BackgroundDetermining the cellular and molecular phenotypes of inflammation in asthma can identify patient populations that may best benefit from targeted therapies. Although elevated IL-6 and polymorphisms in IL-6 signalling are associated with lung dysfunction in asthma, it remains unknown if elevated IL-6 levels are associated with a specific cellular inflammatory phenotype, and how IL-6 blockade might impact such inflammatory responses.MethodsPatients undergoing exacerbations of asthma were phenotyped according to their airway inflammatory characteristics (normal cell count, eosinophilic, neutrophilic, mixed granulocytic), sputum cytokine profiles, and lung function. Mice were exposed to the common allergen, house dust-mite (HDM), in the presence or absence of endogenous IL-6. The intensity and nature of lung inflammation, and levels of pro-granulocytic cytokines and chemokines under these conditions were analyzed.ResultsElevated IL-6 was associated with a lower FEV1 in patients with mixed eosinophilic-neutrophilic bronchitis. In mice, allergen exposure increased lung IL-6 and IL-6 was produced by dendritic cells and alveolar macrophages. Loss-of-function of IL-6 signalling (knockout or antibody-mediated neutralization) abrogated elevations of eosinophil and neutrophil recruiting cytokines/chemokines and allergen-induced airway inflammation in mice.ConclusionsWe demonstrate the association of pleiotropic cellular airway inflammation with IL-6 using human and animal data. These data suggest that exacerbations of asthma, particularly those with a combined eosinophilic and neutrophilic bronchitis, may respond to therapies targeting the IL-6 pathway and therefore, provide a rational basis for initiation of clinical trials to evaluate this.
Asthmatic patients are currently classified as either severe or non-severe based primarily on their response to glucocorticoids. However, because this classification is based on a post-hoc assessment of treatment response, it does not inform the rational staging of disease or therapy. Recent studies in other diseases suggest that a classification which includes molecular information could lead to more accurate diagnoses and prediction of treatment response. We therefore measured cytokine values in bronchoalveolar lavage (BAL) samples of the lower respiratory tract obtained from 83 asthma patients, and used bipartite network visualizations with associated quantitative measures to conduct an exploratory analysis of the co-occurrence of cytokines across patients. The analysis helped to identify three clusters of patients which had a complex but understandable interaction with three clusters of cytokines, leading to insights for a state-based classification of asthma patients. Furthermore, while the patient clusters were significantly different based on key pulmonary functions, they appeared to have no significant relationship to the current classification of asthma patients. These results suggest the need to define a molecular-based classification of asthma patients, which could improve the diagnosis and treatment of this disease.
Asthma is a chronic inflammatory disease of the airways that leads to various degrees of recurrent respiratory symptoms affecting patients globally. Specific subgroups of asthma patients have severe disease leading to increased healthcare costs and socioeconomic burden. Despite the overwhelming prevalence of the asthma, there are limitations in predicting response to therapy and identifying patients who are at increased risk of morbidity. This syndrome presents with common clinical signs and symptoms; however, awareness of subgroups of asthma patients with distinct characteristics has surfaced in recent years. Investigators attempt to describe the phenotypes of asthma to ultimately assist with diagnostic and therapeutic applications. Approaches to asthma phenotyping are multifold; however, it can be partitioned into 2 essential groups, clinical phenotyping and molecular phenotyping. Innovative techniques such as bipartite network analysis and visual analytics introduce a new dimension of data analysis to identify underlying mechanistic pathways.
Asthma is an inflammatory disorder characterized by airway obstruction, airway hyperresponsiveness, and airway inflammation, all of which are variable among patients and variable in time within any specific patient. Understanding the mechanism that underlies this observed variability, and using that understanding to advance the science of asthma and the care of asthmatic patients, is an essential purpose of developing phenotypes. Clinical phenotypes have been used for decades, but overlap each other, and do not map cleanly to either pathophysiologic mechanism or with therapeutic response. Molecular phenotyping, although as yet only partially developed, offers the promise of dissecting the mechanistic underpinnings of the variability of asthma and of providing predictive therapeutics for the benefit of patients with this common and troubling disease.
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