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One major function of lncRNA is to regulate the expression of mRNA, but the patterns of their interactions were largely unknown. We attempted to construct lncRNA-mRNA interaction modules at a genome-wide scale. We performed a genome-wide lncRNA-mRNA eQTL analysis in peripheral blood mononuclear cells of 43 individuals, followed by weighted gene co-expression network analysis and functional enrichment analysis which sought to detect functional modules. There were 4627 significant cis lnc-eQTL pairs (P < 1.4 × 10 -6 ) and 1,587,128 significant trans lnc-eQTL pairs (P < 3.46 × 10 -9 ). We detected 11 eQTL modules for the lnc-eQTL networks. Among them, five modules showed significant enrichments in GO terms, and three modules showed significant enrichments in specific KEGG pathways (e.g., Toll-like receptor, PI3K-Akt, NF-kappa B, and TNF signaling pathways). lncRNA-protein interaction analysis showed that some well-known functional lncRNAs (HOTAIR, CCDC26, RHPN1-AS1, WT1-AS, and TCL6) in the eQTL module interacted with genes in focal adhesion and PI3K-Akt signaling pathway. We identified biologically functional lncRNA-mRNA interaction modules by integrating eQTL and weighted gene co-expression network analysis. Integrative analysis of lncRNA and mRNA data by applying eQTL analysis and weighted gene co-expression network analysis methods could be helpful for functional annotation of lncRNAs.

Objectives: To identify novel DNA methylation sites significant for rheumatoid arthritis (RA) and comprehensively understand their underlying pathological mechanism., Methods: We performed (1) genome-wide DNA methylation and mRNA expression profiling in peripheral blood mononuclear cells from RA patients and health controls; (2) correlation analysis and causal inference tests for DNA methylation and mRNA expression data; (3) differential methylation genes regulatory network construction; (4) validation tests of 10 differential methylation positions (DMPs) of interest and corresponding gene expressions; (5) correlation between PARP9 methylation and its mRNA expression level in Jurkat cells and T cells from patients with RA; (6) testing the pathological functions of PARP9 in Jurkat cells., Results: A total of 1046 DNA methylation positions were associated with RA. The identified DMPs have regulatory effects on mRNA expressions. Causal inference tests identified six DNA methylation-mRNA-RA regulatory chains (eg, cg00959259-PARP9-RA). The identified DMPs and genes formed an interferon-inducible gene interaction network (eg, MX1 , IFI44L , DTX3L and PARP9 ). Key DMPs and corresponding genes were validated their differences in additional samples. Methylation of PARP9 was correlated with mRNA level in Jurkat cells and T lymphocytes isolated from patients with RA. The PARP9 gene exerted significant effects on Jurkat cells (eg, cell cycle, cell proliferation, cell activation and expression of inflammatory factor IL-2)., Conclusions: This multistage study identified an interferon-inducible gene interaction network associated with RA and highlighted the importance of PARP9 gene in RA pathogenesis. The results enhanced our understanding of the important role of DNA methylation in pathology of RA., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.)

DNA methylation is an important regulator on the mRNA expression. However, a genome-wide correlation pattern between DNA methylation and mRNA expression in human peripheral blood mononuclear cells (PBMCs) is largely unknown. The comprehensive relationship between mRNA and DNA methylation was explored by using four types of correlation analyses and a genome-wide methylation-mRNA expression quantitative trait locus (eQTL) analysis in PBMCs in 46 unrelated female subjects. An enrichment analysis was performed to detect biological function for the detected genes. Single pair correlation coefficient (r T1 ) between methylation level and mRNA is moderate (-0.63-0.62) in intensity, and the negative and positive correlations are nearly equal in quantity. Correlation analysis on each gene (T4) found 60.1% genes showed correlations between mRNA and gene-based methylation at P < 0.05 and more than 5.96% genes presented very strong correlation (R T4  > 0.8). Methylation sites have regulation effects on mRNA expression in eQTL analysis, with more often observations in region of transcription start site (TSS). The genes under significant methylation regulation both in correlation analysis and eQTL analysis tend to cluster to the categories (e.g., transcription, translation, regulation of transcription) that are essential for maintaining the basic life activities of cells. Our findings indicated that DNA methylation has predictive regulation effect on mRNA with a very complex pattern in PBMCs. The results increased our understanding on correlation of methylation and mRNA and also provided useful clues for future epigenetic studies in exploring biological and disease-related regulatory mechanisms in PBMC.

Aim: To detect SNP-miRNA-mRNA interaction networks and to elucidate miRNA-mediated regulation effects on mRNA expression., Materials & Methods: In human peripheral blood mononuclear cells of 43 females, SNP-miRNA-mRNA interaction networks were established through an integrative analysis. Then causal inference test was followed to detect miRNA-mediated effects on mRNA expressions., Results: About 167 trios corresponding to 56 SNPs, 20 miRNAs and 47 target-mRNAs have the SNP-miRNA-mRNA interactions, but only 22 trios have miRNA-mediated effects between SNP and mRNA. For the three miRNAs (hsa-miR-222-3p, hsa-miR-181b-5p and hsa-miR-106b-5p), each mediates at least four correlations between SNP and mRNA. The mRNAs in interaction play an important role in energy metabolism, cellular and tissue homeostasis., Conclusion: This study represents the first effort of constructing an integrative interaction network of SNP-miRNA-mRNA and miRNA-mediated regulatory effects that provide helpful clues for future investigations of peripheral blood mononuclear cell-related physiological process and immunological diseases.

Aim: To identify sets of functionally related long noncoding RNAs (lncRNAs) and mRNAs and to evaluate the importance of lncRNAs in an lncRNA-mRNA network., Methods: We carried out weighted gene co-expression network analysis and enrichment analyses to identify functional modules of co-expressed lncRNAs and mRNAs in human peripheral blood mononuclear cells of 43 females., Results: We identified seven modules and found hub lncRNAs in each module. Four of the seven modules had significant gene ontology enrichments. Some of the hub lncRNAs (e.g., SSX8, UCA1, HOXA-AS2, STARD4-AS1 and PCBP1-AS1) have known functions related with diseases such as cancers., Conclusion: We identified seven biologically important lncRNA and mRNA integrative modules in females and showed that lncRNAs might play important roles in lncRNA-mRNA co-expression modules.

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.