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Large‐scale protein identification of human urine proteome by multi‐dimensional LC and MS/MS
Author(s) -
Chen YiTing,
Tsao ChaoYun,
Li JenMing,
Tsai ChihYen,
Chiu SuFeng,
Tseng TzuLing
Publication year - 2007
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.200600769
Subject(s) - proteome , urine , computational biology , urinary system , proteomics , human proteome project , biomarker discovery , database search engine , gene , biology , bioinformatics , chemistry , biochemistry , computer science , endocrinology , information retrieval , search engine
Urine is a human specimen that is easily obtained non‐invasively for clinical diagnosis. We attempted to enhance the resolution of current human urine proteomes and construct a comprehensive reference database for advanced studies, such as the discovery of biomarkers for renal diseases. Multi‐dimensional LC‐MS/MS was coupled with de novo sequencing and database matching. The proposed approach improved the identification of not only the proteins, but also the post‐translational sites of urinary proteins. We identified 165, 200 and 259 unique gene products in the urine proteomes from males, females and pregnant women, respectively. When all of the results were combined and the redundancies removed, a total of 1095 distinct peptides were identified. Of these, 1016 peptides were associated with 334 unique gene products. In this study, over 100 gene products, including some disease‐related proteins, were detected in urine for the first time by proteomic approaches. Various proteins with novel post‐translational hydroxylation were identified using the MASCOT program and de novo sequencing. All proteins with peptide information were summarized into a comprehensive urine protein database. We believe that this comprehensive urine proteome database will assist in the identification of urinary proteins/polypeptides whose spectra are difficult to interpret in the discovery of urinary biomarkers.