Detection of lncRNA-mRNA interaction modules by integrating eQTL with weighted gene co-expression network analysis.

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.

SAMD9 is a (epi-) genetically regulated anti-inflammatory factor activated in RA patients.

To identify PBMC-expressed genes significant for RA, and to ascertain their upstream regulatory factors, as well as downstream functional effects relevant to RA pathogenesis. We performed peripheral blood mononuclear cells (PBMCs) transcriptome-wide mRNA expression profiling in a case-control discovery sample. Differentially expressed genes (DEGs) were identified and validated in PBMCs in independent samples. We also generated genome-wide SNP genotyping data, and collected miRNA expression data and DNA methylation data from PBMCs of the discovery sample. Pearson correlation analyses were conducted to identify miRNAs/DNA methylations influencing DEG expression. Association analyses were conducted to identify expression-regulating SNPs. The key DEG, SAMD9, which was reported to function as a tumor suppressor gene, was assessed for its effects on T cell proliferation, apoptosis, and inflammatory cytokine expression. A total of 181 DEGs (Fold Change ≥ 2.0, Bonferroni adjusted p ≤ 0.05) were discovered in PBMCs. Four DEGs (SAMD9, CKLF, PARP9, and GUSB), upregulated with RA, were validated independently in PBMCs. Specifically, SAMD9 mRNA expression level was significantly upregulated in PHA-activated Jurkat T cells in vitro, and correlated with 8 miRNAs and associated with 22 SNPs in PBMCs in vivo. Knockdown of SAMD9 could transiently promote Jurkat T cell proliferation within 48 h and significantly induce TNF-α and IL-8 expression in T cells. SAMD9 expression is (epi-) genetically regulated, and significantly upregulated in PBMCs in RA patients and in activated T cells in vitro. SAMD9 might serve as a T cell activation marker but act as an anti-inflammatory factor.

Identification of expression quantitative trait loci (eQTLs) in human peripheral blood mononuclear cells (PBMCs) and shared with liver and brain.

PBMCs are essential for immunity and involved in various diseases. To identify genetic variations contributing to PBMCs transcriptome-wide gene expression, we performed a genome-wide eQTL analysis by using genome-wide SNPs data and transcriptome-wide mRNA expression data. To assess whether there are common regulation patterns shared among different tissues/organs, public datasets were utilized to identify common eQTLs shared with PBMCs in lymphoblastoid, monocytes, liver, and brain. Allelic expression imbalance (AEI) assay was employed to validate representative eQTLs identified. We identified 443 cis- and 2386 trans-eSNPs (FDR <0.05), which regulated 128 and 635 target genes, respectively. A transcriptome-wide expression regulation network was constructed, highlighting the importance of 28 pleiotropic eSNPs and 18 dually (cis- and trans-) regulated genes. Three genes, that is, TIPRL, HSPB8, and EGLN3, were commonly regulated by hundreds of eSNPs and constituted a very complex interaction network. Strikingly, the missense SNP rs371513 trans- regulated 25 target genes, which were functionally related to poly(A) RNA binding. Among 8904 eQTLs (P < 0.001) identified herein in PBMCs, a minority (163) was overlapped with lymphoblastoid, monocytes, liver, and/or brain. Besides, two cis-eSNPs in PBMC were confirmed by AEI. The present results demonstrated a comprehensive expression regulation network for human PBMCs and may provide novel insights into the pathogenesis of immunological diseases related to PBMCs.

Multiple correlation analyses revealed complex relationship between DNA methylation and mRNA expression in human peripheral blood mononuclear cells.

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.

Correlation analyses revealed global microRNA-mRNA expression associations in human peripheral blood mononuclear cells.

MicroRNAs (miRNAs) can regulate gene expression through binding to complementary sites in the 3'-untranslated regions of target mRNAs, which will lead to existence of correlation in expression between miRNA and mRNA. However, the miRNA-mRNA correlation patterns are complex and remain largely unclear yet. To establish the global correlation patterns in human peripheral blood mononuclear cells (PBMCs), multiple miRNA-mRNA correlation analyses and expression quantitative trait locus (eQTL) analysis were conducted in this study. We predicted and achieved 861 miRNA-mRNA pairs (65 miRNAs, 412 mRNAs) using multiple bioinformatics programs, and found global negative miRNA-mRNA correlations in PBMC from all 46 study subjects. Among the 861 pairs of correlations, 19.5% were significant (P < 0.05) and ~70% were negative. The correlation network was complex and highlighted key miRNAs/genes in PBMC. Some miRNAs, such as hsa-miR-29a, hsa-miR-148a, regulate a cluster of target genes. Some genes, e.g., TNRC6A, are regulated by multiple miRNAs. The identified genes tend to be enriched in molecular functions of DNA and RNA binding, and biological processes such as protein transport, regulation of translation and chromatin modification. The results provided a global view of the miRNA-mRNA expression correlation profile in human PBMCs, which would facilitate in-depth investigation of biological functions of key miRNAs/mRNAs and better understanding of the pathogenesis underlying PBMC-related diseases.

Integrative multi-omics analysis revealed SNP-lncRNA-mRNA (SLM) networks in human peripheral blood mononuclear cells.

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.

Identification and evaluation of lncRNA and mRNA integrative modules in human peripheral blood mononuclear cells.

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.

Common Marker Genes Identified from Various Sample Types for Systemic Lupus Erythematosus.

OBJECTIVE: Systemic lupus erythematosus (SLE) is a complex auto-immune disease. Gene expression studies have been conducted to identify SLE-related genes in various types of samples. It is unknown whether there are common marker genes significant for SLE but independent of sample types, which may have potentials for follow-up translational research. The aim of this study is to identify common marker genes across various sample types for SLE. METHODS: Based on four public microarray gene expression datasets for SLE covering three representative types of blood-born samples (monocyte; peripheral blood mononuclear cell, PBMC; whole blood), we utilized three statistics (fold-change, FC; t-test p value; false discovery rate adjusted p value) to scrutinize genes simultaneously regulated with SLE across various sample types. For common marker genes, we conducted the Gene Ontology enrichment analysis and Protein-Protein Interaction analysis to gain insights into their functions. RESULTS: We identified 10 common marker genes associated with SLE (IFI6, IFI27, IFI44L, OAS1, OAS2, EIF2AK2, PLSCR1, STAT1, RNASE2, and GSTO1). Significant up-regulation of IFI6, IFI27, and IFI44L with SLE was observed in all the studied sample types, though the FC was most striking in monocyte, compared with PBMC and whole blood (8.82-251.66 vs. 3.73-74.05 vs. 1.19-1.87). Eight of the above 10 genes, except RNASE2 and GSTO1, interact with each other and with known SLE susceptibility genes, participate in immune response, RNA and protein catabolism, and cell death. CONCLUSION: Our data suggest that there exist common marker genes across various sample types for SLE. The 10 common marker genes, identified herein, deserve follow-up studies to dissert their potentials as diagnostic or therapeutic markers to predict SLE or treatment response.