Noninvasive analysis of the sputum transcriptome discriminates clinical phenotypes of asthma

Activated sputum eosinophils associated with exacerbations in children on mepolizumab

BACKGROUND

MUPPITS-2 was a randomized, placebo-controlled clinical trial that demonstrated mepolizumab (anti-IL-5) reduced exacerbations and blood and airway eosinophils in urban children with severe eosinophilic asthma. Despite this reduction in eosinophilia, exacerbation risk persisted in certain patients treated with mepolizumab. This raises the possibility that subpopulations of airway eosinophils exist that contribute to breakthrough exacerbations.

OBJECTIVE

We aimed to determine the effect of mepolizumab on airway eosinophils in childhood asthma.

METHODS

Sputum samples were obtained from 53 MUPPITS-2 participants. Airway eosinophils were characterized using mass cytometry and grouped into subpopulations using unsupervised clustering analyses of 38 surface and intracellular markers. Differences in frequency and immunophenotype of sputum eosinophil subpopulations were assessed based on treatment arm and frequency of exacerbations.

RESULTS

Median sputum eosinophils were significantly lower among participants treated with mepolizumab compared with placebo (58% lower, 0.35% difference [95% CI 0.01, 0.74], P = .04). Clustering analysis identified 3 subpopulations of sputum eosinophils with varied expression of CD62L. CD62Lint and CD62Lhi eosinophils exhibited significantly elevated activation marker and eosinophil peroxidase expression, respectively. In mepolizumab-treated participants, CD62Lint and CD62Lhi eosinophils were more abundant in participants who experienced exacerbations than in those who did not (100% higher for CD62Lint, 0.04% difference [95% CI 0.0, 0.13], P = .04; 93% higher for CD62Lhi, 0.21% difference [95% CI 0.0, 0.77], P = .04).

CONCLUSIONS

Children with eosinophilic asthma treated with mepolizumab had significantly lower sputum eosinophils. However, CD62Lint and CD62Lhi eosinophils were significantly elevated in children on mepolizumab who had exacerbations, suggesting that eosinophil subpopulations exist that contribute to exacerbations despite anti-IL-5 treatment.

Chronic rhinosinusitis with nasal polyps and allergic rhinitis as different multimorbid treatable traits in asthma

Background

Respiratory multimorbidities are linked to asthma, such as allergic rhinitis (AR) with early allergic asthma and chronic rhinosinusitis (CRS) with nasal polyps (CRSwNP) with late nonallergic asthma.

Objective

Our aim was to investigate the association of asthma severity and control with specific upper airway phenotypes.

Method

Patients with asthma were prospectively recruited from 23 pulmonology and ear, nose, and throat clinics. Asthma severity and control, as well as upper airway comorbidities (AR and non-AR [NAR], CRSwNP, and CRS without nasal polyps [CRSsNP]) were assessed according to international consensus guidelines definitions.

Results

A total of 492 asthmatic patients were included. Half of the asthmatic patients (49.6%) had associated rhinitis (37.0% had AR and 12.6% had NAR) and 36.2% had CRS (16.7% had CRSsNP and 19.5% had CRSwNP), whereas 14.2% had no sinonasal symptoms. Most cases of AR (78%) and NAR (84%) were present in patients with mild-to-moderate asthma, whereas CRSwNP was more frequent in patients with severe asthma (35% [P < .001]), mainly nonatopic asthma (44% [P < .001]). Patients with severe asthma with CRSwNP had worse asthma control, which was correlated (r = 0.249 [P = .034]) with sinus occupancy. Multiple logistic regression analysis showed that late-onset asthma, intolerance of aspirin and/or nonsteroidal anti-inflammatory drugs, and CRSwNP were independently associated with severe asthma.

Conclusion

Severe asthma is associated with CRSwNP, with sinus occupancy affecting asthma control. This study has identified 2 main different upper airway treatable traits, AR and CRSwNP, which need further evaluation to improve management and control of patients with asthma.

Asthma Phenotypes in the Era of Personalized Medicine

Asthma is a widespread disease affecting approximately 300-million people globally. This condition leads to significant morbidity, mortality, and economic strain worldwide. Recent clinical and laboratory research advancements have illuminated the immunological factors contributing to asthma. As of now, asthma is understood to be a heterogeneous disease. Personalized medicine involves categorizing asthma by its endotypes, linking observable characteristics to specific immunological mechanisms. Identifying these endotypic mechanisms is paramount in accurately profiling patients and tailoring therapeutic approaches using innovative biological agents targeting distinct immune pathways. This article presents a synopsis of the key immunological mechanisms implicated in the pathogenesis and manifestation of the disease's phenotypic traits and individualized treatments for severe asthma subtypes.

Transcriptomic responses in the blood and sputum of cigarette smokers compared to e-cigarette vapers

RATIONALE

Electronic (e)-cigarettes are popular among youth and cigarette smokers attempting to quit. Studies to date have focused on the utility of e-cigarettes as a smoking cessation tool, but the biological effects are largely unknown.

OBJECTIVES

To identify transcriptomic differences in the blood and sputum of e-cigarette users compared to conventional cigarettes smokers and healthy controls and describe biological pathways affected by these tobacco products.

METHODS

Cross-sectional analysis of whole blood and sputum RNA-sequencing data from 8 smokers, 9 e-cigarette users (e-cigs) and 4 controls. Weighted gene co-network analysis (WGCNA) identified gene module associations. Ingenuity Pathway Analysis (IPA) identified canonical pathways associated with tobacco products.

MAIN RESULTS

In blood, a three-group comparison showed 16 differentially expressed genes (DEGs); pair-wise comparison showed 7 DEGs between e-cigs and controls, 35 DEGs between smokers and controls, and 13 DEGs between smokers and e-cigs. In sputum, 438 DEGs were in the three-group comparison. In pair-wise comparisons, there were 2 DEGs between e-cigs and controls, 270 DEGs between smokers and controls, and 468 DEGs between smokers and e-cigs. Only 2 genes in the smokers vs. control comparison overlapped between blood and sputum. Most gene modules identified through WGCNA associated with tobacco product exposures also were associated with cotinine and exhaled CO levels. IPA showed more canonical pathways altered by conventional cigarette smoking than by e-cigarette use.

CONCLUSION

Cigarette smoking and e-cigarette use led to transcriptomic changes in both blood and sputum. However, conventional cigarettes induced much stronger transcriptomic responses in both compartments.

Classifications of moderate to severe asthma phenotypes in Japan and analysis of serum biomarkers: A Nationwide Cohort Study in Japan (NHOM Asthma Study)

BACKGROUND

Asthma is a heterogeneous disease, and phenotyping can facilitate understanding of disease pathogenesis and direct appropriate asthma treatment. This nationwide cohort study aimed to phenotype asthma patients in Japan and identify potential biomarkers to classify the phenotypes.

METHODS

Adult asthma patients (n = 1925) from 27 national hospitals in Japan were enrolled and divided into Global Initiative for Asthma (GINA) steps 4 or 5 (GINA 4, 5) and GINA Steps 1, 2, or 3 (GINA 1-3) for therapy. Clinical data and questionnaires were collected. Biomarker levels among GINA 4, 5 patients were measured. Ward's minimum variance hierarchical clustering method and tree analysis were performed for phenotyping. Analysis of variance, the Kruskal-Wallis, and chi-square tests were used to compare cluster differences.

RESULTS

The following five clusters were identified: 1) late-onset, old, less-atopic; 2) late-onset, old, eosinophilic, low FEV₁; 3) early-onset, long-duration, atopic, poorly controlled; 4) early-onset, young, female-dominant, atopic; and 5) female-dominant, T1/T2-mixed, most severe. Age of onset, disease duration, blood eosinophils and neutrophils, asthma control questionnaire Sum 6, number of controllers, FEV₁, body mass index (BMI), and hypertension were the phenotype-classifying variables determined by tree analysis that assigned 79.5% to the appropriate cluster. Among the cytokines measured, IL-1RA, YKL40/CHI3L1, IP-10/CXCL10, RANTES/CCL5, and TIMP-1 were useful biomarkers for classifying GINA 4, 5 phenotypes.

CONCLUSIONS

Five distinct phenotypes were identified for moderate to severe asthma and may be classified using clinical and molecular variables (Registered in UMIN-CTR; UMIN000027776.).

Functional immunophenotyping of children with critical status asthmaticus identifies differential gene expression responses in neutrophils exposed to a poly(I:C) stimulus

The host immune response to a viral immune stimulus has not been examined in children during a life-threatening asthma attack. We determined whether we could identify clusters of children with critical asthma by functional immunophenotyping using an intracellular viral analog stimulus. We performed a single-center, prospective, observational cohort study of 43 children ages 6-17 years admitted to a pediatric intensive care unit for an asthma attack between July 2019 to February 2021. Neutrophils were isolated from children, stimulated overnight with LyoVec poly(I:C), and mRNA was analyzed using a targeted Nanostring immunology array. Network analysis of the differentially expressed transcripts for the paired LyoVec poly(I:C) samples was performed. We identified two clusters by functional immunophenotyping that differed by the Asthma Control Test score. Cluster 1 (n = 23) had a higher proportion of children with uncontrolled asthma in the four weeks prior to PICU admission compared with cluster 2 (n = 20). Pathways up-regulated in cluster 1 versus cluster 2 included chemokine receptor/chemokines, interleukin-10 (IL-10), IL-4, and IL-13 signaling. Larger validation studies and clinical phenotyping of children with critical asthma are needed to determine the predictive utility of these clusters in a larger clinical setting.

Phenotyping, Precision Medicine, and Asthma

The traditional one-size-fits all approach based on asthma severity is archaic. Asthma is a heterogenous syndrome rather than a single disease entity. Studies evaluating observable characteristics called phenotypes have elucidated this heterogeneity. Asthma clusters demonstrate overlapping features, are generally stable over time and are reproducible. What the identification of clusters may have failed to do, is move the needle of precision medicine meaningfully in asthma. This may be related to the lack of a straightforward and clinically meaningful way to apply what we have learned about asthma clusters. Clusters are based on both clinical factors and biomarkers. The use of biomarkers is slowly gaining popularity, but phenotyping based on biomarkers is generally greatly underutilized even in subspecialty care. Biomarkers are more often used to evaluate type 2 (T2) inflammatory signatures and eosinophils (sputum and blood), fractional exhaled nitric oxide (FeNO) and serum total and specific immunoglobulin (Ig) E reliably characterize the underlying inflammatory pathways. Biomarkers perform variably and clinicians must be familiar with their advantages and disadvantages to accurately apply them in clinical care. In addition, it is increasingly clear that clinical features are critical in understanding not only phenotypic characterization but in predicting response to therapy and future risk of poor outcomes. Strategies for asthma management will need to leverage our knowledge of biomarkers and clinical features to create composite scores and risk prediction tools that are clinically applicable. Despite significant progress, many questions remain, and more work is required to accurately identify non-T2 biomarkers. Adoption of phenotyping and more consistent use of biomarkers is needed, and we should continue to encourage this incorporation into practice.

Subtyping children with asthma by clustering analysis of mRNA expression data

Background: Asthma is a heterogeneous disease. There are several phenotypic classifications for childhood asthma.

Methods: Unsupervised consensus cluster analysis was used to classify 36 children with persistent asthma from the GSE65204 dataset. The differentially expressed genes (DEGs) between different asthma subtypes were identified, and weighted gene co-expression network analysis (WGCNA) was carried out. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis was performed for DEGs and critical gene modules. Protein–protein interactions (PPI) were constructed to obtain the hub genes. Finally, differences in the immune microenvironment were analyzed between different subtypes.

Results: Two subtypes (C1, C2) were identified using unsupervised consensus clustering. The DEGs between different asthma subtypes were mainly enriched in immune regulation and the release of inflammatory mediators. The important modular genes screened by WGCNA were mainly enriched in aspects of inflammatory mediator regulation. PPI analysis found 10 hub genes (DRC1, TTC25, DNALI1, DNAI1, DNAI2, PIH1D3, ARMC4, RSPH1, DNAAF3, and DNAH5), and ROC analysis demonstrated that 10 hub genes had a reliably ability to distinguish C1 from C2. And we observed differences between C1 and C2 in their immune microenvironment.

Conclusion: Using the gene expression profiles of children’s nasal epithelium, we identified two asthma subtypes that have different gene expression patterns, biological characteristics, and immune microenvironments. This will provide a reference point for future childhood asthma typing and personalized therapy.

The new era of add-on asthma treatments: where do we stand?

Globally, a small proportion (5-12%) of asthma patients are estimated to have severe disease. However, severe asthma accounts for disproportionately high healthcare resource utilization. The Global Initiative for Asthma (GINA) management committee recommends treating patients with asthma with inhaled corticosteroids plus long-acting β2-agonists and, when needed, adding a long-acting muscarinic receptor antagonist or biologic agent. Five biologics, targeting different effectors in the type 2 inflammatory pathway, are approved for asthma treatment. However, biologics have not been compared against each other or add-on inhaled therapies in head-to-head clinical trials. As a result, their positioning versus that of current and anticipated small-molecule strategies is largely unknown. Furthermore, with the emergence of biomarkers for predicting response to biologics, a more personalized treatment approach-currently lacking with inhaled therapies-may be possible. To gain perspective, we reviewed recent advances in asthma pathophysiology, phenotypes, and biomarkers; the place of biologics in the management and personalized treatment of severe asthma; and the future of biologics and small-molecule drugs. We propose an algorithm for the stepwise treatment of severe asthma based on recommendations in the GINA strategy document that accounts for the broad range of phenotypes targeted by inhaled therapies and the specificity of biologics. In the future, both biologics and small molecules will continue to play key roles in the individualized treatment of severe asthma. However, as targeted therapies, their application will continue to be focused on patients with certain phenotypes who meet the specific criteria for use as identified in pivotal clinical trials.

Association of increased basic salivary proline-rich protein 1 levels in induced sputum with type 2-high asthma

BACKGROUND

The aim of this study is to reveal whether basic salivary proline-rich protein BstNI subfamily 1 (PRB1) may be used as a diagnostic biomarker for type 2-high asthma.

METHODS

PRB1 protein levels in the induced sputum of 67 subjects with asthma and 27 controls were determined by an enzyme-linked immunosorbent assay. Correlation analyses between PRB1 in the induced sputum and airway inflammatory indicators were also performed.

RESULTS

PRB1 protein levels were significantly upregulated in the induced sputum of asthmatic patients (p =0.0098) and correlated with clinical eosinophil-related indicators and type 2 airway inflammation. These results indicate that PRB1 is a promising biomarker for type 2-high asthma.

CONCLUSIONS

The expression of PRB1 in induced sputum is a potential biomarker for type 2-high asthma. The results of this study present new insights into the diagnosis and treatment of asthma.

Childhood asthma- pathogenesis and phenotypes

In the pathogenesis of asthma in children there is a pivotal role for a type 2 inflammatory response to early life exposures or events. Interactions between infections, atopy, genetic susceptibility, and environmental exposures (such as farmyard environment, air pollution, tobacco smoke exposure) influence the development of wheezing illness and the risk for progression to asthma. The immune system, lung function and the microbiome in gut and airways develop in parallel and dysbiosis of the microbiome may be a critical factor in asthma development. Increased infant weight gain and preterm birth are other risk factors for development of asthma and reduced lung function. The complex interplay between these factors explains the heterogeneity of asthma in children. Subgroups of patients can be identified as phenotypes based on clinical parameters, or endotypes, based on a specific pathophysiological mechanism. Paediatric asthma phenotypes and endotypes may ultimately help to improve diagnosis of asthma, prediction of asthma development and treatment of individual children, based on clinical, temporal, developmental or inflammatory characteristics. Unbiased, data-driven clustering, using a multidimensional or systems biology approach may be needed to better define phenotypes. The present knowledge on inflammatory phenotypes of childhood asthma has now been successfully applied in the treatment with biologicals of children with severe therapy resistant asthma, and it is to be expected that more personalized treatment options may become available.

Repetitive aeroallergen challenges elucidate maladaptive epithelial and inflammatory traits that underpin allergic airway diseases

BACKGROUND

Signifying the 2-compartments/1-disease paradigm, allergic rhinoconjunctivitis (ARC) and asthma (AA) are prevalent, comorbid conditions triggered by environmental factors (eg, house dust mites [HDMs]). However, despite the ubiquity of triggers, progression to severe ARC/AA is infrequent, suggesting either resilience or adaptation.

OBJECTIVE

We sought to determine whether ARC/AA severity relates to maladaptive responses to disease triggers.

METHODS

Adults with HDM-associated ARC were challenged repetitively with HDMs in an aeroallergen challenge chamber. Mechanistic traits associated with disease severity were identified.

RESULTS

HDM challenges evoked maladaptive (persistently higher ARC symptoms), adaptive (progressive symptom reduction), and resilient (resistance to symptom induction) phenotypes. Symptom severity in the natural environment was an imprecise correlate of the phenotypes. Nasal airway traits, defined by low inflammation-effectual epithelial integrity, moderate inflammation-effectual epithelial integrity, and higher inflammation-ineffectual epithelial integrity, were hallmarks of the resilient, adaptive, and maladaptive evoked phenotypes, respectively. Highlighting a crosstalk mechanism, peripheral blood inflammatory tone calibrated these traits: ineffectual epithelial integrity associated with CD8⁺ T cells, whereas airway inflammation associated with both CD8⁺ T cells and eosinophils. Hallmark peripheral blood maladaptive traits were increased natural killer and CD8⁺ T cells, lower CD4⁺ mucosal-associated invariant T cells, and deficiencies along the TLR-IRF-IFN antiviral pathway. Maladaptive traits tracking HDM-associated ARC also contributed to AA risk and severity models.

CONCLUSIONS

Repetitive challenges with HDMs revealed that maladaptation to disease triggers may underpin ARC/AA disease severity. A combinatorial therapeutic approach may involve reversal of loss-of-beneficial-function traits (ineffectual epithelial integrity, TLR-IRF-IFN deficiencies), mitigation of gain-of-adverse-function traits (inflammation), and blocking of a detrimental crosstalk between the peripheral blood and airway compartments.

SPLUNC1: a novel marker of cystic fibrosis exacerbations

Acute pulmonary Exacerbations (AE) are episodes of clinical worsening in cystic fibrosis (CF), often precipitated by infection. Timely detection is critical to minimise morbidity and lung function declines associated with acute inflammation during AE. Based on our previous observations that airway protein Short Palate Lung Nasal epithelium Clone 1 (SPLUNC1) is regulated by inflammatory signals, we investigated the use of SPLUNC1 fluctuations to diagnose and predict AE in CF.We enrolled CF participants from two independent cohorts to measure AE markers of inflammation in sputum and recorded clinical outcomes for a 1-year follow-up period.SPLUNC1 levels were high in healthy controls (n=9, 10.7 μg mL^-1), and significantly decreased in CF participants without AE (n=30, 5.7 μg mL^-1, p=0.016). SPLUNC1 levels were 71.9% lower during AE (n=14, 1.6 μg mL^-1, p=0.0034) regardless of age, sex, CF-causing mutation, or microbiology findings. Cytokines Il-1β and TNFα were also increased in AE, whereas lung function did not consistently decrease. Stable CF participants with lower SPLUNC1 levels were much more likely to have an AE at 60 days (HR: 11.49, Standard Error: 0.83, p=0.0033). Low-SPLUNC1 stable participants remained at higher AE risk even one year after sputum collection (HR: 3.21, Standard Error: 0.47, p=0.0125). SPLUNC1 was downregulated by inflammatory cytokines and proteases increased in sputum during AE.In acute CF care, low SPLUNC1 levels could support a decision to increase airway clearance or to initiate pharmacological interventions. In asymptomatic, stable patients, low SPLUNC1 levels could inform changes in clinical management to improve long-term disease control and clinical outcomes in CF.

Single-cell characterization of a model of poly I:C-stimulated peripheral blood mononuclear cells in severe asthma

Background

Asthma has been associated with impaired interferon response. Multiple cell types have been implicated in such response impairment and may be responsible for asthma immunopathology. However, existing models to study the immune response in asthma are limited by bulk profiling of cells. Our objective was to Characterize a model of peripheral blood mononuclear cells (PBMCs) of patients with severe asthma (SA) and its response to the TLR3 agonist Poly I:C using two single-cell methods.

Methods

Two complementary single-cell methods, DropSeq for single-cell RNA sequencing (scRNA-Seq) and mass cytometry (CyTOF), were used to profile PBMCs of SA patients and healthy controls (HC). Poly I:C-stimulated and unstimulated cells were analyzed in this study.

Results

PBMCs (n = 9414) from five SA (n = 6099) and three HC (n = 3315) were profiled using scRNA-Seq. Six main cell subsets, namely CD4 + T cells, CD8 + T cells, natural killer (NK) cells, B cells, dendritic cells (DCs), and monocytes, were identified. CD4 + T cells were the main cell type in SA and demonstrated a pro-inflammatory profile characterized by increased JAK1 expression. Following Poly I:C stimulation, PBMCs from SA had a robust induction of interferon pathways compared with HC. CyTOF profiling of Poly I:C stimulated and unstimulated PBMCs (n = 160,000) from the same individuals (SA = 5; HC = 3) demonstrated higher CD8 + and CD8 + effector T cells in SA at baseline, followed by a decrease of CD8 + effector T cells after poly I:C stimulation.

Conclusions

Single-cell profiling of an in vitro model using PBMCs in patients with SA identified activation of pro-inflammatory pathways at baseline and strong response to Poly I:C, as well as quantitative changes in CD8 + effector cells. Thus, transcriptomic and cell quantitative changes are associated with immune cell heterogeneity in this model to evaluate interferon responses in severe asthma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-021-01709-9.

The inflammatory biomarker YKL-40 is elevated in the serum, but not the sputum, of E-cigarette users

METHODS

We conducted a cross-sectional study of adults between 18 and 55 years old. Inclusion criteria were: exclusive e-cigarette use or cigarette smoking for ≥ 1 year or no history of tobacco use. Participants with a history of pulmonary illness, atopy, medications (except birth control pills), marijuana, and illegal substance use were excluded. Custom Multiplex ELISA was used to measure YKL-40 and other biomarker levels in the serum and induced sputum of the participants. Multivariable linear regression was used to compare the levels of YLK-40 in healthy participants, e-cigarette, and cigarette users after adjusting for age, sex, and BMI.

RESULTS

We recruited 20 healthy controls, 23 cigarette smokers, and 22 exclusive e-cigarette users. Serum YKL-40 (ng/ml) was significantly higher in e-cigarette users (Median 21.2 [IQR 12.1-24.0] ng/ml) when compared to controls (12.2 [IQR 8.7-18.1] ng/ml, p = 0.016) but comparable to cigarette smokers (21.6 [IQR 11.62-51.7] ng/ml, p = 0.31). No significant differences were found in the serum or sputum of the other biomarkers tested.

CONCLUSION

The inflammatory biomarker, YKL-40 is elevated in the serum but not the sputum of e-cigarette users with no reported pulmonary disease. Further research is necessary to characterize this association.

Regulation of immune responses by the airway epithelial cell landscape

The community of cells lining our airways plays a collaborative role in the preservation of immune homeostasis in the lung and provides protection from the pathogens and pollutants in the air we breathe. In addition to its structural attributes that provide effective mucociliary clearance of the lower airspace, the airway epithelium is an immunologically active barrier surface that senses changes in the airway environment and interacts with resident and recruited immune cells. Single-cell RNA-sequencing is illuminating the cellular heterogeneity that exists in the airway wall and has identified novel cell populations with unique molecular signatures, trajectories of differentiation and diverse functions in health and disease. In this Review, we discuss how our view of the airway epithelial landscape has evolved with the advent of transcriptomic approaches to cellular phenotyping, with a focus on epithelial interactions with the local neuronal and immune systems.

Role of nuclear factor of activated T cells 2 (NFATc2) in allergic asthma

BACKGROUND

We recently described increased NFATc1, IRF4, and NIP45 messenger RNA (mRNA) expression in peripheral blood mononuclear cells (PBMCs) of asthmatic children and adults with multiple allergies.

OBJECTIVE

NFATc2 has been described to associate with IRF4 to induce interleukin-4, and to be inhibited by T-bet. Here, we analyzed the role of NFATc2 in asthmatic children and adults.

METHODS

PBMCs were isolated from the blood of control of asthmatics subjects. Some PBMCs were analyzed untreated and some cultured with and without phytohemagglutinin. Then, RNA was extracted from the cells and cytokines were measured in the supernatants via enzyme-linked immunosorbent assay or multiplex analysis. RNA was then reverse-transcribed and NFATc1, NFATC2, IRF4, and T-bet mRNA were analyzed by real-time polymerase chain reaction. In addition, in peripheral blood cells, NFATc2 expression was analyzed, in a population of asthmatic children and adults from the Asthma BRIDGE study.

RESULTS

In addition to NFATc1 and NIP45, also NFATc2 was found upregulated in PBMCs and peripheral blood cells from asthmatic children and adults with allergic asthma. Moreover, NFATc1 directly correlated with lymphocytes number whereas NFATc2 correlated with peripheral eosinophilia in asthma.

CONCLUSIONS

In addition to NFATc1 and NIP45, NFATc2 was found upregulated in asthma. Moreover, NFATc1 mRNA correlated with lymphocytes both in control and asthma, and NFATC1 and NFATc2 mRNA showed a direct correlation with eosinophils in controls but not in asthma, indicating that NFATc1 is associated with lymphocytes and not eosinophils in asthma.

CLINICAL SIGNIFICANCE

Targeting NFATc2 in T lymphocytes might ameliorate the allergic phenotype in asthmatic subjects.

Exploration of the sputum methylome and omics deconvolution by quadratic programming in molecular profiling of asthma and COPD: the road to sputum omics 2.0

BACKGROUND

To date, most studies involving high-throughput analyses of sputum in asthma and COPD have focused on identifying transcriptomic signatures of disease. No whole-genome methylation analysis of sputum cells has been performed yet. In this context, the highly variable cellular composition of sputum has potential to confound the molecular analyses.

METHODS

Whole-genome transcription (Agilent Human 4 × 44 k array) and methylation (Illumina 450 k BeadChip) analyses were performed on sputum samples of 9 asthmatics, 10 healthy and 10 COPD subjects. RNA integrity was checked by capillary electrophoresis and used to correct in silico for bias conferred by RNA degradation during biobank sample storage. Estimates of cell type-specific molecular profiles were derived via regression by quadratic programming based on sputum differential cell counts. All analyses were conducted using the open-source R/Bioconductor software framework.

RESULTS

A linear regression step was found to perform well in removing RNA degradation-related bias among the main principal components of the gene expression data, increasing the number of genes detectable as differentially expressed in asthma and COPD sputa (compared to controls). We observed a strong influence of the cellular composition on the results of mixed-cell sputum analyses. Exemplarily, upregulated genes derived from mixed-cell data in asthma were dominated by genes predominantly expressed in eosinophils after deconvolution. The deconvolution, however, allowed to perform differential expression and methylation analyses on the level of individual cell types and, though we only analyzed a limited number of biological replicates, was found to provide good estimates compared to previously published data about gene expression in lung eosinophils in asthma. Analysis of the sputum methylome indicated presence of differential methylation in genomic regions of interest, e.g. mapping to a number of human leukocyte antigen (HLA) genes related to both major histocompatibility complex (MHC) class I and II molecules in asthma and COPD macrophages. Furthermore, we found the SMAD3 (SMAD family member 3) gene, among others, to lie within differentially methylated regions which has been previously reported in the context of asthma.

CONCLUSIONS

In this methodology-oriented study, we show that methylation profiling can be easily integrated into sputum analysis workflows and exhibits a strong potential to contribute to the profiling and understanding of pulmonary inflammation. Wherever RNA degradation is of concern, in silico correction can be effective in improving both sensitivity and specificity of downstream analyses. We suggest that deconvolution methods should be integrated in sputum omics analysis workflows whenever possible in order to facilitate the unbiased discovery and interpretation of molecular patterns of inflammation.

Latent-space embedding of expression data identifies gene signatures from sputum samples of asthmatic patients

Background

The pathogenesis of asthma is a complex process involving multiple genes and pathways. Identifying biomarkers from asthma datasets, especially those that include heterogeneous subpopulations, is challenging. Potentially, autoencoders provide ideal frameworks for such tasks as they can embed complex, noisy high-dimensional gene expression data into a low-dimensional latent space in an unsupervised fashion, enabling us to extract distinguishing features from expression data.

Results

Here, we developed a framework combining a denoising autoencoder and a supervised learning classifier to identify gene signatures related to asthma severity. Using the trained autoencoder with 50 hidden units, we found that hierarchical clustering on the low-dimensional embedding corresponds well with previously defined and clinically relevant clusters of patients. Moreover, each hidden unit has contributions from each of the genes, and pathway analysis of these contributions shows that the hidden units are significantly enriched in known asthma-related pathways. We then used genes that contribute most to the hidden units to develop a secondary random-forest classifier for directly predicting asthma severity. The feature importance metric from this classifier identified a signature based on 50 key genes, which are associated with severity. Furthermore, we can use these key genes to successfully estimate FEV1/FVC ratios across patients, via support-vector-machine regression.

Conclusion

We found that the denoising autoencoder framework can extract meaningful patterns corresponding to functional gene groups and patient clusters from the gene expression of asthma patients.

Profiling cellular heterogeneity in asthma with single cell multiparameter CyTOF

Asthma is a chronic inflammatory disease of the airways that afflicts over 30 million individuals in the United States and over 300 million individuals worldwide. The inflammatory response in the airways is often characterized by the analysis of sputum, which contains multiple types of cells including neutrophils, macrophages, lymphocytes, and rare bronchial epithelial cells. Subtyping patients using microscopy of the sputum has identified both neutrophilic and eosinophilic infiltrates in airway inflammation. However, with the extensive heterogeneity among these cell types, a higher resolution understanding of the inflammatory cell types present in the sputum is needed to dissect the heterogeneity of disease. Improved recognition of the distinct phenotypes and sources of inflammation in asthmatic granulocytes may identify relevant pathways for clinical management or investigation of novel therapeutic mediators. Here, we employed mass cytometry or cytometry by time-of-flight to quantify frequency and define functional status of sputum derived airway cells in asthmatic patients and healthy controls. This in-depth single cell analysis method identified multiple distinct subtypes of airway immune cells, especially in neutrophils. Significance was discovered by statistical analysis as well as a data-driven unbiased clustering approach. Our multidimensional assessment method identifies differences in cellular function and supports identification of cellular status that may contribute to diverse clinical responses. This technical advance is relevant for studies of pathogenesis and may provide meaningful insights to advance our knowledge of asthmatic inflammation.

A Sputum 6 Gene Expression Signature Predicts Inflammatory Phenotypes and Future Exacerbations of COPD

Background

The 6 gene expression signature (6GS) predicts inflammatory phenotype, exacerbation risk, and corticosteroid responsiveness in asthma. In COPD, patterns of airway inflammation are similar, suggesting the 6GS may be useful. This study determines the diagnostic and prognostic ability of 6GS in predicting inflammatory phenotypes and exacerbation risk in COPD.

Methods

We performed 2 studies: a cross-sectional phenotype prediction study in stable COPD (total N=132; n=34 eosinophilic (E)-COPD, n=42 neutrophilic (N)-COPD, n=39 paucigranulocytic (PG)-COPD, n=17 mixed-granulocytic (MG)-COPD) that assessed 6GS ability to discriminate phenotypes (eosinophilia≥3%; neutrophilia≥61%); and a prospective cohort study (total n=54, n=8 E-COPD; n=18 N-COPD; n=20 PG-COPD; n=8 MG-COPD, n=21 exacerbation prone (≥2/year)) that investigated phenotype and exacerbation prediction utility. 6GS was measured by qPCR and evaluated using multiple logistic regression and area under the curve (AUC). Short-term reproducibility (intra-class correlation) and phenotyping method agreement (κ statistic) were assessed.

Results

In the phenotype prediction study, 6GS could accurately identify and discriminate patients with E-COPD from N-COPD (AUC=96.4%; p<0.0001), PG-COPD (AUC=88.2%; p<0.0001) or MG-COPD (AUC=86.2%; p=0.0001), as well as N-COPD from PG-COPD (AUC=83.6%; p<0.0001) or MG-COPD (AUC=87.4%; p<0.0001) and was reproducible. In the prospective cohort study, 6GS had substantial agreement for neutrophilic inflammation (82%, κ=0.63, p<0.001) and moderate agreement for eosinophilic inflammation (78%, κ=0.42, p<0.001). 6GS could significantly discriminate exacerbation prone patients (AUC=77.2%; p=0.034). Higher IL1B levels were associated with poorer lung function and increased COPD severity.

Conclusion

6GS can significantly and reproducibly discriminate COPD inflammatory phenotypes and predict exacerbation prone patients and may become a useful molecular diagnostic tool assisting COPD management.

Are We Meeting the Promise of Endotypes and Precision Medicine in Asthma?

While the term asthma has long been known to describe heterogeneous groupings of patients, only recently have data evolved which enable a molecular understanding of the clinical differences. The evolution of transcriptomics (and other 'omics platforms) and improved statistical analyses in combination with large clinical cohorts opened the door for molecular characterization of pathobiologic processes associated with a range of asthma patients. When linked with data from animal models and clinical trials of targeted biologic therapies, emerging distinctions arose between patients with and without elevations in type 2 immune and inflammatory pathways, leading to the confirmation of a broad categorization of type 2-Hi asthma. Differences in the ratios, sources, and location of type 2 cytokines and their relation to additional immune pathway activation appear to distinguish several different (sub)molecular phenotypes, and perhaps endotypes of type 2-Hi asthma, which respond differently to broad and targeted anti-inflammatory therapies. Asthma in the absence of type 2 inflammation is much less well defined, without clear biomarkers, but is generally linked with poor responses to corticosteroids. Integration of "big data" from large cohorts, over time, using machine learning approaches, combined with validation and iterative learning in animal (and human) model systems is needed to identify the biomarkers and tightly defined molecular phenotypes/endotypes required to fulfill the promise of precision medicine.

A Network of Sputum MicroRNAs Is Associated with Neutrophilic Airway Inflammation in Asthma

Rationale: MicroRNAs are potent regulators of biologic systems that are critical to tissue homeostasis. Individual microRNAs have been identified in airway samples. However, a systems analysis of the microRNA-mRNA networks present in the sputum that contribute to airway inflammation in asthma has not been published.Objectives: Identify microRNA and mRNA networks in the sputum of patients with asthma.Methods: We conducted a genome-wide analysis of microRNA and mRNA in the sputum from patients with asthma and correlated expression with clinical phenotypes. Weighted gene correlation network analysis was implemented to identify microRNA networks (modules) that significantly correlate with clinical features of asthma and mRNA expression networks. MicroRNA expression in peripheral blood neutrophils and lymphocytes and in situ hybridization of the sputum were used to identify the cellular sources of microRNAs. MicroRNA expression obtained before and after ozone exposure was also used to identify changes associated with neutrophil counts in the airway.Measurements and Main Results: Six microRNA modules were associated with clinical features of asthma. A single module (nely) was associated with a history of hospitalizations, lung function impairment, and numbers of neutrophils and lymphocytes in the sputum. Of the 12 microRNAs in the nely module, hsa-miR-223-3p was the highest expressed microRNA in neutrophils and was associated with increased neutrophil counts in the sputum in response to ozone exposure. Multiple microRNAs in the nely module correlated with two mRNA modules enriched for TLR (Toll-like receptor) and T-helper cell type 17 (Th17) signaling and endoplasmic reticulum stress. hsa-miR-223-3p was a key regulator of the TLR and Th17 pathways in the sputum of subjects with asthma.Conclusions: This study of sputum microRNA and mRNA expression from patients with asthma demonstrates the existence of microRNA networks and genes that are associated with features of asthma severity. Among these, hsa-miR-223-3p, a neutrophil-derived microRNA, regulates TLR/Th17 signaling and endoplasmic reticulum stress.

Approaches for integrating heterogeneous RNA-seq data reveal cross-talk between microbes and genes in asthmatic patients

Sputum induction is a non-invasive method to evaluate the airway environment, particularly for asthma. RNA sequencing (RNA-seq) of sputum samples can be challenging to interpret due to the complex and heterogeneous mixtures of human cells and exogenous (microbial) material. In this study, we develop a pipeline that integrates dimensionality reduction and statistical modeling to grapple with the heterogeneity. LDA(Latent Dirichlet allocation)-link connects microbes to genes using reduced-dimensionality LDA topics. We validate our method with single-cell RNA-seq and microscopy and then apply it to the sputum of asthmatic patients to find known and novel relationships between microbes and genes.

Phenotypes and endotypes of adult asthma: Moving toward precision medicine

Asthma is a chronic inflammatory disease of the airways that is challenging to dissect into subgroups because of the heterogeneity present across the spectrum of the disease. Efforts to subclassify asthma using advanced computational methods have identified a number of different phenotypes that suggest that multiple pathobiologically driven clusters of disease exist. The main phenotypes that have been identified include (1) early-onset allergic asthma, (2) early-onset allergic moderate-to-severe remodeled asthma, (3) late-onset nonallergic eosinophilic asthma, and (4) late-onset nonallergic noneosinophilic asthma. Subgroups of these phenotypes also exist but have not been as consistently identified. Advances in our understanding of the diverse immunologic perturbations that drive airway inflammation are consistent with clinical traits associated with these phenotypes and their response to biologic therapies. This has improved the clinician's approach to characterizing asthmatic patients in the clinic. Being able to define asthma endotypes using clinical characteristics and biomarkers will move physicians toward even more personalized management of asthma and precision-based care in the future. Here we will review the most prominent phenotypes and immunologic advances that suggest these disease subtypes represent asthma endotypes.

An endothelial microRNA-1-regulated network controls eosinophil trafficking in asthma and chronic rhinosinusitis

BACKGROUND

Airway eosinophilia is a prominent feature of asthma and chronic rhinosinusitis (CRS), and the endothelium plays a key role in eosinophil trafficking. To date, microRNA-1 (miR-1) is the only microRNA known to be regulated in the lung endothelium in asthma models.

OBJECTIVE

We sought to determine the role of endothelial miR-1 in allergic airway inflammation.

METHODS

We measured microRNA and mRNA expression using quantitative RT-PCR. We used ovalbumin and house dust mite models of asthma. Endothelium-specific overexpression of miR-1 was achieved through lentiviral vector delivery or induction of a transgene. Tissue eosinophilia was quantified by using Congo red and anti-eosinophil peroxidase staining. We measured eosinophil binding with a Sykes-Moore adhesion chamber. Target recruitment to RNA-induced silencing complex was assessed by using anti-Argonaute2 RNA immunoprecipitation. Surface P-selectin levels were measured by using flow cytometry.

RESULTS

Serum miR-1 levels had inverse correlations with sputum eosinophilia, airway obstruction, and number of hospitalizations in asthmatic patients and sinonasal tissue eosinophilia in patients with CRS. IL-13 stimulation decreased miR-1 levels in human lung endothelium. Endothelium-specific overexpression of miR-1 reduced airway eosinophilia and asthma phenotypes in murine models and inhibited IL-13-induced eosinophil binding to endothelial cells. miR-1 recruited P-selectin, thymic stromal lymphopoietin, eotaxin-3, and thrombopoietin receptor to the RNA-induced silencing complex; downregulated these genes in the lung endothelium; and reduced surface P-selectin levels in IL-13-stimulated endothelial cells. In our asthma and CRS cohorts, miR-1 levels correlated inversely with its target genes.

CONCLUSION

Endothelial miR-1 regulates eosinophil trafficking in the setting of allergic airway inflammation. miR-1 has therapeutic potential in asthmatic patients and patients with CRS.

Dysregulation of sputum columnar epithelial cells and products in distinct asthma phenotypes

BACKGROUND

Dysfunction of the bronchial epithelium plays an important role in asthma; however, its measurement is challenging. Columnar epithelial cells are often quantified, yet rarely analysed, in induced sputum studies.

OBJECTIVE

We aimed to test whether sputum columnar epithelial cell proportion and count are altered in asthma, and whether they are associated with clinical and inflammatory variables. We aimed to test whether sputum-based measures could provide a relatively non-invasive means through which to monitor airway epithelial activation status.

METHODS

We examined the relationship of sputum columnar epithelial cells with clinical and inflammatory variables of asthma in a large retrospective cross-sectional cohort (901 participants with asthma and 138 healthy controls). In further studies, we used flow cytometry, microarray, qPCR and ELISA to characterize sputum columnar epithelial cells and their products.

RESULTS

Multivariate analysis and generation of 90th centile cut-offs (≥11% or ≥18.1 × 10⁴ /mL) to identify columnar epithelial cell "high" asthma revealed a significant relationship between elevated sputum columnar cells and male gender, severe asthma and non-neutrophilic airway inflammation. Flow cytometry showed viable columnar epithelial cells were present in all sputum samples tested. An epithelial gene signature (SCGB3A1, LDLRAD1, FOXJ1, DNALI1, CFAP157, CFAP53) was detected in columnar epithelial cell-high sputum. CLCA1 mRNA and periostin protein, previously identified biomarkers of IL-13-mediated epithelial activation, were elevated in columnar epithelial cell-high sputum samples, but only when accompanied by eosinophilia.

CONCLUSIONS & CLINICAL RELEVANCE

Sputum columnar epithelial cells are related to important clinical and inflammatory variables in asthma. Measurement of epithelial biomarkers in sputum samples could allow non-invasive assessment of altered bronchial epithelium status in asthma.

The Genetics and Genomics of Asthma

Asthma is a common, clinically heterogeneous disease with strong evidence of heritability. Progress in defining the genetic underpinnings of asthma, however, has been slow and hampered by issues of inconsistency. Recent advances in the tools available for analysis-assaying transcription, sequence variation, and epigenetic marks on a genome-wide scale-have substantially altered this landscape. Applications of such approaches are consistent with heterogeneity at the level of causation and specify patterns of commonality with a wide range of alternative disease traits. Looking beyond the individual as the unit of study, advances in technology have also fostered comprehensive analysis of the human microbiome and its varied roles in health and disease. In this article, we consider the implications of these technological advances for our current understanding of the genetics and genomics of asthma.

Phenotypes/endotypes-driven treatment in asthma

PURPOSE OF REVIEW

Target therapy is the necessary step towards personalized medicine. The definition of asthma phenotypes and underlying mechanisms (endotypes) represent a key point in the development of new asthma treatments. Big data analysis, biomarker research and the availability of monoclonal antibodies, targeting specific cytokines is leading to the rapid evolution of knowledge. In this review, we sought to outline many of the recent advances in the field.

RECENT FINDINGS

Several attempts have been made to identify asthma phenotypes, sometimes with contrasting results. More success has been obtained concerning the pathogenetic mechanism of specific asthma patterns with the consequent identification of biomarkers and development of effective ad hoc treatment.

SUMMARY

We are in the middle of an extraordinary revolution of our mode of thinking about and approaching asthma. All the effort in the identification of clusters of patients with different disease clinical patterns, prognosis and response to treatment is closely linked to the identification of endotypes (Th2-low and Th2-high). This approach has allowed the development of the specific treatments (anti IgE, Anti IL5 and IL5R) that are now available and is leading to new ones.

A sputum 6-gene signature predicts future exacerbations of poorly controlled asthma

BACKGROUND

Improved diagnostic tools for predicting future exacerbation frequency in asthmatic patients are required. A sputum gene expression signature of 6 biomarkers (6-gene signature [6GS], including Charcot-Leyden crystal galectin [CLC]; carboxypeptidase 3 [CPA3]; deoxyribonuclease 1-like 3 [DNASE1L3]; alkaline phosphatase, liver/bone/kidney [ALPL]; CXCR2; and IL1B) predicts inflammatory and treatment response phenotypes in patients with stable asthma. Recently, we demonstrated that azithromycin (AZM) add-on treatment in patients with uncontrolled moderate-to-severe asthma significantly reduced asthma exacerbations (AMAZES clinical trial).

OBJECTIVES

We sought to test whether the 6GS predicts future exacerbation and inflammatory phenotypes in a subpopulation of AMAZES and to test the effect of AZM therapy on 6GS expression and prognostic capacity.

METHODS

One hundred forty-two patients (73 placebo-treated and 69 AZM-treated patients) had sputum stored for quantitative PCR of 6GS markers at baseline and after 48 weeks of treatment. Logistic regression and receiver operating characteristic and area under the curve (AUC) determination were performed on baseline measures, and in an exploratory analysis the predictive value of the 6GS was compared with conventional biomarkers for exacerbation and inflammatory phenotypes.

RESULTS

The 6GS significantly predicted all future exacerbation phenotypes tested. Calculated AUCs for the 6GS were significantly greater than AUCs for peripheral blood eosinophil counts, sputum neutrophil counts, and combined sputum eosinophil and neutrophil counts. 6GS AUCs were also numerically but not significantly greater than those for fractional exhaled nitric oxide values and sputum eosinophil counts. AZM treatment altered neither 6GS expression nor the predictive capacity of the 6GS for future exacerbation phenotypes. The 6GS was a significant predictor of airway inflammatory phenotype in this population.

CONCLUSION

We demonstrate that a sputum gene signature can predict future exacerbation phenotypes of asthma, with the greatest biomarker performance in identifying those who would experience frequent severe exacerbations. AZM therapy did not modify 6GS expression or biomarker performance, suggesting the therapeutic action of AZM is independent of 6GS-related inflammatory pathways.

Genomic Predictors of Asthma Phenotypes and Treatment Response

Asthma is a complex respiratory disease considered as the most common chronic condition in children. A large genetic contribution to asthma susceptibility is predicted by the clustering of asthma and allergy symptoms among relatives and the large disease heritability estimated from twin studies, ranging from 55 to 90%. Genetic basis of asthma has been extensively investigated in the past 40 years using linkage analysis and candidate-gene association studies. However, the development of dense arrays for polymorphism genotyping has enabled the transition toward genome-wide association studies (GWAS), which have led the discovery of several unanticipated asthma genes in the last 11 years. Despite this, currently known risk variants identified using many thousand samples from distinct ethnicities only explain a small proportion of asthma heritability. This review examines the main findings of the last 2 years in genomic studies of asthma using GWAS and admixture mapping studies, as well as the direction of studies fostering integrative perspectives involving omics data. Additionally, we discuss the need for assessing the whole spectrum of genetic variation in association studies of asthma susceptibility, severity, and treatment response in order to further improve our knowledge of asthma genes and predictive biomarkers. Leveraging the individual's genetic information will allow a better understanding of asthma pathogenesis and will facilitate the transition toward a more precise diagnosis and treatment.

Molecular Endotypes Contribute to the Heterogeneity of Asthma

Diagnosis and management of asthma is commonly implemented based on clinical assessment. Although these nonmolecular biomarkers have been useful, limited resolution of the heterogeneity among asthmatic patients and little information regarding the underlying pathobiology of disease in individuals have been provided. Molecular endotying using global transcriptome expression profiling associated with clinical features of asthma has improved our understanding of disease mechanisms, risk assessment of asthma exacerbations, and treatment responses, especially in patients with type 2 inflammation. Further advances in establishing pathobiological subgroups, bioactive pathways, and true disease endotypes hold potential for a more personalized medical approach in asthmatic patients.

Implication of fraction of exhaled nitric oxide and blood eosinophil count in severe asthma

BACKGROUND

Severe asthma is a complex disease with heterogeneous features and involves type 2 airway inflammation, including eosinophil accumulation. Surrogate biomarkers, fraction of exhaled nitric oxide (FeNO) and blood eosinophil count (b-EOS), may predict eosinophilic airway inflammation. Here we investigated clinical characteristics of severe asthma phenotype using a combined analysis of FeNO and b-EOS.

METHODS

This retrospective study examined clinical data of patients with severe asthma (N = 107; median age, 64 years) treated at Saitama Medical University Hospital from 2009 to 2016. Thresholds of FeNO and b-EOS for sputum eosinophil ratio ≥2% were determined using receiver operating characteristic curve (ROC) analysis. Clinical characteristics were analyzed after classifying patients into four subgroups according to these thresholds.

RESULTS

Of 39 induced sputum samples examined, ROC area under the curve for predicting sputum eosinophilia was 82.0% (p = 0.001) for b-EOS and 77.0% (p = 0.006) for FeNO at optimal cut-off values of ≥300/μL and ≥25 ppb, respectively. The number of sensitized allergens was higher in the high FeNO/low b-EOS and high FeNO/high b-EOS subgroups (p < 0.05). The prevalence of chronic sinusitis was higher in the low FeNO/high b-EOS and high FeNO/high b-EOS subgroups (p = 0.04). The high FeNO/high b-EOS subgroup included the largest proportion (approximately 40%) of patients experiencing frequent severe exacerbations. Both low FeNO/low b-EOS and high FeNO/low b-EOS subgroups showed less severe exacerbations.

CONCLUSIONS

Combined evaluation of FeNO and b-EOS can identify patients with frequent exacerbations and stratify the appropriate therapy for type 2 inflammation-predominant severe asthma.

Genetic profiles of transcriptomic clusters of childhood asthma determine specific severe subtype

BACKGROUND

Previous studies have defined transcriptomic subtypes of adult asthma using samples of induced sputum and bronchial epithelium; however, those procedures are not readily applicable in the clinic, especially for childhood asthma.

OBJECTIVE

We aim to dissect the transcriptomic clusters of childhood asthma using highly variably expressed genes of peripheral blood mononuclear cells (PBMC) among patients.

METHODS

Gene expression of PBMC from 133 asthmatic children and 11 healthy controls was measured with Illumina microarrays. We applied the k-means clustering algorithm of 2048 genes to assign asthmatic children into clusters. Genes with differential expression between asthma clusters and healthy controls were used to investigate whether they could identify severe asthma of children and adults.

RESULTS

We identified 3 asthma clusters with distinct inflammatory profiles in peripheral blood. Cluster 1 had the highest eosinophil count. Cluster 2 showed lower counts of both eosinophils and neutrophils. Cluster 3 had the highest neutrophil count and the poorest treatment control. Compared with other patients, Cluster 3 exhibited a unique gene expression pattern which was associated with changes in the glucocorticoid signalling and activation of the T helper 1/T helper 17 (T_H 1/T_H 17) immune pathways. In the validation studies, an 84-gene signature could identify severe asthma in children on leucocytes, as well as severe asthma in adults on CD8⁺ T cells.

CONCLUSIONS AND CLINICAL RELEVANCE

Gene expression profiling of PBMC is useful for the identification of T_H 1/T_H 17-mediated asthma with poor treatment control. PBMC and CD8⁺ T cells could be important targets for the investigation and identification of severe asthma.

Large-Scale Label-Free Quantitative Mapping of the Sputum Proteome

Analysis of induced sputum supernatant is a minimally invasive approach to study the epithelial lining fluid and, thereby, provide insight into normal lung biology and the pathobiology of lung diseases. We present here a novel proteomics approach to sputum analysis developed within the U-BIOPRED (unbiased biomarkers predictive of respiratory disease outcomes) international project. We present practical and analytical techniques to optimize the detection of robust biomarkers in proteomic studies. The normal sputum proteome was derived using data-independent HDMS^E applied to 40 healthy nonsmoking participants, which provides an essential baseline from which to compare modulation of protein expression in respiratory diseases. The "core" sputum proteome (proteins detected in ≥40% of participants) was composed of 284 proteins, and the extended proteome (proteins detected in ≥3 participants) contained 1666 proteins. Quality control procedures were developed to optimize the accuracy and consistency of measurement of sputum proteins and analyze the distribution of sputum proteins in the healthy population. The analysis showed that quantitation of proteins by HDMS^E is influenced by several factors, with some proteins being measured in all participants' samples and with low measurement variance between samples from the same patient. The measurement of some proteins is highly variable between repeat analyses, susceptible to sample processing effects, or difficult to accurately quantify by mass spectrometry. Other proteins show high interindividual variance. We also highlight that the sputum proteome of healthy individuals is related to sputum neutrophil levels, but not gender or allergic sensitization. We illustrate the importance of design and interpretation of disease biomarker studies considering such protein population and technical measurement variance.

Persistent activation of interlinked type 2 airway epithelial gene networks in sputum-derived cells from aeroallergen-sensitized symptomatic asthmatics

Atopic asthma is a persistent disease characterized by intermittent wheeze and progressive loss of lung function. The disease is thought to be driven primarily by chronic aeroallergen-induced type 2-associated inflammation. However, the vast majority of atopics do not develop asthma despite ongoing aeroallergen exposure, suggesting additional mechanisms operate in conjunction with type 2 immunity to drive asthma pathogenesis. We employed RNA-Seq profiling of sputum-derived cells to identify gene networks operative at baseline in house dust mite-sensitized (HDM^S) subjects with/without wheezing history that are characteristic of the ongoing asthmatic state. The expression of type 2 effectors (IL-5, IL-13) was equivalent in both cohorts of subjects. However, in HDM^S-wheezers they were associated with upregulation of two coexpression modules comprising multiple type 2- and epithelial-associated genes. The first module was interlinked by the hubs EGFR, ERBB2, CDH1 and IL-13. The second module was associated with CDHR3 and mucociliary clearance genes. Our findings provide new insight into the molecular mechanisms operative at baseline in the airway mucosa in atopic asthmatics undergoing natural aeroallergen exposure, and suggest that susceptibility to asthma amongst these subjects involves complex interactions between type 2- and epithelial-associated gene networks, which are not operative in equivalently sensitized/exposed atopic non-asthmatics.

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.

Near-fatal asthma: a heterogeneous clinical entity

PURPOSE OF REVIEW

The aims of the present review were to describe the heterogeneous nature of near-fatal asthma (NFA) and to summarize the distinctive phenotypes identified in this subtype of asthma.

RECENT FINDINGS

Clinical, physiological, and histological studies have shown a large number of triggers, pathological mechanisms, and risk factors associated with NFA. Based on the demographic and clinical characteristics of the patients, the circumstances surrounding the asthma exacerbation and some distinctive features of the disease, several clinical profiles of asthma patients with NFA have been described. Recent data show new associations between some gene expression patterns and fatal asthma, as well as with some biological markers related to inflammatory or immunologic mechanisms in the airways. Also, the use of statistical methods, such as cluster analysis, allowed identifying and confirming different phenotypes of life-threatening asthma patients.

SUMMARY

NFA is a heterogeneous clinical entity, in which different patients' clinical profiles may coexist [e.g. rapid-onset NFA, NFA in patients with dyspnea hypoperception or sensitized to certain pneumoallergens (Alternaria alternata, soybean), NFA related to the menstrual cycle, brittle asthma]. Knowledge of these phenotypes as well as adequate and specific management strategies can reduce morbidity and mortality in patients suffering from NFA.

The R213G polymorphism in SOD3 protects against allergic airway inflammation

Oxidative stress is important in the pathogenesis of allergic asthma. Extracellular superoxide dismutase (EC-SOD; SOD3) is the major antioxidant in lungs, but its role in allergic asthma is unknown. Here we report that asthmatics have increased SOD3 transcript levels in sputum and that a single nucleotide polymorphism (SNP) in SOD3 (R213G; rs1799895) changes lung distribution of EC-SOD, and decreases likelihood of asthma-related symptoms. Knockin mice analogous to the human R213G SNP had lower airway hyperresponsiveness, inflammation, and mucus hypersecretion with decreased interleukin-33 (IL-33) in bronchoalveolar lavage fluid and reduced type II innate lymphoid cells (ILC2s) in lungs. SOD mimetic (Mn (III) tetrakis (N-ethylpyridinium-2-yl) porphyrin) attenuated Alternaria-induced expression of IL-33 and IL-8 release in BEAS-2B cells. These results suggest that R213G SNP potentially benefits its carriers by resulting in high EC-SOD in airway-lining fluid, which ameliorates allergic airway inflammation by dampening the innate immune response, including IL-33/ST2-mediated changes in ILC2s.

A Transcriptome-driven Analysis of Epithelial Brushings and Bronchial Biopsies to Define Asthma Phenotypes in U-BIOPRED

RATIONALE

Asthma is a heterogeneous disease driven by diverse immunologic and inflammatory mechanisms.

OBJECTIVES

Using transcriptomic profiling of airway tissues, we sought to define the molecular phenotypes of severe asthma.

METHODS

The transcriptome derived from bronchial biopsies and epithelial brushings of 107 subjects with moderate to severe asthma were annotated by gene set variation analysis using 42 gene signatures relevant to asthma, inflammation, and immune function. Topological data analysis of clinical and histologic data was performed to derive clusters, and the nearest shrunken centroid algorithm was used for signature refinement.

MEASUREMENTS AND MAIN RESULTS

Nine gene set variation analysis signatures expressed in bronchial biopsies and airway epithelial brushings distinguished two distinct asthma subtypes associated with high expression of T-helper cell type 2 cytokines and lack of corticosteroid response (group 1 and group 3). Group 1 had the highest submucosal eosinophils, as well as high fractional exhaled nitric oxide levels, exacerbation rates, and oral corticosteroid use, whereas group 3 patients showed the highest levels of sputum eosinophils and had a high body mass index. In contrast, group 2 and group 4 patients had an 86% and 64% probability, respectively, of having noneosinophilic inflammation. Using machine learning tools, we describe an inference scheme using the currently available inflammatory biomarkers sputum eosinophilia and fractional exhaled nitric oxide levels, along with oral corticosteroid use, that could predict the subtypes of gene expression within bronchial biopsies and epithelial cells with good sensitivity and specificity.

CONCLUSIONS

This analysis demonstrates the usefulness of a transcriptomics-driven approach to phenotyping that segments patients who may benefit the most from specific agents that target T-helper cell type 2-mediated inflammation and/or corticosteroid insensitivity.

Biomarkers and asthma management: an update

PURPOSE OF REVIEW

Asthma is heterogeneous with different endotypes/phenotypes. Response to corticosteroids is variable and novel biological therapies are proving useful. Biomarkers allow individualization of treatment. This review provides an update on available data regarding asthma biomarkers with focus on their utility for prediction of response to steroidal and new biological therapies.

RECENT FINDINGS

Blood eosinophils are a biomarker with acceptable accuracy as a surrogate for sputum eosinophilia, are associated with relevant outcomes, and are more readily measureable. New evidence supports fraction of exhaled nitric oxide (FENO)-based treatment algorithms for cost-effective maintenance of asthma control/quality of life. Serum and sputum-derived periostin are biomarkers of lung function decline and associated with eosinophilic airway inflammation. Transcriptomics show promise for endotyping; their role in management remains to be determined. Biomarker panels may improve predictive value as shown for the combination of FENO/urinary bromotyrosine in prediction of steroid responsiveness. Novel biological therapies are proving effective in biomarker-selected populations.

SUMMARY

Biomarkers including blood eosinophils and FENO are proving to have utility for the effective administration of steroidal and novel biological therapies in asthma, allowing individualized treatment. Transcriptomics can discriminate subtypes of asthma and may have a role in delivery of individualized therapy.

The Reemergence of the Asthma-COPD Overlap Syndrome: Characterizing a Syndrome in the Precision Medicine Era

PURPOSE OF REVIEW

The asthma-COPD overlap syndrome (ACOS) has reemerged in the medical literature. This review addresses our current understanding of ACOS as a clinical and biological entity and how new and existing therapies may be targeted to this group.

RECENT FINDINGS

Many studies suggest that ACOS is common and associated with more morbidity than asthma and COPD in general. However, there is no consensus on an ACOS definition, likely due to the heterogeneity of the disease. Variable definitions have led to variable results in ACOS studies. Given this clinical variability, biomarkers (e.g., eosinophils and type 2 inflammatory markers) are increasingly being used to identify an ACOS molecular phenotype which appears to be more responsive to inhaled corticosteroids. Although ACOS has become a popular diagnosis, it is unclear whether identifying ACOS as a separate disease entity is clinically useful. Future studies should focus on identifying key clinical features and biomarkers that characterize vulnerable and treatment-responsive patients.

Biomarkers of Airway Type-2 Inflammation and Integrating Complex Phenotypes to Endotypes in Asthma

PURPOSE OF REVIEW

Over the past decade, the most important advance in the field of asthma has been the widespread recognition that asthma is a heterogeneous disease driven by multiple molecular processes.

RECENT FINDINGS

The most well-established molecular mechanism in asthma is increased airway type-2 inflammation, and consequently, non-invasive biomarkers of increased airway type-2 inflammation, such as blood eosinophil counts or blood periostin levels, have proven important in stratifying asthma patients in clinical trials of type-2 cytokine inhibitors. However, it remains ambiguous how well these non-invasive biomarkers represent airway measures of type-2 inflammation in asthma. As a result, the utility of these biomarkers to assist with asthma management or as research tools to better understand asthma pathogenesis remains unclear. This article reviews primary data assessing biomarkers of airway type-2 inflammation in asthma and describes how the use of biomarkers can advance a precision medicine approach to asthma treatment.

Spectrum of T-lymphocyte activities regulating allergic lung inflammation

Despite advances in the treatment of asthma, optimization of symptom control remains an unmet need in many patients. These patients, labeled severe asthma, are responsible for a substantial fraction of the disease burden. In these patients, research is needed to define the cellular and molecular pathways contributing to disease which in large part are refractory to corticosteroid treatment. The causes of steroid-resistant asthma are multifactorial and result from complex interactions of genetics, environmental factors, and innate and adaptive immunity. Adaptive immunity, addressed here, integrates the activities of distinct T-cell subsets and by definition is dynamic and responsive to an ever-changing environment and the influences of epigenetic modifications. These T-cell subsets exhibit different susceptibilities to the actions of corticosteroids and, in some, corticosteroids enhance their functional activation. Moreover, these subsets are not fixed in lineage differentiation but can undergo transcriptional reprogramming in a bidirectional manner between protective and pathogenic effector states. Together, these factors contribute to asthma heterogeneity between patients but also in the same patient at different stages of their disease. Only by carefully defining mechanistic pathways, delineating their sensitivity to corticosteroids, and determining the balance between regulatory and effector pathways will precision medicine become a reality with selective and effective application of targeted therapies.

A novel pathway-based distance score enhances assessment of disease heterogeneity in gene expression

BACKGROUND

Distance based unsupervised clustering of gene expression data is commonly used to identify heterogeneity in biologic samples. However, high noise levels in gene expression data and relatively high correlation between genes are often encountered, so traditional distances such as Euclidean distance may not be effective at discriminating the biological differences between samples. An alternative method to examine disease phenotypes is to use pre-defined biological pathways. These pathways have been shown to be perturbed in different ways in different subjects who have similar clinical features. We hypothesize that differences in the expressions of genes in a given pathway are more predictive of differences in biological differences compared to standard approaches and if integrated into clustering analysis will enhance the robustness and accuracy of the clustering method. To examine this hypothesis, we developed a novel computational method to assess the biological differences between samples using gene expression data by assuming that ontologically defined biological pathways in biologically similar samples have similar behavior.

RESULTS

Pre-defined biological pathways were downloaded and genes in each pathway were used to cluster samples using the Gaussian mixture model. The clustering results across different pathways were then summarized to calculate the pathway-based distance score between samples. This method was applied to both simulated and real data sets and compared to the traditional Euclidean distance and another pathway-based clustering method, Pathifier. The results show that the pathway-based distance score performs significantly better than the Euclidean distance, especially when the heterogeneity is low and genes in the same pathways are correlated. Compared to Pathifier, we demonstrated that our approach achieves higher accuracy and robustness for small pathways. When the pathway size is large, by downsampling the pathways into smaller pathways, our approach was able to achieve comparable performance.

CONCLUSIONS

We have developed a novel distance score that represents the biological differences between samples using gene expression data and pre-defined biological pathway information. Application of this distance score results in more accurate, robust, and biologically meaningful clustering results in both simulated data and real data when compared to traditional methods. It also has comparable or better performance compared to Pathifier.

Gene Expression Profiling in Blood Provides Reproducible Molecular Insights into Asthma Control

RATIONALE

Maintaining optimal symptom control remains the primary objective of asthma treatment. Better understanding of the biologic underpinnings of asthma control may lead to the development of improved clinical and pharmaceutical approaches.

OBJECTIVES

To identify molecular pathways and interrelated genes whose differential expression was associated with asthma control.

METHODS

We performed gene set enrichment analyses of asthma control in 1,170 adults with asthma, each with gene expression data derived from either whole blood (WB) or unstimulated CD4+ T lymphocytes (CD4), and a self-reported asthma control score representing either the preceding 6 months (chronic) or 7 days (acute). Our study comprised a discovery WB cohort (n = 245, chronic) and three independent, nonoverlapping replication cohorts: a second WB set (n = 448, acute) and two CD4 sets (n = 300, chronic; n = 77, acute).

MEASUREMENTS AND MAIN RESULTS

In the WB discovery cohort, we found significant overrepresentation of genes associated with asthma control in 1,106 gene sets from the Molecular Signatures Database (false discovery rate, <5%). Of these, 583 (53%) replicated in at least one replication cohort (false discovery rate, <25%). Suboptimal control was associated with signatures of eosinophilic and granulocytic inflammatory signals, whereas optimal control signatures were enriched for immature lymphocytic patterns. These signatures included two related biologic processes related to activation by TREM-1 (triggering receptor expressed on myeloid cells 1) and lipopolysaccharide.

CONCLUSIONS

Together, these results demonstrate the existence of specific, reproducible transcriptomic components in blood that vary with degree of asthma control and implicate a novel biologic target (TREM-1).