A Common Type 2 Diabetes Risk Variant Potentiates Activity of an Evolutionarily Conserved Islet Stretch Enhancer and Increases C2CD4A and C2CD4B Expression

Abstract

Genome-wide association studies (GWASs) and functional genomics approaches implicate enhancer disruption in islet dysfunction and type 2 diabetes (T2D) risk. We applied genetic fine-mapping and functional (epi)genomic approaches to a T2D- and proinsulin-associated 15q22.2 locus to identify a most likely causal variant, determine its direction of effect, and elucidate plausible target genes. Fine-mapping and conditional analyses of proinsulin levels of 8,635 non-diabetic individuals from the METSIM study support a single association signal represented by a cluster of 16 strongly associated (p < 10) variants in high linkage disequilibrium (r > 0.8) with the GWAS index SNP rs7172432. These variants reside in an evolutionarily and functionally conserved islet and β cell stretch or super enhancer; the most strongly associated variant (rs7163757, p = 3 × 10) overlaps a conserved islet open chromatin site. DNA sequence containing the rs7163757 risk allele displayed 2-fold higher enhancer activity than the non-risk allele in reporter assays (p < 0.01) and was differentially bound by β cell nuclear extract proteins. Transcription factor NFAT specifically potentiated risk-allele enhancer activity and altered patterns of nuclear protein binding to the risk allele in vitro, suggesting that it could be a factor mediating risk-allele effects. Finally, the rs7163757 proinsulin-raising and T2D risk allele (C) was associated with increased expression of C2CD4B, and possibly C2CD4A, both of which were induced by inflammatory cytokines, in human islets. Together, these data suggest that rs7163757 contributes to genetic risk of islet dysfunction and T2D by increasing NFAT-mediated islet enhancer activity and modulating C2CD4B, and possibly C2CD4A, expression in (patho)physiologic states.

Integration of multiple epigenomic marks improves prediction of variant impact in saturation mutagenesis reporter assay

“…deltaSVM is usually trained on histone ChIP‐seq, DHS‐seq, ATAC‐seq, or TF ChIP‐seq data from a single cell type. After this approach, we chose the most closely matched cell line DHS‐seq data set from ENCODE for all cell lines tested (HepG2, K562, HEK293, NHEK, Melano), except MIN6, for which we used ATAC‐seq data from (Kycia et al, ), and trained gkm‐SVM to determine sequence features. We then generated deltaSVM scores for each locus using the appropriate cell‐specific trained gkm‐SVM model.…”

“…Comparison of the leading three prediction groups indicated the important classes of features, and each leading group arrived at a similar method to successfully combine features from different cell types using the training data. for which we used ATAC-seq data from (Kycia et al, 2018), and trained gkm-SVM to determine sequence features. We then generated deltaSVM scores for each locus using the appropriate cell-specific trained gkm-SVM model.…”

Single cell chromatin accessibility reveals pancreatic islet cell type- and state-specific regulatory programs of diabetes risk

“…Our findings provide a roadmap demonstrating how single cell accessible chromatin data derived from disease-relevant primary tissue can be utilized to define the cell types, cell states, cis regulatory elements and genes involved in the genetic basis of complex disease. Over 400 known risk signals for T2D have been identified, yet only a handful have been characterized molecularly 16,18,27, 85–91 . Identifying the genes affected by non-coding risk variants is paramount for understanding the molecular pathways dysregulated in disease and can inform therapeutic target discovery.…”

Single cell ATAC-seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures

“…61 Collectively, these clusters of cells work together to maintain insulin production and glucose 62 homeostasis. Disruption of the complex interplay between the cell types, their organization, and their 63 underlying regulatory interaction is known to be associated with type-2-diabetes (T2D) pathophysiology 64 [2]. However, the exact cellular mechanisms through which different risk factors contribute to the 65 disease risk are not completely understood.…”