Paucigranulocytic Asthma: Potential Pathogenetic Mechanisms, Clinical Features and Therapeutic Management.

Asthma is a heterogeneous disease usually characterized by chronic airway inflammation, in which several phenotypes have been described, related to the age of onset, symptoms, inflammatory characteristics and treatment response. The identification of the inflammatory phenotype in asthma is very useful, since it allows for both the recognition of the asthmatic triggering factor as well as the optimization of treatment The paucigranulocytic phenotype of asthma (PGA) is characterized by sputum eosinophil levels <1−3% and sputum neutrophil levels < 60%. The precise characteristics and the pathobiology of PGA are not fully understood, and, in some cases, it seems to represent a previous eosinophilic phenotype with a good response to anti-inflammatory treatment. However, many patients with PGA remain uncontrolled and experience asthmatic symptoms and exacerbations, irrespective of the low grade of airway inflammation. This observation leads to the hypothesis that PGA might also be either a special phenotype driven by different kinds of cells, such as macrophages or mast cells, or a non-inflammatory phenotype with a low grade of eosinophilic inflammation. In this review, we aim to describe the special characteristics of PGA and the potential therapeutic interventions that could be offered to these patients.

Weighted gene co-expression network analysis to identify key modules and hub genes associated with paucigranulocytic asthma.

BACKGROUND: Asthma is a heterogeneous disease that can be divided into four inflammatory phenotypes: eosinophilic asthma (EA), neutrophilic asthma (NA), mixed granulocytic asthma (MGA), and paucigranulocytic asthma (PGA). While research has mainly focused on EA and NA, the understanding of PGA is limited. In this study, we aimed to identify underlying mechanisms and hub genes of PGA. METHODS: Based on the dataset from Gene Expression Omnibus(GEO), weighted gene coexpression network analysis (WGCNA), differentially expressed genes (DEGs) analysis and protein-protein interaction (PPI) network analysis were conducted to construct a gene network and to identify key gene modules and hub genes. Functional enrichment analyses were performed to investigate the biological process, pathways and immune status of PGA. The hub genes were validated in a separate dataset. RESULTS: Compared to non-PGA, PGA had a different gene expression pattern, in which 449 genes were differentially expressed. One gene module significantly associated with PGA was identified. Intersection between the differentially expressed genes (DEGs) and the genes from the module that were most relevant to PGA were mainly enriched in inflammation and immune response regulation. The single sample Gene Set Enrichment Analysis (ssGSEA) suggested a decreased immune infiltration and function in PGA. Finally six hub genes of PGA were identified, including ADCY2, CXCL1, FPRL1, GPR109B, GPR109A and ADCY3, which were validated in a separate dataset of GSE137268. CONCLUSIONS: Our study characterized distinct gene expression patterns, biological processes and immune status of PGA and identified hub genes, which may improve the understanding of underlying mechanism and provide potential therapeutic targets for PGA.

Heterogeneity of Paucigranulocytic Asthma: A Prospective Cohort Study with Hierarchical Cluster Analysis.

BACKGROUND: Asthma, a heterogeneous disease, can be divided into 4 inflammatory phenotypes using induced sputum cell counts-eosinophilic asthma (EA), neutrophilic asthma (NA), mixed granulocytic asthma, and paucigranulocytic asthma (PGA). Although research has focused on EA and NA, there is little known about PGA. OBJECTIVE: To study the heterogeneity of PGA and identify possible PGA clusters to guide clinical treatment. METHODS: Patients with PGA were grouped by hierarchical cluster analysis and enrolled into a prospective cohort study to validate the clusters, relative to future risk of asthma exacerbations in a real-world setting. Clusters were validated by tree analysis in a separate population. Finally, we explored PGA stability. RESULTS: Cluster analysis of 145 patients with PGA identified 3 clusters: cluster 1 (n = 110, 75.9%) was "mild PGA," cluster 2 (n = 20, 13.8%) was "PGA with psychological dysfunction and rhinoconjunctivitis and other allergic diseases," and cluster 3 (n = 15, 10.3%) was "smoking-associated PGA." Cluster 3 had significantly increased risk of severe exacerbation (relative risk [RR] = 6.43, P = .01), emergency visit (RR = 8.61, P = .03), and hospitalization (RR = 12.94, P < .01). Results of the cluster analysis were successfully validated in an independent PGA population classified using decision tree analysis. Although PGA can transform into or develop from other phenotypes, 70% were stable over time. CONCLUSIONS: Among 3 identified PGA clusters, cluster 3 had a higher risk of severe exacerbation. PGA heterogeneity indicates the requirement of novel targeted interventions.

Mechanisms and therapeutic strategies for non-T2 asthma.

Non-T2 asthma is traditionally defined as asthma without features of T2 asthma. The definition is arbitrary and is generally based on the presence of neutrophils in sputum, or the absence (or normal levels) of eosinophils or other T2 markers in sputum (paucigranulocytic), airway biopsies or in blood. This definition may be imprecise as we gain more knowledge from applying transcriptomics and proteomics to blood and airway samples. The prevalence of non-T2 asthma is also difficult to estimate as most studies are cross-sectional and influenced by concomitant treatment with glucocorticosteroids, and by the presence of recognized or unrecognized airway infections. No specific therapies have shown any clinical benefits in patients with asthma that is associated with a non-T2 inflammatory process. It remains to be seen if such an endotype truly exists and to identify treatments to target that endotype. Meanwhile, identifying intense airway neutrophilia as an indicator of airway infection and airway hyperresponsiveness as an indicator of smooth muscle dysfunction, and treating them appropriately, and not increasing glucocorticosteroids in patients who do not have obvious T2 inflammation, seem reasonable.