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

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          Highlights

          • A novel brain-specific metabolic model was reconstructed, covering 570 genes.

          • The model, iMS570, correctly predicts major metabolic fluxes.

          • It is used as a scaffold to map transcriptome data for neurodegenerative diseases.

          • Identified reporter metabolites are potential biomarkers for the disease pathology.

          • Pathway-specific effect of the diseases on brain metabolism is revealed.

          Abstract

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

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          Most cited references125

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          JASPAR: an open-access database for eukaryotic transcription factor binding profiles.

          The analysis of regulatory regions in genome sequences is strongly based on the detection of potential transcription factor binding sites. The preferred models for representation of transcription factor binding specificity have been termed position-specific scoring matrices. JASPAR is an open-access database of annotated, high-quality, matrix-based transcription factor binding site profiles for multicellular eukaryotes. The profiles were derived exclusively from sets of nucleotide sequences experimentally demonstrated to bind transcription factors. The database is complemented by a web interface for browsing, searching and subset selection, an online sequence analysis utility and a suite of programming tools for genome-wide and comparative genomic analysis of regulatory regions. JASPAR is available at http://jaspar. cgb.ki.se.
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            The effects of alternate optimal solutions in constraint-based genome-scale metabolic models.

            Genome-scale constraint-based models of several organisms have now been constructed and are being used for model driven research. A key issue that may arise in the use of such models is the existence of alternate optimal solutions wherein the same maximal objective (e.g., growth rate) can be achieved through different flux distributions. Herein, we investigate the effects that alternate optimal solutions may have on the predicted range of flux values calculated using currently practiced linear (LP) and quadratic programming (QP) methods. An efficient LP-based strategy is described to calculate the range of flux variability that can be present in order to achieve optimal as well as suboptimal objective states. Sample results are provided for growth predictions of E. coli using glucose, acetate, and lactate as carbon substrates. These results demonstrate the extent of flux variability to be highly dependent on environmental conditions and network composition. In addition we examined the impact of alternate optima for growth under gene knockout conditions as calculated using QP-based methods. It was observed that calculations using QP-based methods can show significant variation in growth rate if the flux variability among alternate optima is high. The underlying biological significance and general source of such flux variability is further investigated through the identification of redundancies in the network (equivalent reaction sets) that lead to alternate solutions. Collectively, these results illustrate the variability inherent in metabolic flux distributions and the possible implications of this heterogeneity for constraint-based modeling approaches. These methods also provide an efficient and robust method to calculate the range of flux distributions that can be derived from quantitative fermentation data.
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              Advances in flux balance analysis.

              Biology is going through a paradigm shift from reductionist to holistic, systems-based approaches. The complete genome sequence for a number of organisms is available and the analysis of genome sequence data is proving very useful. Thus, genome sequencing projects and bioinformatic analyses are leading to a complete 'parts catalog' of the molecular components in many organisms. The next challenge will be to reconstruct and simulate overall cellular functions based on the extensive reductionist information. Recent advances have been made in the area of flux balance analysis, a mathematical modeling approach often utilized by metabolic engineers to quantitatively simulate microbial metabolism.
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                Author and article information

                Contributors
                Journal
                FEBS Open Bio
                FEBS Open Bio
                FEBS Open Bio
                Elsevier
                2211-5463
                6 June 2014
                6 June 2014
                2014
                : 4
                : 542-553
                Affiliations
                [a ]Department of Bioengineering, Gebze Institute of Technology, Gebze, Kocaeli, Turkey
                [b ]Department of Chemical Engineering, Boğaziçi University, 34342 Bebek, Istanbul, Turkey
                Author notes
                [* ]Corresponding author. Tel.: +90 262 605 2407; fax: +90 262 653 06 75. tcakir@ 123456gyte.edu.tr
                Article
                S2211-5463(14)00054-0
                10.1016/j.fob.2014.05.006
                4104795
                25061554
                6d06cda3-d512-4fd9-87ff-ffbd6a6fc059
                © 2014 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

                History
                : 28 April 2014
                : 23 May 2014
                : 27 May 2014
                Categories
                Article

                ad, alzheimer’s disease,als, amyotrophic lateral sclerosis,fba, flux balance analysis,gaba, gamma-aminobutyric acid,hd, huntington’s disease,kiv, ketoisovalerate,klf, krüppel-like factor,kmv, alpha-keto-beta-methylvalerate,ms, multiple sclerosis,pca, principal component analysis,pd, parkinson’s disease,rma, reporter metabolite analysis,rpa, reporter pathway analysis,schz, schizophrenia,tca, tricarboxylic acid,usf, upstream stimulatory factor,neurometabolism,brain metabolic network,reporter metabolite,computational systems biology,transcriptome,neurodegenerative diseases

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