Mechanistic Characterization of RASGRP1 Variants Identifies an hnRNP-K-Regulated Transcriptional Enhancer Contributing to SLE Susceptibility

Systemic lupus erythematosus (SLE) is an autoimmune disease with a strong genetic component. We recently identified a novel SLE susceptibility locus near RASGRP1, which governs the ERK/MAPK kinase cascade and B-/T-cell differentiation and development. However, precise causal RASGRP1 functional variant(s) and their mechanisms of action in SLE pathogenesis remain undefined. Our goal was to fine-map this locus, prioritize genetic variants likely to be functional, experimentally validate their biochemical mechanisms, and determine the contribution of these SNPs to SLE risk. We performed a meta-analysis across six Asian and European cohorts (9,529 cases; 22,462 controls), followed by in silico bioinformatic and epigenetic analyses to prioritize potentially functional SNPs. We experimentally validated the functional significance and mechanism of action of three SNPs in cultured T-cells. Meta-analysis identified 18 genome-wide significant (p < 5 × 10−8) SNPs, mostly concentrated in two haplotype blocks, one intronic and the other intergenic. Epigenetic fine-mapping, allelic, eQTL, and imbalance analyses predicted three transcriptional regulatory regions with four SNPs (rs7170151, rs11631591-rs7173565, and rs9920715) prioritized for functional validation. Luciferase reporter assays indicated significant allele-specific enhancer activity for intronic rs7170151 and rs11631591-rs7173565 in T-lymphoid (Jurkat) cells, but not in HEK293 cells. Following up with EMSA, mass spectrometry, and ChIP-qPCR, we detected allele-dependent interactions between heterogeneous nuclear ribonucleoprotein K (hnRNP-K) and rs11631591. Furthermore, inhibition of hnRNP-K in Jurkat and primary T-cells downregulated RASGRP1 and ERK/MAPK signaling. Comprehensive association, bioinformatics, and epigenetic analyses yielded putative functional variants of RASGRP1, which were experimentally validated. Notably, intronic variant (rs11631591) is located in a cell type-specific enhancer sequence, where its risk allele binds to the hnRNP-K protein and modulates RASGRP1 expression in Jurkat and primary T-cells. As risk allele dosage of rs11631591 correlates with increased RASGRP1 expression and ERK activity, we suggest that this SNP may underlie SLE risk at this locus

Evaluation of SLE Susceptibility Genes in Malaysians

Systemic Lupus Erythematosus (SLE) is a clinically heterogeneous autoimmune disease with strong genetic and environmental components. Our objective was to replicate 25 recently identified SLE susceptibility genes in two distinct populations (Chinese (CH) and Malays (MA)) from Malaysia. We genotyped 347 SLE cases and 356 controls (CH and MA) using the ImmunoChip array and performed an admixture corrected case-control association analysis. Associated genes were grouped into five immune-related pathways. While CH were largely homogenous, MA had three ancestry components (average 82.3% Asian, 14.5% European, and 3.2% African). Ancestry proportions were significantly different between cases and controls in MA. We identified 22 genes with at least one associated SNP (P<0.05). The strongest signal was at HLA-DRA (PMeta=9.96×10-9; PCH=6.57×10-8, PMA=6.73×10-3); the strongest non-HLA signal occurred at STAT4 (PMeta=1.67×10-7; PCH=2.88×10-6, PMA=2.99×10-3). Most of these genes were associated with B- and T-cell function and signaling pathways. Our exploratory study using high-density fine-mapping suggests that most of the established SLE genes are also associated in the major ethnicities of Malaysia. However, these novel SNPs showed stronger association in these Asian populations than with the SNPs reported in previous studies

Comprehensive epigenomic profiling reveals the extent of disease-specific chromatin states and informs target discovery in ankylosing spondylitis

International audienceAnkylosing spondylitis (AS) is a common, highly heritable inflammatory arthritis characterized by enthesitis of the spine and sacroiliac joints. Genome-wide association studies (GWASs) have revealed more than 100 genetic associations whose functional effects remain largely unresolved. Here, we present a comprehensive transcriptomic and epigenomic map of disease-relevant blood immune cell subsets from AS patients and healthy controls. We find that, while CD14+ monocytes and CD4+ and CD8+ T cells show disease-specific differences at the RNA level, epigenomic differences are only apparent upon multi-omics integration. The latter reveals enrichment at disease-associated loci in monocytes. We link putative functional SNPs to genes using high-resolution Capture-C at 10 loci, including PTGER4 and ETS1, and show how disease-specific functional genomic data can be integrated with GWASs to enhance therapeutic target discovery. This study combines epigenetic and transcriptional analysis with GWASs to identify disease-relevant cell types and gene regulation of likely pathogenic relevance and prioritize drug targets

Amino acid signatures of HLA Class-I and II molecules are strongly associated with SLE susceptibility and autoantibody production in Eastern Asians.

Human leukocyte antigen (HLA) is a key genetic factor conferring risk of systemic lupus erythematosus (SLE), but precise independent localization of HLA effects is extremely challenging. As a result, the contribution of specific HLA alleles and amino-acid residues to the overall risk of SLE and to risk of specific autoantibodies are far from completely understood. Here, we dissected (a) overall SLE association signals across HLA, (b) HLA-peptide interaction, and (c) residue-autoantibody association. Classical alleles, SNPs, and amino-acid residues of eight HLA genes were imputed across 4,915 SLE cases and 13,513 controls from Eastern Asia. We performed association followed by conditional analysis across HLA, assessing both overall SLE risk and risk of autoantibody production. DR15 alleles HLA-DRB1*15:01 (P = 1.4x10-27, odds ratio (OR) = 1.57) and HLA-DQB1*06:02 (P = 7.4x10-23, OR = 1.55) formed the most significant haplotype (OR = 2.33). Conditioned protein-residue signals were stronger than allele signals and mapped predominantly to HLA-DRB1 residue 13 (P = 2.2x10-75) and its proxy position 11 (P = 1.1x10-67), followed by HLA-DRB1-37 (P = 4.5x10-24). After conditioning on HLA-DRB1, novel associations at HLA-A-70 (P = 1.4x10-8), HLA-DPB1-35 (P = 9.0x10-16), HLA-DQB1-37 (P = 2.7x10-14), and HLA-B-9 (P = 6.5x10-15) emerged. Together, these seven residues increased the proportion of explained heritability due to HLA to 2.6%. Risk residues for both overall disease and hallmark autoantibodies (i.e., nRNP: DRB1-11, P = 2.0x10-14; DRB1-13, P = 2.9x10-13; DRB1-30, P = 3.9x10-14) localized to the peptide-binding groove of HLA-DRB1. Enrichment for specific amino-acid characteristics in the peptide-binding groove correlated with overall SLE risk and with autoantibody presence. Risk residues were in primarily negatively charged side-chains, in contrast with rheumatoid arthritis. We identified novel SLE signals in HLA Class I loci (HLA-A, HLA-B), and localized primary Class II signals to five residues in HLA-DRB1, HLA-DPB1, and HLA-DQB1. These findings provide insights about the mechanisms by which the risk residues interact with each other to produce autoantibodies and are involved in SLE pathophysiology

Genome-Wide Association Study in an Amerindian Ancestry Population Reveals Novel Systemic Lupus Erythematosus Risk Loci and the Role of European Admixture

OBJECTIVES: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with a strong genetic component. Our aim was to perform the first genome-wide association study on individuals from the Americas enriched for Native American heritage. MATERIALS AND METHODS: We analyzed 3,710 individuals from four countries of Latin America and the Unites States diagnosed with SLE and healthy controls. Samples were genotyped with the HumanOmni1 BeadChip. Data of out-of-study controls was obtained for the HumanOmni2.5. Statistical analyses were performed using SNPTEST and SNPGWA. Data was adjusted for genomic control and FDR. Imputation was done using IMPUTE2, and HiBAG for classical HLA alleles. RESULTS: The IRF5-TNPO3 region showed the strongest association and largest odds ratio (OR) (rs10488631, P(gcadj) = 2.61×10(−29), OR = 2.12, 95% CI: 1.88–2.39) followed by the HLA class II on the DQA2-DQB1 loci (rs9275572, P(gcadj) = 1.11 × 10(−16), OR = 1.62, 95% CI: 1.46–1.80; rs9271366, P(gcadj)=6.46 × 10(−12), OR = 2.06, 95% CI: 1.71–2.50). Other known SLE loci associated were ITGAM, STAT4, TNIP1, NCF2 and IRAK1. We identified a novel locus on 10q24.33 (rs4917385, P(gcadj) =1.4×10(−8)) with a eQTL effect (P(eqtl)=8.0 × 10(−37) at USMG5/miR1307), and describe novel loci. We corroborate SLE-risk loci previously identified in European and Asians. Local ancestry estimation showed that HLA allele risk contribution is of European ancestral origin. Imputation of HLA alleles suggested that autochthonous Native American haplotypes provide protection. CONCLUSIONS: Our results show the insight gained by studying admixed populations to delineate the genetic architecture that underlies autoimmune and complex diseases

Genome-Wide Association Study in an Amerindian Ancestry Population Reveals Novel Systemic Lupus Erythematosus Risk Loci and the Role of European Admixture

OBJECTIVES: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with a strong genetic component. Our aim was to perform the first genome-wide association study on individuals from the Americas enriched for Native American heritage. MATERIALS AND METHODS: We analyzed 3,710 individuals from four countries of Latin America and the Unites States diagnosed with SLE and healthy controls. Samples were genotyped with the HumanOmni1 BeadChip. Data of out-of-study controls was obtained for the HumanOmni2.5. Statistical analyses were performed using SNPTEST and SNPGWA. Data was adjusted for genomic control and FDR. Imputation was done using IMPUTE2, and HiBAG for classical HLA alleles. RESULTS: The IRF5-TNPO3 region showed the strongest association and largest odds ratio (OR) (rs10488631, P(gcadj) = 2.61×10(−29), OR = 2.12, 95% CI: 1.88–2.39) followed by the HLA class II on the DQA2-DQB1 loci (rs9275572, P(gcadj) = 1.11 × 10(−16), OR = 1.62, 95% CI: 1.46–1.80; rs9271366, P(gcadj)=6.46 × 10(−12), OR = 2.06, 95% CI: 1.71–2.50). Other known SLE loci associated were ITGAM, STAT4, TNIP1, NCF2 and IRAK1. We identified a novel locus on 10q24.33 (rs4917385, P(gcadj) =1.4×10(−8)) with a eQTL effect (P(eqtl)=8.0 × 10(−37) at USMG5/miR1307), and describe novel loci. We corroborate SLE-risk loci previously identified in European and Asians. Local ancestry estimation showed that HLA allele risk contribution is of European ancestral origin. Imputation of HLA alleles suggested that autochthonous Native American haplotypes provide protection. CONCLUSIONS: Our results show the insight gained by studying admixed populations to delineate the genetic architecture that underlies autoimmune and complex diseases

Binding assay for rs13023380 and molecular model of <i>IFIH1</i>.

<p>(A) EMSA was performed using nuclear protein extracts from K562 cells (A) with 141-bp PCR products including either the protective (‘G’) or risk (‘A’) sequence at rs13023380. Both ‘G’ and ‘A’ allele-containing PCR products bound to a protein complex in the nuclear extracts. However, the ‘A’ allele bound with at least 2-fold reduced efficiency compared to the ‘G’ allele-carrying PCR product, as measured by the intensity of the shifted band relative to the free DNA band in the same lane. As a nonspecific (NS) DNA control, a 140-bp DNA sequence not present in the genome was created by PCR amplification of bisulfite-modified genomic DNA. (B, C) EMSA for purified recombinant Nucleolin and Ku70/80 protein with PCR products carrying the ‘G’ or ‘A’ allele of rs13023380. In both the cases, the ‘G’ allele binds both of these proteins with increased efficiency. +signs are used to denote the increasing amount of protein added in the reaction. Numbers below EMSA pictures denote the ratio between the intensities of protein bound DNA to the free DNA. (D) Luciferase activities of intronic DNA sequences carrying ancestral ‘G’ or risk allele ‘A’. The protective allele has approximately 2-fold higher promoter activity (luciferase units) than risk allele ‘A’ carrying sequences. <i>Tkmin</i>-only vector, MCS-vector with multiple cloning sites, 380G-protective allele, 380A-risk allele. (E) Crystal structure of RIG-I in complex with dsRNA (from PDB 3TMI) <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003222#pgen.1003222-Jiang1" target="_blank">[27]</a>. Side-chains are shown in red for the positions corresponding to the two coding SNPs in <i>IFIH1</i>. Both mutations are in close proximity to the dsRNA-binding pocket. (F) Close-up of the side-chain of Ala946, modeled from 3TMI. The side-chain makes close contact with the opposing helicase “cap” domain; together these two domains regulate dsRNA entry and processing. Threonine is shown in transparent colors. (G) Superimposition of the RIG-I ATP-binding domain (PDB 4A2W) in blue, and the human <i>IFIH1</i> ATP-binding domain (PDB 3B6E) in green. The <i>IFIH1</i> structure contains the histidine side-chain resulting from the rs10930046 risk allele. Large portions of the <i>IFIH1</i> structure are absent in the 3B6E model, and the two helices are shifted by 1.5 Å. In the ancestral protein, Arg460 likely interacts with the Leu421 main-chain oxygen, as well as the negative helix dipole and the side-chains of Gln433, and Glu425 and 428 (not present in 3B6E).</p

Study design.

<p>Our study design had four stages (sample sizes for cases/controls are in parentheses). In Stage 1, we performed admixture mapping of African Americans (AA) in a case-only analysis with 1032 cases and in a case-control analysis with 1726 controls. In Stage 2, the major admixture mapping signal at 2q22–24 was followed by a candidate gene analysis using case-control association in CC_AA (1525/1810) and European Americans (CC_EA) (3968/3542), with 737 cases used for stages 1 and 2. In order to focus on our best candidate locus (<i>IFIH1</i>), we used out-of-study controls to increase control sample sizes to 4485 for AA and 9750 for EA. In Stage 3, we performed imputation based analysis on AA (1525/4485) and EA (3968/9750) to confirm our candidate gene analysis. In Stage 4, we performed functional analyses for the three confirmed SNPs. For the coding SNPs rs10930046 and rs1990760, we used an apoptosis assay to assess possible changes in protein function, and a gene expression assay to evaluate the effects of these SNPs on expression of genes related to apoptosis, inflammation and viral response. For the intronic variant rs13023380, we used EMSA to investigate whether the variant affected binding of the local DNA sequence to nuclear proteins.</p

Admixture mapping and conditional analysis.

<p>(A) A whole-genome admixture scan on AA SLE cases identified 7 admixture signals that achieved the predefined LOD score >2 (red dashed line). (B) We performed an imputation-based association analysis of <i>IFIH1</i>, which was identified as the most promising candidate gene by a case-control study on 20 candidate genes in the largest peak (2q22–24), followed by 4-SNP haplotype conditional analysis (C). Filled dots indicate the −log₁₀ P values for the association to SLE, and color coding represents inter-marker correlation (r²) between the strongest associated SNP, rs10930046 (“purple diamond”), and the individual SNPs, as shown in the color bar. (C) After conditioning the 4-marker haplotypes for the three markers rs1990760–rs10930046–rs13023380, all individual SNP associations are explained as shown. (D) We analyzed the LD between SNPs on the ImmunoChip, and these LD values were used as a reference panel for imputation in AA. Darker color denotes higher correlation between markers (r²). The LD pattern showed high correlation between markers, making it possible to increase SNP density by imputation. The three independently associated SNPs identified in (B) are denoted by arrows. (E) We performed an imputation based case-control association analysis in EA. Filled dots indicate the −log₁₀ P values for each SNP, and color coding represents the inter-marker correlation (r²) between each individual SNP and the strongest associated SNP, rs13023380 (“purple diamond”), as shown in the color bar. (F) We then performed a two SNP haplotype analysis followed by a three marker haplotype analysis conditioned on the two independent variants rs10930046 and rs13023380. (G) LD analysis of SNPs on the ImmunoChip reference panel showed low inter-marker correlation, which largely precluded imputation based association. Darker color indicates greater r². Arrows indicate the position of the independent SNPs.</p