Nguyen Thanh-Phuong, Liu Wei-chung, Jordán Ferenc
The Microsoft Research-University of Trento, Centre for Computational and Systems Biology, Povo/Trento, Italy.
BMC Syst Biol. 2011 Oct 31;5:179. doi: 10.1186/1752-0509-5-179.
Earlier, we identified proteins connecting different disease proteins in the human protein-protein interaction network and quantified their mediator role. An analysis of the networks of these mediators shows that proteins connecting heart disease and diabetes largely overlap with the ones connecting heart disease and obesity.
We quantified their overlap, and based on the identified topological patterns, we inferred the structural disease-relatedness of several proteins. Literature data provide a functional look of them, well supporting our findings. For example, the inferred structurally important role of the PDZ domain-containing protein GIPC1 in diabetes is supported despite the lack of this information in the Online Mendelian Inheritance in Man database. Several key mediator proteins identified here clearly has pleiotropic effects, supported by ample evidence for their general but always of only secondary importance.
We suggest that studying central nodes in mediator networks may contribute to better understanding and quantifying pleiotropy. Network analysis provides potentially useful tools here, as well as helps in improving databases.
此前,我们在人类蛋白质-蛋白质相互作用网络中鉴定出连接不同疾病蛋白质的蛋白质,并对它们的介导作用进行了量化。对这些介导因子网络的分析表明,连接心脏病和糖尿病的蛋白质与连接心脏病和肥胖症的蛋白质在很大程度上重叠。
我们对它们的重叠进行了量化,并基于所确定的拓扑模式,推断了几种蛋白质与疾病相关的结构。文献数据提供了它们的功能视角,很好地支持了我们的发现。例如,尽管在线人类孟德尔遗传数据库中缺乏相关信息,但含PDZ结构域的蛋白质GIPC1在糖尿病中推断出的重要结构作用得到了支持。这里鉴定出的几种关键介导蛋白显然具有多效性,有充分证据表明它们具有普遍作用,但始终只是次要的。
我们认为,研究介导网络中的中心节点可能有助于更好地理解和量化多效性。网络分析在此提供了潜在有用的工具,也有助于改进数据库。