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eISSN: 1643-3750

Systematic Tracking of Disrupted Modules Identifies Altered Pathways Associated with Congenital Heart Defects in Down's Syndrome

Denghong Chen, Zhenhua Zhang, Yuxiu Meng

Department of Obstetrics, Jining No. 1 People’s Hospital, Jining, Shandong, China (mainland)

Med Sci Monit 2015; 21:3334-3342

DOI: 10.12659/MSM.896001

Available online:

Published: 2015-11-02

BACKGROUND: This work aimed to identify altered pathways in congenital heart defects (CHD) in Down syndrome (DS) by systematically tracking the dysregulated modules of reweighted protein-protein interaction (PPI) networks.
MATERIAL AND METHODS: We performed systematic identification and comparison of modules across normal and disease conditions by integrating PPI and gene-expression data. Based on Pearson correlation coefficient (PCC), normal and disease PPI networks were inferred and reweighted. Then, modules in the PPI network were explored by clique-merging algorithm; altered modules were identified via maximum weight bipartite matching and ranked in non-increasing order. Finally, pathways enrichment analysis of genes in altered modules was carried out based on Database for Annotation, Visualization, and Integrated Discovery (DAVID) to study the biological pathways in CHD in DS.
RESULTS: Our analyses revealed that 348 altered modules were identified by comparing modules in normal and disease PPI networks. Pathway functional enrichment analysis of disrupted module genes showed that the 4 most significantly altered pathways were: ECM-receptor interaction, purine metabolism, focal adhesion, and dilated cardiomyopathy.
CONCLUSIONS: We successfully identified 4 altered pathways and we predicted that these pathways would be good indicators for CHD in DS.

Keywords: Cardiomyopathy, Dilated - metabolism, Algorithms, Cell Adhesion, Computational Biology, Databases, Factual, Down Syndrome - metabolism, Extracellular Matrix - metabolism, Gene Expression Profiling, Gene Expression Regulation, Genome, Human, Heart Defects, Congenital - metabolism, Models, Statistical, Oligonucleotide Array Sequence Analysis, Protein Interaction Mapping, Purines - metabolism