Why SNP rs227584 is associated with human BMD and fracture risk? A molecular and cellular study in bone cells.

A large number of SNPs significant for osteoporosis (OP) had been identified by genome-wide association studies. However, the underlying association mechanisms were largely unknown. From the perspective of protein phosphorylation, gene expression regulation, and bone cell activity, this study aims to illustrate association mechanisms for representative SNPs of interest. We utilized public databases and bioinformatics tool to identify OP-associated SNPs which potentially influence protein phosphorylation (phosSNPs). Associations with hip/spine BMD, as well as fracture risk, in human populations for one significant phosSNP, that is, rs227584 (major/minor allele: C/A, EAS population) located in C17orf53 gene, were suggested in prior meta-analyses. Specifically, carriers of allele C had significant higher BMD and lower risk of low-trauma fractures than carriers of A. We pursued to test the molecular and cellular functions of rs227584 in bone through osteoblastic cell culture and multiple assays. We identified five phosSNPs significant for OP (P < 0.01). The osteoblastic cells, which was transfected with wild-type C17orf53 (allele C at rs227584, P126), demonstrated specific interaction with NEK2 kinase, increased expression levels of osteoblastic genes significantly (OPN, OCN, COL1A1, P < 0.05), and promoted osteoblast growth and ALP activity, in contrast to those transfected with mutant C17orf53 (allele A at rs227584, T126). In the light of the consistent evidences between the present functional study in human bone cells and the prior association studies in human populations, we conclude that the SNP rs227584, via altering protein-kinase interaction, regulates osteoblastic gene expression, influences osteoblast growth and activity, hence to affect BMD and fracture risk in humans.

Rheumatoid arthritis-associated DNA methylation sites in peripheral blood mononuclear cells.

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.

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.

Gene-Based Genome-Wide Association Analysis in European and Asian Populations Identified Novel Genes for Rheumatoid Arthritis.

OBJECTIVE: Rheumatoid arthritis (RA) is a complex autoimmune disease. Using a gene-based association research strategy, the present study aims to detect unknown susceptibility to RA and to address the ethnic differences in genetic susceptibility to RA between European and Asian populations. METHODS: Gene-based association analyses were performed with KGG 2.5 by using publicly available large RA datasets (14,361 RA cases and 43,923 controls of European subjects, 4,873 RA cases and 17,642 controls of Asian Subjects). For the newly identified RA-associated genes, gene set enrichment analyses and protein-protein interactions analyses were carried out with DAVID and STRING version 10.0, respectively. Differential expression verification was conducted using 4 GEO datasets. The expression levels of three selected 'highly verified' genes were measured by ELISA among our in-house RA cases and controls. RESULTS: A total of 221 RA-associated genes were newly identified by gene-based association study, including 71'overlapped', 76 'European-specific' and 74 'Asian-specific' genes. Among them, 105 genes had significant differential expressions between RA patients and health controls at least in one dataset, especially for 20 genes including 11 'overlapped' (ABCF1, FLOT1, HLA-F, IER3, TUBB, ZKSCAN4, BTN3A3, HSP90AB1, CUTA, BRD2, HLA-DMA), 5 'European-specific' (PHTF1, RPS18, BAK1, TNFRSF14, SUOX) and 4 'Asian-specific' (RNASET2, HFE, BTN2A2, MAPK13) genes whose differential expressions were significant at least in three datasets. The protein expressions of two selected genes FLOT1 (P value = 1.70E-02) and HLA-DMA (P value = 4.70E-02) in plasma were significantly different in our in-house samples. CONCLUSION: Our study identified 221 novel RA-associated genes and especially highlighted the importance of 20 candidate genes on RA. The results addressed ethnic genetic background differences for RA susceptibility between European and Asian populations and detected a long list of overlapped or ethnic specific RA genes. The study not only greatly increases our understanding of genetic susceptibility to RA, but also provides important insights into the ethno-genetic homogeneity and heterogeneity of RA in both ethnicities.

Functional relevance for type 1 diabetes mellitus-associated genetic variants by using integrative analyses.

AIM: Type 1 diabetes mellitus (type 1 DM) is an autoimmune disease. Although genome-wide association studies (GWAS) and meta-analyses have successfully identified numerous type 1 DM-associated susceptibility loci, the underlying mechanisms for these susceptibility loci are currently largely unclear. METHODS: Based on publicly available datasets, we performed integrative analyses (i.e., integrated gene relationships among implicated loci, differential gene expression analysis, functional prediction and functional annotation clustering analysis) and combined with expression quantitative trait loci (eQTL) results to further explore function mechanisms underlying the associations between genetic variants and type 1 DM. RESULTS: Among a total of 183 type 1 DM-associated SNPs, eQTL analysis showed that 17 SNPs with cis-regulated eQTL effects on 9 genes. All the 9 eQTL genes enrich in immune-related pathways or Gene Ontology (GO) terms. Functional prediction analysis identified 5 SNPs located in transcription factor (TF) binding sites. Of the 9 eQTL genes, 6 (TAP2, HLA-DOB, HLA-DQB1, HLA-DQA1, HLA-DRB5 and CTSH) were differentially expressed in type 1 DM-associated related cells. Especially, rs3825932 in CTSH has integrative functional evidence supporting the association with type 1 DM. CONCLUSIONS: These findings indicated that integrative analyses can yield important functional information to link genetic variants and type 1 DM.

Identification of novel risk genes associated with type 1 diabetes mellitus using a genome-wide gene-based association analysis.

AIMS/INTRODUCTION: Type 1 diabetes mellitus is a serious disorder characterized by destruction of pancreatic ß-cells, culminating in absolute insulin deficiency. Genetic factors contribute to the susceptibility of type 1 diabetes mellitus. The aim of the present study was to identify more susceptibility genes of type 1 diabetes mellitus. MATERIALS AND METHODS: We carried out an initial gene-based genome-wide association study in a total of 4,075 type 1 diabetes mellitus cases and 2,604 controls by using the Gene-based Association Test using Extended Simes procedure. Furthermore, we carried out replication studies, differential expression analysis and functional annotation clustering analysis to support the significance of the identified susceptibility genes. RESULTS: We identified 452 genes associated with type 1 diabetes mellitus, even after adapting the genome-wide threshold for significance (P < 9.05E-04). Among these genes, 171 were newly identified for type 1 diabetes mellitus, which were ignored in single-nucleotide polymorphism-based association analysis and were not previously reported. We found that 53 genes have supportive evidence from replication studies and/or differential expression studies. In particular, seven genes including four non-human leukocyte antigen (HLA) genes (RASIP1, STRN4, BCAR1 and MYL2) are replicated in at least one independent population and also differentially expressed in peripheral blood mononuclear cells or monocytes. Furthermore, the associated genes tend to enrich in immune-related pathways or Gene Ontology project terms. CONCLUSIONS: The present results suggest the high power of gene-based association analysis in detecting disease-susceptibility genes. Our findings provide more insights into the genetic basis of type 1 diabetes mellitus.

Pharmacogenetics and pharmacogenomics for rheumatoid arthritis responsiveness to methotrexate treatment: the 2013 update.

Rheumatoid arthritis (RA) is a complex, systemic autoimmune disease characterized by chronic inflammation of multiple peripheral joints, which leads to serious destruction of cartilage and bone, progressive deformity and severe disability. Methotrexate (MTX) is one of the first-line drugs commonly used in RA therapy owing to its excellent long-term efficacy and cheapness. However, the efficacy and toxicity of MTX treatment have significant interpatient variability. Genetic factors contribute to this variability. In this review, we have summarized and updated the progress of RA response to MTX treatment since 2009 by focusing on the fields of pharmacogenetics and pharmacogenomics. Identification of genetic factors involved in MTX treatment response will increase the understanding of RA pathology and the development of new personalized treatments.