Integrative Analysis of Transcriptomic and Epigenomic Data to Reveal Regulation Patterns for BMD Variation.
Integrative Analysis of Transcriptomic and Epigenomic Data to Reveal Regulation Patterns for BMD Variation.
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转录组和表观基因组数据的综合分析揭示 BMD 变异的调控模式
DOI：
10.1371/journal.pone.0138524
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Deng HW
中科院分区：
文献类型：
--
作者：
Zhang JG;Tan LJ;Xu C;He H;Tian Q;Zhou Y;Qiu C;Chen XD;Deng HW
多种分析数据的整合和功能基因网络的构建可能为复杂疾病的分子机制提供更多的见解。骨质疏松症是一个全球性的公共卫生问题，但复杂的基因-基因相互作用、转录后修饰和功能网络调控仍不清楚。为了全面了解骨质疏松的病因，我们对5名高髋关节骨密度（Bone Mineral Density，骨密度）受试者和5名低髋关节骨密度受试者同时进行转录组基因表达芯片、表观基因组miRNA芯片和甲基化组测序。采用SPIA （Signaling Pathway Impact Analysis）和PCST （Prize Collecting Steiner Tree）算法进行路径富集分析并构建相互作用网络。通过整合转录组学和表观基因组学数据，我们首先从基因表达和甲基化数据中鉴定出3个基因（FAM50A、ZNF473和TMEM55B）和1个miRNA (hsa-mir-4291)，显示出一致的关联证据；其次，在网络分析中，我们确定了一个与先前研究中与BMD相关的12个基因和11个mirna的相互作用网络模块，包括AKT1、STAT3、STAT5A、FLT3、hsa-mir-141和hsa-mir-34a。该模块揭示了mirna、mrna和DNA甲基化之间的串扰，并显示了影响BMD状态的四种潜在基因表达调控模式。综上所述，多层组学的整合比单独分析组学数据能产生更深入的结果。转录组学和表观基因组学数据的整合分析提高了我们识别骨质疏松病因遗传因素的能力，更重要的是揭示骨质疏松病因多组学的功能调控模式。
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.