42 results on '"Jamshidi N"'
Search Results
2. Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions.
- Author
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Bordbar A, Lewis NE, Schellenberger J, Palsson BØ, and Jamshidi N
- Subjects
- Adenosine Triphosphate, Computer Simulation, Databases, Genetic, Genes, Bacterial, Host-Pathogen Interactions, Humans, Macrophages, Alveolar metabolism, Metabolic Networks and Pathways, Monte Carlo Method, Mycobacterium tuberculosis genetics, Mycobacterium tuberculosis pathogenicity, Nitric Oxide metabolism, Computational Biology methods, Macrophages, Alveolar microbiology, Models, Biological, Mycobacterium tuberculosis metabolism
- Abstract
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.
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- 2010
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3. Formulating genome-scale kinetic models in the post-genome era.
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Jamshidi N and Palsson BØ
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- Algorithms, Computational Biology methods, Computer Simulation, Genomics, Glycolysis, Kinetics, Models, Biological, Models, Theoretical, Thermodynamics, Time Factors, Genome, Systems Biology
- Abstract
The biological community is now awash in high-throughput data sets and is grappling with the challenge of integrating disparate data sets. Such integration has taken the form of statistical analysis of large data sets, or through the bottom-up reconstruction of reaction networks. While progress has been made with statistical and structural methods, large-scale systems have remained refractory to dynamic model building by traditional approaches. The availability of annotated genomes enabled the reconstruction of genome-scale networks, and now the availability of high-throughput metabolomic and fluxomic data along with thermodynamic information opens the possibility to build genome-scale kinetic models. We describe here a framework for building and analyzing such models. The mathematical analysis challenges are reflected in four foundational properties, (i) the decomposition of the Jacobian matrix into chemical, kinetic and thermodynamic information, (ii) the structural similarity between the stoichiometric matrix and the transpose of the gradient matrix, (iii) the duality transformations enabling either fluxes or concentrations to serve as the independent variables and (iv) the timescale hierarchy in biological networks. Recognition and appreciation of these properties highlight notable and challenging new in silico analysis issues.
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- 2008
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4. Systems biology of SNPs.
- Author
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Jamshidi N and Palsson BØ
- Subjects
- Humans, Mitochondria genetics, Polymorphism, Single Nucleotide genetics, Systems Biology
- Abstract
Genome-scale networks can now be reconstructed based on high-throughput data sets. Mathematical analyses of these networks are used to compute their candidate functional or phenotypic states. Analysis of functional states of networks shows that the activity of biochemical reactions can be highly correlated in physiological states, forming so-called co-sets representing functional modules of the network. Thus, detrimental sequence defects in any one of the genes encoding members of a co-set can result in similar phenotypic consequences. Here we show that causal single nucleotide polymorphisms in genes encoding mitochondrial components can be classified and correlated using co-sets.
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- 2006
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5. Is the kinetome conserved?
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Palsson, Bernhard O and Yurkovich, James T
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CELL physiology ,BIOLOGISTS ,BIOLOGY - Abstract
Computational biologists have labored for decades to produce kinetic models to mechanistically explain complex metabolic phenomena. The estimation of numerical values for the large number of kinetic parameters required for constructing large‐scale models has been a major challenge. This collection of kinetic constants has recently been termed the kinetome (Nilsson et al, 2017). In this Commentary, we discuss the recent advances in the field that suggest that the kinetome may be more conserved than expected. A conserved kinetome will accelerate the development of future kinetic models of integrated cellular functions and expand their scope and usability in many fields of biology and biomedicine. [ABSTRACT FROM AUTHOR]
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- 2022
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6. COVID19 Disease Map, a computational knowledge repository of virus–host interaction mechanisms.
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Ostaszewski, Marek, Niarakis, Anna, Mazein, Alexander, Kuperstein, Inna, Phair, Robert, Orta‐Resendiz, Aurelio, Singh, Vidisha, Aghamiri, Sara Sadat, Acencio, Marcio Luis, Glaab, Enrico, Ruepp, Andreas, Fobo, Gisela, Montrone, Corinna, Brauner, Barbara, Frishman, Goar, Monraz Gómez, Luis Cristóbal, Somers, Julia, Hoch, Matti, Kumar Gupta, Shailendra, and Scheel, Julia
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COVID-19 ,DISEASE mapping ,COVID-19 pandemic ,SARS-CoV-2 ,INSTITUTIONAL repositories - Abstract
We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS‐CoV‐2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large‐scale community effort to build an open access, interoperable and computable repository of COVID‐19 molecular mechanisms. The COVID‐19 Disease Map (C19DMap) is a graphical, interactive representation of disease‐relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph‐based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS‐CoV‐2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID‐19 or similar pandemics in the long‐term perspective. SYNOPSIS: COVID‐19 Disease Map is a large‐scale collection of curated computational models and diagrams of molecular mechanisms involved in SARS‐CoV‐2 infection. The map supports the computational exploration of pathways affected by the virus. COVID‐19 Disease Map was built by over 20 independent biocuration teams and harmonised using systems biology standards.Biocuration efforts were assisted by the systematic use of text‐ and AI‐assisted mining of relevant bioinformatic databases and platforms.Case studies illustrate the applications of the map for visual exploration and computational analysis of SARS‐CoV‐2 pathways in combination with omic data.The map is an open‐access effort, with all content and code shared in public repositories. [ABSTRACT FROM AUTHOR]
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- 2021
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7. Personalized whole‐body models integrate metabolism, physiology, and the gut microbiome.
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Thiele, Ines, Sahoo, Swagatika, Heinken, Almut, Hertel, Johannes, Heirendt, Laurent, Aurich, Maike K, and Fleming, Ronan MT
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GUT microbiome ,METABOLIC models ,BASAL metabolism ,BLOOD groups ,PHYSIOLOGY - Abstract
Comprehensive molecular‐level models of human metabolism have been generated on a cellular level. However, models of whole‐body metabolism have not been established as they require new methodological approaches to integrate molecular and physiological data. We developed a new metabolic network reconstruction approach that used organ‐specific information from literature and omics data to generate two sex‐specific whole‐body metabolic (WBM) reconstructions. These reconstructions capture the metabolism of 26 organs and six blood cell types. Each WBM reconstruction represents whole‐body organ‐resolved metabolism with over 80,000 biochemical reactions in an anatomically and physiologically consistent manner. We parameterized the WBM reconstructions with physiological, dietary, and metabolomic data. The resulting WBM models could recapitulate known inter‐organ metabolic cycles and energy use. We also illustrate that the WBM models can predict known biomarkers of inherited metabolic diseases in different biofluids. Predictions of basal metabolic rates, by WBM models personalized with physiological data, outperformed current phenomenological models. Finally, integrating microbiome data allowed the exploration of host–microbiome co‐metabolism. Overall, the WBM reconstructions, and their derived computational models, represent an important step toward virtual physiological humans. Synopsis: Sex‐specific, whole‐body human metabolic models were developed and constrained with physiological, dietary, and metabolomic data. They recapitulate known whole‐body metabolic functions and enable mechanistic exploration of host‐microbiome co‐metabolism. Sex‐specific whole‐body metabolic reconstructions represent the integrated function of 26 organs and six blood cell types.Stoichiometric reconstructions of metabolism can be constrained with whole‐body physiological and metabolomic data to generate personalized models.Whole‐body metabolic models recapitulate known inter‐organ metabolic cycles and energy use, successfully predict known biomarkers of inherited metabolic diseases, and explore potential host‐microbiome co‐metabolism. [ABSTRACT FROM AUTHOR]
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- 2020
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8. De novo MYC addiction as an adaptive response of cancer cells to CDK4/6 inhibition.
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Tarrado‐Castellarnau, Míriam, de Atauri, Pedro, Tarragó‐Celada, Josep, Perarnau, Jordi, Yuneva, Mariia, Thomson, Timothy M, and Cascante, Marta
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CYCLIN-dependent kinases ,THERAPEUTICS ,TUMORS ,HYPOXEMIA ,MYC proteins - Abstract
Cyclin-dependent kinases (CDK) are rational cancer therapeutic targets fraught with the development of acquired resistance by tumor cells. Through metabolic and transcriptomic analyses, we show that the inhibition of CDK4/6 leads to a metabolic reprogramming associated with gene networks orchestrated by the MYC transcription factor. Upon inhibition of CDK4/6, an accumulation of MYC protein ensues which explains an increased glutamine metabolism, activation of the mTOR pathway and blunting of HIF- 1a-mediated responses to hypoxia. These MYC-driven adaptations to CDK4/6 inhibition render cancer cells highly sensitive to inhibitors of MYC, glutaminase or mTOR and to hypoxia, demonstrating that metabolic adaptations to antiproliferative drugs unveil new vulnerabilities that can be exploited to overcome acquired drug tolerance and resistance by cancer cells. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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9. Transcriptomics resources of human tissues and organs.
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Uhlén, Mathias, Hallström, Björn M, Lindskog, Cecilia, Mardinoglu, Adil, Pontén, Fredrik, and Nielsen, Jens
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GENETIC regulation ,POSTHUMOUS conception ,PROTEOMICS ,HUMAN physiology ,ENDOMETRIUM ,ANATOMY - Abstract
Quantifying the differential expression of genes in various human organs, tissues, and cell types is vital to understand human physiology and disease. Recently, several large-scale transcriptomics studies have analyzed the expression of protein-coding genes across tissues. These datasets provide a framework for defining the molecular constituents of the human body as well as for generating comprehensive lists of proteins expressed across tissues or in a tissue-restricted manner. Here, we review publicly available human transcriptome resources and discuss body-wide data from independent genome-wide transcriptome analyses of different tissues. Gene expression measurements from these independent datasets, generated using samples from fresh frozen surgical specimens and postmortem tissues, are consistent. Overall, the different genome-wide analyses support a distribution in which many proteins are found in all tissues and relatively few in a tissue-restricted manner. Moreover, we discuss the applications of publicly available omics data for building genome-scale metabolic models, used for analyzing cell and tissue functions both in physiological and in disease contexts. [ABSTRACT FROM AUTHOR]
- Published
- 2016
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10. Modeling cancer metabolism on a genome scale.
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Yizhak, Keren, Chaneton, Barbara, Gottlieb, Eyal, and Ruppin, Eytan
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CANCER cells ,CELL metabolism ,TARGETED drug delivery ,BIOMARKERS ,CYTOLOGY - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2015
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11. A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration.
- Author
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Yizhak, Keren, Le Dévédec, Sylvia E, Rogkoti, Vasiliki Maria, Baenke, Franziska, Boer, Vincent C, Frezza, Christian, Schulze, Almut, Water, Bob, and Ruppin, Eytan
- Subjects
CANCER prevention ,CELL proliferation ,CELL migration ,ANTINEOPLASTIC agents ,COMPUTATIONAL biology ,GENE silencing ,LUNG cancer - Abstract
Over the last decade, the field of cancer metabolism has mainly focused on studying the role of tumorigenic metabolic rewiring in supporting cancer proliferation. Here, we perform the first genome-scale computational study of the metabolic underpinnings of cancer migration. We build genome-scale metabolic models of the NCI-60 cell lines that capture the Warburg effect (aerobic glycolysis) typically occurring in cancer cells. The extent of the Warburg effect in each of these cell line models is quantified by the ratio of glycolytic to oxidative ATP flux ( AFR), which is found to be highly positively associated with cancer cell migration. We hence predicted that targeting genes that mitigate the Warburg effect by reducing the AFR may specifically inhibit cancer migration. By testing the anti-migratory effects of silencing such 17 top predicted genes in four breast and lung cancer cell lines, we find that up to 13 of these novel predictions significantly attenuate cell migration either in all or one cell line only, while having almost no effect on cell proliferation. Furthermore, in accordance with the predictions, a significant reduction is observed in the ratio between experimentally measured ECAR and OCR levels following these perturbations. Inhibiting anti-migratory targets is a promising future avenue in treating cancer since it may decrease cytotoxic-related side effects that plague current anti-proliferative treatments. Furthermore, it may reduce cytotoxic-related clonal selection of more aggressive cancer cells and the likelihood of emerging resistance. [ABSTRACT FROM AUTHOR]
- Published
- 2014
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12. Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling.
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Agren, Rasmus, Mardinoglu, Adil, Asplund, Anna, Kampf, Caroline, Uhlen, Mathias, and Nielsen, Jens
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ANTINEOPLASTIC agents ,LIVER cancer ,IMMUNOHISTOCHEMISTRY ,TUMOR growth ,ANTIMETABOLITES ,CELL lines - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2014
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13. Dissecting specific and global transcriptional regulation of bacterial gene expression.
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Gerosa, Luca, Kochanowski, Karl, Heinemann, Matthias, and Sauer, Uwe
- Abstract
Gene expression is regulated by specific transcriptional circuits but also by the global expression machinery as a function of growth. Simultaneous specific and global regulation thus constitutes an additional—but often neglected—layer of complexity in gene expression. Here, we develop an experimental-computational approach to dissect specific and global regulation in the bacterium Escherichia coli. By using fluorescent promoter reporters, we show that global regulation is growth rate dependent not only during steady state but also during dynamic changes in growth rate and can be quantified through two promoter-specific parameters. By applying our approach to arginine biosynthesis,we obtain a quantitative understanding of both specific and global regulation that allows accurate prediction of the temporal response to simultaneous perturbations in arginine availability and growth rate. We thereby uncover two principles of joint regulation: (i) specific regulation by repression dominates the transcriptional response during metabolic steady states, largely repressing the biosynthesis genes even when biosynthesis is required and (ii) global regulation sets the maximum promoter activity that is exploited during the transition between steady states. [ABSTRACT FROM AUTHOR]
- Published
- 2013
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14. Dissecting the energy metabolism in Mycoplasma pneumoniae through genome-scale metabolic modeling.
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Wodke, Judith A. H., Puchałka, Jacek, Lluch-Senar, Maria, Marcos, Josep, Yus, Eva, Godinho, Miguel, Gutiérrez-Gallego, Ricardo, Martins dos Santos, Vitor A. P., Serrano, Luis, Klipp, Edda, and Maier, Tobias
- Abstract
Mycoplasma pneumoniae, a threatening pathogen with a minimal genome, is a model organism for bacterial systems biology forwhich substantial experimental information is available.With the goal of understanding the complex interactions underlying its metabolism, we analyzed and characterized themetabolic network of M. pneumoniae in great detail, integrating data fromdifferent omics analyses under a range of conditions into a constraint-based model backbone. Iterating model predictions, hypothesis generation, experimental testing, and model refinement, we accurately curated the network and quantitatively explored the energy metabolism. In contrast to other bacteria, M. pneumoniae uses most of its energy for maintenance tasks instead of growth.We show that in highly linear networks the prediction of flux distributions for different growth times allows analysis of timedependent changes, albeit using a staticmodel. By performing an in silico knock-out study as well as analyzing flux distributions in single and doublemutant phenotypes,we demonstrated that themodel accurately represents the metabolism of M. pneumoniae. The experimentally validated model provides a solid basis for understanding its metabolic regulatory mechanisms. [ABSTRACT FROM AUTHOR]
- Published
- 2013
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15. Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli.
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McCloskey, Douglas, Palsson, Bernhard Ø, and Feist, Adam M
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The genome-scale model (GEM) of metabolism in the bacterium Escherichia coli K-12 has been in development for over a decade and is now in wide use. GEM-enabled studies of E. coli have been primarily focused on six applications: (1) metabolic engineering, (2) model-driven discovery, (3) prediction of cellular phenotypes, (4) analysis of biological network properties, (5) studies of evolutionary processes, and (6) models of interspecies interactions. In this review,we provide an overview of these applications along with a critical assessment of their successes and limitations, and a perspective on likely future developments in the field. Taken together, the studies performed over the past decade have established a genomescale mechanistic understanding of genotype–phenotype relationships in E. coli metabolism that forms the basis for similar efforts for other microbial species. Future challenges include the expansion of GEMs by integrating additional cellular processes beyond metabolism, the identification of key constraints based on emerging data types, and the development of computational methods able to handle such large-scale network models with sufficient accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2013
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16. Glutamine-driven oxidative phosphorylation is a major ATP source in transformed mammalian cells in both normoxia and hypoxia.
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Fan, Jing, Kamphorst, Jurre J, Mathew, Robin, Chung, Michelle K, White, Eileen, Shlomi, Tomer, and Rabinowitz, Joshua D
- Abstract
Mammalian cells can generate ATP via glycolysis or mitochondrial respiration. Oncogene activation and hypoxia promote glycolysis and lactate secretion. The significance of these metabolic changes to ATP production remains however ill defined. Here, we integrate LC-MS-based isotope tracer studies with oxygen uptake measurements in a quantitative redox-balanced metabolic flux model of mammalian cellular metabolism. We then apply this approach to assess the impact of Ras and Akt activation and hypoxia on energy metabolism. Both oncogene activation and hypoxia induce roughly a twofold increase in glycolytic flux. Ras activation and hypoxia also strongly decrease glucose oxidation. Oxidative phosphorylation, powered substantially by glutamine-driven TCA turning, however, persists and accounts for the majority of ATP production. Consistent with this, in all cases, pharmacological inhibition of oxidative phosphorylation markedly reduces energy charge, and glutamine but not glucose removal markedly lowers oxygen uptake. Thus, glutamine-driven oxidative phosphorylation is a major means of ATP production even in hypoxic cancer cells. [ABSTRACT FROM AUTHOR]
- Published
- 2013
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17. Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction.
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O'Brien, Edward J, Lerman, Joshua A, Chang, Roger L, Hyduke, Daniel R, and Palsson, Bernhard Ø
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Growth is a fundamental process of life. Growth requirements are well-characterized experimentally for many microbes; however,we lack a unified model for cellular growth. Such a model must be predictive of events at the molecular scale and capable of explaining the high-level behavior of the cell as a whole. Here, we construct an ME-Model for Escherichia coli—a genome-scale model that seamlessly integrates metabolic and gene product expression pathways. The model computes ~ 80% of the functional proteome (by mass), which is used by the cell to support growth under a given condition. Metabolism and gene expression are interdependent processes that affect and constrain each other.We formalize these constraints and apply the principle of growth optimization to enable the accurate prediction of multi-scale phenotypes, ranging from coarse-grained (growth rate, nutrient uptake, by-product secretion) to fine-grained (metabolic fluxes, gene expression levels). Our results unify many existing principles developed to describe bacterial growth. [ABSTRACT FROM AUTHOR]
- Published
- 2013
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18. Integration of clinical data with a genome-scale metabolic model of the human adipocyte.
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Mardinoglu, Adil, Agren, Rasmus, Kampf, Caroline, Asplund, Anna, Nookaew, Intawat, Jacobson, Peter, Walley, Andrew J, Froguel, Philippe, Carlsson, Lena M, Uhlen, Mathias, and Nielsen, Jens
- 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. [ABSTRACT FROM AUTHOR]
- Published
- 2013
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19. Prediction and identification of sequences coding for orphan enzymes using genomic and metagenomic neighbours.
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Yamada, Takuji, Waller, Alison S, Raes, Jeroen, Zelezniak, Aleksej, Perchat, Nadia, Perret, Alain, Salanoubat, Marcel, Patil, Kiran R, Weissenbach, Jean, and Bork, Peer
- Abstract
Despite the current wealth of sequencing data, one‐third of all biochemically characterized metabolic enzymes lack a corresponding gene or protein sequence, and as such can be considered orphan enzymes. They represent a major gap between our molecular and biochemical knowledge, and consequently are not amenable to modern systemic analyses. As 555 of these orphan enzymes have metabolic pathway neighbours, we developed a global framework that utilizes the pathway and (meta)genomic neighbour information to assign candidate sequences to orphan enzymes. For 131 orphan enzymes (37% of those for which (meta)genomic neighbours are available), we associate sequences to them using scoring parameters with an estimated accuracy of 70%, implying functional annotation of 16 345 gene sequences in numerous (meta)genomes. As a case in point, two of these candidate sequences were experimentally validated to encode the predicted activity. In addition, we augmented the currently available genome‐scale metabolic models with these new sequence–function associations and were able to expand the models by on average 8%, with a considerable change in the flux connectivity patterns and improved essentiality prediction. [ABSTRACT FROM AUTHOR]
- Published
- 2012
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20. Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation.
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Bordbar, Aarash, Mo, Monica L, Nakayasu, Ernesto S, Schrimpe‐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, and Palsson, Bernhard O
- Abstract
Macrophages are central players in immune response, manifesting divergent phenotypes to control inflammation and innate immunity through release of cytokines and other signaling factors. Recently, 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 that are critical for macrophage activation. We constructed a genome-scale metabolic network for the RAW 264.7 cell line to determine metabolic modulators of activation. Metabolites well-known to be associated with immunoactivation (glucose and arginine) and immunosuppression (tryptophan and vitamin D3) were among the most critical effectors. Intracellular metabolic mechanisms were assessed, identifying a suppressive role for de-novo nucleotide synthesis. Finally, 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 connections between activation and metabolic effectors. [ABSTRACT FROM AUTHOR]
- Published
- 2012
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21. Integrative genome-scale metabolic analysis of Vibrio vulnificus for drug targeting and discovery.
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Kim, Hyun Uk, Kim, Soo Young, Jeong, Haeyoung, Kim, Tae Yong, Kim, Jae Jong, Choy, Hyon E, Yi, Kyu Yang, Rhee, Joon Haeng, and Lee, Sang Yup
- Abstract
Although the genomes of many microbial pathogens have been studied to help identify effective drug targets and novel drugs, such efforts have not yet reached full fruition. In this study,we report a systems biological approach that efficiently utilizes genomic information for drug targeting and discovery, and apply this approach to the opportunistic pathogen Vibrio vulnificus CMCP6. First, we partially re-sequenced and fully re-annotated the V. vulnificus CMCP6 genome, and accordingly reconstructed its genome-scale metabolic network, VvuMBEL943. The validated network model was employed to systematically predict drug targets using the concept of metabolite essentiality, along with additional filtering criteria. Target genes encoding enzymes that interact with the five essential metabolites finally selected were experimentally validated. These five essential metabolites are critical to the survival of the cell, and hence were used to guide the cost-effective selection of chemical analogs, which were then screened for antimicrobial activity in a whole-cell assay. This approach is expected to help fill the existing gap between genomics and drug discovery. [ABSTRACT FROM AUTHOR]
- Published
- 2011
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22. Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism.
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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, and Papin, Jason A
- Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2011
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23. Protein localization as a principal feature of the etiology and comorbidity of genetic diseases.
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Park, Solip, Yang, Jae‐Seong, Shin, Young‐Eun, Park, Juyong, Jang, Sung Key, and Kim, Sanguk
- Abstract
Proteins targeting the same subcellular localization tend to participate in mutual protein–protein interactions (PPIs) and are often functionally associated. Here, we investigated the relationship between disease-associated proteins and their subcellular localizations, based on the assumption that protein pairs associated with phenotypically similar diseases are more likely to be connected via subcellular localization. The spatial constraints from subcellular localization significantly strengthened the disease associations of the proteins connected by subcellular localizations. In particular, certain disease types were more prevalent in specific subcellular localizations. We analyzed the enrichment of disease phenotypes within subcellular localizations, and found that there exists a significant correlation between disease classes and subcellular localizations. Furthermore, we found that two diseases displayed high comorbidity when disease-associated proteins were connected via subcellular localization. We newly explained 7584 disease pairs by using the context of protein subcellular localization, which had not been identified using shared genes or PPIs only. Our result establishes a direct correlation between protein subcellular localization and disease association, and helps to understand the mechanism of human disease progression. [ABSTRACT FROM AUTHOR]
- Published
- 2011
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24. Predicting selective drug targets in cancer through metabolic networks.
- Author
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Folger, Ori, Jerby, Livnat, Frezza, Christian, Gottlieb, Eyal, Ruppin, Eytan, and Shlomi, Tomer
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The interest in studying metabolic alterations in cancer and their potential role as novel targets for therapy has been rejuvenated in recent years. Here, we report the development of the first genome‐scale network model of cancer metabolism, validated by correctly identifying genes essential for cellular proliferation in cancer cell lines. The model predicts 52 cytostatic drug targets, of which 40% are targeted by known, approved or experimental anticancer drugs, and the rest are new. It further predicts combinations of synthetic lethal drug targets, whose synergy is validated using available drug efficacy and gene expression measurements across the NCI‐60 cancer cell line collection. Finally, potential selective treatments for specific cancers that depend on cancer type‐specific downregulation of gene expression and somatic mutations are compiled. [ABSTRACT FROM AUTHOR]
- Published
- 2011
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25. A comprehensive genome-scale reconstruction of Escherichia coli metabolism—2011.
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Orth, Jeffrey D, Conrad, Tom M, Na, Jessica, Lerman, Joshua A, Nam, Hojung, Feist, Adam M, and Palsson, Bernhard Ø
- Abstract
The initial genome-scale reconstruction of the metabolic network of Escherichia coli K-12 MG1655 was assembled in 2000. It has been updated and periodically released since then based on new and curated genomic and biochemical knowledge. An update has now been built, named iJO1366, which accounts for 1366 genes, 2251 metabolic reactions, and 1136 unique metabolites. iJO1366 was (1) updated in part using a new experimental screen of 1075 gene knockout strains, illuminating cases where alternative pathways and isozymes are yet to be discovered, (2) compared with its predecessor and to experimental data sets to confirm that it continues to make accurate phenotypic predictions of growth on different substrates and for gene knockout strains, and (3) mapped to the genomes of all available sequenced E. coli strains, including pathogens, leading to the identification of hundreds of unannotated genes in these organisms. Like its predecessors, the iJO1366 reconstruction is expected to be widely deployed for studying the systems biology of E. coli and for metabolic engineering applications. [ABSTRACT FROM AUTHOR]
- Published
- 2011
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26. HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology.
- Author
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Gille, Christoph, Bölling, Christian, Hoppe, Andreas, Bulik, Sascha, Hoffmann, Sabrina, Hübner, Katrin, Karlstädt, Anja, Ganeshan, Ramanan, König, Matthias, Rother, Kristian, Weidlich, Michael, Behre, Jörn, and Holzhütter, Herrmann‐Georg
- Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2010
- Full Text
- View/download PDF
27. Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism.
- Author
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Jerby, Livnat, Shlomi, Tomer, and Ruppin, Eytan
- Abstract
The computational study of human metabolism has been advanced with the advent of the first generic (non-tissue specific) stoichiometric model of human metabolism. In this study, we present a new algorithm for rapid reconstruction of tissue-specific genome-scale models of human metabolism. The algorithm generates a tissue-specific model from the generic human model by integrating a variety of tissue-specific molecular data sources, including literature-based knowledge, transcriptomic, proteomic, metabolomic and phenotypic data. Applying the algorithm, we constructed the first genome-scale stoichiometric model of hepatic metabolism. The model is verified using standard cross-validation procedures, and through its ability to carry out hepatic metabolic functions. The model’s flux predictions correlate with flux measurements across a variety of hormonal and dietary conditions, and improve upon the predictive performance obtained using the original, generic human model (prediction accuracy of 0.67 versus 0.46). Finally, the model better predicts biomarker changes in genetic metabolic disorders than the generic human model (accuracy of 0.67 versus 0.59). The approach presented can be used to construct other human tissue-specific models, and be applied to other organisms. [ABSTRACT FROM AUTHOR]
- Published
- 2010
- Full Text
- View/download PDF
28. Reconstruction and flux-balance analysis of the Plasmodium falciparum metabolic network.
- Author
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Plata, Germán, Hsiao, Tzu‐Lin, Olszewski, Kellen L, Llinás, Manuel, and Vitkup, Dennis
- Abstract
Genome‐scale metabolic reconstructions can serve as important tools for hypothesis generation and high‐throughput data integration. Here, we present a metabolic network reconstruction and flux‐balance analysis (FBA) of Plasmodium falciparum, the primary agent of malaria. The compartmentalized metabolic network accounts for 1001 reactions and 616 metabolites. Enzyme–gene associations were established for 366 genes and 75% of all enzymatic reactions. Compared with other microbes, the P. falciparum metabolic network contains a relatively high number of essential genes, suggesting little redundancy of the parasite metabolism. The model was able to reproduce phenotypes of experimental gene knockout and drug inhibition assays with up to 90% accuracy. Moreover, using constraints based on gene‐expression data, the model was able to predict the direction of concentration changes for external metabolites with 70% accuracy. Using FBA of the reconstructed network, we identified 40 enzymatic drug targets (i.e. in silico essential genes), with no or very low sequence identity to human proteins. To demonstrate that the model can be used to make clinically relevant predictions, we experimentally tested one of the identified drug targets, nicotinate mononucleotide adenylyltransferase, using a recently discovered small‐molecule inhibitor. [ABSTRACT FROM AUTHOR]
- Published
- 2010
- Full Text
- View/download PDF
29. Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models.
- Author
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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, König, Rainer, Smith, Richard D, and Palsson, Bernhard Ø
- Abstract
After hundreds of generations of adaptive evolution at exponential growth, Escherichia coli grows as predicted using flux balance analysis (FBA) on genome-scale metabolic models (GEMs). However, it is not known whether the predicted pathway usage in FBA solutions is consistent with gene and protein expression in the wild-type and evolved strains. Here, we report that >98% 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 evolved strains upregulate genes within the optimal growth predictions, and downregulate genes outside of the optimal growth solutions. In addition, bottlenecks from dosage limitations of computationally predicted essential genes are overcome in the evolved strains.We also identify regulatory processes that may contribute to the development of the optimal growth phenotype in the evolved strains, such as the downregulation of known regulons and stringent response suppression. Thus, differential gene and protein expression from wild-type and adaptively evolved strains supports observed growth phenotype changes, and is consistent with GEM-computed optimal growth states. [ABSTRACT FROM AUTHOR]
- Published
- 2010
- Full Text
- View/download PDF
30. Finding multiple target optimal intervention in disease-related molecular network.
- Author
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Yang, Kun, Bai, Hongjun, Ouyang, Qi, Lai, Luhua, and Tang, Chao
- Abstract
Drugs against multiple targets may overcome the many limitations of single targets and achieve a more effective and safer control of the disease. Numerous high-throughput experiments have been performed in this emerging field. However, systematic identification of multiple drug targets and their best intervention requires knowledge of the underlying disease network and calls for innovative computational methods that exploit the network structure and dynamics. Here, we develop a robust computational algorithm for finding multiple target optimal intervention (MTOI) solutions in a disease network. MTOI identifies potential drug targets and suggests optimal combinations of the target intervention that best restore the network to a normal state, which can be customer designed. We applied MTOI to an inflammation-related network. The well-known side effects of the traditional non-steriodal anti-inflammatory drugs and the recently recalled Vioxx were correctly accounted for in our network model. A number of promising MTOI solutions were found to be both effective and safer. [ABSTRACT FROM AUTHOR]
- Published
- 2008
- Full Text
- View/download PDF
31. Metabolic model integration of the bibliome, genome, metabolome and reactome of Aspergillus niger.
- Author
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Andersen, Mikael Rørdam, Nielsen, Michael Lynge, and Nielsen, Jens
- Abstract
The release of the genome sequences of two strains of Aspergillus niger has allowed systems-level investigations of this important microbial cell factory. To this end, tools for doing data integration of multi-ome data are necessary, and especially interesting in the context of metabolism. On the basis of an A. niger bibliome survey, we present the largest model reconstruction of a metabolic network reported for a fungal species. The reconstructed gapless metabolic network is based on the reportings of 371 articles and comprises 1190 biochemically unique reactions and 871 ORFs. Inclusion of isoenzymes increases the total number of reactions to 2240. A graphical map of the metabolic network is presented. All levels of the reconstruction process were based on manual curation. From the reconstructed metabolic network, a mathematical model was constructed and validated with data on yields, fluxes and transcription. The presented metabolic network and map are useful tools for examining systemwide data in a metabolic context. Results from the validated model show a great potential for expanding the use of A. niger as a high-yield production platform. [ABSTRACT FROM AUTHOR]
- Published
- 2008
- Full Text
- View/download PDF
32. Systems analysis of metabolism in the pathogenic trypanosomatid Leishmania major.
- Author
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Chavali, Arvind K, Whittemore, Jeffrey D, Eddy, James A, Williams, Kyle T, and Papin, Jason A
- Abstract
Systems analyses have facilitated the characterization of metabolic networks of several organisms. We have reconstructed the metabolic network of Leishmania major, a poorly characterized organism that causes cutaneous leishmaniasis in mammalian hosts. This network reconstruction accounts for 560 genes, 1112 reactions, 1101 metabolites and 8 unique subcellular localizations. Using a systemsbased approach, we hypothesized a comprehensive set of lethal single and double gene deletions, some of which were validated using published data with approximately 70%accuracy. Additionally, we generated hypothetical annotations to dozens of previously uncharacterized genes in the L. major genome and proposed a minimal medium for growth. We further demonstrated the utility of a network reconstruction with two proof-of-concept examples that yielded insight into robustness of the network in the presence of enzymatic inhibitors and delineation of promastigote/amastigote stage-specific metabolism. This reconstruction and the associated network analyses of L. major is the first of its kind for a protozoan. It can serve as a tool for clarifying discrepancies between data sources, generating hypotheses that can be experimentally validated and identifying ideal therapeutic targets. [ABSTRACT FROM AUTHOR]
- Published
- 2008
- Full Text
- View/download PDF
33. Reply to "Do genome-scale models need exact solvers or clearer standards?".
- Author
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Chindelevitch, Leonid, Trigg, Jason, Regev, Aviv, and Berger, Bonnie
- Subjects
GENOMES ,STRUCTURAL analysis (Science) ,METABOLIC models - Abstract
Chindelevitch et al address the issues raised by Ebrahim et al and advocate that both improved standards and exact arithmetic are needed to advance the field. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
34. Towards whole-body systems physiology.
- Author
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Kuepfer, Lars
- Published
- 2010
- Full Text
- View/download PDF
35. Comprehensive quantitative analysis of central carbon and amino-acid metabolism in Saccharomyces cerevisiae under multiple conditions by targeted proteomics.
- Author
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Costenoble, Roeland, Picotti, Paola, Reiter, Lukas, Stallmach, Robert, Heinemann, Matthias, Sauer, Uwe, and Aebersold, Ruedi
- Subjects
SYSTEMS biology ,BIOCHEMICAL engineering ,SACCHAROMYCES cerevisiae ,METABOLISM ,PROTEOMICS - Abstract
Decades of biochemical research have identified most of the enzymes that catalyze metabolic reactions in the yeast Saccharomyces cerevisiae. The adaptation of metabolism to changing nutritional conditions, in contrast, is much less well understood. As an important stepping stone toward such understanding, we exploit the power of proteomics assays based on selected reaction monitoring (SRM) mass spectrometry to quantify abundance changes of the 228 proteins that constitute the central carbon and amino-acid metabolic network in the yeast Saccharomyces cerevisiae, at five different metabolic steady states. Overall, 90% of the targeted proteins, including families of isoenzymes, were consistently detected and quantified in each sample, generating a proteomic data set that represents a nutritionally perturbed biological system at high reproducibility. The data set is near comprehensive because we detect 95-99% of all proteins that are required under a given condition. Interpreted through flux balance modeling, the data indicate that S. cerevisiae retains proteins not necessarily used in a particular environment. Further, the data suggest differential functionality for several metabolic isoenzymes. [ABSTRACT FROM AUTHOR]
- Published
- 2011
- Full Text
- View/download PDF
36. Predicting metabolic biomarkers of human inborn errors of metabolism.
- Author
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Shlomi, Tomer, Cabili, Moran N, and Ruppin, Eytan
- Subjects
BIOMARKERS ,GENETIC mutation ,ERYTHROCYTES ,MEDICAL research ,ENZYMES - Abstract
Early diagnosis of inborn errors of metabolism is commonly performed through biofluid metabolomics, which detects specific metabolic biomarkers whose concentration is altered due to genomic mutations. The identification of new biomarkers is of major importance to biomedical research and is usually performed through data mining of metabolomic data. After the recent publication of the genome-scale network model of human metabolism, we present a novel computational approach for systematically predicting metabolic biomarkers in stochiometric metabolic models. Applying the method to predict biomarkers for disruptions of red-blood cell metabolism demonstrates a marked correlation with altered metabolic concentrations inferred through kinetic model simulations. Applying the method to the genome-scale human model reveals a set of 233 metabolites whose concentration is predicted to be either elevated or reduced as a result of 176 possible dysfunctional enzymes. The method's predictions are shown to significantly correlate with known disease biomarkers and to predict many novel potential biomarkers. Using this method to prioritize metabolite measurement experiments to identify new biomarkers can provide an order of a 10-fold increase in biomarker detection performance. [ABSTRACT FROM AUTHOR]
- Published
- 2009
- Full Text
- View/download PDF
37. Quantifying epistatic interactions among the components constituting the protein translation system.
- Author
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Matsuura, Tomoaki, Kazuta, Yasuaki, Aita, Takuyo, Adachi, Jiro, and Yomo, Tetsuya
- Subjects
PROTEIN synthesis ,BIOLOGICAL systems ,MATHEMATICAL optimization ,PROTEINS ,BIOMOLECULES - Abstract
In principle, the accumulation of knowledge regarding the molecular basis of biological systems should allow the development of large-scale kinetic models of their functions. However, the development of such models requires vast numbers of parameters, which are difficult to obtain in practice. Here, we used an in vitro translation system, consisting of 69 defined components, to quantify the epistatic interactions among changes in component concentrations through Bahadur expansion, thereby obtaining a coarse-grained model of protein synthesis activity. Analyses of the data measured using various combinations of component concentrations indicated that the contributions of larger than 2-body inter-component epistatic interactions are negligible, despite the presence of larger than 2-body physical interactions. These findings allowed the prediction of protein synthesis activity at various combinations of component concentrations from a small number of samples, the principle of which is applicable to analysis and optimization of other biological systems. Moreover, the average ratio of 2- to 1-body terms was estimated to be as small as 0.1, implying high adaptability and evolvability of the protein translation system. [ABSTRACT FROM AUTHOR]
- Published
- 2009
- Full Text
- View/download PDF
38. Applications of genome-scale metabolic reconstructions.
- Author
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Oberhardt, Matthew A, Palsson, Bernhard Ø, and Papin, Jason A
- Subjects
GENOMES ,METABOLISM ,BIOCHEMISTRY ,GENETICS ,BIOLOGY - Abstract
The availability and utility of genome-scale metabolic reconstructions have exploded since the first genome-scale reconstruction was published a decade ago. Reconstructions have now been built for a wide variety of organisms, and have been used toward five major ends: (1) contextualization of high-throughput data, (2) guidance of metabolic engineering, (3) directing hypothesis-driven discovery, (4) interrogation of multi-species relationships, and (5) network property discovery. In this review, we examine the many uses and future directions of genome-scale metabolic reconstructions, and we highlight trends and opportunities in the field that will make the greatest impact on many fields of biology. [ABSTRACT FROM AUTHOR]
- Published
- 2009
- Full Text
- View/download PDF
39. The Edinburgh human metabolic network reconstruction and its functional analysis.
- Author
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Ma, Hongwu, Sorokin, Anatoly, Mazein, Alexander, Selkov, Alex, Selkov, Evgeni, Demin, Oleg, and Goryanin, Igor
- Subjects
METABOLISM ,BIOLOGICAL systems ,METABOLITES ,GENOMES ,BIOMOLECULES - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2007
- Full Text
- View/download PDF
40. Genome‐scale metabolic modeling reveals SARS‐CoV‐2‐induced metabolic changes and antiviral targets
- Author
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Cheng, Kuoyuan, Martin‐Sancho, Laura, Pal, Lipika R, Pu, Yuan, Riva, Laura, Yin, Xin, Sinha, Sanju, Nair, Nishanth Ulhas, Chanda, Sumit K, and Ruppin, Eytan
- Published
- 2021
- Full Text
- View/download PDF
41. An integrated computational and experimental study uncovers FUT9 as a metabolic driver of colorectal cancer
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Auslander, Noam, Cunningham, Chelsea E, Toosi, Behzad M, McEwen, Emily J, Yizhak, Keren, Vizeacoumar, Frederick S, Parameswaran, Sreejit, Gonen, Nir, Freywald, Tanya, Bhanumathy, Kalpana K, Freywald, Andrew, Vizeacoumar, Franco J, and Ruppin, Eytan
- Published
- 2017
- Full Text
- View/download PDF
42. Subspecies in the global human gut microbiome
- Author
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Costea, Paul I, Coelho, Luis Pedro, Sunagawa, Shinichi, Munch, Robin, Huerta‐Cepas, Jaime, Forslund, Kristoffer, Hildebrand, Falk, Kushugulova, Almagul, Zeller, Georg, and Bork, Peer
- Published
- 2017
- Full Text
- View/download PDF
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