ABSTRACT
Diesel exhaust particles (DEP) affect health adversely. Our previous studies revealed that DEP extracts up-regulated expression of genes related to drug metabolism, antioxidant enzymes, cell cycle/apoptosis and coagulation, and in addition, n-hexane soluble fraction (n-HSF) of DEP extracts contains aliphatic and polycyclic aromatic hydrocarbons, and n-hexane insoluble fraction (n-HISF) contains oxygenated compounds and has strong oxidative property. However, the relationship between the properties of chemicals in DEP extracts and the gene expression has not been fully elucidated. Here, we used a microarray analysis to identify and characterize genes whose expression is regulated by exposure to fractions of DEP extracts. A dichloromethane-soluble fraction (DMSF) of DEP was further fractionated into n-HSF and n-HISF. We exposed rat alveolar epithelial (SV40T2) cells to these fractions (30microg/ml) for 6h and identified regulated genes. DMSF predominantly up-regulated genes associated with drug metabolism (Cyp1a1, Gsta3), oxidative stress response (HO-1, Srxn1) and cell cycle/apoptosis (Okl38). Genes up-regulated by n-HSF were mainly associated with drug metabolism (Cyp1a1, Gsta3). The genes up-regulated by n-HISF included antioxidant enzymes (HO-1, Srxn1); genes response to cell damage, such as those functioning in cell cycle regulation or apoptosis (Okl38); and genes in coagulation pathways. Our present results suggested that n-HSF and n-HISF regulated characteristic genes which respond to chemical properties of each fraction.
Subject(s)
Epithelial Cells/drug effects , Pulmonary Alveoli/drug effects , Up-Regulation/drug effects , Vehicle Emissions/toxicity , Animals , Cell Line , Cytochrome P-450 CYP1A1/drug effects , Cytochrome P-450 CYP1A1/genetics , Epithelial Cells/metabolism , Glutathione Transferase/drug effects , Glutathione Transferase/genetics , Heme Oxygenase-1/drug effects , Heme Oxygenase-1/genetics , Hexanes/chemistry , Methylene Chloride/chemistry , Oligonucleotide Array Sequence Analysis , Oxidoreductases Acting on Sulfur Group Donors/drug effects , Oxidoreductases Acting on Sulfur Group Donors/genetics , Proteins/drug effects , Proteins/genetics , Pulmonary Alveoli/metabolism , RatsABSTRACT
A variety of cardiovascular, neurological, and neoplastic conditions have been associated with oxidative stress, i.e., conditions under which levels of reactive oxygen species (ROS) are elevated over significant periods. Nuclear factor erythroid 2-related factor (Nrf2) regulates the transcription of several gene products involved in the protective response to oxidative stress. The transcriptional regulatory and signaling relationships linking gene products involved in the response to oxidative stress are, currently, only partially resolved. Microarray data constitute RNA abundance measures representing gene expression patterns. In some cases, these patterns can identify the molecular interactions of gene products. They can be, in effect, proxies for protein-protein and protein-DNA interactions. Traditional techniques used for clustering coregulated genes on high-throughput gene arrays are rarely capable of distinguishing between direct transcriptional regulatory interactions and indirect ones. In this study, newly developed information-theoretic algorithms that employ the concept of mutual information were used: the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE), and Context Likelihood of Relatedness (CLR). These algorithms captured dependencies in the gene expression profiles of the mouse lung, allowing the regulatory effect of Nrf2 in response to oxidative stress to be determined more precisely. In addition, a characterization of promoter sequences of Nrf2 regulatory targets was conducted using a Support Vector Machine classification algorithm to corroborate ARACNE and CLR predictions. Inferred networks were analyzed, compared, and integrated using the Collective Analysis of Biological Interaction Networks (CABIN) plug-in of Cytoscape. Using the two network inference algorithms and one machine learning algorithm, a number of both previously known and novel targets of Nrf2 transcriptional activation were identified. Genes predicted as novel Nrf2 targets include Atf1, Srxn1, Prnp, Sod2, Als2, Nfkbib, and Ppp1r15b. Furthermore, microarray and quantitative RT-PCR experiments following cigarette-smoke-induced oxidative stress in Nrf2(+/+) and Nrf2(-/-) mouse lung affirmed many of the predictions made. Several new potential feed-forward regulatory loops involving Nrf2, Nqo1, Srxn1, Prdx1, Als2, Atf1, Sod1, and Park7 were predicted. This work shows the promise of network inference algorithms operating on high-throughput gene expression data in identifying transcriptional regulatory and other signaling relationships implicated in mammalian disease.
