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Network approaches have successfully been used to help reveal complex mechanisms of diseases including Chronic Obstructive Pulmonary Disease (COPD). However despite recent advances, we remain limited in our ability to incorporate protein-protein interaction (PPI) network information with omics data for disease prediction. New deep learning methods including convolution Graph Neural Network (ConvGNN) has shown great potential for disease classification using transcriptomics data and known PPI networks from existing databases. In this study, we first reconstructed the COPD-associated PPI network through the AhGlasso (Augmented High-Dimensional Graphical Lasso Method) algorithm based on one independent transcriptomics dataset including COPD cases and controls. Then we extended the existing ConvGNN methods to successfully integrate COPD-associated PPI, proteomics, and transcriptomics data and developed a prediction model for COPD classification. This approach improves accuracy over several conventional classification methods and neural networks that do not incorporate network information. We also demonstrated that the updated COPD-associated network developed using AhGlasso further improves prediction accuracy. Although deep neural networks often achieve superior statistical power in classification compared to other methods, it can be very difficult to explain how the model, especially graph neural network(s), makes decisions on the given features and identifies the features that contribute the most to prediction generally and individually. To better explain how the spectral-based Graph Neural Network model(s) works, we applied one unified explainable machine learning method, SHapley Additive exPlanations (SHAP), and identified CXCL11, IL-2, CD48, KIR3DL2, TLR2, BMP10 and several other relevant COPD genes in subnetworks of the ConvGNN model for COPD prediction. Finally, Gene Ontology (GO) enrichment analysis identified glycosaminoglycan, heparin signaling, and carbohydrate derivative signaling pathways significantly enriched in the top important gene/proteins for COPD classifications.

Multiple -omics (genomics, proteomics, etc.) profiles are commonly generated to gain insight into a disease or physiological system. Constructing multi-omics networks with respect to the trait(s) of interest provides an opportunity to understand relationships between molecular features but integration is challenging due to multiple data sets with high dimensionality. One approach is to use canonical correlation to integrate one or two omics types and a single trait of interest. However, these types of methods may be limited due to (1) not accounting for higher-order correlations existing among features, (2) computational inefficiency when extending to more than two omics data when using a penalty term-based sparsity method, and (3) lack of flexibility for focusing on specific correlations (e.g., omics-to-phenotype correlation versus omics-to-omics correlations). In this work, we have developed a novel multi-omics network analysis pipeline called Sparse Generalized Tensor Canonical Correlation Analysis Network Inference (SGTCCA-Net) that can effectively overcome these limitations. We also introduce an implementation to improve the summarization of networks for downstream analyses. Simulation and real-data experiments demonstrate the effectiveness of our novel method for inferring omics networks and features of interest.

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.

Background Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease characterized by multiple subtypes and variable disease progression. Blood biomarkers have been variably associated with subtype, severity, and disease progression. Just as combined clinical variables are more highly predictive of outcomes than individual clinical variables, we hypothesized that multiple biomarkers may be more informative than individual biomarkers to predict subtypes, disease severity, disease progression, and mortality. Methods Fibrinogen, C-Reactive Protein (CRP), surfactant protein D (SP-D), soluble Receptor for Advanced Glycation Endproducts (sRAGE), and Club Cell Secretory Protein (CC16) were measured in the plasma of 1465 subjects from the COPDGene cohort and 2746 subjects from the ECLIPSE cohort. Regression analysis was performed to determine whether these biomarkers, individually or in combination, were predictive of subtypes, disease severity, disease progression, or mortality, after adjustment for clinical covariates. Results In COPDGene, the best combinations of biomarkers were: CC16, sRAGE, fibrinogen, CRP, and SP-D for airflow limitation ( p  < 10 −4 ), SP-D, CRP, sRAGE and fibrinogen for emphysema ( p  < 10 −3 ), CC16, fibrinogen, and sRAGE for decline in FEV 1 ( p  < 0.05) and progression of emphysema ( p  < 10 −3 ), and all five biomarkers together for mortality ( p  < 0.05). All associations except mortality were validated in ECLIPSE. The combination of SP-D, CRP, and fibrinogen was the best model for mortality in ECLIPSE ( p  < 0.05), and this combination was also significant in COPDGene. Conclusion This comprehensive analysis of two large cohorts revealed that combinations of biomarkers improve predictive value compared with clinical variables and individual biomarkers for relevant cross-sectional and longitudinal COPD outcomes.

There is an unmet need for blood biomarkers in diagnosis and prognosis of chronic obstructive pulmonary disease (COPD). The search for these biomarkers has been revolutionized by high-throughput sequencing techniques and multiplex platforms that can measure thousands of gene transcripts, proteins, or metabolites. We review COPDGene (Genetic Epidemiology of COPD) project publications that include DNA methylation, transcriptomic, proteomic, and metabolomic blood biomarkers and discuss their impact on COPD. Key contributions from COPDGene include identification of DNA methylation effects from smoking and genetic variation, new transcriptomic signatures in the blood, identification of protein biomarkers associated with severity and progression (e.g., sRAGE [soluble receptor for advanced glycosylation end products], inflammatory cytokines IL-6 and IL-8), and identification of small molecules (ceramides and sphingomyelin) that may be pathogenic. COPDGene studies have revealed that some of the COPD genome-wide association study polymorphisms are strongly associated with blood biomarkers (e.g., rs2070600 in AGER is a pQTL [protein quantitative trait locus] for sRAGE), underscoring the importance of combining omics results. Investigators have developed molecular networks identifying lower CD4+ resting memory cells associated with COPD. Genes, proteins, and metabolite networks are particularly important because the explanatory value of any single molecule is small (1–10%) compared with panels of multiple markers. COPDGene has been a useful resource in the identification and validation of multiple biomarkers for COPD. These biomarkers, either combined in multiple biomarker panels or integrated with other omics data types, may lead to novel diagnostic and prognostic tests for COPD phenotypes and may be relevant for assessing novel therapies.

Cigarette smoking is the primary cause of Chronic Obstructive Pulmonary Disease (COPD), which is characterized by chronic inflammation of the airways and destruction of lung parenchyma. Repeated and sustained bacterial infections are clearly linked to disease pathogenesis (e.g., exacerbations) and a huge burden on health care costs. The airway epithelium constitutes the first line of host defense against infection and our previous study indicated that Fatty Acid Binding Protein 5 (FABP5) is down regulated in airway epithelial cells of smokers with COPD as compared to smokers without COPD. We hypothesized that cigarette smoke (CS) exposure down regulates FABP5, thus, contributing to a more sustained inflammation in response to bacterial infection. In this report, we show that FABP5 is increased following bacterial infection but decreased following CS exposure of primary normal human bronchial epithelial (NHBE) cells. The goal of this study was to address FABP5 function by knocking down or overexpressing FABP5 in primary NHBE cells exposed to CS. Our data indicate that FABP5 down regulation results in increased P. aeruginosa bacterial load and inflammatory cytokine levels (e.g., IL-8) and decreased expression of the anti-bacterial peptide, β defensin-2. On the contrary, FABP5 overexpression exerts a protective function in airway epithelial cells against P. aeruginosa infection by limiting the production of IL-8 and increasing the expression of β defensin-2. Our study indicates that FABP5 exerts immunomodulatory functions in the airway epithelium against CS exposure and subsequent bacterial infection through its modulation of the nuclear receptor peroxisome proliferator-activated receptor (PPAR)-γ activity. These findings support the development of FABP5/PPAR-γ-targeted therapeutic approach to prevent airway inflammation by restoring antimicrobial immunity during COPD exacerbations.

Flavored electronic cigarettes (ECs) present a serious health challenge globally. Currently, it is unknown whether the addition of highly popular menthol flavoring to e-liquid is associated with changes in the number of aerosolized particles generated or altered lung function. Here, we first performed preclinical studies using our novel robotic platform Human Vaping Mimetic Real-Time Particle Analyzer (HUMITIPAA). HUMITIPAA generates fresh aerosols for any desired EC in a very controlled and user-definable manner and utilizes an optical sensing system to quantitate and analyze sub-micron and microparticles from every puff over the course of vaping session in real-time while emulating clinically relevant breathing mechanics and vaping topography. We discovered that addition of menthol flavoring to freshly prepared e-liquid base propylene glycol–vegetable glycerin leads to enhanced particle counts in all tested size fractions, similar to the effect of adding vitamin E acetate to e-liquid we previously reported. Similarly, we found that menthol vs. non-menthol (tobacco) flavored pods from commercially available ECs leads to generation of significantly higher quantities of 1–10 µm particles upon inhalation. We then retrospectively analyzed data from the COPDGene study and identified an association between the use of menthol flavored ECs and reduced FEV1% predicted and FEV1/FVC independent of age, gender, race, pack-years of smoking, and use of nicotine or cannabis-containing vaping products. Our results reveal an association between enhanced inhaled particle due to menthol addition to ECs and worse lung function indices. Detailed causal relation remains to be demonstrated in future large-scale prospective clinical studies. Importantly, here we demonstrate utility of the HUMITIPAA as a predictive enabling technology to identify inhalation toxicological potential of emerging ECs as the chemical formulation of e-liquid gets modified.

In this study, the investigators found a strong association, in two cohorts, between future exacerbations of COPD and the ratio of the diameter of the pulmonary artery to the diameter of the aorta (with both diameters measured from a baseline CT scan) that is greater than 1. Acute exacerbations of chronic obstructive pulmonary disease (COPD) are critical events in the natural history of the disease and are associated with accelerated loss of lung function and poor quality of life. 1 , 2 Hospitalizations for exacerbations account for $18 billion in direct costs annually in the United States and are associated with 1-year mortality of 21% and 5-year mortality of 55%. 3 Identification of patients at risk for these events is therefore of major importance. Acute exacerbations of COPD are defined as an increase in dyspnea, cough, or sputum production warranting a change in therapy. These acute exacerbations often result from . . .

Chronic obstructive pulmonary disease (COPD) is a common and progressive disease that is influenced by both genetic and environmental factors. For many years, knowledge of the genetic basis of COPD was limited to Mendelian syndromes, such as alpha-1 antitrypsin deficiency and cutis laxa, caused by rare genetic variants. Over the past decade, the proliferation of genome-wide association studies, the accessibility of whole-genome sequencing, and the development of novel methods for analyzing genetic variation data have led to a substantial increase in the understanding of genetic variants that play a role in COPD susceptibility and COPD-related phenotypes. COPDGene (Genetic Epidemiology of COPD), a multicenter, longitudinal study of over 10,000 current and former cigarette smokers, has been pivotal to these breakthroughs in understanding the genetic basis of COPD. To date, over 20 genetic loci have been convincingly associated with COPD affection status, with additional loci demonstrating association with COPD-related phenotypes such as emphysema, chronic bronchitis, and hypoxemia. In this review, we discuss the contributions of the COPDGene study to the discovery of these genetic associations as well as the ongoing genetic investigations of COPD subtypes, protein biomarkers, and post-genome-wide association study analysis.

Acute exacerbations of chronic obstructive pulmonary disease (COPD) increase the risk of death and drive healthcare costs, but whether they accelerate loss of lung function remains controversial. Whether exacerbations in subjects with mild COPD or similar acute respiratory events in smokers without airflow obstruction affect lung function decline is unknown. To determine the association between acute exacerbations of COPD (and acute respiratory events in smokers without COPD) and the change in lung function over 5 years of follow-up. We examined data on the first 2,000 subjects who returned for a second COPDGene visit 5 years after enrollment. Baseline data included demographics, smoking history, and computed tomography emphysema. We defined exacerbations (and acute respiratory events in those without established COPD) as acute respiratory symptoms requiring either antibiotics or systemic steroids, and severe events by the need for hospitalization. Throughout the 5-year follow-up period, we collected self-reported acute respiratory event data at 6-month intervals. We used linear mixed models to fit FEV decline based on reported exacerbations or acute respiratory events. In subjects with COPD, exacerbations were associated with excess FEV decline, with the greatest effect in Global Initiative for Chronic Obstructive Lung Disease stage 1, where each exacerbation was associated with an additional 23 ml/yr decline (95% confidence interval, 2-44; P = 0.03), and each severe exacerbation with an additional 87 ml/yr decline (95% confidence interval, 23-151; P = 0.008); statistically significant but smaller effects were observed in Global Initiative for Chronic Obstructive Lung Disease stage 2 and 3 subjects. In subjects without airflow obstruction, acute respiratory events were not associated with additional FEV decline. Exacerbations are associated with accelerated lung function loss in subjects with established COPD, particularly those with mild disease. Trials are needed to test existing and novel therapies in subjects with early/mild COPD to potentially reduce the risk of progressing to more advanced lung disease. Clinical trial registered with www.clinicaltrials.gov (NCT 00608764).