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. Author manuscript; available in PMC: 2025 Nov 1.
Published in final edited form as: Allergy. 2024 May 26;79(11):3047–3061. doi: 10.1111/all.16174

Multi-omic integration reveals alterations in nasal mucosal biology that mediate air pollutant effects on allergic rhinitis

Haritz Irizar 1, Yoojin Chun 1, Hsiao-Hsien Leon Hsu 2,3, Yan-Chak Li 1, Lingdi Zhang 1, Zoe Arditi 1, Galina Grishina 4, Alexander Grishin 4, Alfin Vicencio 4, Gaurav Pandey 1, Supinda Bunyavanich 1,4,*
PMCID: PMC11560721  NIHMSID: NIHMS1995316  PMID: 38796780

Abstract

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.

Keywords: Allergic rhinitis, air pollution, DNA methylation, nasal, transcriptome, gene expression, epigenome, multi-omic, systems biology, allergy, causal mediation

Graphical Abstract

graphic file with name nihms-1995316-f0001.jpg

  • In a cohort of 505 participants, we examined the effects of air toxics on allergic rhinitis development and their mediation by nasal DNA methylation and nasal gene expression.

  • Multi-omic integration revealed methylation and gene expression paths involving THBS1 and RHOH mediating the effects of acrylic acid and phosphine on AR development.

  • Increased mast cell degranulation and TGF-β1activation are potential mechanisms of air toxic effects on AR development.

Abbreviations: FcεRI, Fc epsilon RI; IgE, immunoglobulin E; IL-33, interleukin 33; RHOH, ras homolog family member H; ST2, suppression of tumorigenicity 2; TGF-β1, transforming growth factor beta 1; THBS1, rhrombospondin 1.

INTRODUCTION

Allergic rhinitis (AR) is a common condition characterized by sneezing, nasal congestion, itching, and rhinorrhea that results from Type 2 mucosal inflammation caused by IgE-mediated reactions to inhaled allergens1. It is one of the most common chronic conditions with a prevalence of up to 40% in some populations2. While the symptoms of AR are relatively mild, its high prevalence results in substantial collective health burden worldwide, including lower labor and academic productivity3, disrupted sleep, decreased outdoor activities, and reduced quality of life4. For example, AR reduces labor productivity by 30 to 50 billion euro per year in the European Union alone3, 5. Although allergen avoidance, antihistamines, intranasal corticosteroids, and allergen-specific immunotherapy are therapeutic options, many patients often remain dissatisfied and have poor long-term adherence6. Better understanding the genetic underpinnings, environmental factors, and molecular mechanisms that influence the development and severity of AR could inform strategies to prevent, manage, and treat AR.

Several genetic and environmental factors are involved in the pathogenesis of AR. Among the genetic factors, genetic variation in or near genes that encode for immune function regulators, such as chemokines and interleukins, is associated with the development of AR, overlapping with the genetics of asthma7. Regarding environmental factors, apart from allergen exposure and viral infections, air pollution and environmental toxicants have also been identified as contributors7, 8.

Several studies have identified significant associations between air pollution and the development and severity of AR9–16. However, those studies have been mostly limited to criteria air pollutants, which are six commonly found outdoor air pollutants including particulate matter, NO2, SO2, O3, CO, and lead17, 18. Evidence for indoor volatile organic compounds playing a role in AR has been inconsistent19. The US Environmental Protection Agency (EPA) inventories 189 hazardous air pollutants known to be associated with adverse health outcomes, also called air toxics, for which nationwide exposure estimates have been generated since 1996 via the National Air Toxic Assessment (NATA, until 2014) and the Air Toxics Screening Assessment (since 2017) (https://www.epa.gov/AirToxScreen). Despite these available air toxics data, no studies thus far have explored the effects of these air toxics on AR.

An increasing body of evidence points to changes in DNA methylation as one of the main mechanisms by which air pollutants affect human biology and health20–26, and gene expression differences in the nasal mucosa have been previously associated with criteria air pollutant exposure27, 28. In particular, pre-natal and early-life exposure to pollution have been shown to have effects not only on the risk and severity of AR11, 29, but also on DNA methylation and gene expression levels across several tissues, in both humans and model animals30–36. A prior study examined associations between particulate matter (PM 2.5), whole blood gene expression, and whole blood DNA methylation.37 Here we were motivated to extend and deepen this area of inquiry by expanding research to the large collection of data on air toxics, connecting these air toxic exposures to the outcome of AR, and targeting epigenomic and transcriptomic data from the tissue most relevant to AR, nasal mucosa. Our rationale for examining both nasal DNA methylation and nasal gene expression was to combine the capacity of DNA methylation to capture environmental exposures and that of gene expression to reflect cellular behavior such that their joint analyses with air toxics exposure and AR as an outcome could enable deeper understanding of mechanisms in AR development. In this study of over 500 individuals from the New York metropolitan area, we addressed the hypotheses that early-life exposure to hazardous air toxics is associated with the development of AR, and that these effects are mediated by DNA methylation and gene expression in the nasal mucosa.

METHODS

An overview of the study design is shown in Figure 1. A detailed representation of the workflow can be found in Figure S1.

Figure 1: Study design.

Figure 1:

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For each participant, early-life air toxic data was obtained from the US EPA National Air Toxics Assessment, nasal samples were collected for DNA methylation and transcriptome profiling, and clinical phenotyping was undertaken. The following effects were systematically assessed: (1) air toxic on AR; (2) air toxic on DNA methylation; (3) DNA methylation on gene expression; and (4) gene expression on AR. Finally, casual mediation analysis (5) was performed to evaluate the degree to which CpG methylation and/or gene expression mediate air toxic effects on AR.

Study population

Participants age ≥ 5 years were recruited from the Mount Sinai Health System, New York, NY, USA between 2015–2018. The study was intended as a case-control study of allergy/asthma. Patients presenting to pediatric practices (general pediatrics, pediatric allergy, and pediatric pulmonology) were approached by study coordinators directly following their clinic visit if the doctor they were seeing approved. Exclusion criteria included sinus disease, bleeding disorder, anticoagulant/aspirin use, or a history of pulmonary disease other than asthma. Participants or their legal guardians provided written informed consent. Phenotyping was performed at a single study visit and included exam, collection of demographic and clinical information via questionnaires, nasal sampling, and phlebotomy. AR was defined based on self-reported physician diagnosis of AR plus supporting responses to questionnaire items regarding current nasal symptoms that are typical of AR and not attributable to concurrent upper respiratory infection. Controls without AR had neither self-reported physician diagnosis of AR or supporting responses to those questionnaire items. Serum allergen-specific IgE testing was performed using ImmunoCAP. The study was approved by the Mount Sinai Institutional Review Board.

Early-life air toxic assessment

Air toxic exposure data was retrieved from the National Air Toxics Assessment (NATA) (https://www.epa.gov/national-air-toxics-assessment). NATA provides exposure estimates for a given year at each census tract based on emissions inventories and computer simulation models, with data available for 1996, 1999, 2002, 2005, 2011 and 2014. We selected the release closest in time following a participant’s birth date and used the data corresponding to the census tract whose geometric centroid was closest to the residential zip code of the participant at enrollment. These data included annual ambient concentration estimates for 125 air toxics. Air toxics with a concentration of 0 in >25% of the samples were removed, leaving 110 air toxics for analysis which were quantized into quartiles (Table S1).

Nasal DNA methylation profiling

All participants were off nasal medications for at least 2 weeks prior to sampling. Nasal samples were obtained using a sterile cotton swab. DNA extraction was performed using the QIAamp DNA Micro Kit by Qiagen (Valencia, CA). The isolated DNA from 384 randomly selected participants underwent bisulfite conversion using the EZ DNA Methylation Kit (Zymo Research, Irvine, CA). Methylome profiling was done on this random subsample of 384 rather than on the whole sample because we had access to four 96-sample Illumina Human MethylationEPIC arrays (Illumina, San Diego, CA). The resulting raw DNA methylation data were QC’ed and pre-processed using the Enmix package in R38 to obtain beta values, which were then transformed into M-values39.

Nasal transcriptome profiling

Nasal brushings were performed with a cytology brush on each contralateral nostril. Brushings were immediately placed in RNALater (Thermo Fisher Scientific, Waltham, MA). RNA was extracted using the RNeasy MinElute Cleanup Kit by Qiagen (Qiagen, Valencia, CA). RNA yield and quality were assessed with a 2100 Bioanalyzer Nano Chip (Agilent Technologies, Santa Clara, CA) and a Qubit Fluorometer (Thermo Fisher Scientific, Waltham, MA). For all 505 participants, RNA sequencing libraries were prepared using the standard TruSeq RNA Sample Prep Kit v2 (Illumina, San Diego, CA) and the resulting libraries were sequenced on an Illumina HiSeq 2500 platform. The raw reads were then mapped using STAR40 and subjected to quality control using RNA-SeQC41. The RNA-seq data showed very high quality, with high sequencing depth (66.6±26.2 million reads per sample) and low base mismatch rates (0.5±0.2%). After QC, the reads that mapped to each gene annotated in Gencode v3042 were counted using featureCounts.43 Expression levels for 18,138 genes remained for analysis following data processing, normalization, and quality control. These data have been submitted to Synapse under accession number syn51232278.

RNA sequence variant calling

To infer genotypes to be used in the expression quantitative trait (eQTL) and Mendelian randomization analyses, we performed variant calling on the RNA sequence (RNA-seq) reads following GATK’s Best Practice Workflow for RNA-seq short variant discovery as previously described44.

eQTL analysis

We then performed eQTL analysis aimed at generating genotype-on-gene expression effects to be used to identify instrument SNPs and build the instrument SNP-score for the Mendelian randomization analysis. The eQTL analysis was performed using the Matrix eQTL R package45. RNA-seq data was prepared by filtering out low count genes and re-scaling by library size using the TMM method. Gene expression values as z-scores and variant calls as minor allele counts were used to estimate cis- and trans-eQTLs, defining a 1Mb window to identify cis-SNPs. An FDR<0.05 threshold was applied for cis- and trans-eQTLs separately to identify statistically significant effects.

Air toxic effects on AR

The effects of individual air toxics on AR were estimated combining elastic-net and logistic regressions46. An elastic-net regression was run with AR as the dependent variable, the 110 air toxics as predictors of interest, and age and sex as covariates. Then, a logistic regression was run on AR using as predictors the air toxics identified as having a non-zero coefficient by the elastic-net regression, with sex and age as covariates.

Air toxic effects on CpG methylation

A similar approach was used to estimate the effects of the 110 air toxics on CpG methylation levels. For each of the CpGs after QC, we first ran an elastic-net regression with the methylation m-values as the dependent variable, the 110 air toxics as predictors of interest and participants’ age and sex, as well as methylation chip slide and array, as covariates. Then, a linear regression was run with the same covariates, but only with the air toxics selected via elastic-net as potential predictors.

CpG methylation effects on gene expression

To explore cis-effects of CpG methylation on gene expression, we first identified a genomic window using a data-driven approach. For each gene, we selected the CpGs within 1Mb on either side of the transcription start site (TSS) and ran a linear regression for each of the CpGs using expression of the gene as dependent variable, the CpG as main predictor and age, sex and RNA-sequencing batch as covariates. We then applied a 1Kb sliding window on the results to calculate average −log10p-values separately for positive and negative effects and plotted them against the distance from the TSS of the corresponding window (Figure S2A). We smoothed the signal by calculating a rolling average of five peaks (Figure S2B). Using these results, we identified a genomic window from 2.3Kb upstream to 25.3Kb downstream of the TSS. Then, for each gene, we selected the CpGs within that genomic window and used the elastic-net + linear regression approach described above to estimate CpG methylation-on-gene expression effects using sex, age, and RNA-sequencing batch as covariates.

Gene expression effect on AR

The effects of gene expression on AR were explored using two approaches. First, we performed a differential expression analysis on the RNA-seq gene counts using DESeq247 to fit a model with AR as main predictor, and age, sex, and RNA-seq batch as covariates. However, as it has been shown that differential expression results often reflect disease-induced alterations48, we complemented this analysis with a Mendelian randomization approach. For each gene, we generated a SNP-score by selecting significant cis- and trans-eQTLs, grouping the resulting SNPs using PLINK249, 50, multiplying the number of effect alleles by the eQTL coefficient, summing the resulting scores across the list of grouped SNPs, and dividing the final score by the number of SNPs. This SNP-score was then used as the instrument variable in a Mendelian randomization analysis performed using the GENIUS algorithm implemented in the MR-GENIUS R package51, with the expression of the corresponding gene as the exposure and AR as the outcome.

Mediation analysis

Last, we performed a mediation analysis to evaluate the degree to which CpG methylation and/or gene expression mediate air toxic effects on AR. We first identified the air toxic – CpG methylation – gene expression – AR paths and selected those associated with increased odds of AR with p-value < 0.1. We then performed a mediation analysis on each path via structural equation modelling (SEM) following Hayes’ PROCESS models52. We reduced all covariates used in upstream regressions into orthogonal dimensions using a factor analysis of mixed data53 and ran elastic-net to identify the most predictive dimensions. We then removed the dimensions most highly correlated with the corresponding predictor of interest until its variance inflation factor was < 3. This approach yielded for each path three regression models containing the most informative covariates and no collinearity. We then defined three indirect effects of interest for the SEM to estimate: serial mediation via CpG methylation and gene expression; mediation only by CpG methylation; and mediation only by gene expression. We then ran the SEM analysis with 50,000 bootstrap replications, and for each path, selected the indirect effect with the lowest p-value.

RESULTS

Study Sample

Of the 505 participants, 275 (54.5%) had physician-diagnosed AR. Table 1 summarizes the demographic and clinical characteristics of the sample overall and stratified by AR. As the study was intended as a case-control study of children with allergy/asthma, and recruitment was from general pediatrics, pediatric allergy, and pediatric pulmonary clinics, rates of asthma and atopic dermatitis were high in this cohort compared to the generation population. The participants with AR were tested for sensitization to 10 environmental allergens by specific IgE, of which 96% tested positive (sIgE ≥ 0.35 kUA/L) to at least one allergen, and specific aeroallergen sensitization rates were as follows: 75% were sensitized to tree pollen mix, 62% to weed pollen mix, 57% to grass pollen mix, 73% to dust mite (Dermatophagoides pteronyssinus or Dermatophagoides farinae), 78% to dog dander, 72% to cat dander, 63% to cockroach (Blatella germanica), 39% to mold mix, and 30% to mouse urine.

Table 1:

Demographic and clinical characteristics of the sample.

All (n=505) AR (n=275) No AR (n=230)
Age (years) 16.4 ± 9.5 15.7± 10.0 17.3±8.9
Year of birth (median, IQR) 2003 [1999–2007] 2005 [2001–2007] 2002 [1998–2006]
Sex (females, %) 267 (52.8%) 130 (47.3%) 137 (59.6%)
Race
 Asian 51 (10.1%) 13 (4.7%) 38 (16.6%)
 Black 109 (21.6%) 61 (22.2%) 47 (20.1%)
 Hawaiian/Pacific Islander 2 (0.4%) 1 (0.4%) 1 (0.4%)
 White 216 (42.8%) 131 (47.6%) 85 (37.1%)
 Multiple races/unknown 127 (25.1%) 69 (25.1%) 58 (25.2%)
Parental atopy 339 (67.1%) 213 (77.5%) 126 (54.8%)
Asthma 315 (62.4%) 209 (76%) 105 (45.7%)
Atopic dermatitis 246 (48.7%) 174 (63.5%) 72 (31.3%)
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Early-life air toxic effects on AR

Elastic-net coupled with logistic regression identified nine air toxic levels in early life associated with developing AR (Step 1 in Figure 1, Figure 2A). Given that all effect coefficients and the corresponding variances were estimated in a single test, no multiple-testing adjustment was indicated. Among the significant air toxics, antimony compounds, acrylic acid, and benzyl chloride showed the strongest positive associations. Two air toxics showed a negative association with AR: cobalt compounds and quinone. Bar plots showing the fraction of AR status by each concentration quartile for the top air toxics, as well as for phosphine and acrylic acid, which feature prominently in downstream results, revealed very strong and clear trends, with the differences in the fraction of AR diagnoses between the 1st and 4th quartiles ranging between 0.93 and 1 (Figure 2B).

Figure 2: Associations between air toxic levels and allergic rhinitis.

Figure 2:

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(A) The volcano plot shows the coefficients (x-axis) and p-values (y-axis) obtained from logistic regression models for the 60 air toxics selected by elastic-net. Air toxics with a significant association (P < 0.05), as well as those that feature prominently in downstream analyses (acrylic acid and phosphine, in bold letters) are colored by red (positive association) or blue (negative association). (B) Bar plots showing the proportion of AR status (yes/no) by the quartiles of selected air toxics. The selected air toxics include the top two negatively-associated air toxics (cobalt compounds and quinone (p-benzoquinone)), the top two positively associated air toxics (antimony compounds and benzyl chloride), and two additional air toxics that feature prominently in downstream results (acrylic acid and phosphine).

Effects of early-life air toxics on DNA methylation

We then set to explore the effects of the air toxics on DNA methylation of nasal mucosa cells (Step 2 in Figure 1) in the subsample of 384 participants with DNA methylation data. An elastic-net coupled with linear regression approach identified 81 associations below the p-value < 9e−8 threshold54, including 38 positive and 43 negative associations (Figure 3A). The strongest positive associations were identified at ARMC9, EPHA6 and C4orf41/RWDD4A for N,N-dimethylaniline (p-values of 1.5e−10, 3.7e−10 and 8.2e−23). Among the negative associations, the strongest signals were at ARMC9, EVX1 and TLE4 for hexachloropentadiene (p-values of 2.8e−12, 3.2e−10 and 1.1e−11) and C4orf41/RWDD4A for chloroprene (p-value=8.3e−31). Interestingly, for two CpGs, those near ARMC9 (cg10683875) and C4orf41/RWDD4A (cg27143824), the two air toxics most strongly associated with methylation levels had opposite effects. Another association that features prominently in downstream analyses was the increase in THBS1 methylation (cg04827020) associated with phosphine (p-value=0.04) (Figure 3A). Boxplots for these top findings showed very strong and clear associations between air toxic quartiles and methylation levels (Figure 3B).

Figure 3: Associations between air toxic levels and CpG methylation.

Figure 3:

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(A) The circos plot shows the 81 associations between air toxic and CpG methylation levels that were significant at P < 9e-8. The green section represents air toxics (ordered alphabetically) and the black/gray section represents CpGs by chromosome location. Edges indicate significant associations, with positive effects shown in red and negative effects shown in blue. The top three positive effects and top four negative effects are shown with thicker edges weighted by absolute t-statistic and labelled in black. Additionally, the association between phosphine and a CpG associated with THBS1, which factors prominently in downstream results, is also shown and labelled in maroon. (B) The boxplots show the covariate-adjusted methylation values for each air toxic by quartile for the eight associations shown in (A). Blue boxes are for negatively associated air toxics and red boxes indicate positively associated air toxics.

Effects of DNA methylation on gene expression

We next explored cis-effects of CpG methylation on gene expression (Step 3 in Figure 1) in the subsample of 384 participants with DNA methylation data. A similar elastic-net followed by linear regression approach identified 374 significant associations at FDR<0.05, including 139 positive and 235 negative effects (Figure 4A). Among the positive associations, WDCF3, SLC2A14 and SKAP2 showed the strongest signals (p-values of 7e−24, 4e−18, and 8e−13, respectively). THBS1 also exhibited a significant positive association (p-value = 0.01). Among the negative associations, MNDA, ARHGAP25, and FCGR2A showed the strongest signals (p-values of 1.4e−21, 3e−16, and 1e−11). Scatterplots of these top findings (Figure 4B) revealed clear trends with high fractions of gene expression variance explained by CpG methylation (18.2–39.0%).

Figure 4: Associations between CpG methylation and gene expression.

Figure 4:

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(A) Miami plot of cis-associations for CpGs located between 2.3KB upstream and 25.3KB downstream of the transcription start site. Positive associations are shown in the top panel and negative associations in the bottom. The effects are ordered by hg38 chromosome coordinates. The 374 significant associations (FDR < 0.05), as well as an additional association that features prominently in downstream results (cg04827020 – THBS1) are highlighted in red (positive) or blue (negative). The top effects by p-value, as well as the cg04827020-THBS1 effect, are labelled. (B) Scatterplots of relationships between gene expression and methylation levels for the associations labelled in (A). The percentage of variance in gene expression explained by variation in methylation of the corresponding CpG is in indicated on each scatterplot.

Effects of gene expression on AR

To complete this systematic examination of molecular paths linking air toxics to AR, we then studied the relationship between nasal gene expression and AR (Step 4 in Figure 1). The differential expression (DE) analysis performed on the total sample of 505 participants revealed 438 differentially expressed genes (FDR<0.05), of which 64 were overexpressed and 374 were under-expressed in AR (Figure 5). Among the overexpressed genes, FETUB (logFC=1.4, FDR=5.3e−5) and PRB1 (logFC=1.5, FDR=6.4e−5) were most significantly associated with AR. RHOH and THBS1, which feature prominently in downstream analyses, were also significantly overexpressed (RHOH: logFC=0.63, FDR=0.004; THBS1: logFC=0.53, FDR=0.015). Among the under-expressed genes, PRTG (logFC=−1.5, FDR=3.5e−6) and FAM25G (logFC=−1.4, p-value=3.2e−6) were most significantly associated with AR.

Figure 5: Gene expression associations with allergic rhinitis.

Figure 5:

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(A) Volcano plot for 19,995 genes associated with AR (FDR < 0.05). Genes overexpressed in AR are shown in red, and those under-expressed in blue. The top two under- and over-expressed genes, along with RHOH and THBS1, are labelled. (B) Miami plot of causal gene expression-phenotype associations identified by Mendelian randomization with −log10p-values shown on the y-axis either as positive associations (upper panel) or negative associations (lower panel) and ordered on the x-axis by hg38 genomic position. The 529 significant associations (FDR < 0.05) are shown either in red (positive associations, n=249) or blue (negative associations, n=280). The top three positively and negatively associated genes ranked by p-value, along with RHOH and THBS1, are labelled. (C) Scatterplot of the relationship between the −log10p-values obtained in the Mendelian randomization (B) and those obtained in the differential expression (A). Spearman’s Rho correlation values are shown. The 300 genes that show a significant association with AR in both MR and DE at p-value < 0.05 with matching directions of effect are highlighted in red (positive associations, n=42) or blue (negative associations, n=258). The top negatively and positively associated genes, ranked by p-value, along with THBS1 and RHOH, are labelled. (D) Bar plots showing the proportion of AR status by the SNP-score-adjusted-expression quartiles of selected genes. The selected genes include the top two negatively-associated genes (NUPR1 and CD74) and the top two positively associated genes (CD69 and ADGRE4P) identified based on the p-values obtained in the DE and MR analyses, as well as THBS1 and RHOH.

The Mendelian randomization (MR) analysis identified 529 genes whose expression levels were causally associated with the odds of developing AR at FDR<0.05, 249 positively and 280 negatively (Figure 5B). The three genes that showed the most significant positive associations were PRR4 (coef=0.03, FDR=5.6e−11), STATH (coef=0.03, FDR=6.8e−11) and CCL4 (coef=0.03, FDR=6.8e−11). RHOH and THBS1 also showed a robust positive association with AR (RHOH: coef=0.04, FDR=3.9e−4; THBS1: coef=0.03, FDR=1.6e−8). Among the negative associations, the three genes most significantly associated with AR were NUPR1 (coef=−0.03, FDR=5.6e−11), ZNF474 (coef=−0.09, FDR=6.8e−11) and ZC3HAV1 (coef=−0.15, FDR=1.1e−9). A scatterplot of the −log10-p-values obtained in the Mendelian randomization (x-axis) and the differential expression (y-axis) analyses signed, in both cases, by the sign of the corresponding coefficient showed a low correlation (Spearman’s Rho=0.16) (Figure 5C). Nonetheless, 300 genes did show a significant association with the same direction of effect (Figure 5C). Among the positively associated genes, ADGRE4P (DE/MR p-values of 9.2e−8/1.1e−13) and CD69 (p-values: 1.5e−7/1.2e−7) showed the most robust signal. However, RHOH and THBS1 were also among the top genes showing a positive association with AR (RHOH p-values: 1.2e−5/3.5e−6; THBS1 p-values: 1.2e−4/2.1e−11). On the opposite side (Figure 5C), NUPR1 (p-values: 1.5−6/4.6e−15) and LCE3D (p-values: 0.003/4e−12) showed the most significant negative associations. The bar plots showing the fraction of AR status (yes/no) by each expression quartile for the top negatively and positively associated genes revealed clear trends, with the differences in the fraction of AR diagnoses between the 1st and 4th quartiles ranging between 0.15 and 0.33. (Figure 5D).

Mediation by methylation and/or gene expression

As a final step, to identify CpGs and/or genes whose respective methylation and/or expression mediate the effects of air toxics on AR (Step 5 in Figure 1), we merged the association results obtained in the upstream analyses to identify 4,755,501 potential paths. Among these, 231 paths with a positive combined effect (increased AR odds) were selected for mediation analysis. Applying a p-value<0.01 threshold, we identified two significant mediation effects: the mediation of the effect of phosphine on AR via methylation of a CpG located in the third intron of THBS1 and the expression of that gene (Figure 6A; coef=0.01, p-value=0.02; THBS1 path), and the mediation of the effect of acrylic acid on AR via expression of RHOH (Figure 6B; coef=0.02, p-value=0.02, RHOH path). The indirect effects explained 5.2% (THBS1 path) and 21.2% (RHOH path) of the total air toxic effects on AR. Sensitivity analyses performed to evaluate the effects of participant’s race, asthma status, and atopic dermatitis status on the models by adding them as covariates yielded still significant results for all indirect effects reported (Table S2), highlighting the robustness of these results.

Figure 6: Significant paths identified in the multiple causal mediation analysis.

Figure 6:

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(A) Positive effect of phosphine concentration on AR mediated by an increase in methylation of a CpG in chromosome 15 (array ID: cg04827020) that leads to an increase in the expression of THBS1, which in turn is associated with increased odds of AR. (B) Positive effect of acrylic acid on AR mediated by the expression of RHOH, whose expression is increased by acrylic acid and is also positively associated with AR. While multiple serial mediation by CpG methylation and gene expression was also tested for this path, mediation by only gene expression yielded a lower p-value here.

DISCUSSION

Early-life exposure to air pollutants has been linked to the development of AR, and exposure to air pollutants is associated with AR exacerbations9–14. However, previous studies have focused on a limited number of pollutants such as the criteria air pollutants ozone, nitrogen dioxide, and particulate matter17, 18, and the molecular mechanisms by which these and other pollutants influence the development of AR is poorly understood. This study expands the research of air pollution and AR to 110 previously unexplored air toxics monitored as hazardous air pollutants by the US EPA. It also shed light on the biological mechanisms by which early-life exposure to these toxics could influence the development of AR by exploring the involvement of nasal DNA methylation and nasal gene expression.

The results of this study, summarized in Figure 7, revealed several air toxics, including benzylchloride, antimony compounds, phosphine, and acrylic acid, that are associated with increased odds of AR. Other air toxics, such as N,N-dimethylaniline and hexachloropentadiene, while not directly associated with AR in this cohort, are associated with the DNA methylation levels of genes that have important functions in the nasal mucosa, such as ARMC9, a regulator of ciliary function in the airway55, 56, and TLE4, a regulator of myeloid immune cell function57, 58. We also identified an increase in the DNA methylation and expression levels of THBS1 with higher phosphine exposures. THBS1 is a secreted homotrimeric protein that is known to participate in the activation of latent TGF-β159–62, which has been shown to promote the differentiation of effector Th2, Th9 and Th17 cells but not of Treg cells in the airway63, thus tilting the balance of effector-regulator T cell phenotypes towards a pro-inflammatory environment. This has been observed to result in a potentiation of the allergic inflammatory response to mite allergens in the airway64 and is aligned with the observed decrease in the number of regulatory CD4+ CD25+ T cells in nasal tissues associated to the immune dysregulation in allergic rhinitis65. On the other hand, acrylic acid is associated with an increase in the expression of RHOH, not via DNA methylation, but via some other mechanism beyond the scope of this study. RHOH is an atypical member of the Rho family of small GTPases necessary for the activation and degranulation of mast cells upon stimulation via FcεR1 or the IL33 receptor66, 67, pointing at the dysregulation of granulocyte function observed in allergic diseases68 as one mechanism through which air toxics contribute to AR. These results indicate that phosphine and acrylic acid might contribute to an AR phenotype by increasing the expression of genes involved in promoting effector T cell and mast cell responses to allergens.

Figure 7: Molecular context for the main findings of this study.

Figure 7:

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Four air toxics (benzyl chloride, antimony compounds, phosphine, and acrylic acid) are associated with increased odds of AR. Two additional air toxics (N,N-dimethylaniline and hexachloropentadiene) are not directly associated with AR but affect nasal mucosal biology by increasing or decreasing the methylation of genes involved in ciliary function and regulation of myeloid cells. Phosphine contributes to a pro-inflammatory environment by promoting DNA methylation of THBS1, resulting in increased gene expression that activates TGF-β1, which is known to promote effector T cell phenotypes in the airway. Acrylic acid increases the expression of RHOH, a gene known to be necessary for FCεR1 and IL33 receptor-mediated mast cell activation and degranulation. We also observed that additional genes known to be present in nasal secretions (e.g. PRB1, PRR4 and STATH) and pro-inflammatory mediators (e.g. CD69 and CCL4) show a positive causal relationship with AR. In contrast, genes previously known to be associated with viral airway infection (e.g. ZC3HAV1 and NUPR1) are negatively associated with AR.

Our results also shed light on other aspects of nasal biology and the changes associated with allergic inflammation. The analysis exploring cis-CpG effects on gene expression revealed that one of the primary functions of the inhibition of gene expression via DNA methylation is the regulation of the immune system, as highlighted by the list of most significant genes, including MNDA, FCGR2A, NCF1 and ARHGAP25, all of which are known to play important roles in immunity and immune-mediated diseases69–76. The importance of the immune compartment in nasal biology and AR is further revealed by the positive causal role we observed for genes like CD69 and CCL4, important pro-inflammatory mediators77–80, and the negative causal association observed for genes like ZC3HAV1 and NUPR181–84, involved in antiviral responses. These observations emphasize the already known role of pro-inflammatory genes in AR and point at effective antiviral responses as protective. Finally, the strong positive association we observed for secreted proteins with a mucosal innate defense function such as PRR4, PRB1 and STATH, all known as salivary proteins but also observed in nasal secretions85–87, reflect the importance of the increase in nasal discharge that clinically characterizes AR, which could be causally involved by increasing the likelihood of a diagnosis. Increase in the secretion of these proteins, as well as the significant effects of N,N-dimethylaniline and hexachloropentadiene exposure on the methylation of ARMC9, a gene involved in ciliary function, highlight the potential central role that the epithelial barrier plays in response to environmental exposures in allergic rhinitis, consistent with the epithelial barrier theory of allergic diseases88.

There are some limitations to this study. Many of the air toxics had correlated concentrations, posing a challenge to the disentanglement of the effect of each toxic, as collinearity results in a loss of power. The selection of the most informative air toxics by elastic-net ameliorated this problem but without complete elimination. Thus, while we are confident that the significant associations reported are robust, our approach might have missed some additional associations. Second, there were variable time intervals between NATA assessment year and participants’ birth dates, leading to heterogeneity in capturing early-life air toxic exposure. Future studies with larger sample sizes should explore the influence of timing of exposure on the effect of these air toxics. Third, it is possible that participants’ residences at the time of study recruitment could differ from their residence at birth. Finally, allergic rhinitis is known to be a heterogeneous condition89, an aspect we did not explore in this study due to sample size limitations. Future studies in larger samples should explore how the effects of air pollution on allergic rhinitis and nasal mucosal biology might vary across different AR subphenotypes based on immunophenotyping89 or different disease trajectories over time90.

In summary, the results of our study greatly expand the range of air pollutants associated with AR and shed light onto potential biological mechanisms underlying those relationships by revealing alterations in DNA methylation and gene expression in the nasal mucosa associated with both air toxic exposure in early-life and AR. These findings deepen our understanding of early-life air pollutants involved in promoting AR, providing information to design targeted air pollution reduction strategies aimed at alleviating the burden of this condition. It has also revealed potential therapeutic targets for air toxic-promoted AR, such as THBS1 and RHOH. Future studies in larger samples with both urban and rural participants, as well as functional studies on animal, cellular, and organoid models will be necessary to dig deeper into the effects of these air toxics on nasal mucosal biology and AR.

Study approval

This study was approved by the Mount Sinai Institutional Review Board. All participants or the parents/legal guardians of minors provided written informed consent. The study was carried out according to the principles of the Declaration of Helsinki.

Supplementary Material

Supinfo

Table S1: List of the 110 air toxics after quantization with the Chemical Abstract Service (CAS) ID numbers, chemical names as listed by the EPA, and the names used in the study. Average concentration levels per quartile are also shown.

Table S2: Sensitivity analyses performed to evaluate the effect of adding participants’ race, asthma status, and atopic dermatitis status as covariates on the significant indirect effects identified in mediation analyses.

Figure S1. Detailed experimental and analytical workflow.

Figure S2. Effects of CpG methylation on the expression of neighboring genes. To identify a data-driven genomic window for examining CpGs that could affect area genes, we first built regression models to explore the effects of individual CpGs on the expression of neighboring genes. For each gene, all CpGs within a 1 Mb up- and downstream of the Transcription Start Site (TSS) were examined. (A) Average −log10p-value obtained for CpGs in a 10 Kb sliding window separately for positive effects (red) and negative effects (blue). X-axis indicates genomic distance of the midpoint of a given 10 Kb window to the TSS, either upstream (−1 Mb to 0) or downstream (0 to 1 Mb). (B) To reduce noise, a rolling average of five peaks was then calculated to identify the genomic window of interest based on that smoothed data. The genomic window identified was −2.3 Kb to 25.3 Kb.

Funding:

This study was supported by the National Institutes of Health R01 AI118833. The work was also supported in part through computational resources provided by Scientific Computing and Data at the Icahn School of Medicine at Mount Sinai supported by the National Center for Advancing Translational Sciences UL1TR004419 and National Institutes of Health S10OD026880 and S10OD030463.

Footnotes

Conflict of interest

The authors have declared that no conflict of interest exists.

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Supplementary Materials

Supinfo

Table S1: List of the 110 air toxics after quantization with the Chemical Abstract Service (CAS) ID numbers, chemical names as listed by the EPA, and the names used in the study. Average concentration levels per quartile are also shown.

Table S2: Sensitivity analyses performed to evaluate the effect of adding participants’ race, asthma status, and atopic dermatitis status as covariates on the significant indirect effects identified in mediation analyses.

Figure S1. Detailed experimental and analytical workflow.

Figure S2. Effects of CpG methylation on the expression of neighboring genes. To identify a data-driven genomic window for examining CpGs that could affect area genes, we first built regression models to explore the effects of individual CpGs on the expression of neighboring genes. For each gene, all CpGs within a 1 Mb up- and downstream of the Transcription Start Site (TSS) were examined. (A) Average −log10p-value obtained for CpGs in a 10 Kb sliding window separately for positive effects (red) and negative effects (blue). X-axis indicates genomic distance of the midpoint of a given 10 Kb window to the TSS, either upstream (−1 Mb to 0) or downstream (0 to 1 Mb). (B) To reduce noise, a rolling average of five peaks was then calculated to identify the genomic window of interest based on that smoothed data. The genomic window identified was −2.3 Kb to 25.3 Kb.

NIHMS1995316-supplement-Supinfo.pdf (460.6KB, pdf)

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