Gao Yi1,2, Min Liang2, Ming Li1,2, Xiangming Fang1, Jifang Liu2, Yuxiong Lai2, Jitao Chen1,2, Wenxia Yao2, Xiao Feng2, Chunyi Lin1, Xinke Zhou3,4, Zhaoyu Liu5,6. 1. Department of Respiratory Medicine, State Key Laboratory of Respiratory Disease, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510700, China. 2. Department of Center Laboratory, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510700, China. 3. Department of Respiratory Medicine, State Key Laboratory of Respiratory Disease, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510700, China. zxkstarr@126.com. 4. Department of Center Laboratory, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510700, China. zxkstarr@126.com. 5. Department of Respiratory Medicine, State Key Laboratory of Respiratory Disease, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510700, China. GYWYer@126.com. 6. Department of Center Laboratory, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510700, China. GYWYer@126.com.
Abstract
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a chronic and progressive lung disease characterized by a mixture of small airway disease and lung tissue parenchymal destruction. Abnormal inflammatory responses to cigarette smoking and other noxious particles are generally thought to be responsible for causing of COPD. Since airway inflammation is a key factor in COPD progress, it is crucial to unravel its underlying molecular mechanisms. Unbiased analysis of genome-wide gene expression profiles in lung small airway epithelial cells provides a powerful tool to investigate this. METHODS: Gene expression data of GSE611906, GSE20257, GSE8545 were downloaded from GEO database. All 288 lung small airway samples in these cohorts, including donors with (n = 61) and without (n = 227) COPD, were chosen for differential gene expression analysis. The gene ontology (GO) function, Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses, gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis were performed. Subsequently, the analyses of IL1B expression level, the Pearson correlation between IL1B and several COPD biomarkers were performed using other cohorts to validate our main findings. RESULTS: With a change ≥ twofold and P value < 0.05 cutoff, we found 38 genes were up-regulated and 114 genes were down-regulated in patients with COPD compared with health controls, while using cutoff fold change 1.5 and P value < 0.05, there were 318 genes up-regulated and 333 genes down-regulated. Among the most up-regulated genes were IL1B, CCL2, CCL23, and CXCL14, all implicated in inflammation triggering. GO, KEGG and WGCNA analysis all disclosed IL1B was highly correlated to COPD disease trait. The expression profile of IL1B was further validated using independent cohorts from COPD airway epithelium, lung tissue, sputum, and blood. We demonstrated higher IL1B gene expression in COPD small airway epithelial cells, but not in COPD lung tissue, sputum, and blood. Strong co-expression of IL1B with COPD biomarkers, such as DUOX2, MMP12, CCL2, and CXCL14, were validated in silico analysis. Finally, PPI network analysis using enriched data showed IL1B, CCL2, CCL7 and BMP7 were in the same hub node with high degrees. CONCLUSIONS: We identified IL1B was significantly up-regulated in COPD small airway epithelial cells and propose IL1B as a novel player in airway inflammation in COPD.
BACKGROUND:Chronic obstructive pulmonary disease (COPD) is a chronic and progressive lung disease characterized by a mixture of small airway disease and lung tissue parenchymal destruction. Abnormal inflammatory responses to cigarette smoking and other noxious particles are generally thought to be responsible for causing of COPD. Since airway inflammation is a key factor in COPD progress, it is crucial to unravel its underlying molecular mechanisms. Unbiased analysis of genome-wide gene expression profiles in lung small airway epithelial cells provides a powerful tool to investigate this. METHODS: Gene expression data of GSE611906, GSE20257, GSE8545 were downloaded from GEO database. All 288 lung small airway samples in these cohorts, including donors with (n = 61) and without (n = 227) COPD, were chosen for differential gene expression analysis. The gene ontology (GO) function, Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses, gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis were performed. Subsequently, the analyses of IL1B expression level, the Pearson correlation between IL1B and several COPD biomarkers were performed using other cohorts to validate our main findings. RESULTS: With a change ≥ twofold and P value < 0.05 cutoff, we found 38 genes were up-regulated and 114 genes were down-regulated in patients with COPD compared with health controls, while using cutoff fold change 1.5 and P value < 0.05, there were 318 genes up-regulated and 333 genes down-regulated. Among the most up-regulated genes were IL1B, CCL2, CCL23, and CXCL14, all implicated in inflammation triggering. GO, KEGG and WGCNA analysis all disclosed IL1B was highly correlated to COPD disease trait. The expression profile of IL1B was further validated using independent cohorts from COPD airway epithelium, lung tissue, sputum, and blood. We demonstrated higher IL1B gene expression in COPD small airway epithelial cells, but not in COPD lung tissue, sputum, and blood. Strong co-expression of IL1B with COPD biomarkers, such as DUOX2, MMP12, CCL2, and CXCL14, were validated in silico analysis. Finally, PPI network analysis using enriched data showed IL1B, CCL2, CCL7 and BMP7 were in the same hub node with high degrees. CONCLUSIONS: We identified IL1B was significantly up-regulated in COPD small airway epithelial cells and propose IL1B as a novel player in airway inflammation in COPD.
Authors: R B Gonçalves; R D Coletta; K G Silvério; L Benevides; M Z Casati; J S da Silva; F H Nociti Journal: Inflamm Res Date: 2011-02-05 Impact factor: 4.575
Authors: Adam M Speen; Jessica R Hoffman; Hye-Young H Kim; Yael N Escobar; Grace E Nipp; Meghan E Rebuli; Ned A Porter; Ilona Jaspers Journal: Chem Res Toxicol Date: 2019-09-11 Impact factor: 3.739