Maternal prenatal immunity, neonatal trained immunity, and early airway microbiota shape childhood asthma development

Abstract

Background: The path to childhood asthma is thought to initiate in utero and be further promoted by postnatal exposures. However, the underlying mechanisms remain underexplored. We hypothesized that prenatal maternal immune dysfunction associated with increased childhood asthma risk (revealed by low IFN-γ:IL-13 secretion during the third trimester of pregnancy) alters neonatal immune training through epigenetic mechanisms and promotes early-life airway colonization by asthmagenic microbiota.

Methods: We examined epigenetic, immunologic, and microbial features potentially related to maternal prenatal immunity (IFN-γ:IL-13 ratio) and childhood asthma in a birth cohort of mother-child dyads sampled pre-, peri-, and postnatally (N = 155). Epigenome-wide DNA methylation and cytokine production were assessed in cord blood mononuclear cells (CBMC) by array profiling and ELISA, respectively. Nasopharyngeal microbiome composition was characterized at age 2-36 months by 16S rRNA sequencing.

Results: Maternal prenatal immune status related to methylome profiles in neonates born to non-asthmatic mothers. A module of differentially methylated CpG sites enriched for microbe-responsive elements was associated with childhood asthma. In vitro responsiveness to microbial products was impaired in CBMCs from neonates born to mothers with the lowest IFN-γ:IL-13 ratio, suggesting defective neonatal innate immunity in those who developed asthma during childhood. These infants exhibited a distinct pattern of upper airway microbiota development characterized by early-life colonization by Haemophilus that transitioned to a Moraxella-dominated microbiota by age 36 months.

Conclusions: Maternal prenatal immune status shapes asthma development in her child by altering the epigenome and trained innate immunity at birth, and is associated with pathologic upper airway microbial colonization in early life.

Keywords: DNA methylation; childhood asthma; maternal prenatal immunity; nasal microbiome; trained innate immunity.

Conflict of interest statement

ADV, KM, DAS, KEF, DF, and DV declare no competing interests. SVL is a co‐founder, board member and consultant of Siolta Therapeutics Inc., and holds stock in this company, she also consults for Solarea Bio.

© 2022 The Authors. Allergy published by European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.

Figures

FIGURE 1

Maternal prenatal IFN‐γ:IL‐13 relates to differences in the CBMC methylome. (A) Differentially methylated CBMC CpGs associated with Q1 versus Q2‐4. The x‐ and y‐axes represent the log2(fold change) and significance, respectively, for differential methylation at each CpG. The dashed line marks the threshold of significance [false discovery rate (FDR) < 0.05]. The most significant DMCs are annotated with their gene symbol (or CpG identifier). (B) Modules identified by hierarchical clustering of maternal prenatal IFN‐γ:IL‐13 Q1 vs. Q2‐4‐associated DMCs (n = 2316) using WGCNA. Distinct modules are denoted by colors represented in the bar below the dendrogram. Gray represents DMCs that did not cluster into a module. (C) Relationships between maternal prenatal IFN‐γ:IL‐13–associated methylation modules at birth and asthma‐related phenotypes in childhood. The table shows Spearman correlation coefficients and p‐values for relationships between module eigengene vectors and asthma‐associated clinical traits. Only modules with >10 CpGs are shown. Bolded associations remained significant after Bonferroni correction (p = .05/14 = .0036). (D) Asthma risk among children with high or low TRQ module eigenvalues at birth (divided at “0”), as determined by logistic regression.

FIGURE 2

Asthma‐associated differential methylation in the TRQ module co‐localizes with microbe‐responsive elements. Heatmap of TRQ module DMCs (rows) that mapped to promoters/enhancers (as assessed by Roadmap Epigenomics consortium chromatin state annotations) and were significantly (p < .05) associated with asthma. The color bar at the top denotes individual neonates (columns) who did or did not develop asthma during childhood. Microbe‐responsive elements (purple text in the right side legend) were defined as DMCs mapping to genes exhibiting dynamic expression, or putative enhancers exhibiting dynamic changes in chromatin marks (DNaseI/ATAC accessibility, H3K27ac, H3K4me1) in response to microbes or their products., , , , , , Statistics and annotations for these DMCs are provided in Table S5.

FIGURE 3

CBMC TRQ module and LPS‐stimulated IL‐6 production co‐associate with childhood asthma development. (A) CBMC TRQ eigengene values and IL‐6 responses to LPS stimulation at birth are significantly correlated. Red and black dots represent neonates with or without a diagnosis of asthma during childhood, respectively. (B) Interaction between TRQ module methylation (eigengene value), LPS‐induced IL‐6 responses at birth, and prevalence of asthma during childhood. p for interaction was assessed using logistic regression. (C) The risk of childhood asthma is related to CBMC TRQ methylation status and IL‐6 responses. Odds ratios were calculated by logistic regression. For panels B and C, TRQ eigengene vector and z‐scored LPS‐stimulated IL‐6 production were each categorized into “High” and “Low” values, divided at zero.

FIGURE 4

Early‐life upper airway microbiota development is influenced by maternal IFN‐γ:IL‐13 and asthma‐associated DNA methylation in the TRQ module. (A) Upper airway microbiota development over the first three years of life relates to maternal IFN‐γ:IL‐13 status. Lines indicate the slopes from linear mixed effects models. Background represents directionality from panel B. (B) Variance in principal coordinate (PC) 3 is driven by Haemophilus (Spearman's ρ > 0.25), and Dolosigranulum and Moraxella (ρ < −0.25; dotted lines represent ρ of 0.5 or − 0.5, respectively). (C) Weighted UniFrac PC1 correlates with the TRQ eigengene at 24 months, but not 36 months of age (line represents linear model and standard error). (D) Weighted UniFrac PC1 is primarily driven by differences in Moraxella decreases (ρ < −0.25) and Staphylococcus and Corynebacterium (ρ > 0.25) abundances.