Li Bo, Guo Tongsheng, Qu Fen, Li Boan, Wang Haibin, Sun Zhiqiang, Li Xiaohan, Gao Zhiqiang, Bao Chunmei, Zhang Chenglong, Li Xiaoxi, Mao Yuanli
Graduate Student Team, Chinese PLA Postgraduate Medical School, Beijing, China (mainland).
Center for Clinical Laboratory, 302 Hospital of PLA, Beijing, China (mainland).
Med Sci Monit Basic Res. 2014 Nov 12;20:176-83. doi: 10.12659/MSMBR.892670.
The increase in the amount of extended spectrum beta-lactamases (ESBL)-producing gram-negative bacteria is seriously threatening human health in recent years. Therefore, it is necessary to develop a rapid and reliable method for identification of ESBLs. The purpose of this study was to establish a novel method to discriminate between ESBL-producing and non- ESBL-producing bacteria by using the matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) technique.
MATERIAL/METHODS: We detected hydrolyzed production of cefotaxime after incubation with 69 gram-negative bacteria by using MALDI-TOF-MS. Then we established genetic algorithm (GA), supervised neural networks (SNN), and quick classifier (QC) models using several peaks to identify ESBL-producing strains. To confirm the clinical applicability of the models established, a blinded validation test was performed in 34 clinical isolated strains.
Using ClinPro Tools software, we identified 4 peaks (456 Da, 396 Da, 370 Da, and 371 Da) in mass spectra of cefotaxime solution that have high enough specificity to discriminate ESBL-producing from non- ESBL-producing strains. Recognition capability of models established were 97.5% (GA), 92.5% (SNN), and 92.5% (QC), and cross validation rates were 90.15% (GA), 97.62 (SNN), and 97.62% (QC). The accuracy rates of the blinded validation test were 82.4% (GA), 88.2% (SNN), and 82.4% (QC).
Our results demonstrate that identification of ESBLs strains by MALDI-TOF-MS has potential clinical value and could be widely used in the future as a routine test in clinical microbiology laboratories.
近年来，产超广谱β-内酰胺酶（ESBL）的革兰氏阴性菌数量增加，严重威胁人类健康。因此，有必要开发一种快速可靠的ESBL鉴定方法。本研究的目的是建立一种利用基质辅助激光解吸/电离飞行时间质谱（MALDI-TOF-MS）技术区分产ESBL菌和非产ESBL菌的新方法。
材料/方法：我们使用MALDI-TOF-MS检测了69株革兰氏阴性菌孵育后头孢噻肟的水解产物。然后，我们利用几个峰建立了遗传算法（GA）、监督神经网络（SNN）和快速分类器（QC）模型，以鉴定产ESBL菌株。为了确认所建立模型的临床适用性，对34株临床分离菌株进行了盲法验证试验。
使用ClinPro Tools软件，我们在头孢噻肟溶液的质谱图中鉴定出4个峰（456 Da、396 Da、370 Da和371 Da），这些峰具有足够高的特异性，可区分产ESBL菌株和非产ESBL菌株。所建立模型的识别能力分别为97.5%（GA）、92.5%（SNN）和92.5%（QC），交叉验证率分别为90.15%（GA）、97.62（SNN）和97.62%（QC）。盲法验证试验的准确率分别为82.4%（GA）、88.2%（SNN）和82.4%（QC）。
我们的结果表明，MALDI-TOF-MS鉴定ESBL菌株具有潜在的临床价值，未来可作为临床微生物实验室的常规检测方法广泛应用。
