Modena Brian D, Tedrow John R, Milosevic Jadranka, Bleecker Eugene R, Meyers Deborah A, Wu Wei, Bar-Joseph Ziv, Erzurum Serpil C, Gaston Benjamin M, Busse William W, Jarjour Nizar N, Kaminski Naftali, Wenzel Sally E
1 Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh School of Medicine, University of Pittsburgh Asthma Institute at UPMC, Pittsburgh, Pennsylvania.
Am J Respir Crit Care Med. 2014 Dec 15;190(12):1363-72. doi: 10.1164/rccm.201406-1099OC.
Although asthma is recognized as a heterogeneous disease associated with clinical phenotypes, the molecular basis of these phenotypes remains poorly understood. Although genomic studies have successfully broadened our understanding in diseases such as cancer, they have not been widely used in asthma studies.
To link gene expression patterns to clinical asthma phenotypes.
We used a microarray platform to analyze bronchial airway epithelial cell gene expression in relation to the asthma biomarker fractional exhaled nitric oxide (FeNO) in 155 subjects with asthma and healthy control subjects from the Severe Asthma Research Program (SARP).
We first identified a diverse set of 549 genes whose expression correlated with FeNO. We used k-means to cluster the patient samples according to the expression of these genes, identifying five asthma clusters/phenotypes with distinct clinical, physiological, cellular, and gene transcription characteristics-termed "subject clusters" (SCs). To then investigate differences in gene expression between SCs, a total of 1,384 genes were identified that highly differentiated the SCs at an unadjusted P value < 10(-6). Hierarchical clustering of these 1,384 genes identified nine gene clusters or "biclusters," whose coexpression suggested biological characteristics unique to each SC. Although genes related to type 2 inflammation were present, novel pathways, including those related to neuronal function, WNT pathways, and actin cytoskeleton, were noted.
These findings show that bronchial epithelial cell gene expression, as related to the asthma biomarker FeNO, can identify distinct asthma phenotypes, while also suggesting the presence of underlying novel gene pathways relevant to these phenotypes.
尽管哮喘被认为是一种与临床表型相关的异质性疾病,但其表型的分子基础仍知之甚少。虽然基因组研究已成功拓宽了我们对癌症等疾病的认识,但尚未广泛应用于哮喘研究。
将基因表达模式与临床哮喘表型联系起来。
我们使用微阵列平台分析了来自重度哮喘研究项目(SARP)的155名哮喘患者和健康对照受试者的支气管气道上皮细胞基因表达,这些表达与哮喘生物标志物呼出一氧化氮分数(FeNO)相关。
我们首先鉴定出一组多样的549个基因,其表达与FeNO相关。我们使用k均值法根据这些基因的表达对患者样本进行聚类,识别出五个具有不同临床、生理、细胞和基因转录特征的哮喘簇/表型,称为“受试者簇”(SCs)。为了进一步研究SCs之间基因表达的差异,共鉴定出1384个基因,这些基因在未校正的P值<10^(-6)时能高度区分SCs。对这1384个基因进行层次聚类,识别出九个基因簇或“双簇”,它们的共表达表明每个SCs具有独特的生物学特征。虽然存在与2型炎症相关的基因,但也注意到了新的途径,包括与神经元功能、WNT途径和肌动蛋白细胞骨架相关的途径。
这些发现表明,与哮喘生物标志物FeNO相关的支气管上皮细胞基因表达可以识别不同的哮喘表型,同时也提示存在与这些表型相关的潜在新基因途径。