Metabolic network topology reveals transcriptional regulatory signatures of type 2 diabetes.
Metabolic network topology reveals transcriptional regulatory signatures of type 2 diabetes.
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DOI：
10.1371/journal.pcbi.1000729
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发表时间：
2010-04-01
影响因子：
4.3
通讯作者：
Patil KR
中科院分区：
文献类型：
--
作者：
Zelezniak A;Pers TH;Soares S;Patti ME;Patil KR
2型糖尿病（T2DM）是一种以胰岛素抵抗和胰岛素分泌受损为特征的疾病。最近与T2DM相关的转录组学研究揭示了多种组织中大量代谢基因的表达变化。由于转录调控的复杂性和代谢网络的高度互联性，鉴定这些转录变化及其对细胞代谢表型的影响的分子机制是一项具有挑战性的任务。在这项研究中，我们将骨骼肌基因表达数据集与人类代谢网络重建相结合，以确定T2DM的关键代谢调节特征。这些特征包括报告代谢物——在相关酶编码基因中具有显著集体转录反应的代谢物，以及在这些基因的启动子区域具有显著富集结合位点的转录因子。除了来自TCA循环、氧化磷酸化和脂质代谢（已知与T2DM相关）的代谢物外，我们还确定了几种代表新型生物标志物候选物的报告代谢物。例如，高度相关的代谢物NAD+/NADH和ATP/ADP也被确定为报告代谢物，可能导致T2DM中观察到的广泛的基因表达变化。一种基于与报告代谢产物相关的基因启动子区域分析的算法揭示了一个连接代谢几个部分的转录因子调控网络。确定的转录因子包括CREB、NRF1和PPAR家族成员等，代表了进一步实验分析的调控靶点。总之，我们的研究结果提供了T2DM发病机制中可能涉及的关键代谢和调节节点的整体图景。2型糖尿病是一种复杂的代谢性疾病，被认为是21世纪人类健康的主要威胁之一。最近对人体组织样本中基因表达水平的研究表明，糖尿病和糖尿病高危人群的多种代谢途径失调；其中哪些是主要的，或者是疾病发病机制的核心，仍然是一个关键问题。细胞代谢网络是高度相互联系的，经常受到严格调控；因此，单个节点上的任何扰动都可以迅速扩散到网络的其余部分。这种复杂性对确定与胰岛素抵抗和2型糖尿病相关的关键分子机制和生物标志物提出了相当大的挑战。在这项研究中，我们通过使用一种将基因表达数据与人类细胞代谢网络相结合的方法来解决这个问题。我们通过分析骨骼肌的基因表达模式来证明我们的方法。该分析确定了转录因子和代谢物，这些转录因子和代谢物代表了2型糖尿病和糖代谢受损的治疗药物和未来临床诊断的潜在靶点。从更广泛的角度来看，该研究为在细胞代谢变化的背景下分析复杂疾病的基因表达数据集提供了一个框架。
Type 2 diabetes mellitus (T2DM) is a disorder characterized by both insulin resistance and impaired insulin secretion. Recent transcriptomics studies related to T2DM have revealed changes in expression of a large number of metabolic genes in a variety of tissues. Identification of the molecular mechanisms underlying these transcriptional changes and their impact on the cellular metabolic phenotype is a challenging task due to the complexity of transcriptional regulation and the highly interconnected nature of the metabolic network. In this study we integrate skeletal muscle gene expression datasets with human metabolic network reconstructions to identify key metabolic regulatory features of T2DM. These features include reporter metabolites—metabolites with significant collective transcriptional response in the associated enzyme-coding genes, and transcription factors with significant enrichment of binding sites in the promoter regions of these genes. In addition to metabolites from TCA cycle, oxidative phosphorylation, and lipid metabolism (known to be associated with T2DM), we identified several reporter metabolites representing novel biomarker candidates. For example, the highly connected metabolites NAD+/NADH and ATP/ADP were also identified as reporter metabolites that are potentially contributing to the widespread gene expression changes observed in T2DM. An algorithm based on the analysis of the promoter regions of the genes associated with reporter metabolites revealed a transcription factor regulatory network connecting several parts of metabolism. The identified transcription factors include members of the CREB, NRF1 and PPAR family, among others, and represent regulatory targets for further experimental analysis. Overall, our results provide a holistic picture of key metabolic and regulatory nodes potentially involved in the pathogenesis of T2DM. Type 2 diabetes mellitus is a complex metabolic disease recognized as one of the main threats to human health in the 21st century. Recent studies of gene expression levels in human tissue samples have indicated that multiple metabolic pathways are dysregulated in diabetes and in individuals at risk for diabetes; which of these are primary, or central to disease pathogenesis, remains a key question. Cellular metabolic networks are highly interconnected and often tightly regulated; any perturbations at a single node can thus rapidly diffuse to the rest of the network. Such complexity presents a considerable challenge in pinpointing key molecular mechanisms and biomarkers associated with insulin resistance and type 2 diabetes. In this study, we address this problem by using a methodology that integrates gene expression data with the human cellular metabolic network. We demonstrate our approach by analyzing gene expression patterns in skeletal muscle. The analysis identified transcription factors and metabolites that represent potential targets for therapeutic agents and future clinical diagnostics for type 2 diabetes and impaired glucose metabolism. In a broader perspective, the study provides a framework for analysis of gene expression datasets from complex diseases in the context of changes in cellular metabolism.
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代谢组数据与代谢网络的集成揭示了报告基因反应。
影响因子：
9.9
作者：
Cakir, Tunahan;Patil, Kiran Raosaheb;Onsan, Zeynep iIsen;Ulgen, Kutlu Ozergin;Kirdar, Betul;Nielsen, Jens
通讯作者：
Nielsen, Jens
影响因子：
11.4
作者：
Brekasis, D;Paget, MSB
通讯作者：
Paget, MSB
影响因子：
64.8
作者：
Kersten, S;Desvergne, B;Wahli, W
通讯作者：
Wahli, W
影响因子：
7.4
作者：
Baxter, Charles J.;Redestig, Henning;Sweetlove, Lee J.
通讯作者：
Sweetlove, Lee J.
影响因子：
4.5
作者：
Laughter, AR;Dunn, CS;Corton, JC
通讯作者：
Corton, JC