中国医学科学院学报

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中国医学科学院学报

中国医学科学院学报 ›› 2011, Vol. 33 ›› Issue (5): 511-516.doi: 10.3881/j.issn.1000-503X.2011.05.007

• 论著 • 上一篇    下一篇

磁珠分离结合生物质谱分析代谢综合征早期肾损害尿液蛋白谱

高碧霞1,2,李明喜1,2,刘雪娇1,2,蔡建芳1,2   

  1. 中国医学科学院 北京协和医学院 北京协和医院 1肾内科 2转化医学中心,北京 100730; 3中国医学科学院 北京协和医学院 基础医学研究所生物医学工程系,北京 100005
  • 收稿日期:2011-04-06 修回日期:2011-10-28 出版日期:2011-10-28 发布日期:2011-10-28
  • 通讯作者: 李明喜 E-mail:mingxili@hotmail.com
  • 作者简介:杨啸林 电话:010-65296438
  • 基金资助:

    卫生部卫生行业专项公益基金(200802007)

Analyzing Urinary Proteome Patterns of Metabolic Syndrome Patients with Early Renal Injury by Magnet Bead Separation and Matrix assisted Laser Desorption Ionization Time of flight Mass Spectrometry

GAO Bi-xia1, 2, LI Ming-xi1, 2, LIU Xue-jiao1, 2, CAI Jian-fang1, 2   

  1. 1Department of Nephrology, 2Translational Medicine Center, PUMC Hospital, CAMS and PUMC, Beijing 100730, China3Department of Biomedical Engineering, Institute of Basic Medical Sciences, CAMS and PUMC, Beijing 100005, China
  • Received:2011-04-06 Revised:2011-10-28 Online:2011-10-28 Published:2011-10-28
  • Contact: LI Ming-xi E-mail:mingxili@hotmail.com
  • Supported by:

    Supported by the Ministry of Health's Special Fund for Public Welfare(200802007)

摘要: 目的 应用纳米磁珠联合基质辅助激光解析电离飞行时间质谱(MALDI-TOF-MS)寻找代谢综合征(MS)早期肾损害尿液生物标志物并建立诊断模型。方法 样本来源于北京平谷地区MS肾损害流行病学研究,应用弱阳离子交换磁珠富集8h过夜尿液蛋白,MALDI-TOF-MS建立尿蛋白谱图,采用Wilcoxon检验和随机森林算法寻找差异蛋白峰,分别联合遗传算法和支持向量机构建诊断模型。结果 入选者包括MS无肾损害54例和MS早期肾损害46例,Wilcoxon检验和随机森林算法分别显示20及12个蛋白峰在MS早期肾损害患者尿液高表达。遗传算法和支持向量机构建模型对MS早期肾损害诊断的敏感性分别为82.6%和89.2%,特异性分别为84.3%和81.1%,准确性分别为83.5%和85.5%。两种机器分类学习方法构建的诊断模型共同包含4个蛋白峰,质荷比分别为2756.98、3019.11、9077.04 和10-054.26。 结论 采用弱阳离子交换磁珠联合MALDI-TOF-MS建立了MS早期肾损害患者尿蛋白谱图。多种机器分类学习方法显示尿液差异蛋白峰,并建立了识别率较好的诊断模型,为差异蛋白的鉴定和功能研究奠定了基础。

关键词: 代谢综合征, 肾脏损害, 尿液生物标志物, 纳米磁珠, 基质辅助激光解析电离飞行时间质谱

Abstract: Objective To determine the potential urinary biomarkers of metabolic syndrome (MS) with early renal injury and establish diagnostic models by magnetic bead-based separation and matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). Methods Participants were selected from the epidemiologic study on MS and renal involvement among residents in Pinggu district, Beijing. Eight-hour overnight urine samples were fractionated by means of magnetic bead-based weak cation exchange chromatography and subsequently analyzed with MALDI-TOF MS. Wilcoxon test and random forests were used to screen differential protein peaks of MS patients with early renal injury, then combined with genetic algorithm and support vector machine, respectively, to establish diagnostic models. Results Totally 54 cases of MS without renal injury and 46 cases of MS with early renal injury were enrolled. Totally twenty protein peaks were up-regulated in the urine of MS patients with early renal injury by Wilcoxon test (P0.005). Genetic algorithm based model showed 82.6% sensitivity, 84.3% specificity, and 83.5% accuracy by a 10-fold cross-validation in identifying MS patients with early renal injury; correspondingly, the support vector machine based model reported 89.2% sensitivity, 81.1% specificity and 85.5 % accuracy. Four protein peaks were included in two diagnostic models with mass-to-charge ratios of 2756.98, 3019.11, 9077.04, and 10054.26. Conclusions The urinary proteome patterns of MS with early renal injury were successfully established with magnetic bead-based separation and MALDI-TOF MS technology. A series of urinary differential expressing protein peaks were identified with bioinformatics tools. Diagnostic models combining cluster of protein peaks are capable of differentiating MS patients with early renal injury from those without renal injury. The different urine protein excretion patterns revealed in this study provide urinary candidate biomarkers of MS patients with early renal injury for future identification and biological roles investigation.

Key words: metabolic syndrome, renal injury, urinary biomarkers, magnetic bead, matrix-assisted laser desorption ionization time-of-flight mass spectrometry

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