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Background: Allergic rhinitis is a common inflammatory condition of the nasal mucosa that imposes a considerable health burden. Air pollution has been observed to increase the risk of developing allergic rhinitis. We addressed the hypotheses that early life exposure to air toxics is associated with developing allergic rhinitis, and that these effects are mediated by DNA methylation and gene expression in the nasal mucosa. Methods: In a case–control cohort of 505 participants, we geocoded participants' early life exposure to air toxics using data from the US Environmental Protection Agency, assessed physician diagnosis of allergic rhinitis by questionnaire, and collected nasal brushings for whole‐genome DNA methylation and transcriptome profiling. We then performed a series of analyses including differential expression, Mendelian randomization, and causal mediation analyses to characterize relationships between early life air toxics, nasal DNA methylation, nasal gene expression, and allergic rhinitis. Results: Among the 505 participants, 275 had allergic rhinitis. The mean age of the participants was 16.4 years (standard deviation = 9.5 years). Early life exposure to air toxics such as acrylic acid, phosphine, antimony compounds, and benzyl chloride was associated with developing allergic rhinitis. These air toxics exerted their effects by altering the nasal DNA methylation and nasal gene expression levels of genes involved in respiratory ciliary function, mast cell activation, pro‐inflammatory TGF‐β1 signaling, and the regulation of myeloid immune cell function. Conclusions: Our results expand the range of air pollutants implicated in allergic rhinitis and shed light on their underlying biological mechanisms in nasal mucosa. [ABSTRACT FROM AUTHOR]

Nasal transcriptomics can provide an accessible window into asthma pathobiology. Our goal was to move beyond gene signatures of asthma to identify master regulator genes that causally regulate genes associated with asthma phenotypes. We recruited 156 children with severe persistent asthma and controls for nasal transcriptome profiling and applied network-based and probabilistic causal methods to identify severe asthma genes and their master regulators. We then took the same approach in an independent cohort of 190 adults with mild/moderate asthma and controls to identify mild/moderate asthma genes and their master regulators. Comparative analysis of the master regulator genes followed by validation testing in independent children with severe asthma (n = 21) and mild/moderate asthma (n = 154) was then performed. Nasal gene signatures for severe persistent asthma and for mild/moderate persistent asthma were identified; both were found to be enriched in coexpression network modules for ciliary function and inflammatory response. By applying probabilistic causal methods to these gene signatures and validation testing in independent cohorts, we identified (1) a master regulator gene common to asthma across severity and ages (FOXJ1); (2) master regulator genes of severe persistent asthma in children (LRRC23, TMEM231, CAPS, PTPRC , and FYB) ; and (3) master regulator genes of mild/moderate persistent asthma in children and adults (C1orf38 and FMNL1). The identified master regulators were statistically inferred to causally regulate the expression of downstream genes that modulate ciliary function and inflammatory response to influence asthma. The identified master regulator genes of asthma provide a novel path forward to further uncovering asthma mechanisms and therapy. [ABSTRACT FROM AUTHOR]

Background Nasal microbiota may influence asthma pathobiology. Objective We sought to characterize the nasal microbiome of subjects with exacerbated asthma, nonexacerbated asthma, and healthy controls to identify nasal microbiota associated with asthma activity. Methods We performed 16S ribosomal RNA sequencing on nasal swabs obtained from 72 primarily adult subjects with exacerbated asthma (n = 20), nonexacerbated asthma (n = 31), and healthy controls (n = 21). Analyses were performed using Quantitative Insights into Microbial (QIIME); linear discriminant analysis effect size (LEfSe); Phylogenetic Investigation of Communities by Reconstruction of Unobserved States; and Statistical Analysis of Metagenomic Profiles (PICRUSt); and Statistical Analysis of Metagenomic Profiles (STAMP). Species found to be associated with asthma activity were validated using quantitative PCR. Metabolic pathways associated with differentially abundant nasal taxa were inferred through metagenomic functional prediction. Results Nasal bacterial composition significantly differed among subjects with exacerbated asthma, nonexacerbated asthma, and healthy controls (permutational multivariate ANOVA, P = 2.2 × 10 −2 ). Relative to controls, the nasal microbiota of subjects with asthma were enriched with taxa from Bacteroidetes (Wilcoxon-Mann-Whitney, r = 0.33, P = 5.1 × 10 −3 ) and Proteobacteria ( r = 0.29, P = 1.4 × 10 −2 ). Four species were differentially abundant based on asthma status after correction for multiple comparisons: Prevotella buccalis , P adj = 1.0 × 10 −2 ; Dialister invisus , P adj = 9.1 × 10 −3 ; Gardnerella vaginalis , P adj = 2.8 × 10 −3 ; Alkanindiges hongkongensis , P adj = 2.6 × 10 −3 . These phyla and species were also differentially abundant based on asthma activity (exacerbated asthma vs nonexacerbated asthma vs controls). Quantitative PCR confirmed species overrepresentation in asthma relative to controls for Prevotella buccalis (fold change = 130, P = 2.1 × 10 −4 ) and Gardnerella vaginalis (fold change = 160, P = 6.8 × 10 −4 ). Metagenomic inference revealed differential glycerolipid metabolism (Kruskal-Wallis, P = 1.9 × 10 −4 ) based on asthma activity. Conclusions Nasal microbiome composition differs in subjects with exacerbated asthma, nonexacerbated asthma, and healthy controls. The identified nasal taxa could be further investigated for potential mechanistic roles in asthma and as possible biomarkers of asthma activity. [ABSTRACT FROM AUTHOR]