Identification of tear fluid biomarkers in dry eye syndrome using iTRAQ quantitative proteomics.
Identification of tear fluid biomarkers in dry eye syndrome using iTRAQ quantitative proteomics.
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DOI：
10.1021/pr900686s
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发表时间：
2009-09
影响因子：
4.4
通讯作者：
Lei Zhou;R. Beuerman;C. M. Chan;Shaozhen Zhao;Xiao Rong Li;He Yang;L. Tong;Shouping Liu;M. Stern;D. Tan
中科院分区：
文献类型：
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作者：
Lei Zhou;R. Beuerman;C. M. Chan;Shaozhen Zhao;Xiao Rong Li;He Yang;L. Tong;Shouping Liu;M. Stern;D. Tan
泪液中发现的蛋白质在维持眼表方面具有重要作用，泪液组分的质量和数量的变化反映了眼表健康的变化。在这项研究中，我们使用定量蛋白质组学，iTRAQ技术结合2D-nanoLC-nano-ESI-MS/MS和统计模型来揭示干眼症患者泪液中显著且可靠变化的蛋白质，以揭示潜在的生物标志物候选者。本研究招募了五十六例干眼症患者和40例健康受试者。总共鉴定了93种泪液蛋白，ProtScore >或=2（>或=99%置信度）。与干眼症相关的是6种上调蛋白，α-烯醇化酶、α-1-酸性糖蛋白1、S100 A8（钙颗粒蛋白A）、S100 A9（钙颗粒蛋白B）、S100 A4和S100 A11（钙离子蛋白）和4种下调蛋白，催乳素诱导蛋白（PIP）、脂质运载蛋白-1、乳铁蛋白和溶菌酶。评估个体生物标志物候选物和生物标志物组的受试者工作曲线（ROC）。使用4-蛋白质生物标志物组，干眼症的诊断准确率为96%（灵敏度，91.0%;特异性，90.0%）。使用ELISA测定法成功验证了从iTRAQ实验产生的两种生物标志物候选物（α-烯醇化酶和S100 A4）。这10种泪液蛋白的水平反映了泪腺的水分泌不足、眼表的炎症状态。通过使用与炎症相关的三种蛋白质，α 1-酸性糖蛋白1、S100 A8和S100 A9，成功地将干眼症严重程度的临床分类与蛋白质组学相关联。九种泪液蛋白生物标志物候选物（除了α 1-酸性糖蛋白1）也使用独立的年龄匹配的患者样本集进行了验证。这项研究表明，iTRAQ技术结合2D-nanoLC-nanoESI-MS/MS定量蛋白质组学是生物标志物发现的有力工具。
The proteins found in tears have an important role in the maintenance of the ocular surface and changes in the quality and quantity of tear components reflect changes in the health of the ocular surface. In this study, we have used quantitative proteomics, iTRAQ technology coupled with 2D-nanoLC-nano-ESI-MS/MS and with a statistical model to uncover proteins that are significantly and reliably changed in the tears of dry eye patients in an effort to reveal potential biomarker candidates. Fifty-six patients with dry eye and 40 healthy subjects were recruited for this study. In total, 93 tear proteins were identified with a ProtScore >or=2 (>or=99% confidence). Associated with dry eye were 6 up-regulated proteins, alpha-enolase, alpha-1-acid glycoprotein 1, S100 A8 (calgranulin A), S100 A9 (calgranulin B), S100 A4 and S100 A11 (calgizzarin) and 4 down-regulated proteins, prolactin-inducible protein (PIP), lipocalin-1, lactoferrin and lysozyme. Receiver operating curves (ROC) were evaluated for individual biomarker candidates and a biomarker panel. With the use of a 4-protein biomarker panel, the diagnostic accuracy for dry eye was 96% (sensitivity, 91.0%; specificity, 90.0%). Two biomarker candidates (alpha-enolase and S100 A4) generated from iTRAQ experiments were successfully verified using an ELISA assay. The levels of these 10 tear proteins reflect aqueous secretion deficiency by lacrimal gland, inflammatory status of the ocular surface. The clinical classification of the severity of the dry eye condition was successfully correlated to the proteomics by using three proteins that are associated with inflammation, alpha1-acid glycoprotein 1, S100 A8 and S100 A9. The nine tear protein biomarker candidates (except alpha1-acid glycoprotein 1) were also verified using an independent age-matched patient sample set. This study demonstrated that iTRAQ technology combined with 2D-nanoLC-nanoESI-MS/MS quantitative proteomics is a powerful tool for biomarker discovery.