Expression Profiling of Human Genetic and Protein Interaction Networks in Type 1 Diabetes

Proteins contributing to a complex disease are often members of the same functional pathways. Elucidation of such pathways may provide increased knowledge about functional mechanisms underlying disease. By combining genetic interactions in Type 1 Diabetes (T1D) with protein interaction data we have previously identified sets of genes, likely to represent distinct cellular pathways involved in T1D risk. Here we evaluate the candidate genes involved in these putative interaction networks not only at the single gene level, but also in the context of the networks of which they form an integral part. mRNA expression levels for each gene were evaluated and profiling was performed by measuring and comparing constitutive expression in human islets versus cytokine-stimulated expression levels, and for lymphocytes by comparing expression levels among controls and T1D individuals. We identified differential regulation of several genes. In one of the networks four out of nine genes showed significant down regulation in human pancreatic islets after cytokine exposure supporting our prediction that the interaction network as a whole is a risk factor. In addition, we measured the enrichment of T1D associated SNPs in each of the four interaction networks to evaluate evidence of significant association at network level. This method provided additional support, in an independent data set, that two of the interaction networks could be involved in T1D and highlights the following processes as risk factors: oxidative stress, regulation of transcription and apoptosis. To understand biological systems, integration of genetic and functional information is necessary, and the current study has used this approach to improve understanding of T1D and the underlying biological mechanisms

Integrative analysis for finding genes and networks involved in diabetes and other complex diseases

An integrative analysis combining genetic interactions and protein interactions can be used to identify candidate genes/proteins for type 1 diabetes and other complex diseases

Genetic Risk Score Modelling for Disease Progression in New-Onset Type 1 Diabetes Patients:Increased Genetic Load of Islet-Expressed and Cytokine-Regulated Candidate Genes Predicts Poorer Glycemic Control

Genome-wide association studies (GWAS) have identified over 40 type 1 diabetes risk loci. The clinical impact of these loci on β-cell function during disease progression is unknown. We aimed at testing whether a genetic risk score could predict glycemic control and residual β-cell function in type 1 diabetes (T1D). As gene expression may represent an intermediate phenotype between genetic variation and disease, we hypothesized that genes within T1D loci which are expressed in islets and transcriptionally regulated by proinflammatory cytokines would be the best predictors of disease progression. Two-thirds of 46 GWAS candidate genes examined were expressed in human islets, and 11 of these significantly changed expression levels following exposure to proinflammatory cytokines (IL-1β + IFNγ + TNFα) for 48 h. Using the GWAS single nucleotide polymorphisms (SNPs) from each locus, we constructed a genetic risk score based on the cumulative number of risk alleles carried in children with newly diagnosed T1D. With each additional risk allele carried, HbA1c levels increased significantly within first year after diagnosis. Network and gene ontology (GO) analyses revealed that several of the 11 candidate genes have overlapping biological functions and interact in a common network. Our results may help predict disease progression in newly diagnosed children with T1D which can be exploited for optimizing treatment

Correlations between islet autoantibody specificity and the SLC30A8 genotype with HLA-DQB1 and metabolic control in new onset type 1 diabetes

We hypothesised that the correlation between autoantibody specificity for the ZnT8 Arg325Trp isoforms and the type 2 diabetes-associated rs13266634 may affect beta-cell function at type 1 diabetes (T ID) onset. To study this, we tested 482 newly diagnosed diabetic probands and 478 healthy siblings from the Danish population-based T1D registry for autoantibodies to ZnT8 (ZnT8A) in addition to GAD65 and IA-2. The prevalence and titres of autoantibodies were correlated with genotypes for rs13266634 and HLA-DQB1, age at diagnosis (AAD) and insulin dose-adjusted HbA1c (IDAA1c), as a proxy for residual beta-cell function. We replicated the correlation between rs13266634 genotypes and specificity for the ZnT8-Argenine (ZnT8R) and ZnT8-Tryptophan (ZnT8W) isoforms previously reported. ZnT8A overlapped substantially with autoantibodies to glutamate decarboxylase 65 (GADA) and IA-2 (IA-2A) and correlated significantly with IA-2A prevalence (p < 2e-16). No effect on IDAA1c was demonstrated for ZnT8A or rs13266634. We found a correlation between ZnT8R positivity and HLA-DQB1*0302 genotypes (p = 0.016), which has not been shown previously. Furthermore, significantly lower ZnT8R and GADA prevalence and titres was found among probands with AAD < 5 years (prevalence: p = 0.004 and p = 0.0001; titres: p = 0.002 and p = 0.001, respectively). The same trend was observed for IA-2A and ZnT8W; however, the difference was non-significant. Our study confirms ZnT8 as a major target for autoantibodies at disease onset in our Danish T1D cohort of children and adolescents, and we have further characterised the relationship between autoantibody specificity for the ZnT8 Arg325Trp epitopes and rs13266634 in relation to established autoantibodies, AAD, measures of beta-cell function and HLA-DQB1 genotypes in T1D

The Type 1 Diabetes - HLA Susceptibility Interactome - Identification of HLA Genotype-Specific Disease Genes for Type 1 Diabetes

Background: The individual contribution of genes in the HLA region to the risk of developing type 1 diabetes (T1D) is confounded by the high linkage disequilibrium (LD) in this region. Using a novel approach we have combined genetic association data with information on functional protein-protein interactions to elucidate risk independent of LD and to place the genetic association into a functional context. Methodology/Principal Findings: Genetic association data from 2300 single nucleotide polymorphisms (SNPs) in the HLA region was analysed in 2200 T1D family trios divided into six risk groups based on HLA-DRB1 genotypes. The best SNP signal in each gene was mapped to proteins in a human protein interaction network and their significance of clustering in functional network modules was evaluated. The significant network modules identified through this approach differed between the six HLA risk groups, which could be divided into two groups based on carrying the DRB1*0301 or the DRB1*0401 allele. Proteins identified in networks specific for DRB1*0301 carriers were involved in stress response and inflammation whereas in DRB1*0401 carriers the proteins were involved in antigen processing and presentation. Conclusions/Significance: In this study we were able to hypothesise functional differences between individuals with T1D carrying specific DRB1 alleles. The results point at candidate proteins involved in distinct cellular processes that could not only help the understanding of the pathogenesis of T1D, but also the distinction between individuals at different genetic risk for developing T1D