Type 1 Diabetes Mellitus-Associated Genetic Variants Contribute to Overlapping Immune Regulatory Networks

Abstract

Type 1 diabetes (T1D) is a chronic metabolic disorder characterized by the autoimmune destruction of insulin-producing pancreatic islet beta cells in genetically predisposed individuals. Genome-wide association studies (GWAS) have identified over 60 risk regions across the human genome, marked by single nucleotide polymorphisms (SNPs), which confer genetic predisposition to T1D. There is increasing evidence that disease-associated SNPs can alter gene expression through spatial interactions that involve distal loci, in a tissue- and development-specific manner. Here, we used three-dimensional (3D) genome organization data to identify genes that physically co-localized with DNA regions that contained T1D-associated SNPs in the nucleus. Analysis of these SNP-gene pairs using the Genotype-Tissue Expression database identified a subset of SNPs that significantly affected gene expression. We identified 246 spatially regulated genes including HLA-DRB1, LAT, MICA, BTN3A2, CTLA4, CD226, NOTCH1, TRIM26, PTEN, TYK2, CTSH, and FLRT3, which exhibit tissue-specific effects in multiple tissues. We observed that the T1D-associated variants interconnect through networks that form part of the immune regulatory pathways, including immune-cell activation, cytokine signaling, and programmed cell death protein-1 (PD-1). Our results implicate T1D-associated variants in tissue and cell-type specific regulatory networks that contribute to pancreatic beta cell inflammation and destruction, adaptive immune signaling, and immune-cell proliferation and activation. A number of other regulatory changes we identified are not typically considered to be central to the pathology of T1D. Collectively, our data represent a novel resource for the hypothesis-driven development of diagnostic, prognostic, and therapeutic interventions in T1D.

Enteroviruses and T1D: Is It the Virus, the Genes or Both which Cause T1D

“…T1D is associated with over 60 genetic risk regions across the human genome, identified by genome-wide association studies (GWAS) [ 124 ], and these T1D-linked SNPs alter the expression of over 200 genes [ 125 ] involved in β-cell inflammation, function and destruction, immune activation and signaling, including viral response, Toll-like receptor, cytokines and NF-𝜅B signaling. Among them, several risks as well as protective single nucleotide polymorphisms within the interferon-induced helicase-1 ( IFIH1 ) gene, which encodes the melanoma differentiation associated protein 5 (MDA5), have been identified in large studies [ 106 , 126 ].…”

Genetic Association Study of IL2RA, IFIH1, and CTLA-4 Polymorphisms With Autoimmune Thyroid Diseases and Type 1 Diabetes

“…In diabetic patients an inappropriate immune reaction results in autoreactive T-cell infiltration and production of tissue specific autoantibodies which cause the destruction and dysfunction of the insulin secreting pancreatic beta cells and insulin deficiency (4). The mechanisms leading to development of these diseases remain unknown, however numerous data indicate that apart from the environmental factors there is a strong genetic susceptibility to the autoimmune diseases (5)(6)(7)(8). The relevance of genetic factors is evident from clustering of AITDs or T1D within families, in particular monozygotic and dizygotic twins (9, 10).…”

Machine Learning SNP Based Prediction for Precision Medicine

“…Of note, the majority of these SNPs are located in non-coding regions and thus must be indirectly involved in their disease association, likely through tissue-specific regulatory activities (Visscher et al, 2017; Schierding et al, 2018). New methods to understand these regulatory activities include the integration of spatial and temporal aspects of gene expression data (Schierding and O’Sullivan, 2015; Schierding et al, 2016; Fadason et al, 2017, 2018; Nyaga et al, 2018). These approaches are providing insights into the impacts of genetic variants that can reassign population based risk to individualized risk.…”