Proteomic profiling of renal allograft rejection in serum using magnetic bead–based sample fractionation and MALDI-TOF MS

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• 作者：Weiguo Sui (1)
  Liling Huang (1)
  Yong Dai (2)
  Jiejing Chen (1)
  Qiang Yan (1)
  He Huang (1)
  
• 关键词：ClinPro tools ; Magnetic bead ; MALDI ; TOF MS ; Renal allograft rejection
• 刊名：Clinical and Experimental Medicine
• 出版年：2010
• 出版时间：December 2010
• 年：2010
• 卷：10
• 期：4
• 页码：259-268
• 全文大小：495KB
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• 作者单位：Weiguo Sui (1)
  Liling Huang (1)
  Yong Dai (2)
  Jiejing Chen (1)
  Qiang Yan (1)
  He Huang (1)
  
  1. First Kidney Transplantation and Hemopurification Center of Chinese PLA, 181st Hospital of Guangzhou Military Area of PLA, 541002, Guilin, Guangxi Province, People’s Republic of China
  2. Clinical Medical Research Center, The Second Clinical Medical College of Jinan University (Shenzhen People’s Hospital), 518020, Shenzhen, Guangdong Province, People’s Republic of China
  

文摘

Proteomics is one of the emerging techniques for biomarker discovery. Biomarkers can be used for early noninvasive diagnosis and prognosis of diseases and treatment efficacy evaluation. In the present study, the well-established research systems of ClinProt Micro solution incorporated unique magnetic bead sample preparation technology, which, based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS), have become very successful in bioinformatics due to its outstanding performance and reproducibility for discovery disease-related biomarker. We collected fasting blood samples from patients with biopsy-confirmed acute renal allograft rejection (n?=?12), chronic rejection (n?=?12), stable graft function (n?=?12) and also from healthy volunteers (n?=?13) to study serum peptidome patterns. Specimens were purified with magnetic bead–based weak cation exchange chromatography and analyzed with a MALDI-TOF mass spectrometer. The results indicated that 18 differential peptide peaks were selected as potential biomarkers of acute renal allograft rejection, and 6 differential peptide peaks were selected as potential biomarkers of chronic rejection. A Quick Classifier Algorithm was used to set up the classification models for acute and chronic renal allograft rejection. The algorithm models recognize 82.64% of acute rejection and 98.96% of chronic rejection episodes, respectively. We were able to identify serum protein fingerprints in small sample sizes of recipients with renal allograft rejection and establish the models for diagnosis of renal allograft rejection. This preliminary study demonstrated that proteomics is an emerging tool for early diagnosis of renal allograft rejection and helps us to better understand the pathogenesis of disease process.