Dynamic Changes in Protein Functional Linkage Networks Revealed by Integration with Gene Expression Data

Response of cells to changing environmental conditions is governed by the dynamics of intricate biomolecular interactions. It may be reasonable to assume, proteins being the dominant macromolecules that carry out routine cellular functions, that understanding the dynamics of protein∶protein interactions might yield useful insights into the cellular responses. The large-scale protein interaction data sets are, however, unable to capture the changes in the profile of protein∶protein interactions. In order to understand how these interactions change dynamically, we have constructed conditional protein linkages for Escherichia coli by integrating functional linkages and gene expression information. As a case study, we have chosen to analyze UV exposure in wild-type and SOS deficient E. coli at 20 minutes post irradiation. The conditional networks exhibit similar topological properties. Although the global topological properties of the networks are similar, many subtle local changes are observed, which are suggestive of the cellular response to the perturbations. Some such changes correspond to differences in the path lengths among the nodes of carbohydrate metabolism correlating with its loss in efficiency in the UV treated cells. Similarly, expression of hubs under unique conditions reflects the importance of these genes. Various centrality measures applied to the networks indicate increased importance for replication, repair, and other stress proteins for the cells under UV treatment, as anticipated. We thus propose a novel approach for studying an organism at the systems level by integrating genome-wide functional linkages and the gene expression data

Candidate gene identification for systemic lupus erythematosus using network centrality measures and gene ontology.

Systemic lupus erythematosus (SLE) commonly accredited as "the great imitator" is a highly complex disease involving multiple gene susceptibility with non-specific symptoms. Many experimental and computational approaches have been used to investigate the disease related candidate genes. But the limited knowledge of gene function and disease correlation and also lack of complete functional details about the majority of genes in susceptible locus, encumbrances the identification of SLE related candidate genes. In this paper, we have studied the human immunome network (undirected) using various graph theoretical centrality measures integrated with the gene ontology terms to predict the new candidate genes. As a result, we have identified 8 candidate genes, which may act as potential targets for SLE disease. We have also carried out the same analysis by replacing the human immunome network with human immunome signaling network (directed) and as an outcome we have obtained 5 candidate genes as potential targets for SLE disease. From the comparison study, we have found these two approaches are complementary in nature

Depending on the number of genes that are considered as top ranking genes, the numbers and combinations of centrality measures may vary for network analysis.

<p>Depending on the number of genes that are considered as top ranking genes, the numbers and combinations of centrality measures may vary for network analysis.</p

Venn diagram of candidate gene prediction, Genes with high network scores having significant GO terms are 38, in that gene set, 30 genes are already known as SLE related genes, so the remaining 8 genes are predicted as new SLE related candidate genes.

<p>Venn diagram of candidate gene prediction, Genes with high network scores having significant GO terms are 38, in that gene set, 30 genes are already known as SLE related genes, so the remaining 8 genes are predicted as new SLE related candidate genes.</p

The correlation coefficients of different pairs of centrality measures shows that the ranking of the nodes differs based on their formalism.

<p>The correlation coefficients of different pairs of centrality measures shows that the ranking of the nodes differs based on their formalism.</p

List of 8 predicted novel SLE related candidate genes.

<p>List of 8 predicted novel SLE related candidate genes.</p

Ranking of predicted candidate genes in various centrality measures in the order Degree-1, Eccentrity-2, Closeness-3, Centroid values-4, SP betweenness-5, CF closeness-6, CF betweenness-7, Katz status-8, Eigen vector-9 and PageRank-10.

<p>The rank of each gene is given inside the braces of the corresponding centrality measures in the top 50 rankings.</p