Network analysis of inflammatory genes and their transcriptional regulators in coronary artery disease.
Network analysis of inflammatory genes and their transcriptional regulators in coronary artery disease.
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DOI：
10.1371/journal.pone.0094328
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Shanker J
中科院分区：
文献类型：
--
作者：
Nair J;Ghatge M;Kakkar VV;Shanker J
网络分析是了解炎症驱动动脉粥样硬化复杂发病机制的一种新方法。使用这种方法，我们试图确定冠状动脉疾病（CAD）中的关键炎症基因及其核心转录调控因子。最初，我们使用Polysearch和CAD基因数据库获得了124个与炎症和CAD相关的候选基因，使用STRING 9.0（检索相互作用基因的搜索工具）生成蛋白质-蛋白质相互作用网络，并使用Cytoscape v 2.8.3可视化。基于介数中心性（BC）和节点度作为关键拓扑参数，我们将白细胞介素-6（IL-6）、血管内皮生长因子A（VEGFA）、白细胞介素-1 β（IL-1B）、肿瘤坏死因子（TNF）和胰高血糖素-内过氧化物合酶2（PTGS 2）确定为枢纽节点。用这五个枢纽基因构建的主干网络显示通过348条边连接的111个节点，其中IL-6的度最大、BC最高。核因子κ B1（NFKB 1）、信号转导和转录激活因子3（STAT 3）和JUN被鉴定为使用MatInspector衍生的调控网络的三个核心转录因子。为了验证枢纽基因，构建了97个测试网络，其揭示了骨干网络的准确度为0.7763，而枢纽节点的频率基本保持不变。用CCLIGO、KEGG和REACTOME进行的途径富集分析显示六种经验证的CAD途径显著富集-平滑肌细胞增殖、急性期反应、骨化二醇1-单加氧酶活性、toll样受体信号传导、NOD样受体信号传导和脂肪细胞因子信号传导途径。在64例病例组和64例对照组中对上述结果进行了实验验证，结果表明，病例组中5个候选基因和3个转录因子的表达均高于对照组（p<0.05）。因此，复杂网络的分析有助于在复杂疾病中优先考虑基因及其转录调节因子。
Network analysis is a novel method to understand the complex pathogenesis of inflammation-driven atherosclerosis. Using this approach, we attempted to identify key inflammatory genes and their core transcriptional regulators in coronary artery disease (CAD). Initially, we obtained 124 candidate genes associated with inflammation and CAD using Polysearch and CADgene database for which protein-protein interaction network was generated using STRING 9.0 (Search Tool for the Retrieval of Interacting Genes) and visualized using Cytoscape v 2.8.3. Based on betweenness centrality (BC) and node degree as key topological parameters, we identified interleukin-6 (IL-6), vascular endothelial growth factor A (VEGFA), interleukin-1 beta (IL-1B), tumor necrosis factor (TNF) and prostaglandin-endoperoxide synthase 2 (PTGS2) as hub nodes. The backbone network constructed with these five hub genes showed 111 nodes connected via 348 edges, with IL-6 having the largest degree and highest BC. Nuclear factor kappa B1 (NFKB1), signal transducer and activator of transcription 3 (STAT3) and JUN were identified as the three core transcription factors from the regulatory network derived using MatInspector. For the purpose of validation of the hub genes, 97 test networks were constructed, which revealed the accuracy of the backbone network to be 0.7763 while the frequency of the hub nodes remained largely unaltered. Pathway enrichment analysis with ClueGO, KEGG and REACTOME showed significant enrichment of six validated CAD pathways - smooth muscle cell proliferation, acute-phase response, calcidiol 1-monooxygenase activity, toll-like receptor signaling, NOD-like receptor signaling and adipocytokine signaling pathways. Experimental verification of the above findings in 64 cases and 64 controls showed increased expression of the five candidate genes and the three transcription factors in the cases relative to the controls (p<0.05). Thus, analysis of complex networks aid in the prioritization of genes and their transcriptional regulators in complex diseases.
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影响因子：
4.6
作者：
Anfossi G;Russo I;Doronzo G;Pomero A;Trovati M
通讯作者：
Trovati M
DOI：
10.1093/bioinformatics/btp193
发表时间：
2009-06-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Du P;Feng G;Flatow J;Song J;Holko M;Kibbe WA;Lin SM
通讯作者：
Lin SM
影响因子：
4
作者：
Hess, J;Angel, P;Schorpp-Kistner, M
通讯作者：
Schorpp-Kistner, M
影响因子：
8.7
作者：
Galea, J;Armstrong, J;Holt, CM
通讯作者：
Holt, CM
DOI：
10.1093/bioinformatics/btp101
发表时间：
2009-04-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Bindea G;Mlecnik B;Hackl H;Charoentong P;Tosolini M;Kirilovsky A;Fridman WH;Pagès F;Trajanoski Z;Galon J
通讯作者：
Galon J