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.

Rationale: Emphysema is a key component of COPD with important prognostic implications. Identifying blood-based biomarkers of emphysema will facilitate early diagnosis and possible development of targeted therapies. Objectives: Discover blood transcriptomic and proteomic biomarkers for chest computed tomography-quantified emphysema in smokers 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 set of 2,370 COPDGene participants with available whole blood RNA sequencing, plasma SomaScan proteomics, and clinical data. Validation was conducted in a testing set of 1,016 COPDGene subjects. Since 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. Predictive models were also developed using elastic net to predict quantitative emphysema from cell blood count, RNA sequencing, and proteomic biomarkers. Model accuracy was assessed by area under the receiver-operator-characteristic-curves (AUROC) for subjects stratified into tertiles of emphysema severity. Measurements and Main Results: 4,913 genes, 1,478 isoforms, 386 exons, and 881 proteins were significantly associated with emphysema (FDR 10%). 75% and 77% of genes and proteins, respectively, were mediated by BMI. The significantly enriched biological pathways were involved in inflammation and cell differentiation, differing between the most and least BMI-mediated genes. The cell blood count plus protein model achieved the highest performance with an AUROC of 0.89. Conclusions: Blood transcriptome and proteome-wide analyses reveal key biological pathways of emphysema and enhance the prediction of emphysema.

Cigarette smoking accounts for approximately one in five deaths in the United States. Previous genomic studies have primarily focused on gene level differential expression to identify related molecular signatures and pathways, but the genome-wide effects of smoking on alternative isoform regulation and posttranscriptional modulation have not yet been described. We conducted RNA sequencing (RNA-seq) in whole-blood samples of 454 current and 767 former smokers in COPDGene Study. We assessed the association of current smoking with differential expression of genes and isoforms and differential usage of isoforms and exons. At 10% FDR, we detected 3,167 differentially expressed genes, 2,014 differentially expressed isoforms, 945 differentially used isoforms and 160 differentially used exons. Genes containing differentially used isoforms were enriched in biological pathways involving GTPase activity and innate immunity. The majority of these genes were not differentially expressed, thus not identifiable from conventional differential gene expression analysis. Isoform switch analysis revealed for the first time widespread 3-prime UTR lengthening associated with cigarette smoking, where current smokers were found to have higher expression and usage of isoforms with markedly longer 3-prime UTRs. The lengthening of 3-prime UTRs appears to be mediated through alternative usage of distal polyadenylation sites, and these extended 3-prime UTR regions are significantly enriched with functional sequence elements including adenylate-uridylate (AU)-rich elements, microRNA and RNA-protein binding sites. Expression quantitative trait locus analyses on differentially used 3-prime UTRs identified 79 known GWAS variants associated with multiple smoking-related human diseases and traits. Smoking elicits widespread transcriptional and posttranscriptional alterations with disease implications. It induces alternative polyadenylation (APA) events resulting in a switch towards the usage of isoforms with strikingly longer 3-prime UTRs in genes related to multiple biological pathways including GTPase activity and innate immunity. The extended 3-prime UTR regions are enriched with functional sequence elements facilitating post-transcriptional regulation of protein expression and mRNA stability. These findings warrant further studies on APA events as potential biomarkers and novel therapeutic targets for smoking-related diseases.

Prior studies have demonstrated associations between emphysema and respiratory symptoms, exacerbations, and mortality. In the current study, we assessed the ability of blood transcriptomics to assist in the identification of CT-quantified emphysema in smokers. METHODS:We randomly split 2,655 non-Hispanic white and African American COPDGene participants with available whole blood RNA sequencing (RNA-Seq), chest CT, and clinical data into 80:20 training and testing samples. We tested for gene expression associations with whole-lung emphysema (Hounsfield units at the 15 th percentile of CT density histogram at total lung capacity, corrected for the inspiratory depth) in the training sample using the voom/limma method and adjusting for age, sex, current smoking, scanner type, white blood cell count proportions, and library prep batch. We constructed three elastic net regression models to predict emphysema using the transcripts that reached statistical significance in the expression analysis, separately or in combination with candidate clinical predictors that consisted of 13 demographic, clinical, spirometric, and functional variables. We then classified subjects into quartiles of emphysema severity and assessed the accuracy of the models in predicting those at highest and lowest risks for emphysema in the testing sample by evaluating the areas under the receiveroperator-characteristic-curves (AUC). All predictors were scaled and their importance scores were defined by the absolute values of their coefficients in the regression models.RESULTS: Subjects were mostly non-Hispanic whites with a balanced representation by sex, a median age of 65, a median BMI of 28, and a median of 40 pack-years of smoking history. Subjects' characteristics were similar in the training and testing samples. A total of 816 genes were associated with emphysema in the expression analysis (FDR 10%). In the testing sample, the combined clinical and RNA-Seq model achieved high performance (AUC = 0.93) and was superior to the clinical only (AUC = 0.88) and RNA-Seq only (AUC = 0.79) models (P-values < 0.05). FEV 1 /FVC, BMI, female sex, MMRC dyspnea score, 6-minute walk distance, age, and four genes (AHRR, EIF1AY, PUDP and CACNA2D3) were among the predictors with the highest importance scores in the combined model.CONCLUSIONS: Blood transcriptomics combined with clinical factors can improve the prediction of emphysema.CLINICAL IMPLICATIONS: Blood-based omics risk scores may provide a valuable non-invasive screening tool for emphysema in smokers. They may also capture important aspects of COPD-related pathophysiology and potentially facilitate the development of personalized therapies.

Introduction: Electronic nicotine delivery systems (ENDS) are driving an epidemic of vaping. Identifying biomarkers of vaping and dual use (concurrent vaping and smoking), will facilitate studies of the health effects of vaping. We conducted a blood biomarker discovery study for vaping and dual use in a longitudinal cohort of current and former adult smokers with high-throughput transcriptomic and proteomic data, and we tested biomarkers for association to multiple health outcomes. Methods: We studied 3,892 COPDGene study participants with blood transcriptomics and/or plasma proteomics data according to their self-reported current vaping and smoking behavior. Biomarkers of vaping and dual use were identified through differential expression analysis and related to prospective health events over six years of follow-up. To assess the predictive accuracy of multi-biomarker panels, we constructed predictive models for vaping and smoking categories and prospective health outcomes. Results: We identified 3 transcriptomic and 3 proteomic associations to vaping, and 90 transcriptomic and 100 proteomic associations to dual use (FDR 10%). Many of these vaping or dual use biomarkers were significantly associated with prospective health outcomes, such as FEV1 decline (3 transcripts and 62 proteins), overall mortality (18 transcripts and 73 proteins), respiratory mortality (2 transcripts and 23 proteins), respiratory exacerbations (13 proteins) and incident cardiovascular disease (24 proteins). Multimarker models showed good performance discriminating between vaping and smoking behavior and produced informative, modestly powerful predictions of future FEV1 decline, mortality and respiratory exacerbations. Conclusion: Vaping and dual use are associated with multiple blood-based biomarkers that are also associated with adverse health outcomes.

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RationaleWhile 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.ObjectivesOur 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.MethodsUsing RNA-sequencing data from 456 samples in the Lung Tissue Research Consortium and 2,370 blood samples from the COPDGene study, we examined the transcriptomic features of computed tomography quantified emphysema. We also queried 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 ResultsIn the lung, 1,055 differentially expressed genes and 29 dysregulated pathways were significantly associated with emphysema. We observed alternative splicing of genes regulating NF-κB and cell adhesion and increased activity in the TGF-β and FoxO signaling pathways. Multiple lung cell types displayed dysregulation of epithelial barrier function pathways, and an imbalance between pro-inflammatory M1 and anti-inflammatory M2 macrophages was detected. Lung tissue and blood samples shared 251 differentially expressed genes and two pathways (oxidative phosphorylation and ribosomal function).ConclusionsThis 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.AT A GLANCE COMMENTARYScientific Knowledge on the SubjectPrior studies have investigated the transcriptomic characteristics of emphysema and its associated biological pathways. However, less is known about alternative splicing mechanisms and cell-type specific transcriptional patterns in emphysema. Additionally, a comparison between dysregulated genes and pathways in blood and lung tissues is needed to better understand the utility of non-invasive diagnostic and prognostic tools for emphysema.What This Study Adds to the FieldUsing lung samples from the Lung Tissue Research Consortium (LTRC) and blood samples from the COPDGene study, we performed differential gene and alternative splicing association analyses for CT-quantified emphysema. We then queried a previously published lung tissue single-cell RNA-sequencing atlas of COPD patients and controls to determine lung cell-type specific expression patterns of the biological pathways identified from the bulk analyses. We demonstrated that multiple pathways, including oxidative phosphorylation and ribosomal function processes, were enriched in both blood and lung tissues. We also observed that in COPD, oxidative phosphorylation was downregulated in pro-inflammatory (M1) macrophages and upregulated in anti-inflammatory (M2) macrophages. Additionally, other immunity-related cell types, including plasma cells, natural killer cells, and T lymphocytes, were linked to epithelial barrier function, such as the Rap1, adherens junction, and TGF-β signaling pathways.

Background: Electronic nicotine delivery systems (ENDS) are driving an epidemic of vaping. Identifying biomarkers of vaping and dual use (concurrent vaping and smoking) will facilitate studies of the health effects of vaping. To identify putative biomarkers of vaping and dual use, we performed association analysis in an observational cohort of 3,892 COPDGene study participants with blood transcriptomics and/or plasma proteomics data and self-reported current vaping and smoking behavior. Methods: Biomarkers of vaping and dual use were identified through differential expression analysis and related to prospective health events over six years of follow-up. To assess the predictive accuracy of multi-biomarker panels, we constructed predictive models for vaping and smoking categories and prospective health outcomes. Results: We identified three transcriptomic and three proteomic associations with vaping, and 90 transcriptomic and 100 proteomic associations to dual use. Many of these vaping or dual use biomarkers were significantly associated with prospective health outcomes, such as FEV1 decline (three transcripts and 62 proteins), overall mortality (18 transcripts and 73 proteins), respiratory mortality (two transcripts and 23 proteins), respiratory exacerbations (13 proteins) and incident cardiovascular disease (24 proteins). Multimarker models showed good performance discriminating between vaping and smoking behavior and produced informative, modestly powerful predictions of future FEV1 decline, mortality, and respiratory exacerbations. Conclusions: In summary, vaping and dual use are associated with RNA and protein blood-based biomarkers that are also associated with adverse health outcomes.