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Open Access Research

Profiling and annotation of human kidney glomerulus proteome

Zenyui Cui1, Yutaka Yoshida1*, Bo Xu1, Ying Zhang1, Masaaki Nameta1, Sameh Magdeldin12, Tomoo Makiguchi1, Toshikazu Ikoma1, Hidehiko Fujinaka13, Eishin Yaoita1 and Tadashi Yamamoto1

Author Affiliations

1 Department of Structural Pathology, Institute of Nephrology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan

2 Department of Physiology, Faculty of Veterinary Medicine, Suez Canal University, Ismailia, Egypt

3 Institute of Clinical Research, Niigata National Hospital, Kashiwazaki, Japan

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Proteome Science 2013, 11:13  doi:10.1186/1477-5956-11-13

Published: 8 April 2013

Abstract

Background

The comprehensive analysis of human kidney glomerulus we previously performed using highly purified glomeruli, provided a dataset of 6,686 unique proteins representing 2,966 distinct genes. This dataset, however, contained considerable redundancy resulting from identification criteria under which all the proteins matched with the same set of peptides and its subset were reported as identified proteins. In this study we reanalyzed the raw data using the Mascot search engine and highly stringent criteria in order to select proteins with the highest scores matching peptides with scores exceeding the “Identity Threshold” and one or more unique peptides. This enabled us to exclude proteins with lower scores which only matched the same set of peptides or its subset. This approach provided a high-confidence, non-redundant dataset of identified proteins for extensive profiling, annotation, and comparison with other proteome datasets that can provide biologically relevant knowledge of glomerulus proteome.

Results

Protein identification using the Mascot search engine under highly stringent, computational strategy generated a non-redundant dataset of 1,817 proteins representing 1,478 genes. These proteins were represented by 2-D protein array specifying observed molecular weight and isoelectric point range of identified proteins to demonstrate differences in the observed and calculated physicochemical properties. Characteristics of glomerulus proteome could be illustrated by GO analysis and protein classification. The depth of proteomic analysis was well documented via comparison of the dynamic range of identified proteins with other proteomic analyses of human glomerulus, as well as a high coverage of biologically important pathways. Comparison of glomerulus proteome with human plasma and urine proteomes, provided by comprehensive analysis, suggested the extent and characteristics of proteins contaminated from plasma and excreted into urine, respectively. Among the latter proteins, several were demonstrated to be highly or specifically localized in the glomerulus by cross-reference analysis with the Human Protein Atlas database, and could be biomarker candidates for glomerular injury. Furthermore, comparison of ortholog proteins identified in human and mouse glomeruli suggest some biologically significant differences in glomerulus proteomes between the two species.

Conclusions

A high-confidence, non-redundant dataset of proteins created by comprehensive proteomic analysis could provide a more extensive understanding of human glomerulus proteome and could be useful as a resource for the discovery of biomarkers and disease-relevant proteins.

Keywords:
Human glomerulus proteome; Human plasma proteome; Human urine proteome; Mouse glomerulus proteome; Cross-reference analysis; Bioinformatics; Biomarkers