Computational Deconvolution of Cell Type-Specific Gene Expression in COPD and IPF Lungs Reveals Disease Severity Associations

RATIONALE

Chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) are debilitating diseases associated with divergent histopathological changes in the lungs. At present, due to cost and technical limitations, profiling cell types is not practical in large epidemiology cohorts (n>1000). Here, we used computational deconvolution to identify cell types in COPD and IPF lungs whose abundances and cell type-specific gene expression are associated with disease diagnosis and severity.

METHODS

We analyzed lung tissue RNA-seq data from 1026 subjects (COPD, n=465; IPF, n=213; control, n=348) from the Lung Tissue Research Consortium. We performed RNA-seq deconvolution, querying thirty-eight discrete cell-type varieties in the lungs. We tested whether deconvoluted cell-type abundance and cell type-specific gene expression were associated with disease severity.

RESULTS

The abundance score of twenty cell types significantly differed between IPF and control lungs. In IPF subjects, eleven and nine cell types were significantly associated with forced vital capacity (FVC) and diffusing capacity for carbon monoxide (DLCO), respectively. Aberrant basaloid cells, a rare cells found in fibrotic lungs, were associated with worse FVC and DLCO in IPF subjects, indicating that this aberrant epithelial population increased with disease severity. Alveolar type 1 and vascular endothelial (VE) capillary A were decreased in COPD lungs compared to controls. An increase in macrophages and classical monocytes was associated with lower DLCO in IPF and COPD subjects. In both diseases, lower non-classical monocytes and VE capillary A cells were associated with increased disease severity. Alveolar type 2 cells and alveolar macrophages had the highest number of genes with cell type-specific differential expression by disease severity in COPD and IPF. In IPF, genes implicated in the pathogenesis of IPF, such as matrix metallopeptidase 7, growth differentiation factor 15, and eph receptor B2, were associated with disease severity in a cell type-specific manner.

CONCLUSION

Utilization of RNA-seq deconvolution enabled us to pinpoint cell types present in the lungs that are associated with the severity of COPD and IPF. This knowledge offers valuable insight into the alterations within tissues in more advanced illness, ultimately providing a better understanding of the underlying pathological processes that drive disease progression.

Colocalization analysis of 3' UTR alternative polyadenylation quantitative trait loci reveals novel mechanisms underlying associations with lung function

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).

HHIP protein interactions in lung cells provide insight into COPD pathogenesis

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. The primary causes of COPD are environmental, including cigarette smoking; however, genetic susceptibility also contributes to COPD risk. Genome-Wide Association Studies (GWASes) have revealed more than 80 genetic loci associated with COPD, leading to the identification of multiple COPD GWAS genes. However, the biological relationships between the identified COPD susceptibility genes are largely unknown. Genes associated with a complex disease are often in close network proximity, i.e. their protein products often interact directly with each other and/or similar proteins. In this study, we use affinity purification mass spectrometry (AP-MS) to identify protein interactions with HHIP , a well-established COPD GWAS gene which is part of the sonic hedgehog pathway, in two disease-relevant lung cell lines (IMR90 and 16HBE). To better understand the network neighborhood of HHIP , its proximity to the protein products of other COPD GWAS genes, and its functional role in COPD pathogenesis, we create HUBRIS, a protein-protein interaction network compiled from 8 publicly available databases. We identified both common and cell type-specific protein-protein interactors of HHIP. We find that our newly identified interactions shorten the network distance between HHIP and the protein products of several COPD GWAS genes, including DSP, MFAP2, TET2 , and FBLN5 . These new shorter paths include proteins that are encoded by genes involved in extracellular matrix and tissue organization. We found and validated interactions to proteins that provide new insights into COPD pathobiology, including CAVIN1 (IMR90) and TP53 (16HBE). The newly discovered HHIP interactions with CAVIN1 and TP53 implicate HHIP in response to oxidative stress.

Biosurfer for systematic tracking of regulatory mechanisms leading to protein isoform diversity

Long-read RNA sequencing has shed light on transcriptomic complexity, but questions remain about the functionality of downstream protein products. We introduce Biosurfer, a computational approach for comparing protein isoforms, while systematically tracking the transcriptional, splicing, and translational variations that underlie differences in the sequences of the protein products. Using Biosurfer, we analyzed the differences in 32,799 pairs of GENCODE annotated protein isoforms, finding a majority (70%) of variable N-termini are due to the alternative transcription start sites, while only 9% arise from 5' UTR alternative splicing. Biosurfer's detailed tracking of nucleotide-to-residue relationships helped reveal an uncommonly tracked source of single amino acid residue changes arising from the codon splits at junctions. For 17% of internal sequence changes, such split codon patterns lead to single residue differences, termed "ragged codons". Of variable C-termini, 72% involve splice- or intron retention-induced reading frameshifts. We found an unusual pattern of reading frame changes, in which the first frameshift is closely followed by a distinct second frameshift that restores the original frame, which we term a "snapback" frameshift. We analyzed long read RNA-seq-predicted proteome of a human cell line and found similar trends as compared to our GENCODE analysis, with the exception of a higher proportion of isoforms predicted to undergo nonsense-mediated decay. Biosurfer's comprehensive characterization of long-read RNA-seq datasets should accelerate insights of the functional role of protein isoforms, providing mechanistic explanation of the origins of the proteomic diversity driven by the alternative splicing. Biosurfer is available as a Python package at https://github.com/sheynkman-lab/biosurfer.

Blood-based Transcriptomic and Proteomic Biomarkers of Emphysema

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.

A Generalized Higher-order Correlation Analysis Framework for Multi-Omics Network Inference

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

Joint clinical and molecular subtyping of COPD with variational autoencoders

Chronic Obstructive Pulmonary Disease (COPD) is a complex, heterogeneous disease. Traditional subtyping methods generally focus on either the clinical manifestations or the molecular endotypes of the disease, resulting in classifications that do not fully capture the disease's complexity. Here, we bridge this gap by introducing a subtyping pipeline that integrates clinical and gene expression data with variational autoencoders. We apply this methodology to the COPDGene study, a large study of current and former smoking individuals with and without COPD. Our approach generates a set of vector embeddings, called Personalized Integrated Profiles (PIPs), that recapitulate the joint clinical and molecular state of the subjects in the study. Prediction experiments show that the PIPs have a predictive accuracy comparable to or better than other embedding approaches. Using trajectory learning approaches, we analyze the main trajectories of variation in the PIP space and identify five well-separated subtypes with distinct clinical phenotypes, expression signatures, and disease outcomes. Notably, these subtypes are more robust to data resampling compared to those identified using traditional clustering approaches. Overall, our findings provide new avenues to establish fine-grained associations between the clinical characteristics, molecular processes, and disease outcomes of COPD.

Activation of CD8+ T Cells in Chronic Obstructive Pulmonary Disease Lung

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 (TRM) 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.

Clonal Somatic Mutations in Chronic Lung Diseases Are Associated with Reduced Lung Function

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.

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.

Heterogeneity and Progression of Chronic Obstructive Pulmonary Disease: Emphysema-Predominant and Non-Emphysema-Predominant Disease

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.

From pre-COPD to COPD: a Simple, Low cost and easy to IMplement (SLIM) risk calculator

BACKGROUND

The lifetime risk of developing clinical COPD among smokers ranges from 13% to 22%. Identifying at-risk individuals who will develop overt disease in a reasonable timeframe may allow for early intervention. We hypothesised that readily available clinical and physiological variables could help identify ever-smokers at higher risk of developing chronic airflow limitation (CAL).

METHODS

Among 2273 Lovelace Smokers' Cohort (LSC) participants, we included 677 (mean age 54 years) with normal spirometry at baseline and a minimum of three spirometries, each 1 year apart. Repeated spirometric measurements were used to determine incident CAL. Using logistic regression, demographics, anthropometrics, smoking history, modified Medical Research Council dyspnoea scale, St George's Respiratory Questionnaire, comorbidities and spirometry, we related variables obtained at baseline to incident CAL as defined by the Global Initiative for Chronic Obstructive Lung Disease and lower limit of normal criteria. The predictive model derived from the LSC was validated in subjects from the COPDGene study.

RESULTS

Over 6.3 years, the incidence of CAL was 26 cases per 1000 person-years. The strongest independent predictors were forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) <0.75, having smoked ≥30 pack-years, body mass index (BMI) ≤25 kg·m2 and symptoms of chronic bronchitis. Having all four predictors increased the risk of developing CAL over 6 years to 85% (area under the receiver operating characteristic curve (AUC ROC) 0.84, 95% CI 0.81-0.89). The prediction model showed similar results when applied to subjects in the COPDGene study with a follow-up period of 10 years (AUC ROC 0.77, 95% CI 0.72-0.81).

CONCLUSION

In middle-aged ever-smokers, a simple predictive model with FEV1/FVC, smoking history, BMI and chronic bronchitis helps identify subjects at high risk of developing CAL.

R2C2+UMI: Combining concatemeric consensus sequencing with unique molecular identifiers enables ultra-accurate sequencing of amplicons on Oxford Nanopore Technologies sequencers

The sequencing of PCR amplicons is a core application of high-throughput sequencing technology. Using unique molecular identifiers (UMIs), individual amplified molecules can be sequenced to very high accuracy on an Illumina sequencer. However, Illumina sequencers have limited read length and are therefore restricted to sequencing amplicons shorter than 600bp unless using inefficient synthetic long-read approaches. Native long-read sequencers from Pacific Biosciences and Oxford Nanopore Technologies can, using consensus read approaches, match or exceed Illumina quality while achieving much longer read lengths. Using a circularization-based concatemeric consensus sequencing approach (R2C2) paired with UMIs (R2C2+UMI) we show that we can sequence ~550nt antibody heavy-chain (IGH) and ~1500nt 16S amplicons at accuracies up to and exceeding Q50 (<1 error in 100,0000 sequenced bases), which exceeds accuracies of UMI-supported Illumina paired sequencing as well as synthetic long-read approaches.

Integrating Genetics, Transcriptomics, and Proteomics in Lung Tissue to Investigate Chronic Obstructive Pulmonary Disease

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.

Deep Learning Utilizing Suboptimal Spirometry Data to Improve Lung Function and Mortality Prediction in the UK Biobank

Background

Spirometry measures lung function by selecting the best of multiple efforts meeting pre-specified quality control (QC), and reporting two key metrics: forced expiratory volume in 1 second (FEV1) and forced vital capacity (FVC). We hypothesize that discarded submaximal and QC-failing data meaningfully contribute to the prediction of airflow obstruction and all-cause mortality.

Methods

We evaluated volume-time spirometry data from the UK Biobank. We identified "best" spirometry efforts as those passing QC with the maximum FVC. "Discarded" efforts were either submaximal or failed QC. To create a combined representation of lung function we implemented a contrastive learning approach, Spirogram-based Contrastive Learning Framework (Spiro-CLF), which utilized all recorded volume-time curves per participant and applied different transformations (e.g. flow-volume, flow-time). In a held-out 20% testing subset we applied the Spiro-CLF representation of a participant's overall lung function to 1) binary predictions of FEV1/FVC < 0.7 and FEV1 Percent Predicted (FEV1PP) < 80%, indicative of airflow obstruction, and 2) Cox regression for all-cause mortality.

Findings

We included 940,705 volume-time curves from 352,684 UK Biobank participants with 2-3 spirometry efforts per individual (66.7% with 3 efforts) and at least one QC-passing spirometry effort. Of all spirometry efforts, 24.1% failed QC and 37.5% were submaximal. Spiro-CLF prediction of FEV1/FVC < 0.7 utilizing discarded spirometry efforts had an Area under the Receiver Operating Characteristics (AUROC) of 0.981 (0.863 for FEV1PP prediction). Incorporating discarded spirometry efforts in all-cause mortality prediction was associated with a concordance index (c-index) of 0.654, which exceeded the c-indices from FEV1 (0.590), FVC (0.559), or FEV1/FVC (0.599) from each participant's single best effort.

Interpretation

A contrastive learning model using raw spirometry curves can accurately predict lung function using submaximal and QC-failing efforts. This model also has superior prediction of all-cause mortality compared to standard lung function measurements.

Funding

MHC is supported by NIH R01HL137927, R01HL135142, HL147148, and HL089856.BDH is supported by NIH K08HL136928, U01 HL089856, and an Alpha-1 Foundation Research Grant.DH is supported by NIH 2T32HL007427-41EKS is supported by NIH R01 HL152728, R01 HL147148, U01 HL089856, R01 HL133135, P01 HL132825, and P01 HL114501.PJC is supported by NIH R01HL124233 and R01HL147326.SPB is supported by NIH R01HL151421 and UH3HL155806.TY, FH, and CYM are employees of Google LLC.

Identifying COPD subtypes using multi-trait genetics

Chronic Obstructive Pulmonary Disease (COPD) has a simple physiological diagnostic criterion but a wide range of clinical characteristics. The mechanisms underlying this variability in COPD phenotypes are unclear. To investigate the potential contribution of genetic variants to phenotypic heterogeneity, we examined the association of genome-wide associated lung function, COPD, and asthma variants with other phenotypes using phenome-wide association results derived in the UK Biobank. Our clustering analysis of the variants-phenotypes association matrix identified three clusters of genetic variants with different effects on white blood cell counts, height, and body mass index (BMI). To assess the potential clinical and molecular effects of these groups of variants, we investigated the association between cluster-specific genetic risk scores and phenotypes in the COPDGene cohort. We observed differences in steroid use, BMI, lymphocyte counts, chronic bronchitis, and differential gene and protein expression across the three genetic risk scores. Our results suggest that multi-phenotype analysis of obstructive lung disease-related risk variants may identify genetically driven phenotypic patterns in COPD.

Hepatitis C and HIV detection by blood RNA-sequencing in cohort of smokers

Detection of viruses by RNA and DNA sequencing has improved the understanding of the human virome. We sought to identify blood viral signatures through secondary use of RNA-sequencing (RNA-seq) data in a large study cohort. The ability to reveal undiagnosed infections with public health implications among study subjects with available sequencing data could enable epidemiologic surveys and may lead to diagnosis and therapeutic interventions, leveraging existing research data in a clinical context. We detected viral RNA in peripheral blood RNA-seq data from a COPD-enriched population of current and former smokers. Correlation between viral detection and both reported infections and relevant disease outcomes was evaluated. We identified Hepatitis C virus RNA in 228 subjects and HIV RNA in 30 subjects. Overall, we observed 31 viral species, including Epstein-Barr virus and Cytomegalovirus. We observed an enrichment of Hepatitis C and HIV infections among subjects reporting liver disease and HIV infections, respectively. Higher interferon expression scores were observed in the subjects with Hepatitis C and HIV infections. Through secondary use of RNA-seq from a cohort of current and former smokers, we detected peripheral blood viral signatures. We identified HIV and Hepatitis C virus (HCV), highlighting potential public health implications for the approach described this study. We observed correlations with reported infections, chronic infection outcomes and the host transcriptomic response, providing evidence to support the validity of the approach.

Protocol for a randomized controlled trial of the Enhanced Smoking Cessation Approach to Promote Empowerment (ESCAPE) digitalized intervention to promote lung health in high-risk individuals who smoke

Low dose computed tomography (LDCT) is an effective screening test to decrease lung cancer deaths. Lung cancer screening may be a teachable moment helping people who smoke to quit, which may result in increased benefit of screening. Innovative strategies are needed to engage high-risk individuals in learning about LDCT screening. More precise methods such as polygenic risk scores quantify genetic predisposition to tobacco use, and optimize lung health interventions. We present the ESCAPE (Enhanced Smoking Cessation Approach to Promote Empowerment) protocol. This study will test a smoking cessation intervention using personal stories and a lung cancer screening decision-aide compared to standard care (brief advice, referral to a quit line, and a lung cancer screening decision-aide), examine the relationship between a polygenic risk score and smoking abstinence, and describe perceptions about integration of genomic information into smoking cessation treatment. A randomized controlled trial followed by a sequential explanatory mixed methods approach will compare the efficacy of the interventions. Interviews will add insight into the use of genomic information and risk perceptions to tailor smoking cessation treatment. Two-hundred and fifty individuals will be recruited from primary care, community-based organizations, mailing lists and through social media. Data will be collected at baseline, 1, 3 and 6-months. The primary outcomes are 7-day point prevalence smoking abstinence and stage of lung cancer screening at 6-months. The results from this study will provide information to refine the ESCAPE intervention and facilitate integration of precision health into future lung health interventions. Clinical trial registration number: NCT0469129T.

Bridging the splicing gap in human genetics with long-read RNA sequencing: finding the protein isoform drivers of disease

Aberrant splicing underlies many human diseases, including cancer, cardiovascular diseases and neurological disorders. Genome-wide mapping of splicing quantitative trait loci (sQTLs) has shown that genetic regulation of alternative splicing is widespread. However, identification of the corresponding isoform or protein products associated with disease-associated sQTLs is challenging with short-read RNA-seq, which cannot precisely characterize full-length transcript isoforms. Furthermore, contemporary sQTL interpretation often relies on reference transcript annotations, which are incomplete. Solutions to these issues may be found through integration of newly emerging long-read sequencing technologies. Long-read sequencing offers the capability to sequence full-length mRNA transcripts and, in some cases, to link sQTLs to transcript isoforms containing disease-relevant protein alterations. Here, we provide an overview of sQTL mapping approaches, the use of long-read sequencing to characterize sQTL effects on isoforms, the linkage of RNA isoforms to protein-level functions and comment on future directions in the field. Based on recent progress, long-read RNA sequencing promises to be part of the human disease genetics toolkit to discover and treat protein isoforms causing rare and complex diseases.

Clustering-based COPD subtypes have distinct longitudinal outcomes and multi-omics biomarkers

Introduction

Chronic obstructive pulmonary disease (COPD) can progress across several domains, complicating the identification of the determinants of disease progression. In our previous work, we applied k-means clustering to spirometric and chest radiological measures to identify four COPD-related subtypes: ‘relatively resistant smokers (RRS)’, ‘mild upper lobe-predominant emphysema (ULE)’, ‘airway-predominant disease (AD)’ and ‘severe emphysema (SE)’. In the current study, we examined the associations of these subtypes to longitudinal COPD-related health measures as well as blood transcriptomic and plasma proteomic biomarkers.

Methods

We included 8266 non-Hispanic white and African-American smokers from the COPDGene study. We used linear regression to investigate cluster associations to 5-year prospective changes in spirometric and radiological measures and to gene expression and protein levels. We used Cox-proportional hazard test to test for cluster associations to prospective exacerbations, comorbidities and mortality.

Results

The RRS, ULE, AD and SE clusters represented 39%, 15%, 26% and 20% of the studied cohort at baseline, respectively. The SE cluster had the greatest 5-year FEV₁ (forced expiratory volume in 1 s) and emphysema progression, and the highest risks of exacerbations, cardiovascular disease and mortality. The AD cluster had the highest diabetes risk. After adjustments, only the SE cluster had an elevated respiratory mortality risk, while the ULE, AD and SE clusters had elevated all-cause mortality risks. These clusters also demonstrated differential protein and gene expression biomarker associations, mostly related to inflammatory and immune processes.

Conclusion

COPD k-means subtypes demonstrate varying rates of disease progression, prospective comorbidities, mortality and associations to transcriptomic and proteomic biomarkers. These findings emphasise the clinical and biological relevance of these subtypes, which call for more study for translation into clinical practice.

Trail registration number

NCT00608764.

Machine Learning Prediction of Progression in Forced Expiratory Volume in 1 Second in the COPDGene® Study

BACKGROUND

The heterogeneous nature of chronic obstructive pulmonary disease (COPD) complicates the identification of the predictors of disease progression. We aimed to improve the prediction of disease progression in COPD by using machine learning and incorporating a rich dataset of phenotypic features.

METHODS

We included 4496 smokers with available data from their enrollment and 5-year follow-up visits in the COPD Genetic Epidemiology (COPDGene^®) study. We constructed linear regression (LR) and supervised random forest models to predict 5-year progression in forced expiratory in 1 second (FEV₁) from 46 baseline features. Using cross-validation, we randomly partitioned participants into training and testing samples. We also validated the results in the COPDGene 10-year follow-up visit.

RESULTS

Predicting the change in FEV₁ over time is more challenging than simply predicting the future absolute FEV₁ level. For random forest, R-squared was 0.15 and the area under the receiver operator characteristic (ROC) curves for the prediction of participants in the top quartile of observed progression was 0.71 (testing) and respectively, 0.10 and 0.70 (validation). Random forest provided slightly better performance than LR. The accuracy was best for Global initiative for chronic Obstructive Lung Disease (GOLD) grades 1-2 participants, and it was harder to achieve accurate prediction in advanced stages of the disease. Predictive variables differed in their relative importance as well as for the predictions by GOLD.

CONCLUSION

Random forest, along with deep phenotyping, predicts FEV₁ progression with reasonable accuracy. There is significant room for improvement in future models. This prediction model facilitates the identification of smokers at increased risk for rapid disease progression. Such findings may be useful in the selection of patient populations for targeted clinical trials.

Blood gene expression risk profiles and interstitial lung abnormalities: COPDGene and ECLIPSE cohort studies

BACKGROUND

Interstitial lung abnormalities (ILA) are radiologic findings that may progress to idiopathic pulmonary fibrosis (IPF). Blood gene expression profiles can predict IPF mortality, but whether these same genes associate with ILA and ILA outcomes is unknown. This study evaluated if a previously described blood gene expression profile associated with IPF mortality is associated with ILA and all-cause mortality.

METHODS

In COPDGene and ECLIPSE study participants with visual scoring of ILA and gene expression data, we evaluated the association of a previously described IPF mortality score with ILA and mortality. We also trained a new ILA score, derived using genes from the IPF score, in a subset of COPDGene. We tested the association with ILA and mortality on the remainder of COPDGene and ECLIPSE.

RESULTS

In 1469 COPDGene (training n = 734; testing n = 735) and 571 ECLIPSE participants, the IPF score was not associated with ILA or mortality. However, an ILA score derived from IPF score genes was associated with ILA (meta-analysis of test datasets OR 1.4 [95% CI: 1.2-1.6]) and mortality (HR 1.25 [95% CI: 1.12-1.41]). Six of the 11 genes in the ILA score had discordant directions of effects compared to the IPF score. The ILA score partially mediated the effects of age on mortality (11.8% proportion mediated).

CONCLUSIONS

An ILA gene expression score, derived from IPF mortality-associated genes, identified genes with concordant and discordant effects on IPF mortality and ILA. These results suggest shared, and unique biologic processes, amongst those with ILA, IPF, aging, and death.

Lung tissue shows divergent gene expression between chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis

BACKGROUND

Chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) are characterized by shared exposures and clinical features, but distinct genetic and pathologic features exist. These features have not been well-studied using large-scale gene expression datasets. We hypothesized that there are divergent gene, pathway, and cellular signatures between COPD and IPF.

METHODS

We performed RNA-sequencing on lung tissues from individuals with IPF (n = 231) and COPD (n = 377) compared to control (n = 267), defined as individuals with normal spirometry. We grouped the overlapping differential expression gene sets based on direction of expression and examined the resultant sets for genes of interest, pathway enrichment, and cell composition. Using gene set variation analysis, we validated the overlap group gene sets in independent COPD and IPF data sets.

RESULTS

We found 5010 genes differentially expressed between COPD and control, and 11,454 genes differentially expressed between IPF and control (1% false discovery rate). 3846 genes overlapped between IPF and COPD. Several pathways were enriched for genes upregulated in COPD and downregulated in IPF; however, no pathways were enriched for genes downregulated in COPD and upregulated in IPF. There were many myeloid cell genes with increased expression in COPD but decreased in IPF. We found that the genes upregulated in COPD but downregulated in IPF were associated with lower lung function in the independent validation cohorts.

CONCLUSIONS

We identified a divergent gene expression signature between COPD and IPF, with increased expression in COPD and decreased in IPF. This signature is associated with worse lung function in both COPD and IPF.

Enhanced protein isoform characterization through long-read proteogenomics

BACKGROUND

The detection of physiologically relevant protein isoforms encoded by the human genome is critical to biomedicine. Mass spectrometry (MS)-based proteomics is the preeminent method for protein detection, but isoform-resolved proteomic analysis relies on accurate reference databases that match the sample; neither a subset nor a superset database is ideal. Long-read RNA sequencing (e.g., PacBio or Oxford Nanopore) provides full-length transcripts which can be used to predict full-length protein isoforms.

RESULTS

We describe here a long-read proteogenomics approach for integrating sample-matched long-read RNA-seq and MS-based proteomics data to enhance isoform characterization. We introduce a classification scheme for protein isoforms, discover novel protein isoforms, and present the first protein inference algorithm for the direct incorporation of long-read transcriptome data to enable detection of protein isoforms previously intractable to MS-based detection. We have released an open-source Nextflow pipeline that integrates long-read sequencing in a proteomic workflow for isoform-resolved analysis.

CONCLUSIONS

Our work suggests that the incorporation of long-read sequencing and proteomic data can facilitate improved characterization of human protein isoform diversity. Our first-generation pipeline provides a strong foundation for future development of long-read proteogenomics and its adoption for both basic and translational research.

Blood RNA sequencing shows overlapping gene expression across COPD phenotype domains

RATIONALE

COPD can be assessed using multidimensional grading systems with components from three domains: pulmonary function tests, symptoms and systemic features. Clinically, measures may be used interchangeably, though it is not known if they share similar pathobiology.

OBJECTIVE

To use RNA sequencing (RNA-seq) to determine if there is an overlap in the underlying biological mechanisms and consequences driving different components of the multidimensional grading systems.

METHODS

Whole blood was collected for RNA-seq from current and former smokers in the Genetic Epidemiology of COPD study. We tested the overlap in gene expression and biological pathways associated with case-control status and quantitative COPD phenotypes within and between the three domains.

RESULTS

In 2647 subjects, there were 3030 genes differentially expressed in any of the three domains or case-control status. There were five genes that overlapped between the three domains and case-control status, including G protein-coupled receptor 15(GPR15), sestrin 1 (SESN1) and interferon-induced guanylate-binding protein 1 (GBP1), which were associated with longitudinal decline in FEV1. The overlap between the three domains was enriched for pathways related to cellular components.

CONCLUSIONS

We identified gene sets and pathways that overlap between 12 COPD-related phenotypes and case-control status. There were no pathways represented in the overlap between the three domains and case-control status, but we identified multiple genes that demonstrated a consistent pattern of expression across several of the phenotypes. Patterns of gene expression correlation were generally similar to the correlation of clinical phenotypes in the PFT and symptom domains but not the systemic features.

Alpha-1 Antitrypsin MZ Heterozygosity Is an Endotype of Chronic Obstructive Pulmonary Disease

Rationale: Multiple studies have demonstrated an increased risk of chronic obstructive pulmonary disease (COPD) in heterozygous carriers of the AAT (alpha-1 antitrypsin) Z allele. However, it is not known if MZ subjects with COPD are phenotypically different from noncarriers (MM genotype) with COPD. Objectives: To assess if MZ subjects with COPD have different clinical features compared with MM subjects with COPD. Methods: Genotypes of SERPINA1 were ascertained by using whole-genome sequencing data in three independent studies. We compared outcomes between MM subjects with COPD and MZ subjects with COPD in each study and combined the results in a meta-analysis. We performed longitudinal and survival analyses to compare outcomes in MM and MZ subjects with COPD over time. Measurements and Main Results: We included 290 MZ subjects with COPD and 6,184 MM subjects with COPD across the three studies. MZ subjects had a lower FEV₁% predicted and greater quantitative emphysema on chest computed tomography scans compared with MM subjects. In a meta-analysis, the FEV₁ was 3.9% lower (95% confidence interval [CI], -6.55% to -1.26%) and emphysema (the percentage of lung attenuation areas <-950 HU) was 4.14% greater (95% CI, 1.44% to 6.84%) in MZ subjects. We found one gene, PGF (placental growth factor), to be differentially expressed in lung tissue from one study between MZ subjects and MM subjects. Conclusions: Carriers of the AAT Z allele (those who were MZ heterozygous) with COPD had lower lung function and more emphysema than MM subjects with COPD. Taken with the subtle differences in gene expression between the two groups, our findings suggest that MZ subjects represent an endotype of COPD.

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.

Lung proteomic biomarkers associated with chronic obstructive pulmonary disease

Identifying protein biomarkers for chronic obstructive pulmonary disease (COPD) has been challenging. Most previous studies have used individual proteins or preselected protein panels measured in blood samples. Mass spectrometry proteomic studies of lung tissue have been based on small sample sizes. We used mass spectrometry proteomic approaches to discover protein biomarkers from 150 lung tissue samples representing COPD cases and controls. Top COPD-associated proteins were identified based on multiple linear regression analysis with false discovery rate (FDR) < 0.05. Correlations between pairs of COPD-associated proteins were examined. Machine learning models were also evaluated to identify potential combinations of protein biomarkers related to COPD. We identified 4,407 proteins passing quality controls. Twenty-five proteins were significantly associated with COPD at FDR < 0.05, including interleukin 33, ferritin (light chain and heavy chain), and two proteins related to caveolae (CAV1 and CAVIN1). Multiple previously reported plasma protein biomarkers for COPD were not significantly associated with proteomic analysis of COPD in lung tissue, although RAGE was borderline significant. Eleven pairs of top significant proteins were highly correlated (r > 0.8), including several strongly correlated with RAGE (EHD2 and CAVIN1). Machine learning models using Random Forests with the top 5% of protein biomarkers demonstrated reasonable accuracy (0.707) and area under the curve (0.714) for COPD prediction. Mass spectrometry-based proteomic analysis of lung tissue is a promising approach for the identification of biomarkers for COPD.

Cigarette smoking-associated isoform switching and 3' UTR lengthening via alternative polyadenylation

Cigarette smoking induces a profound transcriptomic and systemic inflammatory response. Previous studies have focused on gene level differential expression of smoking, but the genome-wide effects of smoking on alternative isoform regulation have not yet been described. We conducted RNA sequencing in whole-blood samples of 454 current and 767 former smokers in the COPDGene Study, and we analyzed the effects of smoking on differential usage of isoforms and exons. At 10% FDR, we detected 3167 differentially expressed genes, 945 differentially used isoforms and 160 differentially used exons. Isoform switch analysis revealed widespread 3' UTR lengthening associated with cigarette smoking. The lengthening of these 3' UTRs was consistent with alternative usage of distal polyadenylation sites, and these extended 3' UTR regions were significantly enriched with functional sequence elements including microRNA and RNA-protein binding sites. These findings warrant further studies on alternative polyadenylation events as potential biomarkers and novel therapeutic targets for smoking-related diseases.

Peripheral blood microbial signatures in current and former smokers

The human microbiome has a role in the development of multiple diseases. Individual microbiome profiles are highly personalized, though many species are shared. Understanding the relationship between the human microbiome and disease may inform future individualized treatments. We hypothesize the blood microbiome signature may be a surrogate for some lung microbial characteristics. We sought associations between the blood microbiome signature and lung-relevant host factors. Based on reads not mapped to the human genome, we detected microbial nucleic acids through secondary use of peripheral blood RNA-sequencing from 2,590 current and former smokers with and without chronic obstructive pulmonary disease (COPD) from the COPDGene study. We used the Genome Analysis Toolkit (GATK) microbial pipeline PathSeq to infer microbial profiles. We tested associations between the inferred profiles and lung disease relevant phenotypes and examined links to host gene expression pathways. We replicated our analyses using a second independent set of blood RNA-seq data from 1,065 COPDGene study subjects and performed a meta-analysis across the two studies. The four phyla with highest abundance across all subjects were Proteobacteria, Actinobacteria, Firmicutes and Bacteroidetes. In our meta-analysis, we observed associations (q-value < 0.05) between Acinetobacter, Serratia, Streptococcus and Bacillus inferred abundances and Modified Medical Research Council (mMRC) dyspnea score. Current smoking status was associated (q < 0.05) with Acinetobacter, Serratia and Cutibacterium abundance. All 12 taxa investigated were associated with at least one white blood cell distribution variable. Abundance for nine of the 12 taxa was associated with sex, and seven of the 12 taxa were associated with race. Host-microbiome interaction analysis revealed clustering of genera associated with mMRC dyspnea score and smoking status, through shared links to several host pathways. This study is the first to identify a bacterial microbiome signature in the peripheral blood of current and former smokers. Understanding the relationships between systemic microbial signatures and lung-related phenotypes may inform novel interventions and aid understanding of the systemic effects of smoking.

Multi-omics subtyping pipeline for chronic obstructive pulmonary disease

Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of mortality in the United States; however, COPD has heterogeneous clinical phenotypes. This is the first large scale attempt which uses transcriptomics, proteomics, and metabolomics (multi-omics) to determine whether there are molecularly defined clusters with distinct clinical phenotypes that may underlie the clinical heterogeneity. Subjects included 3,278 subjects from the COPDGene cohort with at least one of the following profiles: whole blood transcriptomes (2,650 subjects); plasma proteomes (1,013 subjects); and plasma metabolomes (1,136 subjects). 489 subjects had all three contemporaneous -omics profiles. Autoencoder embeddings were performed individually for each -omics dataset. Embeddings underwent subspace clustering using MineClus, either individually by -omics or combined, followed by recursive feature selection based on Support Vector Machines. Clusters were tested for associations with clinical variables. Optimal single -omics clustering typically resulted in two clusters. Although there was overlap for individual -omics cluster membership, each -omics cluster tended to be defined by unique molecular pathways. For example, prominent molecular features of the metabolome-based clustering included sphingomyelin, while key molecular features of the transcriptome-based clusters were related to immune and bacterial responses. We also found that when we integrated the -omics data at a later stage, we identified subtypes that varied based on age, severity of disease, in addition to diffusing capacity of the lungs for carbon monoxide, and precent on atrial fibrillation. In contrast, when we integrated the -omics data at an earlier stage by treating all data sets equally, there were no clinical differences between subtypes. Similar to clinical clustering, which has revealed multiple heterogenous clinical phenotypes, we show that transcriptomics, proteomics, and metabolomics tend to define clusters of COPD patients with different clinical characteristics. Thus, integrating these different -omics data sets affords additional insight into the molecular nature of COPD and its heterogeneity.

Soluble receptor for advanced glycation end products (sRAGE) as a biomarker of COPD

BACKGROUND

Soluble receptor for advanced glycation end products (sRAGE) is a proposed emphysema and airflow obstruction biomarker; however, previous publications have shown inconsistent associations and only one study has investigate the association between sRAGE and emphysema. No cohorts have examined the association between sRAGE and progressive decline of lung function. There have also been no evaluation of assay compatibility, receiver operating characteristics, and little examination of the effect of genetic variability in non-white population. This manuscript addresses these deficiencies and introduces novel data from Pittsburgh COPD SCCOR and as well as novel work on airflow obstruction. A meta-analysis is used to quantify sRAGE associations with clinical phenotypes.

METHODS

sRAGE was measured in four independent longitudinal cohorts on different analytic assays: COPDGene (n = 1443); SPIROMICS (n = 1623); ECLIPSE (n = 2349); Pittsburgh COPD SCCOR (n = 399). We constructed adjusted linear mixed models to determine associations of sRAGE with baseline and follow up forced expiratory volume at one second (FEV₁) and emphysema by quantitative high-resolution CT lung density at the 15th percentile (adjusted for total lung capacity).

RESULTS

Lower plasma or serum sRAGE values were associated with a COPD diagnosis (P < 0.001), reduced FEV₁ (P < 0.001), and emphysema severity (P < 0.001). In an inverse-variance weighted meta-analysis, one SD lower log₁₀-transformed sRAGE was associated with 105 ± 22 mL lower FEV₁ and 4.14 ± 0.55 g/L lower adjusted lung density. After adjusting for covariates, lower sRAGE at baseline was associated with greater FEV₁ decline and emphysema progression only in the ECLIPSE cohort. Non-Hispanic white subjects carrying the rs2070600 minor allele (A) and non-Hispanic African Americans carrying the rs2071288 minor allele (A) had lower sRAGE measurements compare to those with the major allele, but their emphysema-sRAGE regression slopes were similar.

CONCLUSIONS

Lower blood sRAGE is associated with more severe airflow obstruction and emphysema, but associations with progression are inconsistent in the cohorts analyzed. In these cohorts, genotype influenced sRAGE measurements and strengthened variance modelling. Thus, genotype should be included in sRAGE evaluations.

Characterization of a COPD-Associated NPNT Functional Splicing Genetic Variant in Human Lung Tissue via Long-Read Sequencing

Chronic obstructive pulmonary disease (COPD) is a leading cause of death worldwide. Genome-wide association studies (GWAS) have identified over 80 loci that are associated with COPD and emphysema, however for most of these loci the causal variant and gene are unknown. Here, we utilize lung splice quantitative trait loci (sQTL) data from the Genotype-Tissue Expression project (GTEx) and short read sequencing data from the Lung Tissue Research Consortium (LTRC) to characterize a locus in nephronectin ( NPNT ) associated with COPD case-control status and lung function. We found that the rs34712979 variant is associated with alternative splice junction use in NPNT , specifically for the junction connecting the 2nd and 4th exons (chr4:105898001-105927336) (p=4.02×10 ^-38 ). This association colocalized with GWAS data for COPD and lung spirometry measures with a posterior probability of 94%, indicating that the same causal genetic variants in NPNT underlie the associations with COPD risk, spirometric measures of lung function, and splicing. Investigation of NPNT short read sequencing revealed that rs34712979 creates a cryptic splice acceptor site which results in the inclusion of a 3 nucleotide exon extension, coding for a serine residue near the N-terminus of the protein. Using Oxford Nanopore Technologies (ONT) long read sequencing we identified 13 NPNT isoforms, 6 of which are predicted to be protein coding. Two of these are full length isoforms which differ only in the 3 nucleotide exon extension whose occurrence differs by genotype. Overall, our data indicate that rs34712979 modulates COPD risk and lung function by creating a novel splice acceptor which results in the inclusion of a 3 nucelotide sequence coding for a serine in the nephronectin protein sequence. Our findings implicate NPNT splicing in contributing to COPD risk, and identify a novel serine insertion in the nephronectin protein that warrants further study.

Genome-Wide Association Study: Functional Variant rs2076295 Regulates Desmoplakin Expression in Airway Epithelial Cells

Rationale: Genetic association studies have identified rs2076295 in association with idiopathic pulmonary fibrosis (IPF). We hypothesized that rs2076295 is the functional variant regulating DSP (desmoplakin) expression in human bronchial epithelial cells, and DSP regulates extracellular matrix-related gene expression and cell migration, which is relevant to IPF development.Objectives: To determine whether rs2076295 regulates DSP expression and the function of DSP in airway epithelial cells.Methods: Using CRISPR (clustered regularly interspaced short palindromic repeat)/Cas9 editing (including regional deletion, indel, CRISPR interference, and single-base editing), we modified rs2076295 and measured DSP expression in edited 16HBE14o- and primary airway epithelial cells. Cellular integrity, migration, and genome-wide gene expression changes were examined in 16HBE14o- single colonies with DSP knockout. The expression of DSP and its relevant matrix genes was measured by quantitative PCR and also analyzed in single-cell RNA-sequencing data from control and IPF lungs.Measurements and Main Results:DSP is expressed predominantly in bronchial and alveolar epithelial cells, with reduced expression in alveolar epithelial cells in IPF lungs. The deletion of the DNA region-spanning rs2076295 led to reduced expression of DSP, and the edited rs2076295GG 16HBE14o- line has lower expression of DSP than the rs2076295TT lines. Knockout of DSP in 16HBE14o- cells decreased transepithelial resistance but increased cell migration, with increased expression of extracellular matrix-related genes, including MMP7 and MMP9. Silencing of MMP7 and MMP9 abolished increased migration in DSP-knockout cells.Conclusions: rs2076295 regulates DSP expression in human airway epithelial cells. The loss of DSP enhances extracellular matrix-related gene expression and promotes cell migration, which may contribute to the pathogenesis of IPF.

Machine Learning and Prediction of All-Cause Mortality in COPD

BACKGROUND

COPD is a leading cause of mortality.

RESEARCH QUESTION

We hypothesized that applying machine learning to clinical and quantitative CT imaging features would improve mortality prediction in COPD.

STUDY DESIGN AND METHODS

We selected 30 clinical, spirometric, and imaging features as inputs for a random survival forest. We used top features in a Cox regression to create a machine learning mortality prediction (MLMP) in COPD model and also assessed the performance of other statistical and machine learning models. We trained the models in subjects with moderate to severe COPD from a subset of subjects in Genetic Epidemiology of COPD (COPDGene) and tested prediction performance in the remainder of individuals with moderate to severe COPD in COPDGene and Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE). We compared our model with the BMI, airflow obstruction, dyspnea, exercise capacity (BODE) index; BODE modifications; and the age, dyspnea, and airflow obstruction index.

RESULTS

We included 2,632 participants from COPDGene and 1,268 participants from ECLIPSE. The top predictors of mortality were 6-min walk distance, FEV1 % predicted, and age. The top imaging predictor was pulmonary artery-to-aorta ratio. The MLMP-COPD model resulted in a C index ≥ 0.7 in both COPDGene and ECLIPSE (6.4- and 7.2-year median follow-ups, respectively), significantly better than all tested mortality indexes (P < .05). The MLMP-COPD model had fewer predictors but similar performance to that of other models. The group with the highest BODE scores (7-10) had 64% mortality, whereas the highest mortality group defined by the MLMP-COPD model had 77% mortality (P = .012).

INTERPRETATION

An MLMP-COPD model outperformed four existing models for predicting all-cause mortality across two COPD cohorts. Performance of machine learning was similar to that of traditional statistical methods. The model is available online at: https://cdnm.shinyapps.io/cgmortalityapp/.

Statistical considerations for the analysis of massively parallel reporter assays data

Noncoding DNA contains gene regulatory elements that alter gene expression, and the function of these elements can be modified by genetic variation. Massively parallel reporter assays (MPRA) enable high-throughput identification and characterization of functional genetic variants, but the statistical methods to identify allelic effects in MPRA data have not been fully developed. In this study, we demonstrate how the baseline allelic imbalance in MPRA libraries can produce biased results, and we propose a novel, nonparametric, adaptive testing method that is robust to this bias. We compare the performance of this method with other commonly used methods, and we demonstrate that our novel adaptive method controls Type I error in a wide range of scenarios while maintaining excellent power. We have implemented these tests along with routines for simulating MPRA data in the Analysis Toolset for MPRA (@MPRA), an R package for the design and analyses of MPRA experiments. It is publicly available at http://github.com/redaq/atMPRA.

Deep neural network analyses of spirometry for structural phenotyping of chronic obstructive pulmonary disease

BACKGROUNDCurrently recommended traditional spirometry outputs do not reflect the relative contributions of emphysema and airway disease to airflow obstruction. We hypothesized that machine-learning algorithms can be trained on spirometry data to identify these structural phenotypes.METHODSParticipants enrolled in a large multicenter study (COPDGene) were included. The data points from expiratory flow-volume curves were trained using a deep-learning model to predict structural phenotypes of chronic obstructive pulmonary disease (COPD) on CT, and results were compared with traditional spirometry metrics and an optimized random forest classifier. Area under the receiver operating characteristic curve (AUC) and weighted F-score were used to measure the discriminative accuracy of a fully convolutional neural network, random forest, and traditional spirometry metrics to phenotype CT as normal, emphysema-predominant (>5% emphysema), airway-predominant (Pi10 > median), and mixed phenotypes. Similar comparisons were made for the detection of functional small airway disease phenotype (>20% on parametric response mapping).RESULTSAmong 8980 individuals, the neural network was more accurate in discriminating predominant emphysema/airway phenotypes (AUC 0.80, 95%CI 0.79-0.81) compared with traditional measures of spirometry, FEV1/FVC (AUC 0.71, 95%CI 0.69-0.71), FEV1% predicted (AUC 0.70, 95%CI 0.68-0.71), and random forest classifier (AUC 0.78, 95%CI 0.77-0.79). The neural network was also more accurate in discriminating predominant emphysema/small airway phenotypes (AUC 0.91, 95%CI 0.90-0.92) compared with FEV1/FVC (AUC 0.80, 95%CI 0.78-0.82), FEV1% predicted (AUC 0.83, 95%CI 0.80-0.84), and with comparable accuracy with random forest classifier (AUC 0.90, 95%CI 0.88-0.91).CONCLUSIONSStructural phenotypes of COPD can be identified from spirometry using deep-learning and machine-learning approaches, demonstrating their potential to identify individuals for targeted therapies.TRIAL REGISTRATIONClinicalTrials.gov NCT00608764.FUNDINGThis study was supported by NIH grants K23 HL133438 and R21EB027891 and an American Thoracic Foundation 2018 Unrestricted Research Grant. The COPDGene study is supported by NIH grants NHLBI U01 HL089897 and U01 HL089856. The COPDGene study (NCT00608764) is also supported by the COPD Foundation through contributions made to an Industry Advisory Committee comprising AstraZeneca, Boehringer-Ingelheim, GlaxoSmithKline, Novartis, and Sunovion.

Unsupervised discovery of phenotype-specific multi-omics networks

MOTIVATION

Complex diseases often involve a wide spectrum of phenotypic traits. Better understanding of the biological mechanisms relevant to each trait promotes understanding of the etiology of the disease and the potential for targeted and effective treatment plans. There have been many efforts towards omics data integration and network reconstruction, but limited work has examined the incorporation of relevant (quantitative) phenotypic traits.

RESULTS

We propose a novel technique, sparse multiple canonical correlation network analysis (SmCCNet), for integrating multiple omics data types along with a quantitative phenotype of interest, and for constructing multi-omics networks that are specific to the phenotype. As a case study, we focus on miRNA-mRNA networks. Through simulations, we demonstrate that SmCCNet has better overall prediction performance compared to popular gene expression network construction and integration approaches under realistic settings. Applying SmCCNet to studies on chronic obstructive pulmonary disease (COPD) and breast cancer, we found enrichment of known relevant pathways (e.g. the Cadherin pathway for COPD and the interferon-gamma signaling pathway for breast cancer) as well as less known omics features that may be important to the diseases. Although those applications focus on miRNA-mRNA co-expression networks, SmCCNet is applicable to a variety of omics and other data types. It can also be easily generalized to incorporate multiple quantitative phenotype simultaneously. The versatility of SmCCNet suggests great potential of the approach in many areas.

AVAILABILITY AND IMPLEMENTATION

The SmCCNet algorithm is written in R, and is freely available on the web at https://cran.r-project.org/web/packages/SmCCNet/index.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

Genetic Advances in Chronic Obstructive Pulmonary Disease. Insights from COPDGene

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

Machine Learning Characterization of COPD Subtypes: Insights From the COPDGene Study

COPD is a heterogeneous syndrome. Many COPD subtypes have been proposed, but there is not yet consensus on how many COPD subtypes there are and how they should be defined. The COPD Genetic Epidemiology Study (COPDGene), which has generated 10-year longitudinal chest imaging, spirometry, and molecular data, is a rich resource for relating COPD phenotypes to underlying genetic and molecular mechanisms. In this article, we place COPDGene clustering studies in context with other highly cited COPD clustering studies, and summarize the main COPD subtype findings from COPDGene. First, most manifestations of COPD occur along a continuum, which explains why continuous aspects of COPD or disease axes may be more accurate and reproducible than subtypes identified through clustering methods. Second, continuous COPD-related measures can be used to create subgroups through the use of predictive models to define cut-points, and we review COPDGene research on blood eosinophil count thresholds as a specific example. Third, COPD phenotypes identified or prioritized through machine learning methods have led to novel biological discoveries, including novel emphysema genetic risk variants and systemic inflammatory subtypes of COPD. Fourth, trajectory-based COPD subtyping captures differences in the longitudinal evolution of COPD, addressing a major limitation of clustering analyses that are confounded by disease severity. Ongoing longitudinal characterization of subjects in COPDGene will provide useful insights about the relationship between lung imaging parameters, molecular markers, and COPD progression that will enable the identification of subtypes based on underlying disease processes and distinct patterns of disease progression, with the potential to improve the clinical relevance and reproducibility of COPD subtypes.

Integrative Genomics Analysis Identifies ACVR1B as a Candidate Causal Gene of Emphysema Distribution

Genome-wide association studies (GWAS) have identified multiple associations with emphysema apicobasal distribution (EABD), but the biological functions of these variants are unknown. To characterize the functions of EABD-associated variants, we integrated GWAS results with 1) expression quantitative trait loci (eQTL) from the Genotype Tissue Expression (GTEx) project and subjects in the COPDGene (Genetic Epidemiology of COPD) study and 2) cell type epigenomic marks from the Roadmap Epigenomics project. On the basis of these analyses, we selected a variant near ACVR1B (activin A receptor type 1B) for functional validation. SNPs from 168 loci with P values less than 5 × 10^-5 in the largest GWAS meta-analysis of EABD were analyzed. Eighty-four loci overlapped eQTL, with 12 of these loci showing greater than 80% likelihood of harboring a single, shared GWAS and eQTL causal variant. Seventeen cell types were enriched for overlap between EABD loci and Roadmap Epigenomics marks (permutation P < 0.05), with the strongest enrichment observed in CD4⁺, CD8⁺, and regulatory T cells. We selected a putative causal variant, rs7962469, associated with ACVR1B expression in lung tissue for additional functional investigation, and reporter assays confirmed allele-specific regulatory activity for this variant in human bronchial epithelial and Jurkat immune cell lines. ACVR1B expression levels exhibit a nominally significant association with emphysema distribution. EABD-associated loci are preferentially enriched in regulatory elements of multiple cell types, most notably T-cell subsets. Multiple EABD loci colocalize to regulatory elements that are active across multiple tissues and cell types, and functional analyses confirm the presence of an EABD-associated functional variant that regulates ACVR1B expression, indicating that transforming growth factor-β signaling plays a role in the EABD phenotype. Clinical trial registered with www.clinicaltrials.gov (NCT00608764).

COPDGene® 2019: Redefining the Diagnosis of Chronic Obstructive Pulmonary Disease

BACKGROUND

Chronic obstructive pulmonary disease (COPD) remains a major cause of morbidity and mortality. Present-day diagnostic criteria are largely based solely on spirometric criteria. Accumulating evidence has identified a substantial number of individuals without spirometric evidence of COPD who suffer from respiratory symptoms and/or increased morbidity and mortality. There is a clear need for an expanded definition of COPD that is linked to physiologic, structural (computed tomography [CT]) and clinical evidence of disease. Using data from the COPD Genetic Epidemiology study (COPDGene®), we hypothesized that an integrated approach that includes environmental exposure, clinical symptoms, chest CT imaging and spirometry better defines disease and captures the likelihood of progression of respiratory obstruction and mortality.

METHODS

Four key disease characteristics - environmental exposure (cigarette smoking), clinical symptoms (dyspnea and/or chronic bronchitis), chest CT imaging abnormalities (emphysema, gas trapping and/or airway wall thickening), and abnormal spirometry - were evaluated in a group of 8784 current and former smokers who were participants in COPDGene® Phase 1. Using these 4 disease characteristics, 8 categories of participants were identified and evaluated for odds of spirometric disease progression (FEV1 > 350 ml loss over 5 years), and the hazard ratio for all-cause mortality was examined.

RESULTS

Using smokers without symptoms, CT imaging abnormalities or airflow obstruction as the reference population, individuals were classified as Possible COPD, Probable COPD and Definite COPD. Current Global initiative for obstructive Lung Disease (GOLD) criteria would diagnose 4062 (46%) of the 8784 study participants with COPD. The proposed COPDGene® 2019 diagnostic criteria would add an additional 3144 participants. Under the new criteria, 82% of the 8784 study participants would be diagnosed with Possible, Probable or Definite COPD. These COPD groups showed increased risk of disease progression and mortality. Mortality increased in patients as the number of their COPD characteristics increased, with a maximum hazard ratio for all cause-mortality of 5.18 (95% confidence interval [CI]: 4.15-6.48) in those with all 4 disease characteristics.

CONCLUSIONS

A substantial portion of smokers with respiratory symptoms and imaging abnormalities do not manifest spirometric obstruction as defined by population normals. These individuals are at significant risk of death and spirometric disease progression. We propose to redefine the diagnosis of COPD through an integrated approach using environmental exposure, clinical symptoms, CT imaging and spirometric criteria. These expanded criteria offer the potential to stimulate both current and future interventions that could slow or halt disease progression in patients before disability or irreversible lung structural changes develop.

Identification of an emphysema-associated genetic variant near TGFB2 with regulatory effects in lung fibroblasts

Murine studies have linked TGF-β signaling to emphysema, and human genome-wide association studies (GWAS) studies of lung function and COPD have identified associated regions near genes in the TGF-β superfamily. However, the functional regulatory mechanisms at these loci have not been identified. We performed the largest GWAS of emphysema patterns to date, identifying 10 GWAS loci including an association peak spanning a 200 kb region downstream from TGFB2. Integrative analysis of publicly available eQTL, DNaseI, and chromatin conformation data identified a putative functional variant, rs1690789, that may regulate TGFB2 expression in human fibroblasts. Using chromatin conformation capture, we confirmed that the region containing rs1690789 contacts the TGFB2 promoter in fibroblasts, and CRISPR/Cas-9 targeted deletion of a ~ 100 bp region containing rs1690789 resulted in decreased TGFB2 expression in primary human lung fibroblasts. These data provide novel mechanistic evidence linking genetic variation affecting the TGF-β pathway to emphysema in humans.