Pulmonary EV miRNA profiles identify disease and distinct inflammatory endotypes in COPD
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ABSTRACT:
PROVIDER: PRJNA904334 | ENA |
REPOSITORIES: ENA
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Project description:COPD is a heterogeneous condition without effective disease modifying therapies. Identification of novel inflammatory endotype markers such as extracellular vesicles (EVs), which are important intercellular messengers carrying microRNA (miRNA), may enable earlier diagnosis and disease stratification for a targeted treatment approach. Our aim was to identify differentially expressed EV miRNA in the lungs of COPD patients compared with healthy ex-smokers and determine whether they can help define inflammatory COPD endotypes. EV miRNA were isolated and sequenced from ex-smoking COPD patients and healthy ex-smoker bronchoalveolar lavage fluid. Results were validated with RT-qPCR and compared to differential inflammatory cell counts. Differential expression analysis identified five upregulated miRNA in COPD (miR-223-3p, miR-2110, miR-182-5p, miR-200b-5p and miR-625-3p) and three downregulated miRNA (miR-138-5p, miR-338-3p and miR-204-5p), all with a log2 fold change of >1/-1, FDR<0.05. These miRNAs correlated with disease defining characteristics such as FEF 25-75% (a small airways disease measure) and DLCO % (a surrogate measure of emphysema). Receiver operator curve analysis demonstrated miR-2110, miR-223-3p and miR-182-5p showed excellent combinatory predictive ability (AUC 0.91, p<0.0001) in differentiating between health and mild COPD. Furthermore, miR-223-3p and miR-338-3p correlated with airway eosinophilia and were able to distinguish “pure eosinophilic” COPD from other airway inflammatory subtypes (AUC 0.94 and 0.85 respectively). This is the first study to identify differentially expressed miRNA in COPD bronchoalveolar lavage fluid EVs. These findings suggest specific lung derived EV miRNA are a strong predictor of disease presence even in mild COPD. Furthermore, specific miRNA correlated with inflammatory cell numbers in COPD, and may have a role in defining inflammatory endotypes for future treatment stratification.
2022-12-05 | GSE218571 | GEO
Project description: Not available
| S-EPMC9797812 | biostudies-literature
Project description:Chronic obstructive pulmonary disease (COPD) Metagenome
| PRJNA647843 | ENA
Project description:Background: CD8 cells seem to play an important role in the pathogenesis of chronic obstructive pulmonary disease (COPD). However, relatively little is known about their phenotype and function. Aims: To define the transcriptome of pulmonary CD8 cells in COPD and compare to paired circulating CD8 cells and smoker control pulmonary CD8 cells. COPD was defined according to the Global initiative for chronic Obstructive Lung Disease guidelines. Severity of disease was defined according to the patients lung function. In particular the forced evpiratroy volume in 1 second (FEV1).
2013-08-01 | E-MTAB-1688 | biostudies-arrayexpress
Project description:Chronic obstructive pulmonary disease (COPD) is an inflammatory lung disease with complex pathological features and largely unknown etiologies. Identification and validation of biomarkers for this disease could facilitate earlier diagnosis, appreciation of disease subtypes and/or determination of response to therapeutic intervention. To identify gene expression markers for COPD, we performed genome-wide expression profiling of lung tissue from 56 subjects using the Affymetrix U133 Plus 2.0 array. Lung function measurements from these subjects ranged from normal, un-obstructed to severely obstructed. Analysis of differential expression between cases (FEV1<70%, FEV1/FVC<0.7) and controls (FEV1>80%, FEV1/FVC>0.7) identified a set of 65 probe sets representing discrete markers associated with COPD. Correlation of gene expression with quantitative measures of airflow obstruction (FEV1 or FEV1/FVC) identified a set of 220 probe sets. A total of 31 probe sets were identified that showed evidence of significant correlation with quantitative traits and differential expression between cases and controls. Keywords: Disease state marker
2008-05-31 | GSE8581 | GEO
Project description:HIV-infected persons are at increased risk for developing pulmonary diseases including chronic obstructive pulmonary disease (COPD), and the fungal opportunistic pathogen, Pneumocystis jirovecii (Pc) has been implicated in the pathogenesis of HIV-related COPD. We previously developed a non-human primate model of HIV-related COPD using simian-human immunodeficiency virus (SHIV) and Pc co-infection in cynomolgus macaques. In the present study we examined gene expression profiles in lung tissue from SHIV/Pc co-infected monkeys with COPD and compared them to SHIV-infected monkeys infected with normal lung function. Microarray technology was used to develop gene profiles, and differential gene expression was determined by a comparative evaluation of competing normalization methods applied to our expression data set followed by validation using quantitative real-time polymerase chain reaction analysis for select genes. Of over 52,000 transcripts representing more than 20,000 genes analyzed, the SHIV/Pc infected macaques with COPD exhibited 243 differentially expressed (DE) genes compared to SHIV-infected monkeys with normal lung function. DE genes fell into a number of functional categories which may be important in COPD development including: inflammation (pulmonary surfactants A2, B, C, D, upregulated; alternative macrophage activation-associated CC chemokine, upregulated), protease/antiprotease balance (cathepsin H, upregulated; alpha-1-chymotrypsin and secretory leukocyte peptidase inhibitor, downregulated), redox balance (glutathione peroxidase 4 and mitochondrial aldehyde dehydrogenase 2, upregulated) and tissue homeostasis (connective tissue growth factor, downregulated; ornithine decarboxylase antizyme, upregulated). These results identify factors and pathways that may be involved in early development of Pneumocystis and SHIV-associated COPD and reveal several novel, potential therapeutic targets.
2010-09-25 | GSE24343 | GEO
Project description:Background: Chronic obstructive pulmonary disease (COPD) is a major risk factor for the development of lung adenocarcinoma (AC). AC often develops on underlying COPD, thus the differentiation of both entities by biomarker is challenging. Although survival of AC patients strongly depends on early diagnosis, a biomarker panel for AC detection and differentiation from COPD is still missing. Methods: Plasma samples from 176 patients with AC with or without underlying COPD, COPD patients, and hospital controls were analyzed using mass spectrometry-based proteomics. We performed univariate statistics and additionally evaluated machine learning algorithms regarding the differentiation of AC vs. COPD and AC with COPD vs. COPD. Results: Univariate statistics revealed significantly regulated proteins that were significantly regulated between the patient groups. Furthermore, Random forest classification yielded the best performance for differentiation of AC vs. COPD (area under the curve (AUC) 0.935) and AC with COPD vs. COPD (AUC 0.916). The most influential proteins were identified by permutation feature importance and compared to those identified by univariate testing. Conclusion: We demonstrate the great potential of machine learning for differentiation of highly similar disease entities and present a panel of biomarker candidates that should be considered for the development of a future biomarker panel.
2022-10-17 | PXD035120 | Pride
Project description:HIV-infected persons are at increased risk for developing pulmonary diseases including chronic obstructive pulmonary disease (COPD), and the fungal opportunistic pathogen, Pneumocystis jirovecii (Pc) has been implicated in the pathogenesis of HIV-related COPD. We previously developed a non-human primate model of HIV-related COPD using simian-human immunodeficiency virus (SHIV) and Pc co-infection in cynomolgus macaques. In the present study we examined gene expression profiles in lung tissue from SHIV/Pc co-infected monkeys with COPD and compared them to SHIV-infected monkeys infected with normal lung function. Microarray technology was used to develop gene profiles, and differential gene expression was determined by a comparative evaluation of competing normalization methods applied to our expression data set followed by validation using quantitative real-time polymerase chain reaction analysis for select genes. Of over 52,000 transcripts representing more than 20,000 genes analyzed, the SHIV/Pc infected macaques with COPD exhibited 243 differentially expressed (DE) genes compared to SHIV-infected monkeys with normal lung function. DE genes fell into a number of functional categories which may be important in COPD development including: inflammation (pulmonary surfactants A2, B, C, D, upregulated; alternative macrophage activation-associated CC chemokine, upregulated), protease/antiprotease balance (cathepsin H, upregulated; alpha-1-chymotrypsin and secretory leukocyte peptidase inhibitor, downregulated), redox balance (glutathione peroxidase 4 and mitochondrial aldehyde dehydrogenase 2, upregulated) and tissue homeostasis (connective tissue growth factor, downregulated; ornithine decarboxylase antizyme, upregulated). These results identify factors and pathways that may be involved in early development of Pneumocystis and SHIV-associated COPD and reveal several novel, potential therapeutic targets. There are totally 11 samples in the experiment. The sample breakdown is as follows: KN14(group 1) is a true control in that the monkey was not infected with simian-human immunodeficiency virus (SHIV) nor was it colonized with Pneumocystis. KN02, KN03, KN07 and KN08 (group 2) are also controls of a sort. They were infected with SHIV but they did not become colonized with Pneumocystis. The remainder (KN01, KN04, KN06, KN11, KN12, KN13) (group3) were both infected with SHIV and colonized with Pneumocystis. The primary interest is in comparing the two SHIV-infected groups (2 and 3)
2010-09-25 | E-GEOD-24343 | biostudies-arrayexpress
Project description:Chronic Obstructive Pulmonary Disease (COPD) is a complex, heterogeneous disease. Traditional subtyping methods generally focus on either the clinical manifestations or the molecular endotypes of the disease, leading to classifications that only partially reflect disease heterogeneity. Here, we introduce a variational autoencoder-based subtyping pipeline that jointly embeds clinical and gene expression data into a single subject-level representation. We evaluate the framework in the COPDGene study, a large study of current and former smoking individuals with and without COPD. Prediction experiments show that the embeddings have predictive accuracy comparable to or better than other unsupervised embedding approaches. Using trajectory learning approaches, we identify five well-separated subtypes with distinct clinical phenotypes, expression signatures, and longitudinal outcomes. Finally, we show that our findings generalize to an external validation cohort. Overall, our approach enables a transition from isolated phenotypic or molecular subtyping toward an integrated and clinically meaningful understanding of COPD heterogeneity. This GEO dataset contains array-based blood gene expression and phenotype data from the ECLIPSE study, which served as a replication population for this study. COPDGene data are available through dbGaP.
2026-03-31 | GSE324136 | GEO
Project description:Chronic Obstructive Pulmonary Disease (COPD) patient microbiome
| PRJEB46960 | ENA