In Silico Pleiotropy Analysis in KEGG Signaling Networks Using a Boolean Network Model

Abstract

Pleiotropy, which refers to the ability of different mutations on the same gene to cause different pathological effects in human genetic diseases, is important in understanding system-level biological diseases. Although some biological experiments have been proposed, still little is known about pleiotropy on gene-gene dynamics, since most previous studies have been based on correlation analysis. Therefore, a new perspective is needed to investigate pleiotropy in terms of gene-gene dynamical characteristics. To quantify pleiotropy in terms of network dynamics, we propose a measure called in silico Pleiotropic Scores (sPS), which represents how much a gene is affected against a pair of different types of mutations on a Boolean network model. We found that our model can identify more candidate pleiotropic genes that are not known to be pleiotropic than the experimental database. In addition, we found that many types of functionally important genes tend to have higher sPS values than other genes; in other words, they are more pleiotropic. We investigated the relations of sPS with the structural properties in the signaling network and found that there are highly positive relations to degree, feedback loops, and centrality measures. This implies that the structural characteristics are principles to identify new pleiotropic genes. Finally, we found some biological evidence showing that sPS analysis is relevant to the real pleiotropic data and can be considered a novel candidate for pleiotropic gene research. Taken together, our results can be used to understand the dynamics pleiotropic characteristics in complex biological systems in terms of gene-phenotype relations.

Keywords: Boolean network dynamics; feedback loops; gene–gene interactions; pleiotropy; signaling networks.

Figure 1

An illustrative example of $sPS$ computation. An example of a signaling network $G\left(V,A\right)$ with a set of update rules $F$. Let $v_1$ a node subject to the knockout or the overexpression mutations. The mutations change $F$ to $F^{\prime }$ where the state value of $v_1$ is frozen to 0 and 1, respectively, for $t\le T$. The sets of genes whose dynamics are influenced by the mutations are identified as $V_k$ and $V_o$, respectively. Accordingly, $sPS$ of $v_1$ is computed as the ratio of the symmetric difference of $V_k$ and $V_o$ over the union of them.

Figure 2

Relationship between $sPS$ and $zPS$ in KEGG network. (a–d) Relations of $sPS$ and $zPS$ in scatter-plot graph for mutation duration time $T=14-20$, respectively. (e) Correlation coefficients of $sPS$ and $zPS$. Only genes with positive $sPS$ values were examined. All p-values are significant (p-value < 0.0001).

Figure 3

Relation of $sPS$ with the functionally important genes in KEGG network. (a–f) Results of cancer genes, drug targets, essential genes, tumor suppressors, oncogenes, and disease genes, respectively. In each subfigure, all genes were classified into ‘non-zero $sPS$’ and ‘zero-$sPS$’ groups. Y-axis is the ratio of the functionally important genes among the total genes in the group. The mutation duration time $T$ was varied from 14 to 20.

Figure 4

Relation of $sPS$ with structural characteristics in KEGG network. (a) Relation to the degree. Y-axis values mean the correlation coefficients between $sPS$ and the number of nodes’ degree, in-degree, and out-degree. (b) Relation to the involvement of feedback loops. All genes were classified into ‘FBL’ and ‘No FBL’ groups, where a gene involves any feedback loops or not, respectively. Y-axis values mean the average of $sPS$ values. (c) Relations to the centrality measures such as betweenness, stress, closeness, and eigenvector. Y-axis values mean the correlation coefficients between $sPS$ and each centrality measure. Mutation duration time $T$ was set to 14–20.

Figure 5

Pleiotropic genes in KEGG sub-network. Arrow-headed and bar-headed lines indicate the activation (positive) and the inhibition (negative) interactions, respectively. The grey circles belong to the non-observed genes. The blue circles represent confirmed pleiotropic genes from the HPO database. The yellow circles represent the novel candidate pleiotropic genes.