DNA methylation signatures in airway cells from adult children of asthmatic mothers reflect subtypes of severe asthma

Maternal asthma (MA) is among the most consistent risk factors for asthma in children. Possible mechanisms for this observation are epigenetic modifications in utero that have lasting effects on developmental programs in children of mothers with asthma. To test this hypothesis, we performed differential DNA methylation analyses of 398,186 individual CpG sites in primary bronchial epithelial cells (BECs) from 42 nonasthma controls and 88 asthma cases, including 56 without MA (NMA) and 32 with MA. We used weighted gene coexpression network analysis (WGCNA) of 69 and 554 differentially methylated CpGs (DMCs) that were specific to NMA and MA cases, respectively, compared with controls. WGCNA grouped 66 NMA-DMCs and 203 MA-DMCs into two and five comethylation modules, respectively. The eigenvector of one MA-associated module (turquoise) was uniquely correlated with 85 genes expressed in BECs and enriched for 36 pathways, 16 of which discriminated between NMA and MA using machine learning. Genes in all 16 pathways were decreased in MA compared with NMA cases (P = 7.1 × 10⁻³), a finding that replicated in nasal epithelial cells from an independent cohort (P = 0.02). Functional interpretation of these pathways suggested impaired T cell signaling and responses to viral and bacterial pathogens. The MA-associated turquoise module eigenvector was additionally correlated with clinical features of severe asthma and reflective of type 2 (T2)-low asthma (i.e., low total serum immunoglobulin E, fractional exhaled nitric oxide, and eosinophilia). Overall, these data suggest that MA alters diverse epigenetically mediated pathways that lead to distinct subtypes of severe asthma in adults, including hard-to-treat T2-low asthma.

DNA methylation and RNA-seq datasets supporting the findings in this article are available in the Gene Expression Omnibus (GEO) repositories under the accession numbers GSE201872 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE201872) and GSE201955 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE201955), respectively.