Leveraging cell-type-specific regulatory networks to interpret genetic variants in abdominal aortic aneurysm

Abstract

Abdominal aortic aneurysm (AAA) is a common degenerative cardiovascular disease whose pathobiology is not clearly understood. The cellular heterogeneity and cell-type-specific gene regulation of vascular cells in human AAA have not been well-characterized. Here, we performed analysis of whole-genome sequencing data in AAA patients versus controls with the aim of detecting disease-associated variants that may affect gene regulation in human aortic smooth muscle cells (AoSMC) and human aortic endothelial cells (HAEC), two cell types of high relevance to AAA disease. To support this analysis, we generated H3K27ac HiChIP data for these cell types and inferred cell-type-specific gene regulatory networks. We observed that AAA-associated variants were most enriched in regulatory regions in AoSMC, compared with HAEC and CD4+ cells. The cell-type-specific regulation defined by this HiChIP data supported the importance of ERG and the KLF family of transcription factors in AAA disease. The analysis of regulatory elements that contain noncoding variants and also are differentially open between AAA patients and controls revealed the significance of the interleukin-6-mediated signaling pathway. This finding was further validated by including information from the deleteriousness effect of nonsynonymous single-nucleotide variants in AAA patients and additional control data from the Medical Genome Reference Bank dataset. These results shed important insights into AAA pathogenesis and provide a model for cell-type-specific analysis of disease-associated variants.

Genetic effects of sequence-conserved enhancer-like elements on human complex traits

“…Indeed, single-cell atlases of chromatin accessibility have been recently established for many human tissues 70 , and single-cell H3K27ac measurements will likely be available for diverse tissues soon with the advent of new technologies 71 . Besides the single-cell extension, our current strategy may need to be adapted to incorporate other data such as chromatin conformation 72,73 and CRISPR screening 32,74 to capture the multifaceted nature of enhancers 6 . Altogether, fine-tuning HC AELEs alongside advances in technologies and resources will markedly increase the resolution and accuracy.…”

“…We used the HOMER 81 command ‘findMotifsGenome.pl’ (version 4.11, http://homer.ucsd.edu/homer/) to identify genomic regions specifically enriched in a target set of sequences against a background set. We used the exact regions provided (‘-size given’) and searched for known motifs (‘-mknown’) in the curated list 72,82 of 1465 unique motifs (https://github.com/suwonglab/peca). Beyond the 1465 known motifs, we also identified de novo motifs, and matched them to known motifs based on similarity of motif matrices (Fig.s 5c-d, 6c-d).…”

“…We used the exact regions provided ('-size given') and searched for known motifs ('-mknown') in the curated list 72,82 of 1465 unique motifs (https:…”

Epigenetic Regulation of Endothelial Cell Lineages During Zebrafish Development—New Insights From Technical Advances

“…For example, interactions involving the RNApolII can reveal the presence of a long-range enhancers interacting with a promoter ( Li et al, 2012 ). These techniques have been successfully used in zebrafish ( Franke et al, 2021 ), as well as in human endothelial cell samples ( Papantonis et al, 2012 ; Nakato et al, 2019 ; Higashijima et al, 2020 ; Ma et al, 2022 ), and have led to the identification of a distal endothelial KLF4 enhancer ( Maejima et al, 2014 ). The importance of protein-protein interactions (PPIs) in mediating the contact of long-range genomic regions has been shown by Weintraub et al in their work on enhancer-promotor loops.…”

Heritability enrichment in context-specific regulatory networks improves phenotype-relevant tissue identification

“…For example, genetic variants in the regulatory network of cranial neural crest cells are elucidated on how they affect human facial morphology (Feng et al, 2021). RSS-NET utilizes gene regulatory networks of multiple contexts and shows better tissue enrichment estimation by decomposing the total effect of a SNP through TF-TG regulations (Zhu et al, 2021) and HiChIP RE-TG regulations (Ma et al, 2022). And the phenotype-associated SNPs often function in a tissue- or cell-type-specific manner (Westra & Franke, 2014).…”