| 1. |
- Caspeta-Guadarrama, Luis, 1974, et al.
(författare)
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Genome-scale metabolic reconstructions of Pichia stipitis and Pichia pastoris and in-silico evaluation of their potentials
- 2012
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Ingår i: BMC Systems Biology. - : Springer Science and Business Media LLC. - 1752-0509. ; 6:24
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundPichia 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. ResultsIn 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. ConclusionsIn 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.
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| 2. |
- Cvijovic, Marija, 1977, et al.
(författare)
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BioMet Toolbox: genome-wide analysis of metabolism
- 2010
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Ingår i: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 38:SUPPL. 2, s. W144-W149
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Tidskriftsartikel (refereegranskat)abstract
- The rapid progress of molecular biology tools for directed genetic modifications, accurate quantitative experimental approaches, high-throughput measurements, together with development of genome sequencing has made the foundation for a new area of metabolic engineering that is driven by metabolic models. Systematic analysis of biological processes by means of modelling and simulations has made the identification of metabolic networks and prediction of metabolic capabilities under different conditions possible. For facilitating such systemic analysis, we have developed the BioMet Toolbox, a web-based resource for stoichiometric analysis and for integration of transcriptome and interactome data, thereby exploiting the capabilities of genome-scale metabolic models. The BioMet Toolbox provides an effective user-friendly way to perform linear programming simulations towards maximized or minimized growth rates, substrate uptake rates and metabolic production rates by detecting relevant fluxes, simulate single and double gene deletions or detect metabolites around which major transcriptional changes are concentrated. These tools can be used for high-throughput in silico screening and allows fully standardized simulations. Model files for various model organisms (fungi and bacteria) are included. Overall, the BioMet Toolbox serves as a valuable resource for exploring the capabilities of these metabolic networks. BioMet Toolbox is freely available at www.sysbio.se/BioMet/.
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| 3. |
- Liu, Liming, 1976, et al.
(författare)
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Use of genome-scale metabolic models for understanding microbial physiology
- 2010
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Ingår i: FEBS Letters. - : Wiley. - 1873-3468 .- 0014-5793. ; 584:12, s. 2556-2564
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Tidskriftsartikel (refereegranskat)abstract
- The exploitation of microorganisms in industrial, medical, food and environmental biotechnology requires a comprehensive understanding of their physiology. The availability of genome sequences and accumulation of high-throughput data allows gaining understanding of microbial physiology at the systems level, and genome-scale metabolic models represent a valuable framework for integrative analysis of metabolism of microorganisms. Genome-scale metabolic models are reconstructed based on a combination of genome sequence information and detailed biochemical information, and these reconstructed models can be used for analyzing and simulating the operation of metabolism in response to different stimuli. Here we discuss the requirement for having detailed physiological insight in order to exploit microorganisms for production of fuels, chemicals and pharmaceuticals. We further describe the reconstruction process of genome-scale metabolic models and different algorithms that can be used to apply these models to gain improved insight into microbial physiology. (C) 2010 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.
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| 4. |
- Mardinoglu, Adil, 1982, et al.
(författare)
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Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease
- 2014
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 5, s. 3083-
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 5. |
- Mardinoglu, Adil, 1982, et al.
(författare)
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Integration of clinical data with a genome-scale metabolic model of the human adipocyte
- 2013
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Ingår i: Molecular Systems Biology. - : EMBO. - 1744-4292 .- 1744-4292. ; 9, s. 649-
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Tidskriftsartikel (refereegranskat)abstract
- We evaluated the presence/absence of proteins encoded by 14 077 genes in adipocytes obtained from different tissue samples using immunohistochemistry. By combining this with previously published adipocyte-specific proteome data, we identified proteins associated with 7340 genes in human adipocytes. This information was used to reconstruct a comprehensive and functional genome-scale metabolic model of adipocyte metabolism. The resulting metabolic model, iAdipocytes1809, enables mechanistic insights into adipocyte metabolism on a genome-wide level, and can serve as a scaffold for integration of omics data to understand the genotype-phenotype relationship in obese subjects. By integrating human transcriptome and fluxome data, we found an increase in the metabolic activity around androsterone, ganglioside GM2 and degradation products of heparan sulfate and keratan sulfate, and a decrease in mitochondrial metabolic activities in obese subjects compared with lean subjects. Our study hereby shows a path to identify new therapeutic targets for treating obesity through combination of high throughput patient data and metabolic modeling.
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| 6. |
- Pabinger, S., et al.
(författare)
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MEMOSys: Bioinformatics platform for genome-scale metabolic models
- 2011
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Ingår i: BMC Systems Biology. - : Springer Science and Business Media LLC. - 1752-0509. ; 5
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 7. |
- Thiele, I., et al.
(författare)
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A community-driven global reconstruction of human metabolism
- 2013
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Ingår i: Nature Biotechnology. - : Springer Science and Business Media LLC. - 1087-0156 .- 1546-1696. ; 31:5, s. 419-
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Tidskriftsartikel (refereegranskat)abstract
- 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 similar to 2x more reactions and similar to 1.7x 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/.
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| 8. |
- Tymoshenko, S., et al.
(författare)
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Metabolic Needs and Capabilities of Toxoplasma gondii through Combined Computational and Experimental Analysis
- 2015
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Ingår i: PLoS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 11:5, s. Art. no. e1004261-
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 9. |
- Velasco, Sergio, 1980, et al.
(författare)
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Sampling the Solution Space in Genome-Scale Metabolic Networks Reveals Transcriptional Regulation in Key Enzymes
- 2010
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Ingår i: PLoS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 6:7, s. 16-16
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Tidskriftsartikel (refereegranskat)abstract
- Genome-scale metabolic models are available for an increasing number of organisms and can be used to define the region of feasible metabolic flux distributions. In this work we use as constraints a small set of experimental metabolic fluxes, which reduces the region of feasible metabolic states. Once the region of feasible flux distributions has been defined, a set of possible flux distributions is obtained by random sampling and the averages and standard deviations for each of the metabolic fluxes in the genome-scale model are calculated. These values allow estimation of the significance of change for each reaction rate between different conditions and comparison of it with the significance of change in gene transcription for the corresponding enzymes. The comparison of flux change and gene expression allows identification of enzymes showing a significant correlation between flux change and expression change (transcriptional regulation) as well as reactions whose flux change is likely to be driven only by changes in the metabolite concentrations (metabolic regulation). The changes due to growth on four different carbon sources and as a consequence of five gene deletions were analyzed for Saccharomyces cerevisiae. The enzymes with transcriptional regulation showed enrichment in certain transcription factors. This has not been previously reported. The information provided by the presented method could guide the discovery of new metabolic engineering strategies or the identification of drug targets for treatment of metabolic diseases.
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| 10. |
- Wang, Hao, 1975, et al.
(författare)
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RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor
- 2018
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Ingår i: PLoS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 14:10, s. e1006541-
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Tidskriftsartikel (refereegranskat)abstract
- RAVEN is a commonly used MATLAB toolbox for genome-scale metabolic model (GEM) reconstruction, curation and constraint-based modelling and simulation. Here we present RAVEN Toolbox 2.0 with major enhancements, including: (i) de novo reconstruction of GEMs based on the MetaCyc pathway database; (ii) a redesigned KEGG-based reconstruction pipeline; (iii) convergence of reconstructions from various sources; (iv) improved performance, usability, and compatibility with the COBRA Toolbox. Capabilities of RAVEN 2.0 are here illustrated through de novo reconstruction of GEMs for the antibiotic-producing bacterium Streptomyces coelicolor. Comparison of the automated de novo reconstructions with the iMK1208 model, a previously published high-quality S. coelicolor GEM, exemplifies that RAVEN 2.0 can capture most of the manually curated model. The generated de novo reconstruction is subsequently used to curate iMK1208 resulting in Sco4, the most comprehensive GEM of S. coelicolor, with increased coverage of both primary and secondary metabolism. This increased coverage allows the use of Sco4 to predict novel genome editing targets for optimized secondary metabolites production. As such, we demonstrate that RAVEN 2.0 can be used not only for de novo GEM reconstruction, but also for curating existing models based on up-to-date databases. Both RAVEN 2.0 and Sco4 are distributed through GitHub to facilitate usage and further development by the community (https://github.com/SysBioChalmers/RAVEN and https://github.com/SysBioChalmers/Streptomyces_coelicolor-GEM).
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