Altered gene expression in blood and sputum in COPD frequent exacerbators in the ECLIPSE cohort

Novel hypermorphic variants in IRF2BP2 identified in patients with common variable immunodeficiency and autoimmunity

The interferon regulatory factor 2 binding protein 2 (IRF2BP2) is a transcriptional regulator, functioning a transcriptional corepressor by interacting with the interferon regulatory factor-2. The ubiquitous expression of IRF2BP2 by diverse cell types and tissues suggests its potential involvement in different cell signalling pathways. Variants inIRF2BP2have been recently identified to cause familial common variable immunodeficiency (CVID) characterized by immune dysregulation. This study investigated three rare novel variants inIRF2BP2, identified in patients with primary antibody deficiency and autoimmunity by whole exome-sequencing (WES). Following transient overexpression of EGFP-fused mutants in HEK293 cells and transfection in Jurkat cell lines, we used fluorescence microscopy, real-time PCR and Western blotting to analyze their effects on IRF2BP2 expression, subcellular localization, nuclear translocation of IRF2, and the transcriptional activation of NFκB1(p50). We found altered IRF2BP2 mRNA and protein expression levels in the mutants compared to the wild type after IRF2BP2 overexpression. In confocal fluorescence microscopy, variants in the C-terminal RING finger domain showed an irregular aggregate formation and distribution instead of the expected nuclear localization compared to the variants in the N-terminal zinc finger domain and their wildtype counterpart. Immunoblotting revealed an impaired IRF2 and NFκB1 (p50) nuclear localization in the mutants compared to the IRF2BP2 wildtype counterpart. LPS stimulation reduced IRF2BP2 mRNA expression in the variants compared to the wild type. Our findings significantly contribute to understanding the clinical significance of IRF2BP2 mutations in the pathogenesis of immunodeficiency and immune dysregulation. We observed impairment of the nuclear translocation of IRF2 and NFκB1 (p50) due to the upregulation of IRF2BP2, potentially affecting specific gene expressions involved in immune regulation.

Pathophysiology and genomics of bronchiectasis

Bronchiectasis is a complex and heterogeneous inflammatory chronic respiratory disease with an unknown cause in around 30-40% of patients. The presence of airway infection together with chronic inflammation, airway mucociliary dysfunction and lung damage are key components of the vicious vortex model that better describes its pathophysiology. Although bronchiectasis research has significantly increased over the past years and different endotypes have been identified, there are still major gaps in the understanding of the pathophysiology. Genomic approaches may help to identify new endotypes, as has been shown in other chronic airway diseases, such as COPD.Different studies have started to work in this direction, and significant contributions to the understanding of the microbiome and proteome diversity have been made in bronchiectasis in recent years. However, the systematic application of omics approaches to identify new molecular insights into the pathophysiology of bronchiectasis (endotypes) is still limited compared with other respiratory diseases.Given the complexity and diversity of these technologies, this review describes the key components of the pathophysiology of bronchiectasis and how genomics can be applied to increase our knowledge, including the study of new techniques such as proteomics, metabolomics and epigenomics. Furthermore, we propose that the novel concept of trained innate immunity, which is driven by microbiome exposures leading to epigenetic modifications, can complement our current understanding of the vicious vortex. Finally, we discuss the challenges, opportunities and implications of genomics application in clinical practice for better patient stratification into new therapies.

Deep Learning Integration of Chest Computed Tomography Imaging and Gene Expression Identifies Novel Aspects of COPD

Rationale

Chronic obstructive pulmonary disease (COPD) is characterized by pathologic changes in the airways, lung parenchyma, and persistent inflammation, but the links between lung structural changes and blood transcriptome patterns have not been fully described.

Objections

The objective of this study was to identify novel relationships between lung structural changes measured by chest computed tomography (CT) and blood transcriptome patterns measured by blood RNA sequencing (RNA-seq).

Methods

CT scan images and blood RNA-seq gene expression from 1223 participants in the COPD Genetic Epidemiology (COPDGene®) study were jointly analyzed using deep learning to identify shared aspects of inflammation and lung structural changes that we labeled image-expression axes (IEAs). We related IEAs to COPD-related measurements and prospective health outcomes through regression and Cox proportional hazards models and tested them for biological pathway enrichment.

Results

We identified 2 distinct IEAs: IEAemph which captures an emphysema-predominant process with a strong positive correlation to CT emphysema and a negative correlation to forced expiratory volume in 1 second and body mass index (BMI); and IEAairway which captures an airway-predominant process with a positive correlation to BMI and airway wall thickness and a negative correlation to emphysema. Pathway enrichment analysis identified 29 and 13 pathways significantly associated with IEAemph and IEAairway, respectively (adjusted p<0.001).

Conclusions

Integration of CT scans and blood RNA-seq data identified 2 IEAs that capture distinct inflammatory processes associated with emphysema and airway-predominant COPD.

Plasma Proteomics Study Between the Frequent Exacerbation and Infrequent Exacerbation Phenotypes of Chronic Obstructive Pulmonary Disease

Background

Frequent exacerbation (FE) and infrequent exacerbation (IE) are two phenotypes of chronic obstructive pulmonary disease (COPD), of which FE is associated with a higher incidence of exacerbation and a serious threat to human health. Because the pathogenesis mechanisms of FE are unclear, this study aims to identify FE-related proteins in the plasma via proteomics for use as predictive, diagnostic, and therapeutic biomarkers of COPD.

Methods

A cross-sectional study was conducted in which plasma protein profiles were analyzed in COPD patients at stable stage, and differentially expressed proteins (DEPs) were screened out between the FE and IE patients. FE-related DEPs were identified using data-independent acquisition-based proteomics and bioinformatics analyses. In addition, FE-related candidates were verified by enzyme-linked immunosorbent assay.

Results

In this study, 47 DEPs were screened out between the FE and IE groups, including 20 upregulated and 27 downregulated proteins. Key biological functions (eg, neutrophil degranulation, extracellular exosome, protein homodimerization activity) and signaling pathways (eg, arginine and proline metabolism) were enriched in association with the FE phenotype. Receiver operating characteristic (ROC) analysis of the 11 combined DEPs revealed an area under the curve of 0.985 (p <0.05) for discriminating FE from IE. Moreover, correlation and ROC curve analyses indicated that creatine kinase, M-type (CKM) and fat storage-inducing transmembrane protein 1 (FITM1) might be clinically significant in patients with the FE phenotype. In addition, plasma expression levels of CKM and FITM1 were validated to be significantly decreased in the FE group compared with the IE group (CKM: p <0.01; FITM1: p <0.05).

Conclusion

In this study, novel insights into COPD pathogenesis were provided by investigating and comparing plasma protein profiles between the FE and IE patients. CKM, FITM1, and a combinative biomarker panel may serve as useful tools for assisting in the precision diagnosis and effective treatment of the FE phenotype of COPD.

Blood Gene Expression and Immune Cell Subtypes Associated with Chronic Obstructive Pulmonary Disease Exacerbations

Rationale: Acute exacerbations of chronic obstructive pulmonary disease (AE-COPDs) are associated with a significant disease burden. Blood immune phenotyping may improve our understanding of a COPD endotype at increased risk of exacerbations. Objective: To determine the relationship between the transcriptome of circulating leukocytes and COPD exacerbations. Methods: Blood RNA sequencing data (n = 3,618) from the COPDGene (Genetic Epidemiology of COPD) study were analyzed. Blood microarray data (n = 646) from the ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) study were used for validation. We tested the association between blood gene expression and AE-COPDs. We imputed the abundance of leukocyte subtypes and tested their association with prospective AE-COPDs. Flow cytometry was performed on blood in SPIROMICS (Subpopulations and Intermediate Outcomes in COPD Study) (n = 127), and activation markers for T cells were tested for association with prospective AE-COPDs. Measurements and Main Results: Exacerbations were reported 4,030 and 2,368 times during follow-up in COPDGene (5.3 ± 1.7 yr) and ECLIPSE (3 yr), respectively. We identified 890, 675, and 3,217 genes associated with a history of AE-COPDs, persistent exacerbations (at least one exacerbation per year), and prospective exacerbation rate, respectively. In COPDGene, the number of prospective exacerbations in patients with COPD (Global Initiative for Chronic Obstructive Lung Disease stage ⩾2) was negatively associated with circulating CD8+ T cells, CD4+ T cells, and resting natural killer cells. The negative association with naive CD4+ T cells was replicated in ECLIPSE. In the flow-cytometry study, an increase in CTLA4 on CD4+ T cells was positively associated with AE-COPDs. Conclusions: Individuals with COPD with lower circulating lymphocyte counts, particularly decreased CD4+ T cells, are more susceptible to AE-COPDs, including persistent exacerbations.

Reduced quantity and function of pneumococcal antibodies are associated with exacerbations of COPD in SPIROMICS

While hypogammaglobulinemia is associated with COPD exacerbations, it is unknown whether frequent exacerbators have specific defects in antibody production/function. We hypothesized that reduced quantity/function of serum pneumococcal antibodies correlate with exacerbation risk in the SPIROMICS cohort. We measured total pneumococcal IgG in n = 764 previously vaccinated participants with COPD. In a propensity-matched subset of n = 200 with vaccination within five years (n = 50 without exacerbations in the previous year; n = 75 with one, n = 75 with ≥2), we measured pneumococcal IgG for 23 individual serotypes, and pneumococcal antibody function for 4 serotypes. Higher total pneumococcal IgG, serotype-specific IgG (17/23 serotypes), and antibody function (3/4 serotypes) were independently associated with fewer prior exacerbations. Higher pneumococcal IgG (5/23 serotypes) predicted lower exacerbation risk in the following year. Pneumococcal antibodies are inversely associated with exacerbations, supporting the presence of immune defects in frequent exacerbators. With further study, pneumococcal antibodies may be useful biomarkers for immune dysfunction in COPD.

Integrative weighted molecular network construction from transcriptomics and genome wide association data to identify shared genetic biomarkers for COPD and lung cancer

Chronic obstructive pulmonary disease (COPD) is a multifactorial progressive airflow obstruction in the lungs, accounting for high morbidity and mortality across the world. This study aims to identify potential COPD blood-based biomarkers by analyzing the dysregulated gene expression patterns in blood and lung tissues with the help of robust computational approaches. The microarray gene expression datasets from blood (136 COPD and 6 controls) and lung tissues (16 COPD and 19 controls) were analyzed to detect shared differentially expressed genes (DEGs). Then these DEGs were used to construct COPD protein network-clusters and functionally enrich them against gene ontology annotation terms. The hub genes in the COPD network clusters were then queried in GWAS catalog and in several cancer expression databases to explore their pathogenic roles in lung cancers. The comparison of blood and lung tissue datasets revealed 63 shared DEGs. Of these DEGs, 12 COPD hub gene-network clusters (SREK1, TMEM67, IRAK2, MECOM, ASB4, C1QTNF2, CDC42BPA, DPF3, DET1, CCDC74B, KHK, and DDX3Y) connected to dysregulations of protein degradation, inflammatory cytokine production, airway remodeling, and immune cell activity were prioritized with the help of protein interactome and functional enrichment analysis. Interestingly, IRAK2 and MECOM hub genes from these COPD network clusters are known for their involvement in different pulmonary diseases. Additional COPD hub genes like SREK1, TMEM67, CDC42BPA, DPF3, and ASB4 were identified as prognostic markers in lung cancer, which is reported in 1% of COPD patients. This study identified 12 gene network- clusters as potential blood based genetic biomarkers for COPD diagnosis and prognosis.

Sputum Cytokine Profiling in COPD: Comparison Between Stable Disease and Exacerbation

Purpose

Cytokines are extracellular signaling proteins that have been widely implicated in the pathogenesis of chronic obstructive pulmonary disease (COPD). Here, we investigated cytokine expression both at the mRNA and protein level in the sputum of healthy individuals, stable COPD patients, and those experiencing a severe acute exacerbation (AECOPD) requiring hospitalization.

Patients and Methods

Sputum was collected in 19 healthy controls, 25 clinically stable COPD patients, and 31 patients with AECOPD. In AECOPD patients sample collection was performed both at the time of hospital admission and at discharge following treatment. Sputum supernatant was analyzed by an antibody microarray detecting 120 cytokines simultaneously, while the mRNA expression of 14 selected cytokines in sputum cells was investigated by real-time PCR (qPCR).

Results

Proteomic analysis identified interleukin (IL)-6 and growth-regulated oncogene (GRO)α as the only sputum cytokines that were differentially expressed between stable COPD patients and healthy controls. At the onset of AECOPD, several cytokines exhibited altered sputum expression compared to stable COPD. Recovery from AECOPD induced significant changes in the sputum cytokine protein profile; however, the length of hospitalization was insufficient for most cytokines to return to stable levels. With regard to gene expression analysis by qPCR, we found that bone morphogenetic protein (BMP)-4 was up-regulated, while IL-1α, monokine-induced by interferon-γ (MIG), and BMP-6 were down-regulated at the mRNA level in patients with AECOPD compared to stable disease.

Conclusion

The sputum cytokine signature of AECOPD differs from that of stable COPD. Protein level changes are asynchronous with changes in gene expression at the mRNA level in AECOPD. The observation that the levels of most cytokines do not stabilize with acute treatment of AECOPD suggests a prolonged effect of exacerbation on the status of COPD patients.

Causal Path of COPD Progression-Associated Genes in Different Biological Samples

Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease with pulmonary and extra-pulmonary complications. Due to the disease's systemic nature, many investigations investigated the genetic alterations in various biological samples. We aimed to infer causal genes in COPD's pathogenesis in different biological samples using elastic-net logistic regression and the Structural Equation Model. Samples of small airway epithelial cells, bronchoalveolar lavage macrophages, lung tissue biopsy, sputum, and blood samples were selected (135, 70, 235, 143, and 226 samples, respectively). Elastic-net Logistic Regression analysis was implemented to identify the most important genes involved in COPD progression. Thirty-three candidate genes were identified as essential factors in the pathogenesis of COPD and regulation of lung function. Recognized candidate genes in small airway epithelial (SAE) cells have the highest area under the ROC curve (AUC = 97%, SD = 3.9%). Our analysis indicates that macrophages and epithelial cells are more influential in COPD progression at the transcriptome level.

Associations of Monocyte Count and Other Immune Cell Types with Interstitial Lung Abnormalities

Rationale: Higher blood monocyte counts are associated with worse survival in adults with clinically diagnosed pulmonary fibrosis. Their association with the development and progression of interstitial lung abnormalities (ILA) in humans is unknown. Objectives: We evaluated the associations of blood monocyte count, and other immune cell types, with ILA, high-attenuation areas, and FVC in four independent cohorts. Methods: We included participants with measured monocyte counts and computed tomographic (CT) imaging enrolled in MESA (Multi-Ethnic Study of Atherosclerosis, n = 484), AGES-Reykjavik (Age/Gene Environment Susceptibility Study, n = 3,547), COPDGene (Genetic Epidemiology of COPD, n = 2,719), and the ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points, n = 646). Measurements and Main Results: After adjustment for covariates, a 1-SD increment in blood monocyte count was associated with ILA in MESA (odds ratio [OR], 1.3; 95% confidence interval [CI], 1.0-1.8), AGES-Reykjavik (OR, 1.2; 95% CI, 1.1-1.3), COPDGene (OR, 1.3; 95% CI, 1.2-1.4), and ECLIPSE (OR, 1.2; 95% CI, 1.0-1.4). A higher monocyte count was associated with ILA progression over 5 years in AGES-Reykjavik (OR, 1.2; 95% CI, 1.0-1.3). Compared with participants without ILA, there was a higher percentage of activated monocytes among those with ILA in MESA. Higher monocyte count was associated with greater high-attenuation areas in MESA and lower FVC in MESA and COPDGene. Associations of other immune cell types were less consistent. Conclusions: Higher blood monocyte counts were associated with the presence and progression of interstitial lung abnormalities and lower FVC.

Development of a Blood-based Transcriptional Risk Score for Chronic Obstructive Pulmonary Disease

Rationale: The ability of peripheral blood biomarkers to assess chronic obstructive pulmonary disease (COPD) risk and progression is unknown. Genetics and gene expression may capture important aspects of COPD-related biology that predict disease activity. Objectives: Develop a transcriptional risk score (TRS) for COPD and assess the contribution of the TRS and a polygenic risk score (PRS) for disease susceptibility and progression. Methods: We randomly split 2,569 COPDGene (Genetic Epidemiology of COPD) participants with whole-blood RNA sequencing into training (n = 1,945) and testing (n = 624) samples and used 468 ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points) COPD cases with microarray data for replication. We developed a TRS using penalized regression (least absolute shrinkage and selection operator) to model FEV1/FVC and studied the predictive value of TRS for COPD (Global Initiative for Chronic Obstructive Lung Disease 2-4), prospective FEV1 change (ml/yr), and additional COPD-related traits. We adjusted for potential confounders, including age and smoking. We evaluated the predictive performance of the TRS in the context of a previously derived PRS and clinical factors. Measurements and Main Results: The TRS included 147 transcripts and was associated with COPD (odds ratio, 3.3; 95% confidence interval [CI], 2.4-4.5; P < 0.001), FEV1 change (β, -17 ml/yr; 95% CI, -28 to -6.6; P = 0.002), and other COPD-related traits. In ECLIPSE cases, we replicated the association with FEV1 change (β, -8.2; 95% CI, -15 to -1; P = 0.025) and the majority of other COPD-related traits. Models including PRS, TRS, and clinical factors were more predictive of COPD (area under the receiver operator characteristic curve, 0.84) and annualized FEV1 change compared with models with one risk score or clinical factors alone. Conclusions: Blood transcriptomics can improve prediction of COPD and lung function decline when added to a PRS and clinical risk factors.

Mmp12 Is Upregulated by in utero Second-Hand Smoke Exposures and Is a Key Factor Contributing to Aggravated Lung Responses in Adult Emphysema, Asthma, and Lung Cancer Mouse Models

Matrix metalloproteinase-12 (Mmp12) is upregulated by cigarette smoke (CS) and plays a critical role in extracellular matrix remodeling, a key mechanism involved in physiological repair processes, and in the pathogenesis of emphysema, asthma, and lung cancer. While cigarette smoking is associated with the development of chronic obstructive pulmonary diseases (COPD) and lung cancer, in utero exposures to CS and second-hand smoke (SHS) are associated with asthma development in the offspring. SHS is an indoor air pollutant that causes known adverse health effects; however, the mechanisms by which in utero SHS exposures predispose to adult lung diseases, including COPD, asthma, and lung cancer, are poorly understood. In this study, we tested the hypothesis that in utero SHS exposure aggravates adult-induced emphysema, asthma, and lung cancer.

Methods: Pregnant BALB/c mice were exposed from gestational days 6–19 to either 3 or 10mg/m³ of SHS or filtered air. At 10, 11, 16, or 17weeks of age, female offspring were treated with either saline for controls, elastase to induce emphysema, house-dust mite (HDM) to initiate asthma, or urethane to promote lung cancer. At sacrifice, specific disease-related lung responses including lung function, inflammation, gene, and protein expression were assessed.

Results: In the elastase-induced emphysema model, in utero SHS-exposed mice had significantly enlarged airspaces and up-regulated expression of Mmp12 (10.3-fold compared to air-elastase controls). In the HDM-induced asthma model, in utero exposures to SHS produced eosinophilic lung inflammation and potentiated Mmp12 gene expression (5.7-fold compared to air-HDM controls). In the lung cancer model, in utero exposures to SHS significantly increased the number of intrapulmonary metastases at 58weeks of age and up-regulated Mmp12 (9.3-fold compared to air-urethane controls). In all lung disease models, Mmp12 upregulation was supported at the protein level.

Conclusion: Our findings revealed that in utero SHS exposures exacerbate lung responses to adult-induced emphysema, asthma, and lung cancer. Our data show that MMP12 is up-regulated at the gene and protein levels in three distinct adult lung disease models following in utero SHS exposures, suggesting that MMP12 is central to in utero SHS-aggravated lung responses.

Identification of Metabolism-Associated Molecular Subtypes of Chronic Obstructive Pulmonary Disease

Purpose

This study aimed to identify the COPD molecular subtypes reflecting pulmonary function damage on the basis of metabolism-related gene expression, which provided the opportunity to study the metabolic heterogeneity and the association of metabolic pathways with pulmonary function damage.

Methods

Univariate linear regression and the Boruta algorithm were used to select metabolism-related genes associated with forced expiratory volume in the first second (FEV1) and FEV1/forced vital capacity (FVC) in the Evaluation of COPD to Longitudinally Identify Predictive Surrogate Endpoints (ECLIPSE) cohort. COPD subtypes were further identified by consensus clustering with best-fit. Then, we analyzed the differences in the clinical characteristics, metabolic pathways, immune cell characteristics, and transcription features among the subtypes.

Results

This study identified two subtypes (C1 and C2). C1 exhibited higher levels of lower pulmonary function and innate immunity than C2. Ten metabolic pathways were confirmed as key metabolic pathways. The pathways related to N-glycan, hexosamine, purine, alanine, aspartate and glutamate tended to be positively associated with the abundance of adaptive immune cells and negatively associated with the abundance of innate immune cells. In addition, other pathways had opposite trends. All results were verified in Genetic Epidemiology of COPD (COPDGene) datasets.

Conclusion

The two subtypes reflect the pulmonary function damage and help to further understand the metabolic mechanism of pulmonary function in COPD. Further studies are needed to prove the prognostic and therapeutic value of the subtypes.

Genetics plays a limited role in predicting chronic obstructive pulmonary disease treatment response and exacerbation

BACKGROUND

Combination treatments, targeting multiple disease processes, benefit subjects with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). However, predicting treatment response and exacerbation risk remain challenging.

OBJECTIVE

To identify genetic associations with AECOPD risk and response to combination therapy (fluticasone furoate, umeclidinium bromide and vilanterol).

METHODS

The genetic basis of AECOPD disease was investigated in 19,841 subjects from 23 clinical studies and 2 disease cohorts to identify exacerbation disease targets. AECOPD pharmacogenetic effects were examined in 8439 moderate to severe COPD patients with exacerbation rate, lung function and quality of life endpoints; results were followed up in an additional 2201 subjects.

RESULTS

We did not identify significant associations in the AECOPD disease analysis. In the AECOPD pharmacogenetics analysis, rs56195836 (MAPK8) was significantly associated with moderate to severe exacerbation rate in subjects on fluticasone furoate with baseline blood eosinophils ≥150 cells/μl (P = 1.8 × 10^-8). Post-hoc, one variant was associated with on-treatment moderate to severe exacerbation rate stratifying by exacerbation history. AZU1 rs1962343 was significantly associated in subjects with frequent moderate exacerbation history when treated with fluticasone furoate/vilanterol (P = 1.1 × 10^-8). Neither of these signals was supported in independent follow-up.

CONCLUSION

Common genetic variants do not play major roles in AECOPD disease nor predict response to triple therapy or its components in moderate to very severe COPD.

Identification of Macrophage Polarization-Related Genes as Biomarkers of Chronic Obstructive Pulmonary Disease Based on Bioinformatics Analyses

OBJECTIVES

Chronic obstructive pulmonary disease (COPD) is characterized by lung inflammation and remodeling. Macrophage polarization is associated with inflammation and tissue remodeling, as well as immunity. Therefore, this study attempts to investigate the diagnostic value and regulatory mechanism of macrophage polarization-related genes for COPD by bioinformatics analysis and to provide a new theoretical basis for experimental research.

METHODS

The raw gene expression profile dataset (GSE124180) was collected from the Gene Expression Omnibus (GEO) database. Next, a weighted gene coexpression network analysis (WGCNA) was conducted to screen macrophage polarization-related genes. The differentially expressed genes (DEGs) between the COPD and normal samples were generated using DESeq2 v3.11 and overlapped with the macrophage polarization-related genes. Moreover, functional annotations of overlapped genes were conducted by Database for Annotation, Visualization and Integrated Discovery (DAVID) Bioinformatics Resource. The immune-related genes were selected, and their correlation with the differential immune cells was analyzed by Pearson. Finally, receiver operating characteristic (ROC) curves were used to verify the diagnostic value of genes.

RESULTS

A total of 4922 coexpressed genes related to macrophage polarization were overlapped with the 203 DEGs between the COPD and normal samples, obtaining 25 genes related to COPD and macrophage polarization. GEM, S100B, and GZMA of them participated in the immune response, which were considered the candidate biomarkers. GEM and S100B were significantly correlated with marker genes of B cells which had a significant difference between the COPD and normal samples. Moreover, GEM was highly associated with the genes in the PI3K/Akt/GSK3β signaling pathway, regulation of actin cytoskeleton, and calcium signaling pathway based on a Pearson correlation analysis of the candidate genes and the genes in the B cell receptor signaling pathway. PPI network analysis also indicated that GEM might participate in the regulation of the PI3K/Akt/GSK3β signaling pathway. The ROC curve showed that GEM possessed an excellent accuracy in distinguishing COPD from normal samples.

CONCLUSIONS

The data provide a transcriptome-based evidence that GEM is related to COPD and macrophage polarization likely contributes to COPD diagnosis. At the same time, it is hoped that in-depth functional mining can provide new ideas for exploring the COPD pathogenesis.

Exploring PI3Kδ Molecular Pathways in Stable COPD and Following an Acute Exacerbation, Two Randomized Controlled Trials

Background

Inhibition of phosphoinositide 3-kinase δ (PI3Kδ) exerts corrective effects on the dysregulated migration characteristics of neutrophils isolated from patients with chronic obstructive pulmonary disease (COPD).

Objective

To develop novel, induced sputum endpoints to demonstrate changes in neutrophil phenotype in the lung by administering nemiralisib, a potent and selective inhaled PI3Kδ inhibitor, to patients with stable COPD or patients with acute exacerbation (AE) of COPD.

Methods

In two randomized, double-blind, placebo-controlled clinical trials patients with A) stable COPD (N=28, randomized 3:1) or B) AECOPD (N=44, randomized 1:1) received treatment with inhaled nemiralisib (1mg). Endpoints included induced sputum at various time points before and during treatment for the measurement of transcriptomics (primary endpoint), inflammatory mediators, functional respiratory imaging (FRI), and spirometry.

Results

In stable COPD patients, the use of nemiralisib was associated with alterations in sputum neutrophil transcriptomics suggestive of an improvement in migration phenotype; however, the same nemiralisib-evoked effects were not observed in AECOPD. Inhibition of sputum inflammatory mediators was also observed in stable but not AECOPD patients. In contrast, a placebo-corrected improvement in forced expiratory volume in 1 sec of 136 mL (95% Credible Intervals -46, 315mL) with a probability that the true treatment ratio was >0% (Pr(θ>0)) of 93% was observed in AECOPD. However, FRI endpoints remained unchanged.

Conclusion

We provide evidence for nemiralisib-evoked changes in neutrophil migration phenotype in stable COPD but not AECOPD, despite improving lung function in the latter group. We conclude that induced sputum can be used for measuring evidence of alteration of neutrophil phenotype in stable patients, and our study provides a data set of the sputum transcriptomic changes during recovery from AECOPD.

Differential expression of inflammatory responsive genes between chronic periodontitis and periodontally affected bronchiectasis patients

The study aimed to investigate differential expression of targeted inflammatory-immune responsive genes [LTA, LTB, TNFSF4, TNFSF11/RANKL, TNFSF13, TNFSF13B, TNFRSF11B/ Osteoprotegerin; OPG and GFPT1/GFA ] in gingival tissues of bronchiectasis patients having chronic periodontitis in North central Indian population. Gingival tissues were collected from 30 systemically healthy chronic periodontitis patients (CP), 30 bronchiectasis patients with chronic periodontitis (B+CP), 3 systemically healthy with healthy gingiva (healthy control; HC) and 3 bronchiectasis with healthy gingiva (bronchiectasis control; BC). Statistical analysis revealed 7 genes to be significantly upregulated on comparing CP with B+CP i.e LTA (P<0.0001) in B+CP while LTB (P<0.0001), TNFSF4 (P=0.0003), TNFSF11 (P<0.0001), TNFSF13 (P=0.0003), TNFSF13B (P<0.0001) and TNFRSF11B (P=0.0004) in CP group. LTA (Lymphotoxin A) gene could be a potential genetic marker in bronchiectasis patients with chronic periodontitis.

Identification of biomarkers associated with clinical severity of chronic obstructive pulmonary disease

We sought to identify the biomarkers related to the clinical severity of stage I to stage IV chronic obstructive pulmonary disease (COPD). Gene expression profiles from the blood samples of COPD patients at each of the four stages were acquired from the Gene Expression Omnibus Database (GEO, accession number: GSE54837). Genes showing expression changes among the different stages were sorted by soft clustering. We performed functional enrichment, protein-protein interaction (PPI), and miRNA regulatory network analyses for the differentially expressed genes. The biomarkers associated with the clinical classification of COPD were selected from logistic regression models and the relationships between TLR2 and inflammatory factors were verified in clinical blood samples by qPCR and ELISA. Gene clusters demonstrating continuously rising or falling changes in expression (clusters 1, 2, and 7 and clusters 5, 6, and 8, respectively) from stage I to IV were defined as upregulated and downregulated genes, respectively, and further analyzed. The upregulated genes were enriched in functions associated with defense, inflammatory, or immune responses. The downregulated genes were associated with lymphocyte activation and cell activation. TLR2, HMOX1, and CD79A were hub proteins in the integrated network of PPI and miRNA regulatory networks. TLR2 and CD79A were significantly correlated with clinical classifications. TLR2 was closely associated with inflammatory responses during COPD progression. Functions associated with inflammatory and immune responses as well as lymphocyte activation may play important roles in the progression of COPD from stage I to IV. TLR2 and CD79A may serve as potential biomarkers for the clinical severity of COPD. TLR2 and CD79A may also serve as independent biomarkers in the clinical classification in COPD. TLR2 may play an important role in the inflammatory responses of COPD.

Vitamin D Metabolism Is Dysregulated in Asthma and Chronic Obstructive Pulmonary Disease

Rationale: Vitamin D deficiency is common in patients with asthma and chronic obstructive pulmonary disease (COPD). Low 25-hydroxyvitamin D (25[OH]D) levels may represent a cause or a consequence of these conditions.Objectives: To determine whether vitamin D metabolism is altered in asthma or COPD.Methods: We conducted a longitudinal study in 186 adults to determine whether the 25(OH)D response to six oral doses of 3 mg vitamin D₃, administered over 1 year, differed between those with asthma or COPD versus control subjects. Serum concentrations of vitamin D₃, 25(OH)D₃, and 1α,25-dihydroxyvitamin D₃ (1α,25[OH]₂D₃) were determined presupplementation and postsupplementation in 93 adults with asthma, COPD, or neither condition, and metabolite-to-parent compound molar ratios were compared between groups to estimate hydroxylase activity. Additionally, we analyzed 14 datasets to compare expression of 1α,25(OH)₂D₃-inducible gene expression signatures in clinical samples taken from adults with asthma or COPD versus control subjects.Measurements and Main Results: The mean postsupplementation 25(OH)D increase in participants with asthma (20.9 nmol/L) and COPD (21.5 nmol/L) was lower than in control subjects (39.8 nmol/L; P = 0.001). Compared with control subjects, patients with asthma and COPD had lower molar ratios of 25(OH)D₃-to-vitamin D₃ and higher molar ratios of 1α,25(OH)₂D₃-to-25(OH)D₃ both presupplementation and postsupplementation (P ≤ 0.005). Intergroup differences in 1α,25(OH)₂D₃-inducible gene expression signatures were modest and variable if statistically significant.Conclusions: Attenuation of the 25(OH)D response to vitamin D supplementation in asthma and COPD associated with reduced molar ratios of 25(OH)D₃-to-vitamin D₃ and increased molar ratios of 1α,25(OH)₂D₃-to-25(OH)D₃ in serum, suggesting that vitamin D metabolism is dysregulated in these conditions.

Exploration of the sputum methylome and omics deconvolution by quadratic programming in molecular profiling of asthma and COPD: the road to sputum omics 2.0

BACKGROUND

To date, most studies involving high-throughput analyses of sputum in asthma and COPD have focused on identifying transcriptomic signatures of disease. No whole-genome methylation analysis of sputum cells has been performed yet. In this context, the highly variable cellular composition of sputum has potential to confound the molecular analyses.

METHODS

Whole-genome transcription (Agilent Human 4 × 44 k array) and methylation (Illumina 450 k BeadChip) analyses were performed on sputum samples of 9 asthmatics, 10 healthy and 10 COPD subjects. RNA integrity was checked by capillary electrophoresis and used to correct in silico for bias conferred by RNA degradation during biobank sample storage. Estimates of cell type-specific molecular profiles were derived via regression by quadratic programming based on sputum differential cell counts. All analyses were conducted using the open-source R/Bioconductor software framework.

RESULTS

A linear regression step was found to perform well in removing RNA degradation-related bias among the main principal components of the gene expression data, increasing the number of genes detectable as differentially expressed in asthma and COPD sputa (compared to controls). We observed a strong influence of the cellular composition on the results of mixed-cell sputum analyses. Exemplarily, upregulated genes derived from mixed-cell data in asthma were dominated by genes predominantly expressed in eosinophils after deconvolution. The deconvolution, however, allowed to perform differential expression and methylation analyses on the level of individual cell types and, though we only analyzed a limited number of biological replicates, was found to provide good estimates compared to previously published data about gene expression in lung eosinophils in asthma. Analysis of the sputum methylome indicated presence of differential methylation in genomic regions of interest, e.g. mapping to a number of human leukocyte antigen (HLA) genes related to both major histocompatibility complex (MHC) class I and II molecules in asthma and COPD macrophages. Furthermore, we found the SMAD3 (SMAD family member 3) gene, among others, to lie within differentially methylated regions which has been previously reported in the context of asthma.

CONCLUSIONS

In this methodology-oriented study, we show that methylation profiling can be easily integrated into sputum analysis workflows and exhibits a strong potential to contribute to the profiling and understanding of pulmonary inflammation. Wherever RNA degradation is of concern, in silico correction can be effective in improving both sensitivity and specificity of downstream analyses. We suggest that deconvolution methods should be integrated in sputum omics analysis workflows whenever possible in order to facilitate the unbiased discovery and interpretation of molecular patterns of inflammation.

Heme metabolism genes Downregulated in COPD Cachexia

INTRODUCTION

Cachexia contributes to increased mortality and reduced quality of life in Chronic Obstructive Pulmonary Disease (COPD) and may be associated with underlying gene expression changes. Our goal was to identify differential gene expression signatures associated with COPD cachexia in current and former smokers.

METHODS

We analyzed whole-blood gene expression data from participants with COPD in a discovery cohort (COPDGene, N = 400) and assessed replication (ECLIPSE, N = 114). To approximate the consensus definition using available criteria, cachexia was defined as weight-loss > 5% in the past 12 months or low body mass index (BMI) (< 20 kg/m2) and 1/3 criteria: decreased muscle strength (six-minute walk distance < 350 m), anemia (hemoglobin < 12 g/dl), and low fat-free mass index (FFMI) (< 15 kg/m2 among women and < 17 kg/m2 among men) in COPDGene. In ECLIPSE, cachexia was defined as weight-loss > 5% in the past 12 months or low BMI and 3/5 criteria: decreased muscle strength, anorexia, abnormal biochemistry (anemia or high c-reactive protein (> 5 mg/l)), fatigue, and low FFMI. Differential gene expression was assessed between cachectic and non-cachectic subjects, adjusting for age, sex, white blood cell counts, and technical covariates. Gene set enrichment analysis was performed using MSigDB.

RESULTS

The prevalence of COPD cachexia was 13.7% in COPDGene and 7.9% in ECLIPSE. Fourteen genes were differentially downregulated in cachectic versus non-cachectic COPD patients in COPDGene (FDR < 0.05) and ECLIPSE (FDR < 0.05).

DISCUSSION

Several replicated genes regulating heme metabolism were downregulated among participants with COPD cachexia. Impaired heme biosynthesis may contribute to cachexia development through free-iron buildup and oxidative tissue damage.

Omics and the Search for Blood Biomarkers in Chronic Obstructive Pulmonary Disease. Insights from COPDGene

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.

Discovering the genes mediating the interactions between chronic respiratory diseases in the human interactome

The molecular and clinical features of a complex disease can be influenced by other diseases affecting the same individual. Understanding disease-disease interactions is therefore crucial for revealing shared molecular mechanisms among diseases and designing effective treatments. Here we introduce Flow Centrality (FC), a network-based approach to identify the genes mediating the interaction between two diseases in a protein-protein interaction network. We focus on asthma and COPD, two chronic respiratory diseases that have been long hypothesized to share common genetic determinants and mechanisms. We show that FC highlights potential mediator genes between the two diseases, and observe similar outcomes when applying FC to 66 additional pairs of related diseases. Further, we perform in vitro perturbation experiments on a widely replicated asthma gene, GSDMB, showing that FC identifies candidate mediators of the interactions between GSDMB and COPD-associated genes. Our results indicate that FC predicts promising gene candidates for further study of disease-disease interactions.

Working Towards a Blood-Derived Gene Expression Biomarker Specific for Alzheimer's Disease

BACKGROUND

The typical approach to identify blood-derived gene expression signatures as a biomarker for Alzheimer's disease (AD) have relied on training classification models using AD and healthy controls only. This may inadvertently result in the identification of markers for general illness rather than being disease-specific.

OBJECTIVE

Investigate whether incorporating additional related disorders in the classification model development process can lead to the discovery of an AD-specific gene expression signature.

METHODS

Two types of XGBoost classification models were developed. The first used 160 AD and 127 healthy controls and the second used the same 160 AD with 6,318 upsampled mixed controls consisting of Parkinson's disease, multiple sclerosis, amyotrophic lateral sclerosis, bipolar disorder, schizophrenia, coronary artery disease, rheumatoid arthritis, chronic obstructive pulmonary disease, and cognitively healthy subjects. Both classification models were evaluated in an independent cohort consisting of 127 AD and 687 mixed controls.

RESULTS

The AD versus healthy control models resulted in an average 48.7% sensitivity (95% CI = 34.7-64.6), 41.9% specificity (95% CI = 26.8-54.3), 13.6% PPV (95% CI = 9.9-18.5), and 81.1% NPV (95% CI = 73.3-87.7). In contrast, the mixed control models resulted in an average of 40.8% sensitivity (95% CI = 27.5-52.0), 95.3% specificity (95% CI = 93.3-97.1), 61.4% PPV (95% CI = 53.8-69.6), and 89.7% NPV (95% CI = 87.8-91.4).

CONCLUSIONS

This early work demonstrates the value of incorporating additional related disorders into the classification model developmental process, which can result in models with improved ability to distinguish AD from a heterogeneous aging population. However, further improvement to the sensitivity of the test is still required.

Gene expression microarray public dataset reanalysis in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) was classified by the Centers for Disease Control and Prevention in 2014 as the 3^rd leading cause of death in the United States (US). The main cause of COPD is exposure to tobacco smoke and air pollutants. Problems associated with COPD include under-diagnosis of the disease and an increase in the number of smokers worldwide. The goal of our study is to identify disease variability in the gene expression profiles of COPD subjects compared to controls, by reanalyzing pre-existing, publicly available microarray expression datasets. Our inclusion criteria for microarray datasets selected for smoking status, age and sex of blood donors reported. Our datasets used Affymetrix, Agilent microarray platforms (7 datasets, 1,262 samples). We re-analyzed the curated raw microarray expression data using R packages, and used Box-Cox power transformations to normalize datasets. To identify significant differentially expressed genes we used generalized least squares models with disease state, age, sex, smoking status and study as effects that also included binary interactions, followed by likelihood ratio tests (LRT). We found 3,315 statistically significant (Storey-adjusted q-value <0.05) differentially expressed genes with respect to disease state (COPD or control). We further filtered these genes for biological effect using results from LRT q-value <0.05 and model estimates’ 10% two-tailed quantiles of mean differences between COPD and control), to identify 679 genes. Through analysis of disease, sex, age, and also smoking status and disease interactions we identified differentially expressed genes involved in a variety of immune responses and cell processes in COPD. We also trained a logistic regression model using the common array genes as features, which enabled prediction of disease status with 81.7% accuracy. Our results give potential for improving the diagnosis of COPD through blood and highlight novel gene expression disease signatures.

Systemic Markers of Adaptive and Innate Immunity Are Associated with Chronic Obstructive Pulmonary Disease Severity and Spirometric Disease Progression

The progression of chronic obstructive pulmonary disease (COPD) is associated with marked alterations in circulating immune cell populations, but no studies have characterized alterations in these cell types across the full spectrum of lung function impairment in current and former smokers. In 6,299 subjects from the COPDGene and ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) studies, we related Coulter blood counts and proportions to cross-sectional forced expiratory volume in 1 second (FEV₁), adjusting for current smoking status. We also related cell count measures to 3-year change in FEV₁ in ECLIPSE subjects. In a subset of subjects with blood gene expression data, we used cell type deconvolution methods to infer the proportions of immune cell subpopulations, and we related these to COPD clinical status. We observed that FEV₁ levels are positively correlated with lymphocytes and negatively correlated with myeloid populations, such as neutrophils and monocytes. In multivariate models, absolute cell counts and proportions were associated with cross-sectional FEV₁, and lymphocytes, monocytes, and eosinophil counts were predictive of 3-year change in lung function. Using cell type deconvolution to study immune cell subpopulations, we observed that subjects with COPD had a lower proportion of CD4⁺ resting memory cells and naive B cells compared with smokers without COPD. Alterations in circulating immune cells in COPD support a mixed pattern of lymphocyte suppression and an enhanced myeloid cell immune response. Cell counts and proportions contribute independent information to models predicting lung function, suggesting a critical role for immune response in long-term COPD outcomes. Cell type deconvolution is a promising method for immunophenotyping in large cohorts.

Elimination of bacterial DNA during RNA isolation from sputum: Bashing bead vortexing is preferable over prolonged DNase treatment

Sputum often contains large amounts of contaminating bacterial DNA that, if not eliminated during RNA isolation, may interfere with gene expression studies. During RNA isolation only repeated DNase treatment can effectively remove contaminating bacterial DNA from samples, but this compromises RNA quality. In this study we tested alternative methods to facilitate the removal of DNA and improve the quality of RNA obtained. Sputum samples obtained from patients with chronic obstructive pulmonary disease were processed with dithiothreitol and subjected to various RNA isolation methods, yet with modified protocols. Modifications included prolonged DNase treatment or vortexing of sputum cells in the presence of beads prior to RNA isolation. Bacterial DNA contamination was tested by PCR using universal bacterial primers, while RNA quality was assessed by real-time PCR using GAPDH primers for amplicons of different length. We found that the RNeasy Plus Mini kit equipped with the gDNA eliminator spin column was able to completely eliminate bacterial DNA, if sputum cells were lysed in the presence of bashing beads. Notably, compared with the standard protocol, the modified procedure yielded better quality RNA as well, as indicated by improved threshold profiles of qPCR. Bead vortexing of cells was less effective when combined with other RNA isolation methods, and the repeated DNase treatment needed to completely remove contaminating DNA from the samples reduced the quality of RNA markedly. Bead vortexing in combination with certain RNA extraction methods greatly facilitates the isolation of sputum RNA that is free of contaminating bacterial DNA, and is suitable for downstream applications.

Batch adjustment by reference alignment (BARA): Improved prediction performance in biological test sets with batch effects

Many biological data acquisition platforms suffer from inadvertent inclusion of biologically irrelevant variance in analyzed data, collectively termed batch effects. Batch effects can lead to difficulties in downstream analysis by lowering the power to detect biologically interesting differences and can in certain instances lead to false discoveries. They are especially troublesome in predictive modelling where samples in training sets and test sets are often completely correlated with batches. In this article, we present BARA, a normalization method for adjusting batch effects in predictive modelling. BARA utilizes a few reference samples to adjust for batch effects in a compressed data space spanned by the training set. We evaluate BARA using a collection of publicly available datasets and three different prediction models, and compare its performance to already existing methods developed for similar purposes. The results show that data normalized with BARA generates high and consistent prediction performances. Further, they suggest that BARA produces reliable performances independent of the examined classifiers. We therefore conclude that BARA has great potential to facilitate the development of predictive assays where test sets and training sets are correlated with batch.

Association study in African-admixed populations across the Americas recapitulates asthma risk loci in non-African populations

Asthma is a complex disease with striking disparities across racial and ethnic groups. Despite its relatively high burden, representation of individuals of African ancestry in asthma genome-wide association studies (GWAS) has been inadequate, and true associations in these underrepresented minority groups have been inconclusive. We report the results of a genome-wide meta-analysis from the Consortium on Asthma among African Ancestry Populations (CAAPA; 7009 asthma cases, 7645 controls). We find strong evidence for association at four previously reported asthma loci whose discovery was driven largely by non-African populations, including the chromosome 17q12-q21 locus and the chr12q13 region, a novel (and not previously replicated) asthma locus recently identified by the Trans-National Asthma Genetic Consortium (TAGC). An additional seven loci reported by TAGC show marginal evidence for association in CAAPA. We also identify two novel loci (8p23 and 8q24) that may be specific to asthma risk in African ancestry populations.

Do sputum or circulating blood samples reflect the pulmonary transcriptomic differences of COPD patients? A multi-tissue transcriptomic network META-analysis

BACKGROUND

Previous studies have identified lung, sputum or blood transcriptomic biomarkers associated with the severity of airflow limitation in COPD. Yet, it is not clear whether the lung pathobiology is mirrored by these surrogate tissues. The aim of this study was to explore this question.

METHODS

We used Weighted Gene Co-expression Network Analysis (WGCNA) to identify shared pathological mechanisms across four COPD gene-expression datasets: two sets of lung tissues (L1 n = 70; L2 n = 124), and one each of induced sputum (S; n = 121) and peripheral blood (B; n = 121).

RESULTS

WGCNA analysis identified twenty-one gene co-expression modules in L1. A robust module preservation between the two L datasets was observed (86%), with less preservation in S (33%) and even less in B (23%). Three modules preserved across lung tissues and sputum (not blood) were associated with the severity of airflow limitation. Ontology enrichment analysis showed that these modules included genes related to mitochondrial function, ion-homeostasis, T cells and RNA processing. These findings were largely reproduced using the consensus WGCNA network approach.

CONCLUSIONS

These observations indicate that major differences in lung tissue transcriptomics in patients with COPD are poorly mirrored in sputum and are unrelated to those determined in blood, suggesting that the systemic component in COPD is independently regulated. Finally, the fact that one of the preserved modules associated with FEV1 was enriched in mitochondria-related genes supports a role for mitochondrial dysfunction in the pathobiology of COPD.

Integration of Molecular Interactome and Targeted Interaction Analysis to Identify a COPD Disease Network Module

The polygenic nature of complex diseases offers potential opportunities to utilize network-based approaches that leverage the comprehensive set of protein-protein interactions (the human interactome) to identify new genes of interest and relevant biological pathways. However, the incompleteness of the current human interactome prevents it from reaching its full potential to extract network-based knowledge from gene discovery efforts, such as genome-wide association studies, for complex diseases like chronic obstructive pulmonary disease (COPD). Here, we provide a framework that integrates the existing human interactome information with experimental protein-protein interaction data for FAM13A, one of the most highly associated genetic loci to COPD, to find a more comprehensive disease network module. We identified an initial disease network neighborhood by applying a random-walk method. Next, we developed a network-based closeness approach (C_AB) that revealed 9 out of 96 FAM13A interacting partners identified by affinity purification assays were significantly close to the initial network neighborhood. Moreover, compared to a similar method (local radiality), the C_AB approach predicts low-degree genes as potential candidates. The candidates identified by the network-based closeness approach were combined with the initial network neighborhood to build a comprehensive disease network module (163 genes) that was enriched with genes differentially expressed between controls and COPD subjects in alveolar macrophages, lung tissue, sputum, blood, and bronchial brushing datasets. Overall, we demonstrate an approach to find disease-related network components using new laboratory data to overcome incompleteness of the current interactome.

A candidate gene identification strategy utilizing mouse to human big-data mining: "3R-tenet" in COPD genetic research

Background

Early life impairments leading to lower lung function by adulthood are considered as risk factors for chronic obstructive pulmonary disease (COPD). Recently, we compared the lung transcriptomic profile between two mouse strains with extreme total lung capacities to identify plausible pulmonary function determining genes using microarray analysis (GSE80078). Advancement of high-throughput techniques like deep sequencing (eg. RNA-seq) and microarray have resulted in an explosion of genomic data in the online public repositories which however remains under-exploited. Strategic curation of publicly available genomic data with a mouse-human translational approach can effectively implement “3R- Tenet” by reducing screening experiments with animals and performing mechanistic studies using physiologically relevant in vitro model systems. Therefore, we sought to analyze the association of functional variations within human orthologs of mouse lung function candidate genes in a publicly available COPD lung RNA-seq data-set.

Methods

Association of missense single nucleotide polymorphisms, insertions, deletions, and splice junction variants were analyzed for susceptibility to COPD using RNA-seq data of a Korean population (GSE57148). Expression of the associated genes were studied using the Gene Paint (mouse embryo) and Human Protein Atlas (normal adult human lung) databases. The genes were also assessed for replication of the associations and expression in COPD−/mouse cigarette smoke exposed lung tissues using other datasets.

Results

Significant association (p <  0.05) of variations in 20 genes to higher COPD susceptibility have been detected within the investigated cohort. Association of HJURP, MCRS1 and TLR8 are novel in relation to COPD. The associated ADAM19 and KIT loci have been reported earlier. The remaining 15 genes have also been previously associated to COPD. Differential transcript expression levels of the associated genes in COPD- and/ or mouse emphysematous lung tissues have been detected.

Conclusion

Our findings suggest strategic mouse-human datamining approaches can identify novel COPD candidate genes using existing datasets in the online repositories. The candidates can be further evaluated for mechanistic role through in vitro studies using appropriate primary cells/cell lines. Functional studies can be limited to transgenic animal models of only well supported candidate genes. This approach will lead to a significant reduction of animal experimentation in respiratory research.

Electronic supplementary material

The online version of this article (10.1186/s12931-018-0795-y) contains supplementary material, which is available to authorized users.

A Bayesian Approach for Learning Gene Networks Underlying Disease Severity in COPD

In this paper, we propose a Bayesian hierarchical approach to infer network structures across multiple sample groups where both shared and differential edges may exist across the groups. In our approach, we link graphs through a Markov random field prior. This prior on network similarity provides a measure of pairwise relatedness that borrows strength only between related groups. We incorporate the computational efficiency of continuous shrinkage priors, improving scalability for network estimation in cases of larger dimensionality. Our model is applied to patient groups with increasing levels of chronic obstructive pulmonary disease severity, with the goal of better understanding the break down of gene pathways as the disease progresses. Our approach is able to identify critical hub genes for four targeted pathways. Furthermore, it identifies gene connections that are disrupted with increased disease severity and that characterize the disease evolution. We also demonstrate the superior performance of our approach with respect to competing methods, using simulated data.

Estimating drivers of cell state transitions using gene regulatory network models

Background

Specific cellular states are often associated with distinct gene expression patterns. These states are plastic, changing during development, or in the transition from health to disease. One relatively simple extension of this concept is to recognize that we can classify different cell-types by their active gene regulatory networks and that, consequently, transitions between cellular states can be modeled by changes in these underlying regulatory networks.

Results

Here we describe MONSTER, MOdeling Network State Transitions from Expression and Regulatory data, a regression-based method for inferring transcription factor drivers of cell state conditions at the gene regulatory network level. As a demonstration, we apply MONSTER to four different studies of chronic obstructive pulmonary disease to identify transcription factors that alter the network structure as the cell state progresses toward the disease-state.

Conclusions

We demonstrate that MONSTER can find strong regulatory signals that persist across studies and tissues of the same disease and that are not detectable using conventional analysis methods based on differential expression. An R package implementing MONSTER is available at github.com/QuackenbushLab/MONSTER.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-017-0517-y) contains supplementary material, which is available to authorized users.

Systems Biology and Clinical Practice in Respiratory Medicine. The Twain Shall Meet

Respiratory diseases are highly complex, being driven by host-environment interactions and manifested by inflammatory, structural, and functional abnormalities that vary over time. Traditional reductionist approaches have contributed vastly to our knowledge of biological systems in health and disease to date; however, they are insufficient to provide an understanding of the behavior of the system as a whole. In this Pulmonary Perspective, we discuss systems biology approaches, especially but not limited to the study of the lung as a complex system. Such integrative approaches take into account the large number of dynamic subunits and their interactions found in biological systems. Borrowing methods from physics and mathematics, it is possible to study the collective behavior of these systems over time and in a multidimensional manner. We first examine the physiological basis for complexity in the respiratory system and its implications for disease. We then expand on the potential applications of systems biology methods to study complex systems, within the context of diagnosis and monitoring of respiratory diseases including asthma, chronic obstructive pulmonary disease (COPD), and critical illness. We summarize the significant advances made in recent years using systems approaches for disease phenotyping, applied to data ranging from the molecular to clinical level, obtained from large-scale asthma and COPD networks. We describe new studies using temporal complexity patterns to characterize asthma and COPD and predict exacerbations as well as predict adverse outcomes in critical care. We highlight new methods that are emerging with this approach and discuss remaining questions that merit greater attention in the field.

Hemosiderin in sputum macrophages may predict infective exacerbations of chronic obstructive pulmonary disease: a retrospective observational study

Background

Infective exacerbations of COPD are common and are accompanied by neutrophilic bronchitis in sputum. Increased respiratory iron content has been associated with respiratory tract infection, though it is unclear if this represents a predisposing factor for infection or the sequelae of inflammation. Iron overload, as assessed in the airways, may be an important biomarker for recurrent infective exacerbations of COPD. The purpose of our study was to determine if hemosiderin in sputum macrophages is related to infective exacerbations of COPD.

Methods

We undertook a retrospective observational study of 54 consecutive patients who presented with an exacerbation of COPD and had sputum examined including assessment for hemosiderin in alveolar macrophages. The relation between infective exacerbations in the previous two years and the percent of hemosiderin-positive macrophages was analyzed with linear regression. To account for the non-parametric distribution of infective exacerbations, negative binomial regression modelling was used to account for other covariates.

Results

The percent of hemosiderin positive alveolar macrophages (hemosiderin index), analyzed parametrically and non-parametrically, demonstrated a significant correlation with increasing numbers of infective exacerbations in the previous two years. In a multivariate regression analysis, hemosiderin index was an independent predictor of infective exacerbations. COPD patients with raised hemosiderin index (≥20%) had higher levels of sputum IL-6 compared to patients with lower levels (<20%).

Conclusions

High hemosiderin index in sputum alveolar macrophages measured at the time of AECOPD may be related to the frequency of infective exacerbations of COPD.

The recent advances of phenotypes in acute exacerbations of COPD

Exacerbations of COPD are clinically relevant events with therapeutic and prognostic implications. Yet, significant heterogeneity of clinical presentation and disease progression exists within acute exacerbations of COPD (AECOPD). Currently, different phenotypes have been widely used to describe the characteristics among patients with AECOPD. This has proved to be significant in the treatment and prediction of the outcomes of the disease. In this review of published literature, the phenotypes of AECOPD were classified according to etiology, inflammatory biomarkers, clinical manifestation, comorbidity, the frequency of exacerbations, and so on. This review concentrates on advancements in the use of phenotypes of AECOPD.

The use of cellular and molecular biomarkers to manage COPD exacerbations

INTRODUCTION

Chronic obstructive pulmonary disease (COPD) exacerbations are a common cause of respiratory morbidity and mortality, and have various etiologies. Multiple cellular and molecular biomarkers have been associated with exacerbations. Quantitative sputum cell counts are able to identify the presence and type of bronchitis, which is an important contributor to exacerbations. Their utility to monitor bronchitis and to help treat exacerbations has been evaluated, yet they are not used in routine clinical practice. Areas covered: This review will provide a brief summary of biomarkers utilized in COPD, with a focus on the application of cellular markers for the management of exacerbations. A case study will demonstrate the application of these methods. With quantitative sputum cell counts, the presence of eosinophilic bronchitis predicts corticosteroid-responsiveness, while neutrophilic bronchitis identifies infection and suggests the need for antibiotics. Gastroesophageal reflux-related aspiration and heart failure can also be identified by examining sputum. Expert commentary: Quantitative sputum cytometry is an essential tool in the management of exacerbations of COPD, particularly those prone to frequent exacerbations. Treatment based on sputum cell counts is superior to current guideline-based recommendations to prevent future exacerbations and hospitalizations in observational and single-centre controlled trials. Large multicentre clinical trials are necessary to confirm this.

Symptom profiles and inflammatory markers in moderate to severe COPD

BACKGROUND

Physical and psychological symptoms are the hallmark of patients' subjective perception of their illness. The purpose of this analysis was to determine if patients with COPD have distinctive symptom profiles and to examine the association of symptom profiles with systemic biomarkers of inflammation.

METHODS

We conducted latent class analyses of three physical (dyspnea, fatigue, and pain) and two psychological symptoms (depression and anxiety) in 302 patients with moderate to severe COPD using baseline data from a longitudinal observational study of depression in COPD. Systemic inflammatory markers included IL1, IL8, IL10, IL12, IL13, INF, GM-CSF, TNF-α (levels >75^thcentile was considered high); and CRP (levels >3 mg/L was considered high). Multinominal logistic regression models were used to examine the association between symptom classes and inflammation while adjusting for key socio-demographic and disease characteristics.

RESULTS

We found that a 4-class model best fit the data: 1) low physical and psychological symptoms (26%, Low-Phys/Low-Psych), 2) low physical but moderate psychological symptoms (18%, Low-Phys/Mod Psych), 3) high physical but moderate psychological symptoms (25%, High-Phys/Mod Psych), and 4) high physical and psychological symptoms (30%, High-Phys/High Psych). Unadjusted analyses showed associations between symptom class with high levels of IL7, IL-8 (p ≤ .10) and CRP (p < .01). In the adjusted model, those with a high CRP level were less likely to be in the High-Phys/Mod-Psych class compared to the Low-Phys/Low-Psych (OR: 0.41, 95%CI 0.19, 0.90) and Low-Phys/Mod-Psych classes (OR: 0.35, 95%CI 0.16, 0.78); elevated CRP was associated with in increased odds of being in the High-Phys/High-Psych compared to the High-Phys/Mod-Psych class (OR: 2.22, 95%CI 1.08, 4.58). Younger age, having at least a college education, oxygen use and depression history were more prominent predictors of membership in the higher symptom classes.

CONCLUSIONS

Patients with COPD can be classified into four distinct symptom classes based on five commonly co-occurring physical and psychological symptoms. Systemic biomarkers of inflammation were not associated with symptom class. Additional work to test the reliability of these symptom classes, their biological drivers and their validity for prognostication and tailoring therapy in larger and more diverse samples is needed.

TRIAL REGISTRATION

Clinicaltrials.gov, NCT01074515 .

Differences in systemic adaptive immunity contribute to the 'frequent exacerbator' COPD phenotype

BACKGROUND

Some COPD patients are more susceptible to exacerbations than others. Mechanisms underlying these differences in susceptibility are not well understood. We hypothesized that altered cell mediated immune responses may underlie a propensity to suffer from frequent exacerbations in COPD.

METHODS

Peripheral blood mononuclear cells (PBMCs) were obtained from 24 stable COPD patients, eight frequent exacerbators (≥3 diary-card exacerbations/year) and 16 infrequent exacerbators (< 3 diary-card exacerbations/year). Detailed multi-parameter flow cytometry was used to study differences in innate and adaptive systemic immune function between frequent and infrequently exacerbating COPD patients.

RESULTS

The 24 COPD patients had a mean (SD) age of 76.3 (9.4) years and FEV₁ 1.43 (0.60)L, 53.3 (18.3)% predicted. PBMCs of frequent exacerbators (FE) contained lower frequencies of CD4+ T central memory cells (CD4+ Tcm) compared to infrequent exacerbators (IE) (FE = 18.7 %; IE = 23.9 %; p = 0.035). This observation was also apparent in absolute numbers of CD4+ Tcm cells (FE = 0.17 × 10^6/mL; IE = 0.25 × 10^6/mL; p = 0.035). PBMCs of FE contained a lower frequency of CD8+ T effector memory cells expressing HLA-DR (Human Leukocyte Antigen - D Related) compared to IE COPD patients (FE = 22.7 %; IE = 31.5 %; p = 0.007).

CONCLUSION

Differences in the adaptive systemic immune system might associate with exacerbation susceptibility in the 'frequent exacerbator' COPD phenotype. These differences include fewer CD4+ T central memory cells and CD8+ T effector memory cells.

TRIAL REGISTRATION

Not applicable.

B cells in chronic obstructive pulmonary disease: moving to center stage

Chronic inflammatory responses in the lungs contribute to the development and progression of chronic obstructive pulmonary disease (COPD). Although research studies focused initially on the contributions of the innate immune system to the pathogenesis of COPD, more recent studies have implicated adaptive immune responses in COPD. In particular, studies have demonstrated increases in B cell counts and increases in the number and size of B cell-rich lymphoid follicles in COPD lungs that correlate directly with COPD severity. There are also increases in lung levels of mediators that promote B cell maturation, activation, and survival in COPD patients. B cell products such as autoantibodies directed against lung cells, components of cells, and extracellular matrix proteins are also present in COPD lungs. These autoantibodies may contribute to lung inflammation and injury in COPD patients, in part, by forming immune complexes that activate complement components. Studies of B cell-deficient mice and human COPD patients have linked B cells most strongly to the emphysema phenotype. However, B cells have protective activities during acute exacerbations of COPD by promoting adaptive immune responses that contribute to host defense against pathogens. This review outlines the evidence that links B cells and B cell-rich lymphoid follicles to the pathogenesis of COPD and the mechanisms involved. It also reviews the potential and limitations of B cells as therapeutic targets to slow the progression of human COPD.

Impaired Blood Neutrophil Function in the Frequent Exacerbator of Chronic Obstructive Pulmonary Disease: A Proof-of-Concept Study

PURPOSE

The underlying biological mechanisms of the frequent exacerbator phenotype of COPD remain unclear. We compared systemic neutrophil function in COPD patients with or without frequent exacerbations.

METHODS

Whole blood from COPD frequent exacerbators (defined as ≥2 moderate-severe exacerbations in the previous 2 years) and non-exacerbators (no exacerbations in the preceding 2 years) was assayed for neutrophil function. Neutrophil function in healthy ex-smoking volunteers was also measured as a control (reference) group.

RESULTS

A total of 52 subjects were included in this study: 26 frequent exacerbators, 18 non-exacerbators and 8 healthy controls. COPD frequent exacerbators had blunted blood neutrophil fMLP-stimulated oxidative burst compared to both non-exacerbators (p < 0.01) and healthy controls (p < 0.001). There were no differences between COPD frequent exacerbators and non-exacerbators in blood neutrophil PMA-stimulated oxidative burst, but both COPD groups had reduced responses compared to healthy controls (p < 0.001). Bacterial-stimulated neutrophil degranulation was greater in frequent exacerbators than non-exacerbators (p < 0.05).

CONCLUSION

This study is the first to report aberrant receptor-mediated blood neutrophil function in the frequent exacerbator of COPD.

Reduced sputum expression of interferon-stimulated genes in severe COPD

BACKGROUND

Exacerbations of COPD are frequent and commonly triggered by respiratory tract infections. The purpose of our study was to investigate innate immunity in stable COPD patients.

METHODS

Induced sputum was collected from 51 stable consecutive COPD patients recruited from the COPD Clinic of CHU Liege and 35 healthy subjects. Expression of interferons beta (IFN-β) and lambda1 (IL-29), IFN-stimulated genes (ISGs) MxA, OAS, and viperin were measured in total sputum cells by reverse transcription quantitative polymerase chain reaction (RT-qPCR). The presence of Picornaviruses was assessed by RT-PCR, while potential pathogenic microorganisms (PPM) were identified by sputum bacteriology.

RESULTS

Expression of IL-29 was found in 16 of 51 COPD patients (31%) and in nine of 35 healthy subjects (26%), while IFN-β was detected in six of 51 COPD patients (12%) and in two of 35 healthy subjects (6%). ISGs were easily detectable in both groups. In the whole group of COPD patients, OAS expression was decreased (P<0.05), while that of viperin was increased (P<0.01) compared to healthy subjects. No difference was found with respect to MxA. COPD patients from group D of Global Initiative for Chronic Obstructive Lung Disease (GOLD) had reduced expression of all three ISGs (P<0.01 for MxA, P<0.05 for OAS, and P<0.01 for viperin) as compared to those of group B patients. Picornaviruses were detected in eight of 51 (16%) COPD patients vs four of 33 (12%) healthy subjects, while PPM were detected in seven of 39 (18%) COPD patients and associated with raised sputum neutrophil counts. IFN-β expression was raised when either picornavirus or PPM were detected (P=0.06), but no difference was seen regarding IL-29 or ISGs.

CONCLUSION

ISGs expression was reduced in severe COPD that may favor exacerbation and contribute to disease progress by altering response to infection.

COPD subtypes identified by network-based clustering of blood gene expression

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-naïve 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.

8th international conference on management and rehabilitation of chronic respiratory failure: the long summaries – part 1

This paper summarizes the Part 1 of the proceedings of the 8^th International Conference on Management and Rehabilitation of Chronic Respiratory Failure, held in Pescara, Italy, on 7 and 8 May, 2015. It summarizes the contributions from numerous experts in the field of chronic respiratory disease and chronic respiratory failure. The outline follows the temporal sequence of presentations.

This paper (Part 1) includes sections regarding: Advances in Asthma and COPD Therapy (Novel Therapeutic Targets for Asthma: Proteinases, Blood Biomarker Changes in COPD Patients); The problem of Hospital Re-Admission following Discharge after the COPD Exacerbation (Characteristics of the Hospitalized COPD Patient, Reducing Hospital Readmissions Following COPD Exacerbation).