Lung Transcriptomics Links Emphysema to Barrier Dysfunction and Macrophage Subpopulations.

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

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.

Inhibiting Airway Smooth Muscle Contraction Using Pitavastatin: A Role for the Mevalonate Pathway in Regulating Cytoskeletal Proteins.

Despite maximal use of currently available therapies, a significant number of asthma patients continue to experience severe, and sometimes life-threatening bronchoconstriction. To fill this therapeutic gap, we examined a potential role for the 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) inhibitor, pitavastatin. Using human airway smooth muscle (ASM) cells and murine precision-cut lung slices, we discovered that pitavastatin significantly inhibited basal-, histamine-, and methacholine (MCh)-induced ASM contraction. This occurred via reduction of myosin light chain 2 (MLC2) phosphorylation, and F-actin stress fiber density and distribution, in a mevalonate (MA)- and geranylgeranyl pyrophosphate (GGPP)-dependent manner. Pitavastatin also potentiated the ASM relaxing effect of a simulated deep breath, a beneficial effect that is notably absent with the ß2-agonist, isoproterenol. Finally, pitavastatin attenuated ASM pro-inflammatory cytokine production in a GGPP-dependent manner. By targeting all three hallmark features of ASM dysfunction in asthma-contraction, failure to adequately relax in response to a deep breath, and inflammation-pitavastatin may represent a unique asthma therapeutic.