Denlinger, L C

Abstract Background Prostaglandin E2 (PGE2) exerts paradoxical effects in asthma, acting as both a protective mediator and a contributor to pathology depending on context. Its role is shaped by type 2 (T2)-status, eosinophilia, and exacerbation frequency. In epithelial cell cultures from asthmatic and healthy individuals, interleukin (IL)-13 suppresses PGE2 synthesis genes (PTGS2 and PGES) while upregulating HPGD, which encodes for the PGE2 catabolic enzyme 15-prostaglandin dehydrogenase (15-PGDH) (PMID:16751012), suggesting diminished PGE2 availability during T2 inflammation. Similarly, in aspirin-exacerbated respiratory disease (AERD), impaired cyclooxygenase disrupts PGE2 biosynthesis, fueling unchecked leukotriene production and eosinophilic inflammation (PMID: 30516547), underscoring PGE2’s protective role in limiting disease progression. Conversely, in nasal polyps, tuft cells are activated by IL-13 to produce PGE2 (PMID: 35608904), highlighting PGE2’s potential pathogenicity. Objective To assess associations between PGE2-pathway gene expression in bronchial brushings and asthma severity, and to determine how nasal polyp history modifies these relationships. Methods We analyzed bronchial epithelial brushings from 109 adults with asthma (mean age 42.4±12.1, 64% female, 52% severe asthma, 18% nasal polyps) using normalized and batch-corrected RNA-seq data from the baseline visit in the Severe Asthma Research Program (SARP)3 (PMID: 36796454). Age-adjusted and nasal polyp-adjusted relationships were calculated between ratios of PGE2 synthesis and metabolism genes and clinical outcomes (ASSESS score, FEV1 %predicted). The composite PGE2 score (PMID: 35608904), derived from nasal polyp analysis, was calculated from the average scaled log-normalized expression of six PGE2-responsive genes. All data were analyzed using a linear regression mixed model with a random effect for site. Results Ratios of PGE2 synthesis genes (PTGS1/2, PTGES2) to HPGD were significantly associated with greater disease severity (change in ASSESS Score) (Fig.1A, solid lines) and lower lung function (change in FEV1 %predicted) (Fig.1B, solid lines). These relationships persisted after adjusting for nasal polyps (Fig.1, dashed lines). The composite PGE2 score was also related to disease severity (Fig.1A, black solid line) but attenuated when nasal polyps were accounted for (Fig.1A, black dashed lines). Interestingly, lower lung function remained associated with the PGE2 score even after nasal polyp adjustment (Fig.1B, dashed black lines). Conclusions Our analysis demonstrates that the balance between PGE2 synthesis and degradation in the bronchial epithelium is closely linked to asthma severity and lung function, independent of nasal polyp status. Future work will integrate transcriptomic and clinical data longitudinally to establish whether PGE2 could serve as a biomarker for asthma trajectory. This abstract is funded by: HL115118 (LCD), U10HL109152 (S.E.W.), U10HL109172 (to E.I. and B.D.L), U10HL109168 (N.N.J.), U10HL109250 (S.C.E), U10HL109164 (W.C.M), U10HL109257 (M.C), and U10HL109146 (to P.G.W. and S.A.C.)

Abstract Rationale Type-1 inflammation in asthma leads to significant airway level changes independent of Type-2 inflammation and may underlie the severity seen in combined Type1/2 high disease. As IFNs can cause airway epithelial injury and impaired repair, we applied a novel interpretable machine learning algorithm, SLIDE, to analyze the transcriptome of airway epithelial brushings of mild-moderate (MMA) and severe asthma (SA) participants in the Immune Mechanisms in Severe Asthma (IMSA) cohort to identify latent/hidden factors of disease that associate with disease severity. Methods The expression of genes identified by SLIDE as part of a co-expression program was assessed in the full IMSA cohort by severity and in an independent cohort of the NHLBI Severe Asthma Research Program (SARP). We also examined associations of these genes with clinical asthma outcomes in both cohorts. Results SLIDE identified aberrant increased expression of membrane-tethered mucin genes, MUC1 and MUC4. MUC1 appeared in each of 5 significant latent factors associated with greater severity and MUC4 in 2/5. Standard DGE analysis also identified the gene CEACAM5 as a predictor of severity. Pathway analysis of latent factor genes revealed airway keratinization which may contribute to airway remodeling and worsening obstruction over time. Expression of MUC1, MUC4, and CEACAM5 were increased in SA compared to HC in both the full IMSA and separate SARP cohorts (p < 0.001 for all genes in both cohorts). Expression in MMA was consistently lower than in SA in both cohorts, reaching significance in IMSA (p < 0.01) but not consistently in SARP (MUC1 p = 0.04, MUC4 p = 0.27, CEACAM5 p = 0.27). Protein analysis of endobronchial biopsies by immunofluorescence staining showed a trend towards increased MUC1 protein content with evidence of secretion. MUC1, MUC4 and CEACAM5 were negatively correlated with FEV1 % predicted in both cohorts (IMSA: MUC1 r=-0.47, p < 0.01, MUC4 r=-0.47, p < 0.01, CEACAM5 r=-0.62, p < 0.01; SARP: MUC1 r=-0.19, p = 0.05, MUC4 r=-0.29, p < 0.01, CEACAM5 r=-0.51, p < 0.01). All three genes positively correlated with prospective exacerbations in both cohorts (IMSA: MUC1 r = 0.345, p = 0.01, MUC4 r = 0.216, p = 0.08, CEACAM5 r = 0.368, p < 0.01; SARP: MUC1 r = 0.22, p = 0.02, MUC4 r = 0.32, p < 0.01, CEACAM5 r = 0.38, p < 0.01). Conclusions The expression of MUC1, MUC4 and CEACAM5 increased with asthma severity in the airway epithelium and correlated with both prospective exacerbations and obstruction. As pathway analysis of genes in the significant latent factors were associated with airway keratinization aberrant expression of these mucins (with known signaling functions) may contribute to maladaptive repair and airway remodeling that drives obstruction over time, contributing to worse asthma outcomes. This abstract is funded by: NIH

Abstract Rationale The activity of fucosyltransferase 2 (FUT2) determines if mucin glycan structures in epithelial mucus include histo-blood group antigens (HBGAs). People with active FUT2 express HBGAs in epithelial mucus secretions and are terms “secretors”, while non-secretors lack HBGAs in their secretions because they have a FUT2 genotype that inactivates the glycosyl transferase. Prior research suggests that secretors who are blood type O (type O secretors) are susceptible to asthma exacerbations1 and that FUT2 knockout mice (functional non-secretors) are protected from mucus plugging in allergen challenge models2,3. We set out here to determine if secretor status influences mucus plug prevalence in asthma and to confirm if type O secretors are susceptible to asthma exacerbations. Methods We determined secretor status in asthma participants from the Severe Asthma Research Program-3 (SARP-3) cohort (N = 507) in two ways, a specific fucose lectin binding assay in sputum and FUT2 genotyping. Blood group type was determined using ABO genotyping. We analyzed mucus plug segment scores, asthma exacerbations rates and other clinical and biological features in patients stratified by secretor status and blood type (non-secretors, type O secretors, and non-type O secretors). Results Secretor status determined by lectin binding in sputum was strongly concordant with genotypic analysis. Within the SARP-3 cohort, 26.2% were non-secretors, 32.3% type were O secretors, and 41.4% were non-type O secretors. Mucus plug segment scores, annualized asthma exacerbation rates, and other measures of asthma severity were similar in non-secretors, type O secretors, and non-type O secretors (Table). Although type O secretors had increased values for fractional exhaled nitric oxide (FeNO) and higher expression of type 2 gene network genes in their sputum cells, measures of sputum eosinophil inflammation were not higher in this subgroup (Table). Conclusion Secretor status does not influence the prevalence of mucus plugging in asthma patients, and we did not confirm a prior report that type O secretors have an increased risk of asthma exacerbation. Although type O secretors have modestly increased FeNO levels and increased expression of type 2 immune genes in their sputum, it is not clear if this is a biologically important signal. 1. Innes et al. AJRCCM; 2011. 2. Saku et al. JACI; 2019. 3. Hara et al. AJRCMB; 2025 This abstract is funded by: The study was supported by the National Institutes of Health grants R01 HL080414 (JVF), P01 HL107202 (JVF), R01 HL164787 (JVF), U10 HL109172 (EI), U10 HL109152 (SEW), U10 HL109257 (MC), U10 HL109250 (SCE), U10 HL109146 (JVF), U10 HL109164 (ERB), U01 HL146002 (BDL), U10 HL109086 (DTM) and Sandler Asthma Basic Research Center at UCSF (JVF). The following companies provided financial support for study activities at the Coordinating and Clinical Centers beyond the third year of patient follow-up: AstraZeneca, Boehringer-Ingelheim, Genentech, GlaxoSmithKline, Sanofi–Genzyme Regeneron, and Teva. These companies had no role in study design or data analysis, and the only restriction on the funds was that they be used to support the SARP initiative.

Abstract Rationale We hypothesize that corticosteroid resistance causes uncontrolled inflammation and severe asthma. The genetic and molecular mechanisms underlying steroid responsiveness in asthma remain poorly understood (PMID28493293). This study aims to characterize transcriptomic profiles distinguishing steroid responders from steroid resistant asthma. Methods Longitudinal bulk RNA-seq of SARP3 sputum samples was analyzed before and after an “induced phenotype” with 40 mg triamcinolone acetonide (TCA) injection (PMID 27967215). Participants were classified as responders or non-responders based on whether their FEV₁ change after TCA exceeded the median. Paired differential gene expression analyses were performed separately for each group using limma and voom, with participant as a factor and adjustment for sputum cell count differentials. Z-scores were calculated to compare gene expression changes after TCA injection between responders and non-responders, and pathway enrichment was assessed using the fgsea package. Results Baseline demographics and clinical characteristics of 696 SARP3 asthma participants receiving TCA are shown in Table 1. The median [IQR] FEV1 change post-TCA was 1.81% [-2.16, 7.17]. Responders had higher baseline FEV1%, FEV1/FVC ratio, blood eosinophil counts, and sputum eosinophil percentages, but lower serum CRP levels. Steroid responsiveness was associated with asthma severity (Table 1). Among 154 participants with paired sputum RNA-seq data, 70 (45%) exhibited >1.81% improvement in FEV₁ and were classified as responders. Following TCA treatment, non-responders showed no significant changes in gene expression. In responders, 18 genes were upregulated and 203 downregulated. Downregulated genes were associated with Th2 immunity (IL13, IL1RL1, CCL17, SIGLEC8), IgE signaling (FCER1A, FCER2), epithelial barrier function and mucus production (MUC2, MUC7, ALOX15, TFF3), tissue remodeling and fibrosis (MMP10, TIMP3), ciliary function (DNAH5, CFAP43, FOXJ1), and airway hyperreactivity (HRH4, RAMP1). Key genes notably downregulated in responders compared to non-responders included IL13, CCL5, PTGDR2, FCER2, and CLEC10A. Pathway analysis revealed enhanced IL-10-mediated anti-inflammatory signaling, suppression of pro-inflammatory pathways (NF-κB, IL-1, Th1/Th2, asthma, arachidonic acid metabolism, chemokine, NOTCH4), and regulation of mitochondrial oxidative phosphorylation. Conclusions Steroid resistance impairs glucocorticoid-mediated suppression of pro-inflammatory pathways, including NF-κB, IL-1, and Th1/Th2 signaling. This persistent airway inflammation limits improvements in lung function and contributes to worse clinical outcomes, including increased airflow obstruction, and symptom burden. Glucocorticoid resistance in asthma may be driven by receptor dysfunction, airway inflammation, epigenetic changes, and environmental exposures resulting in persistent airway inflammation and remodeling. Elucidating the molecular mechanisms underlying steroid resistance is critical for developing personalized targeted therapies in asthma. This abstract is funded by: NIH-NHLBI - R01 HL161674 (PI: Joe Zein)

Abstract Rationale Asthma contributes substantially to healthcare burden and morbidity through frequent exacerbations. Emerging evidence suggests that genetic factors play a crucial role in modulating susceptibility to asthma exacerbations. To better understand the genetic architecture of exacerbations, we conducted a genome-wide association study (GWAS) in the Tapestry cohort. Methods The Tapestry Study, a large-scale, decentralized and clinical grade genomic sequencing study, conducted Exome+® sequencing at Helix, Inc. on 98,075 Mayo Clinic participants. After excluding patients with ≥10 pack-years of smoking and/or emphysema, we performed a GWAS of asthma exacerbations in 3,960 adults with asthma based on ICD codes. Cases (n = 1,059) were defined as individuals who experienced at least two exacerbations between 2020 and 2025; the remaining 2,901 individuals served as controls. Asthma exacerbations were defined by oral or systemic corticosteroid prescriptions with durations ranging from 3 days up to 28 days. Logistic regression models adjusted for age, sex, BMI, and the first six principal components of ancestry. Replication was performed in 1,273 asthma patients (439 African Americans and 834 non-Hispanic Whites) from the NHLBI-sponsored Severe Asthma Research Program (SARP 1-3), with adjustments for age, sex, BMI, and the first 10 principal components of ancestry. Results Based on a minor allele frequency (MAF) threshold of > 0.05, we analyzed 5,256,135 genome-wide variants. Genome-wide significant associations for asthma exacerbations (p < 5.0 × 10⁻8) included loci at a pseudogene in SMPD4P1 (rs680548, MAF = 0.39, OR = 1.37, p = 9.10 × 10⁻9). Additional variants showing suggestive associations included loci within ENDOD1 (rs638776, OR = 0.69, p = 1.20 × 10⁻6), PCSK2 (rs2206454, OR = 1.32, p = 3.53 × 10⁻6), RBFOX1 (rs17667084, OR = 0.60, p = 5.11 × 10⁻6), and CDC42EP3 (rs721660, OR = 1.29, p = 5.68 × 10⁻6). Subthreshold associations in Tapestry including loci at CDC42EP3 (rs4670213, OR = 1.27, p = 1.41x10-5, MAF = 0.36), and SDR39U1 (rs12587663,OR = 0.83, p = 5.86x10-4, MAF = 0.46) were replicated in the SARP longitudinal visit 5 with p-values of 8.523x10-3 (rs4670213, OR = 1.78), and 2.88x10-4 (rs12587663,OR=0.43), respectively. Conclusion Our GWAS for asthma exacerbations in the Tapestry cohort identified variant loci relevant to mitophagy mechanisms (CDC42EP3, PMID: 40783597), immune signaling and RNA splicing regulation (RBFOX1, PMID: 31301374), and neuromodulatory pathways (PCSK2, PMID: 32794589), all previously implicated in asthma pathophysiology. Collectively, these findings highlight potential novel genetic contributors to asthma exacerbations and provide a foundation for improving risk stratification and therapeutic targeting in asthma management. This abstract is funded by: This abstract is funded by: Mayo Clinic Funding: X01 from the NHLBI for TOPMed whole-genome sequencing and NIH grants R01 HL142992, HL69116, HL69167, HL69170, HL69174, U10 HL109164, U10 HL109257, U10 HL109146, U10 HL109172, U10 HL109250, U10 HL109250, U10 HL109250, U10 HL109168, U10 HL109152, U10 HL109086, U10 HL109046.

Abstract Rationale Higher African genetic ancestry has been associated with lower lung function, among individuals from specific racial-ethnic groups. The influence and interactions of genetic variation and ancestry in lung function is poorly understood. This study investigates ancestry-associated loci influencing lung function via admixture mapping and fine-scale allelic association. Methods 512 African American patients with asthma (≥12 years) from the Severe Asthma Research Program and Collaborative Study on the Genetics of Asthma (SARP/CSGA) cohorts were genotyped on the OmniExpress and HumanHap1M arrays or had whole-genome sequencing (WGS). For admixture mapping, global ancestry was inferred using ADMIXTURE and local ancestry estimated using RFMix v1.5.4 with CEU (N = 99) and YRI (N = 108) references across 15,095 local ancestry segments. Peak regions and contiguous segments (p < 10-3) were fine mapped in SARP using local ancestry estimated from WGS with MAF ≥0.03. The Local Ancestry Adjusted Allelic Association (LAAA) model was used to fine map local genetic effect, local ancestry effect, and the interaction between the two, adjusted for age, sex, height, BMI, cohort, and percentage global African ancestry. SNPs with local genetic or interaction p < 10⁻³ were tested for replication in 282 adolescent/adult participants from the Best African Response to Asthma Drugs (BARD) cohort genotyped with the MEGA array imputed to TOPMed (MAF ≥0.03). Results Mean African genetic ancestry was 81.3% in SARP and 80.1% in BARD. In SARP, African genetic ancestry was inversely associated with lung function (FEV₁:β = −7.9, p = 0.005; FVC:β = −9.0, p = 0.004), with stronger effects in females (FEV₁: β = −9.3, p = 0.003; FVC: β = −11.4, p = 0.001) than males (FEV₁:β = −6.4, p = 0.237; FVC:β = −5.7, p = 0.340). Similar total and sex-specific associations were identified in BARD. Admixture mapping in SARP/CSGA identified significant peaks for maximum FVC (p < 1.87 × 10⁻4) on chromosomes 8 and 17 near RP1 and RBFOX3, and near-significant peaks on chromosome 5 near IQGAP2, SNRPCP2, SV2C and for the maximum FEV₁/FVC ratio on chromosome 10 near PCDH15. Fine mapping identified the top SNP on chromosome 8 (rs73587686; p-allele-dose=9.0x10-5; RGS20; q11.23), with 62 additional SNPs overlapping RGS20. SNP rs7825816 was also identified in SARP (β = 2.35, p-allele-dose =1.03 × 10⁻4) and replicated in BARD (β = 1.15, p-allele-dose =0.03). In BARD, LAAA also identified eight additional SNPs (p ≤ 0.05) in the same LD block (chr8:53914976-53918297) overlapping with RGS20, ATP6V1H, and TCEA1. Conclusions We identified multiple chromosomal regions associated with lung function in asthma patients of predominantly African genetic ancestry. Lung function associations were strongest at genetic variation near a key that was replicated in an independent asthma cohort. This abstract is funded by: R01 K08 HL118128, R01 HL142992, R01 NR013700, R01 HL69167, R01 HL161674, HL69116, HL69167, HL69170, HL69174, U10 HL109164, U10 HL109257, U10 HL109146, U10 HL109172, U10 HL109250, U10 HL109250, U10 HL109250, U10 HL109168, U10 HL109152, U10 HL109086-04, U10 HL098115, HL098102, HL098096, HL098075, HL098090, HL098177, HL098098, HL098107, HL098112, HL098103, and K12 HL89992