Three Major Efforts to Phenotype Asthma: Severe Asthma Research Program, Asthma Disease Endotyping for Personalized Therapeutics, and Unbiased Biomarkers for the Prediction of Respiratory Disease Outcome

Identification of severe acute pediatric asthma phenotypes using unsupervised machine learning

RATIONALE

More targeted management of severe acute pediatric asthma could improve clinical outcomes.

OBJECTIVES

To identify distinct clinical phenotypes of severe acute pediatric asthma using variables obtained in the first 12 h of hospitalization.

METHODS

We conducted a retrospective cohort study in a quaternary care children's hospital from 2014 to 2022. Encounters for children ages 2-18 years admitted to the hospital for asthma were included. We used consensus k means clustering with patient demographics, vital signs, diagnostics, and laboratory data obtained in the first 12 h of hospitalization.

MEASUREMENTS AND MAIN RESULTS

The study population included 683 encounters divided into derivation (80%) and validation (20%) sets, and two distinct clusters were identified. Compared to Cluster 1 in the derivation set, Cluster 2 encounters (177 [32%]) were older (11 years [8; 14] vs. 5 years [3; 8]; p < .01) and more commonly males (63% vs. 53%; p = .03) of Black race (51% vs. 40%; p = .03) with non-Hispanic ethnicity (96% vs. 84%; p < .01). Cluster 2 encounters had smaller improvements in vital signs at 12-h including percent change in heart rate (-1.7 [-11.7; 12.7] vs. -7.8 [-18.5; 1.7]; p < .01), and respiratory rate (0.0 [-20.0; 22.2] vs. -11.4 [-27.3; 9.0]; p < .01). Encounters in Cluster 2 had lower percentages of neutrophils (70.0 [55.0; 83.0] vs. 85.0 [77.0; 90.0]; p < .01) and higher percentages of lymphocytes (17.0 [8.0; 32.0] vs. 9.0 [5.3; 14.0]; p < .01). Cluster 2 encounters had higher rates of invasive mechanical ventilation (23% vs. 5%; p < .01), longer hospital length of stay (4.5 [2.6; 8.8] vs. 2.9 [2.0; 4.3]; p < .01), and a higher mortality rate (7.3% vs. 0.0%; p < .01). The predicted cluster assignments in the validation set shared the same ratio (~2:1), and many of the same characteristics.

CONCLUSIONS

We identified two clinical phenotypes of severe acute pediatric asthma which exhibited distinct clinical features and outcomes.

Association between pre-biologic T2-biomarker combinations and response to biologics in patients with severe asthma

Background

To date, studies investigating the association between pre-biologic biomarker levels and post-biologic outcomes have been limited to single biomarkers and assessment of biologic efficacy from structured clinical trials.

Aim

To elucidate the associations of pre-biologic individual biomarker levels or their combinations with pre-to-post biologic changes in asthma outcomes in real-life.

Methods

This was a registry-based, cohort study using data from 23 countries, which shared data with the International Severe Asthma Registry (May 2017-February 2023). The investigated biomarkers (highest pre-biologic levels) were immunoglobulin E (IgE), blood eosinophil count (BEC) and fractional exhaled nitric oxide (FeNO). Pre- to approximately 12-month post-biologic change for each of three asthma outcome domains (i.e. exacerbation rate, symptom control and lung function), and the association of this change with pre-biologic biomarkers was investigated for individual and combined biomarkers.

Results

Overall, 3751 patients initiated biologics and were included in the analysis. No association was found between pre-biologic BEC and pre-to-post biologic change in exacerbation rate for any biologic class. However, higher pre-biologic BEC and FeNO were both associated with greater post-biologic improvement in FEV1 for both anti-IgE and anti-IL5/5R, with a trend for anti-IL4Rα. Mean FEV1 improved by 27-178 mL post-anti-IgE as pre-biologic BEC increased (250 to 1000 cells/µL), and by 43-216 mL and 129-250 mL post-anti-IL5/5R and -anti-IL4Rα, respectively along the same BEC gradient. Corresponding improvements along a FeNO gradient (25-100 ppb) were 41-274 mL, 69-207 mL and 148-224 mL for anti-IgE, anti-IL5/5R, and anti-IL4Rα, respectively. Higher baseline BEC was also associated with lower probability of uncontrolled asthma (OR 0.392; p=0.001) post-biologic for anti-IL5/5R. Pre-biologic IgE was a poor predictor of subsequent pre-to-post-biologic change for all outcomes assessed for all biologics. The combination of BEC + FeNO marginally improved the prediction of post-biologic FEV1 increase (adjusted R2: 0.751), compared to BEC (adjusted R2: 0.747) or FeNO alone (adjusted R2: 0.743) (p=0.005 and <0.001, respectively); however, this prediction was not improved by the addition of IgE.

Conclusions

The ability of higher baseline BEC, FeNO and their combination to predict biologic-associated lung function improvement may encourage earlier intervention in patients with impaired lung function or at risk of accelerated lung function decline.

Dual inhibition of airway inflammation and fibrosis by common β cytokine receptor blockade

BACKGROUND

Patients with severe asthma can present with eosinophilic type 2 (T2), neutrophilic, or mixed inflammation that drives airway remodeling and exacerbations and represents a major treatment challenge. The common β (βc) receptor signals for 3 cytokines, GM-CSF, IL-5, and IL-3, which collectively mediate T2 and neutrophilic inflammation.

OBJECTIVE

To determine the pathogenesis of βc receptor-mediated inflammation and remodeling in severe asthma and to investigate βc antagonism as a therapeutic strategy for mixed granulocytic airway disease.

METHODS

βc gene expression was analyzed in bronchial biopsy specimens from patients with mild-to-moderate and severe asthma. House dust mite extract and Aspergillus fumigatus extract (ASP) models were used to establish asthma-like pathology and airway remodeling in human βc transgenic mice. Lung tissue gene expression was analyzed by RNA sequencing. The mAb CSL311 targeting the shared cytokine binding site of βc was used to block βc signaling.

RESULTS

βc gene expression was increased in patients with severe asthma. CSL311 potently reduced lung neutrophils, eosinophils, and interstitial macrophages and improved airway pathology and lung function in the acute steroid-resistant house dust mite extract model. Chronic intranasal ASP exposure induced airway inflammation and fibrosis and impaired lung function that was inhibited by CSL311. CSL311 normalized the ASP-induced fibrosis-associated extracellular matrix gene expression network and strongly reduced signatures of cellular inflammation in the lung.

CONCLUSIONS

βc cytokines drive steroid-resistant mixed myeloid cell airway inflammation and fibrosis. The anti-βc antibody CSL311 effectively inhibits mixed T2/neutrophilic inflammation and severe asthma-like pathology and reverses fibrosis gene signatures induced by exposure to commonly encountered environmental allergens.

Consensus clustering with missing labels (ccml): a consensus clustering tool for multi-omics integrative prediction in cohorts with unequal sample coverage

Multi-omics data integration is a complex and challenging task in biomedical research. Consensus clustering, also known as meta-clustering or cluster ensembles, has become an increasingly popular downstream tool for phenotyping and endotyping using multiple omics and clinical data. However, current consensus clustering methods typically rely on ensembling clustering outputs with similar sample coverages (mathematical replicates), which may not reflect real-world data with varying sample coverages (biological replicates). To address this issue, we propose a new consensus clustering with missing labels (ccml) strategy termed ccml, an R protocol for two-step consensus clustering that can handle unequal missing labels (i.e. multiple predictive labels with different sample coverages). Initially, the regular consensus weights are adjusted (normalized) by sample coverage, then a regular consensus clustering is performed to predict the optimal final cluster. We applied the ccml method to predict molecularly distinct groups based on 9-omics integration in the Karolinska COSMIC cohort, which investigates chronic obstructive pulmonary disease, and 24-omics handprint integrative subgrouping of adult asthma patients of the U-BIOPRED cohort. We propose ccml as a downstream toolkit for multi-omics integration analysis algorithms such as Similarity Network Fusion and robust clustering of clinical data to overcome the limitations posed by missing data, which is inevitable in human cohorts consisting of multiple data modalities. The ccml tool is available in the R language (https://CRAN.R-project.org/package=ccml, https://github.com/pulmonomics-lab/ccml, or https://github.com/ZhoulabCPH/ccml).

New insights into the pathophysiology and therapeutic targets of asthma and comorbid chronic rhinosinusitis with or without nasal polyposis

Asthma and chronic rhinosinusitis with nasal polyps (CRSwNP) or without (CRSsNP) are chronic respiratory diseases. These two disorders often co-exist based on common anatomical, immunological, histopathological, and pathophysiological basis. Usually, asthma with comorbid CRSwNP is driven by type 2 (T2) inflammation which predisposes to more severe, often intractable, disease. In the past two decades, innovative technologies and detection techniques in combination with newly introduced targeted therapies helped shape our understanding of the immunological pathways underlying inflammatory airway diseases and to further identify several distinct clinical and inflammatory subsets to enhance the development of more effective personalized treatments. Presently, a number of targeted biologics has shown clinical efficacy in patients with refractory T2 airway inflammation, including anti-IgE (omalizumab), anti-IL-5 (mepolizumab, reslizumab)/anti-IL5R (benralizumab), anti-IL-4R-α (anti-IL-4/IL-13, dupilumab), and anti-TSLP (tezepelumab). In non-type-2 endotypes, no targeted biologics have consistently shown clinical efficacy so far. Presently, multiple therapeutical targets are being explored including cytokines, membrane molecules and intracellular signalling pathways to further expand current treatment options for severe asthma with and without comorbid CRSwNP. In this review, we discuss existing biologics, those under development and share some views on new horizons.

Molecular Accounting and Profiling of Human Respiratory Microbial Communities: Toward Precision Medicine by Targeting the Respiratory Microbiome for Disease Diagnosis and Treatment

The wide diversity of microbiota at the genera and species levels across sites and individuals is related to various causes and the observed differences between individuals. Efforts are underway to further understand and characterize the human-associated microbiota and its microbiome. Using 16S rDNA as a genetic marker for bacterial identification improved the detection and profiling of qualitative and quantitative changes within a bacterial population. In this light, this review provides a comprehensive overview of the basic concepts and clinical applications of the respiratory microbiome, alongside an in-depth explanation of the molecular targets and the potential relationship between the respiratory microbiome and respiratory disease pathogenesis. The paucity of robust evidence supporting the correlation between the respiratory microbiome and disease pathogenesis is currently the main challenge for not considering the microbiome as a novel druggable target for therapeutic intervention. Therefore, further studies are needed, especially prospective studies, to identify other drivers of microbiome diversity and to better understand the changes in the lung microbiome along with the potential association with disease and medications. Thus, finding a therapeutic target and unfolding its clinical significance would be crucial.

Systems Biology in Asthma

The application of mathematical and computational analysis, together with the modelling of biological and physiological processes, is transforming our understanding of the pathophysiology of complex diseases. This systems biology approach incorporates large amounts of genomic, transcriptomic, proteomic, metabolomic, breathomic, metagenomic and imaging data from disease sites together with deep clinical phenotyping, including patient-reported outcomes. Integration of these datasets will provide a greater understanding of the molecular pathways associated with severe asthma in each individual patient and determine their personalised treatment regime. This chapter describes some of the data integration methods used to combine data sets and gives examples of the results obtained using single datasets and merging of multiple datasets (data fusion and data combination) from several consortia including the severe asthma research programme (SARP) and the Unbiased Biomarkers Predictive of Respiratory Disease Outcomes (U-BIOPRED) consortia. These results highlight the involvement of several different immune and inflammatory pathways and factors in distinct subsets of patients with severe asthma. These pathways often overlap in patients with distinct clinical features of asthma, which may explain the incomplete or no response in patients undergoing specific targeted therapy. Collaboration between groups will improve the predictions obtained using a systems medicine approach in severe asthma.

The 5T approach in asthma: Triple Therapy Targeting Treatable Traits

Using a therapeutic strategy that is free from traditional diagnostic labels and based on the identification of "treatable traits" (TTs), which are influential in clinical presentations in each patient, might overcome the difficulties in identifying and validating asthma phenotypes and endotypes. Growing evidence is documenting the importance of using the triple therapy with ICS, LABA, and LAMAs in a single inhaler (SITT) in cases of asthma not controlled by ICS/LABA and in the prevention of exacerbations. The identification of TTs may overcome the possibility of using SITT without considering the specific needs of the patient. In effect, it allows a treatment strategy that is closer to the precision strategy now widely advocated for the management of patients with asthma. There are different TTs in asthma that may benefit from treatment with SITT, regardless of guideline recommendations. The airflow limitation and small airway dysfunction are key TTs that are present in different phenotypes/endotypes, do not depend on the degree of T2 inflammation, and respond better than other treatments to SITT. We suggest that the 5T (Triple Therapy Targeting Treatable Traits) approach should be applied to the full spectrum of asthma, not just severe asthma, and, consequently, SITT should begin earlier than currently recommended.

Allergic and eosinophilic asthma in the era of biomarkers and biologics: similarities, differences and misconceptions

OBJECTIVE

Severe asthma is associated with substantial personal and economic burden; maintaining disease control is the key management goal. Increased understanding of asthma heterogeneity and development of type 2 (T2)-targeting biologics has substantially advanced disease management and outcomes; however, despite both being driven by T2 inflammation, allergic and eosinophilic asthma have different treatment recommendations. We sought to better understand the similarities and differences between allergic and eosinophilic asthma and highlight where misconceptions may arise.

DATA SOURCES

Published articles, pivotal trials, post hoc analyses, and asthma clinical guidelines sourced from PubMed.

STUDY SELECTIONS

Sources reporting allergic and eosinophilic asthma classifications, disease mechanisms, and biomarkers associated with treatment response.

RESULTS

This review highlights that severe allergic and eosinophilic asthma are both driven by T2 inflammation with eosinophils playing a cardinal role. Despite this overlap, treatment recommendations differ based on asthma classification. T2 cytokine gene expression is a reasonably well-established research tool, but not a well-established biomarker in clinical practice, unlike blood eosinophil counts, fractional exhaled nitric oxide, and immunoglobulin E; the clinical relevance of immunoglobulin E as a predictive biomarker remains unclear.

CONCLUSION

Asthma classifications that can be easily characterized at patient level to ensure accurate diagnosis, predict disease trajectory, and treatment response are required. The current dichotomy of allergic and eosinophilic asthma classifications is likely too simplistic, given the similar eosinophil-mediated disease pathophysiology in both classifications. Our results provide future directions to guide clinically meaningful interpretation of asthma endophenotypes, which may improve understanding of severe asthma characterization and aid future advances in defining responders more precisely with personalized medicine approaches.

Microbiome Research and Multi-Omics Integration for Personalized Medicine in Asthma

Asthma is a multifactorial inflammatory disorder of the respiratory system characterized by high diversity in clinical manifestations, underlying pathological mechanisms and response to treatment. It is generally established that human microbiota plays an essential role in shaping a healthy immune response, while its perturbation can cause chronic inflammation related to a wide range of diseases, including asthma. Systems biology approaches encompassing microbiome analysis can offer valuable platforms towards a global understanding of asthma complexity and improving patients' classification, status monitoring and therapeutic choices. In the present review, we summarize recent studies exploring the contribution of microbiota dysbiosis to asthma pathogenesis and heterogeneity in the context of asthma phenotypes-endotypes and administered medication. We subsequently focus on emerging efforts to gain deeper insights into microbiota-host interactions driving asthma complexity by integrating microbiome and host multi-omics data. One of the most prominent achievements of these research efforts is the association of refractory neutrophilic asthma with certain microbial signatures, including predominant pathogenic bacterial taxa (such as Proteobacteria phyla, Gammaproteobacteria class, especially species from Haemophilus and Moraxella genera). Overall, despite existing challenges, large-scale multi-omics endeavors may provide promising biomarkers and therapeutic targets for future development of novel microbe-based personalized strategies for diagnosis, prevention and/or treatment of uncontrollable asthma.

Future Directions for Clinical Respiratory Fungal Research

There has been a growing appreciation of the importance of respiratory fungal diseases in recent years, with better understanding of their prevalence as well as their global distribution. In step with the greater awareness of these complex infections, we are currently poised to make major advances in the characterization and treatment of these fungal diseases, which in itself is largely a consequence of post-genomic technologies which have enabled rational drug development and a path towards personalized medicines. These advances are set against a backdrop of globalization and anthropogenic change, which have impacted the world-wide distribution of fungi and antifungal resistance, as well as our built environment. The current revolution in immunomodulatory therapies has led to a rapidly evolving population at-risk for respiratory fungal disease. Whilst challenges are considerable, perhaps the tools we now have to manage these infections are up to this challenge. There has been a welcome acceleration of the antifungal pipeline in recent years, with a number of new drug classes in clinical or pre-clinical development, as well as new focus on inhaled antifungal drug delivery. The "post-genomic" revolution has opened up metagenomic diagnostic approaches spanning host immunogenetics to the fungal mycobiome that have allowed better characterization of respiratory fungal disease endotypes. When these advances are considered together the key challenge is clear: to develop a personalized medicine framework to enable a rational therapeutic approach.

G-CSFR antagonism reduces mucosal injury and airways fibrosis in a virus-dependent model of severe asthma

BACKGROUND AND PURPOSE

Asthma is a chronic disease that displays heterogeneous clinical and molecular features. A phenotypic subset of late-onset severe asthmatics has debilitating fixed airflow obstruction, increased neutrophilic inflammation and a history of pneumonia. Influenza A virus (IAV) is an important viral cause of pneumonia and asthmatics are frequently hospitalised during IAV epidemics. This study aims to determine whether antagonising granulocyte colony stimulating factor receptor (G-CSFR) prevents pneumonia-associated severe asthma.

EXPERIMENTAL APPROACH

Mice were sensitised to house dust mite (HDM) to establish allergic airway inflammation and subsequently infected with IAV (HKx31/H3N2 subtype). A neutralising monoclonal antibody against G-CSFR was therapeutically administered.

KEY RESULTS

In IAV-infected mice with prior HDM sensitisation, a significant increase in airway fibrotic remodelling and airways hyper-reactivity was observed. A mixed granulocytic inflammatory profile consisting of neutrophils, macrophages and eosinophils was prominent and at a molecular level, G-CSF expression was significantly increased in HDMIAV-treated mice. Blockage of G-CSFR reduced neutrophilic inflammation in the bronchoalveolar and lungs by over 80% in HDMIAV-treated mice without altering viral clearance. Markers of NETosis (dsDNA and myeloperoxidase in bronchoalveolar), tissue injury (LDH activity in bronchoalveolar) and oedema (total bronchoalveolar-fluid protein) were also significantly reduced with anti-G-CSFR treatment. In addition, anti-G-CSFR antagonism significantly reduced bronchoalveolar gelatinase activity, active TFGβ lung levels, collagen lung expression, airways fibrosis and airways hyper-reactivity in HDMIAV-treated mice.

CONCLUSIONS AND IMPLICATIONS

We have shown that antagonising G-CSFR-dependent neutrophilic inflammation reduced pathological disruption of the mucosal barrier and airways fibrosis in an IAV-induced severe asthma model.

Supervised Analysis for Phenotype Identification: The Case of Heart Failure Ejection Fraction Class

Artificial Intelligence is creating a paradigm shift in health care, with phenotyping patients through clustering techniques being one of the areas of interest.

OBJECTIVE

To develop a predictive model to classify heart failure (HF) patients according to their left ventricular ejection fraction (LVEF), by using available data from Electronic Health Records (EHR).

SUBJECTS AND METHODS

2854 subjects over 25 years old with a diagnosis of HF and LVEF, measured by echocardiography, were selected to develop an algorithm to predict patients with reduced EF using supervised analysis. The performance of the developed algorithm was tested in heart failure patients from Primary Care. To select the most influentual variables, the LASSO algorithm setting was used, and to tackle the issue of one class exceeding the other one by a large amount, we used the Synthetic Minority Oversampling Technique (SMOTE). Finally, Random Forest (RF) and XGBoost models were constructed.

RESULTS

The full XGBoost model obtained the maximum accuracy, a high negative predictive value, and the highest positive predictive value. Gender, age, unstable angina, atrial fibrillation and acute myocardial infarct are the variables that most influence EF value. Applied in the EHR dataset, with a total of 25,594 patients with an ICD-code of HF and no regular follow-up in cardiology clinics, 6170 (21.1%) were identified as pertaining to the reduced EF group.

CONCLUSION

The obtained algorithm was able to identify a number of HF patients with reduced ejection fraction, who could benefit from a protocol with a strong possibility of success. Furthermore, the methodology can be used for studies using data extracted from the Electronic Health Records.

Precision medicine in severe pediatric asthma: opportunities and challenges

Purpose of review

Severe pediatric asthma exerts a substantial burden on patients, their families and society. This review provides an update on the latest insights and needs regarding the implementation of precision medicine in severe pediatric asthma.

Recent findings

Biologicals targeting underlying inflammatory pathways are increasingly available to treat children with severe asthma, holding the promise to enable precision medicine in this heterogeneous patient population with high unmet clinical needs. However, the current understanding of which child would benefit from which type or combination of biologicals is still limited, as most evidence comes from adult studies and might not be generalizable to the pediatric population. Studies in pediatric severe asthma are scarce due to the time-consuming effort to diagnose severe asthma and the challenge to recruit sufficient study participants. The application of innovative systems medicine approaches in international consortia might provide novel leads for – preferably noninvasive – new biomarkers to guide precision medicine in severe pediatric asthma.

Summary

Despite the increased availability of targeted treatments for severe pediatric asthma, clinical decision-making tools to guide these therapies are still lacking for the individual pediatric patient.

Precision medicine and treatable traits in chronic airway diseases - where do we stand?

PURPOSE OF REVIEW

To provide an update on the implementation of precision medicine, based on treatable traits and mechanisms, in the daily clinical management of chronic airways diseases.

RECENT FINDINGS

Recent insights into the complex and heterogeneous nature of chronic airway diseases including chronic obstructive pulmonary disease (COPD) and asthma identified several clinical and inflammatory phenotypes. This shifted the management focus of these diseases away from the prototypic disease labels and paved the way for developing novel targeted therapies.The concept of precision medicine aims to link the right patient to the right treatment, while minimizing the risk of adverse effects. Several treatable features ('treatable traits') have now been identified for these chronic airway diseases, including pulmonary, extra-pulmonary, and psychological/lifestyle/environmental traits. As the next step, innovative detection techniques should clarify underlying mechanisms and molecular pathways of these treatable traits and novel reliable point-of-care (composite) biomarkers to help predict responders to targeted therapies must be developed.

SUMMARY

Precision medicine links the right patient to the right treatment. Identification of treatable traits in asthma and COPD will help optimize the treatment approach in these heterogeneous diseases. Furthermore, in-depth identification of underlying molecular pathways and reliable biomarkers in chronic airways diseases to guide targeted treatment in individual patients is in progress.

Plasma P-Selectin Is Inversely Associated with Lung Function and Corticosteroid Responsiveness in Asthma

BACKGROUND

Severe asthma has multiple phenotypes for which biomarkers are still being defined. Plasma P-selectin reports endothelial and/or platelet activation.

OBJECTIVE

To determine if P-selectin is associated with features of asthma in a longitudinal study.

METHODS

Plasmas from 70 adult patients enrolled in the Severe Asthma Research Program (SARP) III at the University of Wisconsin-Madison were analyzed for concentration of P-selectin at several points over the course of 3 years, namely, at baseline (BPS), after intramuscular triamcinolone acetonide (TA) injection, and at 36 months after baseline. Thirty-four participants also came in during acute exacerbation and 6 weeks after exacerbation.

RESULTS

BPS correlated inversely with forced expiratory volume in 1 s (FEV1) and with residual volume/total lung capacity, an indicator of air trapping. BPS was inversely associated with FEV1 change after TA, by regression analysis. FEV1 did not change significantly after TA if BPS was above the median, whereas patients with BPS below the median had significantly increased FEV1 after TA. BPS was higher in and predicted assignment to SARP phenotype cluster 5 ("severe fixed-airflow asthma"). P-selectin was modestly but significantly increased at exacerbation but returned to baseline within 3 years.

CONCLUSIONS

High BPS is associated with airway obstruction, air trapping, the "severe fixed-airflow" cluster, and lack of FEV1 improvement in response to TA injection. P-selectin concentration, which is a stable trait with only modest elevation during exacerbation, may be a useful biomarker for a severe asthma pheno- or endotype characterized by low pulmonary function and lack of corticosteroid responsiveness.

Sputum transcriptomics implicates increased p38 signalling activity in severe asthma

BACKGROUND AND OBJECTIVE

Severe asthma is responsible for a disproportionate burden of illness and healthcare costs spent on asthma. This study analyses sputum transcriptomics to investigate the mechanisms and novel treatment targets of severe asthma.

METHODS

Induced sputum samples were collected in a cross-sectional study from participants with severe asthma (n = 12, defined as per GINA criteria), non-severe uncontrolled (n = 21) and controlled asthma (n = 21) and healthy controls (n = 15). Sputum RNA was extracted and transcriptomic profiles were generated (Illumina HumanRef-8 V2) and analysed (GeneSpring). Sputum protein lysates were analysed for p38 activation in a validation study (n = 24 asthma, n = 8 healthy) by western blotting.

RESULTS

There were 2166 genes differentially expressed between the four groups. In severe asthma, the expression of 1875, 1308 and 563 genes was altered compared to healthy controls, controlled and uncontrolled asthma, respectively. Of the 1875 genes significantly different to healthy controls, 123 were >2-fold change from which four networks were identified. Thirty genes (>2-fold change) were significantly different in severe asthma compared to both controlled asthma and healthy controls. There was enrichment of genes in the p38 signalling pathway that were associated with severe asthma. Phosphorylation of p38 was increased in a subset of severe asthma samples, correlating with neutrophilic airway inflammation.

CONCLUSION

Severe asthma is associated with substantial differences in sputum gene expression that underlie unique cellular mechanisms. The p38 signalling pathway may be important in the pathogenesis of severe asthma, and future investigations into p38 inhibition are warranted as a 'non-Th2' therapeutic option.

Targeting HDAC Complexes in Asthma and COPD

Around three million patients die due to airway inflammatory diseases each year. The most notable of these diseases are asthma and chronic obstructive pulmonary disease (COPD). Therefore, new therapies are urgently needed. Promising targets are histone deacetylases (HDACs), since they regulate posttranslational protein acetylation. Over a thousand proteins are reversibly acetylated, and acetylation critically influences aberrant intracellular signaling pathways in asthma and COPD. The diverse set of selective and non-selective HDAC inhibitors used in pre-clinical models of airway inflammation show promising results, but several challenges still need to be overcome. One such challenge is the design of HDAC inhibitors with unique selectivity profiles, such as selectivity towards specific HDAC complexes. Novel strategies to disrupt HDAC complexes should be developed to validate HDACs further as targets for new anti-inflammatory pulmonary treatments.