Repositioning drugs by targeting network modules: a Parkinson's disease case study.
Repositioning drugs by targeting network modules: a Parkinson's disease case study.
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DOI：
10.1186/s12859-017-1889-0
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发表时间：
2017-12-28
影响因子：
3
通讯作者：
Chen JY
中科院分区：
文献类型：
--
作者：
Yue Z;Arora I;Zhang EY;Laufer V;Bridges SL;Chen JY
迄今为止，许多努力都致力于发现药物与单一靶点之间的特定机制。然而，由于生物系统在控制内部环境的功能网络水平上保持稳态，因此这些网络通常包含多个冗余机制，旨在抵消网络单个成员的丢失或扰动。因此，靶向失调的途径或过程而不是单一靶标的治疗剂的研究可以鉴定在与复杂疾病如帕金森病（PD）的病理学更相关的生物组织水平上起作用的药剂。PD的全基因组关联研究（GWAS）已经确定了疾病易感性的常见变异，而基因表达微阵列数据提供了全基因组转录谱。这些基因组研究可以说明上游扰动引起的信号通路和下游生化机制，导致PD表型的功能障碍。我们假设，药物作用于特定于PD的基因表达模块水平，可以克服与靶向多基因疾病中的单个基因相关的疗效缺乏。因此，这种方法代表了基于模块的药物发现在人类疾病，如PD一个有前途的新方向。我们建立了一个框架，将GWAS数据与来自代表三个大脑区域的组织的基因共表达模块整合在一起-PD患者的额回、外侧实质和内侧实质。利用R语言的加权基因相关网络分析软件包（WGCNA），对帕金森病GWAS数据进行富集分析。这导致鉴定出两个过度代表的PD特异性基因共表达网络模块：包含449个基因的棕色模块（Br）和包含905个基因的绿松石模块（T）。进一步的富集分析确定了Br模块内的四个功能途径（细胞呼吸、细胞内转运、对抗电化学梯度的能量耦合质子转运和基于微管的运动）和T模块内的一个功能途径（M-相）。接下来，我们利用药物-蛋白质调控关系数据库（DMAP）并开发了药物效应总和评分（DESS）来评估所有可能在Br和T模块中将基因表达恢复到正常水平的候选药物。在DESS评分最高的12种药物中，5种被报告为PD的潜在治疗药物，6种被报告为潜在的重新定位应用。在这项研究中，我们提出了一个系统药理学框架，它利用GWAS和基因表达微阵列数据的遗传数据来重新定位PD药物。我们的创新方法将基因共表达模块与生物分子相互作用网络分析相结合，以识别对PD途径和疾病机制至关重要的网络模块。我们在DESS评分中量化药物的积极作用，该评分基于已知的药物靶点活性特征。我们的研究结果表明，这种模块化的方法是有前途的重新定位药物用于多基因疾病，如PD，并能够解决的挑战，阻碍基因靶点的药物重新定位方法的日期。本文的在线版本（10.1186/s12859-017-1889-0）包含补充材料，可供授权用户使用。
Much effort has been devoted to the discovery of specific mechanisms between drugs and single targets to date. However, as biological systems maintain homeostasis at the level of functional networks robustly controlling the internal environment, such networks commonly contain multiple redundant mechanisms designed to counteract loss or perturbation of a single member of the network. As such, investigation of therapeutics that target dysregulated pathways or processes, rather than single targets, may identify agents that function at a level of the biological organization more relevant to the pathology of complex diseases such as Parkinson’s Disease (PD). Genome-wide association studies (GWAS) in PD have identified common variants underlying disease susceptibility, while gene expression microarray data provide genome-wide transcriptional profiles. These genomic studies can illustrate upstream perturbations causing the dysfunction in signaling pathways and downstream biochemical mechanisms leading to the PD phenotype. We hypothesize that drugs acting at the level of a gene expression module specific to PD can overcome the lack of efficacy associated with targeting a single gene in polygenic diseases. Thus, this approach represents a promising new direction for module-based drug discovery in human diseases such as PD. We built a framework that integrates GWAS data with gene co-expression modules from tissues representing three brain regions—the frontal gyrus, the lateral substantia, and the medial substantia in PD patients. Using weighted gene correlation network analysis (WGCNA) software package in R, we conducted enrichment analysis of data from a GWAS of PD. This led to the identification of two over-represented PD-specific gene co-expression network modules: the Brown Module (Br) containing 449 genes and the Turquoise module (T) containing 905 genes. Further enrichment analysis identified four functional pathways within the Br module (cellular respiration, intracellular transport, energy coupled proton transport against the electrochemical gradient, and microtubule-based movement), and one functional pathway within the T module (M-phase). Next, we utilized drug-protein regulatory relationship databases (DMAP) and developed a Drug Effect Sum Score (DESS) to evaluate all candidate drugs that might restore gene expression to normal level across the Br and T modules. Among the drugs with the 12 highest DESS scores, 5 had been reported as potential treatments for PD and 6 hold potential repositioning applications. In this study, we present a systems pharmacology framework which draws on genetic data from GWAS and gene expression microarray data to reposition drugs for PD. Our innovative approach integrates gene co-expression modules with biomolecular interaction network analysis to identify network modules critical to the PD pathway and disease mechanism. We quantify the positive effects of drugs in a DESS score that is based on known drug-target activity profiles. Our results illustrate that this modular approach is promising for repositioning drugs for use in polygenic diseases such as PD, and is capable of addressing challenges of the hindered gene target in drug repositioning approaches to date. The online version of this article (10.1186/s12859-017-1889-0) contains supplementary material, which is available to authorized users.
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WGCNA：用于加权相关网络分析的 R 包。
影响因子：
3
作者：
Langfelder P;Horvath S
通讯作者：
Horvath S
影响因子：
14.9
作者：
Ritchie ME;Phipson B;Wu D;Hu Y;Law CW;Shi W;Smyth GK
通讯作者：
Smyth GK
影响因子：
5.1
作者：
Fujita, Kazuhiro A.;Ostaszewski, Marek;Matsuoka, Yukiko;Ghosh, Samik;Glaab, Enrico;Trefois, Christophe;Crespo, Isaac;Perumal, Thanneer M.;Jurkowski, Wiktor;Antony, Paul M. A.;Diederich, Nico;Buttini, Manuel;Kodama, Akihiko;Satagopam, Venkata P.;Eifes, Serge;del Sol, Antonio;Schneider, Reinhard;Kitano, Hiroaki;Balling, Rudi
通讯作者：
Balling, Rudi
影响因子：
--
作者：
Okun, MS;Fernandez, HH;Crucian, G
通讯作者：
Crucian, G
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