title: 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.
authors.affiliation: Programa de Pós-Graduação em Ciências Biológicas: Bioquímica, Departamento de Bioquímica, Instituto de Ciências Básicas da Saúde, Universidade Federal do Rio Grande do SulPorto Alegre, Brazil; Department of Periodontology, Institute of Dentistry, University of TurkuTurku, Finland.
authors.forename: Fares
authors.initials: F
authors.lastname: Zeidán-Chuliá
authors.affiliation: Department of Periodontology, Institute of Dentistry, University of Turku Turku, Finland.
authors.forename: Mervi
authors.initials: M
authors.lastname: Gürsoy
authors.affiliation: Programa de Pós-Graduação em Ciências Biológicas: Bioquímica, Departamento de Bioquímica, Instituto de Ciências Básicas da Saúde, Universidade Federal do Rio Grande do Sul Porto Alegre, Brazil.
authors.forename: Ben-Hur
authors.initials: BH
authors.lastname: Neves de Oliveira
authors.affiliation: Faculty of Communications and Office of the President, International Technology and Innovation Policy, Gaziantep UniversityGaziantep, Turkey; Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham (Amrita University)Kollam, India.
authors.forename: Vural
authors.initials: V
authors.lastname: Özdemir
authors.affiliation: Department of Periodontology, Institute of Dentistry, University of TurkuTurku, Finland; Oral Health Care, Welfare DivisionTurku, Finland.
authors.forename: Eija
authors.initials: E
authors.lastname: Könönen
authors.affiliation: Department of Periodontology, Institute of Dentistry, University of Turku Turku, Finland.
authors.forename: Ulvi K
authors.initials: UK
authors.lastname: Gürsoy
mesh_terms: D017209:Apoptosis; D015415:Biomarkers / Q000032:analysis*; D019295:Computational Biology; D053263:Gene Regulatory Networks; D006801:Humans; D020782:Matrix Metalloproteinases / Q000378:metabolism; D053858:Metabolic Networks and Pathways; D009569:Nitric Oxide / Q000378:metabolism; D010084:Oxidation-Reduction; D010518:Periodontitis / Q000175:diagnosis* / Q000473:pathology; D060066:Protein Interaction Maps; D000090063:Proto-Oncogene Mas; D012463:Saliva / Q000737:chemistry*; D015398:Signal Transduction; D049490:Systems Biology
chemical_list: D015415:Biomarkers; C000715470:MAS1 protein, human; D000090063:Proto-Oncogene Mas; D009569:Nitric Oxide; D020782:Matrix Metalloproteinases
keywords: computational biology; drug target; gelatinases; in silico; network; omics; oxidative stress; saliva