Using Network Analysis to Understand Severe Asthma Phenotypes.

Asthma is a highly complex and heterogeneous disease that comprises multiple subphenotypes. In most patients, the disease is characterized by reversible airflow limitation; however, a subset of patients suffer from a more severe form of the disease, which is associated with symptoms that are less controllable; they are unresponsive to treatment with conventional glucocorticoid (GC) therapy and require increasingly high doses of inhaled and/or oral GC in addition to combination therapy. Other characteristics associated with severe asthma include atopy, rhinosinusitis, reduced lung function, frequent and/or severe exacerbations, and variations in age of onset and airway inflammatory profiles (eosinophilic, neutrophilic, paucigranulocytic) (1). Understanding the pathogenesis of severe asthma is a daunting task, given that numerous candidate susceptibility genes have been identified and that these genes interact in complex ways with environmental cofactors (2). The application of high-throughput molecular profiling technologies (e.g., microarray, RNA-Seq) to the study of gene expression patterns in asthma has produced vast amounts of data that are now available in the public domain. These data can be downloaded and reanalyzed with the latest algorithms, unveiling new biological insights (3). Previous gene expression studies in asthma have primarily focused on identification of differentially expressed genes. However, this approach is limited, as genes do not exist or function in isolation; instead, they work together. A significant advance in this context was the application of network graph theory to genomic data analysis (4). The underlying concept here is that a functioning biological system can be represented as a “ball-and-stick” diagram, where “balls” represent genes, and “sticks” or links represent coexpression relationships between genes. Rather than focusing on gene expression levels, network analysis interrogates coexpression relationships across the samples to infer the wiring diagram of the underlying gene networks. Coexpression network analysis reveals two fundamental organizing principles of biological systems: the presence of highly connected “hub” genes, which dominate the structure of biological networks, and modules, which are sets of coexpressed genes that function in the same biological pathway or process (4). In this issue of the Journal, Modena and colleagues (pp. 1449– 1463) used coexpression network analysis to interrogate the molecular mechanisms underlying severe asthma (Figure 1) (5). They reanalyzed data they previously published and deposited in the Gene Expression Omnibus repository (6), which consisted of microarray profiles of gene expression derived from epithelial cell brushings collected from adult patients with asthma (n = 129) and healthy controls (n = 26) from the Severe Asthma Research Program cohort. Of note, around one third of the patients met the American Thoracic Society criteria for severe asthma, and two thirds of the patients with severe asthma were using oral corticosteroids. A coexpression network was constructed from all expressed genes (30,889) employing weighted gene coexpression network analysis (WGCNA) (7), resulting in the identification of 64 modules. The expression of each module was summarized using principal component analysis, and the first principal component was correlated with all available clinical traits, unveiling module–trait associations. The clinical traits most significantly associated with gene network patterns were body mass index, ethnicity, chronic rhinosinusitis, fractional exhaled nitric oxide, asthma severity, Asthma Quality of Life Questionnaire scores, FEV1% predicted, emergency room visits/hospitalization, and use of inhaled or oral corticosteroids. Notably, a module of chromosome Y-linked genes was strongly associated with male sex, thus demonstrating the plausibility of the overall approach. The authors found that four modules (color coded as magenta, mediumpurple3, red, and thistle1) were up-regulated in association with American Thoracic Society–defined asthma severity. Of these modules, magenta and red were most strongly and positively correlated with disease severity, emergency presentations/hospitalization, and use of inhaled and oral corticosteroids. They were also inversely related to lung function and quality-of-life scores (note that low Asthma Quality of Life Questionnaire scores indicate more severe symptoms). Magenta was enriched with proteins involved in mitosis and cell division. The red module was enriched with Th2-associated genes (e.g., IL-13, CCL26, CST1, CHI3L1, CLCA1, MUC5AC, and POSTN) (8), and expression of this module was strongly correlated with fractional exhaled nitric oxide and body mass index and was elevated in African Americans compared with subjects of European ancestry. Thistle1 was enriched with histones and related proteins, and mediumpurple3 was enriched with GC-induced genes. The authors also identified a Th1/interferon-associated module (CXCL9, CXCL10, CXCL11, IDO1, IL-15, PARP9, PSMB8, PSMB9, PSMB10, and STAT1), and although this module was up-regulated in 26% of total patients with asthma, it was not significantly correlated with asthma severity. Two modules (grey60 and thistle2) were identified that were inversely related to disease severity. Grey60 was enriched with genes involved in epithelial growth and repair, and thistle2 was enriched with genes involved in neuronal development and function. Overall, the data show that severe asthma and related traits are associated with up-regulation of gene networks involved in mitosis and Th2 inflammation and down-regulation of networks involved in epithelial growth and repair and neuronal development and function. To identify genes that were strongly associated with clinical traits and central components of the modules, the authors selected