Attractors of hypertrophic cardiomyopathy using maximal cliques and attract methods. (April 2017)

• Record Type: Journal Article
• Authors: Feng, Ming-Jun; Chu, Hui-Min; Shen, Cai-Jie; He, Bin; Du, Xian-Feng; Yu, Yi-Bo; Liu, Jing; Chen, Xiao-Min
• Is Part Of: Computational biology and chemistry. Volume 67(2017)
• Page Start: 194
• Page End: 199
• Publication Date: 2017-04
• Subjects: HCM hypertrophic cardiomyopathy -- PPI protein–protein interaction -- SCC spearman correlation coefficient -- RMA robust multiarray average -- MAS micro array suite -- STRING Search Tool for the Retrieval of Interacting Genes -- WID weighted interaction density -- WIC weighted inter-connectivity -- MCD module correlation density -- FDR false discovery rate -- KEGG Kyoto Encyclopedia of Genes and Genomes; Hypertrophic cardiomyopathy -- Attract -- Module correlation density -- Attractor modules -- Pathways; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2017.01.006
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Totally, 32 and 55 modules were obtained from the PPI networks of normal and HCM, respectively. A total of 5 modules were differential attractors. Proteasome, ribosome and oxidative phosphorylation were the significant pathways. Abstract: Background: Our study was designed to identify the differential attractor modules related with hypertrophic cardiomyopathy (HCM) by integrating clustering-based on maximal cliques algorithm and Attract method. Methods: We firstly recruited the HCM-related microarray data from ArrayExpress database. Next, protein–protein interaction (PPI) networks of normal and HCM were constructed and re-weighted using spearman correlation coefficient (SCC). Then, maximal cliques were found from the PPI networks through the clustering-based on maximal cliques approach. Afterwards, highly overlapped cliques were eliminated or merged according to the interconnectivity, and then modules were obtained. Subsequently, we used Attract method to identify differential attractor modules, following by the pathway enrichment analyses for genes in differential attractor modules. Results: After removing the cliques with nodes less than or equal to 4, 926 and 1118 maximal cliques in normal and HCM PPI networks were obtained for module analysis. Then, we obtained 32 and 55 modules from the PPI networks of normal and HCM, respectively. By comparing with normal condition, there were 5 module pairs with the same or similar gene composition. Significantly, based on attract method, we found that these 5 modules were differential attractors. Pathway enrichment analyses indicated that proteasome, ribosome and oxidative phosphorylation were the significant pathways. Conclusions: Proteasome, ribosome and oxidative phosphorylation might play pathophysiological roles in HCM.