龚杰, 张沈茜, 张慧芳, 肖盟, 何利华, 赵飞, 徐英春, 张建中, 肖迪. 基于基质辅助激光解吸电离飞行时间质谱的克柔念珠菌遗传分化研究[J]. 疾病监测, 2019, 34(11): 969-973. DOI: 10.3784/j.issn.1003-9961.2019.11.006
引用本文: 龚杰, 张沈茜, 张慧芳, 肖盟, 何利华, 赵飞, 徐英春, 张建中, 肖迪. 基于基质辅助激光解吸电离飞行时间质谱的克柔念珠菌遗传分化研究[J]. 疾病监测, 2019, 34(11): 969-973. DOI: 10.3784/j.issn.1003-9961.2019.11.006
Jie Gong, Shenxi Zhang, Huifang Zhang, Meng Xiao, Lihua He, Fei Zhao, Yingchun Xu, Jianzhong Zhang, Di Xiao. Genetic differentiation of Candida krusei based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry[J]. Disease Surveillance, 2019, 34(11): 969-973. DOI: 10.3784/j.issn.1003-9961.2019.11.006
Citation: Jie Gong, Shenxi Zhang, Huifang Zhang, Meng Xiao, Lihua He, Fei Zhao, Yingchun Xu, Jianzhong Zhang, Di Xiao. Genetic differentiation of Candida krusei based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry[J]. Disease Surveillance, 2019, 34(11): 969-973. DOI: 10.3784/j.issn.1003-9961.2019.11.006

基于基质辅助激光解吸电离飞行时间质谱的克柔念珠菌遗传分化研究

Genetic differentiation of Candida krusei based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry

  • 摘要:
    目的在蛋白质水平明确克柔念珠菌的遗传分化特征,建立基于基质辅助激光解吸电离飞行时间质谱(MALDI-TOF MS)的不同谱系克柔念珠菌的鉴定方法。
    方法选取20株克柔念珠菌,采用MALDI-TOF MS结合分析软件ClinProTools,确定克柔念珠菌不同谱系的质谱标志峰,构建基于遗传算法(GA)的分类模型,用于区分不同谱系的克柔念珠菌菌株,选择25株克柔念珠菌对该模型进行验证。
    结果利用20株克柔念珠菌构建的GA分类模型,反映模型正确鉴别能力的识别能力值(RC)为100%,反映模型处理测试谱图变异能力的交叉验证值(CV)为97.89%。 25株克柔念珠菌、150张谱图外部验证模型显示其分类能力为95.30%。 确定13个克柔念珠菌谱系相关的特征峰(m/z 3 971.40、3 136.95、3 427.33、2 405.28、2 996.73、2 913.95、3 376.97、6 736.13、5 819.03、4 045.16、5 869.00、3 618.10及3 946.14),可用于正确区分来自不同谱系的克柔念珠菌。
    结论本研究论证并确认了克柔念珠菌的群体遗传分化事件,同时也建立了一种简便而有效的快速谱系分型方法。

     

    Abstract:
    ObjectiveTo identify the genetic differentiation characteristics of Candida krusei at the level of protein and establish different lineage identification methods based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS).
    MethodsMALDI-TOF MS coupled to ClinProTools was used to discover MALDI-TOF MS biomarker peaks and establish a classification model based on a genetic algorithm (GA) to differentiate the different lineages of C. krusei. Twenty strains of C. krusei were used to establish an analysis model, and 25 strains of were used for the validation.
    ResultsFor the GA typing model using 20 strains of C. krusei, the recognition capability (RC) value, which reflects the model’s ability to correctly identify its component spectra was 100%, and the cross-validation (CV) value, which reflects the ability of the model to handle variability among the test spectra was 97.89% and the classification power value of the validation model for 25 C. krusei strains and 150 mass spectrums was 95.30%. This model contained 13 biomarker peaks (m/z 3 971.40, 3 136.95, 3 427.33, 2 405.28, 2 996.73, 2 913.95, 3 376.97, 6 736.13, 5 819.03, 4 045.16, 5 869.00, 3 618.10 and 3 946.14) and can be used to correctly identify different lineages of C. krusei.
    ConclusionThis study demonstrated not only the population genetic differentiation of C. krusei from the peptide level, but also confirmed the existence of this event, and more importantly, a simple and effective rapid lineage typing method was established in this study.

     

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