Barr, R. Graham

Abstract Rationale High-attenuation area (HAA) is a computed tomography (CT) tool that correlates with lung inflammation and fibrosis. Systemic molecular correlates of HAA (e.g., plasma proteins) may inform biological processes involved in interstitial lung disease. Objectives To identify plasma proteins that associate with HAA and correlate with a higher probability of developing new-onset fibrotic or subpleural interstitial lung abnormalities (ILAs). Methods Plasma protein levels were measured using a semiquantitative aptamer-based platform in MESA (the Multi-Ethnic Study of Atherosclerosis; N = 5,486) and SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study; N = 1,781). Linear regression models identified HAA-associated proteins after adjustment for demographic and socioeconomic factors, CT scanner parameters, study center, and batch. Associations of HAA-related proteins with new-onset fibrotic or subpleural ILAs were examined in MESA participants with ILA assessments on full-lung CT 10 years later. Immunohistochemical staining of select proteins was performed in lung tissue from pulmonary fibrosis cases. Measurements and Main Results There were 75 proteins detected that were significantly associated with HAA in MESA and SPIROMICS. Gene Ontology analysis of these proteins identified processes involved in immune cell chemotaxis and cellular growth and apoptosis. Seven proteins were associated with a higher probability of new-onset fibrotic or subpleural ILAs in MESA, and two of these, junctional adhesion molecule-like protein and GTP cyclohydrolase 1 feedback regulatory protein, stained in areas of fibrosis in lung tissue from patients with interstitial lung disease. Conclusions Plasma proteins associated with more HAA are involved in immune and cellular processes and associate with new-onset fibrotic-subpleural ILA.

Rationale Immunoglobulin A (IgA) deficiency, a rare, highly heritable trait, is associated with frequent pulmonary infections, emphysema, airway changes and low lung function; however, it is unclear if reduced IgA levels may affect lung structure and function. Methods Serum IgA, IgA1 and galactose-deficient IgA1 (Gd-IgA1) levels were measured in the population-based Multi-Ethnic Study on Atherosclerosis (MESA). The MESA Lung Study measured percent emphysema on cardiac CT and airway dimensions on chest CT, and performed spirometry. Regression models were evaluated after adjustment for demographic and CT factors. Mendelian randomisation (MR) analyses were conducted using genetic variants from the Trans-Omics for Precision Medicine (TOPMed) programme. A replication analysis was performed in the SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS). Measurements and main results Among 5497 participants, lower log-normalised serum IgA levels were associated with greater percent emphysema (β=−0.084; 95% CI −0.14 to –0.026; p=0.005), which was confirmed on MR (β=−0.79; 95% CI −1.4 to –0.18; p=0.011). Greater log-normalised serum Gd-IgA1 levels were associated with airway wall thickness (β=0.0079; 95% CI 0.0017 to 0.014; p=0.012; n=2580) and decline in the forced expiratory volume in one second (FEV1) (β=−0.012; 95% CI −0.021 to –0.0036; p=0.0055; n=2778) and FEV1/forced vital capacity (FVC) ratio (β=−0.0028; 95% CI −0.0048 to –0.00084; p=0.0054; n=2778). Conclusion Lower serum IgA levels were associated with greater percent emphysema. Additionally, higher Gd-IgA1 levels were associated with airway wall thickness and lung function decline. These findings support a protective role of IgA in emphysema pathogenesis and possible deleterious role of Gd-IgA1 in airway diseases.

Abstract RATIONALE: Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of mortality worldwide. We previously published the polygenic transcriptome risk score (PTRS) which uses the cumulative effect of predicted gene expression to construct genetic predictors for complex diseases, and we demonstrated the value of PTRS for COPD built on Genotype-Tissue Expression (GTEx) Lung tissue with significantly improved cross-ancestry portability. However, the postmortem collection and lack of lung disease status on GTEx samples likely affect results. We hypothesized that the performance of PTRS will be improved by using disease-relevant expression quantitative trait loci (eQTLs) to construct the score. Here, we aimed to improve performance of PTRS by leveraging disease-relevant eQTLs. METHODS: We constructed two transcriptome prediction models, Elastic Net (EN) and Prediction Using Models Informed by Chromatin conformation and Epigenomics (PUMICE), using TOPMed lung RNA-seq data from the Lung Tissue Research Consortium (LTRC), which included COPD patients undergoing lung surgery. We compared their performance with two published models built on RNA-seq from GTEx Lung tissue, GTEx-EN and GTEx-PUMICE. We first integrated each of four transcriptome models with multi-ancestry GWAS of FEV1/FVC ratio (Shrine et al. 2023) to generate transcriptome-wide association study (TWAS) results, which produced trait-associated genes. Pathway analysis was then conducted on significant TWAS genes. Finally, the PTRS was computed on TWAS genes that were included in the top 20 significantly enriched pathways. We tested performance of PTRS in race-stratified analysis for moderate-to-severe and severe COPD in COPDGene. RESULTS: Our models produced more significant TWAS genes (FDR<0.05) and had more overlaps with genes identified for FEV1/FVC ratio from Shrine et al. 2023 compared to two GTEx models (Fig. A). Additionally, our models had significantly higher AUC for predicting both moderate-to-severe and severe COPD in race-stratified analysis. The mean AUC from LTRC-EN for predicting severe COPD was 0.57 and 0.55 respectively for EUR and AFA, which was significantly higher than mean AUC of 0.54 and 0.51 from GTEx-EN (Delong p-value=8.15x10-10 for EUR and 1.42x10-3 for AFA) (Fig. B). Similarly, the PTRS derived from our models produced higher Odds Ratios (OR) per standard deviation. The mean OR from LTRC-PUMICE for moderate-to-severe COPD was 1.25 and 1.18 respectively for EUR and AFA, while it was 1.17 and 1.12 from GTEx-PUMICE (Fig. C). CONCLUSIONS: Our study demonstrates the value of leveraging disease-relevant RNA-seq to construct transcriptome prediction models and their improvement on identifying lung function genes and predicting COPD across ancestries.