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

BMC Pulmonary Medicine

Paige F. S. Willey¹  Albert M. Levin²  Alexei Federov³  Tian Chen³  Xiaolu Zhang⁴  Shuhao Qiu⁵  Jiyoun Yeo⁶  Thomas M. Blomquist⁶  Peter J. Mazzone⁷  Steven D. Nathan⁸  David E. Midthun⁹  Patrick Nana-Sinkam^1,10  Youngsook Yoon^1,11  Sadik A. Khuder^1,11  Fadi Safi^1,11  Jeffrey R. Hammersley^1,11  James C. Willey^1,11  Diego A. Morales^1,11  Dawn-Alita R. Hernandez^1,11  Erin L. Crawford^1,11  Karanbir Padda^1,12  Ronald J. Wainz^1,13  Taylor J. Arend^1,14  Pierre P. Massion^1,15  Douglas A. Arenberg^1,16 

[1] American Enterprise Institute;Department of Biostatistics, Henry Ford Health System;Department of Mathematics and Statistics, The University of Toledo;Department of Medicine, The University of Toledo College of Medicine;Department of Medicine, The University of Toledo Medical Center;Department of Pathology, The University of Toledo College of Medicine;Department of Pulmonary Medicine, Cleveland Clinic;Department of Pulmonary Medicine, Inova Fairfax Hospital;Department of Pulmonary and Critical Care Medicine, Mayo Clinic;Division of Pulmonary Diseases and Critical Care Medicine, Virginia Commonwealth University;Division of Pulmonary and Critical Care Medicine, Department of Medicine, The University of Toledo College of Medicine;Emory University School of Medicine;The Toledo Hospital;The University of Toledo College of Medicine;Thoracic Program, Vanderbilt Ingram Cancer Center;University of Michigan;

关键词: COPD;    eQTL;    cis-regulation;    GWAS;    ERCC5;    CAT;   

DOI  :  10.1186/s12890-018-0603-y

来源: DOAJ

【 摘 要 】

Abstract 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.

【 授权许可】

Unknown