The Utility of Resolving Asthma Molecular Signatures Using Tissue-Specific Transcriptome Data.
The Utility of Resolving Asthma Molecular Signatures Using Tissue-Specific Transcriptome Data.
复制标题
利用组织特异性转录组数据解决哮喘分子特征的效用。
DOI：
10.1534/g3.120.401718
复制
发表时间：
2020-11-05
期刊：
影响因子：
--
通讯作者：
Mersha TB
中科院分区：
文献类型：
--
作者：
Ghosh D;Ding L;Bernstein JA;Mersha TB
尚未对哮喘进行多组织转录组学的综合分析。组织特异性DEG在许多多组织分析中仍未检测到，这影响了疾病相关途径和潜在候选药物的鉴定。从Gene Expression Omnibus（GEO）检索了来自609例病例和196例对照的转录组数据，这些转录组数据使用气道上皮、支气管、鼻、气道巨噬细胞、远端肺成纤维细胞、近端肺成纤维细胞、来自全血和诱导痰样品的CD 4+淋巴细胞、CD 8+淋巴细胞生成。从每种样品类型中鉴定的差异调节的哮喘相关基因用于鉴定（a）组织特异性和组织共享的哮喘途径，（B）它们与GWAS鉴定的疾病基因的连接以鉴定用于功能研究的候选组织，（c）选择侵袭性组织的替代样品，以及最后（d）通过连接图分析鉴定潜在的候选药物。我们发现，组织间的基因表达的相似性是更明显的途径/功能水平比在基因水平与支气管上皮细胞和肺成纤维细胞之间的相似性最高，气道上皮细胞和全血样品之间的最低。尽管公共领域的基因表达数据受到未充分注释的每个样本的人口统计学和临床信息的限制，这限制了分析，但我们的组织解析分析清楚地表明了独特和共享的哮喘通路的相对重要性。在通路水平，IL-1b信号传导和ERK信号传导在许多组织类型中是显著的，而胰岛素样生长因子和TGF-β信号传导仅在气道上皮组织中是相关的。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）是最高表达的途径。总体而言，IL-1信号传导基因（炎症）在气道隔室中相关，而包括IL-13和STAT 6的pro-Th 2基因在成纤维细胞、淋巴细胞、巨噬细胞和支气管活检中更相关。这些基因在GWAS目录中也与哮喘相关。支持向量机结果表明，基于巨噬细胞和上皮细胞的DEG分别具有最高和最低的判别准确率。药物（恩替司他，BMS-345541）和遗传干扰素（KLF 6，BCL 10，INFB 1和BAMBI）在多组织水平上与疾病呈负相关，可能会重新用于治疗哮喘。总的来说，我们的研究表明，DEG，扰动和疾病的连接差异取决于组织/细胞类型。虽然大多数现有的文献描述了哮喘转录组数据从个别样本类型，目前的工作证明了多组织转录组数据的效用。未来的研究应侧重于收集来自多个组织、年龄和种族群体、遗传背景、疾病亚型的转录组学数据，以及在公共领域获得更好的注释数据。
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.
登录
查看更多内容
影响因子：
11
作者：
Koester SE;Insel TR
通讯作者：
Insel TR
影响因子：
16.6
作者：
Soler Artigas M;Wain LV;Miller S;Kheirallah AK;Huffman JE;Ntalla I;Shrine N;Obeidat M;Trochet H;McArdle WL;Alves AC;Hui J;Zhao JH;Joshi PK;Teumer A;Albrecht E;Imboden M;Rawal R;Lopez LM;Marten J;Enroth S;Surakka I;Polasek O;Lyytikäinen LP;Granell R;Hysi PG;Flexeder C;Mahajan A;Beilby J;Bossé Y;Brandsma CA;Campbell H;Gieger C;Gläser S;González JR;Grallert H;Hammond CJ;Harris SE;Hartikainen AL;Heliövaara M;Henderson J;Hocking L;Horikoshi M;Hutri-Kähönen N;Ingelsson E;Johansson Å;Kemp JP;Kolcic I;Kumar A;Lind L;Melén E;Musk AW;Navarro P;Nickle DC;Padmanabhan S;Raitakari OT;Ried JS;Ripatti S;Schulz H;Scott RA;Sin DD;Starr JM;UK BiLEVE;Viñuela A;Völzke H;Wild SH;Wright AF;Zemunik T;Jarvis DL;Spector TD;Evans DM;Lehtimäki T;Vitart V;Kähönen M;Gyllensten U;Rudan I;Deary IJ;Karrasch S;Probst-Hensch NM;Heinrich J;Stubbe B;Wilson JF;Wareham NJ;James AL;Morris AP;Jarvelin MR;Hayward C;Sayers I;Strachan DP;Hall IP;Tobin MD
通讯作者：
Tobin MD
DOI：
10.1164/rccm.200907-1071oc
发表时间：
2010-05-01
影响因子：
24.7
作者：
Kicic, Anthony;Hallstrand, Teal S.;Knight, Darryl A.
通讯作者：
Knight, Darryl A.
影响因子：
2.8
作者：
Duan, Q. L.;Lasky-Su, J.;Tantisira, K. G.
通讯作者：
Tantisira, K. G.
DOI：
10.1016/j.jtemb.2017.08.011
发表时间：
2017-01-01
影响因子：
3.5
作者：
Besiktepe, Neziha;Kayalar, Ozgecan;Oztay, Fusun
通讯作者：
Oztay, Fusun