PDF(Pubmed)
Abstract：
Protein-protein interactions (PPI) play an essential role in the biological processes that occur in the cell. Therefore, the dissection of PPI networks becomes decisive to model functional coordination and predict pathological de-regulation. Cellular networks are dynamic and proteins display varying roles depending on the tissue-interactomic context. Thus, the use of centrality measures in individual proteins fall short to dissect the functional properties of the cell. For this reason, there is a need for more comprehensive, relational, and context-specific ways to analyze the multiple actions of proteins in different cells and identify specific functional assemblies within global biomolecular networks. Under this framework, we define Biological Interacting units (BioInt-U) as groups of proteins that interact physically and are enriched in a common Gene Ontology. A search strategy was applied on 33 tissue-specific (TS) PPI networks to generate BioInt libraries associated with each particular human tissue. The cross-tissue comparison showed that housekeeping assemblies incorporate different proteins and exhibit distinct network properties depending on the tissue. Furthermore, disease genes (DGs) of tissue-associated pathologies preferentially accumulate in units in the expected tissues, which in turn were more central in the TS networks. Overall, the study reveals a tissue-specific functional diversification based on the identification of specific protein units and suggests vulnerabilities specific of each tissue network, which can be applied to refine protein-disease association methods.
摘要：
蛋白质-蛋白质相互作用（PPI）在细胞中发生的生物过程中起着至关重要的作用。因此,PPI网络的解剖对于建立功能协调模型和预测病理性失调具有决定性意义.细胞网络是动态的，蛋白质根据组织相互作用的背景表现出不同的作用。因此，在单个蛋白质中使用中心性措施不足以剖析细胞的功能特性。出于这个原因,需要更全面，关系,和上下文特定的方法来分析蛋白质在不同细胞中的多种作用，并识别全球生物分子网络中的特定功能组件。在这个框架下,我们将生物相互作用单位（BioInt-U）定义为物理相互作用并在共同的基因本体论中富集的蛋白质组。在33个组织特异性(TS)PPI网络上应用搜索策略以产生与每个特定人组织相关的BioInt文库。跨组织比较表明，看家组件掺入了不同的蛋白质，并根据组织表现出不同的网络特性。此外，组织相关病理的疾病基因（DG）优先在预期组织中的单位中积累，这反过来在TS网络中更重要。总的来说,该研究揭示了基于特定蛋白质单位的组织特异性功能多样化，并提出了每个组织网络特有的脆弱性，可用于改进蛋白质-疾病关联方法。
