代谢组学与糖尿病：分析方法和计算方法
Metabolomics and diabetes: analytical and computational approaches.
作者信息
Sas Kelli M, Karnovsky Alla, Michailidis George, Pennathur Subramaniam
机构信息
Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI.
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI.
出版信息
Diabetes. 2015 Mar;64(3):718-32. doi: 10.2337/db14-0509.
Diabetes is characterized by altered metabolism of key molecules and regulatory pathways. The phenotypic expression of diabetes and associated complications encompasses complex interactions between genetic, environmental, and tissue-specific factors that require an integrated understanding of perturbations in the network of genes, proteins, and metabolites. Metabolomics attempts to systematically identify and quantitate small molecule metabolites from biological systems. The recent rapid development of a variety of analytical platforms based on mass spectrometry and nuclear magnetic resonance have enabled identification of complex metabolic phenotypes. Continued development of bioinformatics and analytical strategies has facilitated the discovery of causal links in understanding the pathophysiology of diabetes and its complications. Here, we summarize the metabolomics workflow, including analytical, statistical, and computational tools, highlight recent applications of metabolomics in diabetes research, and discuss the challenges in the field.
糖尿病的特征在于关键分子和调节途径的代谢改变。糖尿病及其相关并发症的表型表达包括遗传、环境和组织特异性因素之间的复杂相互作用，这需要对基因、蛋白质和代谢物网络中的扰动有综合的理解。代谢组学试图系统地识别和定量生物系统中的小分子代谢物。基于质谱和核磁共振的各种分析平台最近的快速发展使得能够识别复杂的代谢表型。生物信息学和分析策略的持续发展促进了在理解糖尿病及其并发症的病理生理学中因果关系的发现。在这里，我们总结了代谢组学工作流程，包括分析、统计和计算工具，突出了代谢组学在糖尿病研究中的最新应用，并讨论了该领域的挑战。
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