Yang Jun-Bo, Luo Rong, Yan Yan, Chen Yan
Department of Pediatrics, The Seventh People's Hospital of Ji-Nan, Ji-Nan, 251400, Shandong Province, China.
Department of Pediatrics, Tongchuan People's Hospital, Tongchuan, Shanxi, 727000, China.
Microb Pathog. 2016 Dec;101:50-55. doi: 10.1016/j.micpath.2016.10.023. Epub 2016 Nov 3.
We aimed to identify key pathways to further explore the molecular mechanism underlying pediatric pneumonia using differential pathway network which integrated protein-protein interactions (PPI) data and pathway information. PPI data and pathway information were obtained from STRING and Reactome database, respectively. Next, pathway interactions were identified on the basis of constructing gene-gene interactions randomly, and a weight value computed using Spearman correlation coefficient was assigned to each pathway-pathway interaction, thereby to further detect differential pathway interactions. Subsequently, construction of differential pathway network was implemented using Cytoscope, following by network clustering analysis using ClusterONE. Finally, topological analysis for differential pathway network was performed to identify hub pathway which had top 5% degree distribution. Significantly, 901 pathways were identified to construct pathway interactions. After discarding the pathway interactions with weight value < 1.2, a differential pathway network was constructed, which contained 499 interactions and 347 pathways. Topological analysis showed 17 hub pathways (FGFR1 fusion mutants, molecules associated with elastic fibres, FGFR1 mutant receptor activation, and so on) were identified. Significantly, signaling by FGFR1 fusion mutants and FGFR1 mutant receptor activation simultaneously appeared in two clusters. Molecules associated with elastic fibres existed in one cluster. Accordingly, differential pathway network method might serve as a predictive tool to help us to further understand the development of pediatric pneumonia. FGFR1 fusion mutants, FGFR1 mutant receptor activation, and molecules associated with elastic fibres might play important roles in the progression of pediatric pneumonia.
我们旨在利用整合了蛋白质-蛋白质相互作用（PPI）数据和通路信息的差异通路网络，来识别关键通路，以进一步探究小儿肺炎潜在的分子机制。PPI数据和通路信息分别从STRING和Reactome数据库获得。接下来，基于随机构建基因-基因相互作用来识别通路相互作用，并使用Spearman相关系数计算权重值，将其分配给每个通路-通路相互作用，从而进一步检测差异通路相互作用。随后，使用Cytoscope构建差异通路网络，接着使用ClusterONE进行网络聚类分析。最后，对差异通路网络进行拓扑分析，以识别度数分布在前5%的枢纽通路。值得注意的是，共识别出901条通路来构建通路相互作用。在舍弃权重值<1.2的通路相互作用后，构建了一个包含499个相互作用和347条通路的差异通路网络。拓扑分析显示，识别出了17条枢纽通路（FGFR1融合突变体、与弹性纤维相关的分子、FGFR1突变受体激活等）。值得注意的是，FGFR1融合突变体信号传导和FGFR1突变受体激活同时出现在两个簇中。与弹性纤维相关的分子存在于一个簇中。因此，差异通路网络方法可能作为一种预测工具，帮助我们进一步了解小儿肺炎的发展。FGFRl融合突变体、FGFR1突变受体激活以及与弹性纤维相关的分子可能在小儿肺炎的进展中起重要作用。
