Risk variants disrupting enhancers of T _H 1 and T _REG cells in type 1 diabetes

Abstract

Genome-wide association studies (GWASs) have revealed 59 genomic loci associated with type 1 diabetes (T1D). Functional interpretation of the SNPs located in the noncoding region of these loci remains challenging. We perform epigenomic profiling of two enhancer marks, H3K4me1 and H3K27ac, using primary TH1 and TREG cells isolated from healthy and T1D subjects. We uncover a large number of deregulated enhancers and altered transcriptional circuitries in both cell types of T1D patients. We identify four SNPs (rs10772119, rs10772120, rs3176792, rs883868) in linkage disequilibrium (LD) with T1D-associated GWAS lead SNPs that alter enhancer activity and expression of immune genes. Among them, rs10772119 and rs883868 disrupt the binding of retinoic acid receptor α (RARA) and Yin and Yang 1 (YY1), respectively. Loss of binding by YY1 also results in the loss of long-range enhancer–promoter interaction. These findings provide insights into how noncoding variants affect the transcriptomes of two T-cell subtypes that play critical roles in T1D pathogenesis.

Transcriptional regulatory network controlling the ontogeny of hematopoietic stem cells

“…We validated our protocol for H3K4me3 with the human lymphoblastoid cell line, GM12878. Treating ChIP-Seq peaks called using a conventional protocol as the gold standard, data generated by our protocol had an area under the ROC curve (auROC) value of 0.97, indicating excellent overall concordance (Gao et al, 2019). We profiled four histone modification marks: H3K4me1, H3K4me3, H3K27me3, H3K27ac across the five developmental stages.…”

“…We developed a low-cell-number protocol to perform ChIP-Seq with as few as 20,000 cells to determine genome-wide distribution of histone modifications (Gao et al, 2019). We validated our protocol for H3K4me3 with the human lymphoblastoid cell line, GM12878.…”

“…Little is known about the transcriptional regulatory networks (TRNs) during HSC formation from arterial endothelium. We recently developed the Target Inference via Physical Connection (TIPC) algorithm for inferring condition-specific TRNs by integrating RNA-Seq and histone mark ChIP-Seq data (Gao et al, 2019). TIPC first computes probability scores for three key components of transcriptional regulation, including probability of a DNA sequence being an enhancer, probability of a TF binding to an enhancer based on the TF motif model and the enhancer sequence, and probability of enhancer-promoter interaction.…”

“…The overall score for a TF regulating a target gene is the product of the three component probabilities and the expression level of the TF ( Figure 5A). Using a set of gold standard TF-target gene pairs in mouse embryonic stem cells, we demonstrated that TIPC achieves improved accuracy compared to several state-of-the-art methods (Gao et al, 2019). To increase robustness, we first scanned the enhancer sequences for each TF motif using the Find Individual Motif Occurrence (FIMO) software (Grant et al, 2011) (P-value < 1e-5).…”

“…To identify key TFs regulating each developmental transition in HSC ontogeny, we first merged developmentally adjacent TRN pairs and represented each gene in the merged network based on its level of differential expression during the transition. Next, we identified key TFs in the differential TRN using a network propagation-based method (Gao et al, 2019). The rationale for our method is that a key TF likely exerts its influence (either directly or indirectly) on the entire set of differentially expressed genes via a shorter regulatory path compared to paths from less important TFs.…”