Prioritizing disease candidate proteins in cardiomyopathy-specific protein-protein interaction networks based on "guilt by association" analysis

• Published in: PloS one 2013-08, Vol.8 (8), p.e71191-e71191
• Main Authors: Li, Wan, Chen, Lina, He, Weiming, Li, Weiguo, Qu, Xiaoli, Liang, Binhua, Gao, Qianping, Feng, Chenchen, Jia, Xu, Lv, Yana, Zhang, Siya, Li, Xia
• Format: Article
• Language: English
• Subjects: Algorithms; Bioinformatics; Biology; Cardiac muscle; Cardiomyopathies - genetics; Cardiomyopathies - metabolism; Cardiomyopathy; Cardiovascular disease; Computational Biology - methods; Congenital diseases; Coronary artery disease; Databases, Genetic; Experimental methods; Gene Regulatory Networks; Genetic aspects; Genome-Wide Association Study - methods; Heart; Heart diseases; Heredity; High-Throughput Screening Assays - methods; Humans; Identification methods; Kinases; Medicine; Metabolic Networks and Pathways; Muscles; Myocardial diseases; Protein Binding - physiology; Protein interaction; Protein Interaction Maps; Protein-protein interactions; Proteins; Proteins - isolation & purification; Proteins - metabolism; Rodents; Seeds
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0071191

The cardiomyopathies are a group of heart muscle diseases which can be inherited (familial). Identifying potential disease-related proteins is important to understand mechanisms of cardiomyopathies. Experimental identification of cardiomyophthies is costly and labour-intensive. In contrast, bioinformatics approach has a competitive advantage over experimental method. Based on "guilt by association" analysis, we prioritized candidate proteins involving in human cardiomyopathies. We first built weighted human cardiomyopathy-specific protein-protein interaction networks for three subtypes of cardiomyopathies using the known disease proteins from Online Mendelian Inheritance in Man as seeds. We then developed a method in prioritizing disease candidate proteins to rank candidate proteins in the network based on "guilt by association" analysis. It was found that most candidate proteins with high scores shared disease-related pathways with disease seed proteins. These top ranked candidate proteins were related with the corresponding disease subtypes, and were potential disease-related proteins. Cross-validation and comparison with other methods indicated that our approach could be used for the identification of potentially novel disease proteins, which may provide insights into cardiomyopathy-related mechanisms in a more comprehensive and integrated way.