The Utility of Resolving Asthma Molecular Signatures Using Tissue-Specific Transcriptome Data

Abstract

An integrative analysis focused on multi-tissue transcriptomics has not been done for asthma. Tissue-specific DEGs remain undetected in many multi-tissue analyses, which influences identification of disease-relevant pathways and potential drug candidates. Transcriptome data from 609 cases and 196 controls, generated using airway epithelium, bronchial, nasal, airway macrophages, distal lung fibroblasts, proximal lung fibroblasts, CD4+ lymphocytes, CD8+ lymphocytes from whole blood and induced sputum samples, were retrieved from Gene Expression Omnibus (GEO). Differentially regulated asthma-relevant genes identified from each sample type were used to identify (a) tissue-specific and tissue-shared asthma pathways, (b) their connection to GWAS-identified disease genes to identify candidate tissue for functional studies, (c) to select surrogate sample for invasive tissues, and finally (d) to identify potential drug candidates via connectivity map analysis. We found that inter-tissue similarity in gene expression was more pronounced at pathway/functional level than at gene level with highest similarity between bronchial epithelial cells and lung fibroblasts, and lowest between airway epithelium and whole blood samples. Although public-domain gene expression data is limited by inadequately annotated per-sample demographic and clinical information which limited the analysis, our tissue-resolved analysis clearly demonstrated relative importance of unique and shared asthma pathways, At the pathway level, IL-1b signaling and ERK signaling were significant in many tissue types, while Insulin-like growth factor and TGF-beta signaling were relevant in only airway epithelial tissue. IL-12 (in macrophages) and Immunoglobulin signaling (in lymphocytes) and chemokines (in nasal epithelium) were the highest expressed pathways. Overall, the IL-1 signaling genes (inflammatory) were relevant in the airway compartment, while pro-Th2 genes including IL-13 and STAT6 were more relevant in fibroblasts, lymphocytes, macrophages and bronchial biopsies. These genes were also associated with asthma in the GWAS catalog. Support Vector Machine showed that DEGs based on macrophages and epithelial cells have the highest and lowest discriminatory accuracy, respectively. Drug (entinostat, BMS-345541) and genetic perturbagens (KLF6, BCL10, INFB1 and BAMBI) negatively connected to disease at multi-tissue level could potentially repurposed for treating asthma. Collectively, our study indicates that the DEGs, perturbagens and disease are connected differentially depending on tissue/cell types. While most of the existing literature describes asthma transcriptome data from individual sample types, the present work demonstrates the utility of multi-tissue transcriptome data. Future studies should focus on collecting transcriptomic data from multiple tissues, age and race groups, genetic background, disease subtypes and on the availability of better-annotated data in the public domain.

Expression profiling of ileal mucosa in asthma reveals upregulation of innate immunity and genes characteristic of Paneth and goblet cells

“…Interestingly, machine learning (support vector machines) showed that differences between asthma and control samples were more discriminatory in macrophages than in the epithelial cells. We found little overlap between the genes that were overexpressed to the highest degree in the ileum of asthma patients, and asthma-specific genes identified across tissues by Ghosh et al [ 37 ]. However, a transcriptomic study of airway epithelial cells by Kicic et al revealed that the response to a biotic stimulus was the top upregulated pathway in asthma [ 38 ], and this theme was also enriched in the ileum.…”

“…A large integrative study of asthma transcriptomics in various tissues [ 37 ] revealed organism-wide changes in IL-1β and ERK signaling. Airway samples in asthma have dysregulated TGFβ, as well as IL-13.…”

Identification of gene biomarkers with expression profiles in patients with allergic rhinitis

“…Inherent gene expression due to potential confounders produced from highly expressed genes in different tissue or cell types might be ignored in an analysis of DEGs in a single source. Recently, some gene expression studies on asthma [ 36 ], musculoskeletal development [ 37 ], and nonalcoholic fatty liver disease [ 38 ] have reported approaches in which different tissues were combined.…”

Cell function and identity revealed by comparative scRNA-seq analysis in human nasal, bronchial and epididymis epithelia

“…Extensive work has compared the similarity of gene expression in the human bronchial and nasal epithelium ( Deprez et al, 2020 ; Ghosh et al, 2020 ; Nguyen et al, 2018 ). This relationship is important both in the context of the healthy and diseased airways, the response to bacterial or viral infection, and the development of therapeutics for airway disease.…”