Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0011854 (type 1 diabetes)
 20,749 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The primary associations of the HLA class II genes, HLA-DRB1 and HLA-DQB1, and the class I genes, HLA-A and HLA-B, with type 1 diabetes (T1D) are well established. However, the role of polymorphism at the HLA-DRB3, HLA-DRB4, and HLA-DRB5 loci remains unclear. In two separate studies, one of 500 subjects and 500 control subjects and one of 366 DRB1*03:01-positive samples from selected multiplex T1D families, we used Roche 454 sequencing with Conexio Genomics ASSIGN ATF 454 HLA genotyping software analysis to analyze sequence variation at these three HLA-DRB loci. Association analyses were performed on the two HLA-DRB loci haplotypes (DRB1-DRB3, -DRB4, or -DRB5). Three common HLA-DRB3 alleles (*01:01, *02:02, *03:01) were observed. DRB1*03:01 haplotypes carrying DRB3*02:02 conferred a higher T1D risk than did DRB1*03:01 haplotypes carrying DRB3*01:01 in DRB1*03:01/*03:01 homozygotes with two DRB3*01:01 alleles (odds ratio [OR] 3.4 [95% CI 1.46-8.09]), compared with those carrying one or two DRB3*02:02 alleles (OR 25.5 [3.43-189.2]) (P = 0.033). For DRB1*03:01/*04:01 heterozygotes, however, the HLA-DRB3 allele did not significantly modify the T1D risk of the DRB1*03:01 haplotype (OR 7.7 for *02:02; 6.8 for *01:01). These observations were confirmed by sequence analysis of HLA-DRB3 exon 2 in a targeted replication study of 281 informative T1D family members and 86 affected family-based association control (AFBAC) haplotypes. The frequency of DRB3*02:02 was 42.9% in the DRB1*03:01/*03:01 patients and 27.6% in the DRB1*03:01/*04 (P = 0.005) compared with 22.6% in AFBAC DRB1*03:01 chromosomes (P = 0.001). Analysis of T1D-associated alleles at other HLA loci (HLA-A, HLA-B, and HLA-DPB1) on DRB1*03:01 haplotypes suggests that DRB3*02:02 on the DRB1*03:01 haplotype can contribute to T1D risk.
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PMID:Next generation sequencing reveals the association of DRB3*02:02 with type 1 diabetes. 2346 45

To explore the expression changes of potential key genes and relevant biological processes in peripheral blood mononuclear cells of children with newly diagnosis of type 1 diabetes (T1D).Microarray data GSE9006 were downloaded from Gene Expression Omnibus (GEO) database, including peripheral blood mononuclear cells samples from 43 children with newly diagnosed T1D (NEW), 19 one-month (1-MO) follow-up samples, 19 4-month (4-MO) follow-up samples and 24 healthy controls. The differentially expressed genes (DEGs) were identified using Affy package in R, and cluster analysis of DEGs were performed following functional enrichment analysis with Database for Annotation, Visualization and Integrated Discovery (DAVID) and construction of protein-protein interaction (PPI) network with STRING database.We identified 73, 73, 96 DEGs in NEW group, 1-MO group and 4-MO group, respectively by comparing with healthy controls with |logFC|>0.58 and P-value<0.05. The cluster analysis of these DEGs showed that 4 genes, including human leukocyte antigen (HLA-DQA1), HLA-DRB4, integrin 3 (ITGB3) and killer cell lectin-like receptor subfamily F member 1 (KLRF1) were all significantly expressed in 3 groups, which were significantly enriched in asthma, T1D and intestinal immune network for IgA production pathway. And 57 genes enriched in cluster 5, which were only differentially expressed in NEW group, were involved in response to wounding, inflammatory response and blood coagulation as well as chemokine signaling pathway. Besides, the hub genes in PPI network of cluster 5 were identified, containing FOS, pro-platelet basic protein (PPBP), interleukin 8 (IL8), formyl peptide receptor-like 2 (FPR2) and platelet factor 4 (PF4).HLA-DQA1, HLA-DRB4, ITGB3 and KLRF1 might be targets for treatment of T1D, and 5 hub proteins, FOS, PPBP, IL8, FPR2 and PF4, were likely to be new markers for diagnosis of T1D.
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PMID:Bioinformatics analysis of gene expression in peripheral blood mononuclear cells from children with type 1 diabetes in 3 periods. 2483 53

Recent genome-wide association studies confirm that human leukocyte antigen (HLA) genes have the strongest associations with several autoimmune diseases, including type 1 diabetes (T1D), providing an impetus to reduce this genetic association to practice through an HLA-based disease predictive model. However, conventional model-building methods tend to be suboptimal when predictors are highly polymorphic with many rare alleles combined with complex patterns of sequence homology within and between genes. To circumvent this challenge, we describe an alternative methodology; treating complex genotypes of HLA genes as "objects" or "exemplars," one focuses on systemic associations of disease phenotype with "objects" via similarity measurements. Conceptually, this approach assigns disease risks base on complex genotype profiles instead of specific disease-associated genotypes or alleles. Effectively, it transforms large, discrete, and sparse HLA genotypes into a matrix of similarity-based covariates. By the Kernel representative theorem and machine learning techniques, it uses a penalized likelihood method to select disease-associated exemplars in building predictive models. To illustrate this methodology, we apply it to a T1D study with eight HLA genes (HLA-DRB1, HLA-DRB3, HLA-DRB4, HLA-DRB5, HLA-DQA1, HLA-DQB1, HLA-DPA1, and HLA-DPB1) to build a predictive model. The resulted predictive model has an area under curve of 0.92 in the training set, and 0.89 in the validating set, indicating that this methodology is useful to build predictive models with complex HLA genotypes.
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PMID:An Object-Oriented Regression for Building Disease Predictive Models with Multiallelic HLA Genes. 2708 Sep 19