A Systems Biology Approach to Reveal Putative Host-Derived Biomarkers of Periodontitis by Network Topology Characterization of MMP-REDOX/NO and Apoptosis Integrated Pathways

Abstract

Periodontitis, a formidable global health burden, is a common chronic disease that destroys tooth-supporting tissues. Biomarkers of the early phase of this progressive disease are of utmost importance for global health. In this context, saliva represents a non-invasive biosample. By using systems biology tools, we aimed to (1) identify an integrated interactome between matrix metalloproteinase (MMP)-REDOX/nitric oxide (NO) and apoptosis upstream pathways of periodontal inflammation, and (2) characterize the attendant topological network properties to uncover putative biomarkers to be tested in saliva from patients with periodontitis. Hence, we first generated a protein-protein network model of interactions (“BIOMARK” interactome) by using the STRING 10 database, a search tool for the retrieval of interacting genes/proteins, with “Experiments” and “Databases” as input options and a confidence score of 0.400. Second, we determined the centrality values (closeness, stress, degree or connectivity, and betweenness) for the “BIOMARK” members by using the Cytoscape software. We found Ubiquitin C (UBC), Jun proto-oncogene (JUN), and matrix metalloproteinase-14 (MMP14) as the most central hub- and non-hub-bottlenecks among the 211 genes/proteins of the whole interactome. We conclude that UBC, JUN, and MMP14 are likely an optimal candidate group of host-derived biomarkers, in combination with oral pathogenic bacteria-derived proteins, for detecting periodontitis at its early phase by using salivary samples from patients. These findings therefore have broader relevance for systems medicine in global health as well.

Cumulative use of salivary markers with an adaptive design improves detection of periodontal disease over fixed biomarker thresholds

“…Specific host-or bacteria-derived biomarkers detected in saliva indicate the presence or progression/remission of periodontitis. With the aid of Omics Technologies, search for biomarkers with high sensitivity and specificity has broadened during the last decade [2]. The use of a single salivary marker in detection of periodontitis is, however, challenged by the episodic and multi-factorial characteristics of the disease [3].…”

Determining the Balance Between Drug Efficacy and Safety by the Network and Biological System Profile of Its Therapeutic Target

“…In total, 32 network properties were calculated and adopted in subsequent analysis. These properties were popular for analyzing a complex biological network, which included: (1) Average Closeness Centrality : the average number of steps required to reach the studied node from any node in a network (Ma et al, 2016 ); (2) Average Shortest Path Length : the average length of shortest paths between the studied node and all other ones (Zhang et al, 2014 ); (3) Betweenness Centrality : the number of times the studied node serving as a linking bridge along shortest path between any two nodes (Zeidán-Chuliá et al, 2015 ); (4) Bridging Centrality : the product of the bridging coefficient and betweenness centrality (Hwang et al, 2008 ); (5) Bridging Coefficient : the extent of the studied node lying between any other densely connected nodes in the network (Paladugu et al, 2008 ); (6) Closeness Centrality Sum : the reciprocal of the sum of the shortest paths between the studied node and all other nodes in the network (Costenbader and ValenteFontanesi, 2003 ); (7) Clustering Coefficient : the number of the connected pairs between all neighbors of node (Watts and Strogatz, 1998 ); (8) Current Flow Betweenness : a centrality index measuring the level of information travels along all possible paths within network (Paladugu et al, 2008 ); (9) Current Flow Closeness : the variant of current flow betweenness (Zhang et al, 2017b ); (10) Degree : the number of edges linked to a node (Braeuning, 2013 ); (11) Degree Centrality : the number of links incident upon a studied node (Batool and Niazi, 2014 ); (12) Deviation : the variation between sum of node distances and network unipolarity (Zhang et al, 2017a ); (13) Distance Deviation : the absolute difference between nodes' distance sum and network's average distance (Rogelj et al, 2013 ); (14) Distance Sum : the sum of all shortest paths starting from the studied node (Bolser et al, 2003 ); (15) Eccentric : the absolute difference between nodes' eccentricities and network's average eccentricity (Zhang et al, 2017a ); (16) Eccentricity : the maximum non-infinite shortest path length between the studied node and all other nodes in the network (Bolser et al, 2003 ); (17) Eccentricity Centrality : the largest geodesic distance between the node and any other node (Batool and Niazi, 2014 ); (18) Eigenvector Centrality : the sum of its neighbors' centrality values (Solá et al, 2013 ); (19) Harmonic Closeness Centrality : the sum of the reciprocals of the average shortest path lengths of each node in network (Zhang et al, 2017b ); (20) Interconnectivity : a connectivity index indicating the quality of the studied nodes being connected together (Emig et al, 2013 ); (21) Load Centrality : the fraction of all th...…”

Using Coexpression Protein Interaction Network Analysis to Identify Mechanisms of Danshensu Affecting Patients with Coronary Heart Disease

“…It indicated that those nodes are key points which control the communication with other nodes in CePIN. Were they removed, the network would be divided into fragments [53]. All nodes with high values of betweenness over threshold are named as “bottlenecks” [54].…”