利用Au蛋白芯片技术快速鉴定铜绿假单胞菌的研究

目的 利用Au蛋白芯片建立铜绿假单胞菌蛋白指纹图谱诊断模型,为铜绿假单胞菌感染快速鉴定奠定基础.方法 收集临床分离鉴定的铜绿假单胞菌64株及对照菌199株,随机分成训练组和验证组.运用表面增强激光解析电离飞行时问质谱技术及Au蛋白芯片(proteinchip golden array)检测铜绿假单胞菌及对照菌的蛋白表达,用Ciphergen Proteinchip和BioMarker Wizard软件自动采集数据,筛选铜绿假单胞菌特征性蛋白.结果 经 BioMarker Patterns 软件建立分类树模型.利用该模型对29株铜绿假单胞菌和64株对照菌进行盲法验证.结果 在相对分子质量3000～20 000范围内共检测到80个蛋白峰,其中具有统计学差异的蛋白峰58个(P＜0.01).BioMarker Patterns软件判别分析,择优选出质荷比(M/Z)为14 045.2的蛋白质峰建立铜绿假单胞菌分类树模型.验证结果表明其对铜绿假单胞菌诊断的灵敏度和特异度分别为96.55%(28/29)、100%(64/64).结论 利用Au蛋白芯片技术可快速鉴定铜绿假单胞菌.

Abstract：

Objective To establish protein fingerprinting identification model of Pseudomonas aeruginosa (P. aeruginosa) and to lay a foundation for rapid identification of P. aeruginosa by proteinchip golden array. Methods Sixty-four P. aeruginosa and one hundred and ninety-nine control bacteria identified in our laboratory were collected and divided into training and testing group. Surface enhanced laser desorption ionization time of flight mass spectrometry (SELDI-TOF-MS) and proteinchip golden array were used to detect the protein profiling of the bacteria. Data were automatically collected by Ciphergen Proteinchip Software and protein markers of P. aeruginosa were screened by BioMarker Wizard Software. Classification tree model was developed and validated by BioMarker Patterns Software. The model was blindly tested with twenty-nine P. aeruginosa and sixty-four control bacteria. Results Eighty protein peaks were detected between 3000 and 20 000, among which fifty-eight ones showed significantly difference between P. aeruginosa and the control bacteria (P＜0.01). By BioMarker Patterns Software, one protein peak ( M/Z at 14 045.2) was chosen to develop a classification tree model. The results exhibited with sensitivity of 96. 55% and specificity of 100%. Conclusion Proteinchip golden array has the potential for rapid identification of P. aeruginosa.

Rapid identification of Pseudomonas aeruginosa by proteinchip golden array

Objective To establish protein fingerprinting identification model of Pseudomonas aeruginosa (P. aeruginosa) and to lay a foundation for rapid identification of P. aeruginosa by proteinchip golden array. Methods Sixty-four P. aeruginosa and one hundred and ninety-nine control bacteria identified in our laboratory were collected and divided into training and testing group. Surface enhanced laser desorption ionization time of flight mass spectrometry (SELDI-TOF-MS) and proteinchip golden array were used to detect the protein profiling of the bacteria. Data were automatically collected by Ciphergen Proteinchip Software and protein markers of P. aeruginosa were screened by BioMarker Wizard Software. Classification tree model was developed and validated by BioMarker Patterns Software. The model was blindly tested with twenty-nine P. aeruginosa and sixty-four control bacteria. Results Eighty protein peaks were detected between 3000 and 20 000, among which fifty-eight ones showed significantly difference between P. aeruginosa and the control bacteria (P＜0.01). By BioMarker Patterns Software, one protein peak ( M/Z at 14 045.2) was chosen to develop a classification tree model. The results exhibited with sensitivity of 96. 55% and specificity of 100%. Conclusion Proteinchip golden array has the potential for rapid identification of P. aeruginosa.