Xia Wei, Zhu Xiao-Wei, Mo Xin-Bo, Wu Long-Fei, Wu Jian, Guo Yu-Fan, Zeng Ke-Qin, Wang Ming-Jun, Lin Xiang, Qiu Ying-Hua, Wang Lan, He Pei, Xie Fang-Fei, Bing Peng-Fei, Lu Xin, Liu Yao-Zhong, Yi Neng-Jun, Deng Fei-Yan, Lei Shu-Feng
Center for Genetic Epidemiology and Genomics, School of Public Health, Soochow University, Suzhou, 215123, Jiangsu, People's Republic of China.
Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, 215123, Jiangsu, People's Republic of China.
Hum Genet. 2017 Apr;136(4):451-462. doi: 10.1007/s00439-017-1771-1. Epub 2017 Feb 28.
Long non-coding RNAs (lncRNAs) serve as important controller of cellular functions via regulating RNA transcription, degradation and translation. However, what are the regulation patterns of lncRNAs on downstream mRNA and how the upstream genetic variants regulate lncRNAs are largely unknown. We first performed a comprehensive expression quantitative trait locus (eQTL) analysis (MatrixeQTL package, R) using genome-wide lncRNA expression and SNP genotype data from human peripheral blood mononuclear cells (PBMCs) of 43 unrelated individuals. Subsequently, multi-omics integrative network analysis was applied to construct SNP-lncRNA-mRNA (SLM) interaction networks. The causal inference test (CIT) was used to identify lncRNA-mediated (epi-) genetic regulation on mRNA expressions. Our eQTL analysis detected 707 pairs of cis-effect associations (p < 5.64E-06) and 6657 trans-effect associations (p < 3.51E-08), respectively. We also found that top significant cis-eSNPs were enriched around the lncRNA transcription start site regions, and that enrichment patterns of cis-eSNPs differs among different lncRNA sizes (small, medium and large).The constructed SLM interaction networks (1 primary networks and four small separate networks) showed various complex interaction patterns. Especially, the in-depth CIT detected 50 significant lncRNA-mediated SLM trios, and some hotspots (e.g., SNPs: rs926370, rs7716167 and rs16880521; lncRNAs: HIT000061975 and ENST00000579057.1). This study represents the first effort of dissecting the SLM interaction patterns in PBMCs by multi-omics integrative network analysis and causal inference test for clearing the regulation chain. The results provide novel insights into the regulation patterns of lncRNA, and may facilitate investigations of PBMC-related immune physiological process and immunological diseases in the future.
长链非编码RNA（lncRNAs）通过调控RNA转录、降解和翻译，充当细胞功能的重要调控因子。然而，lncRNAs对下游mRNA的调控模式以及上游遗传变异如何调控lncRNAs在很大程度上尚不清楚。我们首先使用来自43名无亲缘关系个体的人类外周血单核细胞（PBMCs）的全基因组lncRNA表达和SNP基因型数据，进行了全面的表达定量性状位点（eQTL）分析（MatrixeQTL软件包，R语言）。随后，应用多组学整合网络分析来构建SNP-lncRNA-mRNA（SLM）相互作用网络。因果推断测试（CIT）用于识别lncRNA介导的对mRNA表达的（表观）遗传调控。我们的eQTL分析分别检测到707对顺式效应关联（p < 5.64E-06）和6657对反式效应关联（p < 3.51E-08）。我们还发现，最显著的顺式eSNPs在lncRNA转录起始位点区域周围富集，并且顺式eSNPs的富集模式在不同lncRNA大小（小、中、大）之间存在差异。构建的SLM相互作用网络（1个主要网络和4个小的独立网络）显示出各种复杂的相互作用模式。特别是，深入的CIT检测到50个显著的lncRNA介导的SLM三联体，以及一些热点（例如，SNPs：rs926370、rs7716167和rs16880521；lncRNAs：HIT000061975和ENST00000579057.1）。本研究首次通过多组学整合网络分析和因果推断测试剖析PBMCs中的SLM相互作用模式，以厘清调控链。研究结果为lncRNA的调控模式提供了新的见解，并可能有助于未来对PBMC相关免疫生理过程和免疫疾病的研究。
