Visualization and Analysis of the Interaction Network of Proteins Associated with Blood-cell targeting Autoimmune Diseases

Abstract

Blood-cell targeting Autoimmune Diseases (BLADs) are complex diseases that affect blood cell formation or prevent blood cell production. Since these clinical conditions are gathering growing attention, experimental approaches are being used to investigate the mechanisms behind their pathogenesis and to identify proteins associated with them. However, computational approaches have not been utilized extensively in the study of BLADs. This study aims to investigate the interaction network of proteins associated with BLADs (BLAD interactome) and to identify novel associations with other human proteins. The method followed in this study combines information regarding protein-protein interaction network properties and autoimmune disease terms. Proteins with high network scores and statistically significant autoimmune disease term enrichment were obtained and 14 of them were designated as candidate proteins associated with BLADs. Additionally, clustering analysis of the BLAD interactome was used and allowed the detection of 17 proteins that act as "connectors" of different BLADs. We expect our findings to further extend experimental efforts for the investigation of the pathogenesis and the relationships of BLADs.

Construction and Analysis of Protein-Protein Interaction Network of Non-Alcoholic Fatty Liver Disease

“…Afterwards a topological analysis was conducted using the NetworkAnalyzer [17], a handy Cytoscape plugin, to estimate simple and complex topology parameters. The three important metricsdegree, betweenness and closeness centralitywere utilized to evaluate the importance of nodes in a network [10,18]. Hub proteins were identified by their very high degree of connectivity.…”

“…Proteins with high betweenness centrality, namely bottlenecks, are key connectors in the PPI network, controlling the flow of information within a network [19]. For the identification of proteins -from which the flow of information passes faster to other network's proteins -are those with high closeness centrality, hereby referred to as PHC (proteins with high closeness centrality) [10]. The top scoring proteins corresponding to about the 5% of the network's proteins were then selected for each of the three aforementioned network centralities.…”

“…3, 6 th Cluster-magenta nodes). [10,39,40]. In the present study, a topological analysis of the NAFLD Interactome was conducted by applying two different approaches (as presented throughout the Methods section), thusly a total of 77 candidate NAFLD-related proteins were identified.…”

“…Therefore, it is necessary to move from single omics to multi-omics analysis, providing a broader window of its pathophysiology that scans different perspectives [9]. Network-based approaches integrate omics data such as protein-protein interaction (PPI) networks which are gaining ground in the scientific community as they provide valuable, quick and inexpensive tools for clarifying disease mechanisms and detecting new candidate disease-related proteins (or genes) [10].…”