Phenotype and severity of asthma determines bronchial epithelial immune responses to a viral mimic

BackgroundAsthma is characterised by an aggravated immune response to respiratory viral infections: This phenomenon is a clinically well-recognised driver of acute exacerbations, but how different phenotypes of asthma respond immunologically to virus is unclear.ObjectivesTo describe the association between different phenotypes and severity of asthma and bronchial epithelial immune responses to viral stimulation.MethodsIn the Immunoreact study, healthy subjects (n=10) and 50 patients with asthma were included; 30 (60%) were atopic, and 34 (68%) were eosinophilic; 14 (28%) had severe asthma. All participants underwent bronchoscopy with collection of bronchial brushings. Bronchial epithelial cells (BECs) were expanded and stimulated with the viral replication mimic poly (I:C) (TLR3 agonist) in vitro. The expression of TLR3-induced pro-inflammatory and anti-viral responses of BECs were analysed using RT-qPCR and multiplex ELISA and compared across asthma phenotypes and severity of disease.ResultsPatients with atopic asthma had increased induction of IL-4, IFN-β, IL-6, TNF-α, and IL-1β after poly (I:C) stimulation compared to non-atopic patients, whereas in patients with eosinophilic asthma only IL-6 and IL-8 induction was higher than in non-eosinophilic asthma. Patients with severe asthma displayed a decreased antiviral IFN-β, and increased expression of IL-8, most pronounced in atopic and eosinophilic asthmatics. Furthermore, induction of IL-33 in response to poly (I:C) was increased in severe atopic and in severe eosinophilic asthma, but TSLP only in severe eosinophilic asthma.ConclusionsThe bronchial epithelial immune response to a viral mimic stimulation differs between asthma phenotypes and severities, which may be important to consider when targeting novel asthma treatments.

Characterization of T2-Low and T2-High Asthma Phenotypes in Real-Life

Asthma is a heterogeneous and complex condition characterized by chronic airway inflammation, which may be clinically stratified into three main phenotypes: type 2 (T2) low, T2-high allergic, and T2-high non-allergic asthma. This real-world study investigated whether phenotyping patients with asthma using non-invasive parameters could be feasible to characterize the T2-low and T2-high asthma phenotypes in clinical practice. This cross-sectional observational study involved asthmatic outpatients (n = 503) referring to the Severe Asthma Centre of the San Luigi Gonzaga University Hospital. Participants were stratified according to the patterns of T2 inflammation and atopic sensitization. Among outpatients, 98 (19.5%) patients had T2-low asthma, 127 (25.2%) T2-high non-allergic, and 278 (55.3%) had T2-high allergic phenotype. In comparison to T2-low, allergic patients were younger (OR 0.945, p < 0.001) and thinner (OR 0.913, p < 0.001), had lower smoke exposure (OR 0.975, p < 0.001) and RV/TLC% (OR 0.950, p < 0.001), higher prevalence of asthma severity grade 5 (OR 2.236, p < 0.05), more frequent rhinitis (OR 3.491, p < 0.001) and chronic rhinosinusitis with (OR 2.650, p < 0.001) or without (OR 1.919, p < 0.05) nasal polyps, but less common arterial hypertension (OR 0.331, p < 0.001). T2-high non-allergic patients had intermediate characteristics. Non-invasive phenotyping of asthmatic patients is possible in clinical practice. Identifying characteristics in the three main asthma phenotypes could pave the way for further investigations on useful biomarkers for precision medicine.

Asthma Phenotypes and Host Risk Factors Associated With Various Asthma-Related Outcomes in Health Workers

Background: Work-related asthma phenotypes in health workers (HWs) exposed to cleaning agents have not been investigated extensively as other occupational exposures. This study aimed to describe asthma phenotypes and to identify important host risk factors associated with various asthma-related outcomes.Methods: A cross-sectional study of 699 HWs was conducted in two large tertiary hospitals. A total of 697 HWs completed questionnaire interviews. Sera collected from 682 HWs were analyzed for atopy (Phadiatop) and IgE to occupational allergens (NRL—Hev b5, Hev b6.02; chlorhexidine and ortho-phthalaldehyde—OPA). Methacholine (MCT), bronchodilator challenge (BDR) and fractional exhaled nitric oxide (FeNO) were performed. An asthma symptom score (ASS) used five asthma-related symptoms reported in the past 12 months. Current asthma was based on use of asthma medication or an asthma attack or being woken up by an attack of shortness of breath in the past 12 months. Nonspecific bronchial hyperresponsiveness (NSBH) was defined as having either a positive MCT or a significant bronchodilator response. Two continuous indices of NSBH [continuous index of responsiveness (CIR) and dose-response slope (DRS)] were calculated.Results: The prevalence of current asthma was 10%, atopic asthma (6%) and non-atopic asthma (4%). Overall, 2% of subjects had work-related asthma. There was a weak positive association between NSBH and FeNO [CIR: Beta coefficient (β) = 0.12; CI: 0.03–0.22 and DRS: β = 0.07; CI: 0.03–0.12]. Combining FeNO ≥ 50 ppb with a BDR [mean ratio (MR) = 5.89; CI: 1.02–34.14] or with NSBH (MR = 4.62; CI: 1.16–18.46) correlated better with ASS than FeNO alone (MR = 2.23; CI: 1.30–3.85). HWs with current asthma were twice as likely to be atopic. FeNO was positively associated with atopy (OR = 3.19; CI: 1.59–6.39) but negatively associated with smoking status (GMR = 0.76; CI: 0.62–0.94). Most HWs sensitized to occupational allergens were atopic.Conclusion: Atopic asthma was more prevalent than non-atopic asthma in HWs. Most asthma-related outcomes were positively associated with allergic predictors suggesting a dominant role for IgE mechanisms for work-related symptoms and asthma associated with sensitization to OPA or chlorhexidine.

Sputum metabolomic profiling revels metabolic pathways and signatures associated with inflammatory phenotypes in patients with asthma

Background: The molecular links between metabolism and inflammation that drive different inflammatory phenotypes in asthma are poorly understood. Objectives: To identify the metabolic signatures and underlying molecular pathways of different inflammatory asthma phenotypes. Method: In the discovery set (n=119), untargeted ultra-high performance liquid chromatography–mass spectrometry (UHPLC-MS) were applied to characterize the induced sputum metabolic profiles from asthmatic patients classified by different inflammatory phenotypes using orthogonal partial least-squares discriminant analysis (OPLS-DA) and pathway topology enrichment analysis. In the validation set (n = 114), differential metabolites were selected to perform targeted quantification. Correlations between targeted metabolites and clinical indexes in asthma patients were analyzed. Logistic and negative binomial regression models were established to assess the association between metabolites and severe asthma exacerbation. Results: 77 differential metabolites were identified in the discovery set. Pathway topology analysis uncovered that histidine metabolism, glycerophospholipid metabolism, nicotinate and nicotinamide metabolism, linoleic acid metabolism, phenylalanine, tyrosine and tryptophan biosynthesis were involved in the pathogenesis of different asthma phenotypes. In the validation set, 24 targeted quantification metabolites were significantly differentially expressed between asthma inflammatory phenotypes. Finally, adenosine 5’-monophosphate (RRadj = 1.000, 95%CI = [1.000, 1.000], P = 0.050), allantoin (RRadj = 1.000, 95%CI = [1.000, 1.000], P = 0.043) and nicotinamide (RRadj = 1.001, 95%CI = [1.000, 1.002], P = 0.021) were demonstrated to predict severe asthma exacerbation rate ratios. Conclusions: Different inflammatory asthma phenotypes have specific metabolic profiles in induced sputum. The potential metabolic signatures may serve as identification and therapeutic target in different inflammatory asthma phenotypes.

Eosinophils as Drivers of Severe Eosinophilic Asthma: Endotypes or Plasticity?

Asthma is now recognized as a heterogeneous disease, encompassing different phenotypes driven by distinct pathophysiological mechanisms called endotypes. Common phenotypes of asthma, referred to as eosinophilic asthma, are characterized by the presence of eosinophilia. Eosinophils are usually considered invariant, terminally differentiated effector cells and have become a primary therapeutic target in severe eosinophilic asthma (SEA) and other eosinophil-associated diseases (EADs). Biological treatments that target eosinophils reveal an unexpectedly complex role of eosinophils in asthma, including in SEA, suggesting that “not all eosinophils are equal”. In this review, we address our current understanding of the role of eosinophils in asthma with regard to asthma phenotypes and endotypes. We further address the possibility that different SEA phenotypes may involve differences in eosinophil biology. We discuss how these differences could arise through eosinophil “endotyping”, viz. adaptations of eosinophil function imprinted during their development, or through tissue-induced plasticity, viz. local adaptations of eosinophil function through interaction with their lung tissue niches. In doing so, we also discuss opportunities, technical challenges, and open questions that, if addressed, might provide considerable benefits in guiding the choice of the most efficient precision therapies of SEA and, by extension, other EADs.

Metabolic Phenotypes in Asthmatic Adults: Relationship with Inflammatory and Clinical Phenotypes and Prognostic Implications

Bronchial asthma is a chronic disease that affects individuals of all ages. It has a high prevalence and is associated with high morbidity and considerable levels of mortality. However, asthma is not a single disease, and multiple subtypes or phenotypes (clinical, inflammatory or combinations thereof) can be detected, namely in aggregated clusters. Most studies have characterised asthma phenotypes and clusters of phenotypes using mainly clinical and inflammatory parameters. These studies are important because they may have clinical and prognostic implications and may also help to tailor personalised treatment approaches. In addition, various metabolomics studies have helped to further define the metabolic features of asthma, using electronic noses or targeted and untargeted approaches. Besides discriminating between asthma and a healthy state, metabolomics can detect the metabolic signatures associated with some asthma subtypes, namely eosinophilic and non-eosinophilic phenotypes or the obese asthma phenotype, and this may prove very useful in point-of-care application. Furthermore, metabolomics also discriminates between asthma and other “phenotypes” of chronic obstructive airway diseases, such as chronic obstructive pulmonary disease (COPD) or Asthma–COPD Overlap (ACO). However, there are still various aspects that need to be more thoroughly investigated in the context of asthma phenotypes in adequately designed, homogeneous, multicentre studies, using adequate tools and integrating metabolomics into a multiple-level approach.