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A database of naturally occurring human urinary peptides and proteins for use in clinical applications
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  • Published: 12 October 2007

A database of naturally occurring human urinary peptides and proteins for use in clinical applications

  • Petra Zürbig1,
  • Joshua Coon2,
  • Hartwig Bauer3,
  • Georg Behrens4,
  • Mohammed Dakna1,
  • Anna Dominiczak5,
  • Stephane Decramer6,
  • Jochen Ehrich7,
  • Danilo Fliser8,
  • Moritz Frommberger9,
  • Arnold Ganser10,
  • Mark Giolami11,
  • Igor Golovko1,
  • David Good12,
  • Wilfried Gwinner8,
  • Marion Haubitz8,
  • Stefan Herget-Rosenthal13,
  • Holger Jahn14,
  • George Jerums15,
  • Bruce Julian16,
  • Markus Kellmann17,
  • Volker Kliem18,
  • Walter Kolch19,
  • Andrzej Krolewski20,
  • Mario Luppi21,
  • Ziad Massy22,
  • Michael Melter23,
  • Christian Neusüss24,
  • Jan Novak25,
  • Karlheinz Peter26,
  • Kasper Rossing27,
  • Harald Rupprecht28,
  • Joost Schanstra6,
  • Eric Schiffer1,
  • Jens-Uwe Stolzenburg29,
  • Lise Tarnow27,
  • Dan Theodorescu30,
  • Visith Thongboonkerd31,
  • Raymond Vanholder32,
  • Eva Weissinger10,
  • Harald Mischak1 &
  • …
  • Philippe Schmitt-Kopplin9 

Nature Precedings (2007)Cite this article

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Abstract

Owing to its availability, ease of collection and correlation with (patho-) physiology, urine is an attractive source for clinical proteomics. However, the lack of comparable datasets from large cohorts has greatly hindered development in this field. Here we report the establishment of a high resolution proteome database of naturally occurring human urinary peptides and proteins - ranging from 800-17,000 Da - from over 3,600 individual samples using capillary electrophoresis coupled to mass spectrometry, yielding an average of 1,500 peptides per sample. All processed data were deposited in an SQL database, currently containing 5,010 relevant unique urinary peptides that serve as classifiers for diagnosis and monitoring of diseases, including kidney and vascular diseases. Of these, 352 have been sequenced to date. To demonstrate the applicability of this database, two examples of disease diagnosis were provided: For renal damage diagnosis, patients with a specific renal disease were identified with high specificity and sensitivity in a blinded cohort of 131 individuals. We further show definition of biomarkers specific for immunosuppression and complications after transplantation (Kaposi’s sarcoma). Due to its high information content, this database will be a powerful tool for the validation of biomarkers for both renal and non-renal diseases.

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Authors and Affiliations

  1. Mosaiques diagnostics & therapeutics AG https://www.nature.com/nature

    Petra Zürbig, Mohammed Dakna, Igor Golovko, Eric Schiffer & Harald Mischak

  2. University of Wisconsin-Madison, Department of Chemistry and Biomolecular Chemistry https://www.nature.com/nature

    Joshua Coon

  3. Ludwig-Maximilians-University Munich https://www.nature.com/nature

    Hartwig Bauer

  4. Hannover Medical School, Department of Clinical Immunology https://www.nature.com/nature

    Georg Behrens

  5. University of Glasgow, BHF Glasgow Cardiovascular Research Centre https://www.nature.com/nature

    Anna Dominiczak

  6. CHU de Toulouse, Pediatric Nephrology Unit https://www.nature.com/nature

    Stephane Decramer & Joost Schanstra

  7. Hannover Medical School, Children’s Hospital https://www.nature.com/nature

    Jochen Ehrich

  8. Hannover Medical School, Department of Nephrology https://www.nature.com/nature

    Danilo Fliser, Wilfried Gwinner & Marion Haubitz

  9. GSF - National Research Centre for Environment and Health, Institute for Ecological Chemistry https://www.nature.com/nature

    Moritz Frommberger & Philippe Schmitt-Kopplin

  10. Hannover Medical School, Department of Hematology, Hemostasis and Oncology https://www.nature.com/nature

    Arnold Ganser & Eva Weissinger

  11. University of Glasgow, Department of Computing Science https://www.nature.com/nature

    Mark Giolami

  12. University of Wisconsin-Madison, Department of Chemistry https://www.nature.com/nature

    David Good

  13. University Duisburg-Essen, Department of Nephrology https://www.nature.com/nature

    Stefan Herget-Rosenthal

  14. University Hospital Hamburg-Eppendorf, Department of Psychiatry https://www.nature.com/nature

    Holger Jahn

  15. University of Melbourne, Austin Health & Northern Health, Department of Medicine https://www.nature.com/nature

    George Jerums

  16. University of Alabama at Birmingham https://www.nature.com/nature

    Bruce Julian

  17. Thermo Fisher Scientific GmbH https://www.nature.com/nature

    Markus Kellmann

  18. Saxony Centre for Nephrology, Transplantation Centre, Department of Nephrology Lower https://www.nature.com/nature

    Volker Kliem

  19. University of Glasgow, The Beatson Institute for Cancer Research & Sir Henry Wellcome Functional Genomics Facility https://www.nature.com/nature

    Walter Kolch

  20. Harvard Medical School, Department of Medicine https://www.nature.com/nature

    Andrzej Krolewski

  21. University of Modena and Reggio Emilia, Department of Oncology and Hematology https://www.nature.com/nature

    Mario Luppi

  22. UPJV, INSERM, ERI-12, and Amiens University Hospital https://www.nature.com/nature

    Ziad Massy

  23. University of Regensburg, Children’s Hospital https://www.nature.com/nature

    Michael Melter

  24. University of Aalen https://www.nature.com/nature

    Christian Neusüss

  25. University of Alabama at Birmingham, Microbiology https://www.nature.com/nature

    Jan Novak

  26. Heart Research Institute, Centre for Thrombosis & Myocardial Infarction https://www.nature.com/nature

    Karlheinz Peter

  27. Steno Diabetes Centre https://www.nature.com/nature

    Kasper Rossing & Lise Tarnow

  28. Clinical Centre Bayreuth, Department of Nephrology https://www.nature.com/nature

    Harald Rupprecht

  29. University of Leipzig, Department of Diagnostic Radiology https://www.nature.com/nature

    Jens-Uwe Stolzenburg

  30. University of Virginia https://www.nature.com/nature

    Dan Theodorescu

  31. Mahidol University, Faculty of Medicine at Siriraj Hospital https://www.nature.com/nature

    Visith Thongboonkerd

  32. University Hospital of Ghent, Department of Internal Medicine https://www.nature.com/nature

    Raymond Vanholder

Authors
  1. Petra Zürbig
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  2. Joshua Coon
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  3. Hartwig Bauer
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  4. Georg Behrens
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  5. Mohammed Dakna
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  6. Anna Dominiczak
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  7. Stephane Decramer
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  8. Jochen Ehrich
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  9. Danilo Fliser
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  10. Moritz Frommberger
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  11. Arnold Ganser
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  12. Mark Giolami
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  13. Igor Golovko
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  14. David Good
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  15. Wilfried Gwinner
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  16. Marion Haubitz
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  17. Stefan Herget-Rosenthal
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  18. Holger Jahn
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  19. George Jerums
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  20. Bruce Julian
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  21. Markus Kellmann
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  22. Volker Kliem
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  23. Walter Kolch
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  24. Andrzej Krolewski
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  25. Mario Luppi
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  26. Ziad Massy
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  27. Michael Melter
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  28. Christian Neusüss
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  29. Jan Novak
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  30. Karlheinz Peter
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  31. Kasper Rossing
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  32. Harald Rupprecht
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  33. Joost Schanstra
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  34. Eric Schiffer
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  35. Jens-Uwe Stolzenburg
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  36. Lise Tarnow
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  37. Dan Theodorescu
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  38. Visith Thongboonkerd
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  39. Raymond Vanholder
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  40. Eva Weissinger
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  41. Harald Mischak
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  42. Philippe Schmitt-Kopplin
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Corresponding author

Correspondence to Petra Zürbig.

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Cite this article

Zürbig, P., Coon, J., Bauer, H. et al. A database of naturally occurring human urinary peptides and proteins for use in clinical applications. Nat Prec (2007). https://doi.org/10.1038/npre.2007.1219.1

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  • Received: 11 October 2007

  • Accepted: 12 October 2007

  • Published: 12 October 2007

  • DOI: https://doi.org/10.1038/npre.2007.1219.1

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Keywords

  • kidney disease
  • database
  • clinical
  • proteomics
  • Peptides
  • biomarkers
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