Urine proteomics for profiling of human disease using high accuracy mass spectrometry Academic Article uri icon

Overview

MeSH Major

  • Computational Biology
  • Models, Biological

abstract

  • Knowledge of the biologically relevant components of human tissues has enabled the invention of numerous clinically useful diagnostic tests, as well as non-invasive ways of monitoring disease and its response to treatment. Recent use of advanced MS-based proteomics revealed that the composition of human urine is more complex than anticipated. Here, we extend the current characterization of the human urinary proteome by extensively fractionating urine using ultra-centrifugation, gel electrophoresis, ion exchange and reverse-phase chromatography, effectively reducing mixture complexity while minimizing loss of material. By using high-accuracy mass measurements of the linear ion trap-Orbitrap mass spectrometer and LC-MS/MS of peptides generated from such extensively fractionated specimens, we identified 2362 proteins in routinely collected individual urine specimens, including more than 1000 proteins not described in previous studies. Many of these are biomedically significant molecules, including glomerularly filtered cytokines and shed cell surface molecules, as well as renally and urogenitally produced transporters and structural proteins. Annotation of the identified proteome reveals distinct patterns of enrichment, consistent with previously described specific physiologic mechanisms, including 336 proteins that appear to be expressed by a variety of distal organs and glomerularly filtered from serum. Comparison of the proteomes identified from 12 individual specimens revealed a subset of generally invariant proteins, as well as individually variable ones, suggesting that our approach may be used to study individual differences in age, physiologic state and clinical condition. Consistent with this, annotation of the identified proteome by using machine learning and text mining exposed possible associations with 27 common and more than 500 rare human diseases, establishing a widely useful resource for the study of human pathophysiology and biomarker discovery.

publication date

  • November 11, 2009

Research

keywords

  • Academic Article

Identity

Language

  • eng

PubMed Central ID

  • PMC2994589

Digital Object Identifier (DOI)

  • 10.1002/prca.200900008

PubMed ID

  • 21127740

Additional Document Info

start page

  • 1052

end page

  • 1061

volume

  • 3

number

  • 9