Integration of Molecular Interactome and Targeted Interaction Analysis to Identify a COPD Disease Network Module.
Integration of Molecular Interactome and Targeted Interaction Analysis to Identify a COPD Disease Network Module.
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DOI：
10.1038/s41598-018-32173-z
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发表时间：
2018-09-27
影响因子：
4.6
通讯作者：
Silverman EK
中科院分区：
文献类型：
--
作者：
Sharma A;Kitsak M;Cho MH;Ameli A;Zhou X;Jiang Z;Crapo JD;Beaty TH;Menche J;Bakke PS;Santolini M;Silverman EK
复杂疾病的多基因性质提供了利用基于网络的方法的潜在机会，该方法利用全面的蛋白质-蛋白质相互作用（人类相互作用组）来识别新的感兴趣基因和相关生物途径。然而，当前人类相互作用组的不完整性使其无法充分发挥从基因发现工作中提取基于网络的知识的潜力，例如针对慢性阻塞性肺病（COPD）等复杂疾病的全基因组关联研究。在这里，我们提供了一个框架，将现有的人类相互作用组信息与 FAM13A（与 COPD 关联性最高的基因位点之一）的实验蛋白质-蛋白质相互作用数据相结合，以找到更全面的疾病网络模块。我们通过应用随机游走方法确定了初始疾病网络邻居。接下来，我们开发了一种基于网络的紧密度方法 (CAB)，该方法显示通过亲和纯化测定鉴定出的 96 个 FAM13A 相互作用伙伴中有 9 个与初始网络邻居显着接近。此外，与类似的方法（局部径向性）相比，CAB 方法将低度基因预测为潜在候选基因。通过基于网络的紧密度方法确定的候选者与初始网络邻域相结合，构建了一个综合的疾病网络模块（163个基因），该模块富含肺泡巨噬细胞、肺组织、痰、血液和支气管刷牙数据集中对照和COPD受试者之间差异表达的基因。总的来说，我们展示了一种使用新的实验室数据寻找疾病相关网络组件的方法，以克服当前相互作用组的不完整性。
The polygenic nature of complex diseases offers potential opportunities to utilize network-based approaches that leverage the comprehensive set of protein-protein interactions (the human interactome) to identify new genes of interest and relevant biological pathways. However, the incompleteness of the current human interactome prevents it from reaching its full potential to extract network-based knowledge from gene discovery efforts, such as genome-wide association studies, for complex diseases like chronic obstructive pulmonary disease (COPD). Here, we provide a framework that integrates the existing human interactome information with experimental protein-protein interaction data for FAM13A, one of the most highly associated genetic loci to COPD, to find a more comprehensive disease network module. We identified an initial disease network neighborhood by applying a random-walk method. Next, we developed a network-based closeness approach (CAB) that revealed 9 out of 96 FAM13A interacting partners identified by affinity purification assays were significantly close to the initial network neighborhood. Moreover, compared to a similar method (local radiality), the CAB approach predicts low-degree genes as potential candidates. The candidates identified by the network-based closeness approach were combined with the initial network neighborhood to build a comprehensive disease network module (163 genes) that was enriched with genes differentially expressed between controls and COPD subjects in alveolar macrophages, lung tissue, sputum, blood, and bronchial brushing datasets. Overall, we demonstrate an approach to find disease-related network components using new laboratory data to overcome incompleteness of the current interactome.
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影响因子：
14.9
作者：
Kamburov A;Stelzl U;Lehrach H;Herwig R
通讯作者：
Herwig R
影响因子：
82.9
作者：
Cloonan SM;Glass K;Laucho-Contreras ME;Bhashyam AR;Cervo M;Pabón MA;Konrad C;Polverino F;Siempos II;Perez E;Mizumura K;Ghosh MC;Parameswaran H;Williams NC;Rooney KT;Chen ZH;Goldklang MP;Yuan GC;Moore SC;Demeo DL;Rouault TA;D'Armiento JM;Schon EA;Manfredi G;Quackenbush J;Mahmood A;Silverman EK;Owen CA;Choi AM
通讯作者：
Choi AM
影响因子：
14.9
作者：
Kamburov, Atanas;Stelzl, Ulrich;Herwig, Ralf
通讯作者：
Herwig, Ralf
DOI：
10.1165/rcmb.2008-0340oc
发表时间：
2009-06-01
影响因子：
6.4
作者：
Cho, Michael H.;Ciulla, Dawn M.;Silverman, Edwin K.
通讯作者：
Silverman, Edwin K.
影响因子：
5.2
作者：
Huang, Jie;Liu, Eric Y.;Li, Yun
通讯作者：
Li, Yun