Varghese Sanju A, Powell T Brian, Budisavljevic Milos N, Oates Jim C, Raymond John R, Almeida Jonas S, Arthur John M
Department of Medicine, Division of Nephrology, Medical University of South Carolina, 96 Jonathan Lucas Street, P.O. Box 250623, Charleston, SC 29425, USA.
J Am Soc Nephrol. 2007 Mar;18(3):913-22. doi: 10.1681/ASN.2006070767. Epub 2007 Feb 14.
Diagnosis of the type of glomerular disease that causes the nephrotic syndrome is necessary for appropriate treatment and typically requires a renal biopsy. The goal of this study was to identify candidate protein biomarkers to diagnose glomerular diseases. Proteomic methods and informatic analysis were used to identify patterns of urine proteins that are characteristic of the diseases. Urine proteins were separated by two-dimensional electrophoresis in 32 patients with FSGS, lupus nephritis, membranous nephropathy, or diabetic nephropathy. Protein abundances from 16 patients were used to train an artificial neural network to create a prediction algorithm. The remaining 16 patients were used as an external validation set to test the accuracy of the prediction algorithm. In the validation set, the model predicted the presence of the diseases with sensitivities between 75 and 86% and specificities from 92 to 67%. The probability of obtaining these results in the novel set by chance is 5 x 10(-8). Twenty-one gel spots were most important for the differentiation of the diseases. The spots were cut from the gel, and 20 were identified by mass spectrometry as charge forms of 11 plasma proteins: Orosomucoid, transferrin, alpha-1 microglobulin, zinc alpha-2 glycoprotein, alpha-1 antitrypsin, complement factor B, haptoglobin, transthyretin, plasma retinol binding protein, albumin, and hemopexin. These data show that diseases that cause nephrotic syndrome change glomerular protein permeability in characteristic patterns. The fingerprint of urine protein charge forms identifies the glomerular disease. The identified proteins are candidate biomarkers that can be tested in assays that are more amenable to clinical testing.
诊断导致肾病综合征的肾小球疾病类型对于恰当治疗是必要的，通常需要进行肾活检。本研究的目的是识别用于诊断肾小球疾病的候选蛋白质生物标志物。采用蛋白质组学方法和信息分析来识别具有疾病特征的尿蛋白模式。对32例患有局灶节段性肾小球硬化（FSGS）、狼疮性肾炎、膜性肾病或糖尿病肾病的患者，通过二维电泳分离尿蛋白。利用16例患者的蛋白质丰度训练人工神经网络以创建预测算法。其余16例患者作为外部验证集来测试预测算法的准确性。在验证集中，该模型预测疾病存在的敏感性在75%至86%之间，特异性在92%至67%之间。在新的数据集里偶然获得这些结果的概率为5×10⁻⁸。21个凝胶斑点对于疾病的鉴别最为重要。从凝胶上切下这些斑点，其中20个通过质谱鉴定为11种血浆蛋白的电荷形式：血清类黏蛋白、转铁蛋白、α-1微球蛋白、锌α-2糖蛋白、α-1抗胰蛋白酶、补体因子B、触珠蛋白、甲状腺素运载蛋白、血浆视黄醇结合蛋白、白蛋白和血红素结合蛋白。这些数据表明，导致肾病综合征的疾病以特征性模式改变肾小球蛋白通透性。尿蛋白电荷形式的指纹图谱可识别肾小球疾病。所鉴定的蛋白质是候选生物标志物，可在更适合临床检测的分析方法中进行测试。
