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The human urinary proteome contains more than 1500 proteins, including a large proportion of membrane proteins

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      Abstract

      A high confidence set of proteins in urine from healthy donors is described as a reference urinary proteome.

      Abstract

      Background

      Urine is a desirable material for the diagnosis and classification of diseases because of the convenience of its collection in large amounts; however, all of the urinary proteome catalogs currently being generated have limitations in their depth and confidence of identification. Our laboratory has developed methods for the in-depth characterization of body fluids; these involve a linear ion trap-Fourier transform (LTQ-FT) and a linear ion trap-orbitrap (LTQ-Orbitrap) mass spectrometer. Here we applied these methods to the analysis of the human urinary proteome.

      Results

      We employed one-dimensional sodium dodecyl sulfate polyacrylamide gel electrophoresis and reverse phase high-performance liquid chromatography for protein separation and fractionation. Fractionated proteins were digested in-gel or in-solution, and digests were analyzed with the LTQ-FT and LTQ-Orbitrap at parts per million accuracy and with two consecutive stages of mass spectrometric fragmentation. We identified 1543 proteins in urine obtained from ten healthy donors, while essentially eliminating false-positive identifications. Surprisingly, nearly half of the annotated proteins were membrane proteins according to Gene Ontology (GO) analysis. Furthermore, extracellular, lysosomal, and plasma membrane proteins were enriched in the urine compared with all GO entries. Plasma membrane proteins are probably present in urine by secretion in exosomes.

      Conclusion

      Our analysis provides a high-confidence set of proteins present in human urinary proteome and provides a useful reference for comparing datasets obtained using different methodologies. The urinary proteome is unexpectedly complex and may prove useful in biomarker discovery in the future.

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      Most cited references 63

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          BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks.

          The Biological Networks Gene Ontology tool (BiNGO) is an open-source Java tool to determine which Gene Ontology (GO) terms are significantly overrepresented in a set of genes. BiNGO can be used either on a list of genes, pasted as text, or interactively on subgraphs of biological networks visualized in Cytoscape. BiNGO maps the predominant functional themes of the tested gene set on the GO hierarchy, and takes advantage of Cytoscape's versatile visualization environment to produce an intuitive and customizable visual representation of the results.
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            Author and article information

            Affiliations
            [1 ]Department of Proteomics and Signal Transduction, Max-Planck Institute for Biochemistry, Am Klopferspitz, D-82152 Martinsried, Germany
            [2 ]Center for Experimental Bioinformatics, University of Southern Denmark, Campusvej, DK-5230 Odense M, Denmark
            [3 ]Current address: Graduate School of Global Environmental Studies, Kyoto University, Yoshida-Honmachi Sakyo-Ku, Kyoto, Japan
            [4 ]Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 101300, China
            Contributors
            Journal
            Genome Biol
            Genome Biology
            BioMed Central (London )
            1465-6906
            1465-6914
            2006
            1 September 2006
            : 7
            : 9
            : R80
            1794545
            gb-2006-7-9-r80
            16948836
            10.1186/gb-2006-7-9-r80
            Copyright © 2006 Adachi et al.; licensee BioMed Central Ltd.

            This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

            Categories
            Research

            Genetics

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