Y Variabilities Of Serum Peptidomics Spectra In Patients With Nephrotic Syndrome

Nephrotic syndrome (NS) can be caused by one or more different diseases which can increase the permeability of glomerular basement membrane (GBM) and induce massive plasma proteins are lost. NS is not a single disease but a syndrome which is a set of symptoms and signs that tend to occur together. NS is characterized by massive proteinuria, which leads to hypoproteinemia, hyperlipidemia with elevated cholesterols, triglicerides and other lipids, and edema. NS have many complications, including infection, thrombosis, acute renal failure, renal tubule hypofunction, abnormal calcium metabolism and endocrine disturbance. At present, the diagnosis of nephrotic syndrome (NS) requires a renal biopsy, but it's invasive and insensitive. Thus, it is important to develop sensitive and specific biomarkers, which are significant for early identification of etiopathogenisis and pathology type with NS. Proteomics is a new emerging technique to discover specific biomarkers of diseases. It can be used in early diagnosis and may have additional value as a prognostic tool.Objective: To develop alternative method and potential new biomarkers for diagnosis, we validated a set of well-integrated tools called ClinProt, which was comprised of magnetic bead based sample preparation, matrix assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) acquisition and a bioinformatics package, to investigate the variabilities of serum peptidomics spectra in patients with NS.Methods: The first-morning serum samples from 49 patients diagnosed with NS by renal biopsy, including 17 MsPGN, 12 MCNS, 10 FSGS and 10 MN, were collected and screened to describe their variability of the serum peptidome. The results in NS were compared to findings in 10 healthy individuals. Specimens were purified with magnetic beads-based weak cation exchange chromatography and analyzed in a MALDI-TOF MS. Results: Compared to normal controls, we screened 5, 7, 6, 5 significantly expressed polypeptides in MsPGN,MCNS,MN,FSGS groups, respectively. Group comparisons by means of t-tests, the significance was set at P<0.05. A Genetic Algorithm was used to set up the classification models. Cross validation of healthy controls from MsPGN, MCNS, MN and FSGS was 96.18%, 100%, 98.53% and 94.12%, respectively. The recognition capabilities were 100%. Conclusions: Proteomic analysis with MALDI-TOF MS permits accurate and stable identification. Our study first established a serum peptide fingerprint model for diagnosis of NS, providing people a new point of view to better understand the pathogenesis of NS and early diagnose in proteomics way.