Delineation of a gene network underlying the pulmonary response to oxidative stress in asthma

Abstract

Cigarette smoke exposure induces a respiratory epithelial response that is mediated in part by oxidative stress. The contribution of oxidative stress to cigarette smoke-induced responses in asthmatic respiratory epithelium is not well understood. We sought to increase this understanding by employing data integration and systems biology approaches to publicly available microarray data deposited over the last several years. In this study, we analyzed 14 publicly available asthma- or tobacco-relevant data series and found 4 (2 mice and 2 human) that fulfilled adequate signal/noise thresholds using unsupervised clustering and F test statistics. Using significance filters and a 4-way Venn diagram approach, we identified 26 overlapping genes in the epithelial transcriptional stress response to cigarette smoke and asthma. This test set corresponded to a 26-member gene/protein network containing 18 members that were highly regulated in a fifth data series of direct lung oxidative stress. Of those network members, 2 stood out (ie, tissue inhibitor of metalloproteinase 1 and thrombospondin 1) owing to central location within the network and marked up-regulation sustained at later times in response to oxidative stress. These analyses identified key relationships and primary hypothetical targets for future studies of cigarette smoke-induced oxidative stress in asthma.

Figure 1

Venn diagram showing gene expression data integration and interpretation of asthma- and tobacco-relevant data series. As described in detail in the text, the four selected data series identified using appropriate unsupervised clustering and F≥0.5 were used in a four-way Venn diagram to generate a 26 gene (i.e. 30 transcripts) shared response set that showed time- and/or dose-related responses to asthma-relevant challenges. The hypothetical shared response set was comprised of the five Venn diagram areas that are designated by a bold N and a preceding superscript numeral 1–5. These areas represent overlap of at least three of the four data series.

Figure 2

A network of commonly expressed genes in asthmatic and cigarette smoke-exposed lung. Ingenuity Pathways Analysis software was used to explore the prominent molecular networks represented in the 30 core transcripts (Figure 1, Table 2). The refined (i.e. groups exploded, indirect relationships and disconnected/peripheral non-focus members removed) top-scoring network contains 26 directly linked members centered on the focus genes for tissue inhibitor of metalloproteinases (TIMP)-1, connective tissue growth factor (CTGF), endothelial PAS domain protein 1 (EPAS1) and insulin-like growth factor binding protein 3 (IGFBP3). The genes that are present in the 30 core transcript group (i.e. focus genes) are colored dark gray.

Figure 3

Time-series hierarchical cluster of phosgene-induced oxidative stress in mouse lung over 72 hours (GSE2565). Mice were exposed to phosgene at time 0 and sacrificed at eight post-exposure time points (0, 0.5, 1, 4, 8, 12, 24, 48, or 72 hours). Lung tissue was isolated and RNA extracted for whole genome expression profiling with the Affymetrix MOE430A microarray platform . A time-series gene cluster was generated in GeneSpring GX10 for the 30 of 42 transcripts (corresponding to 26 network genes) that showed statistically significant (corrected p≤0.05) up- (red) or down- (blue) regulation over time. Inspection of these 30 transcripts showed coordinated expression characterized by several distinct patterns, including early, sustained, and late up- or down-regulation.

Figure 4

Highly up- or down- regulated genes in phosgene-induced oxidative stress in mouse lung. Eighteen genes (Figure 3) showed ≥1.5-fold up- or down-regulation at one or more time points relative to time=0 in GSE2565. The graph is split into up-regulated (top) and down-regulated (bottom) genes for clarity. Expression shown for genes with multiple probe sets reflects averaging within each time point.