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 are 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.

尚未针对哮喘进行针对多组织转录组学的综合分析。在许多多组织分析中仍未检测到组织特异性DEG，这会影响疾病相关途径和潜在候选药物的鉴定。从Gene Expression Omnibus（GEO）检索了使用气道上皮，支气管，鼻腔，气道巨噬细胞，远端肺成纤维细胞，近端肺成纤维细胞，全血CD4 +淋巴细胞，CD8 +淋巴细胞生成的609例病例和196例对照的转录组数据。 ）。从每种样品类型中鉴定出的差异调节的与哮喘相关的基因被用于鉴定（a）组织特异性和组织共有的哮喘途径，（b）与GWAS鉴定的疾病基因的联系，以鉴定功能研究的候选组织，通过连接图分析。我们发现组织表达的组织间相似性在途径/功能水平上比在基因水平上更为明显，其中支气管上皮细胞与肺成纤维细胞之间的相似性最高，而气道上皮与全血样品之间的相似性最低。尽管公共域基因表达数据受到每个样本的人口统计学和临床​​信息的注释不足，从而限制了分析，但我们的组织分辨分析清楚地表明了独特和共享的哮喘途径的相对重要性，在途径水平，IL-1b信号传导和ERK信号在许多组织类型中都很重要，而胰岛素样生长因子和TGF-β信号仅在气道上皮组织中相关。IL-12（在巨噬细胞中）和免疫球蛋白信号传导（在淋巴细胞中）和趋化因子（在鼻上皮中）是表达最高的途径。总体而言，IL-1信号基因（炎性）在气道腔内相关，而促Th2基因（包括IL-13和STAT6）在成纤维细胞，淋巴细胞，巨噬细胞和支气管活检中更相关。在GWAS目录中，这些基因也与哮喘有关。支持向量机显示，基于巨噬细胞和上皮细胞的DEG分别具有最高和最低的区分精度。在多组织水平上与疾病负相关的药物（恩替司他，BMS-345541）和遗传性不稳定药物（KLF6，BCL10，INFB1和BAMBI）有可能重新用于治疗哮喘。总体而言，我们的研究表明，DEG，皮疹和疾病的连接取决于组织/细胞类型。虽然大多数现有文献描述了来自单个样本类型的哮喘转录组数据，但本工作证明了多组织转录组数据的实用性。未来的研究应侧重于从多个组织，年龄和种族，遗传背景，疾病亚型中收集转录组数据，并关注公共领域中注释更好的数据的可用性。