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Application of systems biology principles to protein biomarker discovery: Urinary exosomal proteome in renal transplantation
Author(s) -
Pisitkun Trairak,
Gandolfo Maria T.,
Das Samarjit,
Knepper Mark A.,
Bagnasco Serena M.
Publication year - 2012
Publication title -
proteomics – clinical applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.948
H-Index - 54
eISSN - 1862-8354
pISSN - 1862-8346
DOI - 10.1002/prca.201100108
Subject(s) - biomarker discovery , biomarker , kegg , proteome , urinary system , computational biology , exosome , microvesicles , proteomics , biology , bioinformatics , transplantation , transcriptome , medicine , gene expression , microrna , gene , biochemistry
Purpose In mass spectrometry ( MS )‐based studies to discover urinary protein biomarkers, an important question is how to analyze the data to find the most promising potential biomarkers to be advanced to large‐scale validation studies. Here, we describe a “systems biology‐based” approach to address this question. Experimental design We analyzed large‐scale liquid chromatography‐tandem mass spectrometry ( LC ‐ MS / MS ) data of urinary exosomes from renal allograft recipients with biopsy‐proven evidence of immunological rejection or tubular injury ( TI ). We asked whether bioinformatic analysis of urinary exosomal proteins can identify biological‐process based protein groups that correlate with biopsy findings and whether the protein groups fit with general knowledge of the pathophysiological mechanisms involved. Results LC ‐ MS / MS analysis of urinary exosomal proteomes identified more than 1000 proteins in each pathologic group. These protein lists were analyzed computationally to identify the Biological Process and Kyoto Encyclopedia of Genes and Genomes ( KEGG ) Pathway terms that are significantly associated with each pathological group. Among the most informative terms for each group were: “sodium ion transport” for TI ; “immune response” for all rejection; “epithelial cell differentiation” for cell‐mediated rejection; and “acute inflammatory response” for antibody‐mediated rejection. Based on these terms, candidate biomarkers were identified using a novel strategy to allow a dichotomous classification between different pathologic categories. Conclusions and clinical relevance The terms and candidate biomarkers identified make rational connections to pathophysiological mechanisms, suggesting that the described bioinformatic approach will be useful in advancing large‐scale biomarker identification studies toward a validation phase.