Connor Susan C, Hansen Michael K, Corner Adam, Smith Randall F, Ryan Terence E
Discovery Research, GlaxoSmithKline, Ware, UK.
Mol Biosyst. 2010 May;6(5):909-21. doi: 10.1039/b914182k. Epub 2010 Mar 23.
Type 2 diabetes (T2D), one of the most common diseases in the western world, is characterized by insulin resistance and impaired beta-cell function but currently it is difficult to determine the precise pathophysiology in individual T2D patients. Non-targeted metabolomics technologies have the potential for providing novel biomarkers of disease and drug efficacy, and are increasingly being incorporated into biomarker exploration studies. Contextualization of metabolomics results is enhanced by integration of study data from other platforms, such as transcriptomics, thus linking known metabolites and genes to relevant biochemical pathways. In the current study, urinary NMR-based metabolomic and liver, adipose, and muscle transcriptomic results from the db/db diabetic mouse model are described. To assist with cross-platform integration, integrative pathway analysis was used. Sixty-six metabolites were identified in urine that discriminate between the diabetic db/db and control db/+ mice. The combined analysis of metabolite and gene expression changes revealed 24 distinct pathways that were altered in the diabetic model. Several of these pathways are related to expected diabetes-related changes including changes in lipid metabolism, gluconeogenesis, mitochondrial dysfunction and oxidative stress, as well as protein and amino acid metabolism. Novel findings were also observed, particularly related to the metabolism of branched chain amino acids (BCAAs), nicotinamide metabolites, and pantothenic acid. In particular, the observed decrease in urinary BCAA catabolites provides direct corroboration of previous reports that have inferred that elevated BCAAs in diabetic patients are caused, in part, by reduced catabolism. In summary, the integration of metabolomics and transcriptomics data via integrative pathway mapping has facilitated the identification and contextualization of biomarkers that, presuming further analytical and biological validation, may be useful in future T2D clinical studies by identifying patient populations that share common disease pathophysiology and therefore may identify those patients that may respond better to a particular class of anti-diabetic drugs.
2型糖尿病（T2D）是西方世界最常见的疾病之一，其特征为胰岛素抵抗和β细胞功能受损，但目前难以确定个体T2D患者的确切病理生理学机制。非靶向代谢组学技术有潜力提供疾病和药物疗效的新型生物标志物，并且越来越多地被纳入生物标志物探索研究中。通过整合来自其他平台（如转录组学）的研究数据，可以增强代谢组学结果的情境化，从而将已知的代谢物和基因与相关生化途径联系起来。在本研究中，描述了基于尿液核磁共振的代谢组学以及db/db糖尿病小鼠模型的肝脏、脂肪和肌肉转录组学结果。为了辅助跨平台整合，使用了整合通路分析。在尿液中鉴定出66种代谢物，可区分糖尿病db/db小鼠和对照db/+小鼠。代谢物和基因表达变化的联合分析揭示了糖尿病模型中24条不同的通路发生了改变。其中一些通路与预期的糖尿病相关变化有关，包括脂质代谢、糖异生、线粒体功能障碍和氧化应激的变化，以及蛋白质和氨基酸代谢。还观察到了一些新发现，特别是与支链氨基酸（BCAAs）、烟酰胺代谢物和泛酸的代谢有关。特别是，观察到尿中BCAA分解代谢产物的减少直接证实了先前的报道，这些报道推断糖尿病患者体内BCAAs升高部分是由分解代谢减少引起的。总之，通过整合通路映射将代谢组学和转录组学数据进行整合，有助于生物标志物的识别和情境化，假定经过进一步的分析和生物学验证，这些生物标志物可能在未来的T2D临床研究中有用，通过识别具有共同疾病病理生理学的患者群体，从而可能识别出那些可能对特定类别的抗糖尿病药物反应更好的患者。
