Bioinformatics analysis of targeted metabolomics - Uncovering old and new tales of diabetic mice under medication
Bioinformatics analysis of targeted metabolomics - Uncovering old and new tales of diabetic mice under medication
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DOI：
10.1210/en.2007-1747
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发表时间：
2008-07-01
期刊：
影响因子：
4.8
通讯作者：
Suhre, Karsten
中科院分区：
文献类型：
--
作者：
Altmaier, Elisabeth;Ramsay, Steven L.;Suhre, Karsten
代谢组学是一个强大的工具，用于识别已知的和新的疾病相关的扰动代谢途径。在临床前药物测试中，它具有早期识别药物脱靶效应的高潜力。高精度高通量质谱的最新进展使代谢组学领域达到了这样一个水平，即用型试剂盒的定量、靶向代谢组学测量允许在大量生物样品中自动进行数百种不同代谢物的内部筛选。今天，代谢组学领域可以说是在转录组学大约5年前的一个点上。因此，该领域迫切需要适应生物信息学工具和方法。在本文中，我们描述了一个有针对性的定量表征的800多个代谢产物的血浆样品中的罗格列酮治疗的健康和糖尿病小鼠的系统分析。我们表明，已知的和新的糖尿病和药物代谢表型可以在统计学上客观的方式恢复。我们发现，甲基戊二酰肉碱的浓度受到罗格列酮治疗的健康和糖尿病小鼠的相反影响。分析代谢物浓度之间的比率大大降低了数据集中的噪声，从而发现了新的潜在糖尿病生物标志物，例如N-羟基酰基鞘氨醇磷酸胆碱SM（OH）28：0和SM（OH）26：0。使用部分eta（2）值的层次聚类技术，我们确定功能相关的代谢物组，表明糖尿病相关的转变，从溶血磷脂酰胆碱磷脂酰胆碱水平。这里介绍的生物信息学数据分析方法可以很容易地推广到其他药物测试场景和其他医学疾病。
Metabolomics is a powerful tool for identifying both known and new disease-related perturbations in metabolic pathways. In preclinical drug testing, it has a high potential for early identification of drug off-target effects. Recent advances in high-precision high-throughput mass spectrometry have brought the metabolomic field to a point where quantitative, targeted, metabolomic measurements with ready-to-use kits allow for the automated in-house screening for hundreds of different metabolites in large sets of biological samples. Today, the field of metabolomics is, arguably, at a point where transcriptomics was about 5 yr ago. This being so, the field has a strong need for adapted bioinformatics tools and methods. In this paper we describe a systematic analysis of a targeted quantitative characterization of more than 800 metabolites in blood plasma samples from healthy and diabetic mice under rosiglitazone treatment. We show that known and new metabolic phenotypes of diabetes and medication can be recovered in a statistically objective manner. We find that concentrations of methylglutaryl carnitine are oppositely impacted by rosiglitazone treatment of both healthy and diabetic mice. Analyzing ratios between metabolite concentrations dramatically reduces the noise in the data set, allowing for the discovery of new potential biomarkers of diabetes, such as the N-hydroxyacyloylsphingosyl-phosphocholines SM(OH) 28:0 and SM(OH) 26:0. Using a hierarchical clustering technique on partial eta(2) values, we identify functionally related groups of metabolites, indicating a diabetes-related shift from lysophosphatidylcholine to phosphatidylcholine levels. The bioinformatics data analysis approach introduced here can be readily generalized to other drug testing scenarios and other medical disorders.