Molecular phenotyping of severe asthma using pattern recognition of bronchoalveolar lavage-derived cytokines

Exhaled breath analysis for the discrimination of asthma and chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) and asthma are the most common chronic respiratory diseases. In middle-aged and elderly patients, it is difficult to distinguish between COPD and asthma based on clinical symptoms and pulmonary function examinations in clinical practice. Thus, an accurate and reliable inspection method is required. In this study, we aimed to identify breath biomarkers and evaluate the accuracy of breathomics-based methods for discriminating between COPD and asthma. In this multi-center cross-sectional study, exhaled breath samples were collected from 89 patients with COPD and 73 with asthma and detected on a high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS) platform from 20 October 2022, to 20 May 2023, in four hospitals. Data analysis was performed from 15 June 2023 to 16 August 2023. The sensitivity, specificity, and accuracy were calculated to assess the overall performance of the volatile organic component (VOC)-based COPD and asthma discrimination models. Potential VOC markers related to COPD and asthma were also analyzed. The age of all participants ranged from to 18-86 years, and 54 (33.3%) were men. The age [median (minimum, maximum)] of COPD and asthma participants were 66.0 (46.0, 86.0), and 44.0 (17.0, 80.0). The male and female ratio of COPD and asthma participants were 14/75 and 40/33, respectively. Based on breathomics feature selection, ten VOCs were identified as COPD and asthma discrimination biomarkers via breath testing. The joint panel of these ten VOCs achieved an area under the curve of 0.843, sensitivity of 75.9%, specificity of 87.5%, and accuracy of 80.0% in COPD and asthma discrimination. Furthermore, the VOCs detected in the breath samples were closely related to the clinical characteristics of COPD and asthma. The VOC-based COPD and asthma discrimination model showed good accuracy, providing a new strategy for clinical diagnosis. Breathomics-based methods may play an important role in the diagnosis of COPD and asthma.

Protein-Protein interactive networks identified in bronchoalveolar lavage of severe compared to nonsevere asthma

INTRODUCTION

Previous bronchoalveolar lavage fluid (BALF) proteomic analysis has evaluated limited numbers of subjects for only a few proteins of interest, which may differ between asthma and normal controls. Our objective was to examine a more comprehensive inflammatory biomarker panel in quantitative proteomic analysis for a large asthma cohort to identify molecular phenotypes distinguishing severe from nonsevere asthma.

METHODS

Bronchoalveolar lavage fluid from 48 severe and 77 nonsevere adult asthma subjects were assessed for 75 inflammatory proteins, normalized to BALF total protein concentration. Validation of BALF differences was sought through equivalent protein analysis of autologous sputum. Subjects' data, stratified by asthma severity, were analysed by standard statistical tests, principal component analysis and 5 machine learning algorithms.

RESULTS

The severe group had lower lung function and greater health care utilization. Significantly increased BALF proteins for severe asthma compared to nonsevere asthma were fibroblast growth factor 2 (FGF2), TGFα, IL1Ra, IL2, IL4, CCL8, CCL13 and CXCL7 and significantly decreased were platelet-derived growth factor a-a dimer (PDGFaa), vascular endothelial growth factor (VEGF), interleukin 5 (IL5), CCL17, CCL22, CXCL9 and CXCL10. Four protein differences were replicated in sputum. FGF2, PDGFaa and CXCL7 were independently identified by 5 machine learning algorithms as the most important variables for discriminating severe and nonsevere asthma. Increased and decreased proteins identified for the severe cluster showed significant protein-protein interactions for chemokine and cytokine signalling, growth factor activity, and eosinophil and neutrophil chemotaxis differing between subjects with severe and nonsevere asthma.

CONCLUSION

These inflammatory protein results confirm altered airway remodelling and cytokine/chemokine activity recruiting leukocytes into the airways of severe compared to nonsevere asthma as important processes even in stable status.

Biomarkers in exhaled breath condensate as fingerprints of asthma, chronic obstructive pulmonary disease and asthma-chronic obstructive pulmonary disease overlap: a critical review

Asthma, chronic obstructive pulmonary disease (COPD) and asthma-COPD overlap are the third leading cause of mortality around the world. They share some common features, which can lead to misdiagnosis. To properly manage these conditions, reliable markers for early and accurate diagnosis are needed. Over the past 20 years, many molecules have been investigated in the exhaled breath condensate to better understand inflammation pathways and mechanisms related to these disorders. Recently, more advanced techniques, such as sensitive metabolomic and proteomic profiling, have been used to obtain a more comprehensive understanding. This article reviews the use of targeted and untargeted metabolomic methodology to study asthma, COPD and asthma-COPD overlap.

Creatine Kinase Is Decreased in Childhood Asthma

Rationale: The identification of novel molecules associated with asthma may provide insights into the mechanisms of disease and their potential clinical implications. Objectives: To conduct a screening of circulating proteins in childhood asthma and to study proteins that emerged from human studies in a mouse model of asthma. Methods: We included 2,264 children from eight birth cohorts from the Mechanisms of the Development of ALLergy project and the Tucson Children's Respiratory Study. In cross-sectional analyses, we tested 46 circulating proteins for association with asthma in the selection stage and carried significant signals forward to a validation and replication stage. As CK (creatine kinase) was the only protein consistently associated with asthma, we also compared whole blood CK gene expression between subjects with and without asthma (n = 249) and used a house dust mite (HDM)-challenged mouse model to gain insights into CK lung expression and its role in the resolution of asthma phenotypes. Measurements and Main Results: As compared with the lowest CK tertile, children in the highest tertile had significantly lower odds for asthma in selection (adjusted odds ratio, 95% confidence interval: 0.31; 0.15-0.65; P = 0.002), validation (0.63; 0.42-0.95; P = 0.03), and replication (0.40; 0.16-0.97; P = 0.04) stages. Both cytosolic CK forms (CKM and CKB) were underexpressed in blood from asthmatics compared with control subjects (P = 0.01 and 0.006, respectively). In the lungs of HDM-challenged mice, Ckb expression was reduced, and after the HDM challenge, a CKB inhibitor blocked the resolution of airway hyperresponsiveness and reduction of airway mucin. Conclusions: Circulating concentrations and gene expression of CK are inversely associated with childhood asthma. Mouse models support a possible direct involvement of CK in asthma protection via inhibition of airway hyperresponsiveness and reduction of airway mucin.

Determining asthma endotypes and outcomes: Complementing existing clinical practice with modern machine learning

There is unprecedented opportunity to use machine learning to integrate high-dimensional molecular data with clinical characteristics to accurately diagnose and manage disease. Asthma is a complex and heterogeneous disease and cannot be solely explained by an aberrant type 2 (T2) immune response. Available and emerging multi-omics datasets of asthma show dysregulation of different biological pathways including those linked to T2 mechanisms. While T2-directed biologics have been life changing for many patients, they have not proven effective for many others despite similar biomarker profiles. Thus, there is a great need to close this gap to understand asthma heterogeneity, which can be achieved by harnessing and integrating the rich multi-omics asthma datasets and the corresponding clinical data. This article presents a compendium of machine learning approaches that can be utilized to bridge the gap between predictive biomarkers and actual causal signatures that are validated in clinical trials to ultimately establish true asthma endotypes.

Current Insights on the Impact of Proteomics in Respiratory Allergies

Respiratory allergies affect humans worldwide, causing extensive morbidity and mortality. They include allergic rhinitis (AR), asthma, pollen food allergy syndrome (PFAS), aspirin-exacerbated respiratory disease (AERD), and nasal polyps (NPs). The study of respiratory allergic diseases requires new technologies for early and accurate diagnosis and treatment. Omics technologies provide the tools required to investigate DNA, RNA, proteins, and other molecular determinants. These technologies include genomics, transcriptomics, proteomics, and metabolomics. However, proteomics is one of the main approaches to studying allergic disorders' pathophysiology. Proteins are used to indicate normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. In this field, the principal goal of proteomics has been to discover new proteins and use them in precision medicine. Multiple technologies have been applied to proteomics, but that most used for identifying, quantifying, and profiling proteins is mass spectrometry (MS). Over the last few years, proteomics has enabled the establishment of several proteins for diagnosing and treating respiratory allergic diseases.

Segmental Bronchial Allergen Challenge Elicits Distinct Metabolic Phenotypes in Allergic Asthma

Asthma is a complex syndrome associated with episodic decompensations provoked by aeroallergen exposures. The underlying pathophysiological states driving exacerbations are latent in the resting state and do not adequately inform biomarker-driven therapy. A better understanding of the pathophysiological pathways driving allergic exacerbations is needed. We hypothesized that disease-associated pathways could be identified in humans by unbiased metabolomics of bronchoalveolar fluid (BALF) during the peak inflammatory response provoked by a bronchial allergen challenge. We analyzed BALF metabolites in samples from 12 volunteers who underwent segmental bronchial antigen provocation (SBP-Ag). Metabolites were quantified using liquid chromatography-tandem mass spectrometry (LC–MS/MS) followed by pathway analysis and correlation with airway inflammation. SBP-Ag induced statistically significant changes in 549 features that mapped to 72 uniquely identified metabolites. From these features, two distinct inducible metabolic phenotypes were identified by the principal component analysis, partitioning around medoids (PAM) and k-means clustering. Ten index metabolites were identified that informed the presence of asthma-relevant pathways, including unsaturated fatty acid production/metabolism, mitochondrial beta oxidation of unsaturated fatty acid, and bile acid metabolism. Pathways were validated using proteomics in eosinophils. A segmental bronchial allergen challenge induces distinct metabolic responses in humans, providing insight into pathogenic and protective endotypes in allergic asthma.

Multi-Omics Profiling Approach to Asthma: An Evolving Paradigm

Asthma is a complex multifactorial and heterogeneous respiratory disease. Although genetics is a strong risk factor of asthma, external and internal exposures and their interactions with genetic factors also play important roles in the pathophysiology of asthma. Over the past decades, the application of high-throughput omics approaches has emerged and been applied to the field of asthma research for screening biomarkers such as genes, transcript, proteins, and metabolites in an unbiased fashion. Leveraging large-scale studies representative of diverse population-based omics data and integrating with clinical data has led to better profiling of asthma risk. Yet, to date, no omic-driven endotypes have been translated into clinical practice and management of asthma. In this article, we provide an overview of the current status of omics studies of asthma, namely, genomics, transcriptomics, epigenomics, proteomics, exposomics, and metabolomics. The current development of the multi-omics integrations of asthma is also briefly discussed. Biomarker discovery following multi-omics profiling could be challenging but useful for better disease phenotyping and endotyping that can translate into advances in asthma management and clinical care, ultimately leading to successful precision medicine approaches.

Characterizing Inflammatory Cell Asthma Associated Phenotypes in Dental Health Workers Using Cytokine Profiling

Cytokines elicit a pro-inflammatory response by modifying the airway microenvironment in patients with acute or chronic asthma. The expression pattern of several distinct cytokines could be a useful discriminator in asthma. This study aimed to identify asthma subject groupings based on common inflammatory patterns and to determine the relationship between these identified patterns and asthma-associated clinical indices. A sub-group of 76 dental healthcare workers (HCWs) identified from a larger cross-sectional study of 454 dental HCWs in five dental institutions were evaluated further. A self-administered questionnaire elicited the health and employment history of subjects. Sera were analyzed for atopic status, latex sensitization, and 12 cytokines (IL-1β, 3, 4, 5, 6, 7, 8, 10, 12p70, eotaxin, GM-CSF, TNF-α). Pre and post-bronchodilator spirometry was performed on all HCWs. Data clustering and factor analysis were used to identify inflammatory cluster patterns of cytokines. Associations between the cytokine cluster groupings and relevant asthma-associated clinical indices were determined using multivariate logistic regression. The classification of asthma subtype based on cytokine patterns demonstrated both eosinophilic and neutrophilic inflammatory responses. Four phenotypically distinct subgroups relating to the severity of inflammation (acute or chronic) of the cell types were identified. Cytokine determinants for the neutrophilic subtype included IL-1β, 6, 8, 10, 12p70, and TNF-α whereas for the eosinophilic subtype these included IL-3, 4, 5, 7, eotaxin, and GM-CSF. The multivariate models showed a significant association between work-related chest symptoms and all four inflammatory patterns. However, stronger associations were observed for the acute neutrophilic (OR = 6.00, p < 0.05) compared to acute and chronic eosinophilic responses (OR = 4.30, p < 0.05; OR = 4.93, p < 0.05), respectively. Subjects with airway obstruction were more likely to have a mixed cellular infiltrate. The odds of work-exacerbated asthma were increased in acute or chronic eosinophilia (OR = 7.75 and 8.12; p < 0.05), respectively as well as with acute neutrophilia (OR = 6) sub-type. This study demonstrated that neutrophilic inflammatory cell asthma phenotypes coexist with eosinophilic inflammatory phenotypes suggesting a possible dual pathway for asthma in dental health workers, probably due to mixed exposures to high molecular weight (e.g., latex) and low molecular weight (e.g., acrylates) agents.

An Integrated View of Virus-Triggered Cellular Plasticity Using Boolean Networks

Virus-related mortality and morbidity are due to cell/tissue damage caused by replicative pressure and resource exhaustion, e.g., HBV or HIV; exaggerated immune responses, e.g., SARS-CoV-2; and cancer, e.g., EBV or HPV. In this context, oncogenic and other types of viruses drive genetic and epigenetic changes that expand the tumorigenic program, including modifications to the ability of cancer cells to migrate. The best-characterized group of changes is collectively known as the epithelial–mesenchymal transition, or EMT. This is a complex phenomenon classically described using biochemistry, cell biology and genetics. However, these methods require enormous, often slow, efforts to identify and validate novel therapeutic targets. Systems biology can complement and accelerate discoveries in this field. One example of such an approach is Boolean networks, which make complex biological problems tractable by modeling data (“nodes”) connected by logical operators. Here, we focus on virus-induced cellular plasticity and cell reprogramming in mammals, and how Boolean networks could provide novel insights into the ability of some viruses to trigger uncontrolled cell proliferation and EMT, two key hallmarks of cancer.

Omics for the future in asthma

Asthma is a common, complex, multifaceted disease. It comprises multiple phenotypes, which might benefit from treatment with different types of innovative targeted therapies. Refining these phenotypes and understanding their underlying biological structure would help to apply precision medicine approaches. Using different omics methods, such as (epi)genomics, transcriptomics, proteomics, metabolomics, microbiomics, and exposomics, allowed to view and investigate asthma from diverse angles. Technological advancement led to a large increase in the application of omics studies in the asthma field. Although the use of omics technologies has reduced the gap between bench to bedside, several design and methodological challenges still need to be tackled before omics can be applied in asthma patient care. Collaborating under a centralized harmonized work frame (such as in consortia, under consistent methodologies) could help worldwide research teams to tackle these challenges. In this review, we discuss the transition of single biomarker research to multi-omics studies. In addition, we deliberate challenges such as the lack of standardization of sampling and analytical methodologies and validation of findings, which comes in between omics and personalized patient care. The future of omics in asthma is encouraging but not completely clear with some unanswered questions, which have not been adequately addressed before. Therefore, we highlight these questions and emphasize on the importance of fulfilling them.

What did we learn from multiple omics studies in asthma?

More than a decade has passed since the finalization of the Human Genome Project. Omics technologies made a huge leap from trendy and very expensive to routinely executed and relatively cheap assays. Simultaneously, we understood that omics is not a panacea for every problem in the area of human health and personalized medicine. Whilst in some areas of research omics showed immediate results, in other fields, including asthma, it only allowed us to identify the incredibly complicated molecular processes. Along with their possibilities, omics technologies also bring many issues connected to sample collection, analyses and interpretation. It is often impossible to separate the intrinsic imperfection of omics from asthma heterogeneity. Still, many insights and directions from applied omics were acquired-presumable phenotypic clusters of patients, plausible biomarkers and potential pathways involved. Omics technologies develop rapidly, bringing improvements also to asthma research. These improvements, together with our growing understanding of asthma subphenotypes and underlying cellular processes, will likely play a role in asthma management strategies.

Towards Team-Centered Informatics: Accelerating Innovation in Multidisciplinary Scientific Teams Through Visual Analytics

A critical goal of multidisciplinary scientific teams is to integrate knowledge from diverse disciplines for the purpose of developing novel insights and innovations. For example, multidisciplinary translational teams (MTTs) which typically include physicians, biologists, statisticians, and informaticians, aim to integrate biological and clinical knowledge leading to innovations for improving health outcomes. However, such teams face numerous barriers in integrating multidisciplinary knowledge, which is further exacerbated by the explosion of molecular and clinical data generated from millions of patients. Here, we explore the use of a visual analytical representation to help MTTs integrate molecular and clinical data with the goal of accelerating translational insights. The results suggest that the visual analytical representation functioned as a “computational evolving boundary object” which (a) evolved through several emergent states that progressively helped integrate diverse disciplinary knowledge, (b) enabled team members to play primary and supportive roles in evolving the data representation resulting in a more egalitarian team structure, and (c) enabled the team to arrive at novel translational insights leading to domain and methodology publications. However, the interventions also revealed limitations in the approach motivating new visual analytical approaches. These results suggest (a) implications for theory related to modeling computational evolving boundary objects (CEBOs) as an instance of team-centered informatics, and (b) implications for practice related to the design and use of interactive features that enable teams to fluidly evolve CEBOs through emergent states, with the goal of deriving novel insights from large multiomics datasets.

Multidimensional endotypes of chronic rhinosinusitis and their association with treatment outcomes

BACKGROUND

The expression of chronic rhinosinusitis (CRS) is multidimensional. Disease heterogeneity in patients with CRS remains poorly understood. This study aimed to identify endotypes of CRS using cluster analysis by integrating multidimensional characteristics and to explore their association with treatment outcomes.

METHODS

A total of 28 clinical variables and 39 mucosal cellular and molecular variables were analyzed using principal component analysis. Cluster analysis was performed on 246 prospectively recruited Chinese CRS patients with at least 1-year postoperative follow-up. Difficult-to-treat CRS was characterized in each generated cluster.

RESULTS

Seven subject clusters were identified. Cluster 1 (13.01%) was comparable to the classic well-defined eosinophilic CRS with polyps, having severe disease and the highest proportion of difficult-to-treat CRS. Patients in cluster 2 (16.26%) and cluster 4 (13.82%) had relatively lower proportions of presence of polyps and presented mild inflammation with moderate proportions of difficult-to-treat cases. Subjects in cluster 2 were highly atopic. Cluster 3 (7.31%) and cluster 6 (21.14%) were characterized by severe or moderate neutrophilic inflammation, respectively, and with elevated levels of IL-8 and high proportions of difficult-to-treat CRS. Cluster 5 (4.07%) was a unique group characterized by the highest levels of IL-10 and lacked difficult-to-treat cases. Cluster 7 (24.39%) demonstrated the lowest symptom severity, a low proportion of difficult-to-treat CRS, and low inflammation load. Finally, we found that difficult-to-treat CRS was associated with distinct clinical features and biomarkers in the different clusters.

CONCLUSIONS

Distinct clinicopathobiologic clusters of CRS display differences in clinical response to treatments and characteristics of difficult-to-treat CRS.

Paucigranulocytic asthma: Uncoupling of airway obstruction from inflammation

Among patients with asthma, heterogeneity exists regarding the pattern of airway inflammation and response to treatment, prompting the necessity of recognizing specific phenotypes. Based on the analysis of inflammatory cell counts in induced sputum, asthmatic patients can be classified into 4 unique phenotypes: eosinophilic asthma, neutrophilic asthma, mixed granulocytic asthma, and paucigranulocytic asthma (PGA). PGA is an asthma phenotype with no evidence of increased numbers of eosinophils or neutrophils in sputum or blood and in which anti-inflammatory therapies are ineffective at controlling symptoms. Although underinvestigated, PGA is the most common asthma phenotype in patients with stable asthma. However, PGA is sometimes underestimated because of the exclusive reliance on induced sputum cell counts, which are variable among cohorts of studies, prompting the necessity of developing improved biomarkers. Importantly, investigators have reported that inhaled corticosteroids had a limited effect on airway inflammatory markers in patients with PGA and therefore defining PGA as a potentially "steroid-insensitive" phenotype that requires exploration of alternative therapies. PGA manifests as an uncoupling of airway obstruction from airway inflammation that can be driven by structural changes within the airways, such as airway smooth muscle tissue hypertrophy. Animal models provide evidence that processes evoking airway hyperresponsiveness and airway smooth muscle thickening occur independent from inflammation and might be a consequence of a loss of negative homeostatic processes. Collectively, further understanding of PGA with a focus on the characterization, prevalence, clinical significance, and pathobiology derived from animal studies will likely provide precision therapies that will improve PGA clinical outcomes.

Precision Medicine in Targeted Therapies for Severe Asthma: Is There Any Place for "Omics" Technology?

According to the current guidelines, severe asthma still represents a controversial topic in terms of definition and management. The introduction of novel biological therapies as a treatment option for severe asthmatic patients paved the way to a personalized approach, which aims at matching the appropriate therapy with the different asthma phenotypes. Traditional asthma phenotypes have been decomposing by an increasing number of asthma subclasses based on functional and physiopathological mechanisms. This is possible thanks to the development and application of different omics technologies. The new asthma classification patterns, particularly concerning severe asthma, include an increasing number of endotypes that have been identified using new omics technologies. The identification of endotypes provides new opportunities for the management of asthma symptoms, but this implies that biological therapies which target inflammatory mediators in the frame of specific patterns of inflammation should be developed. However, the pathway leading to a precision approach in asthma treatment is still at its beginning. The aim of this review is providing a synthetic overview of the current asthma management, with a particular focus on severe asthma, in the light of phenotype and endotype approach, and summarizing the current knowledge about "omics" science and their therapeutic relevance in the field of bronchial asthma.

Bacterial biogeography of adult airways in atopic asthma

BACKGROUND

Perturbations to the composition and function of bronchial bacterial communities appear to contribute to the pathophysiology of asthma. Unraveling the nature and mechanisms of these complex associations will require large longitudinal studies, for which bronchoscopy is poorly suited. Studies of samples obtained by sputum induction and nasopharyngeal brushing or lavage have also reported asthma-associated microbiota characteristics. It remains unknown, however, whether the microbiota detected in these less-invasive sample types reflect the composition of bronchial microbiota in asthma.

RESULTS

Bacterial microbiota in paired protected bronchial brushings (BB; n = 45), induced sputum (IS; n = 45), oral wash (OW; n = 45), and nasal brushings (NB; n = 27) from adults with mild atopic asthma (AA), atopy without asthma (ANA), and healthy controls (HC) were profiled using 16S rRNA gene sequencing. Though microbiota composition varied with sample type (p < 0.001), compositional similarity was greatest for BB-IS, particularly in AAs and ANAs. The abundance of genera detected in BB correlated with those detected in IS and OW (r median [IQR] 0.869 [0.748-0.942] and 0.822 [0.687-0.909] respectively), but not with those in NB (r = 0.004 [- 0.003-0.011]). The number of taxa shared between IS-BB and NB-BB was greater in AAs than in HCs (p < 0.05) and included taxa previously associated with asthma. Of the genera abundant in NB, only Moraxella correlated positively with abundance in BB; specific members of this genus were shared between the two compartments only in AAs. Relative abundance of Moraxella in NB of AAs correlated negatively with that of Corynebacterium but positively with markers of eosinophilic inflammation in the blood and BAL fluid. The genus, Corynebacterium, trended to dominate all NB samples of HCs but only half of AAs (p = 0.07), in whom abundance of this genus was negatively associated with markers of eosinophilic inflammation.

CONCLUSIONS

Induced sputum is superior to nasal brush or oral wash for assessing bronchial microbiota composition in asthmatic adults. Although compositionally similar to the bronchial microbiota, the microbiota in induced sputum are distinct, reflecting enrichment of oral bacteria. Specific bacterial genera are shared between the nasal and the bronchial mucosa which are associated with markers of systemic and bronchial inflammation.

NKT cells contribute to basal IL-4 production but are not required to induce experimental asthma

CD1d-deficiency results in a selective deletion of NKT cells in mice that is reported to prevent murine allergic airway disease (AAD). Because we find 2–3 fold lower basal IL-4 production in CD1d^- mice than in wild-type (WT) mice, we hypothesized that the contribution made by NKT cells to AAD would depend on the strength of the stimulus used to induce the disease. Consequently, we compared CD1d-deficient mice to WT mice in the development of AAD, using several models of disease induction that differed in the type and dose of allergen, the site of sensitization and the duration of immunization. Surprisingly we found equivalent allergic inflammation and airway disease in WT and CD1d^- mice in all models investigated. Consistent with this, NKT cells constituted only ~2% of CD4⁺ T cells in the lungs of mice with AAD, and IL-4-transcribing NKT cells did not expand with disease induction. Concerned that the congenital absence of NKT cells might have caused a compensatory shift within the immune response, we administered an anti-CD1d monoclonal Ab (mAb) to block NKT function before airway treatments, before or after systemic sensitization to antigen. Such Ab treatment did not affect disease severity. We suggest that the differences reported in the literature regarding the significance of NKT cells in the induction of allergic airway disease may have less to do with the methods used to study the disease and more to do with the animals themselves and/or the facilities used to house them.

Asthma Endotypes and an Overview of Targeted Therapy for Asthma

Guidelines for the management of severe asthma do not emphasize the measurement of the inflammatory component of airway disease to indicate appropriate treatments or to monitor response to treatment. Inflammation is a central component of asthma and contributes to symptoms, physiological, and structural abnormalities. It can be assessed by a number of endotyping strategies based on “omics” technology such as proteomics, transcriptomics, and metabolomics. It can also be assessed using simple cellular responses by quantitative cytometry in sputum. Bronchitis may be eosinophilic, neutrophilic, mixed-granulocytic, or paucigranulocytic (eosinophils and neutrophils not elevated). Eosinophilic bronchitis is usually a Type 2 (T2)-driven process and therefore a sputum eosinophilia of greater than 3% usually indicates a response to treatment with corticosteroids or novel therapies directed against T2 cytokines such as IL-4, IL-5, and IL-13. Neutrophilic bronchitis represents a non-T2-driven disease, which is generally a predictor of response to antibiotics and may be a predictor to therapies targeted at pathways that lead to neutrophil recruitment such as TNF, IL-1, IL-6, IL-8, IL-23, and IL-17. Paucigranulocytic disease may not warrant anti-inflammatory therapy. These patients, whose symptoms may be driven largely by airway hyper-responsiveness may benefit from smooth muscle-directed therapies such as bronchial thermoplasty or mast-cell directed therapies. This review will briefly summarize the current knowledge regarding “omics-based signatures” and cellular endotyping of severe asthma and give an overview of segmentation of asthma therapeutics guided by the endotype.

IgE and eosinophils as therapeutic targets in asthma

PURPOSE OF REVIEW

Asthma is a chronic inflammatory disorder characterized by reversible airflow obstruction, which is being more widely recognized as a broad-spectrum disease that encompasses multiple patient characteristics and pathophysiologic mechanisms. Suboptimal asthma control leads to increasing burden of healthcare costs and loss of productivity to society. Biologic therapies targeted at IgE and eosinophils can be used in poorly controlled allergic and eosinophilic asthma, respectively. The purpose of this review is to analyze the advancements in currently available biologic therapies targeted at IgE and eosinophils in asthma and to identify how these therapies may impact overall healthcare costs.

RECENT FINDINGS

Omalizumab is an anti-IgE antibody that is approved for use of poorly controlled moderate-to-severe asthma. Many studies have confirmed that omalizumab not only improves quality of life and symptom scores, but also decreases urgent care and emergency department visits and hospitalizations. Dupilumab is a biologic agent targeted at TH2 cytokines, but indirectly impacts IgE and is an important biologic agent for atopic disease. Mepolizumab, reslizumab, and benralizumab target IL-5, a key cytokine for eosinophils. For patients with poorly controlled eosinophilic asthma, these biologic agents improve asthma symptoms, reduce exacerbations, and reduce emergency visits and hospitalizations.

SUMMARY

Poorly controlled severe asthma affects a small portion of patients with asthma in the United States and yet it accounts for large portion of healthcare utilization. Biological therapies in poorly controlled severe persistent asthma have been identified to reduce healthcare utilization, including emergency visits and hospitalizations. Biologic agents have a clear beneficial role in the management of severe asthma, and further evaluations should be continued in identifying optimal patient characteristics for the various agents and overall benefit toward healthcare utilization and cost.

Severe Asthma: Have We Made Progress?

Tremendous efforts have been invested in research to (1) discover risk factors, biomarkers, and clinical characteristics; (2) understand the pathophysiology and treatment response variability in severe asthma; and (3) design new therapies. However, to combat severe asthma, many questions concerning the pathogenesis of severe asthma, including its natural history, genetic and environmental risk factors, and disease mechanisms, must be answered. In this article we highlight some of the major discoveries concerning the pathogenesis of severe asthma and its therapeutic development. We conclude that discoveries on numerous fronts of severe asthma, from disease heterogeneity, features of airway remodeling, cytokine mediators and signaling pathways underlying disease pathogenesis, disease mechanisms, potential biomarkers, to new therapeutic targets, demonstrate that progress has been made in understanding and developing more effective treatments for this difficult-to-treat disease.

Asthma Biomarkers: Do They Bring Precision Medicine Closer to the Clinic?

Measurement of biomarkers has been incorporated within clinical research of asthma to characterize the population and to associate the disease with environmental and therapeutic effects. Regrettably, at present, there are no specific biomarkers, none is validated or qualified, and endotype-driven choices overlap. Biomarkers have not yet reached clinical practice and are not included in current asthma guidelines. Last but not least, the choice of the outcome upholding the value of the biomarkers is extremely difficult, since it has to reflect the mechanistic intervention while being relevant to both the disease and the particular person. On the verge of a new age of asthma healthcare standard, we must embrace and adapt to the key drivers of change. Disease endotypes, biomarkers, and precision medicine represent an emerging model of patient care building on large-scale biologic databases, omics and diverse cellular assays, health information technology, and computational tools for analyzing sizable sets of data. A profound transformation of clinical and research pattern from population to individual risk and from investigator-imposed subjective disease clustering (hypothesis driven) to unbiased, data-driven models is facilitated by the endotype/biomarker-driven approach.

Resolving the etiology of atopic disorders by using genetic analysis of racial ancestry

Atopic dermatitis (AD), food allergy, allergic rhinitis, and asthma are common atopic disorders of complex etiology. The frequently observed atopic march from early AD to asthma, allergic rhinitis, or both later in life and the extensive comorbidity of atopic disorders suggest common causal mechanisms in addition to distinct ones. Indeed, both disease-specific and shared genomic regions exist for atopic disorders. Their prevalence also varies among races; for example, AD and asthma have a higher prevalence in African Americans when compared with European Americans. Whether this disparity stems from true genetic or race-specific environmental risk factors or both is unknown. Thus far, the majority of the genetic studies on atopic diseases have used populations of European ancestry, limiting their generalizability. Large-cohort initiatives and new analytic methods, such as admixture mapping, are currently being used to address this knowledge gap. Here we discuss the unique and shared genetic risk factors for atopic disorders in the context of ancestry variations and the promise of high-throughput "-omics"-based systems biology approach in providing greater insight to deconstruct their genetic and nongenetic etiologies. Future research will also focus on deep phenotyping and genotyping of diverse racial ancestry, gene-environment, and gene-gene interactions.

Selected CC and CXC chemokines in children with atopic asthma

INTRODUCTION

There are only limited data on CC and CXC chemokines regulation in children with asthma.

AIM

We compared the serum profile of selected CC and CXC chemokines in patients with atopic asthma and healthy children.

MATERIAL AND METHODS

Serum concentration of CC chemokines RANTES, MCP-1, and CXC chemokines IP-10, MIG, IL-8, RANTES was measured using cytometric bead array in 44 children with atopic asthma and 17 healthy subjects.

RESULTS

The concentration of RANTES was significantly higher and the MIG level was lower in all children with asthma as compared to their control counterparts. We observed increased RANTES and decreased MIG levels also in patients with stable asthma when compared with children in the control group. The IP-10 concentration was similar between the whole asthma group and healthy controls, while significantly increased levels of this chemokine in acute asthma have been observed when compared to stable asthma. For MCP-1 and IL-8, the serum concentration was similar in all compared groups. The MIG concentration correlated positively with IP-10, IL-8, and CRP levels and negatively with the eosinophil count. A negative correlation between the IP-10 and eosinophil count and a negative correlation between FEV1 and IP-10 were found.

CONCLUSIONS

An increased serum RANTES level in children with asthma may result in enhancement of Th2 lymphocyte recruitment into the airway. A decreased expression of Th1 chemokine MIG in children with stable asthma may contribute to a diminished antagonizing effect on Th2 cytokine production and hence intensify Th2 predominance. An increased IP-10 level in children during an asthma attack suggest that this chemokine is a serological marker of disease exacerbation.

Allergen Immunotherapy (AIT): a prototype of Precision Medicine

Precision medicine is a medical model aiming to deliver customised healthcare - with medical decisions, practices, and/or products tailored to the individual patient informed but not directed by guidelines. Allergen immunotherapy has unique immunological rationale, since the approach is tailored to the specific IgE spectrum of an individual and modifies the natural course of the disease as it has a persistent efficacy after completion of treatment. In this perspective Allergen Immunotherapy - AIT has to be presently considered a prototype of Precision Medicine.

Precise information and biomarkers provided by systems medicine and network medicine will address the discovery of Allergen immunotherapy biomarkers for (i) identification of the causes, (ii) stratification of eligible patients for AIT and (iii) the assessment of AIT efficacy.

This area of medical technology is evolving rapidly and, compelemented by e-health, will change the way we practice medicine. It will help to monitor patients’ disease control and data for (i) patient stratification, (ii) clinical trials, (iii) monitoring the efficacy and safety of targeted therapies which are critical for reaching an appropriate reimbursement. Biomarkers associated with e-health combined with a clinical decision support system (CDSS) will change the scope of Allergen immunotherapy.

The cost/effectiveness of Allergen immunotherapy is a key issue for successful implementation. It should include the long-term benefits in the pharmaco-economic evaluation, since no other allergy treatment has this specific characteristic.

AIT is the prototype of current and future precision medicine.

Analysis of a Panel of 48 Cytokines in BAL Fluids Specifically Identifies IL-8 Levels as the Only Cytokine that Distinguishes Controlled Asthma from Uncontrolled Asthma, and Correlates Inversely with FEV1

We sought to identify cells and cytokines in bronchoalveolar lavage (BAL) fluids that distinguish asthma from healthy control subjects and those that distinguish controlled asthma from uncontrolled asthma. Following informed consent, 36 human subjects were recruited for this study. These included 11 healthy control subjects, 15 subjects with controlled asthma with FEV1≥80% predicted and 10 subjects with uncontrolled asthma with FEV1 <80% predicted. BAL fluid was obtained from all subjects. The numbers of different cell types and the levels of 48 cytokines were measured in these fluids. Compared to healthy control subjects, patients with asthma had significantly more percentages of eosinophils and neutrophils, IL-1RA, IL-1α, IL-1β, IL-2Rα, IL-5, IL-6, IL-7, IL-8, G-CSF, GROα (CXCL1), MIP-1β (CCL4), MIG (CXCL9), RANTES (CCL5) and TRAIL in their BAL fluids. The only inflammatory markers that distinguished controlled asthma from uncontrolled asthma were neutrophil percentage and IL-8 levels, and both were inversely correlated with FEV1. We examined whether grouping asthma subjects on the basis of BAL eosinophil % or neutrophil % could identify specific cytokine profiles. The only differences between neutrophil-normal asthma (neutrophil≤2.4%) and neutrophil-high asthma (neutrophils%>2.4%) were a higher BAL fluid IL-8 levels, and a lower FEV1 in the latter group. By contrast, compared to eosinophil-normal asthma (eosinophils≤0.3%), eosinophil-high asthma (eosinophils>0.3%) had higher levels of IL-5, IL-13, IL-16, and PDGF-bb, but same neutrophil percentage, IL-8, and FEV1. Our results identify neutrophils and IL-8 are the only inflammatory components in BAL fluids that distinguish controlled asthma from uncontrolled asthma, and both correlate inversely with FEV1.

Clinical asthma phenotyping: A trial for bridging gaps in asthma management

Asthma is a common disease affecting millions of people worldwide and exerting an enormous strain on health resources in many countries. Evidence is increasing that asthma is unlikely to be a single disease but rather a series of complex, overlapping individual diseases or phenotypes, each defined by its unique interaction between genetic and environmental factors. Asthma phenotypes were initially focused on combinations of clinical characteristics, but they are now evolving to link pathophysiological mechanism to subtypes of asthma. Better characterization of those phenotypes is expected to be most useful for allocating asthma therapies. This article reviews different published researches in terms of unbiased approaches to phenotype asthma and emphasizes how the phenotyping exercise is an important step towards proper asthma treatment. It is structured into three sections; the heterogeneity of asthma, the impact of asthma heterogeneity on asthma management and different trials for phenotyping asthma.

Unlocking proteomic heterogeneity in complex diseases through visual analytics

Despite years of preclinical development, biological interventions designed to treat complex diseases such as asthma often fail in phase III clinical trials. These failures suggest that current methods to analyze biomedical data might be missing critical aspects of biological complexity such as the assumption that cases and controls come from homogeneous distributions. Here we discuss why and how methods from the rapidly evolving field of visual analytics can help translational teams (consisting of biologists, clinicians, and bioinformaticians) to address the challenge of modeling and inferring heterogeneity in the proteomic and phenotypic profiles of patients with complex diseases. Because a primary goal of visual analytics is to amplify the cognitive capacities of humans for detecting patterns in complex data, we begin with an overview of the cognitive foundations for the field of visual analytics. Next, we organize the primary ways in which a specific form of visual analytics called networks has been used to model and infer biological mechanisms, which help to identify the properties of networks that are particularly useful for the discovery and analysis of proteomic heterogeneity in complex diseases. We describe one such approach called subject-protein networks, and demonstrate its application on two proteomic datasets. This demonstration provides insights to help translational teams overcome theoretical, practical, and pedagogical hurdles for the widespread use of subject-protein networks for analyzing molecular heterogeneities, with the translational goal of designing biomarker-based clinical trials, and accelerating the development of personalized approaches to medicine.

Advances in pharmacotherapy for the treatment of allergic rhinitis; MP29-02 (a novel formulation of azelastine hydrochloride and fluticasone propionate in an advanced delivery system) fills the gaps

INTRODUCTION

Effective pharmacologic treatment exists for most patients suffering from allergic rhinitis (AR). However, both in clinical trials and in real-life studies, many patients are dissatisfied with treatment. Physicians often use multiple therapies, in an attempt to improve symptom control, often with limited evidence of success. Novel treatment options are needed and must consider unmet medical needs.

AREAS COVERED

This article reviews the clinical data for a new AR treatment. MP29-02 (Dymista®, Meda, Solna, Sweden) contains azelastine hydrochloride (AZE) and fluticasone propionate (FP), in a novel formulation and delivered in an improved device as a single nasal spray. It has shown superior efficacy in AR patients than either commercially available AZE or FP monotherapy for both nasal and ocular symptom relief, regardless of disease severity. MP29-02 also provided more effective and rapid symptom relief than either AZE or FP monotherapy delivered in the MP29-02 formulation and device. However, the effect was less than that observed versus commercial comparators, suggesting the impact of formulation and device on clinical efficacy.

EXPERT OPINION

MP29-02 simplifies AR management, surpassing the efficacy of gold standard treatment, intranasal corticosteroids (INS), for the first time. It is indicated for the treatment of moderate-to-severe seasonal allergic rhinitis and perennial allergic rhinitis when monotherapy with either intranasal antihistamine or INS is NOT considered sufficient. Most patients present with moderate/severe disease, with evidence of current or previous treatment insufficiency. MP29-02 should be the treatment of choice for these patients.

Respiratory proteomics: from descriptive studies to personalized medicine

Respiratory diseases are highly prevalent and affect humankind worldwide, causing extensive morbidity and mortality with the environment playing an important role. Given the complex structure of the airways, sophisticated tools are required for early diagnosis; initial symptoms are nonspecific, and the clinical diagnosis is made frequently late. Over the past few years, proteomics has made high technological progress in mass-spectrometry-based protein identification and has allowed us to gain new insights into disease mechanisms and identify potential novel therapeutic targets. This review will highlight the contributions of proteomics toward the understanding of the respiratory proteome listing potential biomarkers and its potential application to the clinic. We also outline the contributions of proteomics to creating a personalized approach in respiratory medicine.

β2 adrenergic agonist attenuates house dust mite-induced allergic airway inflammation through dendritic cells

Background

Long-acting β2 adrenergic agonists (LABAs) are commonly used combined with inhaled corticosteroids (ICS) to treat asthmatic patients. Previous reports suggest that LABAs have an anti-inflammatory effect in bronchial asthma, and this should be further investigated. The aim of this study was to investigate whether LABAs inhibit allergic airway inflammation and how this occurs.

Results

We assessed the effect of the LABA formoterol (FORM) on inflammatory cell responses in airway, lung and regional lymph nodes, using an HDM-induced murine allergic asthma model in vivo. The effect of FORM on cytokine production from bone marrow derived dendritic cells (BMDCs) stimulated with HDM was evaluated in vitro. Adoptive transfer of BMDCs pulsed with HDM in the presence or absence of FORM to naïve mice was performed and the inflammatory response to subsequent HDM challenge was analyzed. FORM treatment suppressed HDM-induced changes and caused an increase in the number of eosinophils and neutrophils in bronchoalveolar lavage. The concentration of IL-4 and IL-17 in lung tissue homogenate was elevated and led to an accumulation of IL-4, IL-13, IL-5 and IL-17 producing cells in regional lymph nodes. FORM inhibited the production of IL-6 and IL-23 from BMDCs stimulated with HDM in vitro, and enhanced IL-10 production. The BMDCs adoptive transfer experiment indicated that dendritic cells mediate the effect of FORM, since FORM treatment of BMDCs in vitro attenuated airway inflammation.

Conclusion

These results suggested that FORM modulates dendritic cell function and attenuates Th2 and Th17 responses induced by HDM. Thus, we propose that the clinical significance of LABAs should be re-investigated taking into account these immune-modulating effects.

Biological clustering supports both "Dutch" and "British" hypotheses of asthma and chronic obstructive pulmonary disease

BACKGROUND

Asthma and chronic obstructive pulmonary disease (COPD) are heterogeneous diseases.

OBJECTIVE

We sought to determine, in terms of their sputum cellular and mediator profiles, the extent to which they represent distinct or overlapping conditions supporting either the "British" or "Dutch" hypotheses of airway disease pathogenesis.

METHODS

We compared the clinical and physiological characteristics and sputum mediators between 86 subjects with severe asthma and 75 with moderate-to-severe COPD. Biological subgroups were determined using factor and cluster analyses on 18 sputum cytokines. The subgroups were validated on independent severe asthma (n = 166) and COPD (n = 58) cohorts. Two techniques were used to assign the validation subjects to subgroups: linear discriminant analysis, or the best identified discriminator (single cytokine) in combination with subject disease status (asthma or COPD).

RESULTS

Discriminant analysis distinguished severe asthma from COPD completely using a combination of clinical and biological variables. Factor and cluster analyses of the sputum cytokine profiles revealed 3 biological clusters: cluster 1: asthma predominant, eosinophilic, high TH2 cytokines; cluster 2: asthma and COPD overlap, neutrophilic; cluster 3: COPD predominant, mixed eosinophilic and neutrophilic. Validation subjects were classified into 3 subgroups using discriminant analysis, or disease status with a binary assessment of sputum IL-1β expression. Sputum cellular and cytokine profiles of the validation subgroups were similar to the subgroups from the test study.

CONCLUSIONS

Sputum cytokine profiling can determine distinct and overlapping groups of subjects with asthma and COPD, supporting both the British and Dutch hypotheses. These findings may contribute to improved patient classification to enable stratified medicine.

Systems biology approaches to understanding Epithelial Mesenchymal Transition (EMT) in mucosal remodeling and signaling in asthma

A pathological hallmark of asthma is chronic injury and repair, producing dysfunction of the epithelial barrier function. In this setting, increased oxidative stress, growth factor- and cytokine stimulation, together with extracellular matrix contact produces transcriptional reprogramming of the epithelial cell. This process results in epithelial-mesenchymal transition (EMT), a cellular state associated with loss of epithelial polarity, expression of mesenchymal markers, enhanced mobility and extracellular matrix remodeling. As a result, the cellular biology of the EMT state produces characteristic changes seen in severe, refractory asthma: myofibroblast expansion, epithelial trans-differentiation and subepithelial fibrosis. EMT also induces profound changes in epithelial responsiveness that affects innate immune signaling that may have impact on the adaptive immune response and effectiveness of glucocorticoid therapy in severe asthma. We discuss how this complex phenotype is beginning to be understood using systems biology-level approaches through perturbations coupled with high throughput profiling and computational modeling. Understanding the distinct changes induced by EMT at the systems level may provide translational strategies to reverse the altered signaling and physiology of refractory asthma.

Sputum neutrophil counts are associated with more severe asthma phenotypes using cluster analysis

BACKGROUND

Clinical cluster analysis from the Severe Asthma Research Program (SARP) identified 5 asthma subphenotypes that represent the severity spectrum of early-onset allergic asthma, late-onset severe asthma, and severe asthma with chronic obstructive pulmonary disease characteristics. Analysis of induced sputum from a subset of SARP subjects showed 4 sputum inflammatory cellular patterns. Subjects with concurrent increases in eosinophil (≥2%) and neutrophil (≥40%) percentages had characteristics of very severe asthma.

OBJECTIVE

To better understand interactions between inflammation and clinical subphenotypes, we integrated inflammatory cellular measures and clinical variables in a new cluster analysis.

METHODS

Participants in SARP who underwent sputum induction at 3 clinical sites were included in this analysis (n = 423). Fifteen variables, including clinical characteristics and blood and sputum inflammatory cell assessments, were selected using factor analysis for unsupervised cluster analysis.

RESULTS

Four phenotypic clusters were identified. Cluster A (n = 132) and B (n = 127) subjects had mild-to-moderate early-onset allergic asthma with paucigranulocytic or eosinophilic sputum inflammatory cell patterns. In contrast, these inflammatory patterns were present in only 7% of cluster C (n = 117) and D (n = 47) subjects who had moderate-to-severe asthma with frequent health care use despite treatment with high doses of inhaled or oral corticosteroids and, in cluster D, reduced lung function. The majority of these subjects (>83%) had sputum neutrophilia either alone or with concurrent sputum eosinophilia. Baseline lung function and sputum neutrophil percentages were the most important variables determining cluster assignment.

CONCLUSION

This multivariate approach identified 4 asthma subphenotypes representing the severity spectrum from mild-to-moderate allergic asthma with minimal or eosinophil-predominant sputum inflammation to moderate-to-severe asthma with neutrophil-predominant or mixed granulocytic inflammation.

Analysis and predictive modeling of asthma phenotypes

Molecular classification using robust biochemical measurements provides a level of diagnostic precision that is unattainable using indirect phenotypic measurements. Multidimensional measurements of proteins, genes, or metabolites (analytes) can identify subtle differences in the pathophysiology of patients with asthma in a way that is not otherwise possible using physiological or clinical assessments. We overview a method for relating biochemical analyte measurements to generate predictive models of discrete (categorical) clinical outcomes, a process referred to as "supervised classification." We consider problems inherent in wide (small n and large p) high-dimensional data, including the curse of dimensionality, collinearity and lack of information content. We suggest methods for reducing the data to the most informative features. We describe different approaches for phenotypic modeling, using logistic regression, classification and regression trees, random forest and nonparametric regression spline modeling. We provide guidance on post hoc model evaluation and methods to evaluate model performance using ROC curves and generalized additive models. The application of validated predictive models for outcome prediction will significantly impact the clinical management of asthma.

Introduction to asthma and phenotyping

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.

Dominance of the strongest: inflammatory cytokines versus glucocorticoids

Pro-inflammatory cytokines are involved in the pathogenesis of many inflammatory diseases, and the excessive expression of many of them is normally counteracted by glucocorticoids (GCs), which are steroids that bind to the glucocorticoid receptor (GR). Hence, GCs are potent inhibitors of inflammation, and they are widely used to treat inflammatory diseases, such as asthma, rheumatoid arthritis and inflammatory bowel disease. However, despite the success of GC therapy, many patients show some degree of GC unresponsiveness, called GC resistance (GCR). This is a serious problem because it limits the full therapeutic exploitation of the anti-inflammatory power of GCs. Patients with reduced GC responses often have higher cytokine levels, and there is a complex interplay between GCs and cytokines: GCs downregulate pro-inflammatory cytokines while cytokines limit GC action. Treatment of inflammatory diseases with GCs is successful when GCs dominate. But when cytokines overrule the anti-inflammatory actions of GCs, patients become GC insensitive. New insights into the molecular mechanisms of GR-mediated actions and GCR are needed for the design of more effective GC-based therapies.

Elevated sputum interleukin-5 and submucosal eosinophilia in obese individuals with severe asthma

RATIONALE

The relationship between airway inflammation and obesity in severe asthma is poorly understood.

OBJECTIVES

We sought to determine the relationship between sputum mediator profiles and the distribution of eosinophilic inflammation and obesity in people with severe asthma.

METHODS

Clinical parameters and eight mediators in sputum were assessed in 131 subjects with severe asthma from a single center categorized into lean, overweight, and obese groups defined by their body mass index. In an independent group of people with severe asthma (n = 45) and healthy control subjects (n = 19) eosinophilic inflammation was enumerated in bronchial submucosa, blood, and sputum and related to their body mass index.

MEASUREMENTS AND MAIN RESULTS

Sputum IL-5 geometric mean (95% confidence interval) (pg/ml) was elevated in the obese (1.8 [1.2-2.6]) compared with overweight (1.1 [0.8-1.3]; P = 0.025) and lean (0.9 [0.6-1.2]; P = 0.018) subjects with asthma and was correlated with body mass index (r = 0.29; P < 0.001). There was no relationship among body mass index, the sputum cell count, or other sputum mediators. In the bronchoscopy group the submucosal eosinophil number in the subjects with asthma was correlated with body mass index (Spearman rank correlation, rs = 0.38; P = 0.013) and the median (interquartile range) number of submucosal eosinophils was increased in obese (19.4 [11.8-31.2]) (cells per square millimeter) versus lean subjects (8.2 [5.4-14.6]) (P = 0.006). There was no significant association between sputum or peripheral blood eosinophil counts and body mass index.

CONCLUSIONS

Sputum IL-5 and submucosal eosinophils, but not sputum eosinophils, are elevated in obese people with severe asthma. Whether specific antieosinophilic therapy is beneficial, or improved diet and lifestyle in obese asthma has antiinflammatory effects beyond weight reduction, requires further study.

Identification of innate immune response endotypes in asthma: implications for personalized medicine

Asthma is an idiopathic disease characterized by episodic inflammation and reversible airway obstruction triggered by exposure to environmental agents. Because this disease is heterogeneous in onset, exacerbations, inflammatory states, and response to therapy, there is intense interest in developing personalized approaches to its management. Of focus in this review, the recognition that a component of the pathophysiology of asthma is mediated by inflammation has implications for understanding its etiology and individualizing its therapy. Despite understanding how Th2 polarization mediates asthma exacerbations by aeroallergen exposure, we do not yet fully understand how RNA virus infections produce asthmatic exacerbations. This review will summarize the explosion of information that has revealed how patterns produced by RNA virus infection trigger the innate immune response (IIR) in sentinel airway cells. When the IIR is triggered, these cells elaborate inflammatory cytokines and protective mucosal interferons whose actions activate long-lived adaptive immunity and limit organismal replication. Recent work has shown the multifaceted way that dysregulation of the IIR is linked to viral-induced exacerbation, steroid insensitivity, and T helper polarization of adaptive immunity. New developments in quantitative proteomics now enable accurate identification of subgroups of individuals that demonstrate activation of IIR ("innate endotype"). Potential applications to clinical research are proposed. Together, these developments open realistic prospects for how identification of the IIR endotype may inform asthma therapy in the future.

Exhaled eicosanoid profiles in children with atopic asthma and healthy controls

RATIONALE

Chronic endobronchial inflammation is a hallmark of pediatric asthma and involves the arachidonic acid pathway. Its non-volatile metabolites can be quantified in the exhaled breath condensate (EBC), and single substances have been studied as non-invasive biomarkers for the diagnosis and monitoring of children with asthma. The aim of this study was to compare the content and profile of a wider range of eicosanoids in the EBC between patients and a control group.

MATERIALS AND METHODS

EBC was sampled from 33 children (aged 12.4 ± 3.1 years) with stable atopic asthma (26 on inhaled steroid treatment) and 25 healthy controls (11.8 ± 3.2 years). Validated high performance liquid chromatography coupled with a tandem mass spectrometry platform (HPLC-MS2 ) was used to measure 13 different compounds. In addition, exhaled nitric oxide levels (FeNO) were measured and bronchial hyperresponsiveness (BHR) was assessed by an exercise challenge test in all subjects. An analytical approach was used for multivariate regression modeling of disease status using the most relevant variables.

RESULTS

The levels of PGEM (P < 0.001), PGD2 (P < 0.001), 6keto-PGF1α (P = 0.03), LTC4 (P < 0.001), trans-LTC4 (P = 0.04), and 5HETE (P = 0.02) were significantly higher in asthmatics compared to healthy children, while 11-dehydro TXB2 was significantly less abundant (P = 0.02). The eicosanoids asthma classification ratio (EACR) was computed as the logistic regression function using four variables: PGEM, PGD2, LTC4, and 5HETE. This composite parameter discriminated asthmatic from healthy children better than FEV1, FeNO, or BHR.

CONCLUSION

Complementary measurements of PGEM, PGD2, LTC4, and 5HETE in small-volume EBC samples are feasible by HPLC-MS2 and showed a specific profile in our study population. EACR should be evaluated further in the context of diagnosing and monitoring childhood asthma.

Strategies for molecular classification of asthma using bipartite network analysis of cytokine expression

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.

Identification of subtypes of refractory asthma in Korean patients by cluster analysis

BACKGROUND

Refractory asthma, a subtype of asthma with uncontrolled symptoms despite antiasthma medications, is a heterogeneous syndrome with variable clinical features, presumably different etiologies, and pathophysiological mechanisms. The heterogeneity of refractory asthma, however, is poorly understood. We aimed to characterize refractory asthma and to improve our understanding of the heterogeneity of refractory asthma patients.

METHODS

We identified refractory asthma patients (n = 96) as defined by the American Thoracic Society's criteria from a cohort of Korean asthma patients (n = 2,187). Then, cluster analysis was conducted to classify subtypes of refractory asthma.

RESULTS

Among the study group, 4.4 % (n = 96) of all asthma patients had refractory asthma. Cluster analysis identified four distinct groups of refractory asthma. Age at onset was younger in clusters 1 and 2 than in clusters 3 and 4. Patients in cluster 1 had the most well-preserved pulmonary function; patients in cluster 2 had a female predominance and the most severe airway obstruction; patients in cluster 3 were mostly female and had the most enhanced bronchial hyperresponsiveness; and patients in cluster 4 were most male and tended to be cigarette smokers.

CONCLUSIONS

The current results suggest that refractory asthma is a heterogeneous syndrome and could be classified into four subtypes. Underlying pathogenesis and therapeutic approaches may differ for the different subtypes and further research is needed.