Mapping effector genes at lupus GWAS loci using promoter Capture-C in follicular helper T cells

Abstract

Systemic lupus erythematosus (SLE) is mediated by autoreactive antibodies that damage multiple tissues. Genome-wide association studies (GWAS) link >60 loci with SLE risk, but the causal variants and effector genes are largely unknown. We generated high-resolution spatial maps of SLE variant accessibility and gene connectivity in human follicular helper T cells (TFH), a cell type required for anti-nuclear antibodies characteristic of SLE. Of thẽ 400 potential regulatory variants identified, 90% exhibit spatial proximity to genes distant in the 1D genome sequence, including variants that loop to regulate the canonical TFH genes BCL6 and CXCR5 as confirmed by genome editing. SLE 'variant-to-gene' maps also implicate genes with no known role in TFH/SLE disease biology, including the kinases HIPK1 and MINK1. Targeting these kinases in TFH inhibits production of IL-21, a cytokine crucial for class-switched B cell antibodies. These studies offer mechanistic insight into the SLEassociated regulatory architecture of the human genome.

Biological constraints on GWAS SNPs at suggestive significance thresholds reveal additional BMI loci

“…In this study, we used our recently described variant-to-gene structural mapping approach ( Chesi et al, 2019 ; Su et al, 2020 ) to conduct retrospective biologically constrained analyses of previous sequential GWAS reports to determine whether we could implicate statistically suggestive SNPs that would subsequently achieve GWS in incrementally larger data sets. Using a combination of high-resolution promoter-focused Capture C and ATAC-seq, this method enables the prioritization of statistically suggestive loci.…”

“…Over the past decade, sequencing technologies such aa RNA-seq ( Wang et al, 2009 ), ATAC-seq ( Buenrostro et al, 2013 ), and high-resolution promoter-focused Capture C Chesi et al, 2019 ; Hughes et al, 2014 ; Su et al, 2020 have been developed to facilitate the annotation of genes and their regulatory elements. Such data have been utilized to identify physical variant-to-gene interactions via three-dimensional genomics to implicate effector genes at GWAS loci where both sequence variant and gene reside within regions of open chromatin ( Arnold et al, 2015 ; Çalışkan et al, 2019 ; Chesi et al, 2019 ; Cousminer et al, 2020 ; Javierre et al, 2016 ; Smemo et al, 2014 ; Su et al, 2019 ; Su et al, 2020 ). By leveraging high-resolution promoter-focused Capture C with ATAC-seq, it is possible to physically connect putatively functional non-coding elements, such as enhancers, harboring disease-relevant SNPs to promoters of specific genes thereby potentially mechanistically implicated in the SNP-associated phenotype.…”

Next steps in the identification of gene targets for type 1 diabetes

“…CRISPR/Cas9 genome editing can be used to confirm that accessible SNPs in one gene reside in novel, cis-regulatory elements for other genes with known roles in function disease risk [21]. In type 2 diabetes, the most strongly associated SNP lies within the TCF7L2 gene [33], with the rs7903146 T allele in intron 3 widely implicated as the causal variant [34].…”

“…generated using the assay for transposase-accessible chromatin using sequencing [ATAC-Seq] [ 20 ] at the multi- or single-cell level) can identify regions of potential regulatory significance across multiple tissues. One example used the open chromatin landscapes of follicular helper T cells (TFH) from human tonsil to identify functional variants implicated by GWAS of systemic lupus erythematosus (SLE) [ 21 ]. The proxies of SLE ‘sentinel’ SNPs (those SNPs in strong linkage disequilibrium with the most associated SNP from the GWAS) are highly enriched in the open chromatin of TFH cells, a cell type critical for the development of autoantibodies characteristic of SLE, compared with naive CD4 + T cells.…”

“…Whole genome, promoter-focused Capture C, a version of chromatin conformation capture, relates SNPs in the distal regulatory regions to changes in expression of their target genes [ 24 , 25 ]. High-resolution spatial epigenomic approaches for common complex traits have been able to physically link strongly associated SNPs with their target genes for traits such as SLE [ 21 ] and bone mineral density [ 26 , 27 ], as well as type 2 diabetes and type 1 diabetes (discussed below). These studies demonstrate that 3D regulatory architectures are a consistent feature of highly expressed, lineage-specific genes involved in specialised functions in disease-relevant cell types (Fig.…”