Dataset Information

An attempt to understand kidney's protein handling function by comparing plasma and urine proteomes.

ABSTRACT:

Background

With 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 findings

After 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/significance

The 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.

SUBMITTER: Jia L

PROVIDER: S-EPMC2668176 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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Publications

An attempt to understand kidney's protein handling function by comparing plasma and urine proteomes.

Jia Lulu L Zhang Ling L Shao Chen C Song Eli E Sun Wei W Li Mingxi M Gao Youhe Y

PloS one 20090420 4

<h4>Background</h4>With 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 b ...[more]

PMID: 19381340

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Project description:BackgroundResidual Kidney Function (RKF) is associated with survival benefits in haemodialysis (HD) but is difficult to measure without urine collection. Middle molecules such as Cystatin C and ?2-microglobulin accumulate in renal disease and plasma levels have been used to estimate kidney function early in this condition. We investigated their use to estimate RKF in patients on HD.DesignCystatin C, ?2-microglobulin, urea and creatinine levels were studied in patients on incremental high-flux HD or hemodiafiltration(HDF). Over sequential HD sessions, blood was sampled pre- and post-session 1 and pre-session 2, for estimation of these parameters. Urine was collected during the whole interdialytic interval, for estimation of residual GFR (GFRResidual = mean of urea and creatinine clearance). The relationships of plasma Cystatin C and ?2-microglobulin levels to GFRResidual and urea clearance were determined.ResultsOf the 341 patients studied, 64% had urine output>100 ml/day, 32.6% were on high-flux HD and 67.4% on HDF. Parameters most closely correlated with GFRResidual were 1/?2-micoglobulin (r2 0.67) and 1/Cystatin C (r2 0.50). Both these relationships were weaker at low GFRResidual. The best regression model for GFRResidual, explaining 67% of the variation, was: GFRResidual = 160.3 · (1/?2m) - 4.2. Where ?2m is the pre-dialysis ?2 microglobulin concentration (mg/L). This model was validated in a separate cohort of 50 patients using Bland-Altman analysis. Areas under the curve in Receiver Operating Characteristic analysis aimed at identifying subjects with urea clearance?2 ml/min/1.73 m2 was 0.91 for ?2-microglobulin and 0.86 for Cystatin C. A plasma ?2-microglobulin cut-off of ?19.2 mg/L allowed identification of patients with urea clearance ?2 ml/min/1.73 m2 with 90% specificity and 65% sensitivity.ConclusionPlasma pre-dialysis ?2-microglobulin levels can provide estimates of RKF which may have clinical utility and appear superior to cystatin C. Use of cut-off levels to identify patients with RKF may provide a simple way to individualise dialysis dose based on RKF.

Dataset Information

An attempt to understand kidney's protein handling function by comparing plasma and urine proteomes.

Background

Methodology/principal findings

Conclusion/significance

Publications

An attempt to understand kidney's protein handling function by comparing plasma and urine proteomes.

Similar Datasets

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