Interpreting Metabolomic Profiles using Unbiased Pathway Models
Interpreting Metabolomic Profiles using Unbiased Pathway Models
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DOI：
10.1371/journal.pcbi.1000692
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发表时间：
2010-02-01
影响因子：
4.3
通讯作者：
Roth, Frederick P.
中科院分区：
文献类型：
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作者：
Deo, Rahul C.;Hunter, Luke;Roth, Frederick P.
人类疾病具有异质性，由于遗传和环境因素的不同组合而导致相似的疾病表型。小分子分析可以通过非侵入性询问血浆代谢物水平来评估个体的潜在生物学状态，从而解决疾病异质性。我们分析了 50 名个体的口服葡萄糖耐量试验 (OGTT) 的代谢谱，其中 25 名正常 (NGT)，25 名糖耐量受损 (IGT)。我们的重点是阐明潜在的生物过程。尽管我们最初发现变化的代谢物与代谢途径的先入为主的定义之间几乎没有重叠，但使用无偏网络方法发现了显着的一致变化。具体来说，我们得出了一个代谢网络，其边缘在各个反应中的反应物和产物节点之间以及各个酶和转运蛋白的所有底物之间绘制。我们搜索了“活性模块”——代谢网络中富含代谢物水平变化的区域。活性模块识别了变化的代谢物之间的关系，并强调了代谢物谱中特定溶质载体的重要性。此外，层次聚类和主成分分析表明，OGTT 中变化的代谢物根据系统 A 和 L 氨基酸转运蛋白的活性自然分组， 渗透剂载体 SLC6A12 和线粒体天冬氨酸-谷氨酸转运蛋白 SLC25A13。 NGT 和 IGT 组之间的比较支持 IGT 组中葡萄糖和/或胰岛素刺激的活动减弱。使用无偏见的途径模型，我们提供了证据支持溶质载体在葡萄糖挑战的生理反应中的重要作用，并得出结论：载体 活性反映在扰动实验的个体代谢物概况中。鉴于转运蛋白参与人类疾病，代谢物分析可能有助于通过询问特定转运蛋白活性来改善疾病分类。
Human disease is heterogeneous, with similar disease phenotypes resulting from distinct combinations of genetic and environmental factors. Small-molecule profiling can address disease heterogeneity by evaluating the underlying biologic state of individuals through non-invasive interrogation of plasma metabolite levels. We analyzed metabolite profiles from an oral glucose tolerance test (OGTT) in 50 individuals, 25 with normal (NGT) and 25 with impaired glucose tolerance (IGT). Our focus was to elucidate underlying biologic processes. Although we initially found little overlap between changed metabolites and preconceived definitions of metabolic pathways, the use of unbiased network approaches identified significant concerted changes. Specifically, we derived a metabolic network with edges drawn between reactant and product nodes in individual reactions and between all substrates of individual enzymes and transporters. We searched for "active modules''-regions of the metabolic network enriched for changes in metabolite levels. Active modules identified relationships among changed metabolites and highlighted the importance of specific solute carriers in metabolite profiles. Furthermore, hierarchical clustering and principal component analysis demonstrated that changed metabolites in OGTT naturally grouped according to the activities of the System A and L amino acid transporters, the osmolyte carrier SLC6A12, and the mitochondrial aspartate-glutamate transporter SLC25A13. Comparison between NGT and IGT groups supported blunted glucose- and/or insulin-stimulated activities in the IGT group. Using unbiased pathway models, we offer evidence supporting the important role of solute carriers in the physiologic response to glucose challenge and conclude that carrier activities are reflected in individual metabolite profiles of perturbation experiments. Given the involvement of transporters in human disease, metabolite profiling may contribute to improved disease classification via the interrogation of specific transporter activities.