An Attempt to Understand Kidney's Protein Handling Function by Comparing Plasma and Urine Proteomes

Abstract

BackgroundWith the help of proteomics technology, the human plasma and urine proteomes, which closely represent the protein compositions of the input and output of the kidney, respectively, have been profiled in much greater detail by different research teams. Many datasets have been accumulated to form “reference profiles” of the plasma and urine proteomes. Comparing these two proteomes may help us understand the protein handling aspect of kidney function in a way, however, which has been unavailable until the recent advances in proteomics technology.Methodology/Principal FindingsAfter removing secreted proteins downstream of the kidney, 2611 proteins in plasma and 1522 in urine were identified with high confidence and compared based on available proteomic data to generate three subproteomes, the plasma-only subproteome, the plasma-and-urine subproteome, and the urine-only subproteome, and they correspond to three groups of proteins that are handled in three different ways by the kidney. The available experimental molecular weights of the proteins in the three subproteomes were collected and analyzed. Since the functions of the overrepresented proteins in the plasma-and-urine subproteome are probably the major functions that can be routinely regulated by excretion from the kidney in physiological conditions, Gene Ontology term enrichment in the plasma-and-urine subproteome versus the whole plasma proteome was analyzed. Protease activity, calcium and growth factor binding proteins, and coagulation and immune response-related proteins were found to be enriched.Conclusion/SignificanceThe comparison method described in this paper provides an illustration of a new approach for studying organ functions with a proteomics methodology. Because of its distinctive input (plasma) and output (urine), it is reasonable to predict that the kidney will be the first organ whose functions are further elucidated by proteomic methods in the near future. It can also be anticipated that there will be more applications for proteomics in organ function research.

Applications of urinary proteomics in biomarker discovery

“…Therefore it is reasonable to assume that comparing the plasma proteome to the urinary proteome may lead to a better understanding of kidney physiology. Jia et al [62] proposed the "black box theory", in which a kidney was treated as a stand-alone black box, with plasma proteins and urine proteins as its input and output. Using bioinformatics methods, changes of proteins after kidney handling were identified to reveal some general rules of kidney function of protein handling.…”

Matching Kidneys and Urines: Establishing Noninvasive Surrogates of Intrarenal Events in Primary Glomerulonephritis

“…One systemsbiology analytic study of publically available data sets illustrated how if one considers the kidney as a "black box" with a defined input (plasma) and output (urine), one could use a subtractive computational approach to identify urinary proteins of renal origin. 54 Although this may be applicable experimentally, this work represents a largely theoretical approach. It remains difficult to determine the origin of all protein species in the urine, particularly in disease states.…”

The Renal Gene Ontology Annotation Initiative

“…1 For example, a number of recent proteomics analyses have identified several novel, potentially susceptible genes and proteins associated with various aspects of renal function, development and disease, whose role and mode of action within the renal system remain ambiguous. [2][3][4][5][6][7][8][9] There is also a number of renal genome and proteome databases that exist, providing the scientific community with a range of central repositories of renal-related physiological data, published and unpublished mass spectrometry data and microarray data. [10][11][12][13] Although these high-throughput resources are extremely powerful for investigating multi-factorial phenotypes such as renal disease, these advances also mean that scientists must cope with the increasingly complex task of identifying, evaluating and managing the existing biological information for these large sequence sets.…”