Project description:Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of mortality in the United States; however, COPD has heterogeneous clinical phenotypes. This is the first large scale attempt which uses transcriptomics, proteomics, and metabolomics (multi-omics) to determine whether there are molecularly defined clusters with distinct clinical phenotypes that may underlie the clinical heterogeneity. Subjects included 3,278 subjects from the COPDGene cohort with at least one of the following profiles: whole blood transcriptomes (2,650 subjects); plasma proteomes (1,013 subjects); and plasma metabolomes (1,136 subjects). 489 subjects had all three contemporaneous -omics profiles. Autoencoder embeddings were performed individually for each -omics dataset. Embeddings underwent subspace clustering using MineClus, either individually by -omics or combined, followed by recursive feature selection based on Support Vector Machines. Clusters were tested for associations with clinical variables. Optimal single -omics clustering typically resulted in two clusters. Although there was overlap for individual -omics cluster membership, each -omics cluster tended to be defined by unique molecular pathways. For example, prominent molecular features of the metabolome-based clustering included sphingomyelin, while key molecular features of the transcriptome-based clusters were related to immune and bacterial responses. We also found that when we integrated the -omics data at a later stage, we identified subtypes that varied based on age, severity of disease, in addition to diffusing capacity of the lungs for carbon monoxide, and precent on atrial fibrillation. In contrast, when we integrated the -omics data at an earlier stage by treating all data sets equally, there were no clinical differences between subtypes. Similar to clinical clustering, which has revealed multiple heterogenous clinical phenotypes, we show that transcriptomics, proteomics, and metabolomics tend to define clusters of COPD patients with different clinical characteristics. Thus, integrating these different -omics data sets affords additional insight into the molecular nature of COPD and its heterogeneity.
Project description:Chronic obstructive pulmonary disease (COPD) is a known risk factor for developing lung cancer suggesting that the COPD stroma contains factors supporting tumorigenesis. Since cancer initiation is complex we used a multi-omic approach to identify gene expression patterns that distinguish COPD stroma in patients with or without lung cancer. We obtained lung tissue from patients with COPD and lung cancer (tumor and adjacent non-malignant tissue) and those with COPD without lung cancer for proteomic and mRNA (cytoplasmic and polyribosomal) profiling. We used the joint and individual variation explained (JIVE) method to integrate and analysis across the three datasets. JIVE identified eight latent patterns that robustly distinguished and separated the three groups of tissue samples. Predictive variables that associated with the tumor, compared to adjacent stroma, were mainly represented in the transcriptomic data, whereas, predictive variables associated with adjacent tissue compared to controls was represented at the translatomic level. Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis revealed extracellular matrix (ECM) and PI3K-Akt signaling pathways as important signals in the pre-malignant stroma. COPD stroma adjacent to lung cancer is unique and differs from non-malignant COPD tissue and is distinguished by the extracellular matrix and PI3K-Akt signaling pathways.
Project description:Identifying protein biomarkers for chronic obstructive pulmonary disease (COPD) has been challenging. Most previous studies have utilized individual proteins or pre-selected protein panels measured in blood samples. To identify COPD protein biomarkers by applying comprehensive mass spectrometry proteomics in lung tissue samples. We utilized mass spectrometry proteomic approaches to identify protein biomarkers from 152 lung tissue samples representing COPD cases and controls.
Project description:Diaphragm muscles in Chronic Obstructive Pulmonary Disease (COPD) patients undergo an adaptive fast to slow transformation that includes cellular adaptations. This project studies the signaling mechanisms responsible for this transformation. Keywords: other
Project description:Objective Osteoporosis, which is now recognized as a major comorbidity of chronic obstructive pulmonary disease (COPD), must be diagnosed by appropriate methods. The aims of this study were to clarify the relationships between bone mineral density (BMD) and COPD-related clinical variables and to explore the association of BMD with the updated Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification in men. Methods We enrolled 50 Japanese men with clinically stable COPD who underwent dual-energy X-ray absorptiometry (DEXA), pulmonary function testing, and computerized tomography (CT) and who had completed a questionnaire (COPD assessment test [CAT]). We determined the association between the T-score and other tested parameters and compared the BMD of patients in each GOLD category. Results Twenty-three of the 50 patients (46.0%) were diagnosed with osteopenia, and 7 (14.0%) were diagnosed with osteoporosis. The BMD findings were significantly correlated with the CAT score, forced expiratory volume in 1 second percentage predicted (FEV1% predicted), low attenuation volume percentage (LAV%), and percentage of cross-sectional area of small pulmonary vessels (%CSA) on CT images. Notably, the median T-score of the GOLD category D participants was significantly lower than that of the participants in each of the other categories (A [-0.98], B [-1.06], C [-1.05], and D [-2.19], p<0.05). Conclusion Reduced BMD was associated with airflow limitation, extent of radiographic findings, and a poor quality of life (QOL) in patients with COPD. The BMD of GOLD category D patients was the lowest of all of the patients evaluated, and category D patients may benefit from active intervention for osteoporosis.
Project description:Assessment of patients with chronic obstructive pulmonary disease (COPD) is important to establish an accurate diagnosis, assist in making therapeutic decisions, measuring outcomes for clinical and research purposes, and determining prognosis. Chest computed tomography (CT) scans are useful in patients who present with airflow limitation and clinical features suggestive of COPD but in whom other diagnoses are being considered. In such cases, a chest CT may indicate another diagnosis. The amount and distribution of emphysema can identify outcomes from lung volume reduction surgery, and chest CT scans are mandatory in assessment of patients for this surgery. Quantitative parameters from chest CT scans have been used to define longitudinal progression of disease. Assessment of patients with COPD for both clinical and research purposes should incorporate a variety of different outcomes. There are outcome measures that have been successfully incorporated in large clinical trials, and the design and outcomes of these trials can be used to plan future clinical investigations in COPD.
Project description:This experiment was carried out to see if there were any miRNA expression differences in pulmonary endothelial cells between patients with and without COPD. COPD is an inflammatory condition and although much work has previously been performed to investigate the inflammatory cells in COPD there has not been as much research looking at the endothelium through which inflammatory cells must pass through to reach the lung tissue. In this experiment pulmonary endothelial cells were extracted from whole lung tissue removed at the time of cardiothoracic surgery. This was performed for patients with and without COPD. RNA was extracted using the Qiagen microRNeasy kits prior to transferring to the University of Birmingham Biosciences department who performed RNA labelling and ran the microarrays. Once the microarrays were performed quality was checked using ArrayQualityMetrics and the COPD group was compared to the non-COPD group using SAM. The experiment was then repeated using another patient group. MiRNAs of interest were validated with qPCR initially before moving on to functional work.
Project description:Chronic obstructive pulmonary disease (COPD) is a critical condition with high morbidity and mortality. Although several medications are available, there are no definite treatments. However, recent advances in the understanding of stem and progenitor cells in the lung, and molecular changes during re-alveolization after pneumonectomy, have made it possible to envisage the regeneration of damaged lungs. With this background, numerous studies of stem cells and various stimulatory molecules have been undertaken, to try and regenerate destroyed lungs in animal models of COPD. Both the cell and drug therapies show promising results. However, in contrast to the successes in laboratories, no clinical trials have exhibited satisfactory efficacy, although they were generally safe and tolerable. In this article, we review the previous experimental and clinical trials, and summarize the recent advances in lung regeneration therapy for COPD. Furthermore, we discuss the current limitations and future perspectives of this emerging field.
Project description:Investigation of whole genome gene expression level changes of the dynamic gene profiling of peripheral blood mononuclear cells (PBMCs) from patients with AECOPD) on day1, 3 and 10, compared to the normal people and stable COPD patients. A five chip study using total RNA recovered from Peripheral Blood Mononuclear Cell of Peripheral Blood.Evaluating the dynamic gene profiling of peripheral blood mononuclear cells (PBMCs) from patients with AECOPD) on day1, 3 and 10 after the hospital admission, to compared with healthy controls or patients with stable COPD. Slides were scanned at 5 μm/pixel resolution using an Axon GenePix 4000B scanner (Molecular Devices Corporation) piloted by GenePix Pro 6.0 software (Axon). Scanned images (TIFF format) were then imported into NimbleScan software (version 2.5) for grid alignment and expression data analysis. Expression data were normalized through quantile normalization and the Robust Multichip Average (RMA) algorithm included in the NimbleScan software. The Probe level (*_norm_RMA.pair) files and Gene level (*_RMA.calls) files were generated after normalization.