Patterns of gene expression in human airway epithelial cells

RNAseq analysis of bronchial epithelial cells to identify COPD-associated genes and SNPs

Background

There is a need for more powerful methods to identify low-effect SNPs that contribute to hereditary COPD pathogenesis. We hypothesized that SNPs contributing to COPD risk through cis-regulatory effects are enriched in genes comprised by bronchial epithelial cell (BEC) expression patterns associated with COPD.

Methods

To test this hypothesis, normal BEC specimens were obtained by bronchoscopy from 60 subjects: 30 subjects with COPD defined by spirometry (FEV1/FVC < 0.7, FEV1% < 80%), and 30 non-COPD controls. Targeted next generation sequencing was used to measure total and allele-specific expression of 35 genes in genome maintenance (GM) genes pathways linked to COPD pathogenesis, including seven TP53 and CEBP transcription factor family members. Shrinkage linear discriminant analysis (SLDA) was used to identify COPD-classification models. COPD GWAS were queried for putative cis-regulatory SNPs in the targeted genes.

Results

On a network basis, TP53 and CEBP transcription factor pathway gene pair network connections, including key DNA repair gene ERCC5, were significantly different in COPD subjects (e.g., Wilcoxon rank sum test for closeness, p-value = 5.0E-11). ERCC5 SNP rs4150275 association with chronic bronchitis was identified in a set of Lung Health Study (LHS) COPD GWAS SNPs restricted to those in putative regulatory regions within the targeted genes, and this association was validated in the COPDgene non-hispanic white (NHW) GWAS. ERCC5 SNP rs4150275 is linked (D’ = 1) to ERCC5 SNP rs17655 which displayed differential allelic expression (DAE) in BEC and is an expression quantitative trait locus (eQTL) in lung tissue (p = 3.2E-7). SNPs in linkage (D’ = 1) with rs17655 were predicted to alter miRNA binding (rs873601). A classifier model that comprised gene features CAT, CEBPG, GPX1, KEAP1, TP73, and XPA had pooled 10-fold cross-validation receiver operator characteristic area under the curve of 75.4% (95% CI: 66.3%–89.3%). The prevalence of DAE was higher than expected (p = 0.0023) in the classifier genes.

Conclusions

GM genes comprised by COPD-associated BEC expression patterns were enriched for SNPs with cis-regulatory function, including a putative cis-rSNP in ERCC5 that was associated with COPD risk. These findings support additional total and allele-specific expression analysis of gene pathways with high prior likelihood for involvement in COPD pathogenesis.

Electronic supplementary material

The online version of this article (10.1186/s12890-018-0603-y) contains supplementary material, which is available to authorized users.

A lung cancer risk classifier comprising genome maintenance genes measured in normal bronchial epithelial cells

Background

Annual low dose CT (LDCT) screening of individuals at high demographic risk reduces lung cancer mortality by more than 20%. However, subjects selected for screening based on demographic criteria typically have less than a 10% lifetime risk for lung cancer. Thus, there is need for a biomarker that better stratifies subjects for LDCT screening. Toward this goal, we previously reported a lung cancer risk test (LCRT) biomarker comprising 14 genome-maintenance (GM) pathway genes measured in normal bronchial epithelial cells (NBEC) that accurately classified cancer (CA) from non-cancer (NC) subjects. The primary goal of the studies reported here was to optimize the LCRT biomarker for high specificity and ease of clinical implementation.

Methods

Targeted competitive multiplex PCR amplicon libraries were prepared for next generation sequencing (NGS) analysis of transcript abundance at 68 sites among 33 GM target genes in NBEC specimens collected from a retrospective cohort of 120 subjects, including 61 CA cases and 59 NC controls. Genes were selected for analysis based on contribution to the previously reported LCRT biomarker and/or prior evidence for association with lung cancer risk. Linear discriminant analysis was used to identify the most accurate classifier suitable to stratify subjects for screening.

Results

After cross-validation, a model comprising expression values from 12 genes (CDKN1A, E2F1, ERCC1, ERCC4, ERCC5, GPX1, GSTP1, KEAP1, RB1, TP53, TP63, and XRCC1) and demographic factors age, gender, and pack-years smoking, had Receiver Operator Characteristic area under the curve (ROC AUC) of 0.975 (95% CI: 0.96–0.99). The overall classification accuracy was 93% (95% CI 88%–98%) with sensitivity 93.1%, specificity 92.9%, positive predictive value 93.1% and negative predictive value 93%. The ROC AUC for this classifier was significantly better (p < 0.0001) than the best model comprising demographic features alone.

Conclusions

The LCRT biomarker reported here displayed high accuracy and ease of implementation on a high throughput, quality-controlled targeted NGS platform. As such, it is optimized for clinical validation in specimens from the ongoing LCRT blinded prospective cohort study. Following validation, the biomarker is expected to have clinical utility by better stratifying subjects for annual lung cancer screening compared to current demographic criteria alone.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-017-3287-4) contains supplementary material, which is available to authorized users.

Comparison of proteomic and transcriptomic profiles in the bronchial airway epithelium of current and never smokers

BACKGROUND

Although prior studies have demonstrated a smoking-induced field of molecular injury throughout the lung and airway, the impact of smoking on the airway epithelial proteome and its relationship to smoking-related changes in the airway transcriptome are unclear.

METHODOLOGY/PRINCIPAL FINDINGS

Airway epithelial cells were obtained from never (n = 5) and current (n = 5) smokers by brushing the mainstem bronchus. Proteins were separated by one dimensional polyacrylamide gel electrophoresis (1D-PAGE). After in-gel digestion, tryptic peptides were processed via liquid chromatography/ tandem mass spectrometry (LC-MS/MS) and proteins identified. RNA from the same samples was hybridized to HG-U133A microarrays. Protein detection was compared to RNA expression in the current study and a previously published airway dataset. The functional properties of many of the 197 proteins detected in a majority of never smokers were similar to those observed in the never smoker airway transcriptome. LC-MS/MS identified 23 proteins that differed between never and current smokers. Western blotting confirmed the smoking-related changes of PLUNC, P4HB1, and uteroglobin protein levels. Many of the proteins differentially detected between never and current smokers were also altered at the level of gene expression in this cohort and the prior airway transcriptome study. There was a strong association between protein detection and expression of its corresponding transcript within the same sample, with 86% of the proteins detected by LC-MS/MS having a detectable corresponding probeset by microarray in the same sample. Forty-one proteins identified by LC-MS/MS lacked detectable expression of a corresponding transcript and were detected in

CONCLUSIONS/SIGNIFICANCE

1D-PAGE coupled with LC-MS/MS effectively profiled the airway epithelium proteome and identified proteins expressed at different levels as a result of cigarette smoke exposure. While there was a strong correlation between protein and transcript detection within the same sample, we also identified proteins whose corresponding transcripts were not detected by microarray. This noninvasive approach to proteomic profiling of airway epithelium may provide additional insights into the field of injury induced by tobacco exposure.

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MESH Headings

• Adult
• Blotting, Western
• Bronchi/cytology
• Bronchi/drug effects
• Bronchi/metabolism
• Chromatography, Liquid/methods
• Electrophoresis, Polyacrylamide Gel/methods
• Epithelial Cells/cytology
• Epithelial Cells/drug effects
• Epithelial Cells/physiology
• Female
• Gene Expression Profiling
• Glycoproteins/metabolism
• Humans
• Male
• Microarray Analysis
• Middle Aged
• Molecular Sequence Data
• Phosphoproteins/metabolism
• Procollagen-Proline Dioxygenase/metabolism
• Protein Disulfide-Isomerases/metabolism
• Proteome/analysis
• Respiratory Mucosa/cytology
• Respiratory Mucosa/drug effects
• Smoking/adverse effects
• Tandem Mass Spectrometry/methods
• Nicotiana
• Uteroglobin/metabolism
• Young Adult

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Grants

• R01 CA124640 NCI NIH HHS
• U01 ES016035 NIEHS NIH HHS
• R01CA124640 NCI NIH HHS
• U01ES016035 NIEHS NIH HHS

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Affiliation(s)

Katrina Steiling The Pulmonary Center, Boston University Medical Center, Boston, Massachusetts, United States of America.
Aran Y Kadar
Agnes Bergerat
James Flanigon
Sriram Sridhar
Vishal Shah
Q Rushdy Ahmad
Jerome S Brody
Marc E Lenburg
Martin Steffen
Avrum Spira

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