title: Characterizing the impact of smoking and lung cancer on the airway transcriptome using RNA-Seq.
abstract: Cigarette smoke creates a molecular field of injury in epithelial cells that line the respiratory tract. We hypothesized that transcriptome sequencing (RNA-Seq) will enhance our understanding of the field of molecular injury in response to tobacco smoke exposure and lung cancer pathogenesis by identifying gene expression differences not interrogated or accurately measured by microarrays. We sequenced the high-molecular-weight fraction of total RNA (>200 nt) from pooled bronchial airway epithelial cell brushings (n = 3 patients per pool) obtained during bronchoscopy from healthy never smoker (NS) and current smoker (S) volunteers and smokers with (C) and without (NC) lung cancer undergoing lung nodule resection surgery. RNA-Seq libraries were prepared using 2 distinct approaches, one capable of capturing non-polyadenylated RNA (the prototype NuGEN Ovation RNA-Seq protocol) and the other designed to measure only polyadenylated RNA (the standard Illumina mRNA-Seq protocol) followed by sequencing generating approximately 29 million 36 nt reads per pool and approximately 22 million 75 nt paired-end reads per pool, respectively. The NuGEN protocol captured additional transcripts not detected by the Illumina protocol at the expense of reduced coverage of polyadenylated transcripts, while longer read lengths and a paired-end sequencing strategy significantly improved the number of reads that could be aligned to the genome. The aligned reads derived from the two complementary protocols were used to define the compendium of genes expressed in the airway epithelium (n = 20,573 genes). Pathways related to the metabolism of xenobiotics by cytochrome P450, retinol metabolism, and oxidoreductase activity were enriched among genes differentially expressed in smokers, whereas chemokine signaling pathways, cytokine-cytokine receptor interactions, and cell adhesion molecules were enriched among genes differentially expressed in smokers with lung cancer. There was a significant correlation between the RNA-Seq gene expression data and Affymetrix microarray data generated from the same samples (P < 0.001); however, the RNA-Seq data detected additional smoking- and cancer-related transcripts whose expression was were either not interrogated by or was not found to be significantly altered when using microarrays, including smoking-related changes in the inflammatory genes S100A8 and S100A9 and cancer-related changes in MUC5AC and secretoglobin (SCGB3A1). Quantitative real-time PCR confirmed differential expression of select genes and non-coding RNAs within individual samples. These results demonstrate that transcriptome sequencing has the potential to provide new insights into the biology of the airway field of injury associated with smoking and lung cancer. The measurement of both coding and non-coding transcripts by RNA-Seq has the potential to help elucidate mechanisms of response to tobacco smoke and to identify additional biomarkers of lung cancer risk and novel targets for chemoprevention.
authors.affiliation: The Pulmonary Center, Department of Medicine, Boston University Medical Center, Boston University, Massachusetts, USA. jbeane@bu.edu
authors.forename: Jennifer
authors.initials: J
authors.lastname: Beane
authors.forename: Jessica
authors.initials: J
authors.lastname: Vick
authors.forename: Frank
authors.initials: F
authors.lastname: Schembri
authors.forename: Christina
authors.initials: C
authors.lastname: Anderlind
authors.forename: Adam
authors.initials: A
authors.lastname: Gower
authors.forename: Joshua
authors.initials: J
authors.lastname: Campbell
authors.forename: Lingqi
authors.initials: L
authors.lastname: Luo
authors.forename: Xiao Hui
authors.initials: XH
authors.lastname: Zhang
authors.forename: Ji
authors.initials: J
authors.lastname: Xiao
authors.forename: Yuriy O
authors.initials: YO
authors.lastname: Alekseyev
authors.forename: Shenglong
authors.initials: S
authors.lastname: Wang
authors.forename: Shawn
authors.initials: S
authors.lastname: Levy
authors.forename: Pierre P
authors.initials: PP
authors.lastname: Massion
authors.forename: Marc
authors.initials: M
authors.lastname: Lenburg
authors.forename: Avrum
authors.initials: A
authors.lastname: Spira
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics*; D001980:Bronchi / Q000166:cytology / Q000187:drug effects / Q000378:metabolism; D004847:Epithelial Cells / Q000187:drug effects / Q000378:metabolism / Q000473:pathology; D020869:Gene Expression Profiling; D006801:Humans; D008175:Lung Neoplasms / Q000209:etiology* / Q000378:metabolism / Q000473:pathology; D020411:Oligonucleotide Array Sequence Analysis; D012333:RNA, Messenger / Q000235:genetics*; D012334:RNA, Neoplasm / Q000235:genetics; D020545:Respiratory Mucosa / Q000166:cytology / Q000187:drug effects / Q000378:metabolism; D020133:Reverse Transcriptase Polymerase Chain Reaction; D012907:Smoking / Q000009:adverse effects*
chemical_list: D014408:Biomarkers, Tumor; D012333:RNA, Messenger; D012334:RNA, Neoplasm