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

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          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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          Lipid droplets finally get a little R-E-S-P-E-C-T.

          Long underappreciated as important cellular organelles, lipid droplets are finally being recognized as dynamic structures with a complex and interesting biology. In light of this newfound respect, we discuss emerging views on lipid droplet biology and speculate on the major advances to come.
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            Ongoing and future developments at the Universal Protein Resource

            The primary mission of Universal Protein Resource (UniProt) is to support biological research by maintaining a stable, comprehensive, fully classified, richly and accurately annotated protein sequence knowledgebase, with extensive cross-references and querying interfaces freely accessible to the scientific community. UniProt is produced by the UniProt Consortium which consists of groups from the European Bioinformatics Institute (EBI), the Swiss Institute of Bioinformatics (SIB) and the Protein Information Resource (PIR). UniProt is comprised of four major components, each optimized for different uses: the UniProt Archive, the UniProt Knowledgebase, the UniProt Reference Clusters and the UniProt Metagenomic and Environmental Sequence Database. UniProt is updated and distributed every 4 weeks and can be accessed online for searches or download at http://www.uniprot.org.
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              Network-based prediction of human tissue-specific metabolism.

              Direct in vivo investigation of mammalian metabolism is complicated by the distinct metabolic functions of different tissues. We present a computational method that successfully describes the tissue specificity of human metabolism on a large scale. By integrating tissue-specific gene- and protein-expression data with an existing comprehensive reconstruction of the global human metabolic network, we predict tissue-specific metabolic activity in ten human tissues. This reveals a central role for post-transcriptional regulation in shaping tissue-specific metabolic activity profiles. The predicted tissue specificity of genes responsible for metabolic diseases and tissue-specific differences in metabolite exchange with biofluids extend markedly beyond tissue-specific differences manifest in enzyme-expression data, and are validated by large-scale mining of tissue-specificity data. Our results establish a computational basis for the genome-wide study of normal and abnormal human metabolism in a tissue-specific manner.
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                Author and article information

                Journal
                Nat Commun
                Nature communications
                Springer Science and Business Media LLC
                2041-1723
                2041-1723
                2014
                : 5
                Affiliations
                [1 ] 1] Department of Chemical and Biological Engineering, Chalmers University of Technology, Kamivangen 10, Gothenburg SE-412 96, Sweden [2].
                [2 ] Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala SE-751 85, Sweden.
                [3 ] 1] Department of Proteomics, KTH-Royal Institute of Technology, Stockholm SE-106 91, Sweden [2] Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-171 21, Sweden.
                [4 ] 1] Department of Chemical and Biological Engineering, Chalmers University of Technology, Kamivangen 10, Gothenburg SE-412 96, Sweden [2] Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-171 21, Sweden.
                Article
                ncomms4083
                10.1038/ncomms4083
                24419221
                735f3e5f-ca2b-4876-879a-b1cce1978ff1
                History

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