National TCM Key Laboratory of Serum Pharmacochemistry, Key Laboratory of Chinmedomics, Heilongjiang University of Chinese Medicine, Harbin 150040, China.
Anal Chem. 2013 Aug 6;85(15):7606-12. doi: 10.1021/ac401793d. Epub 2013 Jul 25.
Metabolomics is the study of metabolic changes in biological systems and provides the small molecule fingerprints related to the disease. Extracting biomedical information from large metabolomics data sets by multivariate data analysis is of considerable complexity. Therefore, more efficient and optimizing metabolomics data processing technologies are needed to improve mass spectrometry applications in biomarker discovery. Here, we report the findings of urine metabolomic investigation of hepatitis C virus (HCV) patients by high-throughput ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) coupled with pattern recognition methods (principal component analysis, partial least-squares, and OPLS-DA) and network pharmacology. A total of 20 urinary differential metabolites (13 upregulated and 7 downregulated) were identified and contributed to HCV progress, involve several key metabolic pathways such as taurine and hypotaurine metabolism, glycine, serine and threonine metabolism, histidine metabolism, arginine and proline metabolism, and so forth. Metabolites identified through metabolic profiling may facilitate the development of more accurate marker algorithms to better monitor disease progression. Network analysis validated close contact between these metabolites and implied the importance of the metabolic pathways. Mapping altered metabolites to KEGG pathways identified alterations in a variety of biological processes mediated through complex networks. These findings may be promising to yield a valuable and noninvasive tool that insights into the pathophysiology of HCV and to advance the early diagnosis and monitor the progression of disease. Overall, this investigation illustrates the power of the UPLC-MS platform combined with the pattern recognition and network analysis methods that can engender new insights into HCV pathobiology.
代谢组学是研究生物系统代谢变化的学科，提供与疾病相关的小分子指纹图谱。通过多元数据分析从大型代谢组学数据集提取生物医学信息具有相当的复杂性。因此，需要更高效和优化的代谢组学数据处理技术来提高质谱在生物标志物发现中的应用。在这里，我们通过高通量超高效液相色谱-质谱联用（UPLC-MS）结合模式识别方法（主成分分析、偏最小二乘法和 OPLS-DA）和网络药理学报告了丙型肝炎病毒（HCV）患者尿液代谢组学研究的结果。共鉴定出 20 种尿液差异代谢物（13 种上调和 7 种下调），这些代谢物与 HCV 的进展有关，涉及牛磺酸和次牛磺酸代谢、甘氨酸、丝氨酸和苏氨酸代谢、组氨酸代谢、精氨酸和脯氨酸代谢等多个关键代谢途径。通过代谢组学鉴定的代谢物可能有助于开发更准确的标记算法，以更好地监测疾病进展。网络分析验证了这些代谢物之间的密切联系，并暗示了代谢途径的重要性。将改变的代谢物映射到 KEGG 途径中，确定了通过复杂网络介导的各种生物过程的改变。这些发现可能为 HCV 的病理生理学提供有价值的非侵入性工具，从而推进早期诊断和监测疾病进展。总的来说，这项研究表明 UPLC-MS 平台与模式识别和网络分析方法相结合，可以深入了解 HCV 的病理生物学，并为 HCV 的早期诊断和监测疾病进展提供新的见解。
