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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

      BackgroundUrine 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.ResultsWe 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.ConclusionOur 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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      Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.

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        • Record: found
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        • Article: not found

        A simple method for displaying the hydropathic character of a protein.

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          • Record: found
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          • Article: not found

          Probability-based protein identification by searching sequence databases using mass spectrometry data.

          Several algorithms have been described in the literature for protein identification by searching a sequence database using mass spectrometry data. In some approaches, the experimental data are peptide molecular weights from the digestion of a protein by an enzyme. Other approaches use tandem mass spectrometry (MS/MS) data from one or more peptides. Still others combine mass data with amino acid sequence data. We present results from a new computer program, Mascot, which integrates all three types of search. The scoring algorithm is probability based, which has a number of advantages: (i) A simple rule can be used to judge whether a result is significant or not. This is particularly useful in guarding against false positives. (ii) Scores can be compared with those from other types of search, such as sequence homology. (iii) Search parameters can be readily optimised by iteration. The strengths and limitations of probability-based scoring are discussed, particularly in the context of high throughput, fully automated protein identification.
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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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