Inferring pleiotropy by network analysis: linked diseases in the human PPI network

Nguyen et al. BMC Systems Biology 2011, 5:179 http://www.biomedcentral.com/1752-0509/5/179 RESEARCH ARTICLE Open Access Inferring pleiotropy by network analysis: linked diseases in the human PPI network Thanh-Phuong Nguyen1, Wei-chung Liu2 and Ferenc Jordán1* Abstract Background: 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. Results: 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. Conclusions: 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. Background The systems perspective on complex biological systems emphasizes that individual genes act in genetic networks and individual proteins play their roles in protein-protein interaction (PPI) networks [1]. There is increasing interest in these networks, as their analysis helps to understand the relationship between the components (i. e. genes, proteins) and how these are positioned in the whole system. Well-connected hubs seem to be of high functional importance [2,3]. Consequently, studies on diseases based on PPI networks had the starting point by analysing the centrality of disease proteins. Genes associated with a particular phenotype or function are not randomly positioned in the PPI network, but tend to exhibit high connectivity; they may cluster together and can occur in central network locations [4,5]. Beyond focusing on the number of neighbours of graph nodes (their degree), wider neighbourhoods, indirect effects and larger subsets of nodes can also be * Correspondence: 1 The Microsoft Research - University of Trento, Centre for Computational and Systems Biology, Povo/Trento, Italy Full list of author information is available at the end of the article analyzed by the rich arsenal of network analytical tools. This non-local information may help, for example, to quantify the structural relationships between different sets of proteins. In an earlier paper [6], we have determined proteins that mediate indirect effects between sets of proteins causing five diseases in the human PPI network. Their mediator role was quantified and they were ranked according to structural importance. Their functional role may be of high interest, as proteins involved in certain pairs of diseases have no direct interactions among them [6]. These findings motivated an appealing problem: „which proteins connect diseases in the human PPI network?”. To be connected to diverse regions of the PPI network may lend a functionally pleiotropic character to a protein in a classical, genetic sense: it has been demonstrated that high connectivity correlates well with pleiotropic effects [7,8]. The most central mediators are especially important in connecting apparently distant nodes in the human PPI network. Specific network positions may render strange but characteristic behaviour (expression pattern) to different proteins [9,10]. Instead of being exceptional, these epistatic effects may be of © 2011 Nguyen et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Nguyen et al. BMC Systems Biology 2011, 5:179 http://www.biomedcentral.com/1752-0509/5/179 primary importance in physiology [11] and in better understanding animal development and adaptation. In this paper, (1) we compare two interaction networks of mediators (mediating indirect effects between heart disease and obesity, and between heart disease and diabetes), (2) we analyse the structure of these two networks and their aggregated total network, (3) we study the overlap between the two mediator networks, and (4) we infer biological functions for some proteins and provide supporting literature data. All in all, we illustrate that network analysis is an excellent tool for identifying pleiotropy and epistasis from complex networks extracted from multiple databases. Results Network analysis We obtained 9 proteins involved in heart diseases (H), as well as 44 and 20 involved in diabetes (D) and obesity (O), respectively. The HD network contains N = 2142 nodes and L = 3537 links, while the HO network contains N = 1746 nodes and L = 2567 links and the total network contains N = 2221 nodes and L = 3686 links. Figure 1 provides a schematic illustration for how the networks had been constructed (see Methods). Figure 2 shows the relationships between mediator proteins in the HD (Figure 2a) and the HO (Figure 2b) networks. The HD network (Figure 3a) contains 25 HD mediators and their 2117 neighbours and the HO network (Figure 3b) contains 12 HO mediators and their 1734 neighbours. In the „total” network (Figure 4), 9 shared mediators appear, so it contains only 28 mediator proteins. In this total network, 1667 nodes are present in both the HD and the HO network, 475 only in the HD and 79 only in the HO network. The distributions of individual structural indices are very similar for all of the three analyzed networks. Additional file 1 shows all values of the six network indices for all nodes in the three networks. Figure 5 shows these distributions only for the total network. We can observe that almost all indices follow a strongly leftskewed distribution where only a few nodes are extremely important. While degree (D), topological importance (TI) and betweenness centrality (BC) have really only one or a few hubs, topological overlap (TO) indicates several key nodes. Closeness centrality (CC) has a unimodal, normal-like distribution. For each network, there seem to be strong and positive rank correlation between all centrality indices but not for the overlap indices (TO 3 0.01 and TO 3 0.005 ). TO indices correlate positively and weakly with other centrality indices whereas they correlate negatively and weakly with CC (see Table 1). D best correlates with TI3 . The TO measure offers different, complementary information than the centrality indices. (...truncated)