Integrative Analysis of Transcriptomic and Epigenomic Data to Reveal Regulation Patterns for BMD Variation

• DOI: 10.1371/journal.pone.0138524
• Publication Date: 2015-09-21T17:51:44Z
• Authors: Ji-Gang Zhang; Li-Jun Tan; Chao Xu; Hao He; Qing Tian; Yu Zhou; Chuan Qiu; Xiang-Ding Chen; Hong-Wen Deng
• Keywords: Adult; Epigenomics; 0301 basic medicine; Science; Gene Expression Profiling; Q; R; Computational Biology; Genetic Variation; 3. Good health; MicroRNAs; Young Adult; 03 medical and health sciences; Bone Density; Medicine; Humans; Osteoporosis; Female; Gene Regulatory Networks; RNA, Messenger; Transcriptome; Research Article; Signal Transduction

Integration of multiple profiling data and construction of functional gene networks may provide additional insights into the molecular mechanisms of complex diseases. Osteoporosis is a worldwide public health problem, but the complex gene-gene interactions, post-transcriptional modifications and regulation of functional networks are still unclear. To gain a comprehensive understanding of osteoporosis etiology, transcriptome gene expression microarray, epigenomic miRNA microarray and methylome sequencing were performed simultaneously in 5 high hip BMD (Bone Mineral Density) subjects and 5 low hip BMD subjects. SPIA (Signaling Pathway Impact Analysis) and PCST (Prize Collecting Steiner Tree) algorithm were used to perform pathway-enrichment analysis and construct the interaction networks. Through integrating the transcriptomic and epigenomic data, firstly we identified 3 genes (FAM50A, ZNF473 and TMEM55B) and one miRNA (hsa-mir-4291) which showed the consistent association evidence from both gene expression and methylation data; secondly in network analysis we identified an interaction network module with 12 genes and 11 miRNAs including AKT1, STAT3, STAT5A, FLT3, hsa-mir-141 and hsa-mir-34a which have been associated with BMD in previous studies. This module revealed the crosstalk among miRNAs, mRNAs and DNA methylation and showed four potential regulatory patterns of gene expression to influence the BMD status. In conclusion, the integration of multiple layers of omics can yield in-depth results than analysis of individual omics data respectively. Integrative analysis from transcriptomics and epigenomic data improves our ability to identify causal genetic factors, and more importantly uncover functional regulation pattern of multi-omics for osteoporosis etiology.