Three allele combinations associated with Multiple Sclerosis

BACKGROUND: Multiple sclerosis (MS) is an immune-mediated disease of polygenic etiology. Dissection of its genetic background is a complex problem, because of the combinatorial possibilities of gene-gene interactions. As genotyping methods improve throughput, approaches that can explore multigene interactions appropriately should lead to improved understanding of MS. METHODS: 286 unrelated patients with definite MS and 362 unrelated healthy controls of Russian descent were genotyped at polymorphic loci (including SNPs, repeat polymorphisms, and an insertion/deletion) of the DRB1, TNF, LT, TGFβ1, CCR5 and CTLA4 genes and TNFa and TNFb microsatellites. Each allele carriership in patients and controls was compared by Fisher's exact test, and disease-associated combinations of alleles in the data set were sought using a Bayesian Markov chain Monte Carlo-based method recently developed by our group. RESULTS: We identified two previously unknown MS-associated tri-allelic combinations: -509TGFβ1*C, DRB1*18(3), CTLA4*G and -238TNF*B1,-308TNF*A2, CTLA4*G, which perfectly separate MS cases from controls, at least in the present sample. The previously described DRB1*15(2) allele, the microsatellite TNFa9 allele and the biallelic combination CCR5Δ32, DRB1*04 were also reidentified as MS-associated. CONCLUSION: These results represent an independent validation of MS association with DRB1*15(2) and TNFa9 in Russians and are the first to find the interplay of three loci in conferring susceptibility to MS. They demonstrate the efficacy of our approach for the identification of complex-disease-associated combinations of alleles

Essential Role of Adhesion GPCR, GPR123, for Human Pluripotent Stem Cells and Reprogramming towards Pluripotency

G-protein-coupled receptors (GPCRs) are the largest family of cell surface receptors. They modulate key physiological functions and are required in diverse developmental processes including embryogenesis, but their role in pluripotency maintenance and acquisition during the reprogramming towards hiPSCs draws little attention. Meanwhile, it is known that more than 106 GPCRs are overexpressed in human pluripotent stem cells (hPSCs). Previously, to identify novel effectors of reprogramming, we performed a high-throughput RNA interference (RNAi) screening assay and identified adhesion GPCR, GPR123, as a potential reprogramming effector. Its role has not been explored before. Herein, by employing GPR123 RNAi we addressed the role of GPR123 for hPSCs. The suppression of GPR123 in hPSCs leads to the loss of pluripotency and differentiation, impacted colony morphology, accumulation of cells at the G2 phase of the cell cycle, and absence of the scratch closure. Application of the GPR123 RNAi at the initiation stage of reprogramming leads to a decrease in the percentage of the “true” hiPSC colonies, a drop in E-cadherin expression, a decrease in the percentage of NANOG+ nuclei, and the absence of actin cytoskeleton remodeling. Together this leads to the absence of the alkaline-phosphatase-positive hiPSCs colonies on the 18th day of the reprogramming process. Overall, these data indicate for the first time the essential role of GPR123 in the maintenance and acquisition of pluripotency

Variants of the Coagulation and Inflammation Genes Are Replicably Associated with Myocardial Infarction and Epistatically Interact in Russians.

In spite of progress in cardiovascular genetics, data on genetic background of myocardial infarction are still limited and contradictory. This applies as well to the genes involved in inflammation and coagulation processes, which play a crucial role in the disease etiopathogenesis.In this study we found genetic variants of TGFB1, FGB and CRP genes associated with myocardial infarction in discovery and replication groups of Russian descent from the Moscow region and the Republic of Bashkortostan (325/185 and 220/197 samples, correspondingly). We also found and replicated biallelic combinations of TGFB1 with FGB, TGFB1 with CRP and IFNG with PTGS1 genetic variants associated with myocardial infarction providing a detectable cumulative effect. We proposed an original two-component procedure for the analysis of nonlinear (epistatic) interactions between the genes in biallelic combinations and confirmed the epistasis hypothesis for the set of alleles of IFNG with PTGS. The procedure is applicable to any pair of logical variables, e.g. carriage of two sets of alleles. The composite model that included three single gene variants and the epistatic pair has AUC of 0.66 both in discovery and replication groups.The genetic impact of TGFB1, FGB, CRP, IFNG, and PTGS and/or their biallelic combinations on myocardial infarction was found and replicated in Russians. Evidence of epistatic interactions between IFNG with PTGS genes was obtained both in discovery and replication groups

The map of possible interactions between components of MI-associated biallelic combination <i>IFNG</i> and <i>PTGS1</i> (black circles) and ten relative partners (gray circles) generated by GeneMania online software [45].

<p>Possible physical interactions (pink), co-expression (violet), pathway (blue), genetic interactions (green), and shared protein domains (yellow) are shown. IDO1 –indoleamine 2,3–dioxygenase 1; IFNG–interferon gamma; IFNGR1 –interferon gamma receptor 1; IFNGR2 –interferon gamma receptor 2; IRF1 –interferon regulatory factor 1; MPO–myeloperoxidase; PTGIS–prostaglandin I2 (prostacyclin) synthase; PRKCD–protein kinase C delta; PTGS1 –prostaglandin–endoperoxide synthase 1; PTGS2 –prostaglandin–endoperoxide synthase 2; PTPN2 –protein tyrosine phosphatase, non–receptor type 2; PTPN6 –protein tyrosine phosphatase, non–receptor type 6.</p

ROC curves demonstrate usefulness of the additive composite model built from all identified genetic markers.

<p><b>A</b>. Comparing performance of the composite model to the performance of each single marker in the Moscow discovery sample. Combining the high specificity of <i>CRP</i> and <i>IFNG</i>+<i>PTGS</i> predictors (the left hump) with relatively high sensitivity of <i>TGFB1</i> and <i>FGB</i> (the right hump) yields a much better classifier. <b>B</b>. Performance of the model stays the same when tested on the independent replication sample (Bashkortostan).</p