Transcriptomic studies of the airway field of injury associated with smoking-related lung disease

Abstract

The "field of injury" hypothesis proposes that exposure to an inhaled insult such as cigarette smoke elicits a common molecular response throughout the respiratory tract. This response can therefore be quantified in any airway tissue, including readily accessible epithelial cells in the bronchus, nose, and mouth. High-throughput technologies, such as whole-genome gene expression microarrays, can be employed to catalog the physiological consequences of such exposures in the airway epithelium. Pulmonary diseases such as chronic obstructive pulmonary disease, lung cancer, and asthma are also thought to be associated with a field of injury, and in patients with these diseases, airway epithelial cells can be a useful surrogate for diseased tissue that is often difficult to obtain. Global measurement of mRNA and microRNA expression in these cells can provide useful information about the molecular pathogenesis of such diseases and may be useful for diagnosis and for predicting prognosis and response to therapy. In this review, our aim is to summarize the history and state of the art of such "transcriptomic" studies in the human airway epithelium, especially in smoking and smoking-related lung diseases, and to highlight future directions for this field.

Figure 1.

Timeline of selected studies involving transcriptomic profiling of airway epithelial cells. Studies are shaded according to the main experimental condition(s) of interest. COPD = chronic obstructive pulmonary disease; PI3K = phosphoinositide-3-kinase; Th2 = helper T-cell type 2.

Figure 2.

Transcriptomic profiling reveals a common transcriptional response to cigarette smoke in epithelia throughout the airway. Four sets of gene expression profiles of epithelial cells from the small (10th−12th generation) or large (third generation) bronchus, trachea, or nose of healthy smokers were retrieved from the Gene Expression Omnibus (GEO). The Robust Multichip Average (RMA) (50) and an Entrez Gene–specific probeset mapping (51) were used to estimate the levels of 74 genes whose expression has previously been reported to be differentially expressed in response to cigarette smoke (4). Genes that are up- or down-regulated in smokers (red and blue sidebars, respectively) were clustered within separate groups using hierarchical clustering. In the same manner, samples from never-smokers (yellow) and current smokers (blue) were clustered separately. Samples from former smokers (green) were ordered (and shaded) according to time since smoking cessation. Gene (row) order was established using the small airway set (GSE11952) and maintained in the remaining heatmaps.