Christenson, S.

Abstract Introduction Resilient smokers, who maintain respiratory health despite smoking, remain poorly characterized. Airway epithelial cell gene expression in resilient smokers can provide insight into mechanisms and pathways that may protect smokers from developing smoking-related lung disease. The objective of this study is to identify genes and potential pathways associated with lung-related resilience. Methods Resilient smokers were defined based on a previously published multi-dimensional definition which includes the absence of: 1) spirometric airflow limitation, 2) symptoms (cough/sputum, dyspnea), 3) radiographic (HRCT) abnormalities, 4) exacerbations, and 5) an accelerated FEV1 decline. “Non-resilient” smokers had preserved spirometry but showed abnormalities in at least one domain of resilience. We analyzed airway epithelial brush samples from 21 resilient and 34 non-resilient smokers from SPIROMICS. Differential gene expression was performed using DESeq2 package in R, which estimates differential expression using generalized linear models. We adjusted for sequencing batch, age (continuous), race, gender, and current smoking status. While controlling for these covariates we evaluated differential gene expression between resilient and non-resilient smokers, the interaction between resilience and smoking status, and in each sub-category of resilient (absence of symptoms, radiographic abnormalities, exacerbations, and accelerated decline in FEV1). Results We identified 98 genes that were differentially expressed (FDR ≤0.1) when comparing resilient to non-resilient smokers, 20 of those genes were up regulated in resilient smokers (Figure 1). Notable genes included cadherin-12 (CDH12), aspartate beta-hydroxylase (ASPH), and protein kinase AMP-activated alpha 2 (PRKAA2). CDH12 is involved in cell adhesion and epithelial integrity. ASPH influences cell adhesion and signaling. PRKAA2 plays role in cellular homeostasis and stress response, which may be altered in non-resilient smokers, affecting cellular metabolism and inflammation. BPI folding containing family A member (BPIFA1) was significantly associated with smoking status in the interaction analysis, suggesting its role in modulating the impact of smoking on resilience. BPIFA1 has anti-microbial properties, regulates immune responses, and contributes mucosal surface protection. Sub-category analysis identified 17 genes associated with resilience to symptoms and 2 genes related to physiologic resilience (FEV1 decline), though no significant genes were associated with radiographic or exacerbation resilience. Conclusion Differential gene expression of airway epithelial cells identified distinct expression patterns between resilient and non-resilient smokers. Specifically, genes associated with epithelial cell adhesion, signaling, and cellular homeostasis were up regulated in resilient smokers. These findings suggest that resilient smokers may have transcriptomic profiles and potential mechanisms that mitigate the effects of cigarette smoke.

Abstract Rationale: MUC5B and MUC5AC are neighboring genes on chromosome 11p15 that encode the two predominant mucins secreted in the respiratory airways. Increased airway mucin concentrations and an imbalance between these two mucins drive COPD symptoms and the development of airflow obstruction or COPD, but the epigenomic regulation of this loci has not been characterized in the airways. Methods: We isolated DNA from bronchial airway brushings of 72 participants with >20 pack-years smoking history from the Subpopulations and Intermediate Outcomes Measures in COPD Study (SPIROMICS). 36 participants had GOLD-based moderate-to-severe COPD (FEV1/FVC<0.70, FEV1<80%) and 36 were without or with mild COPD. We used the Illumina Infinium Methylation EPIC BeadChip Array to assess proportion of DNA methylation (β) spanning 689,937 CpG sites. We evaluated proportional DNA methylation (β) at 96 CpG loci within a 103kB region using matrixQTL for cis-expression quantitative trait methylation (eQTM: 10kB flanks relative to MUC5AC/MUC5B) integrating bulk RNA-seq data and mQTL models integrating gene sequencing data to identify DNA variation associated with proportion eQTM methylation. Models included age, sex, BMI, clinical site, current smoking vs past smoking, total pack-years, three ancestral components of ancestry, chipset, and position. Results: We identified a top eQTM, cg00524486, marginally associated with MUC5B expression (coefficient estimate=8.75, punadjusted=0.0014, pFDR=0.065, Figure 1B). This eQTM was associated with CT scan parametric response mapping-based functional small airways disease (PRM-fSAD, beta coefficient[β]=3.64, p=0.011). We tested for DNA variation associated with methylation of this top eQTM which identified two methylation quantitative trait loci (mQTL: rs885454 [β=0.044, minor allele frequency[MAF]=0.09, pFDR=0.01], rs868902 [β=0.020, MAF=0.43, pFDR=0.08, Figure 1A) associated with cg00524486 methylation, of which the more frequent mQTL, rs868902, was marginally associated with PRM-fSAD (p=0.07) in the larger SPIROMICS cohort with MUC5B DNA sequencing data. Conclusions: This preliminary multi-omic study of the lower airway epithelium demonstrates that the expression of MUC5B is epigenetically regulated by neighboring CpG probe methylation (eQTM) represented by DNA variation (mQTL). These studies require further confirmation in a larger dataset of airway samples and support a role for epigenetic variation as a biologic contributor and biomarker of early COPD pathogenesis.

Abstract Rationale: The club cell secretory protein (CC16), encoded by the SCGB1A1 gene, has an anti-inflammatory role in airways diseases, including asthma (Li, AJRCCM 2023; Voraphani, AJRCCM 2023). We hypothesize that additional pathway genes regulated or co-expressed with CC16 have cumulative effects on asthma severity outcomes by modulating airway inflammation and maintaining lung homeostasis. Methods: As published previously, four genes were downregulated in CC16 knockout experiments and correlated with CC16 levels: BPIFA1, SFTPD, LTF, and LYZ (Iannuzo, Front. Immunol 2023). We developed a four-gene biomarker score in 94 patients with asthma from the NHLBI-sponsored Severe Asthma Research Program (SARP3) who underwent research bronchoscopy for lower airway epithelial brushings, RNA isolation, and bulk RNA sequencing. We log-transformed the raw values of each transcript; obtained standardized residuals from regression models that included sex, age, and batch effects; and summed the standardized residuals of all four proteins to estimate a standardized sum score (Zhai, JACI Allergy 2024). Logistic regression models tested the four-gene score, three-gene score without LTF, and individual transcripts for associations with exacerbations during 12-month follow-up and zero-inflated negative binomial regression for number of exacerbations. Results: 30.85% percent of 94 patients experienced at least one exacerbation at the end of follow-up. The CC16-associated four-gene biomarker score showed protective effects on asthma exacerbation risk with a 54% percent reduction in exacerbation risk for every one-fold higher score (OR=0.46, 95% CI: 0.27-0.78, p =0.0040, Figure 1). Transcript expression of BPIFA1 (OR=0.87, 95% CI:0.66-1.16, p=0.337), SFTPD (OR=0.33, 95% CI:0.04-2.52, p=0.283), LYZ (OR=0.62, 95%CI:0.28-1.39, p=0.248), and LTF (OR=0.36, 95% CI:0.18-0.74, p=0.0053) all indicated protective directionality with respect to exacerbation risk. However, only LTF demonstrated a statistically significant association. When we excluded LTF, the remaining three-gene score showed a trend toward association with risk (OR=0.64, 95% CI:0.40-1.03, p=0.068) and was significantly associated with number of exacerbations (RR=0.49, 95% CI:0.26-0.95, p=0.034). Compared to LTF alone (OR=0.48, PPV=0.50, NPV=0.71, Sensitivity=0.14, AUC=0.65), the combined four-gene score (OR=0.46, PPV=0.60, NPV= 0.73, Sensitivity=0.21, AUC=0.68) showed improved predictive performance. Conclusions: We identified a CC16-associated transcriptomic predictive score for asthma exacerbations, with LTF showing the strongest effect. Although the other three genes showed individually weak and non-significant associations with exacerbations, the three gene score was significant for number of exacerbations.We demonstrate the potential of this transcriptomic score as a predictive biomarker for asthma severity-related outcomes. Further research investigating these genes and their interaction with other CC16-associated immune pathways could provide insight into therapeutic targets.

Abstract Asthma is a heterogeneous disease with variable presentation and characteristics. There is a critical need to identify underlying molecular endotypes of asthma. We performed the largest transcriptomic analysis of 808 bronchial epithelial cell samples across 11 independent cohorts, including 3 cohorts from the Severe Asthma Research Program. Using seven datasets (218 patients with asthma, 148 healthy control subjects) as discovery cohorts, we identified 505 differentially expressed genes, which we validated in the remaining four datasets. Unsupervised clustering using the 505 differentially expressed genes identified four reproducible clusters of patients with asthma across all datasets, corresponding to healthy control subjects, patients with mild/moderate asthma, and patients with severe asthma with significant differences in several clinical markers of severity, including pulmonary function, Type 2 inflammation, fractional exhaled nitric oxide, and maximum bronchodilator reversibility. Importantly, we found the same clusters in pediatric patients using nasal lavage fluid cells, demonstrating the gene signature and clusters are not confounded by age and are conserved in both lower and upper airways. The four asthma clusters may represent a unifying framework for understanding the molecular heterogeneity of asthma. Further study could potentially enable a precision medicine approach of matching therapies with patients with asthma most likely to benefit.