantification using QExactiveTM Orbitrap MS detected 19 highly abundant proteins (> 1.0% of the intracellular proteome), including CysK (cysteine synthase, 5.41%) and EF-Tu (elongation factor Tu, 4.91%), which are involved in cell survival against environmental stresses. Metabolic pathway annotation of LM1 proteome using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database showed that half of the proteins expressed are important for basic metabolic and biosynthetic processes, and the other half might be structurally important or involved in basic cellular processes. In addition, glycogen biosynthesis was activated in the early stationary phase, which is important for energy storage and maintenance. The proteogenomic data presented in this study provide a suitable reference to understand the protein expression pattern of lactobacilli in standard conditions. PMID:26761899

  3. The Genomic HyperBrowser: an analysis web server for genome-scale data

    PubMed Central

    Sandve, Geir K.; Gundersen, Sveinung; Johansen, Morten; Glad, Ingrid K.; Gunathasan, Krishanthi; Holden, Lars; Holden, Marit; Liestøl, Knut; Nygård, Ståle; Nygaard, Vegard; Paulsen, Jonas; Rydbeck, Halfdan; Trengereid, Kai; Clancy, Trevor; Drabløs, Finn; Ferkingstad, Egil; Kalaš, Matúš; Lien, Tonje; Rye, Morten B.; Frigessi, Arnoldo; Hovig, Eivind

    2013-01-01

    The immense increase in availability of genomic scale datasets, such as those provided by the ENCODE and Roadmap Epigenomics projects, presents unprecedented opportunities for individual researchers to pose novel falsifiable biological questions. With this opportunity, however, researchers are faced with the challenge of how to best analyze and interpret their genome-scale datasets. A powerful way of representing genome-scale data is as feature-specific coordinates relative to reference genome assemblies, i.e. as genomic tracks. The Genomic HyperBrowser (http://hyperbrowser.uio.no) is an open-ended web server for the analysis of genomic track data. Through the provision of several highly customizable components for processing and statistical analysis of genomic tracks, the HyperBrowser opens for a range of genomic investigations, related to, e.g., gene regulation, disease association or epigenetic modifications of the genome. PMID:23632163

  4. Parallel Mutual Information Based Construction of Genome-Scale Networks on the Intel® Xeon Phi™ Coprocessor.

    PubMed

    Misra, Sanchit; Pamnany, Kiran; Aluru, Srinivas

    2015-01-01

    Construction of whole-genome networks from large-scale gene expression data is an important problem in systems biology. While several techniques have been developed, most cannot handle network reconstruction at the whole-genome scale, and the few that can, require large clusters. In this paper, we present a solution on the Intel Xeon Phi coprocessor, taking advantage of its multi-level parallelism including many x86-based cores, multiple threads per core, and vector processing units. We also present a solution on the Intel® Xeon® processor. Our solution is based on TINGe, a fast parallel network reconstruction technique that uses mutual information and permutation testing for assessing statistical significance. We demonstrate the first ever inference of a plant whole genome regulatory network on a single chip by constructing a 15,575 gene network of the plant Arabidopsis thaliana from 3,137 microarray experiments in only 22 minutes. In addition, our optimization for parallelizing mutual information computation on the Intel Xeon Phi coprocessor holds out lessons that are applicable to other domains. PMID:26451815

  5. A Genome-Scale Investigation of Incongruence in Culicidae Mosquitoes

    PubMed Central

    Wang, Yuyu; Zhou, Xiaofan; Yang, Ding; Rokas, Antonis

    2015-01-01

    Comparison of individual gene trees in several recent phylogenomic studies from diverse lineages has revealed a surprising amount of topological conflict or incongruence, but we still know relatively little about its distribution across the tree of life. To further our understanding of incongruence, the factors that contribute to it and how it can be ameliorated, we examined its distribution in a clade of 20 Culicidae mosquito species through the reconstruction and analysis of the phylogenetic histories of 2,007 groups of orthologous genes. Levels of incongruence were generally low, the three exceptions being the internodes concerned with the branching of Anopheles christyi, with the branching of the subgenus Anopheles as well as the already reported incongruence within the Anopheles gambiae species complex. Two of these incongruence events (A. gambiae species complex and A. christyi) are likely due to biological factors, whereas the third (subgenus Anopheles) is likely due to analytical factors. Similar to previous studies, the use of genes or internodes with high bootstrap support or internode certainty values, both of which were positively correlated with gene alignment length, substantially reduced the observed incongruence. However, the clade support values of the internodes concerned with the branching of the subgenus Anopheles as well as within the A. gambiae species complex remained very low. Based on these results, we infer that the prevalence of incongruence in Culicidae mosquitoes is generally low, that it likely stems from both analytical and biological factors, and that it can be ameliorated through the selection of genes with strong phylogenetic signal. More generally, selection of genes with strong phylogenetic signal may be a general empirical solution for reducing incongruence and increasing the robustness of inference in phylogenomic studies. PMID:26608059

  6. Metabolic Constraint-Based Refinement of Transcriptional Regulatory Networks

    PubMed Central

    Chandrasekaran, Sriram; Price, Nathan D.

    2013-01-01

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

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

    PubMed

    Chandrasekaran, Sriram; Price, Nathan D

    2013-01-01

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

  8. Comparative Biochemistry and In Vitro Pathway Reconstruction as Powerful Partners in Studies of Metabolic Diversity.

    PubMed

    Fan, P; Moghe, G D; Last, R L

    2016-01-01

    There are estimated to be >300,000 plant species, producing >200,000 metabolites. Many of these metabolites are restricted to specific plant lineages and are referred to as "specialized" metabolites. These serve varied functions in plants including defense against biotic and abiotic stresses, plant-plant and plant-microbe communication, and pollinator attraction. These compounds also have important applications in agriculture, medicine, skin care, and in diverse aspects of human culture. The specialized metabolic repertoire of plants can vary even within and between closely related species, in terms of the number and classes of specialized metabolites as well as their structural variants. This phenotypic variation can be exploited to discover the underlying variation in the metabolic enzymes. We describe approaches for using the diversity of specialized metabolites and variation in enzyme structure and function to identify novel enzymatic activities and understand the structural basis for these differences. The knowledge obtained from these studies will provide new modules for the synthetic biology toolbox. PMID:27480680

  9. Genomic reconstruction of transcriptional regulatory networks in lactic acid bacteria

    PubMed Central

    2013-01-01

    Background Genome scale annotation of regulatory interactions and reconstruction of regulatory networks are the crucial problems in bacterial genomics. The Lactobacillales order of bacteria collates various microorganisms having a large economic impact, including both human and animal pathogens and strains used in the food industry. Nonetheless, no systematic genome-wide analysis of transcriptional regulation has been previously made for this taxonomic group. Results A comparative genomics approach was used for reconstruction of transcriptional regulatory networks in 30 selected genomes of lactic acid bacteria. The inferred networks comprise regulons for 102 orthologous transcription factors (TFs), including 47 novel regulons for previously uncharacterized TFs. Numerous differences between regulatory networks of the Streptococcaceae and Lactobacillaceae groups were described on several levels. The two groups are characterized by substantially different sets of TFs encoded in their genomes. Content of the inferred regulons and structure of their cognate TF binding motifs differ for many orthologous TFs between the two groups. Multiple cases of non-orthologous displacements of TFs that control specific metabolic pathways were reported. Conclusions The reconstructed regulatory networks substantially expand the existing knowledge of transcriptional regulation in lactic acid bacteria. In each of 30 studied genomes the obtained regulatory network contains on average 36 TFs and 250 target genes that are mostly involved in carbohydrate metabolism, stress response, metal homeostasis and amino acids biosynthesis. The inferred networks can be used for genetic experiments, functional annotations of genes, metabolic reconstruction and evolutionary analysis. All reconstructed regulons are captured within the Streptococcaceae and Lactobacillaceae collections in the RegPrecise database (http://regprecise.lbl.gov). PMID:23398941

  10. Genomic reconstruction of Shewanella oneidensis MR-1 metabolism reveals previously uncharacterized machinery for lactate utilization

    SciTech Connect

    Pinchuk, Grigoriy E.; Rodionov, Dmitry A.; Yang, Chen; Li, Xiaoqing; Osterman, Andrei L.; Dervyn, Etienne; Geydebrekht, Oleg V.; Reed, Samantha B.; Romine, Margaret F.; Collart, Frank R.; Scott, J.; Fredrickson, Jim K.; Beliaev, Alex S.

    2009-02-24

    The ability to utilize lactate as a sole source of carbon and energy is one of the key metabolic signatures of Shewanellae, a diverse group of dissimilatory metal reducing bacteria commonly found in aquatic and sedimentary environments. Nonetheless, homology searches failed to recognize orthologs of previously described bacterial D- or L-lactate oxidizing enzymes (Escherichia coli genes dld and lldD) in any of the 13 analyzed genomes of Shewanella spp. Using comparative genomic techniques, we identified a conserved chromosomal gene cluster in Shewanella oneidensis MR-1 (locus tag: SO1522-SO1518) containing lactate permease and candidate genes for both D- and L-lactate dehydrogenase enzymes. The predicted D-LDH gene (dldD, SO1521) is a distant homolog of FAD-dependent lactate dehydrogenase from yeast, whereas the predicted L-LDH is encoded by three genes with previously unknown functions (lldEGF, SO1520-19-18). Through a combination of genetic and biochemical techniques, we experimentally confirmed the predicted physiological role of these novel genes in S. oneidensis MR-1 and carried out successful functional validation studies in Escherichia coli and Bacillus subtilis. We conclusively showed that dldD and lldEFG encode fully functional D-and L-LDH enzymes, which catalyze the oxidation of the respective lactate stereoisomers to pyruvate. Notably, the S. oneidensis MR-1 LldEFG enzyme is the first described example of a multi-subunit lactate oxidase. Comparative analysis of >400 bacterial species revealed the presence of LldEFG and Dld in a broad range of diverse species accentuating the potential importance of these previously unknown proteins in microbial metabolism.

  11. Community structure and metabolism through reconstruction of microbial genomes from the environment

    SciTech Connect

    Tyson, Gene W.; Chapman, Jarrod; Hugenholtz, Phillip; Allen, Eric E.; Rachna, Ram J.; Richardson, Paul M.; Solovyev, Victor V.; Rubin, Edward M.; Rokhsar, Daniel S.; Banfield, Jillian F.

    2004-01-01

    Microbial communities are vital in the functioning of all ecosystems; however, most microorganisms are uncultivated, and their roles in natural systems are unclear. Here, using random shotgun sequencing of DNA from a natural acidophilic biofilm, we report reconstruction of near-complete genomes of Leptospirillum group II and Ferroplasma type II, and partial recovery of three other genomes. This was possible because the biofilm was dominated by a small number of species populations and the frequency of genomic rearrangements and gene insertions or deletions was relatively low. Because each sequence read came from a different individual, we could determine that single-nucleotide polymorphisms are the predominant form of heterogeneity at the strain level. The Leptospirillum group II genome had remarkably few nucleotide polymorphisms, despite the existence of low-abundance variants. The Ferroplasma type II genome seems to be a composite from three ancestral strains that have undergone homologous recombination to form a large population of mosaic genomes. Analysis of the gene complement for each organism revealed the pathways for carbon and nitrogen fixation and energy generation, and provided insights into survival strategies in an extreme environment.

  12. Evolution of Mitochondria Reconstructed from the Energy Metabolism of Living Bacteria

    PubMed Central

    Degli Esposti, Mauro; Chouaia, Bessem; Comandatore, Francesco; Crotti, Elena; Sassera, Davide; Lievens, Patricia Marie-Jeanne; Daffonchio, Daniele; Bandi, Claudio

    2014-01-01

    The ancestors of mitochondria, or proto-mitochondria, played a crucial role in the evolution of eukaryotic cells and derived from symbiotic α-proteobacteria which merged with other microorganisms - the basis of the widely accepted endosymbiotic theory. However, the identity and relatives of proto-mitochondria remain elusive. Here we show that methylotrophic α-proteobacteria could be the closest living models for mitochondrial ancestors. We reached this conclusion after reconstructing the possible evolutionary pathways of the bioenergy systems of proto-mitochondria with a genomic survey of extant α-proteobacteria. Results obtained with complementary molecular and genetic analyses of diverse bioenergetic proteins converge in indicating the pathway stemming from methylotrophic bacteria as the most probable route of mitochondrial evolution. Contrary to other α-proteobacteria, methylotrophs show transition forms for the bioenergetic systems analysed. Our approach of focusing on these bioenergetic systems overcomes the phylogenetic impasse that has previously complicated the search for mitochondrial ancestors. Moreover, our results provide a new perspective for experimentally re-evolving mitochondria from extant bacteria and in the future produce synthetic mitochondria. PMID:24804722

  13. Evolution of mitochondria reconstructed from the energy metabolism of living bacteria.

    PubMed

    Degli Esposti, Mauro; Chouaia, Bessem; Comandatore, Francesco; Crotti, Elena; Sassera, Davide; Lievens, Patricia Marie-Jeanne; Daffonchio, Daniele; Bandi, Claudio

    2014-01-01

    The ancestors of mitochondria, or proto-mitochondria, played a crucial role in the evolution of eukaryotic cells and derived from symbiotic α-proteobacteria which merged with other microorganisms - the basis of the widely accepted endosymbiotic theory. However, the identity and relatives of proto-mitochondria remain elusive. Here we show that methylotrophic α-proteobacteria could be the closest living models for mitochondrial ancestors. We reached this conclusion after reconstructing the possible evolutionary pathways of the bioenergy systems of proto-mitochondria with a genomic survey of extant α-proteobacteria. Results obtained with complementary molecular and genetic analyses of diverse bioenergetic proteins converge in indicating the pathway stemming from methylotrophic bacteria as the most probable route of mitochondrial evolution. Contrary to other α-proteobacteria, methylotrophs show transition forms for the bioenergetic systems analysed. Our approach of focusing on these bioenergetic systems overcomes the phylogenetic impasse that has previously complicated the search for mitochondrial ancestors. Moreover, our results provide a new perspective for experimentally re-evolving mitochondria from extant bacteria and in the future produce synthetic mitochondria. PMID:24804722

  14. Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models

    SciTech Connect

    Lewis, Nathan E.; Hixson, Kim K.; Conrad, Tom M.; Lerman, Joshua A.; Charusanti, Pep; Polpitiya, Ashoka D.; Adkins, Joshua N.; Schramm, Gunnar; Purvine, Samuel O.; Lopez-Ferrer, Daniel; Weitz, Karl K.; Eils, Roland; Konig, Rainer; Smith, Richard D.; Palsson, Bernhard O.

    2010-07-27

    After hundreds of generations of mid log phase growth, Escherichia coli acquires a higher growth rate as predicted using flux balance analysis (FBA) on genome-scale metabolic models (GEMs). FBA solutions contain hundreds of variables that can be examined using omics methods. We report that 99% of active reactions from FBA optimal growth solutions are supported by transcriptomic and proteomic data. Moreover, when E. coli adapts to growth rate selective pressure, the resulting evolved strains reinforce the optimal growth predictions. Specifically, through constraint-based analysis of the proteomic and transcriptomic data, we find: 1) selective pressure for the predicted optimal growth states and a minimization of network flux; 2) suppression of genes outside of the optimal growth solutions; and 3) a trend towards usage of more efficient metabolic pathways. For processes not in GEMs, we find 4) an increase in the transcription/translation machinery and stringent response suppression, and 5) that established regulons are significantly down-regulated. Thus, differential expression supports observed growth phenotype changes, and observed expression in evolved strains is consistent with GEM computed optimal growth states.

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

    SciTech Connect

    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

  16. Expression induction of P450 genes by imidacloprid in Nilaparvata lugens: A genome-scale analysis.

    PubMed

    Zhang, Jianhua; Zhang, Yixi; Wang, Yunchao; Yang, Yuanxue; Cang, Xinzhu; Liu, Zewen

    2016-09-01

    The overexpression of P450 monooxygenase genes is a main mechanism for the resistance to imidacloprid, a representative neonicotinoid insecticide, in Nilaparvata lugens (brown planthopper, BPH). However, only two P450 genes (CYP6AY1 and CYP6ER1), among fifty-four P450 genes identified from BPH genome database, have been reported to play important roles in imidacloprid resistance until now. In this study, after the confirmation of important roles of P450s in imidacloprid resistance by the synergism analysis, the expression induction by imidacloprid was determined for all P450 genes. In the susceptible (Sus) strain, eight P450 genes in Clade4, eight in Clade3 and two in Clade2 were up-regulated by imidacloprid, among which three genes (CYP6CS1, CYP6CW1 and CYP6ER1, all in Clade3) were increased to above 4.0-fold and eight genes to above 2.0-fold. In contrast, no P450 genes were induced in Mito clade. Eight genes induced to above 2.0-fold were selected to determine their expression and induced levels in Huzhou population, in which piperonyl butoxide showed the biggest effects on imidacloprid toxicity among eight field populations. The expression levels of seven P450 genes were higher in Huzhou population than that in Sus strain, with the biggest differences for CYP6CS1 (9.8-fold), CYP6ER1 (7.7-fold) and CYP6AY1 (5.1-fold). The induction levels for all tested genes were bigger in Sus strain than that in Huzhou population except CYP425B1. Screening the induction of P450 genes by imidacloprid in the genome-scale will provide an overall view on the possible metabolic factors in the resistance to neonicotinoid insecticides. The further work, such as the functional study of recombinant proteins, will be performed to validate the roles of these P450s in imidacloprid resistance. PMID:27521914

  17. Direct coupling of a genome-scale microbial in silico model and a groundwater reactive transport model

    SciTech Connect

    Fang, Yilin; Scheibe, Timothy D.; Mahadevan, Radhakrishnan; Garg, Srinath; Long, Philip E.; Lovley, Derek R.

    2011-02-28

    The activity of microorganisms often plays an important role in dynamic natural attenuation or engineered bioremediation of subsurface contaminants, such as chlorinated solvents, metals, and radionuclides. To evaluate and/or design bioremediated systems, quantitative reactive transport models are needed. State-of-the-art reactive transport models often ignore the microbial effects or simulate the microbial effects with static growth yield and constant reaction rate parameters over simulated conditions, while in reality microorganisms can dynamically modify their functionality (such as utilization of alternative respiratory pathways) in response to spatial and temporal variations in environmental conditions. Constraint-based genome-scale microbial in silico models, using genomic data and multiple-pathway reaction networks, have been shown to be able to simulate transient metabolism of some well studied microorganisms and identify growth rate, substrate uptake rates, and byproduct rates under different growth conditions. These rates can be identified and used to replace specific microbially-mediated reaction rates in a reactive transport model using local geochemical conditions as constraints. We previously demonstrated the potential utility of integrating a constraint based microbial metabolism model with a reactive transport simulator as applied to bioremediation of uranium in groundwater. However, that work relied on an indirect coupling approach that was effective for initial demonstration but may not be extensible to more complex problems that are of significant interest (e.g., communities of microbial species, multiple constraining variables). Here, we extend that work by presenting and demonstrating a method of directly integrating a reactive transport model (FORTRAN code) with constraint-based in silico models solved with IBM ILOG CPLEX linear optimizer base system (C library). The models were integrated with BABEL, a language interoperability tool. The

  18. Direct coupling of a genome-scale microbial in silico model and a groundwater reactive transport model.

    PubMed

    Fang, Yilin; Scheibe, Timothy D; Mahadevan, Radhakrishnan; Garg, Srinath; Long, Philip E; Lovley, Derek R

    2011-03-25

    The activity of microorganisms often plays an important role in dynamic natural attenuation or engineered bioremediation of subsurface contaminants, such as chlorinated solvents, metals, and radionuclides. To evaluate and/or design bioremediated systems, quantitative reactive transport models are needed. State-of-the-art reactive transport models often ignore the microbial effects or simulate the microbial effects with static growth yield and constant reaction rate parameters over simulated conditions, while in reality microorganisms can dynamically modify their functionality (such as utilization of alternative respiratory pathways) in response to spatial and temporal variations in environmental conditions. Constraint-based genome-scale microbial in silico models, using genomic data and multiple-pathway reaction networks, have been shown to be able to simulate transient metabolism of some well studied microorganisms and identify growth rate, substrate uptake rates, and byproduct rates under different growth conditions. These rates can be identified and used to replace specific microbially-mediated reaction rates in a reactive transport model using local geochemical conditions as constraints. We previously demonstrated the potential utility of integrating a constraint-based microbial metabolism model with a reactive transport simulator as applied to bioremediation of uranium in groundwater. However, that work relied on an indirect coupling approach that was effective for initial demonstration but may not be extensible to more complex problems that are of significant interest (e.g., communities of microbial species and multiple constraining variables). Here, we extend that work by presenting and demonstrating a method of directly integrating a reactive transport model (FORTRAN code) with constraint-based in silico models solved with IBM ILOG CPLEX linear optimizer base system (C library). The models were integrated with BABEL, a language interoperability tool. The

  19. Direct coupling of a genome-scale microbial in silico model and a groundwater reactive transport model

    NASA Astrophysics Data System (ADS)

    Fang, Yilin; Scheibe, Timothy D.; Mahadevan, Radhakrishnan; Garg, Srinath; Long, Philip E.; Lovley, Derek R.

    2011-03-01

    The activity of microorganisms often plays an important role in dynamic natural attenuation or engineered bioremediation of subsurface contaminants, such as chlorinated solvents, metals, and radionuclides. To evaluate and/or design bioremediated systems, quantitative reactive transport models are needed. State-of-the-art reactive transport models often ignore the microbial effects or simulate the microbial effects with static growth yield and constant reaction rate parameters over simulated conditions, while in reality microorganisms can dynamically modify their functionality (such as utilization of alternative respiratory pathways) in response to spatial and temporal variations in environmental conditions. Constraint-based genome-scale microbial in silico models, using genomic data and multiple-pathway reaction networks, have been shown to be able to simulate transient metabolism of some well studied microorganisms and identify growth rate, substrate uptake rates, and byproduct rates under different growth conditions. These rates can be identified and used to replace specific microbially-mediated reaction rates in a reactive transport model using local geochemical conditions as constraints. We previously demonstrated the potential utility of integrating a constraint-based microbial metabolism model with a reactive transport simulator as applied to bioremediation of uranium in groundwater. However, that work relied on an indirect coupling approach that was effective for initial demonstration but may not be extensible to more complex problems that are of significant interest (e.g., communities of microbial species and multiple constraining variables). Here, we extend that work by presenting and demonstrating a method of directly integrating a reactive transport model (FORTRAN code) with constraint-based in silico models solved with IBM ILOG CPLEX linear optimizer base system (C library). The models were integrated with BABEL, a language interoperability tool. The

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

    PubMed

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

    2016-01-01

    The solution space of genome-scale models of cellular metabolism provides a map between physically viable flux configurations and cellular metabolic phenotypes described, at the most basic level, by the corresponding growth rates. By sampling the solution space of E. coli's metabolic network, we show that empirical growth rate distributions recently obtained in experiments at single-cell resolution can be explained in terms of a trade-off between the higher fitness of fast-growing phenotypes and the higher entropy of slow-growing ones. Based on this, we propose a minimal model for the evolution of a large bacterial population that captures this trade-off. The scaling relationships observed in experiments encode, in such frameworks, for the same distance from the maximum achievable growth rate, the same degree of growth rate maximization, and/or the same rate of phenotypic change. Being grounded on genome-scale metabolic network reconstructions, these results allow for multiple implications and extensions in spite of the underlying conceptual simplicity. PMID:27232645

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

    NASA Astrophysics Data System (ADS)

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

    2016-06-01

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

  2. Improved Metabolic Models for E. coli and Mycoplasma genitalium from GlobalFit, an Algorithm That Simultaneously Matches Growth and Non-Growth Data Sets

    PubMed Central

    Hartleb, Daniel

    2016-01-01

    Constraint-based metabolic modeling methods such as Flux Balance Analysis (FBA) are routinely used to predict the effects of genetic changes and to design strains with desired metabolic properties. The major bottleneck in modeling genome-scale metabolic systems is the establishment and manual curation of reliable stoichiometric models. Initial reconstructions are typically refined through comparisons to experimental growth data from gene knockouts or nutrient environments. Existing methods iteratively correct one erroneous model prediction at a time, resulting in accumulating network changes that are often not globally optimal. We present GlobalFit, a bi-level optimization method that finds a globally optimal network, by identifying the minimal set of network changes needed to correctly predict all experimentally observed growth and non-growth cases simultaneously. When applied to the genome-scale metabolic model of Mycoplasma genitalium, GlobalFit decreases unexplained gene knockout phenotypes by 79%, increasing accuracy from 87.3% (according to the current state-of-the-art) to 97.3%. While currently available computers do not allow a global optimization of the much larger metabolic network of E. coli, the main strengths of GlobalFit are already played out when considering only one growth and one non-growth case simultaneously. Application of a corresponding strategy halves the number of unexplained cases for the already highly curated E. coli model, increasing accuracy from 90.8% to 95.4%. PMID:27482704

  3. Improved Metabolic Models for E. coli and Mycoplasma genitalium from GlobalFit, an Algorithm That Simultaneously Matches Growth and Non-Growth Data Sets.

    PubMed

    Hartleb, Daniel; Jarre, Florian; Lercher, Martin J

    2016-08-01

    Constraint-based metabolic modeling methods such as Flux Balance Analysis (FBA) are routinely used to predict the effects of genetic changes and to design strains with desired metabolic properties. The major bottleneck in modeling genome-scale metabolic systems is the establishment and manual curation of reliable stoichiometric models. Initial reconstructions are typically refined through comparisons to experimental growth data from gene knockouts or nutrient environments. Existing methods iteratively correct one erroneous model prediction at a time, resulting in accumulating network changes that are often not globally optimal. We present GlobalFit, a bi-level optimization method that finds a globally optimal network, by identifying the minimal set of network changes needed to correctly predict all experimentally observed growth and non-growth cases simultaneously. When applied to the genome-scale metabolic model of Mycoplasma genitalium, GlobalFit decreases unexplained gene knockout phenotypes by 79%, increasing accuracy from 87.3% (according to the current state-of-the-art) to 97.3%. While currently available computers do not allow a global optimization of the much larger metabolic network of E. coli, the main strengths of GlobalFit are already played out when considering only one growth and one non-growth case simultaneously. Application of a corresponding strategy halves the number of unexplained cases for the already highly curated E. coli model, increasing accuracy from 90.8% to 95.4%. PMID:27482704

  4. Genome-scale approaches to the epigenetics of common human disease

    PubMed Central

    2011-01-01

    Traditionally, the pathology of human disease has been focused on microscopic examination of affected tissues, chemical and biochemical analysis of biopsy samples, other available samples of convenience, such as blood, and noninvasive or invasive imaging of varying complexity, in order to classify disease and illuminate its mechanistic basis. The molecular age has complemented this armamentarium with gene expression arrays and selective analysis of individual genes. However, we are entering a new era of epigenomic profiling, i.e., genome-scale analysis of cell-heritable nonsequence genetic change, such as DNA methylation. The epigenome offers access to stable measurements of cellular state and to biobanked material for large-scale epidemiological studies. Some of these genome-scale technologies are beginning to be applied to create the new field of epigenetic epidemiology. PMID:19844740

  5. The gut microbiota modulates host amino acid and glutathione metabolism in mice.

    PubMed

    Mardinoglu, Adil; Shoaie, Saeed; Bergentall, Mattias; Ghaffari, Pouyan; Zhang, Cheng; Larsson, Erik; Bäckhed, Fredrik; Nielsen, Jens

    2015-10-01

    The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. PMID:26475342

  6. The gut microbiota modulates host amino acid and glutathione metabolism in mice

    PubMed Central

    Mardinoglu, Adil; Shoaie, Saeed; Bergentall, Mattias; Ghaffari, Pouyan; Zhang, Cheng; Larsson, Erik; Bäckhed, Fredrik; Nielsen, Jens

    2015-01-01

    The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. PMID:26475342

  7. Metabolism

    MedlinePlus

    Metabolism refers to all the physical and chemical processes in the body that convert or use energy, ... Tortora GJ, Derrickson BH. Metabolism. In: Tortora GJ, Derrickson BH. Principles of Anatomy and Physiology . 14th ed. Hoboken, NJ: John H Wiley and Sons; 2013: ...

  8. Metabolic Needs and Capabilities of Toxoplasma gondii through Combined Computational and Experimental Analysis

    PubMed Central

    Tymoshenko, Stepan; Oppenheim, Rebecca D.; Agren, Rasmus; Nielsen, Jens; Soldati-Favre, Dominique; Hatzimanikatis, Vassily

    2015-01-01

    Toxoplasma gondii is a human pathogen prevalent worldwide that poses a challenging and unmet need for novel treatment of toxoplasmosis. Using a semi-automated reconstruction algorithm, we reconstructed a genome-scale metabolic model, ToxoNet1. The reconstruction process and flux-balance analysis of the model offer a systematic overview of the metabolic capabilities of this parasite. Using ToxoNet1 we have identified significant gaps in the current knowledge of Toxoplasma metabolic pathways and have clarified its minimal nutritional requirements for replication. By probing the model via metabolic tasks, we have further defined sets of alternative precursors necessary for parasite growth. Within a human host cell environment, ToxoNet1 predicts a minimal set of 53 enzyme-coding genes and 76 reactions to be essential for parasite replication. Double-gene-essentiality analysis identified 20 pairs of genes for which simultaneous deletion is deleterious. To validate several predictions of ToxoNet1 we have performed experimental analyses of cytosolic acetyl-CoA biosynthesis. ATP-citrate lyase and acetyl-CoA synthase were localised and their corresponding genes disrupted, establishing that each of these enzymes is dispensable for the growth of T. gondii, however together they make a synthetic lethal pair. PMID:26001086

  9. In silico metabolic engineering of Bacillus subtilis for improved production of riboflavin, Egl-237, (R,R)-2,3-butanediol and isobutanol.

    PubMed

    Hao, Tong; Han, Binbin; Ma, Hongwu; Fu, Jing; Wang, Hui; Wang, Zhiwen; Tang, Bincai; Chen, Tao; Zhao, Xueming

    2013-08-01

    Bacillus subtilis is a Gram-positive sporiferous bacterium widely used in a variety of industrial fields as a producer of high-quality vitamins, enzymes and proteins. Many genetic modifications and evolutionary engineering optimisations aiming at obtaining a better performing strain for its products have been studied. As genome-scale metabolic network models have gained significant popularity as effective tools in metabolic phenotype studies, we reconstructed a genome-scale metabolic network of B. subtilis-iBsu1147. The accuracy of iBsu1147 is validated by growth on various carbon sources, single gene knockout and large fragment non-essential gene knockout simulations. The model is used for the in silico metabolic engineering design of reactions over/underexpressed or knockout for increasing the production of four important products of B. subtilis: riboflavin, cellulase Egl-237, (R,R)-2,3-butanediol and isobutanol. The simulation predicted candidate reactions related to the improvement of strain performance on related products. The prediction is partly supported by previously published results. Due to the complexity of the biological system, it is difficult to manually find the factors that are not directly related to the production of the target compounds. The in silico predictions provide more choices for further strain improvement for these products. PMID:23666098

  10. Quantitative Proteomics-Based Reconstruction and Identification of Metabolic Pathways and Membrane Transport Proteins Related to Sugar Accumulation in Developing Fruits of Pear (Pyrus communis).

    PubMed

    Reuscher, Stefan; Fukao, Yoichiro; Morimoto, Reina; Otagaki, Shungo; Oikawa, Akira; Isuzugawa, Kanji; Shiratake, Katsuhiro

    2016-03-01

    During their 6 month development, pear (Pyrus communis) fruits undergo drastic changes in their morphology and their chemical composition. To gain a better understanding of the metabolic pathways and transport processes active during fruit development, we performed a time-course analysis using mass spectrometry (MS)-based protein identification and quantification of fruit flesh tissues. After pre-fractionation of the samples, 2,841 proteins were identified. A principal component analysis (PCA) separated the samples from seven developmental stages into three distinct clusters representing the early, mid and late developmental phase. Over-representation analysis of proteins characteristic of each developmental phase revealed both expected and novel biological processes relevant at each phase. A high abundance of aquaporins was detected in samples from fruits in the cell expansion stage. We were able quantitatively to reconstruct basic metabolic pathways such as the tricarboxylic acid (TCA) cycle, which indicates sufficient coverage to reconstruct other metabolic pathways. Most of the enzymes that presumably contribute to sugar accumulation in pear fruits could be identified. Our data indicate that invertases do not play a major role in the sugar conversions in developing pear fruits. Rather, sucrose might be broken down by sucrose synthases. Further focusing on sugar transporters, we identified several putative sugar transporters from diverse families which showed developmental regulation. In conclusion, our data set comprehensively describes the proteome of developing pear fruits and provides novel insights about sugar accumulation as well as candidate genes for key reactions and transport steps. PMID:26755692

  11. Rapid prototyping of microbial cell factories via genome-scale engineering.

    PubMed

    Si, Tong; Xiao, Han; Zhao, Huimin

    2015-11-15

    Advances in reading, writing and editing genetic materials have greatly expanded our ability to reprogram biological systems at the resolution of a single nucleotide and on the scale of a whole genome. Such capacity has greatly accelerated the cycles of design, build and test to engineer microbes for efficient synthesis of fuels, chemicals and drugs. In this review, we summarize the emerging technologies that have been applied, or are potentially useful for genome-scale engineering in microbial systems. We will focus on the development of high-throughput methodologies, which may accelerate the prototyping of microbial cell factories. PMID:25450192

  12. BABELOMICS: a systems biology perspective in the functional annotation of genome-scale experiments

    PubMed Central

    Al-Shahrour, Fátima; Minguez, Pablo; Tárraga, Joaquín; Montaner, David; Alloza, Eva; Vaquerizas, Juan M.; Conde, Lucía; Blaschke, Christian; Vera, Javier; Dopazo, Joaquín

    2006-01-01

    We present a new version of Babelomics, a complete suite of web tools for functional analysis of genome-scale experiments, with new and improved tools. New functionally relevant terms have been included such as CisRed motifs or bioentities obtained by text-mining procedures. An improved indexing has considerably speeded up several of the modules. An improved version of the FatiScan method for studying the coordinate behaviour of groups of functionally related genes is presented, along with a similar tool, the Gene Set Enrichment Analysis. Babelomics is now more oriented to test systems biology inspired hypotheses. Babelomics can be found at . PMID:16845052

  13. Identifying Branched Metabolic Pathways by Merging Linear Metabolic Pathways

    NASA Astrophysics Data System (ADS)

    Heath, Allison P.; Bennett, George N.; Kavraki, Lydia E.

    This paper presents a graph-based algorithm for identifying complex metabolic pathways in multi-genome scale metabolic data. These complex pathways are called branched pathways because they can arrive at a target compound through combinations of pathways that split compounds into smaller ones, work in parallel with many compounds, and join compounds into larger ones. While most previous work has focused on identifying linear metabolic pathways, branched metabolic pathways predominate in metabolic networks. Automatic identification of branched pathways has a number of important applications in areas that require deeper understanding of metabolism, such as metabolic engineering and drug target identification. Our algorithm utilizes explicit atom tracking to identify linear metabolic pathways and then merges them together into branched metabolic pathways. We provide results on two well-characterized metabolic pathways that demonstrate that this new merging approach can efficiently find biologically relevant branched metabolic pathways with complex structures.

  14. Integration of a constraint-based metabolic model of Brassica napus developing seeds with 13C-metabolic flux analysis

    DOE PAGESBeta

    Hay, Jordan O.; Shi, Hai; Heinzel, Nicolas; Hebbelmann, Inga; Rolletschek, Hardy; Schwender, Jorg

    2014-12-19

    The use of large-scale or genome-scale metabolic reconstructions for modeling and simulation of plant metabolism and integration of those models with large-scale omics and experimental flux data is becoming increasingly important in plant metabolic research. Here we report an updated version of bna572, a bottom-up reconstruction of oilseed rape (Brassica napus L.; Brassicaceae) developing seeds with emphasis on representation of biomass-component biosynthesis. New features include additional seed-relevant pathways for isoprenoid, sterol, phenylpropanoid, flavonoid, and choline biosynthesis. Being now based on standardized data formats and procedures for model reconstruction, bna572+ is available as a COBRA-compliant Systems Biology Markup Language (SBML) modelmore » and conforms to the Minimum Information Requested in the Annotation of Biochemical Models (MIRIAM) standards for annotation of external data resources. Bna572+ contains 966 genes, 671 reactions, and 666 metabolites distributed among 11 subcellular compartments. It is referenced to the Arabidopsis thaliana genome, with gene-protein-reaction (GPR) associations resolving subcellular localization. Detailed mass and charge balancing and confidence scoring were applied to all reactions. Using B. napus seed specific transcriptome data, expression was verified for 78% of bna572+ genes and 97% of reactions. Alongside bna572+ we also present a revised carbon centric model for 13C-Metabolic Flux Analysis (13C-MFA) with all its reactions being referenced to bna572+ based on linear projections. By integration of flux ratio constraints obtained from 13C-MFA and by elimination of infinite flux bounds around thermodynamically infeasible loops based on COBRA loopless methods, we demonstrate improvements in predictive power of Flux Variability Analysis (FVA). In conclusion, using this combined approach we characterize the difference in metabolic flux of developing seeds of two B. napus genotypes contrasting in starch

  15. Integration of a constraint-based metabolic model of Brassica napus developing seeds with 13C-metabolic flux analysis

    PubMed Central

    Hay, Jordan O.; Shi, Hai; Heinzel, Nicolas; Hebbelmann, Inga; Rolletschek, Hardy; Schwender, Jorg

    2014-01-01

    The use of large-scale or genome-scale metabolic reconstructions for modeling and simulation of plant metabolism and integration of those models with large-scale omics and experimental flux data is becoming increasingly important in plant metabolic research. Here we report an updated version of bna572, a bottom-up reconstruction of oilseed rape (Brassica napus L.; Brassicaceae) developing seeds with emphasis on representation of biomass-component biosynthesis. New features include additional seed-relevant pathways for isoprenoid, sterol, phenylpropanoid, flavonoid, and choline biosynthesis. Being now based on standardized data formats and procedures for model reconstruction, bna572+ is available as a COBRA-compliant Systems Biology Markup Language (SBML) model and conforms to the Minimum Information Requested in the Annotation of Biochemical Models (MIRIAM) standards for annotation of external data resources. Bna572+ contains 966 genes, 671 reactions, and 666 metabolites distributed among 11 subcellular compartments. It is referenced to the Arabidopsis thaliana genome, with gene-protein-reaction (GPR) associations resolving subcellular localization. Detailed mass and charge balancing and confidence scoring were applied to all reactions. Using B. napus seed specific transcriptome data, expression was verified for 78% of bna572+ genes and 97% of reactions. Alongside bna572+ we also present a revised carbon centric model for 13C-Metabolic Flux Analysis (13C-MFA) with all its reactions being referenced to bna572+ based on linear projections. By integration of flux ratio constraints obtained from 13C-MFA and by elimination of infinite flux bounds around thermodynamically infeasible loops based on COBRA loopless methods, we demonstrate improvements in predictive power of Flux Variability Analysis (FVA). Using this combined approach we characterize the difference in metabolic flux of developing seeds of two B. napus genotypes contrasting in starch and oil content. PMID

  16. The stimulatory effect of mannitol on levan biosynthesis: Lessons from metabolic systems analysis of Halomonas smyrnensis AAD6(T.).

    PubMed

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

    2013-01-01

    Halomonas smyrnensis AAD(T) is a halophilic, gram-negative bacterium that can efficiently produce levan from sucrose as carbon source via levansucrase activity. However, systems-based approaches are required to further enhance its metabolic performance for industrial application. As an important step toward this goal, the genome-scale metabolic network of Chromohalobacter salexigens DSM3043, which is considered a model organism for halophilic bacteria, has been reconstructed based on its genome annotation, physiological information, and biochemical information. In the present work, the genome-scale metabolic network of C. salexigens was recruited, and refined via integration of the available biochemical, physiological, and phenotypic features of H. smyrnensis AAD6(T) . The generic metabolic model, which comprises 1,393 metabolites and 1,108 reactions, was then systematically analyzed in silico using constraints-based simulations. To elucidate the relationship between levan biosynthesis and other metabolic processes, an enzyme-graph representation of the metabolic network and a graph decomposition technique were employed. Using the concept of control effective fluxes, significant links between several metabolic processes and levan biosynthesis were estimated. The major finding was the elucidation of the stimulatory effect of mannitol on levan biosynthesis, which was further verified experimentally via supplementation of mannitol to the fermentation medium. The optimal concentration of 30 g/L mannitol supplemented to the 50 g/L sucrose-based medium resulted in a twofold increase in levan production in parallel with increased sucrose hydrolysis rate, accumulated extracellular glucose, and decreased fructose uptake rate. PMID:24123998

  17. Dermal penetration and metabolism of p-aminophenol and p-phenylenediamine: application of the EpiDerm human reconstructed epidermis model.

    PubMed

    Hu, Ting; Bailey, Ruth E; Morrall, Stephen W; Aardema, Marilyn J; Stanley, Lesley A; Skare, Julie A

    2009-07-24

    To address the provision of the 7th Amendment to the EU Cosmetics Directive banning the use of in vivo genotoxicity assays for testing cosmetic ingredients in 2009, the 3D EpiDerm reconstructed human skin micronucleus assay has been developed. To further characterise the EpiDerm tissue for potential use in genotoxicity testing, we have evaluated the dermal penetration and metabolism of two hair dye ingredients, p-aminophenol (PAP) and p-phenylenediamine (PPD) in this reconstructed epidermis model. When EpiDerm tissue was topically exposed to PAP or PPD for 30 min (typical for a hair dye exposure), the majority (80->90%) of PAP or PPD was excluded from skin tissue and removed by rinsing. After a 23.5h recovery period, the PAP fraction that did penetrate was completely N-acetylated to acetaminophen (APAP). Similarly, 30 min topical application of PPD resulted in the formation of the N-mono- and N,N'-diacetylated metabolites of PPD. These results are consistent with published data on the dermal metabolism of these compounds from other in vitro systems as well as from in vivo studies. When tissue was exposed topically (PAP) or via the culture media (PPD) for 24h, there was good batch-to-batch and donor-to-donor reproducibility in the penetration and metabolism of PAP and PPD. Overall, the results demonstrate that these two aromatic amines are biotransformed in 3D EpiDerm tissue via N-acetylation. Characterising the metabolic capability of EpiDerm tissue is important for the evaluation of this model for use in genotoxicity testing. PMID:19446244

  18. Informed Consent in Genome-Scale Research: What Do Prospective Participants Think?

    PubMed Central

    Trinidad, Susan Brown; Fullerton, Stephanie M.; Bares, Julie M.; Jarvik, Gail P.; Larson, Eric B.; Burke, Wylie

    2012-01-01

    Background To promote effective genome-scale research, genomic and clinical data for large population samples must be collected, stored, and shared. Methods We conducted focus groups with 45 members of a Seattle-based integrated healthcare delivery system to learn about their views and expectations for informed consent in genome-scale studies. Results Participants viewed information about study purpose, aims, and how and by whom study data could be used to be at least as important as information about risks and possible harms. They generally supported a tiered consent approach for specific issues, including research purpose, data sharing, and access to individual research results. Participants expressed a continuum of opinions with respect to the acceptability of broad consent, ranging from completely acceptable to completely unacceptable. Older participants were more likely to view the consent process in relational – rather than contractual – terms, compared with younger participants. The majority of participants endorsed seeking study subjects’ permission regarding material changes in study purpose and data sharing. Conclusions Although this study sample was limited in terms of racial and socioeconomic diversity, our results suggest a strong positive interest in genomic research on the part of at least some prospective participants and indicate a need for increased public engagement, as well as strategies for ongoing communication with study participants. PMID:23493836

  19. Genome scale transcriptional response diversity among ten ecotypes of Arabidopsis thaliana during heat stress

    PubMed Central

    Barah, Pankaj; Jayavelu, Naresh D.; Mundy, John; Bones, Atle M.

    2013-01-01

    In the scenario of global warming and climate change, heat stress is a serious threat to crop production worldwide. Being sessile, plants cannot escape from heat. Plants have developed various adaptive mechanisms to survive heat stress. Several studies have focused on diversity of heat tolerance levels in divergent Arabidopsis thaliana (A. thaliana) ecotypes, but comprehensive genome scale understanding of heat stress response in plants is still lacking. Here we report the genome scale transcript responses to heat stress of 10 A. thaliana ecotypes (Col, Ler, C24, Cvi, Kas1, An1, Sha, Kyo2, Eri, and Kond) originated from different geographical locations. During the experiment, A. thaliana plants were subjected to heat stress (38°C) and transcript responses were monitored using Arabidopsis NimbleGen ATH6 microarrays. The responses of A. thaliana ecotypes exhibited considerable variation in the transcript abundance levels. In total, 3644 transcripts were significantly heat regulated (p < 0.01) in the 10 ecotypes, including 244 transcription factors and 203 transposable elements. By employing a systems genetics approach- Network Component Analysis (NCA), we have constructed an in silico transcript regulatory network model for 35 heat responsive transcription factors during cellular responses to heat stress in A. thaliana. The computed activities of the 35 transcription factors showed ecotype specific responses to the heat treatment. PMID:24409190

  20. Integrating Cellular Metabolism into a Multiscale Whole-Body Model

    PubMed Central

    Krauss, Markus; Schaller, Stephan; Borchers, Steffen; Findeisen, Rolf; Lippert, Jörg; Kuepfer, Lars

    2012-01-01

    Cellular metabolism continuously processes an enormous range of external compounds into endogenous metabolites and is as such a key element in human physiology. The multifaceted physiological role of the metabolic network fulfilling the catalytic conversions can only be fully understood from a whole-body perspective where the causal interplay of the metabolic states of individual cells, the surrounding tissue and the whole organism are simultaneously considered. We here present an approach relying on dynamic flux balance analysis that allows the integration of metabolic networks at the cellular scale into standardized physiologically-based pharmacokinetic models at the whole-body level. To evaluate our approach we integrated a genome-scale network reconstruction of a human hepatocyte into the liver tissue of a physiologically-based pharmacokinetic model of a human adult. The resulting multiscale model was used to investigate hyperuricemia therapy, ammonia detoxification and paracetamol-induced toxication at a systems level. The specific models simultaneously integrate multiple layers of biological organization and offer mechanistic insights into pathology and medication. The approach presented may in future support a mechanistic understanding in diagnostics and drug development. PMID:23133351

  1. Genomic Reconstruction of the Transcriptional Regulatory Network in Bacillus subtilis

    PubMed Central

    Leyn, Semen A.; Kazanov, Marat D.; Sernova, Natalia V.; Ermakova, Ekaterina O.; Novichkov, Pavel S.

    2013-01-01

    The adaptation of microorganisms to their environment is controlled by complex transcriptional regulatory networks (TRNs), which are still only partially understood even for model species. Genome scale annotation of regulatory features of genes and TRN reconstruction are challenging tasks of microbial genomics. We used the knowledge-driven comparative-genomics approach implemented in the RegPredict Web server to infer TRN in the model Gram-positive bacterium Bacillus subtilis and 10 related Bacillales species. For transcription factor (TF) regulons, we combined the available information from the DBTBS database and the literature with bioinformatics tools, allowing inference of TF binding sites (TFBSs), comparative analysis of the genomic context of predicted TFBSs, functional assignment of target genes, and effector prediction. For RNA regulons, we used known RNA regulatory motifs collected in the Rfam database to scan genomes and analyze the genomic context of new RNA sites. The inferred TRN in B. subtilis comprises regulons for 129 TFs and 24 regulatory RNA families. First, we analyzed 66 TF regulons with previously known TFBSs in B. subtilis and projected them to other Bacillales genomes, resulting in refinement of TFBS motifs and identification of novel regulon members. Second, we inferred motifs and described regulons for 28 experimentally studied TFs with previously unknown TFBSs. Third, we discovered novel motifs and reconstructed regulons for 36 previously uncharacterized TFs. The inferred collection of regulons is available in the RegPrecise database (http://regprecise.lbl.gov/) and can be used in genetic experiments, metabolic modeling, and evolutionary analysis. PMID:23504016

  2. Genomic reconstruction of the transcriptional regulatory network in Bacillus subtilis.

    PubMed

    Leyn, Semen A; Kazanov, Marat D; Sernova, Natalia V; Ermakova, Ekaterina O; Novichkov, Pavel S; Rodionov, Dmitry A

    2013-06-01

    The adaptation of microorganisms to their environment is controlled by complex transcriptional regulatory networks (TRNs), which are still only partially understood even for model species. Genome scale annotation of regulatory features of genes and TRN reconstruction are challenging tasks of microbial genomics. We used the knowledge-driven comparative-genomics approach implemented in the RegPredict Web server to infer TRN in the model Gram-positive bacterium Bacillus subtilis and 10 related Bacillales species. For transcription factor (TF) regulons, we combined the available information from the DBTBS database and the literature with bioinformatics tools, allowing inference of TF binding sites (TFBSs), comparative analysis of the genomic context of predicted TFBSs, functional assignment of target genes, and effector prediction. For RNA regulons, we used known RNA regulatory motifs collected in the Rfam database to scan genomes and analyze the genomic context of new RNA sites. The inferred TRN in B. subtilis comprises regulons for 129 TFs and 24 regulatory RNA families. First, we analyzed 66 TF regulons with previously known TFBSs in B. subtilis and projected them to other Bacillales genomes, resulting in refinement of TFBS motifs and identification of novel regulon members. Second, we inferred motifs and described regulons for 28 experimentally studied TFs with previously unknown TFBSs. Third, we discovered novel motifs and reconstructed regulons for 36 previously uncharacterized TFs. The inferred collection of regulons is available in the RegPrecise database (http://regprecise.lbl.gov/) and can be used in genetic experiments, metabolic modeling, and evolutionary analysis. PMID:23504016

  3. Benchmarking Procedures for High-Throughput Context Specific Reconstruction Algorithms

    PubMed Central

    Pacheco, Maria P.; Pfau, Thomas; Sauter, Thomas

    2016-01-01

    Recent progress in high-throughput data acquisition has shifted the focus from data generation to processing and understanding of how to integrate collected information. Context specific reconstruction based on generic genome scale models like ReconX or HMR has the potential to become a diagnostic and treatment tool tailored to the analysis of specific individuals. The respective computational algorithms require a high level of predictive power, robustness and sensitivity. Although multiple context specific reconstruction algorithms were published in the last 10 years, only a fraction of them is suitable for model building based on human high-throughput data. Beside other reasons, this might be due to problems arising from the limitation to only one metabolic target function or arbitrary thresholding. This review describes and analyses common validation methods used for testing model building algorithms. Two major methods can be distinguished: consistency testing and comparison based testing. The first is concerned with robustness against noise, e.g., missing data due to the impossibility to distinguish between the signal and the background of non-specific binding of probes in a microarray experiment, and whether distinct sets of input expressed genes corresponding to i.e., different tissues yield distinct models. The latter covers methods comparing sets of functionalities, comparison with existing networks or additional databases. We test those methods on several available algorithms and deduce properties of these algorithms that can be compared with future developments. The set of tests performed, can therefore serve as a benchmarking procedure for future algorithms. PMID:26834640

  4. Unraveling the Light-Specific Metabolic and Regulatory Signatures of Rice through Combined in Silico Modeling and Multiomics Analysis.

    PubMed

    Lakshmanan, Meiyappan; Lim, Sun-Hyung; Mohanty, Bijayalaxmi; Kim, Jae Kwang; Ha, Sun-Hwa; Lee, Dong-Yup

    2015-12-01

    Light quality is an important signaling component upon which plants orchestrate various morphological processes, including seed germination and seedling photomorphogenesis. However, it is still unclear how plants, especially food crops, sense various light qualities and modulate their cellular growth and other developmental processes. Therefore, in this work, we initially profiled the transcripts of a model crop, rice (Oryza sativa), under four different light treatments (blue, green, red, and white) as well as in the dark. Concurrently, we reconstructed a fully compartmentalized genome-scale metabolic model of rice cells, iOS2164, containing 2,164 unique genes, 2,283 reactions, and 1,999 metabolites. We then combined the model with transcriptome profiles to elucidate the light-specific transcriptional signatures of rice metabolism. Clearly, light signals mediated rice gene expressions, differentially regulating numerous metabolic pathways: photosynthesis and secondary metabolism were up-regulated in blue light, whereas reserve carbohydrates degradation was pronounced in the dark. The topological analysis of gene expression data with the rice genome-scale metabolic model further uncovered that phytohormones, such as abscisate, ethylene, gibberellin, and jasmonate, are the key biomarkers of light-mediated regulation, and subsequent analysis of the associated genes' promoter regions identified several light-specific transcription factors. Finally, the transcriptional control of rice metabolism by red and blue light signals was assessed by integrating the transcriptome and metabolome data with constraint-based modeling. The biological insights gained from this integrative systems biology approach offer several potential applications, such as improving the agronomic traits of food crops and designing light-specific synthetic gene circuits in microbial and mammalian systems. PMID:26453433

  5. Zooming-in on cancer metabolic rewiring with tissue specific constraint-based models.

    PubMed

    Di Filippo, Marzia; Colombo, Riccardo; Damiani, Chiara; Pescini, Dario; Gaglio, Daniela; Vanoni, Marco; Alberghina, Lilia; Mauri, Giancarlo

    2016-06-01

    The metabolic rearrangements occurring in cancer cells can be effectively investigated with a Systems Biology approach supported by metabolic network modeling. We here present tissue-specific constraint-based core models for three different types of tumors (liver, breast and lung) that serve this purpose. The core models were extracted and manually curated from the corresponding genome-scale metabolic models in the Human Metabolic Atlas database with a focus on the pathways that are known to play a key role in cancer growth and proliferation. Along similar lines, we also reconstructed a core model from the original general human metabolic network to be used as a reference model. A comparative Flux Balance Analysis between the reference and the cancer models highlighted both a clear distinction between the two conditions and a heterogeneity within the three different cancer types in terms of metabolic flux distribution. These results emphasize the need for modeling approaches able to keep up with this tumoral heterogeneity in order to identify more suitable drug targets and develop effective treatments. According to this perspective, we identified key points able to reverse the tumoral phenotype toward the reference one or vice-versa. PMID:27085310

  6. Use of randomized sampling for analysis of metabolic networks.

    PubMed

    Schellenberger, Jan; Palsson, Bernhard Ø

    2009-02-27

    Genome-scale metabolic network reconstructions in microorganisms have been formulated and studied for about 8 years. The constraint-based approach has shown great promise in analyzing the systemic properties of these network reconstructions. Notably, constraint-based models have been used successfully to predict the phenotypic effects of knock-outs and for metabolic engineering. The inherent uncertainty in both parameters and variables of large-scale models is significant and is well suited to study by Monte Carlo sampling of the solution space. These techniques have been applied extensively to the reaction rate (flux) space of networks, with more recent work focusing on dynamic/kinetic properties. Monte Carlo sampling as an analysis tool has many advantages, including the ability to work with missing data, the ability to apply post-processing techniques, and the ability to quantify uncertainty and to optimize experiments to reduce uncertainty. We present an overview of this emerging area of research in systems biology. PMID:18940807

  7. Targeted and genome-scale methylomics reveals gene body signatures in human cell lines

    PubMed Central

    Ball, Madeleine Price; Li, Jin Billy; Gao, Yuan; Lee, Je-Hyuk; LeProust, Emily; Park, In-Hyun; Xie, Bin; Daley, George Q.; Church, George M.

    2012-01-01

    Cytosine methylation, an epigenetic modification of DNA, is a target of growing interest for developing high throughput profiling technologies. Here we introduce two new, complementary techniques for cytosine methylation profiling utilizing next generation sequencing technology: bisulfite padlock probes (BSPPs) and methyl sensitive cut counting (MSCC). In the first method, we designed a set of ~10,000 BSPPs distributed over the ENCODE pilot project regions to take advantage of existing expression and chromatin immunoprecipitation data. We observed a pattern of low promoter methylation coupled with high gene body methylation in highly expressed genes. Using the second method, MSCC, we gathered genome-scale data for 1.4 million HpaII sites and confirmed that gene body methylation in highly expressed genes is a consistent phenomenon over the entire genome. Our observations highlight the usefulness of techniques which are not inherently or intentionally biased in favor of only profiling particular subsets like CpG islands or promoter regions. PMID:19329998

  8. GENOME-SCALE SEQUENCING TO IDENTIFY GENES INVOLVED IN MENDELIAN DISORDERS

    PubMed Central

    Markello, Thomas C; Adams, David R

    2014-01-01

    The analysis of genome-scale sequence data can be defined as the interrogation of a complete set of genetic instructions in a search for individual loci that produce or contribute to a pathological state. Bioinformatic analysis of sequence data requires sufficient discriminant power to find this needle in a haystack. Current approaches make choices about selectivity and specificity thresholds, and the quality, quantity and completeness of the data in these analyses. There are many software tools available for individual analytic component-tasks, including commercial and open source options. Three major types of techniques have been included in most published exome projects to date: frequency/population genetic analysis, inheritance state consistency, and predictions of deleteriousness. We discuss the required infrastructure and use of each technique during analysis of genomic sequence data for clinical and research applications. Future developments will alter the strategies and sequence of using these tools and are speculated on in the closing section. PMID:24510651

  9. Moving image analysis to the cloud: A case study with a genome-scale tomographic study

    NASA Astrophysics Data System (ADS)

    Mader, Kevin; Stampanoni, Marco

    2016-01-01

    Over the last decade, the time required to measure a terabyte of microscopic imaging data has gone from years to minutes. This shift has moved many of the challenges away from experimental design and measurement to scalable storage, organization, and analysis. As many scientists and scientific institutions lack training and competencies in these areas, major bottlenecks have arisen and led to substantial delays and gaps between measurement, understanding, and dissemination. We present in this paper a framework for analyzing large 3D datasets using cloud-based computational and storage resources. We demonstrate its applicability by showing the setup and costs associated with the analysis of a genome-scale study of bone microstructure. We then evaluate the relative advantages and disadvantages associated with local versus cloud infrastructures.

  10. A genome scale resource for in vivo tag-based protein function exploration in C. elegans

    PubMed Central

    Sarov, Mihail; Murray, John; Schanze, Kristin; Pozniakovski, Andrei; Niu, Wei; Angermann, Karolin; Hasse, Susanne; Rupprecht, Michaela; Vinis, Elisabeth; Tinney, Matthew; Preston, Elicia; Zinke, Andrea; Enst, Susanne; Teichgraber, Tina; Janette, Judith; Reis, Kadri; Janosch, Stephan; Schloissnig, Siegfried; Ejsmont, Radoslaw K.; Slightam, Cindie; Xu, Xiao; Kim, Stuart K.; Reinke, Valerie; Stewart, A. Francis; Snyder, Michael; Waterston, Robert; Hyman, Anthony A.

    2012-01-01

    Understanding the in vivo dynamics of protein localization and their physical interactions is important for many problems in Biology. To enable systematic protein function interrogation in a multicelluar context, we built a genome-scale transgenic platform for in vivo expression of fluorescent and affinity tagged proteins in Caenorhabditis elegans under endogenous cis regulatory control. The platform combines computer-assisted transgene design, massively parallel DNA engineering and next generation sequencing to generate a resource of 14637 genomic DNA transgenes, which covers 73% of the proteome. The multipurpose tag used allows any protein of interest to be localized in vivo or affinity purified using standard tag-based assays. We illustrate the utility of the resource by systematic chromatin immunopurification and automated 4D imaging, which produced detailed DNA binding and cell/tissue distribution maps for key transcription factor proteins PMID:22901814

  11. Genome-scale phylogenetic function annotation of large and diverse protein families

    PubMed Central

    Engelhardt, Barbara E.; Jordan, Michael I.; Srouji, John R.; Brenner, Steven E.

    2011-01-01

    The Statistical Inference of Function Through Evolutionary Relationships (SIFTER) framework uses a statistical graphical model that applies phylogenetic principles to automate precise protein function prediction. Here we present a revised approach (SIFTER version 2.0) that enables annotations on a genomic scale. SIFTER 2.0 produces equivalently precise predictions compared to the earlier version on a carefully studied family and on a collection of 100 protein families. We have added an approximation method to SIFTER 2.0 and show a 500-fold improvement in speed with minimal impact on prediction results in the functionally diverse sulfotransferase protein family. On the Nudix protein family, previously inaccessible to the SIFTER framework because of the 66 possible molecular functions, SIFTER achieved 47.4% accuracy on experimental data (where BLAST achieved 34.0%). Finally, we used SIFTER to annotate all of the Schizosaccharomyces pombe proteins with experimental functional characterizations, based on annotations from proteins in 46 fungal genomes. SIFTER precisely predicted molecular function for 45.5% of the characterized proteins in this genome, as compared with four current function prediction methods that precisely predicted function for 62.6%, 30.6%, 6.0%, and 5.7% of these proteins. We use both precision-recall curves and ROC analyses to compare these genome-scale predictions across the different methods and to assess performance on different types of applications. SIFTER 2.0 is capable of predicting protein molecular function for large and functionally diverse protein families using an approximate statistical model, enabling phylogenetics-based protein function prediction for genome-wide analyses. The code for SIFTER and protein family data are available at http://sifter.berkeley.edu. PMID:21784873

  12. Genome-scale protein expression and structural biology of Plasmodium falciparum and related Apicomplexan organisms.

    PubMed

    Vedadi, Masoud; Lew, Jocelyne; Artz, Jennifer; Amani, Mehrnaz; Zhao, Yong; Dong, Aiping; Wasney, Gregory A; Gao, Mian; Hills, Tanya; Brokx, Stephen; Qiu, Wei; Sharma, Sujata; Diassiti, Angelina; Alam, Zahoor; Melone, Michelle; Mulichak, Anne; Wernimont, Amy; Bray, James; Loppnau, Peter; Plotnikova, Olga; Newberry, Kate; Sundararajan, Emayavaram; Houston, Simon; Walker, John; Tempel, Wolfram; Bochkarev, Alexey; Kozieradzki, Ivona; Edwards, Aled; Arrowsmith, Cheryl; Roos, David; Kain, Kevin; Hui, Raymond

    2007-01-01

    Parasites from the protozoan phylum Apicomplexa are responsible for diseases, such as malaria, toxoplasmosis and cryptosporidiosis, all of which have significantly higher rates of mortality and morbidity in economically underdeveloped regions of the world. Advances in vaccine development and drug discovery are urgently needed to control these diseases and can be facilitated by production of purified recombinant proteins from Apicomplexan genomes and determination of their 3D structures. To date, both heterologous expression and crystallization of Apicomplexan proteins have seen only limited success. In an effort to explore the effectiveness of producing and crystallizing proteins on a genome-scale using a standardized methodology, over 400 distinct Plasmodium falciparum target genes were chosen representing different cellular classes, along with select orthologues from four other Plasmodium species as well as Cryptosporidium parvum and Toxoplasma gondii. From a total of 1008 genes from the seven genomes, 304 (30.2%) produced purified soluble proteins and 97 (9.6%) crystallized, culminating in 36 crystal structures. These results demonstrate that, contrary to previous findings, a standardized platform using Escherichia coli can be effective for genome-scale production and crystallography of Apicomplexan proteins. Predictably, orthologous proteins from different Apicomplexan genomes behaved differently in expression, purification and crystallization, although the overall success rates of Plasmodium orthologues do not differ significantly. Their differences were effectively exploited to elevate the overall productivity to levels comparable to the most successful ongoing structural genomics projects: 229 of the 468 target genes produced purified soluble protein from one or more organisms, with 80 and 32 of the purified targets, respectively, leading to crystals and ultimately structures from one or more orthologues. PMID:17125854

  13. Dynamic metabolic models in context: biomass backtracking.

    PubMed

    Tummler, Katja; Kühn, Clemens; Klipp, Edda

    2015-08-01

    Mathematical modeling has proven to be a powerful tool to understand and predict functional and regulatory properties of metabolic processes. High accuracy dynamic modeling of individual pathways is thereby opposed by simplified but genome scale constraint based approaches. A method that links these two powerful techniques would greatly enhance predictive power but is so far lacking. We present biomass backtracking, a workflow that integrates the cellular context in existing dynamic metabolic models via stoichiometrically exact drain reactions based on a genome scale metabolic model. With comprehensive examples, for different species and environmental contexts, we show the importance and scope of applications and highlight the improvement compared to common boundary formulations in existing metabolic models. Our method allows for the contextualization of dynamic metabolic models based on all available information. We anticipate this to greatly increase their accuracy and predictive power for basic research and also for drug development and industrial applications. PMID:26189715

  14. Dynamic Analysis of Integrated Signaling, Metabolic, and Regulatory Networks

    PubMed Central

    Eddy, James A.; Papin, Jason A.

    2008-01-01

    Extracellular cues affect signaling, metabolic, and regulatory processes to elicit cellular responses. Although intracellular signaling, metabolic, and regulatory networks are highly integrated, previous analyses have largely focused on independent processes (e.g., metabolism) without considering the interplay that exists among them. However, there is evidence that many diseases arise from multifunctional components with roles throughout signaling, metabolic, and regulatory networks. Therefore, in this study, we propose a flux balance analysis (FBA)–based strategy, referred to as integrated dynamic FBA (idFBA), that dynamically simulates cellular phenotypes arising from integrated networks. The idFBA framework requires an integrated stoichiometric reconstruction of signaling, metabolic, and regulatory processes. It assumes quasi-steady-state conditions for “fast” reactions and incorporates “slow” reactions into the stoichiometric formalism in a time-delayed manner. To assess the efficacy of idFBA, we developed a prototypic integrated system comprising signaling, metabolic, and regulatory processes with network features characteristic of actual systems and incorporating kinetic parameters based on typical time scales observed in literature. idFBA was applied to the prototypic system, which was evaluated for different environments and gene regulatory rules. In addition, we applied the idFBA framework in a similar manner to a representative module of the single-cell eukaryotic organism Saccharomyces cerevisiae. Ultimately, idFBA facilitated quantitative, dynamic analysis of systemic effects of extracellular cues on cellular phenotypes and generated comparable time-course predictions when contrasted with an equivalent kinetic model. Since idFBA solves a linear programming problem and does not require an exhaustive list of detailed kinetic parameters, it may be efficiently scaled to integrated intracellular systems that incorporate signaling, metabolic, and

  15. Genome-wide metabolic (re-) annotation of Kluyveromyces lactis

    PubMed Central

    2012-01-01

    Background Even before having its genome sequence published in 2004, Kluyveromyces lactis had long been considered a model organism for studies in genetics and physiology. Research on Kluyveromyces lactis is quite advanced and this yeast species is one of the few with which it is possible to perform formal genetic analysis. Nevertheless, until now, no complete metabolic functional annotation has been performed to the proteins encoded in the Kluyveromyces lactis genome. Results In this work, a new metabolic genome-wide functional re-annotation of the proteins encoded in the Kluyveromyces lactis genome was performed, resulting in the annotation of 1759 genes with metabolic functions, and the development of a methodology supported by merlin (software developed in-house). The new annotation includes novelties, such as the assignment of transporter superfamily numbers to genes identified as transporter proteins. Thus, the genes annotated with metabolic functions could be exclusively enzymatic (1410 genes), transporter proteins encoding genes (301 genes) or have both metabolic activities (48 genes). The new annotation produced by this work largely surpassed the Kluyveromyces lactis currently available annotations. A comparison with KEGG’s annotation revealed a match with 844 (~90%) of the genes annotated by KEGG, while adding 850 new gene annotations. Moreover, there are 32 genes with annotations different from KEGG. Conclusions The methodology developed throughout this work can be used to re-annotate any yeast or, with a little tweak of the reference organism, the proteins encoded in any sequenced genome. The new annotation provided by this study offers basic knowledge which might be useful for the scientific community working on this model yeast, because new functions have been identified for the so-called metabolic genes. Furthermore, it served as the basis for the reconstruction of a compartmentalized, genome-scale metabolic model of Kluyveromyces lactis, which is

  16. Integration of a constraint-based metabolic model of Brassica napus developing seeds with 13C-metabolic flux analysis

    SciTech Connect

    Hay, Jordan O.; Shi, Hai; Heinzel, Nicolas; Hebbelmann, Inga; Rolletschek, Hardy; Schwender, Jorg

    2014-12-19

    The use of large-scale or genome-scale metabolic reconstructions for modeling and simulation of plant metabolism and integration of those models with large-scale omics and experimental flux data is becoming increasingly important in plant metabolic research. Here we report an updated version of bna572, a bottom-up reconstruction of oilseed rape (Brassica napus L.; Brassicaceae) developing seeds with emphasis on representation of biomass-component biosynthesis. New features include additional seed-relevant pathways for isoprenoid, sterol, phenylpropanoid, flavonoid, and choline biosynthesis. Being now based on standardized data formats and procedures for model reconstruction, bna572+ is available as a COBRA-compliant Systems Biology Markup Language (SBML) model and conforms to the Minimum Information Requested in the Annotation of Biochemical Models (MIRIAM) standards for annotation of external data resources. Bna572+ contains 966 genes, 671 reactions, and 666 metabolites distributed among 11 subcellular compartments. It is referenced to the Arabidopsis thaliana genome, with gene-protein-reaction (GPR) associations resolving subcellular localization. Detailed mass and charge balancing and confidence scoring were applied to all reactions. Using B. napus seed specific transcriptome data, expression was verified for 78% of bna572+ genes and 97% of reactions. Alongside bna572+ we also present a revised carbon centric model for 13C-Metabolic Flux Analysis (13C-MFA) with all its reactions being referenced to bna572+ based on linear projections. By integration of flux ratio constraints obtained from 13C-MFA and by elimination of infinite flux bounds around thermodynamically infeasible loops based on COBRA loopless methods, we demonstrate improvements in predictive power of Flux Variability Analysis (FVA). In conclusion, using this combined approach we characterize the difference in metabolic flux of developing seeds of two B. napus

  17. Genome-wide functional annotation and structural verification of metabolic ORFeome of Chlamydomonas reinhardtii

    PubMed Central

    2011-01-01

    ,400 JGI predicted metabolic ORFs that can facilitate the reconstruction and refinement of a genome-scale metabolic network. The unveiling of the metabolic potential of this organism, along with structural verification of the relevant ORFs, facilitates the selection of metabolic engineering targets with applications in bioenergy and biopharmaceuticals. The ORF clones are a resource for downstream studies. PMID:21810206

  18. Genome-scale cold stress response regulatory networks in ten Arabidopsis thaliana ecotypes

    PubMed Central

    2013-01-01

    Background Low temperature leads to major crop losses every year. Although several studies have been conducted focusing on diversity of cold tolerance level in multiple phenotypically divergent Arabidopsis thaliana (A. thaliana) ecotypes, genome-scale molecular understanding is still lacking. Results In this study, we report genome-scale transcript response diversity of 10 A. thaliana ecotypes originating from different geographical locations to non-freezing cold stress (10°C). To analyze the transcriptional response diversity, we initially compared transcriptome changes in all 10 ecotypes using Arabidopsis NimbleGen ATH6 microarrays. In total 6061 transcripts were significantly cold regulated (p < 0.01) in 10 ecotypes, including 498 transcription factors and 315 transposable elements. The majority of the transcripts (75%) showed ecotype specific expression pattern. By using sequence data available from Arabidopsis thaliana 1001 genome project, we further investigated sequence polymorphisms in the core cold stress regulon genes. Significant numbers of non-synonymous amino acid changes were observed in the coding region of the CBF regulon genes. Considering the limited knowledge about regulatory interactions between transcription factors and their target genes in the model plant A. thaliana, we have adopted a powerful systems genetics approach- Network Component Analysis (NCA) to construct an in-silico transcriptional regulatory network model during response to cold stress. The resulting regulatory network contained 1,275 nodes and 7,720 connections, with 178 transcription factors and 1,331 target genes. Conclusions A. thaliana ecotypes exhibit considerable variation in transcriptome level responses to non-freezing cold stress treatment. Ecotype specific transcripts and related gene ontology (GO) categories were identified to delineate natural variation of cold stress regulated differential gene expression in the model plant A. thaliana. The predicted

  19. Unraveling the Light-Specific Metabolic and Regulatory Signatures of Rice through Combined in Silico Modeling and Multiomics Analysis1[OPEN

    PubMed Central

    Lim, Sun-Hyung; Kim, Jae Kwang; Ha, Sun-Hwa

    2015-01-01

    Light quality is an important signaling component upon which plants orchestrate various morphological processes, including seed germination and seedling photomorphogenesis. However, it is still unclear how plants, especially food crops, sense various light qualities and modulate their cellular growth and other developmental processes. Therefore, in this work, we initially profiled the transcripts of a model crop, rice (Oryza sativa), under four different light treatments (blue, green, red, and white) as well as in the dark. Concurrently, we reconstructed a fully compartmentalized genome-scale metabolic model of rice cells, iOS2164, containing 2,164 unique genes, 2,283 reactions, and 1,999 metabolites. We then combined the model with transcriptome profiles to elucidate the light-specific transcriptional signatures of rice metabolism. Clearly, light signals mediated rice gene expressions, differentially regulating numerous metabolic pathways: photosynthesis and secondary metabolism were up-regulated in blue light, whereas reserve carbohydrates degradation was pronounced in the dark. The topological analysis of gene expression data with the rice genome-scale metabolic model further uncovered that phytohormones, such as abscisate, ethylene, gibberellin, and jasmonate, are the key biomarkers of light-mediated regulation, and subsequent analysis of the associated genes’ promoter regions identified several light-specific transcription factors. Finally, the transcriptional control of rice metabolism by red and blue light signals was assessed by integrating the transcriptome and metabolome data with constraint-based modeling. The biological insights gained from this integrative systems biology approach offer several potential applications, such as improving the agronomic traits of food crops and designing light-specific synthetic gene circuits in microbial and mammalian systems. PMID:26453433

  20. In silico analysis of Clostridium acetobutylicum ATCC 824 metabolic response to an external electron supply.

    PubMed

    Gallardo, Roberto; Acevedo, Alejandro; Quintero, Julián; Paredes, Ivan; Conejeros, Raúl; Aroca, Germán

    2016-02-01

    The biological production of butanol has become an important research field and thanks to genome sequencing and annotation; genome-scale metabolic reconstructions have been developed for several Clostridium species. This work makes use of the iCAC490 model of Clostridium acetobutylicum ATCC 824 to analyze its metabolic capabilities and response to an external electron supply through a constraint-based approach using the Constraint-Based Reconstruction Analysis Toolbox. Several analyses were conducted, which included sensitivity, production envelope, and phenotypic phase planes. The model showed that the use of an external electron supply, which acts as co-reducing agent along with glucose-derived reducing power (electrofermentation), results in an increase in the butanol-specific productivity. However, a proportional increase in the butyrate uptake flux is required. Besides, the uptake of external butyrate leads to the coupling of butanol production and growth, which coincides with results reported in literature. Phenotypic phase planes showed that the reducing capacity becomes more limiting for growth at high butyrate uptake fluxes. An electron uptake flux allows the metabolism to reach the growth optimality line. Although the maximum butanol flux does not coincide with the growth optimality line, a butyrate uptake combined with an electron uptake flux would result in an increased butanol volumetric productivity, being a potential strategy to optimize the production of butanol by C. acetobutylicum ATCC 824. PMID:26650720

  1. Exploring metabolic pathway reconstruction and genome-wide expression profiling in Lactobacillus reuteri to define functional probiotic features.

    PubMed

    Saulnier, Delphine M; Santos, Filipe; Roos, Stefan; Mistretta, Toni-Ann; Spinler, Jennifer K; Molenaar, Douwe; Teusink, Bas; Versalovic, James

    2011-01-01

    The genomes of four Lactobacillus reuteri strains isolated from human breast milk and the gastrointestinal tract have been recently sequenced as part of the Human Microbiome Project. Preliminary genome comparisons suggested that these strains belong to two different clades, previously shown to differ with respect to antimicrobial production, biofilm formation, and immunomodulation. To explain possible mechanisms of survival in the host and probiosis, we completed a detailed genomic comparison of two breast milk-derived isolates representative of each group: an established probiotic strain (L. reuteri ATCC 55730) and a strain with promising probiotic features (L. reuteri ATCC PTA 6475). Transcriptomes of L. reuteri strains in different growth phases were monitored using strain-specific microarrays, and compared using a pan-metabolic model representing all known metabolic reactions present in these strains. Both strains contained candidate genes involved in the survival and persistence in the gut such as mucus-binding proteins and enzymes scavenging reactive oxygen species. A large operon predicted to encode the synthesis of an exopolysaccharide was identified in strain 55730. Both strains were predicted to produce health-promoting factors, including antimicrobial agents and vitamins (folate, vitamin B(12)). Additionally, a complete pathway for thiamine biosynthesis was predicted in strain 55730 for the first time in this species. Candidate genes responsible for immunomodulatory properties of each strain were identified by transcriptomic comparisons. The production of bioactive metabolites by human-derived probiotics may be predicted using metabolic modeling and transcriptomics. Such strategies may facilitate selection and optimization of probiotics for health promotion, disease prevention and amelioration. PMID:21559529

  2. High precision multi-genome scale reannotation of enzyme function by EFICAz

    PubMed Central

    Arakaki, Adrian K; Tian, Weidong; Skolnick, Jeffrey

    2006-01-01

    biologically significant hypotheses and can be useful for comparative genome analysis and automated metabolic pathway reconstruction. PMID:17166279

  3. Comparison of xenobiotic metabolizing enzyme activities in ex vivo human skin and reconstructed human skin models from SkinEthic.

    PubMed

    Eilstein, Joan; Léreaux, Guillaume; Budimir, Natali; Hussler, Georges; Wilkinson, Simon; Duché, Daniel

    2014-09-01

    Skin function is not limited to a physical barrier. According to its total surface area, it is also considered as an extra-hepatic metabolizing organ. In vitro engineered human skins have been developed to replace limited ex vivo normal human skin samples (NHS). Thus, assessing and comparing skin models from SkinEthic [Episkin™, RHE™ and the full thickness model (FTM)] with NHS in terms of metabolic capability are essential. The apparent activities of main cutaneous isoforms of cytochrome P450-dependent monooxygenases (CYP1A1/1B1, 2B6/2C18/2E1, 3A5/3A7), esterase, glutathione-S-[(GST), A, M, P, T], N-acetyl-(NAT1), uridinyl-diphosphate glucuronyl-(UDPGT 1A family) and sulfo-(SULT1A1) transferases were determined using probe substrates. Mean activities indicative of CYP1A1/1B1 (expressed as pmol/mg protein/6 h) in RHE™ (2.8) and FTM (2.6) were very similar to NHS (3.0) while Episkin™ showed a higher activity (9.1). Activities of CYP3A5/3A7 in FTM (3.3) and Episkin™ (3.6) were similar to NHS (3.8) while activity in RHE™ (13.3) was higher. CYP2B6/2C18/2E1 activity was below LOQ (0.5) in all skin models and NHS. Comparable intrinsic metabolic clearances were measured between NHS and skin models for esterase, UDPGT, GST and NAT1 activities. SULT1A1 activity toward probe substrates was not detected in skin models and observed at the limit of detection in NHS. Weak cytochrome P450-dependent monooxygenases, high esterase and transferase activities suggested that NHS and skin models exhibited limited functionalization and much greater detoxification (hydrolytic and conjugating) capacities. These results demonstrate that skin models are similar to NHS in terms of metabolic functionality toward xenobiotics investigated and useful tools to assess both the local efficiency and safety of cosmetics. PMID:24658324

  4. Genome-scale quantitative characterization of bacterial protein localization dynamics throughout the cell cycle

    PubMed Central

    Kuwada, Nathan J; Traxler, Beth; Wiggins, Paul A

    2015-01-01

    Bacterial cells display both spatial and temporal organization, and this complex structure is known to play a central role in cellular function. Although nearly one-fifth of all proteins in Escherichia coli localize to specific subcellular locations, fundamental questions remain about how cellular-scale structure is encoded at the level of molecular-scale interactions. One significant limitation to our understanding is that the localization behavior of only a small subset of proteins has been characterized in detail. As an essential step toward a global model of protein localization in bacteria, we capture and quantitatively analyze spatial and temporal protein localization patterns throughout the cell cycle for nearly every protein in E. coli that exhibits nondiffuse localization. This genome-scale analysis reveals significant complexity in patterning, notably in the behavior of DNA-binding proteins. Complete cell-cycle imaging also facilitates analysis of protein partitioning to daughter cells at division, revealing a broad and robust assortment of asymmetric partitioning behaviors. PMID:25353361

  5. Genome-Scale Phylogenetics: Inferring the Plant Tree of Life from 18,896 Gene Trees

    PubMed Central

    Burleigh, J. Gordon; Bansal, Mukul S.; Eulenstein, Oliver; Hartmann, Stefanie; Wehe, André; Vision, Todd J.

    2011-01-01

    Phylogenetic analyses using genome-scale data sets must confront incongruence among gene trees, which in plants is exacerbated by frequent gene duplications and losses. Gene tree parsimony (GTP) is a phylogenetic optimization criterion in which a species tree that minimizes the number of gene duplications induced among a set of gene trees is selected. The run time performance of previous implementations has limited its use on large-scale data sets. We used new software that incorporates recent algorithmic advances to examine the performance of GTP on a plant data set consisting of 18,896 gene trees containing 510,922 protein sequences from 136 plant taxa (giving a combined alignment length of >2.9 million characters). The relationships inferred from the GTP analysis were largely consistent with previous large-scale studies of backbone plant phylogeny and resolved some controversial nodes. The placement of taxa that were present in few gene trees generally varied the most among GTP bootstrap replicates. Excluding these taxa either before or after the GTP analysis revealed high levels of phylogenetic support across plants. The analyses supported magnoliids sister to a eudicot + monocot clade and did not support the eurosid I and II clades. This study presents a nuclear genomic perspective on the broad-scale phylogenic relationships among plants, and it demonstrates that nuclear genes with a history of duplication and loss can be phylogenetically informative for resolving the plant tree of life. PMID:21186249

  6. Cancer Biomarkers from Genome-Scale DNA Methylation: Comparison of Evolutionary and Semantic Analysis Methods

    PubMed Central

    Valavanis, Ioannis; Pilalis, Eleftherios; Georgiadis, Panagiotis; Kyrtopoulos, Soterios; Chatziioannou, Aristotelis

    2015-01-01

    DNA methylation profiling exploits microarray technologies, thus yielding a wealth of high-volume data. Here, an intelligent framework is applied, encompassing epidemiological genome-scale DNA methylation data produced from the Illumina’s Infinium Human Methylation 450K Bead Chip platform, in an effort to correlate interesting methylation patterns with cancer predisposition and, in particular, breast cancer and B-cell lymphoma. Feature selection and classification are employed in order to select, from an initial set of ~480,000 methylation measurements at CpG sites, predictive cancer epigenetic biomarkers and assess their classification power for discriminating healthy versus cancer related classes. Feature selection exploits evolutionary algorithms or a graph-theoretic methodology which makes use of the semantics information included in the Gene Ontology (GO) tree. The selected features, corresponding to methylation of CpG sites, attained moderate-to-high classification accuracies when imported to a series of classifiers evaluated by resampling or blindfold validation. The semantics-driven selection revealed sets of CpG sites performing similarly with evolutionary selection in the classification tasks. However, gene enrichment and pathway analysis showed that it additionally provides more descriptive sets of GO terms and KEGG pathways regarding the cancer phenotypes studied here. Results support the expediency of this methodology regarding its application in epidemiological studies.

  7. On the road to synthetic life: the minimal cell and genome-scale engineering.

    PubMed

    Juhas, Mario

    2016-06-01

    Synthetic biology employs rational engineering principles to build biological systems from the libraries of standard, well characterized biological parts. Biological systems designed and built by synthetic biologists fulfill a plethora of useful purposes, ranging from better healthcare and energy production to biomanufacturing. Recent advancements in the synthesis, assembly and "booting-up" of synthetic genomes and in low and high-throughput genome engineering have paved the way for engineering on the genome-wide scale. One of the key goals of genome engineering is the construction of minimal genomes consisting solely of essential genes (genes indispensable for survival of living organisms). Besides serving as a toolbox to understand the universal principles of life, the cell encoded by minimal genome could be used to build a stringently controlled "cell factory" with a desired phenotype. This review provides an update on recent advances in the genome-scale engineering with particular emphasis on the engineering of minimal genomes. Furthermore, it presents an ongoing discussion to the scientific community for better suitability of minimal or robust cells for industrial applications. PMID:25578717

  8. How Archiving by Freezing Affects the Genome-Scale Diversity of Escherichia coli Populations.

    PubMed

    Sprouffske, Kathleen; Aguilar-Rodríguez, José; Wagner, Andreas

    2016-01-01

    In the experimental evolution of microbes such as Escherichia coli, many replicate populations are evolved from a common ancestor. Freezing a population sample supplemented with the cryoprotectant glycerol permits later analysis or restarting of an evolution experiment. Typically, each evolving population, and thus each sample archived in this way, consists of many unique genotypes and phenotypes. The effect of archiving on such a heterogeneous population is unknown. Here, we identified optimal archiving conditions for E. coli. We also used genome sequencing of archived samples to study the effects that archiving has on genomic population diversity. We observed no allele substitutions and mostly small changes in allele frequency. Nevertheless, principal component analysis of genome-scale allelic diversity shows that archiving affects diversity across many loci. We showed that this change in diversity is due to selection rather than drift. In addition, ∼1% of rare alleles that occurred at low frequencies were lost after treatment. Our observations imply that archived populations may be used to conduct fitness or other phenotypic assays of populations, in which the loss of a rare allele may have negligible effects. However, caution is appropriate when sequencing populations restarted from glycerol stocks, as well as when using glycerol stocks to restart or replay evolution. This is because the loss of rare alleles can alter the future evolutionary trajectory of a population if the lost alleles were strongly beneficial. PMID:26988250

  9. Genome Scale Evolution of Myxoma Virus Reveals Host-Pathogen Adaptation and Rapid Geographic Spread

    PubMed Central

    Kerr, Peter J.; Rogers, Matthew B.; Fitch, Adam; DePasse, Jay V.; Cattadori, Isabella M.; Twaddle, Alan C.; Hudson, Peter J.; Tscharke, David C.; Read, Andrew F.; Holmes, Edward C.

    2013-01-01

    The evolutionary interplay between myxoma virus (MYXV) and the European rabbit (Oryctolagus cuniculus) following release of the virus in Australia in 1950 as a biological control is a classic example of host-pathogen coevolution. We present a detailed genomic and phylogeographic analysis of 30 strains of MYXV, including the Australian progenitor strain Standard Laboratory Strain (SLS), 24 Australian viruses isolated from 1951 to 1999, and three isolates from the early radiation in Britain from 1954 and 1955. We show that in Australia MYXV has spread rapidly on a spatial scale, with multiple lineages cocirculating within individual localities, and that both highly virulent and attenuated viruses were still present in the field through the 1990s. In addition, the detection of closely related virus lineages at sites 1,000 km apart suggests that MYXV moves freely in geographic space, with mosquitoes, fleas, and rabbit migration all providing means of transport. Strikingly, despite multiple introductions, all modern viruses appear to be ultimately derived from the original introductions of SLS. The rapidity of MYXV evolution was also apparent at the genomic scale, with gene duplications documented in a number of viruses. Duplication of potential virulence genes may be important in increasing the expression of virulence proteins and provides the basis for the evolution of novel functions. Mutations leading to loss of open reading frames were surprisingly frequent and in some cases may explain attenuation, but no common mutations that correlated with virulence or attenuation were identified. PMID:24067966

  10. How Archiving by Freezing Affects the Genome-Scale Diversity of Escherichia coli Populations

    PubMed Central

    Sprouffske, Kathleen; Aguilar-Rodríguez, José; Wagner, Andreas

    2016-01-01

    In the experimental evolution of microbes such as Escherichia coli, many replicate populations are evolved from a common ancestor. Freezing a population sample supplemented with the cryoprotectant glycerol permits later analysis or restarting of an evolution experiment. Typically, each evolving population, and thus each sample archived in this way, consists of many unique genotypes and phenotypes. The effect of archiving on such a heterogeneous population is unknown. Here, we identified optimal archiving conditions for E. coli. We also used genome sequencing of archived samples to study the effects that archiving has on genomic population diversity. We observed no allele substitutions and mostly small changes in allele frequency. Nevertheless, principal component analysis of genome-scale allelic diversity shows that archiving affects diversity across many loci. We showed that this change in diversity is due to selection rather than drift. In addition, ∼1% of rare alleles that occurred at low frequencies were lost after treatment. Our observations imply that archived populations may be used to conduct fitness or other phenotypic assays of populations, in which the loss of a rare allele may have negligible effects. However, caution is appropriate when sequencing populations restarted from glycerol stocks, as well as when using glycerol stocks to restart or replay evolution. This is because the loss of rare alleles can alter the future evolutionary trajectory of a population if the lost alleles were strongly beneficial. PMID:26988250

  11. Genome-scale RNAi profiling of cell division in human tissue culture cells.

    PubMed

    Kittler, Ralf; Pelletier, Laurence; Heninger, Anne-Kristine; Slabicki, Mikolaj; Theis, Mirko; Miroslaw, Lukasz; Poser, Ina; Lawo, Steffen; Grabner, Hannes; Kozak, Karol; Wagner, Jan; Surendranath, Vineeth; Richter, Constance; Bowen, Wayne; Jackson, Aimee L; Habermann, Bianca; Hyman, Anthony A; Buchholz, Frank

    2007-12-01

    Cell division is fundamental for all organisms. Here we report a genome-scale RNA-mediated interference screen in HeLa cells designed to identify human genes that are important for cell division. We have used a library of endoribonuclease-prepared short interfering RNAs for gene silencing and have used DNA content analysis to identify genes that induced cell cycle arrest or altered ploidy on silencing. Validation and secondary assays were performed to generate a nine-parameter loss-of-function phenoprint for each of the genes. These phenotypic signatures allowed the assignment of genes to specific functional classes by combining hierarchical clustering, cross-species analysis and proteomic data mining. We highlight the richness of our dataset by ascribing novel functions to genes in mitosis and cytokinesis. In particular, we identify two evolutionarily conserved transcriptional regulatory networks that govern cytokinesis. Our work provides an experimental framework from which the systematic analysis of novel genes necessary for cell division in human cells can begin. PMID:17994010

  12. Modeling and small-angle neutron scattering spectra of chromatin supernucleosomal structures at genome scale

    NASA Astrophysics Data System (ADS)

    Ilatovskiy, Andrey V.; Lebedev, Dmitry V.; Filatov, Michael V.; Grigoriev, Mikhail; Petukhov, Michael G.; Isaev-Ivanov, Vladimir V.

    2011-11-01

    Eukaryotic genome is a highly compacted nucleoprotein complex organized in a hierarchical structure based on nucleosomes. Detailed organization of this structure remains unknown. In the present work we developed algorithms for geometry modeling of the supernucleosomal chromatin structure and for computing distance distribution functions and small-angle neutron scattering (SANS) spectra of the genome-scale (˜106 nucleosomes) chromatin structure at residue resolution. Our physical nucleosome model was based on the mononucleosome crystal structure. A nucleosome was assumed to be rigid within a local coordinate system. Interface parameters between nucleosomes can be set for each nucleosome independently. Pair distance distributions were computed with Monte Carlo simulation. SANS spectra were calculated with Fourier transformation of weighted distance distribution; the concentration of heavy water in solvent and probability of H/D exchange were taken into account. Two main modes of supernucleosomal structure generation were used. In a free generation mode all interface parameters were chosen randomly, whereas nucleosome self-intersections were not allowed. The second generation mode (generation in volume) enabled spherical or cubical wall restrictions. It was shown that calculated SANS spectra for a number of our models were in general agreement with available experimental data.

  13. Model-guided identification of gene deletion targets for metabolic engineering in Saccharomyces cerevisiae.

    PubMed

    Brochado, Ana Rita; Patil, Kiran Raosaheb

    2014-01-01

    Identification of metabolic engineering strategies for rerouting intracellular fluxes towards a desired product is often a challenging task owing to the topological and regulatory complexity of metabolic networks. Genome-scale metabolic models help tackling this complexity through systematic consideration of mass balance and reaction directionality constraints over the entire network. Here, we describe how genome-scale metabolic models can be used for identifying gene deletion targets leading to increased production of the desired product. Vanillin production in Saccharomyces cerevisiae is used as a case study throughout this chapter. PMID:24744040

  14. Modeling Neisseria meningitidis metabolism: from genome to metabolic fluxes

    PubMed Central

    Baart, Gino JE; Zomer, Bert; de Haan, Alex; van der Pol, Leo A; Beuvery, E Coen; Tramper, Johannes; Martens, Dirk E

    2007-01-01

    Background Neisseria meningitidis is a human pathogen that can infect diverse sites within the human host. The major diseases caused by N. meningitidis are responsible for death and disability, especially in young infants. In general, most of the recent work on N. meningitidis focuses on potential antigens and their functions, immunogenicity, and pathogenicity mechanisms. Very little work has been carried out on Neisseria primary metabolism over the past 25 years. Results Using the genomic database of N. meningitidis serogroup B together with biochemical and physiological information in the literature we constructed a genome-scale flux model for the primary metabolism of N. meningitidis. The validity of a simplified metabolic network derived from the genome-scale metabolic network was checked using flux-balance analysis in chemostat cultures. Several useful predictions were obtained from in silico experiments, including substrate preference. A minimal medium for growth of N. meningitidis was designed and tested succesfully in batch and chemostat cultures. Conclusion The verified metabolic model describes the primary metabolism of N. meningitidis in a chemostat in steady state. The genome-scale model is valuable because it offers a framework to study N. meningitidis metabolism as a whole, or certain aspects of it, and it can also be used for the purpose of vaccine process development (for example, the design of growth media). The flux distribution of the main metabolic pathways (that is, the pentose phosphate pathway and the Entner-Douderoff pathway) indicates that the major part of pyruvate (69%) is synthesized through the ED-cleavage, a finding that is in good agreement with literature. PMID:17617894

  15. PSAMM: A Portable System for the Analysis of Metabolic Models.

    PubMed

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

    2016-02-01

    The genome-scale models of metabolic networks have been broadly applied in phenotype prediction, evolutionary reconstruction, community functional analysis, and metabolic engineering. Despite the development of tools that support individual steps along the modeling procedure, it is still difficult to associate mathematical simulation results with the annotation and biological interpretation of metabolic models. In order to solve this problem, here we developed a Portable System for the Analysis of Metabolic Models (PSAMM), a new open-source software package that supports the integration of heterogeneous metadata in model annotations and provides a user-friendly interface for the analysis of metabolic models. PSAMM is independent of paid software environments like MATLAB, and all its dependencies are freely available for academic users. Compared to existing tools, PSAMM significantly reduced the running time of constraint-based analysis and enabled flexible settings of simulation parameters using simple one-line commands. The integration of heterogeneous, model-specific annotation information in PSAMM is achieved with a novel format of YAML-based model representation, which has several advantages, such as providing a modular organization of model components and simulation settings, enabling model version tracking, and permitting the integration of multiple simulation problems. PSAMM also includes a number of quality checking procedures to examine stoichiometric balance and to identify blocked reactions. Applying PSAMM to 57 models collected from current literature, we demonstrated how the software can be used for managing and simulating metabolic models. We identified a number of common inconsistencies in existing models and constructed an updated model repository to document the resolution of these inconsistencies. PMID:26828591

  16. PSAMM: A Portable System for the Analysis of Metabolic Models

    PubMed Central

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

    2016-01-01

    The genome-scale models of metabolic networks have been broadly applied in phenotype prediction, evolutionary reconstruction, community functional analysis, and metabolic engineering. Despite the development of tools that support individual steps along the modeling procedure, it is still difficult to associate mathematical simulation results with the annotation and biological interpretation of metabolic models. In order to solve this problem, here we developed a Portable System for the Analysis of Metabolic Models (PSAMM), a new open-source software package that supports the integration of heterogeneous metadata in model annotations and provides a user-friendly interface for the analysis of metabolic models. PSAMM is independent of paid software environments like MATLAB, and all its dependencies are freely available for academic users. Compared to existing tools, PSAMM significantly reduced the running time of constraint-based analysis and enabled flexible settings of simulation parameters using simple one-line commands. The integration of heterogeneous, model-specific annotation information in PSAMM is achieved with a novel format of YAML-based model representation, which has several advantages, such as providing a modular organization of model components and simulation settings, enabling model version tracking, and permitting the integration of multiple simulation problems. PSAMM also includes a number of quality checking procedures to examine stoichiometric balance and to identify blocked reactions. Applying PSAMM to 57 models collected from current literature, we demonstrated how the software can be used for managing and simulating metabolic models. We identified a number of common inconsistencies in existing models and constructed an updated model repository to document the resolution of these inconsistencies. PMID:26828591

  17. Genome-scale data suggest reclassifications in the Leisingera-Phaeobacter cluster including proposals for Sedimentitalea gen. nov. and Pseudophaeobacter gen. nov.

    PubMed Central

    Breider, Sven; Scheuner, Carmen; Schumann, Peter; Fiebig, Anne; Petersen, Jörn; Pradella, Silke; Klenk, Hans-Peter; Brinkhoff, Thorsten; Göker, Markus

    2014-01-01

    Earlier phylogenetic analyses of the marine Rhodobacteraceae (class Alphaproteobacteria) genera Leisingera and Phaeobacter indicated that neither genus might be monophyletic. We here used phylogenetic reconstruction from genome-scale data, MALDI-TOF mass-spectrometry analysis and a re-assessment of the phenotypic data from the literature to settle this matter, aiming at a reclassification of the two genera. Neither Phaeobacter nor Leisingera formed a clade in any of the phylogenetic analyses conducted. Rather, smaller monophyletic assemblages emerged, which were phenotypically more homogeneous, too. We thus propose the reclassification of Leisingera nanhaiensis as the type species of a new genus as Sedimentitalea nanhaiensis gen. nov., comb. nov., the reclassification of Phaeobacter arcticus and Phaeobacter leonis as Pseudophaeobacter arcticus gen. nov., comb. nov. and Pseudophaeobacter leonis comb. nov., and the reclassification of Phaeobacter aquaemixtae, Phaeobacter caeruleus, and Phaeobacter daeponensis as Leisingera aquaemixtae comb. nov., Leisingera caerulea comb. nov., and Leisingera daeponensis comb. nov. The genera Phaeobacter and Leisingera are accordingly emended. PMID:25157246

  18. Predicting genome-scale Arabidopsis-Pseudomonas syringae interactome using domain and interolog-based approaches

    PubMed Central

    2014-01-01

    Background Every year pathogenic organisms cause billions of dollars' worth damage to crops and livestock. In agriculture, study of plant-microbe interactions is demanding a special attention to develop management strategies for the destructive pathogen induced diseases that cause huge crop losses every year worldwide. Pseudomonas syringae is a major bacterial leaf pathogen that causes diseases in a wide range of plant species. Among its various strains, pathovar tomato strain DC3000 (PstDC3000) is asserted to infect the plant host Arabidopsis thaliana and thus, has been accepted as a model system for experimental characterization of the molecular dynamics of plant-pathogen interactions. Protein-protein interactions (PPIs) play a critical role in initiating pathogenesis and maintaining infection. Understanding the PPI network between a host and pathogen is a critical step for studying the molecular basis of pathogenesis. The experimental study of PPIs at a large scale is very scarce and also the high throughput experimental results show high false positive rate. Hence, there is a need for developing efficient computational models to predict the interaction between host and pathogen in a genome scale, and find novel candidate effectors and/or their targets. Results In this study, we used two computational approaches, the interolog and the domain-based to predict the interactions between Arabidopsis and PstDC3000 in genome scale. The interolog method relies on protein sequence similarity to conduct the PPI prediction. A Pseudomonas protein and an Arabidopsis protein are predicted to interact with each other if an experimentally verified interaction exists between their respective homologous proteins in another organism. The domain-based method uses domain interaction information, which is derived from known protein 3D structures, to infer the potential PPIs. If a Pseudomonas and an Arabidopsis protein contain an interacting domain pair, one can expect the two

  19. Noise analysis of genome-scale protein synthesis using a discrete computational model of translation

    SciTech Connect

    Racle, Julien; Hatzimanikatis, Vassily; Stefaniuk, Adam Jan

    2015-07-28

    Noise in genetic networks has been the subject of extensive experimental and computational studies. However, very few of these studies have considered noise properties using mechanistic models that account for the discrete movement of ribosomes and RNA polymerases along their corresponding templates (messenger RNA (mRNA) and DNA). The large size of these systems, which scales with the number of genes, mRNA copies, codons per mRNA, and ribosomes, is responsible for some of the challenges. Additionally, one should be able to describe the dynamics of ribosome exchange between the free ribosome pool and those bound to mRNAs, as well as how mRNA species compete for ribosomes. We developed an efficient algorithm for stochastic simulations that addresses these issues and used it to study the contribution and trade-offs of noise to translation properties (rates, time delays, and rate-limiting steps). The algorithm scales linearly with the number of mRNA copies, which allowed us to study the importance of genome-scale competition between mRNAs for the same ribosomes. We determined that noise is minimized under conditions maximizing the specific synthesis rate. Moreover, sensitivity analysis of the stochastic system revealed the importance of the elongation rate in the resultant noise, whereas the translation initiation rate constant was more closely related to the average protein synthesis rate. We observed significant differences between our results and the noise properties of the most commonly used translation models. Overall, our studies demonstrate that the use of full mechanistic models is essential for the study of noise in translation and transcription.

  20. Noise analysis of genome-scale protein synthesis using a discrete computational model of translation

    NASA Astrophysics Data System (ADS)

    Racle, Julien; Stefaniuk, Adam Jan; Hatzimanikatis, Vassily

    2015-07-01

    Noise in genetic networks has been the subject of extensive experimental and computational studies. However, very few of these studies have considered noise properties using mechanistic models that account for the discrete movement of ribosomes and RNA polymerases along their corresponding templates (messenger RNA (mRNA) and DNA). The large size of these systems, which scales with the number of genes, mRNA copies, codons per mRNA, and ribosomes, is responsible for some of the challenges. Additionally, one should be able to describe the dynamics of ribosome exchange between the free ribosome pool and those bound to mRNAs, as well as how mRNA species compete for ribosomes. We developed an efficient algorithm for stochastic simulations that addresses these issues and used it to study the contribution and trade-offs of noise to translation properties (rates, time delays, and rate-limiting steps). The algorithm scales linearly with the number of mRNA copies, which allowed us to study the importance of genome-scale competition between mRNAs for the same ribosomes. We determined that noise is minimized under conditions maximizing the specific synthesis rate. Moreover, sensitivity analysis of the stochastic system revealed the importance of the elongation rate in the resultant noise, whereas the translation initiation rate constant was more closely related to the average protein synthesis rate. We observed significant differences between our results and the noise properties of the most commonly used translation models. Overall, our studies demonstrate that the use of full mechanistic models is essential for the study of noise in translation and transcription.

  1. Genetic Analysis of Genome-Scale Recombination Rate Evolution in House Mice

    PubMed Central

    Dumont, Beth L.; Payseur, Bret A.

    2011-01-01

    The rate of meiotic recombination varies markedly between species and among individuals. Classical genetic experiments demonstrated a heritable component to population variation in recombination rate, and specific sequence variants that contribute to recombination rate differences between individuals have recently been identified. Despite these advances, the genetic basis of species divergence in recombination rate remains unexplored. Using a cytological assay that allows direct in situ imaging of recombination events in spermatocytes, we report a large (∼30%) difference in global recombination rate between males of two closely related house mouse subspecies (Mus musculus musculus and M. m. castaneus). To characterize the genetic basis of this recombination rate divergence, we generated an F2 panel of inter-subspecific hybrid males (n = 276) from an intercross between wild-derived inbred strains CAST/EiJ (M. m. castaneus) and PWD/PhJ (M. m. musculus). We uncover considerable heritable variation for recombination rate among males from this mapping population. Much of the F2 variance for recombination rate and a substantial portion of the difference in recombination rate between the parental strains is explained by eight moderate- to large-effect quantitative trait loci, including two transgressive loci on the X chromosome. In contrast to the rapid evolution observed in males, female CAST/EiJ and PWD/PhJ animals show minimal divergence in recombination rate (∼5%). The existence of loci on the X chromosome suggests a genetic mechanism to explain this male-biased evolution. Our results provide an initial map of the genetic changes underlying subspecies differences in genome-scale recombination rate and underscore the power of the house mouse system for understanding the evolution of this trait. PMID:21695226

  2. Proteomics-based metabolic modeling and characterization of the cellulolytic bacterium Thermobifida fusca

    PubMed Central

    2014-01-01

    Background Thermobifida fusca is a cellulolytic bacterium with potential to be used as a platform organism for sustainable industrial production of biofuels, pharmaceutical ingredients and other bioprocesses due to its capability of potential to convert plant biomass to value-added chemicals. To best develop T. fusca as a bioprocess organism, it is important to understand its native cellular processes. In the current study, we characterize the metabolic network of T. fusca through reconstruction of a genome-scale metabolic model and proteomics data. The overall goal of this study was to use multiple metabolic models generated by different methods and comparison to experimental data to gain a high-confidence understanding of the T. fusca metabolic network. Results We report the generation of three versions of a metabolic model of Thermobifida fusca sp. XY developed using three different approaches (automated, semi-automated, and proteomics-derived). The model closest to in vivo growth was the proteomics-derived model that consists of 975 reactions involving 1382 metabolites and account for 316 EC numbers (296 genes). The model was optimized for biomass production with the optimal flux of 0.48 doublings per hour when grown on cellobiose with a substrate uptake rate of 0.25 mmole/h. In vivo activity of the DXP pathway for terpenoid biosynthesis was also confirmed using real-time PCR. Conclusions iTfu296 provides a platform to understand and explore the metabolic capabilities of the actinomycete T. fusca for the potential use in bioprocess industries for the production of biofuel and pharmaceutical ingredients. By comparing different model reconstruction methods, the use of high-throughput proteomics data as a starting point proved to be the most accurate to in vivo growth. PMID:25115351

  3. Partial reconstruction of in vitro gluconeogenesis arising from mitochondrial l-lactate uptake/metabolism and oxaloacetate export via novel L-lactate translocators.

    PubMed Central

    De Bari, Lidia; Atlante, Anna; Valenti, Daniela; Passarella, Salvatore

    2004-01-01

    In the light of the occurrence of L-lactate dehydrogenase inside the mitochondrial matrix, we looked at whether isolated rat liver mitochondria can take up and metabolize L-lactate, and provide oxaloacetate outside mitochondria, thus contributing to a partial reconstruction of gluconeogenesis in vitro. We found that: (1) L-lactate (10 mM), added to mitochondria in the presence of a cocktail of glycolysis/gluconeogenesis enzymes and cofactors, can lead to synthesis of glyceraldehyde-3-phosphate at a rate of about 7 nmol/min per mg mitochondrial protein. (2) Three novel translocators exist to mediate L-lactate traffic across the inner mitochondrial membrane. An L-lactate/H+ symporter was identified by measuring fluorimetrically the rate of endogenous pyridine nucleotide reduction. Consistently, L-lactate oxidation was found to occur with P/O ratio=3 (where P/O ratio is the ratio of mol of ATP synthesized to mol of oxygen atoms reduced to water during oxidative phosphorylation) and with generation of membrane potential. Proton uptake, which occurred as a result of addition of L-lactate to RLM together with electron flow inhibitors, and mitochondrial swelling in ammonium L-lactate solutions were also monitored. L-Lactate/oxaloacetate and L-lactate/pyruvate anti-porters were identified by monitoring photometrically the appearance of L-lactate counter-anions outside mitochondria. These L-lactate translocators, which are distinct from the monocarboxylate carrier, were found to differ from each other in V(max) values and in inhibition and pH profiles, and proved to regulate mitochondrial L-lactate metabolism in vitro. The role of lactate/mitochondria interactions in gluconeogenesis is discussed. PMID:14960150

  4. Metabolic Burden: Cornerstones in Synthetic Biology and Metabolic Engineering Applications.

    PubMed

    Wu, Gang; Yan, Qiang; Jones, J Andrew; Tang, Yinjie J; Fong, Stephen S; Koffas, Mattheos A G

    2016-08-01

    Engineering cell metabolism for bioproduction not only consumes building blocks and energy molecules (e.g., ATP) but also triggers energetic inefficiency inside the cell. The metabolic burdens on microbial workhorses lead to undesirable physiological changes, placing hidden constraints on host productivity. We discuss cell physiological responses to metabolic burdens, as well as strategies to identify and resolve the carbon and energy burden problems, including metabolic balancing, enhancing respiration, dynamic regulatory systems, chromosomal engineering, decoupling cell growth with production phases, and co-utilization of nutrient resources. To design robust strains with high chances of success in industrial settings, novel genome-scale models (GSMs), (13)C-metabolic flux analysis (MFA), and machine-learning approaches are needed for weighting, standardizing, and predicting metabolic costs. PMID:26996613

  5. Modeling Genome-Scale mRNA Expression Datasets: From Matrix Algebra to Genetic Networks

    NASA Astrophysics Data System (ADS)

    Alter, Orly

    2003-03-01

    for two genome-scale datasets. GSVD is a unique data-driven linear transformation of the two datasets from the two genes × arrays spaces to two reduced and diagonal ``genelets'' × ``arraylets'' spaces. Some of the genelets can be associated with independent regulatory programs that are common to both datasets. Other genelets can be associated with independent biological processes or experimental artifacts that are almost exclusive to one of the datasets or the other. I will conclude with a discussion of the insights that these models may offer into the biology, chemistry and physics of gene expression.

  6. Weighted Statistical Binning: Enabling Statistically Consistent Genome-Scale Phylogenetic Analyses

    PubMed Central

    Bayzid, Md Shamsuzzoha; Mirarab, Siavash; Boussau, Bastien; Warnow, Tandy

    2015-01-01

    Because biological processes can result in different loci having different evolutionary histories, species tree estimation requires multiple loci from across multiple genomes. While many processes can result in discord between gene trees and species trees, incomplete lineage sorting (ILS), modeled by the multi-species coalescent, is considered to be a dominant cause for gene tree heterogeneity. Coalescent-based methods have been developed to estimate species trees, many of which operate by combining estimated gene trees, and so are called "summary methods". Because summary methods are generally fast (and much faster than more complicated coalescent-based methods that co-estimate gene trees and species trees), they have become very popular techniques for estimating species trees from multiple loci. However, recent studies have established that summary methods can have reduced accuracy in the presence of gene tree estimation error, and also that many biological datasets have substantial gene tree estimation error, so that summary methods may not be highly accurate in biologically realistic conditions. Mirarab et al. (Science 2014) presented the "statistical binning" technique to improve gene tree estimation in multi-locus analyses, and showed that it improved the accuracy of MP-EST, one of the most popular coalescent-based summary methods. Statistical binning, which uses a simple heuristic to evaluate "combinability" and then uses the larger sets of genes to re-calculate gene trees, has good empirical performance, but using statistical binning within a phylogenomic pipeline does not have the desirable property of being statistically consistent. We show that weighting the re-calculated gene trees by the bin sizes makes statistical binning statistically consistent under the multispecies coalescent, and maintains the good empirical performance. Thus, "weighted statistical binning" enables highly accurate genome-scale species tree estimation, and is also statistically

  7. Modelling metabolism of the diatom Phaeodactylum tricornutum.

    PubMed

    Singh, Dipali; Carlson, Ross; Fell, David; Poolman, Mark

    2015-12-01

    Marine diatoms have potential as a biotechnological production platform, especially for lipid-derived products, including biofuels. Here we introduce some features of diatom metabolism, particularly with respect to photosynthesis, photorespiration and lipid synthesis and their differences relative to other photosynthetic eukaryotes. Since structural metabolic modelling of other photosynthetic organisms has been shown to be capable of representing their metabolic capabilities realistically, we briefly review the main approaches to this type of modelling. We then propose that genome-scale modelling of the diatom Phaeodactylum tricornutum, in response to varying light intensity, could uncover the novel aspects of the metabolic potential of this organism. PMID:26614658

  8. Using Gene Essentiality and Synthetic Lethality Information to Correct Yeast and CHO Cell Genome-Scale Models

    PubMed Central

    Chowdhury, Ratul; Chowdhury, Anupam; Maranas, Costas D.

    2015-01-01

    Essentiality (ES) and Synthetic Lethality (SL) information identify combination of genes whose deletion inhibits cell growth. This information is important for both identifying drug targets for tumor and pathogenic bacteria suppression and for flagging and avoiding gene deletions that are non-viable in biotechnology. In this study, we performed a comprehensive ES and SL analysis of two important eukaryotic models (S. cerevisiae and CHO cells) using a bilevel optimization approach introduced earlier. Information gleaned from this study is used to propose specific model changes to remedy inconsistent with data model predictions. Even for the highly curated Yeast 7.11 model we identified 50 changes (metabolic and GPR) leading to the correct prediction of an additional 28% of essential genes and 36% of synthetic lethals along with a 53% reduction in the erroneous identification of essential genes. Due to the paucity of mutant growth phenotype data only 12 changes were made for the CHO 1.2 model leading to an additional correctly predicted 11 essential and eight non-essential genes. Overall, we find that CHO 1.2 was 76% less accurate than the Yeast 7.11 metabolic model in predicting essential genes. Based on this analysis, 14 (single and double deletion) maximally informative experiments are suggested to improve the CHO cell model by using information from a mouse metabolic model. This analysis demonstrates the importance of single and multiple knockout phenotypes in assessing and improving model reconstructions. The advent of techniques such as CRISPR opens the door for the global assessment of eukaryotic models. PMID:26426067

  9. Using Gene Essentiality and Synthetic Lethality Information to Correct Yeast and CHO Cell Genome-Scale Models.

    PubMed

    Chowdhury, Ratul; Chowdhury, Anupam; Maranas, Costas D

    2015-01-01

    Essentiality (ES) and Synthetic Lethality (SL) information identify combination of genes whose deletion inhibits cell growth. This information is important for both identifying drug targets for tumor and pathogenic bacteria suppression and for flagging and avoiding gene deletions that are non-viable in biotechnology. In this study, we performed a comprehensive ES and SL analysis of two important eukaryotic models (S. cerevisiae and CHO cells) using a bilevel optimization approach introduced earlier. Information gleaned from this study is used to propose specific model changes to remedy inconsistent with data model predictions. Even for the highly curated Yeast 7.11 model we identified 50 changes (metabolic and GPR) leading to the correct prediction of an additional 28% of essential genes and 36% of synthetic lethals along with a 53% reduction in the erroneous identification of essential genes. Due to the paucity of mutant growth phenotype data only 12 changes were made for the CHO 1.2 model leading to an additional correctly predicted 11 essential and eight non-essential genes. Overall, we find that CHO 1.2 was 76% less accurate than the Yeast 7.11 metabolic model in predicting essential genes. Based on this analysis, 14 (single and double deletion) maximally informative experiments are suggested to improve the CHO cell model by using information from a mouse metabolic model. This analysis demonstrates the importance of single and multiple knockout phenotypes in assessing and improving model reconstructions. The advent of techniques such as CRISPR opens the door for the global assessment of eukaryotic models. PMID:26426067

  10. Using proteomic data to assess a genome-scale "in silico" model of metal reducing bacteria in the simulation of field-scale uranium bioremediation

    NASA Astrophysics Data System (ADS)

    Yabusaki, S.; Fang, Y.; Wilkins, M. J.; Long, P.; Rifle IFRC Science Team

    2011-12-01

    A series of field experiments in a shallow alluvial aquifer at a former uranium mill tailings site have demonstrated that indigenous bacteria can be stimulated with acetate to catalyze the conversion of hexavalent uranium in a groundwater plume to immobile solid-associated uranium in the +4 oxidation state. While this bioreduction of uranium has been shown to lower groundwater concentrations below actionable standards, a viable remediation methodology will need a mechanistic, predictive and quantitative understanding of the microbially-mediated reactions that catalyze the reduction of uranium in the context of site-specific processes, properties, and conditions. At the Rifle IFRC site, we are investigating the impacts on uranium behavior of pulsed acetate amendment, acetate-oxidizing iron and sulfate reducing bacteria, seasonal water table variation, spatially-variable physical (hydraulic conductivity, porosity) and geochemical (reactive surface area) material properties. The simulation of three-dimensional, variably saturated flow and biogeochemical reactive transport during a uranium bioremediation field experiment includes a genome-scale in silico model of Geobacter sp. to represent the Fe(III) terminal electron accepting process (TEAP). The Geobacter in silico model of cell-scale physiological metabolic pathways is comprised of hundreds of intra-cellular and environmental exchange reactions. One advantage of this approach is that the TEAP reaction stoichiometry and rate are now functions of the metabolic status of the microorganism. The linkage of in silico model reactions to specific Geobacter proteins has enabled the use of groundwater proteomic analyses to assess the accuracy of the model under evolving hydrologic and biogeochemical conditions. In this case, the largest predicted fluxes through in silico model reactions generally correspond to high abundances of proteins linked to those reactions (e.g. the condensation reaction catalyzed by the protein

  11. Structural analysis of metabolic networks based on flux centrality.

    PubMed

    Koschützki, Dirk; Junker, Björn H; Schwender, Jörg; Schreiber, Falk

    2010-08-01

    Metabolic reactions are fundamental to living organisms, and a large number of reactions simultaneously occur at a given time in living cells transforming diverse metabolites into each other. There has been an ongoing debate on how to classify metabolites with respect to their importance for metabolic performance, usually based on the analysis of topological properties of genome scale metabolic networks. However, none of these studies have accounted quantitatively for flux in metabolic networks, thus lacking an important component of a cell's biochemistry. We therefore analyzed a genome scale metabolic network of Escherichia coli by comparing growth under 19 different growth conditions, using flux balance analysis and weighted network centrality investigation. With this novel concept of flux centrality we generated metabolite rankings for each particular growth condition. In contrast to the results of conventional analysis of genome scale metabolic networks, different metabolites were top-ranking dependent on the growth condition. At the same time, several metabolites were consistently among the high ranking ones. Those are associated with pathways that have been described by biochemists as the most central part of metabolism, such as glycolysis, tricarboxylic acid cycle and pentose phosphate pathway. The values for the average path length of the analyzed metabolite networks were between 10.5 and 12.6, supporting recent findings that the metabolic network of E. coli is not a small-world network. PMID:20471988

  12. A genome-scale CRISPR-Cas9 screening method for protein stability reveals novel regulators of Cdc25A.

    PubMed

    Wu, Yuanzhong; Zhou, Liwen; Wang, Xin; Lu, Jinping; Zhang, Ruhua; Liang, Xiaoting; Wang, Li; Deng, Wuguo; Zeng, Yi-Xin; Huang, Haojie; Kang, Tiebang

    2016-01-01

    The regulation of stability is particularly crucial for unstable proteins in cells. However, a convenient and unbiased method of identifying regulators of protein stability remains to be developed. Recently, a genome-scale CRISPR-Cas9 library has been established as a genetic tool to mediate loss-of-function screening. Here, we developed a protein stability regulators screening assay (Pro-SRSA) by combining the whole-genome CRISPR-Cas9 library with a dual-fluorescence-based protein stability reporter and high-throughput sequencing to screen for regulators of protein stability. Using Cdc25A as an example, Cul4B-DDB1(DCAF8) was identified as a new E3 ligase for Cdc25A. Moreover, the acetylation of Cdc25A at lysine 150, which was acetylated by p300/CBP and deacetylated by HDAC3, prevented the ubiquitin-mediated degradation of Cdc25A by the proteasome. This is the first study to report that acetylation, as a novel posttranslational modification, modulates Cdc25A stability, and we suggest that this unbiased CRISPR-Cas9 screening method at the genome scale may be widely used to globally identify regulators of protein stability. PMID:27462461

  13. From genome-scale data to models of infectious disease: A Bayesian network-based strategy to drive model development.

    PubMed

    Yin, Weiwei; Kissinger, Jessica C; Moreno, Alberto; Galinski, Mary R; Styczynski, Mark P

    2015-12-01

    High-throughput, genome-scale data present a unique opportunity to link host to pathogen on a molecular level. Forging such connections will help drive the development of mathematical models to better understand and predict both pathogen behavior and the epidemiology of infectious diseases, including malaria. However, the datasets that can aid in identifying these links and models are vast and not amenable to simple, reductionist, and univariate analyses. These datasets require data mining in order to identify the truly important measurements that best describe clinical and molecular observations. Moreover, these datasets typically have relatively few samples due to experimental limitations (particularly for human studies or in vivo animal experiments), making data mining extremely difficult. Here, after first providing a brief overview of common strategies for data reduction and identification of relationships between variables for inclusion in mathematical models, we present a new generalized strategy for performing these data reduction and relationship inference tasks. Our approach emphasizes the importance of robustness when using data to drive model development, particularly when using genome-scale, small-sample in vivo data. We identify the use of appropriate feature reduction combined with data permutations and subsampling strategies as being critical to enable increasingly robust results from network inference using high-dimensional, low-observation data. PMID:26093035

  14. Integrated genome-scale analysis of the transcriptional regulatory landscape in a blood stem/progenitor cell model.

    PubMed

    Wilson, Nicola K; Schoenfelder, Stefan; Hannah, Rebecca; Sánchez Castillo, Manuel; Schütte, Judith; Ladopoulos, Vasileios; Mitchelmore, Joanna; Goode, Debbie K; Calero-Nieto, Fernando J; Moignard, Victoria; Wilkinson, Adam C; Jimenez-Madrid, Isabel; Kinston, Sarah; Spivakov, Mikhail; Fraser, Peter; Göttgens, Berthold

    2016-03-31

    Comprehensive study of transcriptional control processes will be required to enhance our understanding of both normal and malignant hematopoiesis. Modern sequencing technologies have revolutionized our ability to generate genome-scale expression and histone modification profiles, transcription factor (TF)-binding maps, and also comprehensive chromatin-looping information. Many of these technologies, however, require large numbers of cells, and therefore cannot be applied to rare hematopoietic stem/progenitor cell (HSPC) populations. The stem cell factor-dependent multipotent progenitor cell line HPC-7 represents a well-recognized cell line model for HSPCs. Here we report genome-wide maps for 17 TFs, 3 histone modifications, DNase I hypersensitive sites, and high-resolution promoter-enhancer interactomes in HPC-7 cells. Integrated analysis of these complementary data sets revealed TF occupancy patterns of genomic regions involved in promoter-anchored loops. Moreover, preferential associations between pairs of TFs bound at either ends of chromatin loops led to the identification of 4 previously unrecognized protein-protein interactions between key blood stem cell regulators. All HPC-7 data sets are freely available both through standard repositories and a user-friendly Web interface. Together with previously generated genome-wide data sets, this study integrates HPC-7 data into a genomic resource on par with ENCODE tier 1 cell lines and, importantly, is the only current model with comprehensive genome-scale data that is relevant to HSPC biology. PMID:26809507

  15. A genome-scale CRISPR-Cas9 screening method for protein stability reveals novel regulators of Cdc25A

    PubMed Central

    Wu, Yuanzhong; Zhou, Liwen; Wang, Xin; Lu, Jinping; Zhang, Ruhua; Liang, Xiaoting; Wang, Li; Deng, Wuguo; Zeng, Yi-Xin; Huang, Haojie; Kang, Tiebang

    2016-01-01

    The regulation of stability is particularly crucial for unstable proteins in cells. However, a convenient and unbiased method of identifying regulators of protein stability remains to be developed. Recently, a genome-scale CRISPR-Cas9 library has been established as a genetic tool to mediate loss-of-function screening. Here, we developed a protein stability regulators screening assay (Pro-SRSA) by combining the whole-genome CRISPR-Cas9 library with a dual-fluorescence-based protein stability reporter and high-throughput sequencing to screen for regulators of protein stability. Using Cdc25A as an example, Cul4B-DDB1DCAF8 was identified as a new E3 ligase for Cdc25A. Moreover, the acetylation of Cdc25A at lysine 150, which was acetylated by p300/CBP and deacetylated by HDAC3, prevented the ubiquitin-mediated degradation of Cdc25A by the proteasome. This is the first study to report that acetylation, as a novel posttranslational modification, modulates Cdc25A stability, and we suggest that this unbiased CRISPR-Cas9 screening method at the genome scale may be widely used to globally identify regulators of protein stability. PMID:27462461

  16. Reverse engineering and analysis of large genome-scale gene networks

    PubMed Central

    Aluru, Maneesha; Zola, Jaroslaw; Nettleton, Dan; Aluru, Srinivas

    2013-01-01

    Reverse engineering the whole-genome networks of complex multicellular organisms continues to remain a challenge. While simpler models easily scale to large number of genes and gene expression datasets, more accurate models are compute intensive limiting their scale of applicability. To enable fast and accurate reconstruction of large networks, we developed Tool for Inferring Network of Genes (TINGe), a parallel mutual information (MI)-based program. The novel features of our approach include: (i) B-spline-based formulation for linear-time computation of MI, (ii) a novel algorithm for direct permutation testing and (iii) development of parallel algorithms to reduce run-time and facilitate construction of large networks. We assess the quality of our method by comparison with ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks) and GeneNet and demonstrate its unique capability by reverse engineering the whole-genome network of Arabidopsis thaliana from 3137 Affymetrix ATH1 GeneChips in just 9 min on a 1024-core cluster. We further report on the development of a new software Gene Network Analyzer (GeNA) for extracting context-specific subnetworks from a given set of seed genes. Using TINGe and GeNA, we performed analysis of 241 Arabidopsis AraCyc 8.0 pathways, and the results are made available through the web. PMID:23042249

  17. E-Flux2 and SPOT: Validated Methods for Inferring Intracellular Metabolic Flux Distributions from Transcriptomic Data

    PubMed Central

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

    2016-01-01

    Background Several methods have been developed to predict system-wide and condition-specific intracellular metabolic fluxes by integrating transcriptomic data with genome-scale metabolic models. While powerful in many settings, existing methods have several shortcomings, and it is unclear which method has the best accuracy in general because of limited validation against experimentally measured intracellular fluxes. Results We present a general optimization strategy for inferring intracellular metabolic flux distributions from transcriptomic data coupled with genome-scale metabolic reconstructions. It consists of two different template models called DC (determined carbon source model) and AC (all possible carbon sources model) and two different new methods called E-Flux2 (E-Flux method combined with minimization of l2 norm) and SPOT (Simplified Pearson cOrrelation with Transcriptomic data), which can be chosen and combined depending on the availability of knowledge on carbon source or objective function. This enables us to simulate a broad range of experimental conditions. We examined E. coli and S. cerevisiae as representative prokaryotic and eukaryotic microorganisms respectively. The predictive accuracy of our algorithm was validated by calculating the uncentered Pearson correlation between predicted fluxes and measured fluxes. To this end, we compiled 20 experimental conditions (11 in E. coli and 9 in S. cerevisiae), of transcriptome measurements coupled with corresponding central carbon metabolism intracellular flux measurements determined by 13C metabolic flux analysis (13C-MFA), which is the largest dataset assembled to date for the purpose of validating inference methods for predicting intracellular fluxes. In both organisms, our method achieves an average correlation coefficient ranging from 0.59 to 0.87, outperforming a representative sample of competing methods. Easy-to-use implementations of E-Flux2 and SPOT are available as part of the open

  18. Modelling osmotic stress by Flux Balance Analysis at the genomic scale.

    PubMed

    Metris, Aline; George, Susan; Baranyi, József

    2012-01-16

    Predictive microbiology for food safety is still primarily based on empirical models describing the effect of the environmental conditions of the food on the kinetics of the growth of foodborne pathogens. One way to make these models more mechanistic is to use systems biology methods such as Flux Balance Analysis (FBA). FBA consists of evaluating the possible fluxes through the metabolic reactions taking place in a cell. Using this method, the specific growth rate of Escherichia coli can be predicted by assuming, as an objective function, that the cells maximise their biomass production during balanced growth. Whilst this works under favourable environmental conditions, our simulations show that this objective function is not sufficient to explain the decrease of the growth rate due to osmotic stress. One feature of the FBA models is that the parameters and objective function in general refer to chemostat experiments where the carbon source is the main limiting factor. This may be relevant to some foods where the carbon to nitrogen balance is limiting but, in general, it is the physico-chemical conditions which are the most stringent. We therefore need to examine the effect of such constraints on the fluxes and/or modify the objective function, or to elaborate the metabolic model by taking into account other functional levels of the cell in order to develop mechanistic predictive models for osmotic stress conditions. PMID:21807434

  19. Deep epistasis in human metabolism

    PubMed Central

    Imielinski, Marcin; Belta, Calin

    2010-01-01

    We extend and apply a method that we have developed for deriving high-order epistatic relationships in large biochemical networks to a published genome-scale model of human metabolism. In our analysis we compute 33 328 reaction sets whose knockout synergistically disables one or more of 43 important metabolic functions. We also design minimal knockouts that remove flux through fumarase, an enzyme that has previously been shown to play an important role in human cancer. Most of these knockout sets employ more than eight mutually buffering reactions, spanning multiple cellular compartments and metabolic subsystems. These reaction sets suggest that human metabolic pathways possess a striking degree of parallelism, inducing “deep” epistasis between diversely annotated genes. Our results prompt specific chemical and genetic perturbation follow-up experiments that could be used to query in vivo pathway redundancy. They also suggest directions for future statistical studies of epistasis in genetic variation data sets. PMID:20590333

  20. Deep epistasis in human metabolism

    NASA Astrophysics Data System (ADS)

    Imielinski, Marcin; Belta, Calin

    2010-06-01

    We extend and apply a method that we have developed for deriving high-order epistatic relationships in large biochemical networks to a published genome-scale model of human metabolism. In our analysis we compute 33 328 reaction sets whose knockout synergistically disables one or more of 43 important metabolic functions. We also design minimal knockouts that remove flux through fumarase, an enzyme that has previously been shown to play an important role in human cancer. Most of these knockout sets employ more than eight mutually buffering reactions, spanning multiple cellular compartments and metabolic subsystems. These reaction sets suggest that human metabolic pathways possess a striking degree of parallelism, inducing "deep" epistasis between diversely annotated genes. Our results prompt specific chemical and genetic perturbation follow-up experiments that could be used to query in vivo pathway redundancy. They also suggest directions for future statistical studies of epistasis in genetic variation data sets.

  1. 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)*

    PubMed Central

    2012-01-01

    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 sequence for the Showa strain of

  2. Genome-Scale Screening of Drug-Target Associations Relevant to Ki Using a Chemogenomics Approach

    PubMed Central

    Cao, Dong-Sheng; Liang, Yi-Zeng; Deng, Zhe; Hu, Qian-Nan; He, Min; Xu, Qing-Song; Zhou, Guang-Hua; Zhang, Liu-Xia; Deng, Zi-xin; Liu, Shao

    2013-01-01

    The identification of interactions between drugs and target proteins plays a key role in genomic drug discovery. In the present study, the quantitative binding affinities of drug-target pairs are differentiated as a measurement to define whether a drug interacts with a protein or not, and then a chemogenomics framework using an unbiased set of general integrated features and random forest (RF) is employed to construct a predictive model which can accurately classify drug-target pairs. The predictability of the model is further investigated and validated by several independent validation sets. The built model is used to predict drug-target associations, some of which were confirmed by comparing experimental data from public biological resources. A drug-target interaction network with high confidence drug-target pairs was also reconstructed. This network provides further insight for the action of drugs and targets. Finally, a web-based server called PreDPI-Ki was developed to predict drug-target interactions for drug discovery. In addition to providing a high-confidence list of drug-target associations for subsequent experimental investigation guidance, these results also contribute to the understanding of drug-target interactions. We can also see that quantitative information of drug-target associations could greatly promote the development of more accurate models. The PreDPI-Ki server is freely available via: http://sdd.whu.edu.cn/dpiki. PMID:23577055

  3. A genome-scale resource for in vivo tag-based protein function exploration in C. elegans.

    PubMed

    Sarov, Mihail; Murray, John I; Schanze, Kristin; Pozniakovski, Andrei; Niu, Wei; Angermann, Karolin; Hasse, Susanne; Rupprecht, Michaela; Vinis, Elisabeth; Tinney, Matthew; Preston, Elicia; Zinke, Andrea; Enst, Susanne; Teichgraber, Tina; Janette, Judith; Reis, Kadri; Janosch, Stephan; Schloissnig, Siegfried; Ejsmont, Radoslaw K; Slightam, Cindie; Xu, Xiao; Kim, Stuart K; Reinke, Valerie; Stewart, A Francis; Snyder, Michael; Waterston, Robert H; Hyman, Anthony A

    2012-08-17

    Understanding the in vivo dynamics of protein localization and their physical interactions is important for many problems in biology. To enable systematic protein function interrogation in a multicellular context, we built a genome-scale transgenic platform for in vivo expression of fluorescent- and affinity-tagged proteins in Caenorhabditis elegans under endogenous cis regulatory control. The platform combines computer-assisted transgene design, massively parallel DNA engineering, and next-generation sequencing to generate a resource of 14,637 genomic DNA transgenes, which covers 73% of the proteome. The multipurpose tag used allows any protein of interest to be localized in vivo or affinity purified using standard tag-based assays. We illustrate the utility of the resource by systematic chromatin immunopurification and automated 4D imaging, which produced detailed DNA binding and cell/tissue distribution maps for key transcription factor proteins. PMID:22901814

  4. A Genome-Scale DNA Repair RNAi Screen Identifies SPG48 as a Novel Gene Associated with Hereditary Spastic Paraplegia

    PubMed Central

    Słabicki, Mikołaj; Theis, Mirko; Krastev, Dragomir B.; Samsonov, Sergey; Mundwiller, Emeline; Junqueira, Magno; Paszkowski-Rogacz, Maciej; Teyra, Joan; Heninger, Anne-Kristin; Poser, Ina; Prieur, Fabienne; Truchetto, Jérémy; Confavreux, Christian; Marelli, Cécilia; Durr, Alexandra; Camdessanche, Jean Philippe; Brice, Alexis; Shevchenko, Andrej; Pisabarro, M. Teresa; Stevanin, Giovanni; Buchholz, Frank

    2010-01-01

    DNA repair is essential to maintain genome integrity, and genes with roles in DNA repair are frequently mutated in a variety of human diseases. Repair via homologous recombination typically restores the original DNA sequence without introducing mutations, and a number of genes that are required for homologous recombination DNA double-strand break repair (HR-DSBR) have been identified. However, a systematic analysis of this important DNA repair pathway in mammalian cells has not been reported. Here, we describe a genome-scale endoribonuclease-prepared short interfering RNA (esiRNA) screen for genes involved in DNA double strand break repair. We report 61 genes that influenced the frequency of HR-DSBR and characterize in detail one of the genes that decreased the frequency of HR-DSBR. We show that the gene KIAA0415 encodes a putative helicase that interacts with SPG11 and SPG15, two proteins mutated in hereditary spastic paraplegia (HSP). We identify mutations in HSP patients, discovering KIAA0415/SPG48 as a novel HSP-associated gene, and show that a KIAA0415/SPG48 mutant cell line is more sensitive to DNA damaging drugs. We present the first genome-scale survey of HR-DSBR in mammalian cells providing a dataset that should accelerate the discovery of novel genes with roles in DNA repair and associated medical conditions. The discovery that proteins forming a novel protein complex are required for efficient HR-DSBR and are mutated in patients suffering from HSP suggests a link between HSP and DNA repair. PMID:20613862

  5. Genome-scale computational analysis of DNA curvature and repeats in Arabidopsis and rice uncovers plant-specific genomic properties

    PubMed Central

    2011-01-01

    Background Due to its overarching role in genome function, sequence-dependent DNA curvature continues to attract great attention. The DNA double helix is not a rigid cylinder, but presents both curvature and flexibility in different regions, depending on the sequence. More in depth knowledge of the various orders of complexity of genomic DNA structure has allowed the design of sophisticated bioinformatics tools for its analysis and manipulation, which, in turn, have yielded a better understanding of the genome itself. Curved DNA is involved in many biologically important processes, such as transcription initiation and termination, recombination, DNA replication, and nucleosome positioning. CpG islands and tandem repeats also play significant roles in the dynamics and evolution of genomes. Results In this study, we analyzed the relationship between these three structural features within rice (Oryza sativa) and Arabidopsis (Arabidopsis thaliana) genomes. A genome-scale prediction of curvature distribution in rice and Arabidopsis indicated that most of the chromosomes of both genomes have maximal chromosomal DNA curvature adjacent to the centromeric region. By analyzing tandem repeats across the genome, we found that frequencies of repeats are higher in regions adjacent to those with high curvature value. Further analysis of CpG islands shows a clear interdependence between curvature value, repeat frequencies and CpG islands. Each CpG island appears in a local minimal curvature region, and CpG islands usually do not appear in the centromere or regions with high repeat frequency. A statistical evaluation demonstrates the significance and non-randomness of these features. Conclusions This study represents the first systematic genome-scale analysis of DNA curvature, CpG islands and tandem repeats at the DNA sequence level in plant genomes, and finds that not all of the chromosomes in plants follow the same rules common to other eukaryote organisms, suggesting that some

  6. Single-cell genome and metatranscriptome sequencing reveal metabolic interactions of an alkane-degrading methanogenic community

    PubMed Central

    Embree, Mallory; Nagarajan, Harish; Movahedi, Narjes; Chitsaz, Hamidreza; Zengler, Karsten

    2014-01-01

    Microbial interactions have a key role in global geochemical cycles. Although we possess significant knowledge about the general biochemical processes occurring in microbial communities, we are often unable to decipher key functions of individual microorganisms within the environment in part owing to the inability to cultivate or study them in isolation. Here, we circumvent this shortcoming through the use of single-cell genome sequencing and a novel low-input metatranscriptomics protocol to reveal the intricate metabolic capabilities and microbial interactions of an alkane-degrading methanogenic community. This methanogenic consortium oxidizes saturated hydrocarbons under anoxic conditions through a thus-far-uncharacterized biochemical process. The genome sequence of a dominant bacterial member of this community, belonging to the genus Smithella, was sequenced and served as the basis for subsequent analysis through metabolic reconstruction. Metatranscriptomic data generated from less than 500 pg of mRNA highlighted metabolically active genes during anaerobic alkane oxidation in comparison with growth on fatty acids. These data sets suggest that Smithella is not activating hexadecane by fumarate addition. Differential expression assisted in the identification of hypothetical proteins with no known homology that may be involved in hexadecane activation. Additionally, the combination of 16S rDNA sequence and metatranscriptomic data enabled the study of other prevalent organisms within the consortium and their interactions with Smithella, thus yielding a comprehensive characterization of individual constituents at the genome scale during methanogenic alkane oxidation. PMID:24152715

  7. Genome-scale investigation of phenotypically distinct but nearly clonal Trichoderma strains

    PubMed Central

    Weld, Richard J.; Cox, Murray P.; Bradshaw, Rosie E.; McLean, Kirstin L.; Stewart, Alison; Steyaert, Johanna M.

    2016-01-01

    Biological control agents (BCA) are beneficial organisms that are applied to protect plants from pests. Many fungi of the genus Trichoderma are successful BCAs but the underlying mechanisms are not yet fully understood. Trichoderma cf. atroviride strain LU132 is a remarkably effective BCA compared to T. cf. atroviride strain LU140 but these strains were found to be highly similar at the DNA sequence level. This unusual combination of phenotypic variability and high DNA sequence similarity between separately isolated strains prompted us to undertake a genome comparison study in order to identify DNA polymorphisms. We further investigated if the polymorphisms had functional effects on the phenotypes. The two strains were clearly identified as individuals, exhibiting different growth rates, conidiation and metabolism. Superior pathogen control demonstrated by LU132 depended on its faster growth, which is a prerequisite for successful distribution and competition. Genome sequencing identified only one non-synonymous single nucleotide polymorphism (SNP) between the strains. Based on this SNP, we successfully designed and validated an RFLP protocol that can be used to differentiate LU132 from LU140 and other Trichoderma strains. This SNP changed the amino acid sequence of SERF, encoded by the previously undescribed single copy gene “small EDRK-rich factor” (serf). A deletion of serf in the two strains did not lead to identical phenotypes, suggesting that, in addition to the single functional SNP between the nearly clonal Trichoderma cf. atroviride strains, other non-genomic factors contribute to their phenotypic variation. This finding is significant as it shows that genomics is an extremely useful but not exhaustive tool for the study of biocontrol complexity and for strain typing. PMID:27190719

  8. Genetic dissection of Flaviviridae host factors through genome-scale CRISPR screens.

    PubMed

    Marceau, Caleb D; Puschnik, Andreas S; Majzoub, Karim; Ooi, Yaw Shin; Brewer, Susan M; Fuchs, Gabriele; Swaminathan, Kavya; Mata, Miguel A; Elias, Joshua E; Sarnow, Peter; Carette, Jan E

    2016-07-01

    The Flaviviridae are a family of viruses that cause severe human diseases. For example, dengue virus (DENV) is a rapidly emerging pathogen causing an estimated 100 million symptomatic infections annually worldwide. No approved antivirals are available to date and clinical trials with a tetravalent dengue vaccine showed disappointingly low protection rates. Hepatitis C virus (HCV) also remains a major medical problem, with 160 million chronically infected patients worldwide and only expensive treatments available. Despite distinct differences in their pathogenesis and modes of transmission, the two viruses share common replication strategies. A detailed understanding of the host functions that determine viral infection is lacking. Here we use a pooled CRISPR genetic screening strategy to comprehensively dissect host factors required for these two highly important Flaviviridae members. For DENV, we identified endoplasmic-reticulum (ER)-associated multi-protein complexes involved in signal sequence recognition, N-linked glycosylation and ER-associated degradation. DENV replication was nearly completely abrogated in cells deficient in the oligosaccharyltransferase (OST) complex. Mechanistic studies pinpointed viral RNA replication and not entry or translation as the crucial step requiring the OST complex. Moreover, we show that viral non-structural proteins bind to the OST complex. The identified ER-associated protein complexes were also important for infection by other mosquito-borne flaviviruses including Zika virus, an emerging pathogen causing severe birth defects. By contrast, the most significant genes identified in the HCV screen were distinct and included viral receptors, RNA-binding proteins and enzymes involved in metabolism. We found an unexpected link between intracellular flavin adenine dinucleotide (FAD) levels and HCV replication. This study shows notable divergence in host-depenency factors between DENV and HCV, and illuminates new host targets for

  9. Genome-scale investigation of phenotypically distinct but nearly clonal Trichoderma strains.

    PubMed

    Lange, Claudia; Weld, Richard J; Cox, Murray P; Bradshaw, Rosie E; McLean, Kirstin L; Stewart, Alison; Steyaert, Johanna M

    2016-01-01

    Biological control agents (BCA) are beneficial organisms that are applied to protect plants from pests. Many fungi of the genus Trichoderma are successful BCAs but the underlying mechanisms are not yet fully understood. Trichoderma cf. atroviride strain LU132 is a remarkably effective BCA compared to T. cf. atroviride strain LU140 but these strains were found to be highly similar at the DNA sequence level. This unusual combination of phenotypic variability and high DNA sequence similarity between separately isolated strains prompted us to undertake a genome comparison study in order to identify DNA polymorphisms. We further investigated if the polymorphisms had functional effects on the phenotypes. The two strains were clearly identified as individuals, exhibiting different growth rates, conidiation and metabolism. Superior pathogen control demonstrated by LU132 depended on its faster growth, which is a prerequisite for successful distribution and competition. Genome sequencing identified only one non-synonymous single nucleotide polymorphism (SNP) between the strains. Based on this SNP, we successfully designed and validated an RFLP protocol that can be used to differentiate LU132 from LU140 and other Trichoderma strains. This SNP changed the amino acid sequence of SERF, encoded by the previously undescribed single copy gene "small EDRK-rich factor" (serf). A deletion of serf in the two strains did not lead to identical phenotypes, suggesting that, in addition to the single functional SNP between the nearly clonal Trichoderma cf. atroviride strains, other non-genomic factors contribute to their phenotypic variation. This finding is significant as it shows that genomics is an extremely useful but not exhaustive tool for the study of biocontrol complexity and for strain typing. PMID:27190719

  10. Positron emission reconstruction tomography for the assessment of regional myocardial metabolism by the administration of substrates labeled with cyclotron produced radionuclides

    NASA Technical Reports Server (NTRS)

    Ter-Pogossian, M. M.; Hoffman, E. J.; Weiss, E. S.; Coleman, R. E.; Phelps, M. E.; Welch, M. J.; Sobel, B. E.

    1975-01-01

    A positron emission transverse tomograph device was developed which provides transaxial sectional images of the distribution of positron-emitting radionuclides in the heart. The images provide a quantitative three-dimensional map of the distribution of activity unencumbered by the superimposition of activity originating from regions overlying and underlying the plane of interest. PETT is used primarily with the cyclotron-produced radionuclides oxygen-15, nitrogen-13 and carbon-11. Because of the participation of these atoms in metabolism, they can be used to label metabolic substrates and intermediary molecules incorporated in myocardial metabolism.

  11. Mapping the landscape of metabolic goals of a cell.

    PubMed

    Zhao, Qi; Stettner, Arion I; Reznik, Ed; Paschalidis, Ioannis Ch; Segrè, Daniel

    2016-01-01

    Genome-scale flux balance models of metabolism provide testable predictions of all metabolic rates in an organism, by assuming that the cell is optimizing a metabolic goal known as the objective function. We introduce an efficient inverse flux balance analysis (invFBA) approach, based on linear programming duality, to characterize the space of possible objective functions compatible with measured fluxes. After testing our algorithm on simulated E. coli data and time-dependent S. oneidensis fluxes inferred from gene expression, we apply our inverse approach to flux measurements in long-term evolved E. coli strains, revealing objective functions that provide insight into metabolic adaptation trajectories. PMID:27215445

  12. Metabolic modeling of clostridia: current developments and applications.

    PubMed

    Dash, Satyakam; Ng, Chiam Yu; Maranas, Costas D

    2016-02-01

    Anaerobic Clostridium spp. is an important bioproduction microbial genus that can produce solvents and utilize a broad spectrum of substrates including cellulose and syngas. Genome-scale metabolic (GSM) models are increasingly being put forth for various clostridial strains to explore their respective metabolic capabilities and suitability for various bioconversions. In this study, we have selected representative GSM models for six different clostridia (Clostridium acetobutylicum, C. beijerinckii, C. butyricum, C. cellulolyticum, C. ljungdahlii and C. thermocellum) and performed a detailed model comparison contrasting their metabolic repertoire. We also discuss various applications of these GSM models to guide metabolic engineering interventions as well as assessing cellular physiology. PMID:26755502

  13. Integrated Approach to Reconstruction of Microbial Regulatory Networks

    SciTech Connect

    Rodionov, Dmitry A; Novichkov, Pavel S

    2013-11-04

    This project had the goal(s) of development of integrated bioinformatics platform for genome-scale inference and visualization of transcriptional regulatory networks (TRNs) in bacterial genomes. The work was done in Sanford-Burnham Medical Research Institute (SBMRI, P.I. D.A. Rodionov) and Lawrence Berkeley National Laboratory (LBNL, co-P.I. P.S. Novichkov). The developed computational resources include: (1) RegPredict web-platform for TRN inference and regulon reconstruction in microbial genomes, and (2) RegPrecise database for collection, visualization and comparative analysis of transcriptional regulons reconstructed by comparative genomics. These analytical resources were selected as key components in the DOE Systems Biology KnowledgeBase (SBKB). The high-quality data accumulated in RegPrecise will provide essential datasets of reference regulons in diverse microbes to enable automatic reconstruction of draft TRNs in newly sequenced genomes. We outline our progress toward the three aims of this grant proposal, which were: Develop integrated platform for genome-scale regulon reconstruction; Infer regulatory annotations in several groups of bacteria and building of reference collections of microbial regulons; and Develop KnowledgeBase on microbial transcriptional regulation.

  14. A semiquantitative metric for evaluating clinical actionability of incidental or secondary findings from genome-scale sequencing

    PubMed Central

    Berg, Jonathan S.; Foreman, Ann Katherine M.; O'Daniel, Julianne M.; Booker, Jessica K.; Boshe, Lacey; Carey, Timothy; Crooks, Kristy R.; Jensen, Brian C.; Juengst, Eric T.; Lee, Kristy; Nelson, Daniel K.; Powell, Bradford C.; Powell, Cynthia M.; Roche, Myra I.; Skrzynia, Cecile; Strande, Natasha T.; Weck, Karen E.; Wilhelmsen, Kirk C.; Evans, James P.

    2016-01-01

    Purpose: As genome-scale sequencing is increasingly applied in clinical scenarios, a wide variety of genomic findings will be discovered as secondary or incidental findings, and there is debate about how they should be handled. The clinical actionability of such findings varies, necessitating standardized frameworks for a priori decision making about their analysis. Genet Med 18 5, 467–475. Methods: We established a semiquantitative metric to assess five elements of actionability: severity and likelihood of the disease outcome, efficacy and burden of intervention, and knowledge base, with a total score from 0 to 15. Genet Med 18 5, 467–475. Results: The semiquantitative metric was applied to a list of putative actionable conditions, the list of genes recommended by the American College of Medical Genetics and Genomics (ACMG) for return when deleterious variants are discovered as secondary/incidental findings, and a random sample of 1,000 genes. Scores from the list of putative actionable conditions (median = 12) and the ACMG list (median = 11) were both statistically different than the randomly selected genes (median = 7) (P < 0.0001, two-tailed Mann-Whitney test). Genet Med 18 5, 467–475. Conclusion: Gene–disease pairs having a score of 11 or higher represent the top quintile of actionability. The semiquantitative metric effectively assesses clinical actionability, promotes transparency, and may facilitate assessments of clinical actionability by various groups and in diverse contexts. Genet Med 18 5, 467–475. PMID:26270767

  15. Genome-scale identification of miRNA-mRNA and miRNA-lncRNA interactions in domestic animals.

    PubMed

    Li, A; Zhang, J; Zhou, Z; Wang, L; Sun, X; Liu, Y

    2015-12-01

    Domestic animals show considerable genetic diversity. Previous studies suggested that animal phenotypes were affected by miRNA-mRNA interplay, but these studies focused mainly on the analysis of one or several miRNA-mRNA interactions. However, in this study, we investigated miRNA-mRNA and miRNA-lncRNA interactions on a genomic scale using miranda and targetscan algorithms. There has been strong directional artificial selection practiced during the domestication of animals. Thus, we investigated SNPs that were located in miRNAs and miRNA binding sites and found that several SNPs located in 3'-UTRs of mRNAs had the potential to affect miRNA-mRNA interactions. In addition, a database, named miRBond, was developed to provide visualization, analysis and downloading of the resulting datasets. Our results open the way to further experimental verification of miRNA-mRNA and miRNA-lncRNA interactions as well as the influence of SNPs upon such interplay. PMID:26360131

  16. A C. elegans genome-scale microRNA network contains composite feedback motifs with high flux capacity

    PubMed Central

    Martinez, Natalia J.; Ow, Maria C.; Barrasa, M. Inmaculada; Hammell, Molly; Sequerra, Reynaldo; Doucette-Stamm, Lynn; Roth, Frederick P.; Ambros, Victor R.; Walhout, Albertha J.M.

    2008-01-01

    MicroRNAs (miRNAs) and transcription factors (TFs) are primary metazoan gene regulators. Whereas much attention has focused on finding the targets of both miRNAs and TFs, the transcriptional networks that regulate miRNA expression remain largely unexplored. Here, we present the first genome-scale Caenorhabditis elegans miRNA regulatory network that contains experimentally mapped transcriptional TF → miRNA interactions, as well as computationally predicted post-transcriptional miRNA → TF interactions. We find that this integrated miRNA network contains 23 miRNA ↔ TF composite feedback loops in which a TF that controls a miRNA is itself regulated by that same miRNA. By rigorous network randomizations, we show that such loops occur more frequently than expected by chance and, hence, constitute a genuine network motif. Interestingly, miRNAs and TFs in such loops are heavily regulated and regulate many targets. This “high flux capacity” suggests that loops provide a mechanism of high information flow for the coordinate and adaptable control of miRNA and TF target regulons. PMID:18794350

  17. A Genome-Scale RNA–Interference Screen Identifies RRAS Signaling as a Pathologic Feature of Huntington's Disease

    PubMed Central

    Miller, John P.; Yates, Bridget E.; Al-Ramahi, Ismael; Berman, Ari E.; Sanhueza, Mario; Kim, Eugene; de Haro, Maria; DeGiacomo, Francesco; Torcassi, Cameron; Holcomb, Jennifer; Gafni, Juliette; Mooney, Sean D.; Botas, Juan; Ellerby, Lisa M.; Hughes, Robert E.

    2012-01-01

    A genome-scale RNAi screen was performed in a mammalian cell-based assay to identify modifiers of mutant huntingtin toxicity. Ontology analysis of suppressor data identified processes previously implicated in Huntington's disease, including proteolysis, glutamate excitotoxicity, and mitochondrial dysfunction. In addition to established mechanisms, the screen identified multiple components of the RRAS signaling pathway as loss-of-function suppressors of mutant huntingtin toxicity in human and mouse cell models. Loss-of-function in orthologous RRAS pathway members also suppressed motor dysfunction in a Drosophila model of Huntington's disease. Abnormal activation of RRAS and a down-stream effector, RAF1, was observed in cellular models and a mouse model of Huntington's disease. We also observe co-localization of RRAS and mutant huntingtin in cells and in mouse striatum, suggesting that activation of R-Ras may occur through protein interaction. These data indicate that mutant huntingtin exerts a pathogenic effect on this pathway that can be corrected at multiple intervention points including RRAS, FNTA/B, PIN1, and PLK1. Consistent with these results, chemical inhibition of farnesyltransferase can also suppress mutant huntingtin toxicity. These data suggest that pharmacological inhibition of RRAS signaling may confer therapeutic benefit in Huntington's disease. PMID:23209424

  18. Genome-scale DNA variant analysis and functional validation of a SNP underlying yellow fruit color in wild strawberry.

    PubMed

    Hawkins, Charles; Caruana, Julie; Schiksnis, Erin; Liu, Zhongchi

    2016-01-01

    Fragaria vesca is a species of diploid strawberry being developed as a model for the octoploid garden strawberry. This work sequenced and compared the genomes of three F. vesca accessions: 'Hawaii 4', 'Rügen', and 'Yellow Wonder'. Genome-scale analyses of shared and distinct SNPs among these three accessions have revealed that 'Rügen' and 'Yellow Wonder' are more similar to each other than they are to 'Hawaii 4'. Though all three accessions are inbred seven generations, each accession still possesses extensive heterozygosity, highlighting the inherent differences between individual plants even of the same accession. The identification of the impact of each SNP as well as the large number of Indel markers provides a foundation for locating candidate mutations underlying phenotypic variations among these F. vesca accessions and for mapping new mutations generated through forward genetics screens. Through systematic analysis of SNP variants affecting genes in anthocyanin biosynthesis and regulation, a candidate SNP in FveMYB10 was identified and then functionally confirmed to be responsible for the yellow color fruits made by many F. vesca accessions. As a whole, this study provides further resources for F. vesca and establishes a foundation for linking traits of economic importance to specific genes and variants. PMID:27377763

  19. Intra-Monozygotic Twin Pair Discordance and Longitudinal Variation of Whole-Genome Scale DNA Methylation in Adults

    PubMed Central

    Zhang, Su-Hua; Chen, Jinzhong; Lu, Daru; Shen, Min; Li, Chengtao

    2015-01-01

    Monozygotic twins share identical genomic DNA and are indistinguishable using conventional genetic markers. Increasing evidence indicates that monozygotic twins are epigenetically distinct, suggesting that a comparison between DNA methylation patterns might be useful to approach this forensic problem. However, the extent of epigenetic discordance between healthy adult monozygotic twins and the stability of CpG loci within the same individual over a short time span at the whole-genome scale are not well understood. Here, we used Infinium HumanMethylation450 Beadchips to compare DNA methylation profiles using blood collected from 10 pairs of monozygotic twins and 8 individuals sampled at 0, 3, 6, and 9 months. Using an effective and unbiased method for calling differentially methylated (DM) CpG sites, we showed that 0.087%–1.530% of the CpG sites exhibit differential methylation in monozygotic twin pairs. We further demonstrated that, on whole-genome level, there has been no significant epigenetic drift within the same individuals for up to 9 months, including one monozygotic twin pair. However, we did identify a subset of CpG sites that vary in DNA methylation over the 9-month period. The magnitude of the intra-pair or longitudinal methylation discordance of the CpG sites inside the CpG islands is greater than those outside the CpG islands. The CpG sites located on shores appear to be more suitable for distinguishing between MZ twins. PMID:26248206

  20. Genome-scale DNA variant analysis and functional validation of a SNP underlying yellow fruit color in wild strawberry

    PubMed Central

    Hawkins, Charles; Caruana, Julie; Schiksnis, Erin; Liu, Zhongchi

    2016-01-01

    Fragaria vesca is a species of diploid strawberry being developed as a model for the octoploid garden strawberry. This work sequenced and compared the genomes of three F. vesca accessions: ‘Hawaii 4′, ‘Rügen’, and ‘Yellow Wonder’. Genome-scale analyses of shared and distinct SNPs among these three accessions have revealed that ‘Rügen’ and ‘Yellow Wonder’ are more similar to each other than they are to ‘Hawaii 4’. Though all three accessions are inbred seven generations, each accession still possesses extensive heterozygosity, highlighting the inherent differences between individual plants even of the same accession. The identification of the impact of each SNP as well as the large number of Indel markers provides a foundation for locating candidate mutations underlying phenotypic variations among these F. vesca accessions and for mapping new mutations generated through forward genetics screens. Through systematic analysis of SNP variants affecting genes in anthocyanin biosynthesis and regulation, a candidate SNP in FveMYB10 was identified and then functionally confirmed to be responsible for the yellow color fruits made by many F. vesca accessions. As a whole, this study provides further resources for F. vesca and establishes a foundation for linking traits of economic importance to specific genes and variants. PMID:27377763

  1. Multiplexed chromosome conformation capture sequencing for rapid genome-scale high-resolution detection of long-range chromatin interactions.

    PubMed

    Stadhouders, Ralph; Kolovos, Petros; Brouwer, Rutger; Zuin, Jessica; van den Heuvel, Anita; Kockx, Christel; Palstra, Robert-Jan; Wendt, Kerstin S; Grosveld, Frank; van Ijcken, Wilfred; Soler, Eric

    2013-03-01

    Chromosome conformation capture (3C) technology is a powerful and increasingly popular tool for analyzing the spatial organization of genomes. Several 3C variants have been developed (e.g., 4C, 5C, ChIA-PET, Hi-C), allowing large-scale mapping of long-range genomic interactions. Here we describe multiplexed 3C sequencing (3C-seq), a 4C variant coupled to next-generation sequencing, allowing genome-scale detection of long-range interactions with candidate regions. Compared with several other available techniques, 3C-seq offers a superior resolution (typically single restriction fragment resolution; approximately 1-8 kb on average) and can be applied in a semi-high-throughput fashion. It allows the assessment of long-range interactions of up to 192 genes or regions of interest in parallel by multiplexing library sequencing. This renders multiplexed 3C-seq an inexpensive, quick (total hands-on time of 2 weeks) and efficient method that is ideal for the in-depth analysis of complex genetic loci. The preparation of multiplexed 3C-seq libraries can be performed by any investigator with basic skills in molecular biology techniques. Data analysis requires basic expertise in bioinformatics and in Linux and Python environments. The protocol describes all materials, critical steps and bioinformatics tools required for successful application of 3C-seq technology. PMID:23411633

  2. Metabolism of halophilic archaea.

    PubMed

    Falb, Michaela; Müller, Kerstin; Königsmaier, Lisa; Oberwinkler, Tanja; Horn, Patrick; von Gronau, Susanne; Gonzalez, Orland; Pfeiffer, Friedhelm; Bornberg-Bauer, Erich; Oesterhelt, Dieter

    2008-03-01

    In spite of their common hypersaline environment, halophilic archaea are surprisingly different in their nutritional demands and metabolic pathways. The metabolic diversity of halophilic archaea was investigated at the genomic level through systematic metabolic reconstruction and comparative analysis of four completely sequenced species: Halobacterium salinarum, Haloarcula marismortui, Haloquadratum walsbyi, and the haloalkaliphile Natronomonas pharaonis. The comparative study reveals different sets of enzyme genes amongst halophilic archaea, e.g. in glycerol degradation, pentose metabolism, and folate synthesis. The carefully assessed metabolic data represent a reliable resource for future system biology approaches as it also links to current experimental data on (halo)archaea from the literature. PMID:18278431

  3. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0.

    PubMed

    Schellenberger, Jan; Que, Richard; Fleming, Ronan M T; Thiele, Ines; Orth, Jeffrey D; Feist, Adam M; Zielinski, Daniel C; Bordbar, Aarash; Lewis, Nathan E; Rahmanian, Sorena; Kang, Joseph; Hyduke, Daniel R; Palsson, Bernhard Ø

    2011-09-01

    Over the past decade, a growing community of researchers has emerged around the use of constraint-based reconstruction and analysis (COBRA) methods to simulate, analyze and predict a variety of metabolic phenotypes using genome-scale models. The COBRA Toolbox, a MATLAB package for implementing COBRA methods, was presented earlier. Here we present a substantial update of this in silico toolbox. Version 2.0 of the COBRA Toolbox expands the scope of computations by including in silico analysis methods developed since its original release. New functions include (i) network gap filling, (ii) (13)C analysis, (iii) metabolic engineering, (iv) omics-guided analysis and (v) visualization. As with the first version, the COBRA Toolbox reads and writes systems biology markup language-formatted models. In version 2.0, we improved performance, usability and the level of documentation. A suite of test scripts can now be used to learn the core functionality of the toolbox and validate results. This toolbox lowers the barrier of entry to use powerful COBRA methods. PMID:21886097

  4. 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)*

    DOE PAGESBeta

    Molnár, István; Lopez, David; Wisecaver, Jennifer H.; Devarenne, Timothy P.; Weiss, Taylor L.; Pellegrini, Matteo; Hackett, Jeremiah D.

    2012-10-30

    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. The 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 thatmore » compete for photosynthetic carbon and energy.« less

  5. 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)*

    SciTech Connect

    Molnár, István; Lopez, David; Wisecaver, Jennifer H.; Devarenne, Timothy P.; Weiss, Taylor L.; Pellegrini, Matteo; Hackett, Jeremiah D.

    2012-10-30

    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. The 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.

  6. Cancer Metabolism: A Modeling Perspective

    PubMed Central

    Ghaffari, Pouyan; Mardinoglu, Adil; Nielsen, Jens

    2015-01-01

    Tumor cells alter their metabolism to maintain unregulated cellular proliferation and survival, but this transformation leaves them reliant on constant supply of nutrients and energy. In addition to the widely studied dysregulated glucose metabolism to fuel tumor cell growth, accumulating evidences suggest that utilization of amino acids and lipids contributes significantly to cancer cell metabolism. Also recent progresses in our understanding of carcinogenesis have revealed that cancer is a complex disease and cannot be understood through simple investigation of genetic mutations of cancerous cells. Cancer cells present in complex tumor tissues communicate with the surrounding microenvironment and develop traits which promote their growth, survival, and metastasis. Decoding the full scope and targeting dysregulated metabolic pathways that support neoplastic transformations and their preservation requires both the advancement of experimental technologies for more comprehensive measurement of omics as well as the advancement of robust computational methods for accurate analysis of the generated data. Here, we review cancer-associated reprogramming of metabolism and highlight the capability of genome-scale metabolic modeling approaches in perceiving a system-level perspective of cancer metabolism and in detecting novel selective drug targets. PMID:26733270

  7. Multi-channel metabolic imaging, with SENSE reconstruction, of hyperpolarized [1- 13C] pyruvate in a live rat at 3.0 tesla on a clinical MR scanner

    NASA Astrophysics Data System (ADS)

    Tropp, James; Lupo, Janine M.; Chen, Albert; Calderon, Paul; McCune, Don; Grafendorfer, Thomas; Ozturk-Isik, Esin; Larson, Peder E. Z.; Hu, Simon; Yen, Yi-Fen; Robb, Fraser; Bok, Robert; Schulte, Rolf; Xu, Duan; Hurd, Ralph; Vigneron, Daniel; Nelson, Sarah

    2011-01-01

    We report metabolic images of 13C, following injection of a bolus of hyperpolarized [1-13C] pyruvate in a live rat. The data were acquired on a clinical scanner, using custom coils for volume transmission and array reception. Proton blocking of all carbon resonators enabled proton anatomic imaging with the system body coil, to allow for registration of anatomic and metabolic images, for which good correlation was achieved, with some anatomic features (kidney and heart) clearly visible in a carbon image, without reference to the corresponding proton image. Parallel imaging with sensitivity encoding was used to increase the spatial resolution in the SI direction of the rat. The signal to noise ratio in was in some instances unexpectedly high in the parallel images; variability of the polarization among different trials, plus partial volume effects, are noted as a possible cause of this.

  8. Genome-scale detection of hypermethylated CpG islands in circulating cell-free DNA of hepatocellular carcinoma patients

    PubMed Central

    Wen, Lu; Li, Jingyi; Guo, Huahu; Liu, Xiaomeng; Zheng, Shengmin; Zhang, Dafang; Zhu, Weihua; Qu, Jianhui; Guo, Limin; Du, Dexiao; Jin, Xiao; Zhang, Yuhao; Gao, Yun; Shen, Jie; Ge, Hao; Tang, Fuchou; Huang, Yanyi; Peng, Jirun

    2015-01-01

    Despite advances in DNA methylome analyses of cells and tissues, current techniques for genome-scale profiling of DNA methylation in circulating cell-free DNA (ccfDNA) remain limited. Here we describe a methylated CpG tandems amplification and sequencing (MCTA-Seq) method that can detect thousands of hypermethylated CpG islands simultaneously in ccfDNA. This highly sensitive technique can work with genomic DNA as little as 7.5 pg, which is equivalent to 2.5 copies of the haploid genome. We have analyzed a cohort of tissue and plasma samples (n = 151) of hepatocellular carcinoma (HCC) patients and control subjects, identifying dozens of high-performance markers in blood for detecting small HCC (≤ 3 cm). Among these markers, 4 (RGS10, ST8SIA6, RUNX2 and VIM) are mostly specific for cancer detection, while the other 15, classified as a novel set, are already hypermethylated in the normal liver tissues. Two corresponding classifiers have been established, combination of which achieves a sensitivity of 94% with a specificity of 89% for the plasma samples from HCC patients (n = 36) and control subjects including cirrhosis patients (n = 17) and normal individuals (n = 38). Notably, all 15 alpha-fetoprotein-negative HCC patients were successfully identified. Comparison between matched plasma and tissue samples indicates that both the cancer and noncancerous tissues contribute to elevation of the methylation markers in plasma. MCTA-Seq will facilitate the development of ccfDNA methylation biomarkers and contribute to the improvement of cancer detection in a clinical setting. PMID:26516143

  9. An Arrayed Genome-Scale Lentiviral-Enabled Short Hairpin RNA Screen Identifies Lethal and Rescuer Gene Candidates

    PubMed Central

    Bhinder, Bhavneet; Antczak, Christophe; Ramirez, Christina N.; Shum, David; Liu-Sullivan, Nancy; Radu, Constantin; Frattini, Mark G.

    2013-01-01

    Abstract RNA interference technology is becoming an integral tool for target discovery and validation.; With perhaps the exception of only few studies published using arrayed short hairpin RNA (shRNA) libraries, most of the reports have been either against pooled siRNA or shRNA, or arrayed siRNA libraries. For this purpose, we have developed a workflow and performed an arrayed genome-scale shRNA lethality screen against the TRC1 library in HeLa cells. The resulting targets would be a valuable resource of candidates toward a better understanding of cellular homeostasis. Using a high-stringency hit nomination method encompassing criteria of at least three active hairpins per gene and filtered for potential off-target effects (OTEs), referred to as the Bhinder–Djaballah analysis method, we identified 1,252 lethal and 6 rescuer gene candidates, knockdown of which resulted in severe cell death or enhanced growth, respectively. Cross referencing individual hairpins with the TRC1 validated clone database, 239 of the 1,252 candidates were deemed independently validated with at least three validated clones. Through our systematic OTE analysis, we have identified 31 microRNAs (miRNAs) in lethal and 2 in rescuer genes; all having a seed heptamer mimic in the corresponding shRNA hairpins and likely cause of the OTE observed in our screen, perhaps unraveling a previously unknown plausible essentiality of these miRNAs in cellular viability. Taken together, we report on a methodology for performing large-scale arrayed shRNA screens, a comprehensive analysis method to nominate high-confidence hits, and a performance assessment of the TRC1 library highlighting the intracellular inefficiencies of shRNA processing in general. PMID:23198867

  10. A genome-scale proteomic screen identifies a role for DnaK in chaperoning of polar autotransporters in Shigella.

    PubMed

    Janakiraman, Anuradha; Fixen, Kathryn R; Gray, Andrew N; Niki, Hironori; Goldberg, Marcia B

    2009-10-01

    Autotransporters are outer membrane proteins that are widely distributed among gram-negative bacteria. Like other autotransporters, the Shigella autotransporter IcsA, which is required for actin assembly during infection, is secreted at the bacterial pole. In the bacterial cytoplasm, IcsA localizes to poles and potential cell division sites independent of the cell division protein FtsZ. To identify bacterial proteins involved in the targeting of IcsA to the pole in the bacterial cytoplasm, we screened a genome-scale library of Escherichia coli proteins tagged with green fluorescent protein (GFP) for those that displayed a localization pattern similar to that of IcsA-GFP in cells that lack functional FtsZ using a strain carrying a temperature-sensitive ftsZ allele. For each protein that mimicked the localization of IcsA-GFP, we tested whether IcsA localization was dependent on the presence of the protein. Although these approaches did not identify a polar receptor for IcsA, the cytoplasmic chaperone DnaK both mimicked IcsA localization at elevated temperatures as a GFP fusion and was required for the localization of IcsA to the pole in the cytoplasm of E. coli. DnaK was also required for IcsA secretion at the pole in Shigella flexneri. The localization of DnaK-GFP to poles and potential cell division sites was dependent on elevated growth temperature and independent of the presence of IcsA or functional FtsZ; native DnaK was found to be enhanced at midcell and the poles. A second Shigella autotransporter, SepA, also required DnaK for secretion, consistent with a role of DnaK more generally in the chaperoning of autotransporter proteins in the bacterial cytoplasm. PMID:19684128

  11. Genome-scale detection of hypermethylated CpG islands in circulating cell-free DNA of hepatocellular carcinoma patients.

    PubMed

    Wen, Lu; Li, Jingyi; Guo, Huahu; Liu, Xiaomeng; Zheng, Shengmin; Zhang, Dafang; Zhu, Weihua; Qu, Jianhui; Guo, Limin; Du, Dexiao; Jin, Xiao; Zhang, Yuhao; Gao, Yun; Shen, Jie; Ge, Hao; Tang, Fuchou; Huang, Yanyi; Peng, Jirun

    2015-11-01

    Despite advances in DNA methylome analyses of cells and tissues, current techniques for genome-scale profiling of DNA methylation in circulating cell-free DNA (ccfDNA) remain limited. Here we describe a methylated CpG tandems amplification and sequencing (MCTA-Seq) method that can detect thousands of hypermethylated CpG islands simultaneously in ccfDNA. This highly sensitive technique can work with genomic DNA as little as 7.5 pg, which is equivalent to 2.5 copies of the haploid genome. We have analyzed a cohort of tissue and plasma samples (n = 151) of hepatocellular carcinoma (HCC) patients and control subjects, identifying dozens of high-performance markers in blood for detecting small HCC (≤ 3 cm). Among these markers, 4 (RGS10, ST8SIA6, RUNX2 and VIM) are mostly specific for cancer detection, while the other 15, classified as a novel set, are already hypermethylated in the normal liver tissues. Two corresponding classifiers have been established, combination of which achieves a sensitivity of 94% with a specificity of 89% for the plasma samples from HCC patients (n = 36) and control subjects including cirrhosis patients (n = 17) and normal individuals (n = 38). Notably, all 15 alpha-fetoprotein-negative HCC patients were successfully identified. Comparison between matched plasma and tissue samples indicates that both the cancer and noncancerous tissues contribute to elevation of the methylation markers in plasma. MCTA-Seq will facilitate the development of ccfDNA methylation biomarkers and contribute to the improvement of cancer detection in a clinical setting. PMID:26516143

  12. Alterations in cancer cell metabolism: the Warburg effect and metabolic adaptation.

    PubMed

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

    2015-05-01

    The Warburg effect means higher glucose uptake of cancer cells compared to normal tissues, whereas a smaller fraction of this glucose is employed for oxidative phosphorylation. With the advent of high throughput technologies and computational systems biology, cancer cell metabolism has been reinvestigated over the last decades toward identifying various events underlying "how" and "why" a cancer cell employs aerobic glycolysis. Significant progress has been shaped to revise the Warburg effect. In this study, we have integrated the gene expression of 13 different cancer cells with the genome-scale metabolic network of human (Recon1) based on the E-Flux method, and analyzed them based on constraint-based modeling. Results show that regardless of significant up- and down-regulated metabolic genes, the distribution of metabolic changes is similar in different cancer types. These findings support the theory that the Warburg effect is a consequence of metabolic adaptation in cancer cells. PMID:25773945

  13. Penile Reconstruction

    PubMed Central

    Salgado, Christopher J.; Chim, Harvey; Tang, Jennifer C.; Monstrey, Stan J.; Mardini, Samir

    2011-01-01

    A variety of surgical options exists for penile reconstruction. The key to success of therapy is holistic management of the patient, with attention to the psychological aspects of treatment. In this article, we review reconstructive modalities for various types of penile defects inclusive of partial and total defects as well as the buried penis, and also describe recent basic science advances, which may promise new options for penile reconstruction. PMID:22851914

  14. Elimination of thermodynamically infeasible loops in steady-state metabolic models.

    PubMed

    Schellenberger, Jan; Lewis, Nathan E; Palsson, Bernhard Ø

    2011-02-01

    The constraint-based reconstruction and analysis (COBRA) framework has been widely used to study steady-state flux solutions in genome-scale metabolic networks. One shortcoming of current COBRA methods is the possible violation of the loop law in the computed steady-state flux solutions. The loop law is analogous to Kirchhoff's second law for electric circuits, and states that at steady state there can be no net flux around a closed network cycle. Although the consequences of the loop law have been known for years, it has been computationally difficult to work with. Therefore, the resulting loop-law constraints have been overlooked. Here, we present a general mixed integer programming approach called loopless COBRA (ll-COBRA), which can be used to eliminate all steady-state flux solutions that are incompatible with the loop law. We apply this approach to improve flux predictions on three common COBRA methods: flux balance analysis, flux variability analysis, and Monte Carlo sampling of the flux space. Moreover, we demonstrate that the imposition of loop-law constraints with ll-COBRA improves the consistency of simulation results with experimental data. This method provides an additional constraint for many COBRA methods, enabling the acquisition of more realistic simulation results. PMID:21281568

  15. KENeV: A web-application for the automated reconstruction and visualization of the enriched metabolic and signaling super-pathways deriving from genomic experiments.

    PubMed

    Pilalis, Eleftherios; Koutsandreas, Theodoros; Valavanis, Ioannis; Athanasiadis, Emmanouil; Spyrou, George; Chatziioannou, Aristotelis

    2015-01-01

    Gene expression analysis, using high throughput genomic technologies,has become an indispensable step for the meaningful interpretation of the underlying molecular complexity, which shapes the phenotypic manifestation of the investigated biological mechanism. The modularity of the cellular response to different experimental conditions can be comprehended through the exploitation of molecular pathway databases, which offer a controlled, curated background for statistical enrichment analysis. Existing tools enable pathway analysis, visualization, or pathway merging but none integrates a fully automated workflow, combining all above-mentioned modules and destined to non-programmer users. We introduce an online web application, named KEGG Enriched Network Visualizer (KENeV), which enables a fully automated workflow starting from a list of differentially expressed genes and deriving the enriched KEGG metabolic and signaling pathways, merged into two respective, non-redundant super-networks. The final networks can be downloaded as SBML files, for further analysis, or instantly visualized through an interactive visualization module. In conclusion, KENeV (available online at http://www.grissom.gr/kenev) provides an integrative tool, suitable for users with no programming experience, for the functional interpretation, at both the metabolic and signaling level, of differentially expressed gene subsets deriving from genomic experiments. PMID:26925206

  16. KENeV: A web-application for the automated reconstruction and visualization of the enriched metabolic and signaling super-pathways deriving from genomic experiments

    PubMed Central

    Pilalis, Eleftherios; Koutsandreas, Theodoros; Valavanis, Ioannis; Athanasiadis, Emmanouil; Spyrou, George; Chatziioannou, Aristotelis

    2015-01-01

    Gene expression analysis, using high throughput genomic technologies,has become an indispensable step for the meaningful interpretation of the underlying molecular complexity, which shapes the phenotypic manifestation of the investigated biological mechanism. The modularity of the cellular response to different experimental conditions can be comprehended through the exploitation of molecular pathway databases, which offer a controlled, curated background for statistical enrichment analysis. Existing tools enable pathway analysis, visualization, or pathway merging but none integrates a fully automated workflow, combining all above-mentioned modules and destined to non-programmer users. We introduce an online web application, named KEGG Enriched Network Visualizer (KENeV), which enables a fully automated workflow starting from a list of differentially expressed genes and deriving the enriched KEGG metabolic and signaling pathways, merged into two respective, non-redundant super-networks. The final networks can be downloaded as SBML files, for further analysis, or instantly visualized through an interactive visualization module. In conclusion, KENeV (available online at http://www.grissom.gr/kenev) provides an integrative tool, suitable for users with no programming experience, for the functional interpretation, at both the metabolic and signaling level, of differentially expressed gene subsets deriving from genomic experiments. PMID:26925206

  17. Penile reconstruction

    PubMed Central

    Garaffa, Giulio; Sansalone, Salvatore; Ralph, David J

    2013-01-01

    During the most recent years, a variety of new techniques of penile reconstruction have been described in the literature. This paper focuses on the most recent advances in male genital reconstruction after trauma, excision of benign and malignant disease, in gender reassignment surgery and aphallia with emphasis on surgical technique, cosmetic and functional outcome. PMID:22426595

  18. Image reconstruction

    SciTech Connect

    Defrise, Michel; Gullberg, Grant T.

    2006-04-05

    We give an overview of the role of Physics in Medicine andBiology in development of tomographic reconstruction algorithms. We focuson imaging modalities involving ionizing radiation, CT, PET and SPECT,and cover a wide spectrum of reconstruction problems, starting withclassical 2D tomogra tomography in the 1970s up to 4D and 5D problemsinvolving dynamic imaging of moving organs.

  19. Adaptive evolution of complex innovations through stepwise metabolic niche expansion.

    PubMed

    Szappanos, Balázs; Fritzemeier, Jonathan; Csörgő, Bálint; Lázár, Viktória; Lu, Xiaowen; Fekete, Gergely; Bálint, Balázs; Herczeg, Róbert; Nagy, István; Notebaart, Richard A; Lercher, Martin J; Pál, Csaba; Papp, Balázs

    2016-01-01

    A central challenge in evolutionary biology concerns the mechanisms by which complex metabolic innovations requiring multiple mutations arise. Here, we propose that metabolic innovations accessible through the addition of a single reaction serve as stepping stones towards the later establishment of complex metabolic features in another environment. We demonstrate the feasibility of this hypothesis through three complementary analyses. First, using genome-scale metabolic modelling, we show that complex metabolic innovations in Escherichia coli can arise via changing nutrient conditions. Second, using phylogenetic approaches, we demonstrate that the acquisition patterns of complex metabolic pathways during the evolutionary history of bacterial genomes support the hypothesis. Third, we show how adaptation of laboratory populations of E. coli to one carbon source facilitates the later adaptation to another carbon source. Our work demonstrates how complex innovations can evolve through series of adaptive steps without the need to invoke non-adaptive processes. PMID:27197754

  20. Breast reconstruction.

    PubMed

    DellaCroce, Frank J; Wolfe, Emily T

    2013-04-01

    As diagnostic technology has progressed and the understanding of the disease process has evolved, the number of mastectomies performed in the United States has increased. Breast reconstructive techniques have commensurately become more sophisticated along the same timeline. The result is that those facing mastectomy have the potential to simultaneously retain physical beauty and wholeness. Only 33% of women who are otherwise candidates for immediate reconstruction at the time of mastectomy choose reconstruction. Patients generally have a high level of satisfaction with the option they choose, contributing to a feeling of overall recovery and physical and emotional wholeness. PMID:23464695

  1. ACL reconstruction

    MedlinePlus

    ... Tissue taken from a donor is called an allograft. The procedure is usually performed with the help ... This increases the chance you may have a meniscus tear. ACL reconstruction may be used for these ...

  2. [Eyebrow reconstruction].

    PubMed

    Baraër, F; Darsonval, V; Lejeune, F; Bochot-Hermouet, B; Rousseau, P

    2013-10-01

    The eyebrow is an essential anatomical area, from a social point of view, so its reconstruction, in case of skin defect, must be as meticulous as possible, with the less residual sequela. Capillary density extremely varies from one person to another and the different methods of restoration of this area should absolutely take this into consideration. We are going to review the various techniques of reconstruction, according to the sex and the surface to cover. PMID:23896574

  3. Genome-based Modeling and Design of Metabolic Interactions in Microbial Communities

    PubMed Central

    Mahadevan, Radhakrishnan; Henson, Michael A.

    2012-01-01

    Biotechnology research is traditionally focused on individual microbial strains that are perceived to have the necessary metabolic functions, or the capability to have these functions introduced, to achieve a particular task. For many important applications, the development of such omnipotent microbes is an extremely challenging if not impossible task. By contrast, nature employs a radically different strategy based on synergistic combinations of different microbial species that collectively achieve the desired task. These natural communities have evolved to exploit the native metabolic capabilities of each species and are highly adaptive to changes in their environments. However, microbial communities have proven difficult to study due to a lack of suitable experimental and computational tools. With the advent of genome sequencing, omics technologies, bioinformatics and genome-scale modeling, researchers now have unprecedented capabilities to analyze and engineer the metabolism of microbial communities. The goal of this review is to summarize recent applications of genome-scale metabolic modeling to microbial communities. A brief introduction to lumped community models is used to motivate the need for genome-level descriptions of individual species and their metabolic interactions. The review of genome-scale models begins with static modeling approaches, which are appropriate for communities where the extracellular environment can be assumed to be time invariant or slowly varying. Dynamic extensions of the static modeling approach are described, and then applications of genome-scale models for design of synthetic microbial communities are reviewed. The review concludes with a summary of metagenomic tools for analyzing community metabolism and an outlook for future research. PMID:24688668

  4. Human metabolic atlas: an online resource for human metabolism.

    PubMed

    Pornputtapong, Natapol; Nookaew, Intawat; Nielsen, Jens

    2015-01-01

    Human tissue-specific genome-scale metabolic models (GEMs) provide comprehensive understanding of human metabolism, which is of great value to the biomedical research community. To make this kind of data easily accessible to the public, we have designed and deployed the human metabolic atlas (HMA) website (http://www.metabolicatlas.org). This online resource provides comprehensive information about human metabolism, including the results of metabolic network analyses. We hope that it can also serve as an information exchange interface for human metabolism knowledge within the research community. The HMA consists of three major components: Repository, Hreed (Human REaction Entities Database) and Atlas. Repository is a collection of GEMs for specific human cell types and human-related microorganisms in SBML (System Biology Markup Language) format. The current release consists of several types of GEMs: a generic human GEM, 82 GEMs for normal cell types, 16 GEMs for different cancer cell types, 2 curated GEMs and 5 GEMs for human gut bacteria. Hreed contains detailed information about biochemical reactions. A web interface for Hreed facilitates an access to the Hreed reaction data, which can be easily retrieved by using specific keywords or names of related genes, proteins, compounds and cross-references. Atlas web interface can be used for visualization of the GEMs collection overlaid on KEGG metabolic pathway maps with a zoom/pan user interface. The HMA is a unique tool for studying human metabolism, ranging in scope from an individual cell, to a specific organ, to the overall human body. This resource is freely available under a Creative Commons Attribution-NonCommercial 4.0 International License. PMID:26209309

  5. Human metabolic atlas: an online resource for human metabolism

    PubMed Central

    Nookaew, Intawat; Nielsen, Jens

    2015-01-01

    Human tissue-specific genome-scale metabolic models (GEMs) provide comprehensive understanding of human metabolism, which is of great value to the biomedical research community. To make this kind of data easily accessible to the public, we have designed and deployed the human metabolic atlas (HMA) website (http://www.metabolicatlas.org). This online resource provides comprehensive information about human metabolism, including the results of metabolic network analyses. We hope that it can also serve as an information exchange interface for human metabolism knowledge within the research community. The HMA consists of three major components: Repository, Hreed (Human REaction Entities Database) and Atlas. Repository is a collection of GEMs for specific human cell types and human-related microorganisms in SBML (System Biology Markup Language) format. The current release consists of several types of GEMs: a generic human GEM, 82 GEMs for normal cell types, 16 GEMs for different cancer cell types, 2 curated GEMs and 5 GEMs for human gut bacteria. Hreed contains detailed information about biochemical reactions. A web interface for Hreed facilitates an access to the Hreed reaction data, which can be easily retrieved by using specific keywords or names of related genes, proteins, compounds and cross-references. Atlas web interface can be used for visualization of the GEMs collection overlaid on KEGG metabolic pathway maps with a zoom/pan user interface. The HMA is a unique tool for studying human metabolism, ranging in scope from an individual cell, to a specific organ, to the overall human body. This resource is freely available under a Creative Commons Attribution-NonCommercial 4.0 International License. Database URL: http://www.metabolicatlas.org. PMID:26209309

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

    PubMed Central

    2011-01-01

    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. PMID:22784571

  7. Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation

    SciTech Connect

    Bordbar, Aarash; Mo, Monica L.; Nakayasu, Ernesto S.; Rutledge, Alexandra C.; Kim, Young-Mo; Metz, Thomas O.; Jones, Marcus B.; Frank, Bryan C.; Smith, Richard D.; Peterson, Scott N.; Hyduke, Daniel R.; Adkins, Joshua N.; Palsson, Bernhard O.

    2012-06-26

    Macrophages are central players in the immune response, manifesting divergent phenotypes to control inflammation and innate immunity through the release of cytokines and other regulatory factor-dependent signaling pathways. In recent years, the focus on metabolism has been reemphasized as critical signaling and regulatory pathways of human pathophysiology, ranging from cancer to aging, often converge on metabolic responses. Here, we used genome-scale modeling and multi-omics (transcriptomics, proteomics, and metabolomics) analysis to assess metabolic features critical for macrophage functions. We constructed a genome-scale metabolic network for the RAW 264.7 cell line to determine metabolic modulators of macrophage activation. Metabolites well-known to be associated with immunoactivation (e.g., glucose and arginine) and immunosuppression (e.g., tryptophan and vitamin D3) were amongst the most critical effectors. Intracellular metabolic mechanisms linked to critical suppressive effectors were then assessed, identifying a suppressive role for de novo nucleotide synthesis. Finally, the underlying metabolic mechanisms of macrophage activation are identified by analyzing multi-omic data obtained from LPS-stimulated RAW cells in the context of our flux-based predictions. Our study demonstrates metabolism's role in regulating activation may be greater than previously anticipated and elucidates underlying metabolic connections between activation and metabolic effectors.

  8. Biogeochemical metabolic modeling of methanogenesis by Methanosarcina barkeri

    NASA Astrophysics Data System (ADS)

    Jensvold, Z. D.; Jin, Q.

    2015-12-01

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

  9. Reconstructing Validity

    ERIC Educational Resources Information Center

    Moss, Pamela A.

    2007-01-01

    In response to Lissitz and Samuelsen (2007), the author reconstructs the historical arguments for the more comprehensive unitary concept of validity and the principles of scientific inquiry underlying it. Her response is organized in terms of four questions: (a) How did validity in educational measurement come to be conceptualized as unitary, and…

  10. Vaginal reconstruction

    SciTech Connect

    Lesavoy, M.A.

    1985-05-01

    Vaginal reconstruction can be an uncomplicated and straightforward procedure when attention to detail is maintained. The Abbe-McIndoe procedure of lining the neovaginal canal with split-thickness skin grafts has become standard. The use of the inflatable Heyer-Schulte vaginal stent provides comfort to the patient and ease to the surgeon in maintaining approximation of the skin graft. For large vaginal and perineal defects, myocutaneous flaps such as the gracilis island have been extremely useful for correction of radiation-damaged tissue of the perineum or for the reconstruction of large ablative defects. Minimal morbidity and scarring ensue because the donor site can be closed primarily. With all vaginal reconstruction, a compliant patient is a necessity. The patient must wear a vaginal obturator for a minimum of 3 to 6 months postoperatively and is encouraged to use intercourse as an excellent obturator. In general, vaginal reconstruction can be an extremely gratifying procedure for both the functional and emotional well-being of patients.

  11. Project Reconstruct.

    ERIC Educational Resources Information Center

    Helisek, Harriet; Pratt, Donald

    1994-01-01

    Presents a project in which students monitor their use of trash, input and analyze information via a database and computerized graphs, and "reconstruct" extinct or endangered animals from recyclable materials. The activity was done with second-grade students over a period of three to four weeks. (PR)

  12. ACL reconstruction - discharge

    MedlinePlus

    Anterior cruciate ligament reconstruction - discharge; ACL reconstruction - discharge ... had surgery to reconstruct your anterior cruciate ligament (ACL). The surgeon drilled holes in the bones of ...

  13. Quantitative analysis of drug effects at the whole-body level: a case study for glucose metabolism in malaria patients.

    PubMed

    Snoep, Jacky L; Green, Kathleen; Eicher, Johann; Palm, Daniel C; Penkler, Gerald; du Toit, Francois; Walters, Nicolas; Burger, Robert; Westerhoff, Hans V; van Niekerk, David D

    2015-12-01

    We propose a hierarchical modelling approach to construct models for disease states at the whole-body level. Such models can simulate effects of drug-induced inhibition of reaction steps on the whole-body physiology. We illustrate the approach for glucose metabolism in malaria patients, by merging two detailed kinetic models for glucose metabolism in the parasite Plasmodium falciparum and the human red blood cell with a coarse-grained model for whole-body glucose metabolism. In addition we use a genome-scale metabolic model for the parasite to predict amino acid production profiles by the malaria parasite that can be used as a complex biomarker. PMID:26614654

  14. A review of metabolic and enzymatic engineering strategies for designing and optimizing performance of microbial cell factories

    PubMed Central

    Fisher, Amanda K.; Freedman, Benjamin G.; Bevan, David R.; Senger, Ryan S.

    2014-01-01

    Microbial cell factories (MCFs) are of considerable interest to convert low value renewable substrates to biofuels and high value chemicals. This review highlights the progress of computational models for the rational design of an MCF to produce a target bio-commodity. In particular, the rational design of an MCF involves: (i) product selection, (ii) de novo biosynthetic pathway identification (i.e., rational, heterologous, or artificial), (iii) MCF chassis selection, (iv) enzyme engineering of promiscuity to enable the formation of new products, and (v) metabolic engineering to ensure optimal use of the pathway by the MCF host. Computational tools such as (i) de novo biosynthetic pathway builders, (ii) docking, (iii) molecular dynamics (MD) and steered MD (SMD), and (iv) genome-scale metabolic flux modeling all play critical roles in the rational design of an MCF. Genome-scale metabolic flux models are of considerable use to the design process since they can reveal metabolic capabilities of MCF hosts. These can be used for host selection as well as optimizing precursors and cofactors of artificial de novo biosynthetic pathways. In addition, recent advances in genome-scale modeling have enabled the derivation of metabolic engineering strategies, which can be implemented using the genomic tools reviewed here as well. PMID:25379147

  15. ScrumPy: metabolic modelling with Python.

    PubMed

    Poolman, M G

    2006-09-01

    ScrumPy is a software package used for the definition and analysis of metabolic models. It is written using the Python programming language that is also used as a user interface. ScrumPy has features for both kinetic and structural modelling, but the emphasis is on structural modelling and those features of most relevance to analysis of large (genome-scale) models. The aim is at describing ScrumPy's functionality to readers with some knowledge of metabolic modelling, but implementation, programming and other computational details are omitted. ScrumPy is released under the Gnu Public Licence, and available for download from http://mudshark.brookes.ac.uk/ ScrumPy. PMID:16986321

  16. A genome-scale in vivo loss-of-function screen identifies Phf6 as a lineage-specific regulator of leukemia cell growth

    PubMed Central

    Meacham, Corbin E.; Lawton, Lee N.; Soto-Feliciano, Yadira M.; Pritchard, Justin R.; Joughin, Brian A.; Ehrenberger, Tobias; Fenouille, Nina; Zuber, Johannes; Williams, Richard T.; Young, Richard A.

    2015-01-01

    We performed a genome-scale shRNA screen for modulators of B-cell leukemia progression in vivo. Results from this work revealed dramatic distinctions between the relative effects of shRNAs on the growth of tumor cells in culture versus in their native microenvironment. Specifically, we identified many “context-specific” regulators of leukemia development. These included the gene encoding the zinc finger protein Phf6. While inactivating mutations in PHF6 are commonly observed in human myeloid and T-cell malignancies, we found that Phf6 suppression in B-cell malignancies impairs tumor progression. Thus, Phf6 is a “lineage-specific” cancer gene that plays opposing roles in developmentally distinct hematopoietic malignancies. PMID:25737277

  17. Breast Reconstruction After Mastectomy

    MedlinePlus

    ... around the cancer removed (lumpectomy or breast-conserving surgery) might not need reconstruction, but sometimes they do. Breast reconstruction is done by a plastic surgeon. Should I have breast reconstruction? Breast reconstruction ...

  18. Characterizing the Metabolism of Dehalococcoides with a Constraint-Based Model

    PubMed Central

    Ahsanul Islam, M.; Edwards, Elizabeth A.; Mahadevan, Radhakrishnan

    2010-01-01

    Dehalococcoides strains respire a wide variety of chloro-organic compounds and are important for the bioremediation of toxic, persistent, carcinogenic, and ubiquitous ground water pollutants. In order to better understand metabolism and optimize their application, we have developed a pan-genome-scale metabolic network and constraint-based metabolic model of Dehalococcoides. The pan-genome was constructed from publicly available complete genome sequences of Dehalococcoides sp. strain CBDB1, strain 195, strain BAV1, and strain VS. We found that Dehalococcoides pan-genome consisted of 1118 core genes (shared by all), 457 dispensable genes (shared by some), and 486 unique genes (found in only one genome). The model included 549 metabolic genes that encoded 356 proteins catalyzing 497 gene-associated model reactions. Of these 497 reactions, 477 were associated with core metabolic genes, 18 with dispensable genes, and 2 with unique genes. This study, in addition to analyzing the metabolism of an environmentally important phylogenetic group on a pan-genome scale, provides valuable insights into Dehalococcoides metabolic limitations, low growth yields, and energy conservation. The model also provides a framework to anchor and compare disparate experimental data, as well as to give insights on the physiological impact of “incomplete” pathways, such as the TCA-cycle, CO2 fixation, and cobalamin biosynthesis pathways. The model, referred to as iAI549, highlights the specialized and highly conserved nature of Dehalococcoides metabolism, and suggests that evolution of Dehalococcoides species is driven by the electron acceptor availability. PMID:20811585

  19. Mapping the landscape of metabolic goals of a cell

    DOE PAGESBeta

    Zhao, Qi; Stettner, Arion I.; Reznik, Ed; Paschalidis, Ioannis Ch.; Segre, Daniel

    2016-05-23

    Here, genome-scale flux balance models of metabolism provide testable predictions of all metabolic rates in an organism, by assuming that the cell is optimizing a metabolic goal known as the objective function. We introduce an efficient inverse flux balance analysis (invFBA) approach, based on linear programming duality, to characterize the space of possible objective functions compatible with measured fluxes. After testing our algorithm on simulated E. coli data and time-dependent S. oneidensis fluxes inferred from gene expression, we apply our inverse approach to flux measurements in long-term evolved E. coli strains, revealing objective functions that provide insight into metabolic adaptationmore » trajectories.« less

  20. Host metabolism regulates intracellular growth of Trypanosoma cruzi.

    PubMed

    Caradonna, Kacey L; Engel, Juan C; Jacobi, David; Lee, Chih-Hao; Burleigh, Barbara A

    2013-01-16

    Metabolic coupling of intracellular pathogens with host cells is essential for successful colonization of the host. Establishment of intracellular infection by the protozoan Trypanosoma cruzi leads to the development of human Chagas' disease, yet the functional contributions of the host cell toward the infection process remain poorly characterized. Here, a genome-scale functional screen identified interconnected metabolic networks centered around host energy production, nucleotide metabolism, pteridine biosynthesis, and fatty acid oxidation as key processes that fuel intracellular T. cruzi growth. Additionally, the host kinase Akt, which plays essential roles in various cellular processes, was critical for parasite replication. Targeted perturbations in these host metabolic pathways or Akt-dependent signaling pathways modulated the parasite's replicative capacity, highlighting the adaptability of this intracellular pathogen to changing conditions in the host. These findings identify key cellular process regulating intracellular T. cruzi growth and illuminate the potential to leverage host pathways to limit T. cruzi infection. PMID:23332160

  1. Host metabolism regulates intracellular growth of Trypanosoma cruzi

    PubMed Central

    Caradonna, Kacey L.; Engel, Juan C.; Jacobi, David; Lee, Chih-Hao; Burleigh, Barbara A.

    2012-01-01

    SUMMARY Metabolic coupling of intracellular pathogens with host cells is essential for successful colonization of the host. Establishment of intracellular infection by the protozoan Trypanosoma cruzi leads to the development of human Chagas disease, yet the functional contributions of the host cell toward the infection process remain poorly characterized. Here, a genome-scale functional screen identified interconnected metabolic networks centered around host energy production, nucleotide metabolism, pteridine biosynthesis, and fatty acid oxidation as key processes that fuel intracellular T. cruzi growth. Additionally, the host kinase Akt, which plays essential roles in various cellular processes, was critical for parasite replication. Targeted perturbations in these host metabolic pathways or Akt-dependent signaling pathways modulated the parasite’s replicative capacity, highlighting the adaptability of this intracellular pathogen to changing conditions in the host. These findings identify key cellular process regulating intracellular T. cruzi growth and illuminate the potential to leverage host pathways to limit T. cruzi infection. PMID:23332160

  2. Temporal Expression-based Analysis of Metabolism

    PubMed Central

    Segrè, Daniel

    2012-01-01

    Metabolic flux is frequently rerouted through cellular metabolism in response to dynamic changes in the intra- and extra-cellular environment. Capturing the mechanisms underlying these metabolic transitions in quantitative and predictive models is a prominent challenge in systems biology. Progress in this regard has been made by integrating high-throughput gene expression data into genome-scale stoichiometric models of metabolism. Here, we extend previous approaches to perform a Temporal Expression-based Analysis of Metabolism (TEAM). We apply TEAM to understanding the complex metabolic dynamics of the respiratorily versatile bacterium Shewanella oneidensis grown under aerobic, lactate-limited conditions. TEAM predicts temporal metabolic flux distributions using time-series gene expression data. Increased predictive power is achieved by supplementing these data with a large reference compendium of gene expression, which allows us to take into account the unique character of the distribution of expression of each individual gene. We further propose a straightforward method for studying the sensitivity of TEAM to changes in its fundamental free threshold parameter θ, and reveal that discrete zones of distinct metabolic behavior arise as this parameter is changed. By comparing the qualitative characteristics of these zones to additional experimental data, we are able to constrain the range of θ to a small, well-defined interval. In parallel, the sensitivity analysis reveals the inherently difficult nature of dynamic metabolic flux modeling: small errors early in the simulation propagate to relatively large changes later in the simulation. We expect that handling such “history-dependent” sensitivities will be a major challenge in the future development of dynamic metabolic-modeling techniques. PMID:23209390

  3. Tracheal reconstructions.

    PubMed

    Srikrishna, S V; Shekar, P S; Shetty, N

    1998-12-01

    Surgical reconstruction of the trachea is a relatively complex procedure. We had 20 cases of tracheal stenosis. We have a modest experience of 16 tracheal reconstructions for acquired tracheal stenosis. Two patients underwent laser treatment while another two died before any intervention. The majority of these cases were a result of prolonged ventilation (14 cases), following organophosphorous poisoning (11 cases), Guillain-Barré syndrome, bullet injury, fat embolism and surprisingly only one tumor, a case of mucoepidermoid carcinoma, who had a very unusual presentation. There were 12 males and 4 females in this series, age ranging from 12-35 years. The duration of ventilation ranged from 1-21 days and the interval from decannulation to development of stridor was between 5-34 days. Six of them were approached by the cervical route, 5 by thoracotomy and cervical approach, 2 via median sternotomy and 3 by thoracotomy alone. Five of them required an additional laryngeal drop and 1 required pericardiotomy and release of pulmonary veins to gain additional length. The excised segments of trachea measured 3 to 5 cms in length. All were end to end anastomosis with interrupted Vicryl sutures. We have had no experience with stents or prosthetic tubes. Three patients developed anastomotic leaks which were controlled conservatively. Almost all of them required postoperative tracheo-bronchial suctioning with fibreoptic bronchoscope. We had one death in this series due to sepsis. PMID:9914459

  4. GIM3E: Condition-specific Models of Cellular Metabolism Developed from Metabolomics and Expression Data

    SciTech Connect

    Schmidt, Brian; Ebrahim, Ali; Metz, Thomas O.; Adkins, Joshua N.; Palsson, Bernard O.; Hyduke, Daniel R.

    2013-11-15

    Motivation: Genome-scale metabolic models have been used extensively to investigate alterations in cellular metabolism. The accuracy of these models to represent cellular metabolism in specific conditions has been improved by constraining the model with omics data sources. However, few practical methods for integrating metabolomics data with other omics data sources into genome-scale models of metabolism have been reported. Results: GIMMME (Gene Inactivation Moderated by Metabolism, Metabolomics, and Expression) is an algorithm that enables the development of condition-specific models based on an objective function, transcriptomics, and intracellular metabolomics data. GIMMME establishes metabolite utilization requirements with metabolomics data, uses model-paired transcriptomics data to find experimentally supported solutions, and also provides calculations of the turnover (production / consumption) flux of metabolites. GIMMME was employed to investigate the effects of integrating additional omics datasets to create increasingly constrained solution spaces of Salmonella Typhimurium metabolism during growth in both rich and virulence media. This integration proved to be informative and resulted in a requirement of additional active reactions (12 in each case) or metabolites (26 or 29, respectively). The addition of constraints from transcriptomics also impacted the allowed solution space, and the cellular metabolites with turnover fluxes that were necessarily altered by the change in conditions increased from 118 to 271 of 1397. Availability: GIMMME has been implemented in Python and requires a COBRApy 0.2.x. The algorithm and sample data described here are freely available at: http://opencobra.sourceforge.net/

  5. Swimming in Light: A Large-Scale Computational Analysis of the Metabolism of Dinoroseobacter shibae

    PubMed Central

    Rex, Rene; Bill, Nelli; Schmidt-Hohagen, Kerstin; Schomburg, Dietmar

    2013-01-01

    The Roseobacter clade is a ubiquitous group of marine α-proteobacteria. To gain insight into the versatile metabolism of this clade, we took a constraint-based approach and created a genome-scale metabolic model (iDsh827) of Dinoroseobacter shibae DFL12T. Our model is the first accounting for the energy demand of motility, the light-driven ATP generation and experimentally determined specific biomass composition. To cover a large variety of environmental conditions, as well as plasmid and single gene knock-out mutants, we simulated 391,560 different physiological states using flux balance analysis. We analyzed our results with regard to energy metabolism, validated them experimentally, and revealed a pronounced metabolic response to the availability of light. Furthermore, we introduced the energy demand of motility as an important parameter in genome-scale metabolic models. The results of our simulations also gave insight into the changing usage of the two degradation routes for dimethylsulfoniopropionate, an abundant compound in the ocean. A side product of dimethylsulfoniopropionate degradation is dimethyl sulfide, which seeds cloud formation and thus enhances the reflection of sunlight. By our exhaustive simulations, we were able to identify single-gene knock-out mutants, which show an increased production of dimethyl sulfide. In addition to the single-gene knock-out simulations we studied the effect of plasmid loss on the metabolism. Moreover, we explored the possible use of a functioning phosphofructokinase for D. shibae. PMID:24098096

  6. Minimal metabolic pathway structure is consistent with associated biomolecular interactions

    PubMed Central

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

    2014-01-01

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

  7. Minimal metabolic pathway structure is consistent with associated biomolecular interactions.

    PubMed

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

    2014-01-01

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

  8. Metabolic neuropathies

    MedlinePlus

    Neuropathy - metabolic ... can be caused by many different things. Metabolic neuropathy may be caused by: A problem with the ... one of the most common causes of metabolic neuropathies. People who are at the highest risk for ...

  9. Influence of genome-scale RNA structure disruption on the replication of murine norovirus—similar replication kinetics in cell culture but attenuation of viral fitness in vivo

    PubMed Central

    McFadden, Nora; Arias, Armando; Dry, Inga; Bailey, Dalan; Witteveldt, Jeroen; Evans, David J.; Goodfellow, Ian; Simmonds, Peter

    2013-01-01

    Mechanisms by which certain RNA viruses, such as hepatitis C virus, establish persistent infections and cause chronic disease are of fundamental importance in viral pathogenesis. Mammalian positive-stranded RNA viruses establishing persistence typically possess genome-scale ordered RNA secondary structure (GORS) in their genomes. Murine norovirus (MNV) persists in immunocompetent mice and provides an experimental model to functionally characterize GORS. Substitution mutants were constructed with coding sequences in NS3/4- and NS6/7-coding regions replaced with sequences with identical coding and (di-)nucleotide composition but disrupted RNA secondary structure (F1, F2, F1/F2 mutants). Mutants replicated with similar kinetics to wild-type (WT) MNV3 in RAW264.7 cells and primary macrophages, exhibited similar (highly restricted) induction and susceptibility to interferon-coupled cellular responses and equal replication fitness by serial passaging of co-cultures. In vivo, both WT and F1/F2 mutant viruses persistently infected mice, although F1, F2 and F1/F2 mutant viruses were rapidly eliminated 1–7 days post-inoculation in competition experiments with WT. F1/F2 mutants recovered from tissues at 9 months showed higher synonymous substitution rates than WT and nucleotide substitutions that potentially restored of RNA secondary structure. GORS plays no role in basic replication of MNV but potentially contributes to viral fitness and persistence in vivo. PMID:23630317

  10. Genome-scale functional analysis of the human genes modulating p53 activity by regulating MDM2 expression in a p53-independent manner.

    PubMed

    Kim, Dong Min; Choi, Seung-Hyun; Yeom, Young Il; Min, Sang-Hyun; Kim, Il-Chul

    2016-09-16

    MDM2, a critical negative regulator of p53, is often overexpressed in leukemia, but few p53 mutations are found, suggesting that p53-independent MDM2 expression occurs due to alterations in MDM2 upstream regulators. In this study, a high MDM2 transcription level was observed (41.17%) regardless of p53 expression in patient with acute myeloid leukemia (AML). Therefore, we performed genome-scale functional screening of the human genes modulating MDM2 expression in a p53-independent manner. We searched co-expression profiles of genes showing a positive or negative pattern with MDM2 expression in a DNA microarray database, selected1089 links, and composed a screening library of 368 genes. Using MDM2 P1 and P2 promoter-reporter systems, we screened clones regulating MDM2 transcriptions in a p53-independent manner by overexpression. Nine clones from the screening library showed enhanced MDM2 promoter activity and MDM2 expression in p53-deficient HCT116 cells. Among them, six clones, including NTRK2, GNA15, SFRS2, EIF5A, ELAVL1, and YWHAB mediated MAPK signaling for expressing MDM2. These results indicate that p53-independent upregulation of MDM2 by increasing selected clones may lead to oncogenesis in AML and that MDM2-modulating genes are novel potential targets for AML treatment. PMID:27524244

  11. Metabolic processes of Methanococcus maripaludis and potential applications.

    PubMed

    Goyal, Nishu; Zhou, Zhi; Karimi, Iftekhar A

    2016-01-01

    Methanococcus maripaludis is a rapidly growing, fully sequenced, genetically tractable model organism among hydrogenotrophic methanogens. It has the ability to convert CO2 and H2 into a useful cleaner energy fuel (CH4). In fact, this conversion enhances in the presence of free nitrogen as the sole nitrogen source due to prolonged cell growth. Given the global importance of GHG emissions and climate change, diazotrophy can be attractive for carbon capture and utilization applications from appropriately treated flue gases, where surplus hydrogen is available from renewable electricity sources. In addition, M. maripaludis can be engineered to produce other useful products such as terpenoids, hydrogen, methanol, etc. M. maripaludis with its unique abilities has the potential to be a workhorse like Escherichia coli and S. cerevisiae for fundamental and experimental biotechnology studies. More than 100 experimental studies have explored different specific aspects of the biochemistry and genetics of CO2 and N2 fixation by M. maripaludis. Its genome-scale metabolic model (iMM518) also exists to study genetic perturbations and complex biological interactions. However, a comprehensive review describing its cell structure, metabolic processes, and methanogenesis is still lacking in the literature. This review fills this crucial gap. Specifically, it integrates distributed information from the literature to provide a complete and detailed view for metabolic processes such as acetyl-CoA synthesis, pyruvate synthesis, glycolysis/gluconeogenesis, reductive tricarboxylic acid (RTCA) cycle, non-oxidative pentose phosphate pathway (NOPPP), nitrogen metabolism, amino acid metabolism, and nucleotide biosynthesis. It discusses energy production via methanogenesis and its relation to metabolism. Furthermore, it reviews taxonomy, cell structure, culture/storage conditions, molecular biology tools, genome-scale models, and potential industrial and environmental applications. Through the

  12. Metabolic Network Prediction of Drug Side Effects.

    PubMed

    Shaked, Itay; Oberhardt, Matthew A; Atias, Nir; Sharan, Roded; Ruppin, Eytan

    2016-03-23

    Drug side effects levy a massive cost on society through drug failures, morbidity, and mortality cases every year, and their early detection is critically important. Here, we describe the array of model-based phenotype predictors (AMPP), an approach that leverages medical informatics resources and a human genome-scale metabolic model (GSMM) to predict drug side effects. AMPP is substantially predictive (AUC > 0.7) for >70 drug side effects, including very serious ones such as interstitial nephritis and extrapyramidal disorders. We evaluate AMPP's predictive signal through cross-validation, comparison across multiple versions of a side effects database, and co-occurrence analysis of drug side effect associations in scientific abstracts (hypergeometric p value = 2.2e-40). AMPP outperforms a previous biochemical structure-based method in predicting metabolically based side effects (aggregate AUC = 0.65 versus 0.59). Importantly, AMPP enables the identification of key metabolic reactions and biomarkers that are predictive of specific side effects. Taken together, this work lays a foundation for future detection of metabolically grounded side effects during early stages of drug development. PMID:27135366

  13. Estimating Metabolic Fluxes Using a Maximum Network Flexibility Paradigm

    PubMed Central

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

    2015-01-01

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

  14. Facial reconstruction in partial lipodystrophy.

    PubMed

    Hurwitz, P J; Sarel, R

    1982-03-01

    Lipodystrophy is a rare disease characterized by progressive disappearance of the subcutaneous fat of the upper part of the body. Accompanying abnormalities of carbohydrate and lipid metabolism, diabetes, nephritis, and low levels of complement are frequent. The most striking clinical features are the extremely hollow cheeks, making the normal facial skeleton rather prominent. Very little has been reported on facial reconstruction in such patients. A 16-year-old girl is presented who was successfully reconstructed after the atrophic process arrested spontaneously. Bilateral dermal fat grafts from the buttocks were used in a one-stage procedure. Nine months later, when no more resorption of fat occurred, some trimming of the grafts was necessary. A good result was achieved. PMID:7103380

  15. Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer.

    PubMed

    Yizhak, Keren; Gaude, Edoardo; Le Dévédec, Sylvia; Waldman, Yedael Y; Stein, Gideon Y; van de Water, Bob; Frezza, Christian; Ruppin, Eytan

    2014-01-01

    Utilizing molecular data to derive functional physiological models tailored for specific cancer cells can facilitate the use of individually tailored therapies. To this end we present an approach termed PRIME for generating cell-specific genome-scale metabolic models (GSMMs) based on molecular and phenotypic data. We build >280 models of normal and cancer cell-lines that successfully predict metabolic phenotypes in an individual manner. We utilize this set of cell-specific models to predict drug targets that selectively inhibit cancerous but not normal cell proliferation. The top predicted target, MLYCD, is experimentally validated and the metabolic effects of MLYCD depletion investigated. Furthermore, we tested cell-specific predicted responses to the inhibition of metabolic enzymes, and successfully inferred the prognosis of cancer patients based on their PRIME-derived individual GSMMs. These results lay a computational basis and a counterpart experimental proof of concept for future personalized metabolic modeling applications, enhancing the search for novel selective anticancer therapies. PMID:25415239

  16. Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer

    PubMed Central

    Le Dévédec, Sylvia; Waldman, Yedael Y; Stein, Gideon Y; van de Water, Bob

    2014-01-01

    Utilizing molecular data to derive functional physiological models tailored for specific cancer cells can facilitate the use of individually tailored therapies. To this end we present an approach termed PRIME for generating cell-specific genome-scale metabolic models (GSMMs) based on molecular and phenotypic data. We build >280 models of normal and cancer cell-lines that successfully predict metabolic phenotypes in an individual manner. We utilize this set of cell-specific models to predict drug targets that selectively inhibit cancerous but not normal cell proliferation. The top predicted target, MLYCD, is experimentally validated and the metabolic effects of MLYCD depletion investigated. Furthermore, we tested cell-specific predicted responses to the inhibition of metabolic enzymes, and successfully inferred the prognosis of cancer patients based on their PRIME-derived individual GSMMs. These results lay a computational basis and a counterpart experimental proof of concept for future personalized metabolic modeling applications, enhancing the search for novel selective anticancer therapies. DOI: http://dx.doi.org/10.7554/eLife.03641.001 PMID:25415239

  17. Metabolic Plasticity and Inter-Compartmental Interactions in Rice Metabolism: An Analysis from Reaction Deletion Study

    PubMed Central

    Shaw, Rahul; Kundu, Sudip

    2015-01-01

    More than 20% of the total caloric intake of human population comes from rice. The expression of rice genes and hence, the concentration of enzymatic proteins might vary due to several biotic and abiotic stresses. It in turn, can influence the overall metabolism and survivability of rice plant. Thus, understanding the rice cellular metabolism, its plasticity and potential readjustments under different perturbations can help rice biotechnologists to design efficient rice cultivars. Here, using the flux balance analysis (FBA) method, with the help of in-silico reaction deletion strategy, we study the metabolic plasticity of genome-scale metabolic model of rice leaf. A set of 131 reactions, essential for the production of primary biomass precursors is identified; deletion of any of them can inhibit the overall biomass production. Usability Index (IU) for the rest of the reactions are estimated and based on this parameter, they are classified into three categories—maximally-favourable, quasi-favourable and unfavourable for the primary biomass production. The lower value of 1 − IU of a reaction suggests that the cell cannot easily bypass it for biomass production. While some of the alternative paths are energetically equally efficient, others demand for higher photon. The variations in (i) ATP/NADPH ratio, (ii) exchange of metabolites through chloroplastic transporters and (iii) total biomass production are also presented here. Mutual metabolic dependencies of different cellular compartments are also demonstrated. PMID:26222686

  18. MiRPara: a SVM-based software tool for prediction of most probable microRNA coding regions in genome scale sequences

    PubMed Central

    2011-01-01

    Background MicroRNAs are a family of ~22 nt small RNAs that can regulate gene expression at the post-transcriptional level. Identification of these molecules and their targets can aid understanding of regulatory processes. Recently, HTS has become a common identification method but there are two major limitations associated with the technique. Firstly, the method has low efficiency, with typically less than 1 in 10,000 sequences representing miRNA reads and secondly the method preferentially targets highly expressed miRNAs. If sequences are available, computational methods can provide a screening step to investigate the value of an HTS study and aid interpretation of results. However, current methods can only predict miRNAs for short fragments and have usually been trained against small datasets which don't always reflect the diversity of these molecules. Results We have developed a software tool, miRPara, that predicts most probable mature miRNA coding regions from genome scale sequences in a species specific manner. We classified sequences from miRBase into animal, plant and overall categories and used a support vector machine to train three models based on an initial set of 77 parameters related to the physical properties of the pre-miRNA and its miRNAs. By applying parameter filtering we found a subset of ~25 parameters produced higher prediction ability compared to the full set. Our software achieves an accuracy of up to 80% against experimentally verified mature miRNAs, making it one of the most accurate methods available. Conclusions miRPara is an effective tool for locating miRNAs coding regions in genome sequences and can be used as a screening step prior to HTS experiments. It is available at http://www.whiov.ac.cn/bioinformatics/mirpara PMID:21504621

  19. The evolution of metabolic networks of E. coli

    PubMed Central

    2011-01-01

    Background Despite the availability of numerous complete genome sequences from E. coli strains, published genome-scale metabolic models exist only for two commensal E. coli strains. These models have proven useful for many applications, such as engineering strains for desired product formation, and we sought to explore how constructing and evaluating additional metabolic models for E. coli strains could enhance these efforts. Results We used the genomic information from 16 E. coli strains to generate an E. coli pangenome metabolic network by evaluating their collective 76,990 ORFs. Each of these ORFs was assigned to one of 17,647 ortholog groups including ORFs associated with reactions in the most recent metabolic model for E. coli K-12. For orthologous groups that contain an ORF already represented in the MG1655 model, the gene to protein to reaction associations represented in this model could then be easily propagated to other E. coli strain models. All remaining orthologous groups were evaluated to see if new metabolic reactions could be added to generate a pangenome-scale metabolic model (iEco1712_pan). The pangenome model included reactions from a metabolic model update for E. coli K-12 MG1655 (iEco1339_MG1655) and enabled development of five additional strain-specific genome-scale metabolic models. These additional models include a second K-12 strain (iEco1335_W3110) and four pathogenic strains (two enterohemorrhagic E. coli O157:H7 and two uropathogens). When compared to the E. coli K-12 models, the metabolic models for the enterohemorrhagic (iEco1344_EDL933 and iEco1345_Sakai) and uropathogenic strains (iEco1288_CFT073 and iEco1301_UTI89) contained numerous lineage-specific gene and reaction differences. All six E. coli models were evaluated by comparing model predictions to carbon source utilization measurements under aerobic and anaerobic conditions, and to batch growth profiles in minimal media with 0.2% (w/v) glucose. An ancestral genome-scale

  20. Simple topological properties predict functional misannotations in a metabolic network

    PubMed Central

    Liberal, Rodrigo; Pinney, John W.

    2013-01-01

    Motivation: Misannotation in sequence databases is an important obstacle for automated tools for gene function annotation, which rely extensively on comparison with sequences with known function. To improve current annotations and prevent future propagation of errors, sequence-independent tools are, therefore, needed to assist in the identification of misannotated gene products. In the case of enzymatic functions, each functional assignment implies the existence of a reaction within the organism’s metabolic network; a first approximation to a genome-scale metabolic model can be obtained directly from an automated genome annotation. Any obvious problems in the network, such as dead end or disconnected reactions, can, therefore, be strong indications of misannotation. Results: We demonstrate that a machine-learning approach using only network topological features can successfully predict the validity of enzyme annotations. The predictions are tested at three different levels. A random forest using topological features of the metabolic network and trained on curated sets of correct and incorrect enzyme assignments was found to have an accuracy of up to 86% in 5-fold cross-validation experiments. Further cross-validation against unseen enzyme superfamilies indicates that this classifier can successfully extrapolate beyond the classes of enzyme present in the training data. The random forest model was applied to several automated genome annotations, achieving an accuracy of in most cases when validated against recent genome-scale metabolic models. We also observe that when applied to draft metabolic networks for multiple species, a clear negative correlation is observed between predicted annotation quality and phylogenetic distance to the major model organism for biochemistry (Escherichia coli for prokaryotes and Homo sapiens for eukaryotes). Contact: j.pinney@imperial.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID

  1. Disorders of Carbohydrate Metabolism

    MedlinePlus

    ... Metabolic Disorders Disorders of Carbohydrate Metabolism Disorders of Amino Acid Metabolism Disorders of Lipid Metabolism Carbohydrates are sugars. ... Metabolic Disorders Disorders of Carbohydrate Metabolism Disorders of Amino Acid Metabolism Disorders of Lipid Metabolism NOTE: This is ...

  2. MPW : the metabolic pathways database.

    SciTech Connect

    Selkov, E., Jr.; Grechkin, Y.; Mikhailova, N.; Selkov, E.; Mathematics and Computer Science; Russian Academy of Sciences

    1998-01-01

    The Metabolic Pathways Database (MPW) (www.biobase.com/emphome.html/homepage. html.pags/pathways.html) a derivative of EMP (www.biobase.com/EMP) plays a fundamental role in the technology of metabolic reconstructions from sequenced genomes under the PUMA (www.mcs.anl.gov/home/compbio/PUMA/Production/ ReconstructedMetabolism/reconstruction.html), WIT (www.mcs.anl.gov/home/compbio/WIT/wit.html ) and WIT2 (beauty.isdn.msc.anl.gov/WIT2.pub/CGI/user.cgi) systems. In October 1997, it included some 2800 pathway diagrams covering primary and secondary metabolism, membrane transport, signal transduction pathways, intracellular traffic, translation and transcription. In the current public release of MPW (beauty.isdn.mcs.anl.gov/MPW), the encoding is based on the logical structure of the pathways and is represented by the objects commonly used in electronic circuit design. This facilitates drawing and editing the diagrams and makes possible automation of the basic simulation operations such as deriving stoichiometric matrices, rate laws, and, ultimately, dynamic models of metabolic pathways. Individual pathway diagrams, automatically derived from the original ASCII records, are stored as SGML instances supplemented by relational indices. An auxiliary database of compound names and structures, encoded in the SMILES format, is maintained to unambiguously connect the pathways to the chemical structures of their intermediates.

  3. Analysis of Sensitive CO2 Pathways and Genes Related to Carbon Uptake and Accumulation in Chlamydomonas reinhardtii through Genomic Scale Modeling and Expe