Proteomic profiling of uremia in serum using magnetic bead-based sample fractionation and MALDI-TOF MS.

BACKGROUNDS

Uremia is a term used to loosely describe the illness accompanying renal failure, in particular the nitrogenous waste products associated with the failure of this organ. At present, the diagnosis of uremia could be implemented by clinical symptoms, laboratory examinations, or image analysis. To develop alternative methods and potential new biomarkers for diagnosis, we employed a novel platform called ClinProt to investigate serum peptidome of uremia.

METHODS

The first-morning serum samples from 30 patients, including 10 non-dialysis (ND), 10 peritoneal dialysis (PD), and 10 hemodialysis (HD) patients, were collected and screened to describe their variability of the serum peptidome. The results in uremia were compared to the findings in 13 normal controls. Specimens were purified with magnetic beads-based weak cation exchange chromatography and analyzed in a matrix-assisted laser desorption/ionization time-of-flight mass spectrometry.

RESULTS

Compared to normal controls, we screened 7, 7, and 9 significantly expressed polypeptides in ND, PD, and HD groups, respectively. Group comparisons were done by means of t-tests, the statistical significance was set at p < 0.05. A genetic algorithm was used to set up the classification models between uremia groups and normal controls. Cross-validation of normal controls from ND, PD, and HD was 99.30%, 95.12%, and 98.61%, respectively. The recognition capabilities were 100%.

CONCLUSIONS

We were able to identify serum protein fingerprints in small sample sizes of recipients with uremia and establish the models for diagnosis of uremia. This preliminary study demonstrated that proteomics is an emerging tool for early diagnosis of uremia and helps us to better understand the pathogenesis of the disease process.