Mauger, D

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 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