Type 2 Diabetic Nephropathy Differentially Expressed Proteins

Part One: Establish and optimize the platform of SELDI-TOF-MSObjective: To establish a reliable surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) platform and to optimize the technological conditions in the experiments.Material and Methods: Urine and Serum samples from 4 type 2 diabetic nephropathy (DN) patients, 2 diabetic patients with normal albuminuria and 2 healthy volunteers were analyzed using SELDI ProteinChip, as well as cortical protein extract of kidneys from a DN patient and a normal control. Protein or peptide spectra was determined by SELDI-TOF-MS measurement after treating the samples onto four types of ProteinChip arrays(WCX2, H4, SAX2, IMAC3-Cu²⁺) for each case. According to the protein profiling results, optimized parameters and suitable chips for urine, serum and tissue extract were screened respectively, and the reliability and stability of the platform were assessed.Results: Protein profiles of serum and kidney tissue showed the best peak spectrum on WCX2 Chips by sensitivity of 8 and laser intensity of 185. Urinary protein profiles were most clearly identifiable on IMAC3-Cu²⁺ chip by sensitivity of 9 and laser intensity of 230. The coefficient variation (CV) of protein intensity and m/z value was 16.72% and 0.0236%, respectively, within the same protein chip. The CV of protein intensity and m/z value was 16. 76% and 0. 0207%, respectively, among different chips.Conclusion: SELDI-TOF-MS is an ideal technological platform for proteomic research because of high reproducibility and stability. Weak cation exchange proteinchip array should be used to provide the fingerprints in the proteomic research of serum and kidney tissue, while IMAC-Cu²⁺ array is suitable for the research of urinary proteomics. Quality control and standardization of analysis conditions could be key issue for the reliability of outcome.Part Two: Urinary protein fingerprint profile and decision tree diagnostic pattern of type 2 diabetic nephropathyObjective: Surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) is a novel method for biomarker discovery. The current study was undertaken to investigate whether SELDI profiling of urine and artificial intelligence analysis could distinguish type 2 diabetic nephropathy (DN) from diabetes without albuminuria and normal controls (nonDN).Material and Methods: Urine samples from 42 DN patients were analyzed and compared to those from 42 nonDN, using SELDI ProteinChip and Biomarker Patterns Software to identify potential differences in protein or polypeptide profile and generate a tree analysis pattern by this training set. The validity of the tree analysis pattern was then challenged with a blinded testing set of 50 samples. The IMAC3-Cu²⁺ ProteinChip Arrays were performed on a ProteinChip PBSâ…¡c reader of the ProteinChip Biomarker System.Results: Totally 120 mass peaks in the range from 2 to 100 kDa were detected in training set. The intensities of 6 peaks detected appeared upregulated, while 11 peaks downregulated, in DN group as compared to nonDN groups more than 2 folds (P<0.01). The algorithm identified a cluster pattern. SELDI-TOF-MS combined with a tree analysis pattern segregated DN from non DN with sensitivity of 86.67% and specificity of 85.00% in blinded test.Conclusions: While further characterization of differentially detected protein peaks is important and necessary, our current work clearly demonstrates that the SELDI is effective in searching biomarkers of DN in urine protein profile. SELDI combined with a tree analysis pattern can both facilitate discriminate DN and provide a novel clinical diagnostic platform which improve the detection of DN.Part Three: Serous protein fingerprint profile and decision tree diagnostic pattern of type 2 diabetic nephropathyObjective: We aimed to use SELDI and bioinformatics to define and validate a DN specific proteomic pattern of serum.Material and Methods: We used SELDI to obtain protein or polypeptide patterns from serum samples of 65 patients with DN and 65 nonDN subjects., From signatures of protein or polypeptide mass, we established a model for diagnosing the presence of DN. We estimated the proportion of correct classifications from the model by applying it to a masked group containing 22 patients with DN, 15 healthy individuals, and 13 diabetic patients with normal urinary albumin.Results: The intensities of 22 peaks detected appeared upregulated, while 24 peaks downregulated, in DN group as compared to nonDN groups more than 2 folds(P<0.01). The algorithm identified a diagnostic DN pattern of 6 protein/polypeptide masses. On masked assessment, prediction models based on these protein/polypeptides obtained a sensitivity of 90. 91% and specificity of 89.29%.Conclusion: These observations suggest that DN patients have a unique cluster of molecular components in sera, which are present in their SELDI profile. Identification and characterization of these molecular components help in the understanding of the pathogenesis of DN. Serous protein signature combined with a tree analysis pattern can provide a novel clinical diagnostic approach of DN.