A phase I clinical research study aimed at determining mechanisms that regulate airway mucosal inflammation in asthma endotypes using intranasal administration of endotoxin (lipopolysaccharide from E. coli) in healthy controls and subjects diagnosed with asthma.
Patients with severe asthma can be broadly classified into three endotypes (T2, T1, and T17) based on inflammatory cell and cytokine profiles. Within these endotypes, many patients have high levels of neutrophils in the airways and mucosal epithelium. Our preliminary data suggest that neutrophils in the airways and blood of patients with severe asthma are heterogenous and that subsets exist. The nature of these subsets appears to differ between T2, T1, and T17 asthma endotypes. In order to advance the field and determine the mechanisms that underpin severe neutrophilic asthma, investigators plan to longitudinally assess transcriptional profiles and functional properties of neutrophil subsets that migrate to the nasal cavity following exposure to a standardized dose of Clinical Reference Center endotoxin (IRB#HS-3131-528, IND 018580). Investigators will recruit 15 subjects of each asthma endotype (45 total participants with asthma) and 15 healthy controls. LPS will be used to elicit migration of neutrophils into the nasal cavities. Neutrophils will be isolated from the nasal cavities using both nasal lavage and nasal brushes 20 minutes, 1 day and 3 days after endotoxin challenge. Neutrophil biology will be assessed using single-cell RNAseq and ex vivo functional assays.
Study Type
INTERVENTIONAL
Allocation
NON_RANDOMIZED
Purpose
BASIC_SCIENCE
Masking
NONE
Enrollment
60
LPS aka endotoxin is a component of bacterial cell walls and is ubiquitous in the environment. It is found in pet dander, house dust and environmental dusts and has been heavily studied in asthma.
National Jewish Health
Denver, Colorado, United States
Neutrophil heterogeneity using single cell RNA sequencing
Neutrophil subset composition as determined by single cell RNAseq is compared to endotype groups using a multivariate linear regression framework for compositional data, employing the additive log ratio transformation. Models will include indicator variables for whether a sample is T1 high, T2 high, T17 high or non-asthmatic and will control for age, sex, and smoking status. AM subset gene expression matrices from scRNAseq will be compared between endotypes using the pseudo bulk approach to account for clustering of cells within subjects. To identify global gene expression programs underlying each endotype, bulk RNAseq gene expression data will be normalized using DESeq2's variance stabilizing transformation and similarly compared between endotypes using linear regression models. A Benjamini-Hochberg adjustment will be used to control the false discovery rate.
Time frame: We anticipate that the study will run over 5 years
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