Statistical and Molecular Genetic Analysis of Systemic Lupus Erythematosus(SLE) Associated Variants Moses Nimo 1,2, A. K Maiti 1, X. Kim-Howard 1, C. Sun 1, S. K Nath 1 1 Arthritis and Clinical Immunology, OMRF, Oklahoma City, 2 Tulsa Community College, Tulsa, OK. Abstract Introduction: Systemic lupus erythematosus (SLE) is a relatively common, complex autoimmune disorder characterized by the formation of a variety of autoantibodies and has a substantial genetic component. Lupus is a significant health problem, that, it is at least 3-5 times more prevalent in people of African and Asian ancestry than Caucasian ancestry. Clinical manifestations and disease severity also vary significantly among different ethnicities. Recently we identified novel SLE predisposing variants from two genes, i.e. HIP1(Huntingtin Interacting Protein 1) and ITGAM (Integrin alpha M). While HIP1 variant (rs ) is identified by imputation-based method in Korean population, the ITGAM variant (rs ) is previously associated in multiple ethnic populations. Here using statistical and molecular genetic analyses on 2 variants, we further provide evidence for their association with SLE. Data and Methods. We used Taqman assay for genotyping the HIP1 variant. Monocytes are sorted from PBMC by FACS. Total RNAs were purified from these monocytes, PCR amplified with ITGAM primers and sequenced. Apoptosis and ligand interactions were studied by flow cytometry. Results. We verified the HIP1 variant is in HWE and associated in Korean population. Monocytes of risk and non- risk allele of ITGAM carrying SLE patients show differential expression in mRNA levels. Stable cells carrying non-risk and risk allele variant show differential apoptosis. They also showed differential ligand binding activities in allele- specific manner. Conclusion. We confirmed the imputation results of HIP1 association in Korean population. We also showed the molecular differences of risk and non-risk variants of ITGAM in RNA level and protein functions. Introduction Systemic lupus erythematosus (SLE) is a complex, multi-organ, autoimmune disease with significant mortality rate. SLE is a clinically heterogeneous autoimmune disease known to have a strong, but complex genetic architecture. Approximately 50 genes have been identified to be associated with SLE and among them almost 40 genes are identified with GWAS (Genome Wide Association Studies). By imputation analysis we identified a novel SNP in HIP1 (Huntingtin Interacting Protein) that is consistently associated with Korean population. Validating the association of HIP1 SNP could identify a novel gene susceptible for SLE. We also identified a genetic association between a novel SNP, rs in exon-3 of ITGAM (Integrin-α-M, CR3, Mac-1) and SLE susceptibility, and confirmed it in multiple ethnic populations (P meta =2.2x10 -67, OR=1.79, N=18,167) (Nath et al, 2008). Our analysis using resequencing data (300 SLE cases) and 1000 Genomes Projects confirmed our imputation-based results that rs is the only ITGAM SLE-predisposing variant. Association was stronger between this variant and specific clinical sub-phenotypes of SLE especially, renal disease and discoid rash (Kim – Howard et al, 2010). We also found genetic association with systemic sclerosis, a skin related autoimmune disease. All available data support rs as being the causative variant best explaining observed association. The rs risk-allele (A) is a missense mutation that changes amino acid arginine to histidine at position 77 (R77H) of the CD11b protein encoded by ITGAM. Although ITGAM surface expression is correlated with disease activity (Mcpherson et al, 2012), functional consequences of R77H for SLE susceptibility are unknown. There is a critical need to assess functional effects of R77H on SLE phenotype. CD11b is the α-chain of CD11b/CD18, an integrin transmembrane glycoprotein found predominantly on monocytes and neutrophils, involved in essential immunological processes (cell-adhesion, migration, phagocytosis, and signaling). This SLE risk variant lies near, and could modulate conformation of the CD11b ‘I’ domain, which mediates key cell surface ligand interactions with molecules such as fibrinogen, C3bi, and ICAM1. Materials and Methods Genotyping. DNA samples (10ng) were mixed with TaqMan Probe rs and TaqMan genotyping master mix (Applied Biosystem). Genotyping was done in ABI 7900HT Fast Real-Time PCR system. Under the allelic discrimination conditions allele (C/G) were assigned to FAM and VIC detectors respectively and SNP rs marker of HIP1 was assigned to both detectors. After the amplification, automatic calling of allele were counted. Statistical analysis. Allele and genotype frequencies were calculated for each locus and tested for Hardy– Weinberg equilibrium (HWE) in controls. This case–control association studies were analyzed by χ 2 test using 2 x 3 and 2 x 2 contingency tables of genotype and allele frequencies, respectively. Allelic OR and 95% CIs were calculated using PLINK. Monocyte Purification from PBM. PBMC (Peripheral Blood Mononuclear cells) were stained with CD14 Antibody conjugated with Alexa-488 and CD14+ (monocytes) cells were sorted by FACS (Fluorescence-Activated Cell Sorting). cDNA preparation and PCR. Total RNA s were purified from these sorted monocytes and reverse transcribed with random primer and Mul V reverse transcriptase (New England Biolab). For PCR amplification,15ng cDNA samples were mixed with 10XdNTP’s, 10XPCR buffer, forward and reverse primers and Taq DNA polymerase. PCR was operated under general PCR condition (95 o C, 5 minutes-1 cycle, 95 o C, 40seconds, 60 o C, 30 seconds, 72 o C, 30 seconds, with 40 cycles and 72 o C 10 minutes-1 cycle). Amplified DNAs were separated by 1.5% Agarose gel electrophoresis and bands were observed (245 bp) under a UV transilluminator and photographs were taken. Apoptosis. Apoptosis was measured according to Annexin apoptotic assay (BD bioscience).Stable cells with ITGAM risk allele and non risk allele were stained with alexa-700 conjugated antibody of CD11b and annexin –PE and 7-AAD. Cells were analyzed for CD11b+ and Annexin/7-AAD. Acknowledgements This project is supported by the National Center for Research Resources and the National Institute of General Medical Sciences of the National Institute of Health (8P20GM103447) and 1R01AR References 1. Nath SK et al, 2008, Nature Genetics,40, Kim-Howard et al, 2010, Annals Rheu.Dis,69, Mcpherson et al, 2011, Jbiol.Chem Fig1. Some clinical criteria for diagnosing SLE Results Association of HIP1 in Korean population After genotyping we had genotypes for 519 SLE cases and 501 controls. This SNP was significantly out of HWE in controls (2.34x10 -5 ), which may indicate that there may have been quality issues with the genotyping. While there was a very modest indication of significant association (P = 0.042, OR = 1.45) these results must be taken with caution because of the low genotyping rate and HWE in controls. Allelic specific expression in heterozygous (GA) patients We enquired whether ITGAM SNP rs affects its own expression; we purified monocytes from heterozygous patients (GA) and prepared total RNA from these monocytes [Fig 2 a,b]. After reverse transcription, these cDNAs were PCR amplified [Fig.3a]with neighboring primers that includes rs When sequenced these PCR products with one of the primers, we observed only non-risk allele (G) in all 4 patients [Fig 3b, arrow shows G, but not heterozygous G/A] implying that in these patients only G allele carrying mRNAs are expressed. It is consistent with our previous experiments where we showed that (by cloning RT- PCR product in TA and sequencing 30 clones to identify G or A transcripts) in GA individuals ‘A’ allele carrying mRNA is very little or does not expresses at all. These suggest that in heterozygous (GA) individuals only non-risk allele was expressed thus reducing the total amount of transcript, which could be pathogenic instead of normal function. Thus risk allele (A) could affect the expression of its own transcription. ITGAM risk allele carrying cells are less apoptotic than nonrisk allele carrying cells. CD11b protein with CD18 forms a MAC1 complex in the surface of monocyte, neutrophils, and dendritic cells. However, MAC1 in monocytes binds to deposited particle in kidney glomerulus leading to phagocytosis and apoptosis. We wanted to enquire whether risk allele inhibits apoptosis in compare to wild type (non-risk) cells. We stained the stable cells carrying risk or non-risk allele and measured the intrinsic apoptotic abilities of these cells. We observed that non-risk allele carrying cells do have more apoptosis than risk allele carrying cells [Fig 4 a,b, Q-2-2/(Q-2-2+Q1-2)]. Consistent with the idea, our results demonstrate that risk allele carrying CD11b in monocytes has less ability to apoptosis that could implicate limited phagocytosis mediated dirt clearance in kidney glomerulei. Conclusions 1.HIP 1 SNP rs is moderately associated with Korean population. 2.Risk allele of ITGAM affects its own mRNA expression. 3.Risk allele of ITGAM also affects its protein function by impairing phagocytosis mediated apoptosis. d)c) a)b) Fig 4: apoptosis assay using flow cytometry Fig 2:Purification of monocyte by FACs Fig 3: PCR products verified in agarose gel electrophoresis and DNA sequencing b) a) 245bp