Cellular Signaling Pathways in Insulin Resistance-Systems Biology Analyses of Microarray Dataset Reveals New Drug Target Gene Signatures of Type 2 Diabetes Mellitus.
Cellular Signaling Pathways in Insulin Resistance-Systems Biology Analyses of Microarray Dataset Reveals New Drug Target Gene Signatures of Type 2 Diabetes Mellitus.
复制标题
微阵列数据集的胰岛素抵抗系统生物学分析中的细胞信号通路揭示了2型糖尿病的新药物靶基因特征。
DOI：
10.3389/fphys.2017.00013
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发表时间：
2017
影响因子：
4
通讯作者：
Chen J
中科院分区：
文献类型：
--
作者：
Muhammad SA;Raza W;Nguyen T;Bai B;Wu X;Chen J
目的：2型糖尿病（T2DM）是一种影响全球大量人群的慢性代谢性疾病。为了扩大对该疾病遗传原因的理解范围，我们进行了相互作用和基于毒物基因组学的系统生物学研究，通过cDNA差异分析发现潜在的T2DM相关基因。方法：从50个差异表达基因列表中（p < 0.05），通过广泛的数据作图发现9-T2DM相关基因。在我们构建的基因网络中，发现t2dm相关的差异表达种子基因（9个基因）与功能相关的基因特征（31个基因）相互作用。基于毒物基因组学和数据管理，t2dm相关种子基因和特征基因的遗传相互作用网络通常与疾病状况密切相关。结果：这些网络中胰岛素信号、胰岛素分泌等t2dm相关通路包括JAK-STAT、MAPK、TGF、toll样受体、p53和mTOR、脂肪细胞因子、FOXO、PPAR、P13-AKT和甘油三酯代谢通路显著富集。我们发现了一些在不同条件下都很常见的富集通路。我们认识到11个信号通路是胰岛素抵抗和T2DM基因特征之间的联系。值得注意的是，在药物-基因网络中，相互作用基因与13种fda批准的药物和少数未批准的药物存在显著重叠。本研究证明了系统遗传学在识别18个与T2DM相关的潜在基因方面的价值，这些基因可能是药物靶点。结论：这种在基因组数据中寻找变异的综合和基于网络的方法有望加速对不同疾病的新药物靶标分子的识别，并可以加快药物发现的结果。
Purpose: Type 2 diabetes mellitus (T2DM) is a chronic and metabolic disorder affecting large set of population of the world. To widen the scope of understanding of genetic causes of this disease, we performed interactive and toxicogenomic based systems biology study to find potential T2DM related genes after cDNA differential analysis. Methods: From the list of 50-differential expressed genes (p < 0.05), we found 9-T2DM related genes using extensive data mapping. In our constructed gene-network, T2DM-related differentially expressed seeder genes (9-genes) are found to interact with functionally related gene signatures (31-genes). The genetic interaction network of both T2DM-associated seeder as well as signature genes generally relates well with the disease condition based on toxicogenomic and data curation. Results: These networks showed significant enrichment of insulin signaling, insulin secretion and other T2DM-related pathways including JAK-STAT, MAPK, TGF, Toll-like receptor, p53 and mTOR, adipocytokine, FOXO, PPAR, P13-AKT, and triglyceride metabolic pathways. We found some enriched pathways that are common in different conditions. We recognized 11-signaling pathways as a connecting link between gene signatures in insulin resistance and T2DM. Notably, in the drug-gene network, the interacting genes showed significant overlap with 13-FDA approved and few non-approved drugs. This study demonstrates the value of systems genetics for identifying 18 potential genes associated with T2DM that are probable drug targets. Conclusions: This integrative and network based approaches for finding variants in genomic data expect to accelerate identification of new drug target molecules for different diseases and can speed up drug discovery outcomes.