Toward Global Metabolomics Analysis with Hydrophilic Interaction Liquid Chromatography-Mass Spectrometry: Improved Metabolite Identification by Retention Time Prediction
Toward Global Metabolomics Analysis with Hydrophilic Interaction Liquid Chromatography-Mass Spectrometry: Improved Metabolite Identification by Retention Time Prediction
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DOI：
10.1021/ac2021823
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发表时间：
2011-11-15
影响因子：
7.4
通讯作者：
Burgess, Karl E. V.
中科院分区：
文献类型：
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作者：
Creek, Darren J.;Jankevics, Andris;Burgess, Karl E. V.
代谢组学是后基因组生物学的一个新兴领域，涉及生物系统中小分子的综合分析。然而，与鉴定检测到的代谢物相关的困难目前限制了其应用。在这里，我们证明了保留时间预测模型可以改善亲水相互作用色谱（HILIC）-高分辨率质谱代谢组学平台上的代谢物鉴定。定量结构保留关系（QSRR）模型，将6个物理化学变量的多重线性回归的基础上，120个真实的标准代谢物，表现出良好的预测能力的保留时间的范围内的代谢物（交叉验证R-2 = 0.82和均方误差= 0.14）。预测的保留时间通过去除40%的仅通过准确质量进行鉴定时发生的错误鉴定来改善代谢物鉴定。这一程序的重要性是证明了690个代谢产物的原生动物寄生虫布氏锥虫提取物的推定鉴定，从而使确定的代谢产物被映射到一个生物体范围内的代谢网络，从全球系统生物学的角度为细胞代谢的未来研究提供了机会。
Metabolomics is an emerging field of postgenomic biology concerned with comprehensive analysis of small molecules in biological systems. However, difficulties associated with the identification of detected metabolites currently limit its application. Here we demonstrate that a retention time prediction model can improve metabolite identification on a hydrophilic interaction chromatography (HILIC)-high-resolution mass spectrometry metabolomics platform. A quantitative structure retention relationship (QSRR) model, incorporating six physicochemical variables in a multiple-linear regression based on 120 authentic standard metabolites, shows good predictive ability for retention times of a range of metabolites (cross-validated R-2 = 0.82 and mean squared error = 0.14). The predicted retention times improved metabolite identification by removing 40% of the false identifications that occurred with identification by accurate mass alone. The importance of this procedure was demonstrated by putative identification of 690 metabolites in extracts of the protozoan parasite Trypanosoma brucei, thus allowing identified metabolites to be mapped onto an organism-wide metabolic network, providing opportunities for future studies of cellular metabolism from a global systems biology perspective.