Project description:Background: COPD is currently the fourth leading cause of death worldwide and predicted to rank third by 2020. Statins are commonly used lipid lowering agents with documented benefits on cardiovascular morbidity and mortality, and have also been shown to have pleiotropic effects including anti-inflammatory and anti-oxidant activity. Objective: Identify a gene signature associated with statin use in the blood of COPD patients, and identify molecular mechanisms and pathways underpinning this signature that could explain any potential benefits in COPD. Methods: Whole blood gene expression was measured on 168 statin users and 452 non-users from the ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) study. Gene expression was measured using the Affymetrix Human Gene 1.1 ST microarray chips. Factor Analysis for Robust Microarray Summarization (FARMS) was used to process the expression data and to filter out non-informative probe sets. Differential gene expression analysis was undertaken using the Linear Models for Microarray data (Limma) package adjusting for propensity score and employing a surrogate variable analysis. Similarity of the expression signal with published gene expression profiles was performed in ProfileChaser. Results: 18 genes were differentially expressed between statin users and non-users at a false discovery rate of 10%. Top genes included LDLR, ABCA1, ABCG1, MYLIP, SC4MOL, and DHCR24. The 18 genes were significantly enriched in pathways and biological processes related to cholesterol homeostasis and metabolism, and were enriched for transcription factor binding sites for sterol regulatory element binding protein 2 (SREBP-2). The resulting gene signature showed correlation with Huntington disease, Parkinson’s disease and acute myeloid leukemia. Conclusion: Statins gene signature was not enriched in any pathways related to respiratory diseases, beyond the drug’s effect on cholesterol homeostasis. Study subjects were a subset of those with COPD from the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) study (Vestbo et al.), funded by GlaxoSmithKline (GSK Study No. SCO104960, NCT00292552). ECLIPSE is a non-interventional, observational, multicentre, three-year study in people with COPD. Blood was collected in PAXGene tubes and frozen at -80oC. In this work we have looked at the effect of statins on gene expression in 620 subjects of whom 168 were statin users. ECLIPSE study was described in: Vestbo J, Anderson W, Coxson HO, et al.: Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE). Eur Respir J. 2008;31(4):869-73
Project description:Background: COPD is currently the fourth leading cause of death worldwide and predicted to rank third by 2020. Statins are commonly used lipid lowering agents with documented benefits on cardiovascular morbidity and mortality, and have also been shown to have pleiotropic effects including anti-inflammatory and anti-oxidant activity. Objective: Identify a gene signature associated with statin use in the blood of COPD patients, and identify molecular mechanisms and pathways underpinning this signature that could explain any potential benefits in COPD. Methods: Whole blood gene expression was measured on 168 statin users and 452 non-users from the ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) study. Gene expression was measured using the Affymetrix Human Gene 1.1 ST microarray chips. Factor Analysis for Robust Microarray Summarization (FARMS) was used to process the expression data and to filter out non-informative probe sets. Differential gene expression analysis was undertaken using the Linear Models for Microarray data (Limma) package adjusting for propensity score and employing a surrogate variable analysis. Similarity of the expression signal with published gene expression profiles was performed in ProfileChaser. Results: 18 genes were differentially expressed between statin users and non-users at a false discovery rate of 10%. Top genes included LDLR, ABCA1, ABCG1, MYLIP, SC4MOL, and DHCR24. The 18 genes were significantly enriched in pathways and biological processes related to cholesterol homeostasis and metabolism, and were enriched for transcription factor binding sites for sterol regulatory element binding protein 2 (SREBP-2). The resulting gene signature showed correlation with Huntington disease, Parkinson’s disease and acute myeloid leukemia. Conclusion: Statins gene signature was not enriched in any pathways related to respiratory diseases, beyond the drug’s effect on cholesterol homeostasis.
Project description:Differential profiles from whole genome human expression arrays on monocytes obtained from peripheral blood in COPD was studied and compared with controls. Monocytes were isolated from Controls (Group 1) which included Control Smokers (Group 1A) and Control Never Smokers (Group 1B) and COPD (Group 2) which included COPD Smokers (Group 2A) and COPD ExSmokers (Group 2B). Differential transcriptomic expression associated with (i) Smoking, (ii) COPD, and (iii) cessation of smoking were identified.
Project description:Chronic Obstructive Pulmonary Disease (COPD) is a respiratory disorder that is the result of extended exposure of the airways to noxious stimuli, principally cigarette smoke (CS). The mechanisms through which COPD evolves are not fully understood though it is believed that the disease process includes a genetic component since not all smokers develop COPD. To investigate the mechanism leading to the development of COPD/emphysema, we performed an experiment in which whole genome gene expression and several COPD-relevant biological endpoints (MMP-9, MMP activity, TIMP-1 and lung weight) were measured in lung tissue after exposure to two doses of CS for various periods of time. A novel and powerful method, known as reverse engineering and forward simulation (REFS(TM)), was employed to identify key molecular drivers by integrating gene expression data and 4 measured COPD-relevant endpoints. An ensemble of molecular networks was generated using REFS(TM). Simulations showed that this ensemble could successfully recover the measured experimental data for gene expression and measured COPD-relevant endpoints. This ensemble of networks was then further employed to simulate thousands of in silico gene knockdown experiments. Based on the in silico gene knockdown, thirty-three molecular key drivers for the above four COPD-relevant endpoints were identified, with the majority of them being enriched in inflammation, emphysema and COPD.