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Short-acting β2-agonist bronchodilators are the most common medications used in treating chronic obstructive pulmonary disease (COPD). Genetic variants determining bronchodilator responsiveness (BDR) in COPD have not been identified. We performed a genome-wide association study (GWAS) of BDR in 5789 current or former smokers with COPD in one African-American and four white populations. BDR was defined as the quantitative spirometric response to inhaled β2-agonists. We combined results in a meta-analysis. In the meta-analysis, single-nucleotide polymorphisms (SNPs) in the genes KCNK1 (P=2.02 × 10(-7)) and KCNJ2 (P=1.79 × 10(-7)) were the top associations with BDR. Among African Americans, SNPs in CDH13 were significantly associated with BDR (P=5.1 × 10(-9)). A nominal association with CDH13 was identified in a gene-based analysis in all subjects. We identified suggestive association with BDR among COPD subjects for variants near two potassium channel genes (KCNK1 and KCNJ2). SNPs in CDH13 were significantly associated with BDR in African Americans.The Pharmacogenomics Journal advance online publication, 27 October 2015; doi:10.1038/tpj.2015.65.

Background: The identification of gene-by-environment interactions is important for understanding the genetic basis of chronic obstructive pulmonary disease (COPD). Many COPD genetic association analyses assume a linear relationship between pack-years of smoking exposure and forced expiratory volume in 1 s (FEV(1)); however, this assumption has not been evaluated empirically in cohorts with a wide spectrum of COPD severity.Methods: The relationship between FEV(1) and pack-years of smoking exposure was examined in four large cohorts assembled for the purpose of identifying genetic associations with COPD. Using data from the Alpha-1 Antitrypsin Genetic Modifiers Study, the accuracy and power of two different approaches to model smoking were compared by performing a simulation study of a genetic variant with a range of gene-by-smoking interaction effects.Results: Non-linear relationships between smoking and FEV(1) were identified in the four cohorts. It was found that, in most situations where the relationship between pack-years and FEV(1) is non-linear, a piecewise linear approach to model smoking and gene-by-smoking interactions is preferable to the commonly used total pack-years approach. The piecewise linear approach was applied to a genetic association analysis of the PI*Z allele in the Norway Case-Control cohort and a potential PI*Z-by-smoking interaction was identified (p=0.03 for FEV(1) analysis, p=0.01 for COPD susceptibility analysis).Conclusion: In study samples of subjects with a wide range of COPD severity, a non-linear relationship between pack-years of smoking and FEV(1) is likely. In this setting, approaches that account for this non-linearity can be more powerful and less biased than the more common approach of using total pack-years to model the smoking effect. [ABSTRACT FROM AUTHOR]

Background: Chronic bronchitis (CB) in patients with COPD is associated with an accelerated lung function decline and an increased risk of respiratory infections. Despite its clinical significance, the chronic bronchitic phenotype in COPD remains poorly defined.Methods: We analyzed data from subjects enrolled in the Genetic Epidemiology of COPD (COPDGene) Study. A total of 1,061 subjects with GOLD (Global Initiative for Chronic Obstructive Lung Disease) stage II to IV were divided into two groups: CB (CB+) if subjects noted chronic cough and phlegm production for ≥ 3 mo/y for 2 consecutive years, and no CB (CB-) if they did not.Results: There were 290 and 771 subjects in the CB+ and CB- groups, respectively. Despite similar lung function, the CB+ group was younger (62.8 ± 8.4 vs 64.6 ± 8.4 years, P = .002), smoked more (57 ± 30 vs 52 ± 25 pack-years, P = .006), and had more current smokers (48% vs 27%, P < .0001). A greater percentage of the CB+ group reported nasal and ocular symptoms, wheezing, and nocturnal awakenings secondary to cough and dyspnea. History of exacerbations was higher in the CB+ group (1.21 ± 1.62 vs 0.63 ± 1.12 per patient, P < .027), and more patients in the CB+ group reported a history of severe exacerbations (26.6% vs 20.0%, P = .024). There was no difference in percent emphysema or percent gas trapping, but the CB+ group had a higher mean percent segmental airway wall area (63.2% ± 2.9% vs 62.6% ± 3.1%, P = .013).Conclusions: CB in patients with COPD is associated with worse respiratory symptoms and higher risk of exacerbations. This group may need more directed therapy targeting chronic mucus production and smoking cessation not only to improve symptoms but also to reduce risk, improve quality of life, and improve outcomes.Trial Registry: ClinicalTrials.gov; No.: NCT00608764; URL: www.clinicaltrials.gov. [ABSTRACT FROM AUTHOR]

Background: Severe α₁-antitrypsin deficiency is a known genetic risk factor for COPD. Heterozygous (protease inhibitor [PI] MZ) individuals have moderately reduced serum levels of α₁-antitrypsin, but whether they have an increased risk of COPD is uncertain.Methods: We compared PI MZ and PI MM individuals in two large populations: a case-control study from Norway (n = 1,669) and a multicenter family-based study from Europe and North America (n = 2,707). We sought to determine whether PI MZ was associated with the specific COPD-related phenotypes of lung function and quantitative CT scan measurements of emphysema and airway disease.Results: PI MZ was associated with a 3.5% lower FEV₁/FVC ratio in the case-control study (P = .035) and 3.9% lower FEV₁/vital capacity (VC) ratio in the family study (P = .009). In the case-control study, PI MZ also was associated with 3.7% more emphysema on quantitative analysis of chest CT scans (P = .003). The emphysema result was not replicated in the family study. PI MZ was not associated with airway wall thickness or COPD status in either population. Among subjects with low smoking exposure (< 20 pack-years), PI MZ individuals had more severe emphysema on chest CT scan than PI MM individuals in both studies.Conclusions: Compared with PI MM individuals, PI MZ heterozygotes had lower FEV₁/(F)VC ratio in two independent studies. Our results suggest that PI MZ individuals may be slightly more susceptible to the development of airflow obstruction than PI MM individuals. [ABSTRACT FROM AUTHOR]

Rationale: Several family-based studies have identified genetic linkage for lung function and airflow obstruction to chromosome 2q.Objectives: We hypothesized that merging results of high-resolution single nucleotide polymorphism (SNP) mapping in four separate populations would lead to the identification of chronic obstructive pulmonary disease (COPD) susceptibility genes on chromosome 2q.Methods: Within the chromosome 2q linkage region, 2,843 SNPs were genotyped in 806 COPD cases and 779 control subjects from Norway, and 2,484 SNPs were genotyped in 309 patients with severe COPD from the National Emphysema Treatment Trial and 330 community control subjects. Significant associations from the combined results across the two case-control studies were followed up in 1,839 individuals from 603 families from the International COPD Genetics Network (ICGN) and in 949 individuals from 127 families in the Boston Early-Onset COPD Study.Measurements and Main Results: Merging the results of the two case-control analyses, 14 of the 790 overlapping SNPs had a combined P < 0.01. Two of these 14 SNPs were consistently associated with COPD in the ICGN families. The association with one SNP, located in the gene XRCC5, was replicated in the Boston Early-Onset COPD Study, with a combined P = 2.51 x 10(-5) across the four studies, which remains significant when adjusted for multiple testing (P = 0.02). Genotype imputation confirmed the association with SNPs in XRCC5.Conclusions: By combining data from COPD genetic association studies conducted in four independent patient samples, we have identified XRCC5, an ATP-dependent DNA helicase, as a potential COPD susceptibility gene. [ABSTRACT FROM AUTHOR]

Rationale: Patients with severe chronic obstructive pulmonary disease (COPD) may have varying levels of disability despite similar levels of lung function. This variation may reflect different COPD subtypes, which may have different genetic predispositions.Objectives: To identify genetic associations for COPD-related phenotypes, including measures of exercise capacity, pulmonary function, and respiratory symptoms.Methods: In 304 subjects from the National Emphysema Treatment Trial, we genotyped 80 markers in 22 positional and/or biologically plausible candidate genes. Regression models were used to test for association, using a test-replication approach to guard against false-positive results. For significant associations, effect estimates were recalculated using the entire cohort. Positive associations with dyspnea were confirmed in families from the Boston Early-Onset COPD Study.Results: The test-replication approach identified four genes-microsomal epoxide hydrolase (EPHX1), latent transforming growth factor-beta binding protein-4 (LTBP4), surfactant protein B (SFTPB), and transforming growth factor-beta1 (TGFB1)-that were associated with COPD-related phenotypes. In all subjects, single-nucleotide polymorphisms (SNPs) in EPHX1 (p < or = 0.03) and in LTBP4 (p < or = 0.03) were associated with maximal output on cardiopulmonary exercise testing. Markers in LTBP4 (p < or = 0.05) and SFTPB (p = 0.005) were associated with 6-min walk test distance. SNPs in EPHX1 were associated with carbon monoxide diffusing capacity (p < or = 0.04). Three SNPs in TGFB1 were associated with dyspnea (p < or = 0.002), one of which replicated in the family study (p = 0.02).Conclusions: Polymorphisms in several genes seem to be associated with COPD-related traits other than FEV(1). These associations may identify genes in pathways important for COPD pathogenesis. [ABSTRACT FROM AUTHOR]

Background: Genetic variants and gene expression predict risk of chronic obstructive pulmonary disease (COPD), but their effect on COPD heterogeneity is unclear. We aimed to define high-risk COPD subtypes using genetics (polygenic risk score, PRS) and blood gene expression (transcriptional risk score, TRS) and assess differences in clinical and molecular characteristics., Methods: We defined high-risk groups based on PRS and TRS quantiles by maximising differences in protein biomarkers in a COPDGene training set and identified these groups in COPDGene and ECLIPSE test sets. We tested multivariable associations of subgroups with clinical outcomes and compared protein-protein interaction networks and drug repurposing analyses between high-risk groups., Findings: We examined two high-risk omics-defined groups in non-overlapping test sets (n = 1133 NHW COPDGene, n = 299 African American (AA) COPDGene, n = 468 ECLIPSE). We defined "high activity" (low PRS, high TRS) and "severe risk" (high PRS, high TRS) subgroups. Participants in both subgroups had lower body-mass index (BMI), lower lung function, and alterations in metabolic, growth, and immune signalling processes compared to a low-risk (low PRS, low TRS) subgroup. "High activity" but not "severe risk" participants had greater prospective FEV 1 decline (COPDGene: -51 mL/year; ECLIPSE: -40 mL/year) and proteomic profiles were enriched in gene sets perturbed by treatment with 5-lipoxygenase inhibitors and angiotensin-converting enzyme (ACE) inhibitors., Interpretation: Concomitant use of polygenic and transcriptional risk scores identified clinical and molecular heterogeneity amongst high-risk individuals. Proteomic and drug repurposing analysis identified subtype-specific enrichment for therapies and suggest prior drug repurposing failures may be explained by patient selection., Funding: National Institutes of Health., Competing Interests: Declaration of interests EKS received grant support from Bayer and Northpond Labs. BDH received grant support from Bayer. MHC has received grant support from Bayer. MM received grant support from Bayer and consulting fees from Sitka, TheaHealth, 2ndMD, and TriNetX. CPH reports grant support from Boehringer-Ingelheim, Novartis, Bayer and Vertex, outside of this study. PJC has received grant support from GlaxoSmithKline and Bayer and consulting fees from GlaxoSmithKline and Novartis. RTS received consulting fees from GSK, AstraZeneca, Roche, Itai and Beyond, Samay Health, Immunomet, ENA Respiratory, Teva, COPD Foundation and Vocalis Health. She is a retiree and shareholder of GSK and holds share options at ENA Respiratory. DDS received honoraria for giving talks on COPD from GSK, Boehringer Ingelheim, and AstraZeneca, is the chair of an NHLBI sponsored clinical trial data safety monitoring board, and deputy editor of European Respiratory Journal. JLC received consulting fees from AstraZeneca PLC, CSL Behring, LLC, and Novartis Corporation. SIR received consulting fees from Verona Pharma, Sanofi, BeyondAir and the Alpha 1 Foundation. He is a founder and president of Great Plains Biometrix. He was an employee of AstraZeneca from 2015 to 2019 during which he received shares as part of his compensation. JHP is supported by NIH K25HL140186. KG is supported by NIH/NHLBI: R01HG011393, R01HL152728, R01HL160008, and R01HL162813., (Published by Elsevier B.V.)

Chronic obstructive pulmonary disease (COPD) is a complex disease influenced by well-established environmental exposures (most notably, cigarette smoking) and incompletely defined genetic factors. The chromosome 4q region harbors multiple genetic risk loci for COPD, including signals near HHIP, FAM13A, GSTCD, TET2, and BTC. Leveraging RNA-Seq data from lung tissue in COPD cases and controls, we estimated the co-expression network for genes in the 4q region bounded by HHIP and BTC (~70MB), through partial correlations informed by protein-protein interactions. We identified several co-expressed gene pairs based on partial correlations, including NPNT-HHIP, BTC-NPNT and FAM13A-TET2, which were replicated in independent lung tissue cohorts. Upon clustering the co-expression network, we observed that four genes previously associated to COPD: BTC, HHIP, NPNT and PPM1K appeared in the same network community. Finally, we discovered a sub-network of genes differentially co-expressed between COPD vs controls (including FAM13A, PPA2, PPM1K and TET2). Many of these genes were previously implicated in cell-based knock-out experiments, including the knocking out of SPP1 which belongs to the same genomic region and could be a potential local key regulatory gene. These analyses identify chromosome 4q as a region enriched for COPD genetic susceptibility and differential co-expression., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: EKS has received grant support from Bayer and Northpond Laboratories. PJC has received grant support from Sanofi and Bayer and consulting fees from Verona Pharma. CPH has received grant support from Bayer, Boehringer-Ingelheim, and Vertex, and consulting fees from Chiesi, Ono, Sanofi, and Takeda. MHC has received grant support from Bayer., (Copyright: © 2024 Gentili et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Rationale: Cigarette smoking (CS) impairs B cell function and antibody production, increasing infection risk. The impact of e-cigarette use ('vaping') and combined CS and vaping ('dual-use') on B cell activity is unclear., Objective: To examine B cell receptor sequencing (BCR-seq) profiles associated with CS, vaping, dual-use, COPD-related outcomes, and demographic factors., Methods: BCR-seq was performed on blood RNA samples from 234 participants in the COPDGene study. We assessed multivariable associations of B cell function measures (immunoglobulin heavy chain (IGH) subclass expression and usage, class-switching, V-segment usage, and clonal expansion) with CS, vaping, dual-use, COPD severity, age, sex, and race. We adjusted for multiple comparisons using the Benjamini-Hochberg method, identifying significant associations at 5% FDR and suggestive associations at 10% FDR., Results: Among 234 non-Hispanic white (NHW) and African American (AA) participants, CS and dual-use were significantly positively associated with increased secretory IgA production, with dual-use showing the strongest associations. Dual-use was positively associated with class switching and B cell clonal expansion, indicating increased B cell activation, with similar trends in those only smoking or only vaping. We observed significant associations between race and IgG antibody usage. AA participants had higher IgG subclass proportions and lower IgM usage compared to NHW participants., Conclusions: CS and vaping additively enhance B cell activation, most notably in dual-users. Self-reported race was strongly associated with IgG isotype usage. These findings highlight associations between B cell activation and antibody transcription, suggesting potential differences in immune and vaccine responses linked to CS, vaping, and race.

Epidemiologic and genetic studies in many complex diseases suggest subgroup disparities (e.g. by sex, race) in disease course and patient outcomes. We consider this from the standpoint of integrative analysis where we combine information from different views (e.g. genomics, proteomics, clinical data). Existing integrative analysis methods ignore the heterogeneity in subgroups, and stacking the views and accounting for subgroup heterogeneity does not model the association among the views. We propose Heterogeneity in Integration and Prediction (HIP), a statistical approach for joint association and prediction that leverages the strengths in each view to identify molecular signatures that are shared by and specific to a subgroup. We apply HIP to proteomics and gene expression data pertaining to chronic obstructive pulmonary disease (COPD) to identify proteins and genes shared by, and unique to, males and females, contributing to the variation in COPD, measured by airway wall thickness. Our COPD findings have identified proteins, genes, and pathways that are common across and specific to males and females, some implicated in COPD, while others could lead to new insights into sex differences in COPD mechanisms. HIP accounts for subgroup heterogeneity in multi-view data, ranks variables based on importance, is applicable to univariate or multivariate continuous outcomes, and incorporates covariate adjustment. With the efficient algorithms implemented using PyTorch, this method has many potential scientific applications and could enhance multiomics research in health disparities. HIP is available at https://github.com/lasandrall/HIP, a video tutorial at https://youtu.be/O6E2OLmeMDo and a Shiny Application at https://multi-viewlearn.shinyapps.io/HIP&#95;ShinyApp/ for users with limited programming experience., (© The Author(s) 2024. Published by Oxford University Press.)

Background: Studies have identified individual blood biomarkers associated with chronic obstructive pulmonary disease (COPD) and related phenotypes. However, complex diseases such as COPD typically involve changes in multiple molecules with interconnections that may not be captured when considering single molecular features., Methods: Leveraging proteomic data from 3,173 COPDGene Non-Hispanic White (NHW) and African American (AA) participants, we applied sparse multiple canonical correlation network analysis (SmCCNet) to 4,776 proteins assayed on the SomaScan v4.0 platform to derive sparse networks of proteins associated with current vs. former smoking status, airflow obstruction, and emphysema quantitated from high-resolution computed tomography scans. We then used NetSHy, a dimension reduction technique leveraging network topology, to produce summary scores of each proteomic network, referred to as NetSHy scores. We next performed a genome-wide association study (GWAS) to identify variants associated with the NetSHy scores, or network quantitative trait loci (nQTLs). Finally, we evaluated the replicability of the networks in an independent cohort, SPIROMICS., Results: We identified networks of 13 to 104 proteins for each phenotype and exposure in NHW and AA, and the derived NetSHy scores significantly associated with the variable of interests. Networks included known (sRAGE, ALPP, MIP1) and novel molecules (CA10, CPB1, HIS3, PXDN) and interactions involved in COPD pathogenesis. We observed 7 nQTL loci associated with NetSHy scores, 4 of which remained after conditional analysis. Networks for smoking status and emphysema, but not airflow obstruction, demonstrated a high degree of replicability across race groups and cohorts., Conclusions: In this work, we apply state-of-the-art molecular network generation and summarization approaches to proteomic data from COPDGene participants to uncover protein networks associated with COPD phenotypes. We further identify genetic associations with networks. This work discovers protein networks containing known and novel proteins and protein interactions associated with clinically relevant COPD phenotypes across race groups and cohorts., (© 2024. The Author(s).)

Background: Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of mortality. Predicting mortality risk in patients with COPD can be important for disease management strategies. Although all-cause mortality predictors have been developed previously, limited research exists on factors directly affecting COPD-specific mortality., Methods: In a retrospective study, we used probabilistic graphs to analyse clinical cross-sectional data (COPDGene cohort), including demographics, spirometry, quantitative chest imaging, and symptom features, as well as gene expression data. COPDGene recruited current and former smokers, aged 45-80 years with >10 pack-years smoking history, from across the USA (Phase 1, 11/2007-4/2011) and invited them for a follow-up visit (Phase 2, 7/2013-7/2017). ECLIPSE cohort recruited current and former smokers (COPD patients and controls from USA and Europe), aged 45-80 with smoking history >10 pack-years (12/2005-11/2007). We applied graphical models on multi-modal data COPDGene Phase 1 participants to identify factors directly affecting all-cause and COPD-specific mortality (primary outcomes); and on Phase 2 follow-up cohort to identify additional molecular and social factors affecting mortality. We used penalized Cox regression with features selected by the causal graph to build VAPORED, a mortality risk prediction model. VAPORED was compared to existing scores (BODE: BMI, airflow obstruction, dyspnoea, exercise capacity; ADO: age, dyspnoea, airflow obstruction) on the ability to rank individuals by mortality risk, using four evaluation metrics (concordance, concordance probability estimate (CPE), cumulative/dynamic (C/D) area under the receiver operating characteristic curve (AUC), and integrated C/D AUC). The results were validated in ECLIPSE., Findings: Graphical models, applied on the COPDGene Phase 1 samples (n = 8610), identified 11 and 7 variables directly linked to all-cause and COPD-specific mortality, respectively. Although many appear in both models, non-lung comorbidities appear only in the all-cause model, while forced vital capacity (FVC %predicted) appears in COPD-specific mortality model only. Additionally, the graph model of Phase 2 data (n = 3182) identified internet access, CD4 T cells and platelets to be linked to lower mortality risk. Furthermore, using the 7 variables linked to COPD-specific mortality (forced expiratory volume in 1 s/forced vital capacity (FEV 1 /FVC) ration, FVC %predicted, age, history of pneumonia, oxygen saturation, 6-min walk distance, dyspnoea) we developed VAPORED mortality risk score, which we validated on the ECLIPSE cohort (3-yr all-cause mortality data, n = 2312). VAPORED performed significantly better than ADO, BODE, and updated BODE indices in predicting all-cause mortality in ECLIPSE in terms of concordance (VAPORED [0.719] vs ADO [0.693; FDR p-value 0.014], BODE [0.695; FDR p-value 0.020], and updated BODE [0.694; FDR p-value 0.021]); CPE (VAPORED [0.714] vs ADO [0.673; FDR p-value <0.0001], BODE [0.662; FDR p-value <0.0001], and updated BODE [0.646; FDR p-value <0.0001]); 3-year C/D AUC (VAPORED [0.728] vs ADO [0.702; FDR p-value 0.017], BODE [0.704; FDR p-value 0.021], and updated BODE [0.703; FDR p-value 0.024]); integrated C/D AUC (VAPORED [0.723] vs ADO [0.698; FDR p-value 0.047], BODE [0.695; FDR p-value 0.024], and updated BODE [0.690; FDR p-value 0.021]). Finally, we developed a web tool to help clinicians calculate VAPORED mortality risk and compare it to ADO and BODE predictions., Interpretation: Our work is an important step towards improving our identification of high-risk patients and generating hypotheses of potential biological mechanisms and social factors driving mortality in patients with COPD at the population level. The main limitation of our study is the fact that the analysed datasets consist of older people with extensive smoking history and limited racial diversity. Thus, the results are relevant to high-risk individuals or those diagnosed with COPD and the VAPORED score is validated for them., Funding: This research was supported by NIH [NHLBI, NLM]. The COPDGene study is supported by the COPD Foundation, through grants from AstraZeneca, Bayer Pharmaceuticals, Boehringer Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer and Sunovion., Competing Interests: PC received grant support from Bayer and Sanofi; and consulting fees from Verona Pharma. FCS has received institutional grant support and consulting fees from Sanofi/Regeneron, AstraZeneca, Verona Pharma, Nuvaira, Gala Therapeutics, GlaxoSmithKline, Boehringer Ingelheim. CPH has received institutional grant support and consulting fees from Alpha-1 Foundation, Bayer, Boehringer Ingelheim, Vertex, Chiesi, Ono, Takeda, Sanofi. TCL, MHR, MJ, PVB declare no competing interests., (© 2024 The Authors.)

Rationale: Identification and validation of circulating biomarkers for lung function decline in COPD remains an unmet need., Objective: Identify prognostic and dynamic plasma protein biomarkers of COPD progression., Methods: We measured plasma proteins using SomaScan from two COPD-enriched cohorts, the Subpopulations and Intermediate Outcomes Measures in COPD Study (SPIROMICS) and Genetic Epidemiology of COPD (COPDGene), and one population-based cohort, Multi-Ethnic Study of Atherosclerosis (MESA) Lung. Using SPIROMICS as a discovery cohort, linear mixed models identified baseline proteins that predicted future change in FEV 1 (prognostic model) and proteins whose expression changed with change in lung function (dynamic model). Findings were replicated in COPDGene and MESA-Lung. Using the COPD-enriched cohorts, Gene Set Enrichment Analysis (GSEA) identified proteins shared between COPDGene and SPIROMICS. Metascape identified significant associated pathways., Measurements and Main Results: The prognostic model found 7 significant proteins in common (p < 0.05) among all 3 cohorts. After applying false discovery rate (adjusted p < 0.2), leptin remained significant in all three cohorts and growth hormone receptor remained significant in the two COPD cohorts. Elevated baseline levels of leptin and growth hormone receptor were associated with slower rate of decline in FEV 1 . Twelve proteins were nominally but not FDR significant in the dynamic model and all were distinct from the prognostic model. Metascape identified several immune related pathways unique to prognostic and dynamic proteins., Conclusion: We identified leptin as the most reproducible COPD progression biomarker. The difference between prognostic and dynamic proteins suggests disease activity signatures may be different from prognosis signatures.

Compared to men, women often develop COPD at an earlier age with worse respiratory symptoms despite lower smoking exposure. However, most preventive, and therapeutic strategies ignore biological sex differences in COPD. Our goal was to better understand sex-specific gene regulatory processes in lung tissue and the molecular basis for sex differences in COPD onset and severity. We analyzed lung tissue gene expression and DNA methylation data from 747 individuals in the Lung Tissue Research Consortium (LTRC), and 85 individuals in an independent dataset. We identified sex differences in COPD-associated gene regulation using gene regulatory networks. We used linear regression to test for sex-biased associations of methylation with lung function, emphysema, smoking, and age. Analyzing gene regulatory networks in the control group, we identified that genes involved in the extracellular matrix (ECM) have higher transcriptional factor targeting in females than in males. However, this pattern is reversed in COPD, with males showing stronger regulatory targeting of ECM-related genes than females. Smoking exposure, age, lung function, and emphysema were all associated with sex-specific differential methylation of ECM-related genes. We identified sex-based gene regulatory patterns of ECM-related genes associated with lung function and emphysema. Multiple factors including epigenetics, smoking, aging, and cell heterogeneity influence sex-specific gene regulation in COPD. Our findings underscore the importance of considering sex as a key factor in disease susceptibility and severity.

Background: The coronary artery calcium score (CACS) and ratio of the pulmonary artery to aorta diameters (PA:A ratio) measured from chest CT scans have been established as predictors of cardiovascular events and COPD exacerbations, respectively. However, little is known about the reciprocal relationship between these predictors and outcomes. Furthermore, the prognostic implications of COPD subtypes on clinical outcomes remain insufficiently characterized., Research Question: How can these two chest CT scan-derived parameters predict subsequent cardiovascular events and COPD exacerbations in different COPD subtypes?, Study Design and Methods: Using COPDGene study data, we assessed prospective cardiovascular disease (CVD) and COPD exacerbation risk in patients with COPD (Global Initiative for Chronic Obstructive Lung Disease spirometric grades 2-4), focusing on CACS and PA:A ratio at study enrollment, with logistic regression models. These outcomes were analyzed in three COPD subtypes: 1,042 patients with non-emphysema-predominant disease (NEPD) (low attenuation area at -950 Hounsfield units [LAA-950] < 5%), 1,324 patients with emphysema-predominant disease (EPD) (LAA-950 ≥ 10%), and 465 patients with intermediate emphysema disease (5% ≤ LAA-950 < 10%)., Results: Our study indicated significantly higher overall risk for cardiovascular events in patients with higher CACS (≥ median; OR, 1.61; 95% CI, 1.30-2.00) and increased COPD exacerbations in those with higher PA:A ratios (≥ 1; OR, 1.80; 95% CI, 1.46-2.23). Notably, patients with NEPD showed a stronger association between these indicators and clinical events than those with EPD (with CACS/CVD, NEPD vs EPD: OR, 2.02 vs 1.41; with PA:A ratio/COPD exacerbation, NEPD vs EPD: OR, 2.50 vs 1.65); the difference in ORs between COPD subtypes was statistically significant for CACS/CVD., Interpretation: Two chest CT scan parameters, CACS and PA:A ratio, hold distinct predictive values for cardiovascular events and COPD exacerbations that are influenced by specific COPD subtypes., Trial Registration: ClinicalTrials.gov; No.: NCT00608764; URL: www., Clinicaltrials: gov., Competing Interests: Financial/Nonfinancial Disclosures The authors have reported to CHEST the following: In the past 3 years, E. K. S. has received grant support from Bayer and Northpond Laboratories. J. M. W. has received grant support from ARCUS-Med, Mereo BioPharma, Medscape, Verona Pharma, Grifols, Alpha-1 Foundation, and InhibrX. P. J. C. has received grant support from Bayer and Sanofi. C. P. H. has received grant support from Alpha-1 Foundation, Bayer, Boehringer-Ingelheim, and Vertex. None declared (H.-M. Y., M. H. R., V. J. C., K. Y., G. L. K., M. T. D., R. C. W., M. B.)., (Copyright © 2024 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Rationale: Genome-wide association studies (GWAS) have identified multiple genetic loci associated with chronic obstructive pulmonary disease (COPD). When integrated with GWAS results, expression quantitative trait locus (eQTL) studies can provide insight into biological mechanisms involved in disease by identifying single nucleotide polymorphisms (SNPs) that contribute to whole gene expression. However, there are multiple genetically driven regulatory and isoform-specific effects which cannot be detected in traditional eQTL analyses. Here, we identify SNPs that are associated with alternative splicing (sQTL) in addition to eQTLs to identify novel functions for COPD associated genetic variants., Methods: We performed RNA sequencing on whole blood from 3743 subjects in the COPDGene Study. RNA sequencing data from lung tissue of 1241 subjects from the Lung Tissue Research Consortium (LTRC), and whole genome sequencing data on all subjects. Associations between all SNPs within 1000 kb of a gene (cis-) and splice and gene expression quantifications were tested using tensorQTL. In COPDGene a total of 11,869,333 SNPs were tested for association with 58,318 splice clusters, and 8,792,206 SNPs were tested for association with 70,094 splice clusters in LTRC. We assessed colocalization with COPD-associated SNPs from a published GWAS[1]., Results: After adjustment for multiple statistical testing, we identified 28,110 splice-sites corresponding to 3,889 unique genes that were significantly associated with genotype in COPDGene whole blood, and 58,258 splice-sites corresponding to 10,307 unique genes associated with genotype in LTRC lung tissue. We found 7,576 sQTL splice-sites corresponding to 2,110 sQTL genes were shared between whole blood and lung, while 20,534 sQTL splice-sites in 3,518 genes were unique to blood and 50,682 splice-sites in 9,677 genes were unique to lung. To determine what proportion of COPD-associated SNPs were associated with transcriptional splicing, we performed colocalization analysis between COPD GWAS and sQTL data, and found that 38 genomic windows, corresponding to 38 COPD GWAS loci had evidence of colocalization between QTLs and COPD. The top five colocalizations between COPD and lung sQTLs include NPNT , FBXO38 , HHIP , NTN4 and BTC ., Conclusions: A total of 38 COPD GWAS loci contain evidence of sQTLs, suggesting that analysis of sQTLs in whole blood and lung tissue can provide novel insights into disease mechanisms.

Rationale: While many studies have examined gene expression in lung tissue, the gene regulatory processes underlying emphysema are still not well understood. Finding efficient non-imaging screening methods and disease-modifying therapies has been challenging, but knowledge of the transcriptomic features of emphysema may help in this effort., Objectives: Our goals were to identify emphysema-associated biological pathways through transcriptomic analysis of bulk lung tissue, to determine the lung cell types in which these emphysema-associated pathways are altered, and to detect unique and overlapping transcriptomic signatures in blood and lung samples., Methods: Using RNA-sequencing data from 446 samples in the Lung Tissue Research Consortium (LTRC) and 3,606 blood samples from the COPDGene study, we examined the transcriptomic features of chest computed tomography-quantified emphysema. We also leveraged publicly available lung single-cell RNA-sequencing data to identify cell types showing COPD-associated differential expression of the emphysema pathways found in the bulk analyses., Measurements and Main Results: In the bulk lung RNA-seq analysis, 1,087 differentially expressed genes and 34 dysregulated pathways were significantly associated with emphysema. We observed alternative splicing of several genes and increased activity in pluripotency and cell barrier function pathways. Lung tissue and blood samples shared differentially expressed genes and biological pathways. Multiple lung cell types displayed dysregulation of epithelial barrier function pathways, and distinct pathway activities were observed among various macrophage subpopulations., Conclusions: This study identified emphysema-related changes in gene expression and alternative splicing, cell-type specific dysregulated pathways, and instances of shared pathway dysregulation between blood and lung.

While many disease-associated single nucleotide polymorphisms (SNPs) are expression quantitative trait loci (eQTLs), a large proportion of genome-wide association study (GWAS) variants are of unknown function. Alternative polyadenylation (APA) plays an important role in posttranscriptional regulation by allowing genes to shorten or extend 3' untranslated regions (UTRs). We hypothesized that genetic variants that affect APA in lung tissue may lend insight into the function of respiratory associated GWAS loci. We generated alternative polyadenylation (apa) QTLs using RNA sequencing and whole genome sequencing on 1241 subjects from the Lung Tissue Research Consortium (LTRC) as part of the NHLBI TOPMed project. We identified 56 179 APA sites corresponding to 13 582 unique genes after filtering out APA sites with low usage. We found that a total of 8831 APA sites were associated with at least one SNP with q-value < 0.05. The genomic distribution of lead APA SNPs indicated that the majority are intronic variants (33%), followed by downstream gene variants (26%), 3' UTR variants (17%), and upstream gene variants (within 1 kb region upstream of transcriptional start site, 10%). APA sites in 193 genes colocalized with GWAS data for at least one phenotype. Genes containing the top APA sites associated with GWAS variants include membrane associated ring-CH-type finger 2 (MARCHF2), nectin cell adhesion molecule 2 (NECTIN2), and butyrophilin subfamily 3 member A2 (BTN3A2). Overall, these findings suggest that APA may be an important mechanism for genetic variants in lung function and chronic obstructive pulmonary disease (COPD)., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Background: Cardiovascular disease (CVD) is the most important comorbidity in patients with chronic obstructive pulmonary disease (COPD). COPD exacerbations not only contribute to COPD progression but may also elevate the risk of CVD. This study aimed to determine whether COPD exacerbations increase the risk of subsequent CVD events using up to 15 years of prospective longitudinal follow-up data from the COPDGene (Genetic Epidemiology of Chronic Obstructive Pulmonary Disease) study., Methods and Results: The COPDGene study is a large, multicenter, longitudinal investigation of COPD, including subjects at enrollment aged 45 to 80 years with a minimum of 10 pack-years of smoking history. Cox proportional hazards models and Kaplan-Meier survival curves were used to assess the risk of a composite end point of CVD based on the COPD exacerbation rate. Frequent exacerbators exhibited a higher cumulative incidence of composite CVD end points than infrequent exacerbators, irrespective of the presence of CVD at baseline. After adjusting for covariates, frequent exacerbators still maintained higher hazard ratios (HRs) than the infrequent exacerbator group (without CVD: HR, 1.81 [95% CI, 1.47-2.22]; with CVD: HR, 1.92 [95% CI, 1.51-2.44]). This observation remained consistently significant in moderate to severe COPD subjects and the preserved ratio impaired spirometry population. In the mild COPD population, frequent exacerbators showed a trend toward more CVD events., Conclusions: COPD exacerbations are associated with an increased risk of subsequent cardiovascular events in subjects with and without preexisting CVD. Patients with COPD experiencing frequent exacerbations may necessitate careful monitoring and additional management for subsequent potential CVD., Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT00608764.

Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous, chronic inflammatory process of the lungs and, like other complex diseases, is caused by both genetic and environmental factors. Detailed understanding of the molecular mechanisms of complex diseases requires the study of the interplay among different biomolecular layers, and thus the integration of different omics data types. In this study, we investigated COPD-associated molecular mechanisms through a correlation-based network integration of lung tissue RNA-seq and DNA methylation data of COPD cases (n = 446) and controls (n = 346) derived from the Lung Tissue Research Consortium. First, we performed a SWIM-network based analysis to build separate correlation networks for RNA-seq and DNA methylation data for our case-control study population. Then, we developed a method to integrate the results into a coupled network of differentially expressed and differentially methylated genes to investigate their relationships across both molecular layers. The functional enrichment analysis of the nodes of the coupled network revealed a strikingly significant enrichment in Immune System components, both innate and adaptive, as well as immune-system component communication (interleukin and cytokine-cytokine signaling). Our analysis allowed us to reveal novel putative COPD-associated genes and to analyze their relationships, both at the transcriptomics and epigenomics levels, thus contributing to an improved understanding of COPD pathogenesis., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Rationale: The European Respiratory Society (ERS) and the American Thoracic Society (ATS) recommend using z-scores, and the ATS has recommended using Global Lung Initiative (GLI)- "Global" race-neutral reference equations for spirometry interpretation. However, these recommendations have been variably implemented and the impact has not been widely assessed, both in clinical and research settings., Objectives: We evaluated the ERS/ATS airflow obstruction severity classification., Methods: In the COPDGene Study (n = 10,108), airflow obstruction has been defined as a forced expiratory volume in one second to forced vital capacity (FEV1/FVC) ratio <0.70, with spirometry severity graded from class 1 to 4 based on race-specific percent predicted (pp) FEV1 cut-points as recommended by the Global Initiative for Chronic Obstructive Lung Disease (GOLD). We compared the GOLD approach, using NHANES III race-specific equations, to the application of GLI-Global equations using the ERS/ATS definition of airflow obstruction as FEV1/FVC ratio < lower limit of normal (LLN) and z-FEV1 cut-points of -1.645, -2.5, and -4 ("zGLI Global"). We tested the four-tier severity scheme for association with COPD outcomes., Measurements and Main Results: The lowest agreement between ERS/ATS with zGLI Global and the GOLD classification was observed in individuals with milder disease (56.9% and 42.5% in GOLD 1 and 2) and race was a major determinant of redistribution. After adjustment for relevant covariates, zGLI Global distinguished all-cause mortality risk between normal spirometry and the first grade of COPD (Hazard Ratio 1.23, 95% CI 1.04-1.44, p=0.014), and showed a linear increase in exacerbation rates with increasing disease severity, in comparison to GOLD., Conclusions: The zGLI Global severity classification outperformed GOLD in the discrimination of survival, exacerbations, and imaging characteristics.

Rationale: Emphysema is a chronic obstructive pulmonary disease phenotype with important prognostic implications. Identifying blood-based biomarkers of emphysema will facilitate early diagnosis and development of targeted therapies. Objectives: To discover blood omics biomarkers for chest computed tomography-quantified emphysema and develop predictive biomarker panels. Methods: Emphysema blood biomarker discovery was performed using differential gene expression, alternative splicing, and protein association analyses in a training sample of 2,370 COPDGene participants with available blood RNA sequencing, plasma proteomics, and clinical data. Internal validation was conducted in a COPDGene testing sample ( n  = 1,016), and external validation was done in the ECLIPSE study ( n  = 526). Because low body mass index (BMI) and emphysema often co-occur, we performed a mediation analysis to quantify the effect of BMI on gene and protein associations with emphysema. Elastic net models with bootstrapping were also developed in the training sample sequentially using clinical, blood cell proportions, RNA-sequencing, and proteomic biomarkers to predict quantitative emphysema. Model accuracy was assessed by the area under the receiver operating characteristic curves for subjects stratified into tertiles of emphysema severity. Measurements and Main Results: Totals of 3,829 genes, 942 isoforms, 260 exons, and 714 proteins were significantly associated with emphysema (false discovery rate, 5%) and yielded 11 biological pathways. Seventy-four percent of these genes and 62% of these proteins showed mediation by BMI. Our prediction models demonstrated reasonable predictive performance in both COPDGene and ECLIPSE. The highest-performing model used clinical, blood cell, and protein data (area under the receiver operating characteristic curve in COPDGene testing, 0.90; 95% confidence interval, 0.85-0.90). Conclusions: Blood transcriptome and proteome-wide analyses revealed key biological pathways of emphysema and enhanced the prediction of emphysema.

Rationale: Despite the importance of inflammation in chronic obstructive pulmonary disease (COPD), the immune cell landscape in the lung tissue of patients with mild-moderate disease has not been well characterized at the single-cell and molecular level. Objectives: To define the immune cell landscape in lung tissue from patients with mild-moderate COPD at single-cell resolution. Methods: We performed single-cell transcriptomic, proteomic, and T-cell receptor repertoire analyses on lung tissue from patients with mild-moderate COPD ( n  = 5, Global Initiative for Chronic Obstructive Lung Disease I or II), emphysema without airflow obstruction ( n  = 5), end-stage COPD ( n  = 2), control ( n  = 6), or donors ( n  = 4). We validated in an independent patient cohort ( N  = 929) and integrated with the Hhip +/- murine model of COPD. Measurements and Main Results: Mild-moderate COPD lungs have increased abundance of two CD8 +  T cell subpopulations: cytotoxic KLRG1 + TIGIT + CX3CR1 +  TEMRA (T effector memory CD45RA + ) cells, and DNAM-1 + CCR5 +  T resident memory (T RM ) cells. These CD8 +  T cells interact with myeloid and alveolar type II cells via IFNG and have hyperexpanded T-cell receptor clonotypes. In an independent cohort, the CD8 + KLRG1 + TEMRA cells are increased in mild-moderate COPD lung compared with control or end-stage COPD lung. Human CD8 + KLRG1 +  TEMRA cells are similar to CD8 +  T cells driving inflammation in an aging-related murine model of COPD. Conclusions: CD8 +  TEMRA cells are increased in mild-moderate COPD lung and may contribute to inflammation that precedes severe disease. Further study of these CD8 +  T cells may have therapeutic implications for preventing severe COPD.

Background: Asthma and chronic obstructive pulmonary disease (COPD) have distinct and overlapping genetic and clinical features., Objective: We sought to test the hypothesis that polygenic risk scores (PRSs) for asthma (PRS Asthma ) and spirometry (FEV 1 and FEV 1 /forced vital capacity; PRS spiro ) would demonstrate differential associations with asthma, COPD, and asthma-COPD overlap (ACO)., Methods: We developed and tested 2 asthma PRSs and applied the higher performing PRS Asthma and a previously published PRS spiro to research (Genetic Epidemiology of COPD study and Childhood Asthma Management Program, with spirometry) and electronic health record-based (Mass General Brigham Biobank and Genetic Epidemiology Research on Adult Health and Aging [GERA]) studies. We assessed the association of PRSs with COPD and asthma using modified random-effects and binary-effects meta-analyses, and ACO and asthma exacerbations in specific cohorts. Models were adjusted for confounders and genetic ancestry., Results: In meta-analyses of 102,477 participants, the PRS Asthma (odds ratio [OR] per SD, 1.16 [95% CI, 1.14-1.19]) and PRS spiro (OR per SD, 1.19 [95% CI, 1.17-1.22]) both predicted asthma, whereas the PRS spiro predicted COPD (OR per SD, 1.25 [95% CI, 1.21-1.30]). However, results differed by cohort. The PRS spiro was not associated with COPD in GERA and Mass General Brigham Biobank. In the Genetic Epidemiology of COPD study, the PRS Asthma (OR per SD: Whites, 1.3; African Americans, 1.2) and PRS spiro (OR per SD: Whites, 2.2; African Americans, 1.6) were both associated with ACO. In GERA, the PRS Asthma was associated with asthma exacerbations (OR, 1.18) in Whites; the PRS spiro was associated with asthma exacerbations in White, LatinX, and East Asian participants., Conclusions: PRSs for asthma and spirometry are both associated with ACO and asthma exacerbations. Genetic prediction performance differs in research versus electronic health record-based cohorts., (Published by Elsevier Inc.)

Rationale: Constantly exposed to the external environment and mutagens such as tobacco smoke, human lungs have one of the highest somatic mutation rates among all human organs. However, the relationship of these mutations to lung disease and function is not known. Objectives: To identify the prevalence and significance of clonal somatic mutations in chronic lung diseases. Methods: We analyzed the clonal somatic mutations from 1,251 samples of normal and diseased noncancerous lung tissue RNA sequencing with paired whole-genome sequencing from the Lung Tissue Research Consortium. We examined the associations of somatic mutations with lung function, disease status, and computationally deconvoluted cell types in two of the most common diseases represented in our dataset, chronic obstructive pulmonary disease (COPD; 29%) and idiopathic pulmonary fibrosis (IPF; 13%). Measurements and Main Results: Clonal somatic mutational burden was associated with reduced lung function in both COPD and IPF. We identified an increased prevalence of clonal somatic mutations in individuals with IPF compared with normal control subjects and individuals with COPD independent of age and smoking status. IPF clonal somatic mutations were enriched in disease-related and airway epithelial-expressed genes such as MUC5B in IPF. Patients who were MUC5B risk variant carriers had increased odds of developing somatic mutations of MUC5B that were explained by increased expression of MUC5B . Conclusions: Our identification of an increased prevalence of clonal somatic mutation in diseased lung that correlates with airway epithelial gene expression and disease severity highlights for the first time the role of somatic mutational processes in lung disease genetics.

Background: Quantitative interstitial abnormalities (QIA) are an automated computed tomography (CT) finding of early parenchymal lung disease, associated with worse lung function, reduced exercise capacity, increased respiratory symptoms, and death. The metabolomic perturbations associated with QIA are not well known. We sought to identify plasma metabolites associated with QIA in smokers. We also sought to identify shared and differentiating metabolomics features between QIA and emphysema, another smoking-related advanced radiographic abnormality., Methods: In 928 former and current smokers in the Genetic Epidemiology of COPD cohort, we measured QIA and emphysema using an automated local density histogram method and generated metabolite profiles from plasma samples using liquid chromatography-mass spectrometry (Metabolon). We assessed the associations between metabolite levels and QIA using multivariable linear regression models adjusted for age, sex, body mass index, smoking status, pack-years, and inhaled corticosteroid use, at a Benjamini-Hochberg False Discovery Rate p-value of ≤ 0.05. Using multinomial regression models adjusted for these covariates, we assessed the associations between metabolite levels and the following CT phenotypes: QIA-predominant, emphysema-predominant, combined-predominant, and neither- predominant. Pathway enrichment analyses were performed using MetaboAnalyst., Results: We found 85 metabolites significantly associated with QIA, with overrepresentation of the nicotinate and nicotinamide, histidine, starch and sucrose, pyrimidine, phosphatidylcholine, lysophospholipid, and sphingomyelin pathways. These included metabolites involved in inflammation and immune response, extracellular matrix remodeling, surfactant, and muscle cachexia. There were 75 metabolites significantly different between QIA-predominant and emphysema-predominant phenotypes, with overrepresentation of the phosphatidylethanolamine, nicotinate and nicotinamide, aminoacyl-tRNA, arginine, proline, alanine, aspartate, and glutamate pathways., Conclusions: Metabolomic correlates may lend insight to the biologic perturbations and pathways that underlie clinically meaningful quantitative CT measurements like QIA in smokers., (© 2023. The Author(s).)

The consequences of returning infectious pathogen test results identified incidentally in research studies have not been well-studied. Concerns include identification of an important health issue for individuals, accuracy of research test results, public health impact, potential emotional distress for participants, and need for IRB permissions. Blood RNA-sequencing analysis for non-human RNA in 3984 participants from the COPDGene study identified 228 participants with evidence suggestive for hepatitis C virus (HCV) infection. We hypothesized that incidentally discovered HCV results could be effectively returned to COPDGene participants with attention to the identified concerns. In conjunction with a COPDGene Participant Advisory Panel, we developed and obtained IRB approval for a process of returning HCV research results and an HCV Follow-Up Study questionnaire to capture information about previous HCV diagnosis and treatment information and participant reactions to return of HCV results. During phone calls following the initial HCV notification letter, 84 of 124 participants who could be contacted (67.7%) volunteered that they had been previously diagnosed with HCV infection. Thirty-one of these 124 COPDGene participants were enrolled in the HCV Follow-Up Study. Five of the 31 HCV Follow-Up Study participants did not report a previous diagnosis of HCV. For four of these participants, subsequent clinical HCV testing confirmed HCV infection. Thus, 30/31 Follow-Up Study participants had confirmed HCV diagnoses, supporting the accuracy of the HCV research test results. However, the limited number of participants in the Follow-Up Study precludes an accurate assessment of the false-positive and false-negative rates of the research RNA sequencing evidence for HCV. Most HCV Follow-Up Study participants (29/31) were supportive of returning HCV research results, and most participants found the process for returning HCV results to be informative and not upsetting. Newly diagnosed participants were more likely to be pleased to learn about a potentially curable infection (p = 0.027) and showed a trend toward being more frightened by the potential health risks of HCV (p = 0.11). We conclude that HCV results identified incidentally during transcriptomic research studies can be successfully returned to research study participants with a carefully designed process., (© 2023. The Author(s).)

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: IEA emph 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 IEA airway 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 IEA emph and IEA airway , 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., (JCOPDF © 2023.)

While variation in emphysema severity between patients with chronic obstructive pulmonary disease (COPD) is well-recognized, clinically applicable definitions of the emphysema-predominant disease (EPD) and non-emphysema-predominant disease (NEPD) subtypes have not been established. To study the clinical relevance of the EPD and NEPD subtypes, we tested the association of these subtypes with prospective decline in forced expiratory volume in 1 second (FEV1) and mortality among 3,427 subjects with Global Initiative for Chronic Obstructive Lung Disease (GOLD) spirometric grade 2-4 COPD at baseline in the Genetic Epidemiology of COPD (COPDGene) Study, an ongoing national multicenter study that started in 2007. NEPD was defined as airflow obstruction with less than 5% computed tomography (CT) quantitative densitometric emphysema at -950 Hounsfield units, and EPD was defined as airflow obstruction with 10% or greater CT emphysema. Mixed-effects models for FEV1 demonstrated larger average annual FEV1 loss in EPD subjects than in NEPD subjects (-10.2 mL/year; P < 0.001), and subtype-specific associations with FEV1 decline were identified. Cox proportional hazards models showed higher risk of mortality among EPD patients versus NEPD patients (hazard ratio = 1.46, 95% confidence interval: 1.34, 1.60; P < 0.001). To determine whether the NEPD/EPD dichotomy is captured by previously described COPDGene subtypes, we used logistic regression and receiver operating characteristic (ROC) curve analysis to predict NEPD/EPD membership using these previous subtype definitions. The analysis generally showed excellent discrimination, with areas under the ROC curve greater than 0.9. The NEPD and EPD COPD subtypes capture important aspects of COPD heterogeneity and are associated with different rates of disease progression and mortality., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Rationale: Cigarette smoking contributes to the risk of death through different mechanisms. Objectives: To determine how causes of and clinical features associated with death vary in tobacco cigarette users by lung function impairment. Methods: We stratified current and former tobacco cigarette users enrolled in Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) into normal spirometry, PRISm (Preserved Ratio Impaired Spirometry), Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1-2 COPD, and GOLD 3-4 COPD. Deaths were identified via longitudinal follow-up and Social Security Death Index search. Causes of death were adjudicated after a review of death certificates, medical records, and next-of-kin interviews. We tested associations between baseline clinical variables and all-cause mortality using multivariable Cox proportional hazards models. Measurements and Main Results: Over a 10.1-year median follow-up, 2,200 deaths occurred among 10,132 participants (age 59.5 ± 9.0 yr; 46.6% women). Death from cardiovascular disease was most frequent in PRISm (31% of deaths). Lung cancer deaths were most frequent in GOLD 1-2 (18% of deaths vs. 9-11% in other groups). Respiratory deaths outpaced competing causes of death in GOLD 3-4, particularly when BODE index ⩾7. St. George's Respiratory Questionnaire score ⩾25 was associated with higher mortality in all groups: Hazard ratio (HR), 1.48 (1.20-1.84) normal spirometry; HR, 1.40 (1.05-1.87) PRISm; HR, 1.80 (1.49-2.17) GOLD 1-2; HR, 1.65 (1.26-2.17) GOLD 3-4. History of respiratory exacerbations was associated with higher mortality in GOLD 1-2 and GOLD 3-4, quantitative emphysema in GOLD 1-2, and airway wall thickness in PRISm and GOLD 3-4. Conclusions: Leading causes of death vary by lung function impairment in tobacco cigarette users. Worse respiratory-related quality of life is associated with all-cause mortality regardless of lung function.

Expression quantitative trait methylation (eQTM) analysis identifies DNA CpG sites at which methylation is associated with gene expression. The present study describes an eQTM resource of CpG-transcript pairs derived from whole blood DNA methylation and RNA sequencing gene expression data in 2115 Framingham Heart Study participants. We identified 70,047 significant cis CpG-transcript pairs at p < 1E-7 where the top most significant eGenes (i.e., gene transcripts associated with a CpG) were enriched in biological pathways related to cell signaling, and for 1208 clinical traits (enrichment false discovery rate [FDR] ≤ 0.05). We also identified 246,667 significant trans CpG-transcript pairs at p < 1E-14 where the top most significant eGenes were enriched in biological pathways related to activation of the immune response, and for 1191 clinical traits (enrichment FDR ≤ 0.05). Independent and external replication of the top 1000 significant cis and trans CpG-transcript pairs was completed in the Women's Health Initiative and Jackson Heart Study cohorts. Using significant cis CpG-transcript pairs, we identified significant mediation of the association between CpG sites and cardiometabolic traits through gene expression and identified shared genetic regulation between CpGs and transcripts associated with cardiometabolic traits. In conclusion, we developed a robust and powerful resource of whole blood eQTM CpG-transcript pairs that can help inform future functional studies that seek to understand the molecular basis of disease., (© 2023. Springer Nature Limited.)

Exonic variants present some of the strongest links between genotype and phenotype. However, these variants can have significant inter-individual pathogenicity differences, known as variable penetrance. In this study, we propose a model where genetically controlled mRNA splicing modulates the pathogenicity of exonic variants. By first cataloging exonic inclusion from RNA-sequencing data in GTEx V8, we find that pathogenic alleles are depleted on highly included exons. Using a large-scale phased whole genome sequencing data from the TOPMed consortium, we observe that this effect may be driven by common splice-regulatory genetic variants, and that natural selection acts on haplotype configurations that reduce the transcript inclusion of putatively pathogenic variants, especially when limiting to haploinsufficient genes. Finally, we test if this effect may be relevant for autism risk using families from the Simons Simplex Collection, but find that splicing of pathogenic alleles has a penetrance reducing effect here as well. Overall, our results indicate that common splice-regulatory variants may play a role in reducing the damaging effects of rare exonic variants., Competing Interests: Conflicts of interest statement TL is a paid advisor to GSK, Pfizer, Goldfinch Bio, and Variant Bio and has equity in Variant Bio., (© The Author(s) 2023. Published by Oxford University Press on behalf of The Genetics Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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.

The integration of transcriptomic and proteomic data from lung tissue with chronic obstructive pulmonary disease (COPD)-associated genetic variants could provide insight into the biological mechanisms of COPD. Here, we assessed associations between lung transcriptomics and proteomics with COPD in 98 subjects from the Lung Tissue Research Consortium. Low correlations between transcriptomics and proteomics were generally observed, but higher correlations were found for COPD-associated proteins. We integrated COPD risk SNPs or SNPs near COPD-associated proteins with lung transcripts and proteins to identify regulatory cis -quantitative trait loci (QTLs). Significant expression QTLs (eQTLs) and protein QTLs (pQTLs) were found regulating multiple COPD-associated biomarkers. We investigated mediated associations from significant pQTLs through transcripts to protein levels of COPD-associated proteins. We also attempted to identify colocalized effects between COPD genome-wide association studies and eQTL and pQTL signals. Evidence was found for colocalization between COPD genome-wide association study signals and a pQTL for RHOB and an eQTL for DSP. We applied weighted gene co-expression network analysis to find consensus COPD-associated network modules. Two network modules generated by consensus weighted gene co-expression network analysis were associated with COPD with a false discovery rate lower than 0.05. One network module is related to the catenin complex, and the other module is related to plasma membrane components. In summary, multiple cis -acting determinants of transcripts and proteins associated with COPD were identified. Colocalization analysis, mediation analysis, and correlation-based network analysis of multiple omics data may identify key genes and proteins that work together to influence COPD pathogenesis.

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., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Zhuang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)