Project description:Multiple gene expression studies have been performed in lung or airway tissues from subjects with chronic obstructive pulmonary disease (COPD). However, in comparison to genome-wide association studies (GWAS), there has been poor replication across these studies. We sought to perform gene expression profiling on a large sample of severe COPD cases and control smokers and use network methods to identify interacting genes and pathways. We collected surgically-resected lung tissue samples from subjects with severe COPD and controls with normal lung function; all subjects were former smokers. Gene expression profiling on homogenized lung tissue was performed using Illumina HumanHT-12 BeadChips. Protein and RNA binding studies, RNA-interference, a murine smoking model, and expression quantitative trait locus analyses were used to identify genes and proteins that may interact with three known COPD GWAS genes: IREB2, HHIP and FAM13A. Weighted Gene Co-Expression Network Analysis (WGCNA) was used to define co-expressed gene modules. Comparing 111 COPD cases and 40 control smokers, 214 genes were differentially expressed; none of these genes were at significant GWAS loci. However, the top differentially expressed gene was HMGB1, which interacts with AGER, a gene implicated through COPD GWAS. The genes differentially expressed in lung tissue showed strong enrichment for putative interactors of IREB2 and HHIP, with less support for FAM13A, based on the gene sets from the in vitro, in vivo, and in silico studies. In the WGCNA, the module most highly associated for COPD was enriched for B-cell pathways, which shared seventeen genes with the results of the Hhip+/- mouse smoking model. As in other common diseases, genes at COPD GWAS loci were not differentially expressed with COPD status; however, using a combination of network methods, experimental studies of interaction and careful phenotype definition, we found differential expression of putative interactors of these genes, and we replicated previous human and mouse microarray results involving B cell pathways.

Project description:Chronic obstructive pulmonary disease (COPD) is one of the most prevalent lung diseases, and involves persistent airflow limitation and incorporates both emphysema and chronic bronchitis. Cigarette smoking has been identified as the main risk factor for disease development and progression. In a basic model of COPD, the disease is initiated when the physiologic response mechanisms to cigarette smoke exposure are overwhelmed; for example, because of long-term exposure effects or other aging-related changes. In this parallel-group case-controlled clinical study we asked to what extent the different transitions in a chronic-exposure-to-disease model are reflected in the proteome and cellular transcriptome of induced sputum samples from the lung. For this, we selected 60 age- and gender-matched individuals for each of four study groups: current healthy smokers, current-smoker COPD patients, former smokers, and never smokers (a total of 240 individuals). Induced sputum was collected, the cell-free supernatant was analyzed by quantitative proteomics (isobaric-tag based), and the cellular mRNA fraction was analyzed by microarray-based expression profiling. The sputum proteome of current smokers (healthy or COPD patients) clearly reflected the common physiological responses to smoke exposure, including alterations in mucin/trefoil proteins (e.g., MUC5AC and TFF1/3up-regulation), peptidase regulators (e.g., TIMP1 up-regulation), and a prominent xenobiotic/oxidative stress response (e.g., NQO1 and ALDH3A1 up-regulation). The latter response also was observed in the sputum transcriptome, which additionally demonstrated an immune-related polarization change (toward a M2 signature). The (long-term) former smoker group showed nearly complete reversal of the observable biological effects. Thirteen differentially abundant proteins between the COPD and healthy smoker groups were identified. These abundant proteins included previously reported COPD-associated proteins (e.g., TIMP1 (up-regulation) and APOA1 (down-regulation)) and novel proteins such as C6orf58 and BPIFB1 (LPLUNC1) (both up-regulated in the COPD group compared with the healthy smokers). In summary, our study demonstrates that sputum proteomics/transcriptomics can capture the complex and reversible physiological response to cigarette smoke exposure, which appears to be only slightly modulated in early-stage COPD patients. The study has been registered on ClinicalTrials.gov with identifier NCT01780298.

Project description:One of the most common smoking-related diseases, chronic obstructive pulmonary disease (COPD), results from a dysregulated, multi-tissue inflammatory response to cigarette smoke. We hypothesized that systemic inflammatory signals in genome-wide blood gene expression can identify clinically important COPD-related disease subtypes, and we leveraged pre-existing gene interaction networks to guide unsupervised clustering of blood microarray expression data. Using network-informed non-negative matrix factorization, we analyzed genome-wide blood gene expression from 229 former smokers in the ECLIPSE Study, and we identified novel, clinically relevant molecular subtypes of COPD. These network-informed clusters were more stable and more strongly associated with measures of lung structure and function than clusters derived from a network-naÃ¯ve approach, and they were associated with subtype-specific enrichment for inflammatory and protein catabolic pathways. These clusters were successfully reproduced in an independent sample of 135 smokers from the COPDGene Study. Briefly, gene expression was derived from whole blood samples in ECLIPSE subjects and peripheral blood mononuclear cells (PBMCs) for the COPDGene subjects. Gene expression profiling was performed using the Affymetrix Human U133 Plus2 array. Gene expression data were log-transformed, and background correction and normalization were performed for the merged ECLIPSE and COPDGene samples using robust multi-array averaging and quantile normalization as implemented in the affy Bioconductor package[27]. Of the 136 COPDGene subjects reported in a previous publication[13], one self-reported African-American subject was removed from analysis, which was conducted on the remaining 135 non-Hispanic white subjects. To identify a set of genes associated with COPD, we performed differential expression analysis for 38,519 probesets in ECLIPSE that passed quality control measures. Normalized probeset intensities were related to measures indicative of two primary dimensions of pulmonary impairment in COPD airway obstruction as indicated by two measures of spirometric lung function (FEV1 (% of predicted) and FEV1/FVC) and lung parenchymal destruction, i.e., emphysema (as quantified by the percentage of low attenuation area less than -950 Hounsfield units on lung computed tomography, %LAA-950). The analysis was conducted using the limma Bioconductor package, and the false discovery rate was controlled at 5%. The following covariates were included in the differential expression analysis age, pack-years of cigarette smoke exposure, and gender. After standardizing gene expression data from 229 ECLIPSE subjects by the variance of each probe set, we applied NMF[29] and NBS[6] to identify meta-patients (i.e. subtypes or subject clusters) and meta-genes (i.e. representative subtype expression profiles). Cross-sectional study of smokers. 229 subjects from the ECLIPSE study were analyzed in the model discovery phase. 135 subjects from the COPDGene Study (GSE42057) were used for replication. Please note that the entire data set for total 364 samples including the re-analyzed samples is provided in the *364samples.txt files.

			Action	DRS
	MTBLS358	Other
	FILES	Other
	a_MTBLS358_CER_mass_spectrometry.txt	Txt
	a_MTBLS358_EICO_mass_spectrometry.txt	Txt
	a_MTBLS358_SHOT_mass_spectrometry-neg.txt	Txt

Dataset Information

Chronic Obstructive Pulmonary Disease (COPD) Biomarker Identification Study: Serum Lipidomics

Dataset's files

Publications

Alterations in Serum Polyunsaturated Fatty Acids and Eicosanoids in Patients with Mild to Moderate Chronic Obstructive Pulmonary Disease (COPD).

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