Chen Wei, Zhao Wenshan, Yang Aiting, Xu Anjian, Wang Huan, Cong Min, Liu Tianhui, Wang Ping, You Hong
Experimental and Translational Research Center, Beijing Friendship Hospital, Capital Medical University, Beijing, China; Beijing Key Laboratory of Tolerance Induction and Organ Protection in Transplantation, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Liver Research Center, Beijing Friendship Hospital, Capital Medical University, Beijing Key Laboratory of Translational Medicine in Liver Cirrhosis, National Clinical Research Center of Digestive Diseases, Beijing, China.
Gene. 2017 Dec 15;636:87-95. doi: 10.1016/j.gene.2017.09.027. Epub 2017 Sep 14.
Liver fibrosis, characterized with the excessive accumulation of extracellular matrix (ECM) proteins, represents the final common pathway of chronic liver inflammation. Ever-increasing evidence indicates microRNAs (miRNAs) dysregulation has important implications in the different stages of liver fibrosis. However, our knowledge of miRNA-gene regulation details pertaining to such disease remains unclear.
The publicly available Gene Expression Omnibus (GEO) datasets of patients suffered from cirrhosis were extracted for integrated analysis. Differentially expressed miRNAs (DEMs) and genes (DEGs) were identified using GEO2R web tool. Putative target gene prediction of DEMs was carried out using the intersection of five major algorithms: DIANA-microT, TargetScan, miRanda, PICTAR5 and miRWalk. Functional miRNA-gene regulatory network (FMGRN) was constructed based on the computational target predictions at the sequence level and the inverse expression relationships between DEMs and DEGs. DAVID web server was selected to perform KEGG pathway enrichment analysis. Functional miRNA-gene regulatory module was generated based on the biological interpretation. Internal connections among genes in liver fibrosis-related module were determined using String database. MiRNA-gene regulatory modules related to liver fibrosis were experimentally verified in recombinant human TGFβ1 stimulated and specific miRNA inhibitor treated LX-2 cells.
We totally identified 85 and 923 dysregulated miRNAs and genes in liver cirrhosis biopsy samples compared to their normal controls. All evident miRNA-gene pairs were identified and assembled into FMGRN which consisted of 990 regulations between 51 miRNAs and 275 genes, forming two big sub-networks that were defined as down-network and up-network, respectively. KEGG pathway enrichment analysis revealed that up-network was prominently involved in several KEGG pathways, in which "Focal adhesion", "PI3K-Akt signaling pathway" and "ECM-receptor interaction" were remarked significant (adjusted p<0.001). Genes enriched in these pathways coupled with their regulatory miRNAs formed a functional miRNA-gene regulatory module that contains 7 miRNAs, 22 genes and 42 miRNA-gene connections. Gene interaction analysis based on String database revealed that 8 out of 22 genes were highly clustered. Finally, we experimentally confirmed a functional regulatory module containing 5 miRNAs (miR-130b-3p, miR-148a-3p, miR-345-5p, miR-378a-3p, and miR-422a) and 6 genes (COL6A1, COL6A2, COL6A3, PIK3R3, COL1A1, CCND2) associated with liver fibrosis.
Our integrated analysis of miRNA and gene expression profiles highlighted a functional miRNA-gene regulatory module associated with liver fibrosis, which, to some extent, may provide important clues to better understand the underlying pathogenesis of liver fibrosis.
肝纤维化以细胞外基质（ECM）蛋白过度积累为特征，是慢性肝脏炎症的最终共同途径。越来越多的证据表明，微小RNA（miRNA）失调在肝纤维化的不同阶段具有重要意义。然而，我们对与这种疾病相关的miRNA-基因调控细节的了解仍不清楚。
提取公开可用的肝硬化患者基因表达综合数据库（GEO）数据集进行综合分析。使用GEO2R网络工具鉴定差异表达的miRNA（DEM）和基因（DEG）。使用DIANA-microT、TargetScan、miRanda、PICTAR5和miRWalk这五种主要算法的交集对DEM进行潜在靶基因预测。基于序列水平的计算靶标预测以及DEM与DEG之间的反向表达关系构建功能性miRNA-基因调控网络（FMGRN）。选择DAVID网络服务器进行KEGG通路富集分析。基于生物学解释生成功能性miRNA-基因调控模块。使用String数据库确定肝纤维化相关模块中基因之间的内部连接。在重组人TGFβ1刺激和特定miRNA抑制剂处理的LX-2细胞中对与肝纤维化相关的miRNA-基因调控模块进行实验验证。
与正常对照相比，我们在肝硬化活检样本中总共鉴定出85个失调的miRNA和923个失调的基因。鉴定出所有明显的miRNA-基因对并将其组装成FMGRN，该网络由51个miRNA与275个基因之间的990条调控组成，形成两个大的子网，分别定义为下调网络和上调网络。KEGG通路富集分析表明，上调网络显著参与了多个KEGG通路，其中“粘着斑”、“PI3K-Akt信号通路”和“ECM-受体相互作用”显著（校正p<0.001）。富集于这些通路的基因及其调控miRNA形成了一个功能性miRNA-基因调控模块，该模块包含7个miRNA、22个基因和42个miRNA-基因连接。基于String数据库的基因相互作用分析表明，22个基因中有8个高度聚集。最后，我们通过实验证实了一个功能性调控模块，该模块包含5个与肝纤维化相关的miRNA（miR-130b-3p、miR-148a-3p、miR-345-5p、miR-378a-3p和miR-422a）和6个基因（COL6A1、COL6A2、COL6A3、PIK3R3、COL1A1、CCND2）。
我们对miRNA和基因表达谱的综合分析突出了一个与肝纤维化相关的功能性miRNA-基因调控模块，这在一定程度上可能为更好地理解肝纤维化的潜在发病机制提供重要线索。
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