Exploring the cellular basis of human disease through a large-scale mapping of deleterious genes to cell types.
Exploring the cellular basis of human disease through a large-scale mapping of deleterious genes to cell types.
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DOI：
10.1186/s13073-015-0212-9
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发表时间：
2015-09-01
期刊：
影响因子：
12.3
通讯作者：
Sternberg MJ
中科院分区：
文献类型：
--
作者：
Cornish AJ;Filippis I;David A;Sternberg MJ
人体内发现的每种细胞类型都有一套不同而独特的功能，这些功能的破坏可能导致疾病。然而，目前还没有系统的映射细胞类型和它们可能导致的疾病之间。在这项研究中，我们将蛋白质-蛋白质相互作用数据与来自FANTOM 5项目的高质量细胞类型特异性基因表达数据相结合，以建立迄今为止创建的最大的细胞类型特异性相互作用组集合。我们开发了一种称为基因集紧凑性（GSC）的新方法，该方法对比了73种细胞类型特异性相互作用组中疾病相关基因的相对位置，以将与196种疾病相关的基因映射到它们影响的细胞类型。我们对PubMed数据库进行文本挖掘，以产生疾病相关细胞类型的独立资源，我们使用该资源来验证我们的方法。GSC方法成功地识别了已知的疾病-细胞类型关联，并突出了值得进一步研究的关联。这包括肥大细胞和多发性硬化症，这是一个目前正在进行多发性硬化症2期临床试验的细胞群体。此外，我们使用被鉴定为表现每种疾病的细胞类型构建了基于细胞类型的疾病体，从而提供了通过病因学联系的疾病的见解。这项研究产生的数据集代表了疾病与其表现的细胞类型的首次大规模映射，因此将有助于疾病系统的研究。总体而言，我们证明我们的方法将疾病相关基因与它们产生的表型联系起来，这是系统医学的一个关键目标。本文的在线版本（doi：10.1186/s13073-015-0212-9）包含补充材料，可供授权用户使用。
Each cell type found within the human body performs a diverse and unique set of functions, the disruption of which can lead to disease. However, there currently exists no systematic mapping between cell types and the diseases they can cause. In this study, we integrate protein–protein interaction data with high-quality cell-type-specific gene expression data from the FANTOM5 project to build the largest collection of cell-type-specific interactomes created to date. We develop a novel method, called gene set compactness (GSC), that contrasts the relative positions of disease-associated genes across 73 cell-type-specific interactomes to map genes associated with 196 diseases to the cell types they affect. We conduct text-mining of the PubMed database to produce an independent resource of disease-associated cell types, which we use to validate our method. The GSC method successfully identifies known disease–cell-type associations, as well as highlighting associations that warrant further study. This includes mast cells and multiple sclerosis, a cell population currently being targeted in a multiple sclerosis phase 2 clinical trial. Furthermore, we build a cell-type-based diseasome using the cell types identified as manifesting each disease, offering insight into diseases linked through etiology. The data set produced in this study represents the first large-scale mapping of diseases to the cell types in which they are manifested and will therefore be useful in the study of disease systems. Overall, we demonstrate that our approach links disease-associated genes to the phenotypes they produce, a key goal within systems medicine. The online version of this article (doi:10.1186/s13073-015-0212-9) contains supplementary material, which is available to authorized users.