| 1. |
- 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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| 2. |
- 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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| 3. |
- 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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| 4. |
- Ågren, Rasmus, 1982, et al.
(författare)
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Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling
- 2014
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Ingår i: Molecular Systems Biology. - : EMBO. - 1744-4292. ; 10:3
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Tidskriftsartikel (refereegranskat)abstract
- Synopsis Personalized GEMs for six hepatocellular carcinoma patients are reconstructed using proteomics data and a task-driven model reconstruction algorithm. These GEMs are used to predict antimetabolites preventing tumor growth in all patients or in individual patients. The presence of proteins encoded by 15,841 genes in tumors from 27 HCC patients is evaluated by immunohistochemistry. Personalized GEMs for six HCC patients and GEMs for 83 healthy cell types are reconstructed based on HMR 2.0 and the tINIT algorithm for task-driven model reconstruction. 101 antimetabolites are predicted to inhibit tumor growth in all patients. Antimetabolite toxicity is tested using the 83 cell type-specific GEMs. 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.
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| 5. |
- Ågren, Rasmus, 1982, et al.
(författare)
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Reconstruction of Genome-Scale Active Metabolic Networks for 69 Human Cell Types and 16 Cancer Types Using INIT
- 2012
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Ingår i: PLoS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 8:5
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 6. |
- Ågren, Rasmus, 1982, et al.
(författare)
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The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum
- 2013
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Ingår i: PLoS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 9:3, s. e1002980-
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 7. |
- Ahmed, Engy, et al.
(författare)
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Archaeal community changes in Lateglacial lake sediments: Evidence from ancient DNA
- 2018
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Ingår i: Quaternary Science Reviews. - : Elsevier BV. - 0277-3791 .- 1873-457X. ; 181, s. 19-29
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Tidskriftsartikel (refereegranskat)abstract
- The Lateglacial/early Holocene sediments from the ancient lake at Hässeldala Port, southern Sweden provide an important archive for the environmental and climatic shifts at the end of the last ice age and the transition into the present Interglacial. The existing multi-proxy data set highlights the complex interplay of physical and ecological changes in response to climatic shifts and lake status changes. Yet, it remains unclear how microorganisms, such as Archaea, which do not leave microscopic features in the sedimentary record, were affected by these climatic shifts. Here we present the metagenomic data set of Hässeldala Port with a special focus on the abundance and biodiversity of Archaea. This allows reconstructing for the first time the temporal succession of major Archaea groups between 13.9 and 10.8 ka BP by using ancient environmental DNA metagenomics and fossil archaeal cell membrane lipids. We then evaluate to which extent these findings reflect physical changes of the lake system, due to changes in lake-water summer temperature and seasonal lake-ice cover. We show that variations in archaeal composition and diversity were related to a variety of factors (e.g., changes in lake water temperature, duration of lake ice cover, rapid sediment infilling), which influenced bottom water conditions and the sediment-water interface. Methanogenic Archaea dominated during the Allerød and Younger Dryas pollen zones, when the ancient lake was likely stratified and anoxic for large parts of the year. The increase in archaeal diversity at the Younger Dryas/Holocene transition is explained by sediment infilling and formation of a mire/peatbog.
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| 8. |
- Bysani, Madhusudhan, et al.
(författare)
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ATAC-seq reveals alterations in open chromatin in pancreatic islets from subjects with type 2 diabetes
- 2019
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322 .- 2045-2322. ; 9:1
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Tidskriftsartikel (refereegranskat)abstract
- Impaired insulin secretion from pancreatic islets is a hallmark of type 2 diabetes (T2D). Altered chromatin structure may contribute to the disease. We therefore studied the impact of T2D on open chromatin in human pancreatic islets. We used assay for transposase-accessible chromatin using sequencing (ATAC-seq) to profile open chromatin in islets from T2D and non-diabetic donors. We identified 57,105 and 53,284 ATAC-seq peaks representing open chromatin regions in islets of nondiabetic and diabetic donors, respectively. The majority of ATAC-seq peaks mapped near transcription start sites. Additionally, peaks were enriched in enhancer regions and in regions where islet-specific transcription factors (TFs), e.g. FOXA2, MAFB, NKX2.2, NKX6.1 and PDX1, bind. Islet ATAC-seq peaks overlap with 13 SNPs associated with T2D (e.g. rs7903146, rs2237897, rs757209, rs11708067 and rs878521 near TCF7L2, KCNQ1, HNF1B, ADCY5 and GCK, respectively) and with additional 67 SNPs in LD with known T2D SNPs (e.g. SNPs annotated to GIPR, KCNJ11, GLIS3, IGF2BP2, FTO and PPARG). There was enrichment of open chromatin regions near highly expressed genes in human islets. Moreover, 1,078 open chromatin peaks, annotated to 898 genes, differed in prevalence between diabetic and non-diabetic islet donors. Some of these peaks are annotated to candidate genes for T2D and islet dysfunction (e.g. HHEX, HMGA2, GLIS3, MTNR1B and PARK2) and some overlap with SNPs associated with T2D (e.g. rs3821943 near WFS1 and rs508419 near ANK1). Enhancer regions and motifs specific to key TFs including BACH2, FOXO1, FOXA2, NEUROD1, MAFA and PDX1 were enriched in differential islet ATAC-seq peaks of T2D versus non-diabetic donors. Our study provides new understanding into how T2D alters the chromatin landscape, and thereby accessibility for TFs and gene expression, in human pancreatic islets.
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| 9. |
- 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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| 10. |
- Charbord, Jeremie, et al.
(författare)
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In vivo screen identifies a SIK inhibitor that induces beta cell proliferation through a transient UPR
- 2021
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Ingår i: Nature Metabolism. - : Springer Science and Business Media LLC. - 2522-5812. ; 3:5, s. 682-700
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Tidskriftsartikel (refereegranskat)abstract
- It is known that beta cell proliferation expands the beta cell mass during development and under certain hyperglycemic conditions in the adult, a process that may be used for beta cell regeneration in diabetes. Here, through a new high-throughput screen using a luminescence ubiquitination-based cell cycle indicator (LUCCI) in zebrafish, we identify HG-9-91-01 as a driver of proliferation and confirm this effect in mouse and human beta cells. HG-9-91-01 is an inhibitor of salt-inducible kinases (SIKs), and overexpression of Sik1 specifically in beta cells blocks the effect of HG-9-91-01 on beta cell proliferation. Single-cell transcriptomic analyses of mouse beta cells demonstrate that HG-9-91-01 induces a wave of activating transcription factor (ATF)6-dependent unfolded protein response (UPR) before cell cycle entry. Importantly, the UPR wave is not associated with an increase in insulin expression. Additional mechanistic studies indicate that HG-9-91-01 induces multiple signalling effectors downstream of SIK inhibition, including CRTC1, CRTC2, ATF6, IRE1 and mTOR, which integrate to collectively drive beta cell proliferation. A high-throughput chemical screen identifies the salt-inducible kinase inhibitor HG-9-91-01 as a driver of beta cell proliferation, acting through an ATF6-dependent unfolded protein response.
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| 11. |
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| 12. |
- Lehmann, Philipp, et al.
(författare)
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Metabolome dynamics of diapause in the butterfly Pieris napi: Distinguishing maintenance, termination and post-diapause phases
- 2018
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Ingår i: Journal of Experimental Biology. - : The Company of Biologists. - 1477-9145 .- 0022-0949. ; 221:2
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Tidskriftsartikel (refereegranskat)abstract
- Diapause is a deep resting stage facilitating temporal avoidance of unfavourable environmental conditions, and is used by many insects to adapt their life cycle to seasonal variation. Although considerable work has been invested in trying to understand each of the major diapause stages (induction, maintenance and termination), we know very little about the transitions between stages, especially diapause termination. Understanding diapause termination is crucial for modelling and predicting spring emergence and winter physiology of insects, including many pest insects. In order to gain these insights, we investigated metabolome dynamics across diapause development in pupae of the butterfly Pieris napi, which exhibits adaptive latitudinal variation in the length of endogenous diapause that is uniquely well characterized. By employing a time-series experiment, we show that the whole-body metabolome is highly dynamic throughout diapause and differs between pupae kept at a diapause-terminating (low) temperature and those kept at a diapause-maintaining (high) temperature. We show major physiological transitions through diapause, separate temperature-dependent from temperature-independent processes and identify significant patterns of metabolite accumulation and degradation. Together, the data show that although the general diapause phenotype (suppressed metabolism, increased cold tolerance) is established in a temperature-independent fashion, diapause termination is temperature dependent and requires a cold signal. This revealed several metabolites that are only accumulated under diapause-terminating conditions and degraded in a temperatureunrelated fashion during diapause termination. In conclusion, our findings indicate that some metabolites, in addition to functioning as cryoprotectants, for example, are candidates for having regulatory roles as metabolic clocks or time-keepers during diapause.
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| 13. |
- 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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| 14. |
- 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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| 15. |
- 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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| 16. |
- 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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| 17. |
- 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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| 18. |
- 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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| 19. |
- Ågren, Rasmus, 1982, et al.
(författare)
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Genome-scale modeling enables metabolic engineering of Saccharomyces cerevisiae for succinic acid production
- 2013
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Ingår i: Journal of Industrial Microbiology and Biotechnology. - : Oxford University Press (OUP). - 1367-5435 .- 1476-5535. ; 40:7, s. 735-747
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Tidskriftsartikel (refereegranskat)abstract
- In this work, we describe the application of a genome-scale metabolic model and flux balance analysis for the prediction of succinic acid overproduction strategies in Saccharomyces cerevisiae. The top three single gene deletion strategies, Delta mdh1, Delta oac1, and Delta dic1, were tested using knock-out strains cultivated anaerobically on glucose, coupled with physiological and DNA microarray characterization. While Delta mdh1 and Delta oac1 strains failed to produce succinate, Delta dic1 produced 0.02 C-mol/C-mol glucose, in close agreement with model predictions (0.03 C-mol/C-mol glucose). Transcriptional profiling suggests that succinate formation is coupled to mitochondrial redox balancing, and more specifically, reductive TCA cycle activity. While far from industrial titers, this proof-of-concept suggests that in silico predictions coupled with experimental validation can be used to identify novel and non-intuitive metabolic engineering strategies.
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| 20. |
- Ågren, Rasmus, 1982
(författare)
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On metabolic networks and multi-omics integration
- 2013
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Cellular metabolism is a highly complex chemical system, involving thousands of interacting metabolites and reactions. The traditional approach to understanding metabolism has been that of reductionism; by isolating and carefully measuring the involved components, the goal has been to understand the whole as the sum of its parts. This reductionist approach has successfully identified most of the components of metabolism but, unfortunately, it fails to capture the long-range and complex interactions that are essential for the functionality. Systems biology is an emerging research field which uses high-throughput data generation and mathematical modelling in order to apply a holistic, or network-centric, view on metabolism. One type of modelling framework, which is in line with this thinking, is genome-scale metabolic modelling. These models, called GEMs, represent very valuable resources, but their applications have been limited due to the large manual effort required to reconstruct them. In this project, we have developed algorithms and software for streamlining the reconstruction process, as well as for novel applications of GEMs. More specifically, we here present: the RAVEN Toolbox, a software suite for automated reconstruction and quality control; the INIT algorithm, an algorithm for inferring GEMs for human cell types; an algorithm which integrates fluxomics and transcriptomics data in order to identify transcriptionally controlled metabolic reactions.The methods and software were used in a number of case studies to address real biological questions. These studies were: 1) Metabolic engineering of Saccharomyces cerevisiae for succinic acid overproduction. The predictions from the modelling were successfully validated experimentally. 2) Study of metabolic regulation in S. cerevisiae. This led to the identification of a small number of transcription factors and enzymes which were predicted to be controlling central parts of metabolism. 3). Penicillin production in Penicillium chrysogenum. This led to the reconstruction of the first GEM for P. chrysogenum, an important resource in itself, and to identification of metabolic engineering targets for more efficient production of penicillin. 4) Human cancer metabolism. This led to the identification of metabolic subnetworks which were predicted to be significantly more active in cancers, and to identification of potential drug targets for treatment. 5) Lipid metabolism in obesity. This led to new insights into the large-scale metabolic rearrangements associated with obesity, and to identification of possible therapeutic strategies. 6) Metabolism in non-alcoholic fatty liver disease. This led to the identification of serine deficiency as a central aspect of the disease, and to proposed therapeutic strategies for remedying it.The work put forward in this thesis has resulted in improvements on several important aspects of genome-scale metabolic modelling, and it has shown how the framework can be applied to gain novel biological insights. As such, it can contribute to further increase the role of the framework in modelling of human health and disease.
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