Epigenetic and transcriptional dysregulation in CD4+ T cells in patients with atopic dermatitis

Abstract

Atopic dermatitis (AD) is one of the most common skin disorders among children. Disease etiology involves genetic and environmental factors, with 29 independent AD risk loci enriched for risk allele-dependent gene expression in the skin and CD4+ T cell compartments. We investigated the potential epigenetic mechanisms responsible for the genetic susceptibility of CD4+ T cells. To understand the differences in gene regulatory activity in peripheral blood T cells in AD, we measured chromatin accessibility (an assay based on transposase-accessible chromatin sequencing, ATAC-seq), nuclear factor kappa B subunit 1 (NFKB1) binding (chromatin immunoprecipitation with sequencing, ChIP-seq), and gene expression levels (RNA-seq) in stimulated CD4+ T cells from subjects with active moderate-to-severe AD, as well as in age-matched and non-allergic controls. Open chromatin regions in stimulated CD4+ T cells were highly enriched for AD genetic risk variants, with almost half of the AD risk loci overlapping AD-dependent ATAC-seq peaks. AD-specific open chromatin regions were strongly enriched for NF-κB DNA-binding motifs. ChIP-seq identified hundreds of NFKB1-occupied genomic loci that were AD- or control-specific. As expected, the AD-specific ChIP-seq peaks were strongly enriched for NF-κB DNA-binding motifs. Surprisingly, control-specific NFKB1 ChIP-seq peaks were not enriched for NFKB1 motifs, but instead contained motifs for other classes of human transcription factors, suggesting a mechanism involving altered indirect NFKB1 binding. Using DNA sequencing data, we identified 63 instances of altered genotype-dependent chromatin accessibility at 36 AD risk variant loci (30% of AD risk loci) that might lead to genotype-dependent gene expression. Based on these findings, we propose that CD4+ T cells respond to stimulation in an AD-specific manner, resulting in disease- and genotype-dependent chromatin accessibility alterations involving NFKB1 binding.

maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks

“…Its etiology involves both genetic and environmental factors, with 29 independent AD risk loci known [52,53]. Here, we take advantage of an important genomics resource in AD: ATAC-seq and RNA-seq of activated CD4 + T cells, along with whole-genome sequencing, of AD patients and agematched controls (Fig 7A) [54]. Previous analysis of this dataset identified several AD risk variants with allele-dependent chromatin accessibility in activated T cells [54].…”

“…the furthest genetic variant positions in LD with risk variant, extended by an additional + or -512bp to contain the most distal variants in LD. We limited TFBS prediction to TFs with nominal mRNA expression (DESeq2 VST counts � 9) in activated CD4 + T cells, measured in parallel RNA-seq [54].…”

“…We derived both DNA sequence and ATAC-seq signal inputs for maxATAC from a genetics atopic dermatitis (AD) study [54], combining whole genome sequencing (WGS) and OMNI-ATAC-seq (of peripheral blood CD4+ T-cells stimulated with anti-CD3 and anti-CD28 beads for 5 hours) for 6 AD patients and 6 age-matched controls. We used patient-specific genetic variant calls, focusing on the nine AD risk variants previously associated with allele-dependent chromatin accessibility [54]. We sought to identify pairs of AD patients and age-matched controls that were homozygous for the risk and non-risk alleles, respectively.…”

“…Fig 7. maxATAC TFBS prediction at atopic dermatitis risk loci in patient-derived CD4 + T cells. (A)In a previous study[54], peripheral CD4 + T cells were isolated from atopic dermatitis (AD) patients and age-matched controls (CTL) and TCR-stimulated prior to ATAC-seq, RNA-seq and whole genome sequencing (WGS) data generation. (B) We identified a pair of donors in which the AD patient (AD2) was homozygous for the risk allele and the age-matched control (CTL2) was homozygous for the non-risk allele at two independent loci: rs1758201 and rs6062490.…”

Omics and Lung Function: A Need for Integration

“…What is needed now is integration—bringing together multiple omics approaches in both lung and blood to understand the pathophysiological processes underlying growth and decline of lung function and the heritable, environmental, and pathological triggers of disease. Such integration of omics data, particularly epigenetic and gene expression data, has been particularly valuable in providing insights into the pathogenesis of a range of acute and chronic diseases including atopic dermatitis ( 14 ), coronavirus disease (COVID-19) ( 15 ) and COPD ( 16 ). Network medicine-approaches may hold the most compelling promise to move DNAm insights into clinical translation ( 17 ).…”