The DNA repair transcriptome in severe COPD

Abstract

Inadequate DNA repair is implicated in the pathogenesis of chronic obstructive pulmonary disease (COPD). However, the mechanisms that underlie inadequate DNA repair in COPD are poorly understood. We applied an integrative genomic approach to identify DNA repair genes and pathways associated with COPD severity.We measured the transcriptomic changes of 419 genes involved in DNA repair and DNA damage tolerance that occur with severe COPD in three independent cohorts (n=1129). Differentially expressed genes were confirmed with RNA sequencing and used for patient clustering. Clinical and genome-wide transcriptomic differences were assessed following cluster identification. We complemented this analysis by performing gene set enrichment analysis, Z-score and weighted gene correlation network analysis to identify transcriptomic patterns of DNA repair pathways associated with clinical measurements of COPD severity.We found 15 genes involved in DNA repair and DNA damage tolerance to be differentially expressed in severe COPD. K-means clustering of COPD cases based on this 15-gene signature identified three patient clusters with significant differences in clinical characteristics and global transcriptomic profiles. Increasing COPD severity was associated with downregulation of the nucleotide excision repair pathway.Systematic analysis of the lung tissue transcriptome of individuals with severe COPD identified DNA repair responses associated with disease severity that may underlie COPD pathogenesis.

FIGURE 1

Study workflow. COPD: chronic obstructive pulmonary disease; SAM: significance analysis of microarrays; LGRC: Lung Genomics Research Consortium; OSU: Ohio State University; Lung eQTL: Lung expression quantitative trait loci; GSEA: gene set enrichment analysis; WGCNA: weighted gene correlation network analysis.

FIGURE 2

K-means clustering of patients based on 15 gene consensus signature. Yellow denotes an increase over the sample mean, and purple denotes a decrease over sample mean.

FIGURE 3

Clinical characteristics of chronic obstructive pulmonary disease by cluster. a) Box and whiskers of percentage emphysema by cluster. b) Box and whiskers of BODE index (body mass index, airflow obstruction, dyspnoea and exercise capacity) by cluster. c) Box and whiskers of forced expiratory volume in 1 s (FEV₁) % predicted by cluster. d) Box and whiskers of St George’s Respiratory Questionnaire (SGRQ) score by cluster. e) Box and whiskers of diffusing capacity of the lung for carbon monoxide (Dlco) % predicted by cluster.f) Box and whiskers of 6-min walk distance (6MWD) by cluster. *: p<0.05; **: p<0.005; ***: p<0.0005.

FIGURE 4

Overrepresented genes in the top 50 enriched pathways. a) Comparison between Cluster 2 and controls. b) Comparison between Cluster 3 and controls. Red denotes upregulated genes. Blue denotes downregulated genes. Lines represent curated associations between genes.

FIGURE 5

Immunohistochemistry for DDB2, NEIL1 and XRCC4. Immunohistochemistry demonstrating localisation and staining intensity for DDB2, NEIL1 and XRCC4 (identified by brown chromogen] performed on lung tissue samples from patients in Cluster 1 and Cluster 3. Nuclear staining appeared particularly localised to bronchiole epithelial cells (arrows], although other cells also demonstrated nuclear staining. Images acquired using a 40× objective lens.

FIGURE 6

The nucleotide excision repair (NER) pathway is downregulated in severe chronic obstructive pulmonary disease. a-d) Enrichment plots from gene set enrichment analysis. The enrichment plots contain profiles of the running enrichment scores (ES) and the barcode plot indicates the position of the genes in each gene set; red represents Spearman correlations with more severe disease, blue represents Spearman correlations with less severe disease. False discovery rates (FDRs) for NER gene set enrichment are reported for a) forced expiratory volume in 1 s (FEV₁), b) percentage emphysema, c) diffusing capacity of the lung for carbon monoxide (Dlco) % pred and d) BODE index (body mass index, airflow obstruction, dyspnoea and exercise capacity). e-h) NER pathway Z-score coefficients for each patient plotted against e) FEV₁ % pred,percentage emphysema, g) Dlco % pred and h) BODE index.

FIGURE 7

Weighted gene co-expression network analysis (WGCNA). WGCNA identified 40 gene modules as demonstrated in this WGCNA heatmap. n represents the number of genes within each module. Positive correlations are red, and negative correlations are blue. The green, sky blue, and turquoise were the three modules most positively correlated with indices of decreased disease severity, and the yellow, salmon, and light yellow were the three modules most negatively correlated with indices of increased disease severity. The most highly enriched process is indicated for each of the top six modules, including the nucleotide excision repair (NER)-base excision repair (BER) process in the yellow module. IL-6: interleukin 6; BODE: body mass index, airflow obstruction, dyspnoea and exercise capacity; FEV₁: forced expiratory volume in 1 s; SGRQ: St George’s Respiratory Questionnaire; Dlco: diffusing capacity of the lung for carbon monoxide; 6MWD: 6-min walk distance.