Metabolomics and gene expression profiles in association with air pollution exposure mixtures among young adults with asthma

Abstract

BACKGROUND AND AIM: Air pollution exposure has been shown to increase severity of various disease outcomes including asthma control, however, the underlying biological mechanisms are not well established. In this study, we aim to leverage transcriptomics and metabolomics to identify biological mechanisms of air pollutants exposure. METHODS: In this cross-sectional study, 102 young adults with childhood asthma history, who were participants of Southern California Children’s Health Study, were enrolled in 2012. Whole blood gene expression data was measured with Illumina HumanHT-12 v4 Expression BeadChip, with 20,869 expression signatures included in the analysis. Serum untargeted metabolomics were analyzed using the Metabolon UPLC-MS/MS, and 937 metabolites were confirmed for all samples. Participants’ regional (NO2, O3, PM10, PM2.5) and near-roadway air pollution exposure were based on nearby central monitoring and modelling during one-month and one-year before the study visit. Multi-omics network analysis (R package ‘xMWAS’) was conducted to identify subnetworks that link metabolomics and transcriptomics to specific air pollutants exposure. Joint-pathway analysis based on MetaboAnalyst (McGill University) was performed to identify pathways associated with air pollutants in each subnetwork. Key covariates such as SES, ethnicity, sex, and smoking were adjusted in all analyses. RESULTS:Network analysis found that 357 gene markers, 92 metabolites, and one-year and one-month exposures to 8 air pollutants were clustered into 9 subnetworks. For the subnetwork including PM10 and one-month O3, gene expression markers were enriched in pathways for insulin secretion, antigen processing and presentation. Another subnetwork including PM2.5 and NO2 exposures was inked to altered metabolism of amino acids such as arginine, serine, and aspartic acid. One-year O3 exposure was clustered with metabolites and genes involved in glycerophospholipid metabolism and N-Glycan biosynthesis. CONCLUSIONS:This study demonstrates that exposure to various air pollutants may induce changes in gene expression and metabolomics in individuals with asthma, potentially affecting disease prognosis. KEYWORDS: Air Pollution, Metabolomics, Transcriptomics, Network Analysis, Pathway Analysis