UC Riverside

Particulate Matter (PM) is a complex mixture of organic and inorganic chemicals, which can trigger systemic health effects including chronic obstructive pulmonary disease (COPD), lung cancer, cardiovascular dysfunction, obesity, and diabetes. The exact mechanisms by which disease progression occurs, however, remain unclear. Therefore, proper chemical characterization of PM and their effects on the development of diseases are required to fully understand PM-induced health effects. In this dissertation, we investigated the toxicological responses and disease progression pathways through transcriptomic analysis to probe the potential molecular mechanisms leading to PM-induced health outcomes. First, the toxicological potency of PM emitted from a modern vehicle equipped with a gasoline direct injection (GDI) engine was examined using eight different fuel blends with varying aromatic hydrocarbon and ethanol contents. Second, the potential health impacts of dimethyl selenide (DMSe)-derived secondary organic aerosols (SOA) were investigated by RNA sequencing (RNA-seq). Third, the lncRNA-mRNA coexpression analysis was conducted to investigate the role of lncRNAs in altered gene expression following DMSe-SOA exposure. Results from these studies indicate that gasoline exhaust particles from eight different fuel blends imbalance the gene expression related to oxidative stress and inflammation. RNA-seq data reveal major biological pathways perturbed by DMSe-derived SOA associated with elevated genotoxicity, DNA damage, and p53-mediated stress responses, as well as downregulated glycolysis and interleukin IL-4/IL-13 signaling that regulate diabetogenesis and allergic airway inflammation, respectively. In addition, we found that four trans-acting lncRNAs known to be associated with human carcinogenesis, including PINCR, PICART1, DLGAP1-AS2, and LINC01629, also differentially expressed in human airway epithelial cells treated with DMSe-derived SOA. Overall, using toxicogenomic approaches, this dissertation contributes to an improved understanding of potential biomarkers in early biological responses to PM exposure derived from traffic and natural sources.