Epigenetics in asthma and COPD

Epigenetic mechanisms are likely to play a role in many complex diseases, the extent of which we only beginning to understand. COPD and asthma are two respiratory diseases subject to strong environmental influences depending on underlying genetic susceptibility. Epigenetic mechanisms such as DNA methylation, histone modification and microRNA may be involved in these processes by modulating environmental effects to influence disease development. Given their demonstrated modifiable nature, epigenetic mechanisms may open new possibilities for therapeutic intervention. Here we give an overview of recent developments in the field of respiratory epigenetics in relation to asthma and COPD in the context of our current understanding of mechanisms leading to such diseases.     

Personalized medicine with biologics for severe type 2 asthma: current status and future prospects

Asthma affects more than 300 million people worldwide and poses a large socioeconomic burden, particularly in the 5% to 10% of severe asthmatics. So far, each entry of new biologics in clinical trials has led to high expectations for treating all severe asthma forms, but the outcome has only been successful if the biologic, as add-on treatment, targeted specific patient subgroups. Indeed, we now realize that asthma is a heterogeneous disease with multiple phenotypes, based on distinct pathophysiological mechanisms, called endotypes. Thus, asthma therapy is gradually moving to a personalized medicine approach, tailored to individual's asthma endotypes identified through biomarkers. Here, we review the clinical efficacy of antibody-related therapeutics undergoing clinical trials, or those already approved, for the treatment of severe type 2 asthma. Biologics targeting type 2 cytokines have shown consistent efficacy, especially in patients with evidence of type 2 inflammation, suggesting that the future of asthma biologics is promising.     

Microbiota: A Missing Link in The Pathogenesis of Chronic Lung Inflammatory Diseases

Chronic respiratory diseases account for high morbidity and mortality, with asthma, chronic obstructive pulmonary disease (COPD), and cystic fibrosis (CF) being the most prevalent globally. Even though the diseases increase in prevalence, the exact underlying mechanisms have still not been fully understood. Despite their differences in nature, pathophysiologies, and clinical phenotypes, a growing body of evidence indicates that the presence of lung microbiota can shape the pathogenic processes underlying chronic inflammation, typically observed in the course of the diseases. Therefore, the characterization of the lung microbiota may shed new light on the pathogenesis of these diseases. Specifically, in chronic respiratory tract diseases, the human microbiota may contribute to the disease’s development and severity. The present review explores the role of the microbiota in the area of chronic pulmonary diseases, especially COPD, asthma, and CF.     

Asthma, the ugly duckling of lung disease proteomics?

The human respiratory system represents a vital but vulnerable system. It is a major target for many diseases such as cancer and asthma. The incidence of these diseases has increased dramatically in the last 40-50 years. In the search for possible new therapies, many experimental tools and methods have been developed to study these diseases, ranging from animal models to in vitro studies. In the last decades, genomic and proteomic approaches have gained a lot of attention. After the major scientific breakthroughs in the field of genomics, it is now widely accepted that to understand biological processes, large-scale protein studies through proteomics techniques are required. In the battle against lung cancer, the proteomics approach has already been successfully implemented. Surprisingly, only a few proteomics studies on the ever-increasing global asthma problem have been published so far. And although proteomics also has its limitations and experimental difficulties, in our opinion, proteomics can definitely contribute to the understanding of a complex disease such as asthma. Therefore, the additional values and possibilities of proteomics in asthma research should be thoroughly investigated. A close collaboration between the different scientific disciplines may eventually lead to the development of new therapeutic strategies against asthma.     

Biomarkers of some pulmonary diseases in exhaled breath

Analysis of various biomarkers in exhaled breath allows completely non-invasive monitoring of inflammation and oxidative stress in the respiratory tract in inflammatory lung diseases, including asthma, chronic obstructive pulmonary disease (COPD), cystic fibrosis (CF), bronchiectasis and interstitial lung diseases. The technique is simple to perform, may be repeated frequently, and can be applied to children, including neonates, and patients with severe disease in whom more invasive procedures are not possible. Several volatile chemicals can be measured in the breath (nitric oxide, carbon monoxide, ammonia), and many non-volatile molecules (mediators, oxidation and nitration products, proteins) may be measured in exhaled breath condensate. Exhaled breath analysis may be used to quantify inflammation and oxidative stress in the respiratory tract, in differential diagnosis of airway disease and in the monitoring of therapy. Most progress has been made with exhaled nitric oxide (NO), which is increased in atopic asthma, is correlated with other inflammatory indices and is reduced by treatment with corticosteroids and antileukotrienes, but not (β₂-agonists. In contrast, exhaled NO is normal in COPD, reduced in CF and diagnostically low in primary ciliary dyskinesia. Exhaled carbon monoxide (CO) is increased in asthma, COPD and CF. Increased concentrations of 8-isoprostane, hydrogen peroxide, nitrite and 3-nitrotyrosine are found in exhaled breath condensate in inflammatory lung diseases. Furthermore, increased levels of lipid mediators are found in these diseases, with a differential pattern depending on the nature of the disease process. In the future it is likely that smaller and more sensitive analysers will extend the discriminatory value of exhaled breath analysis and that these techniques may be available to diagnose and monitor respiratory diseases in the general practice and home setting.     

Data-Driven Asthma Endotypes Defined from Blood Biomarker and Gene Expression Data

The diagnosis and treatment of childhood asthma is complicated by its mechanistically distinct subtypes (endotypes) driven by genetic susceptibility and modulating environmental factors. Clinical biomarkers and blood gene expression were collected from a stratified, cross-sectional study of asthmatic and non-asthmatic children from Detroit, MI. This study describes four distinct asthma endotypes identified via a purely data-driven method. Our method was specifically designed to integrate blood gene expression and clinical biomarkers in a way that provides new mechanistic insights regarding the different asthma endotypes. For example, we describe metabolic syndrome-induced systemic inflammation as an associated factor in three of the four asthma endotypes. Context provided by the clinical biomarker data was essential in interpreting gene expression patterns and identifying putative endotypes, which emphasizes the importance of integrated approaches when studying complex disease etiologies. These synthesized patterns of gene expression and clinical markers from our research may lead to development of novel serum-based biomarker panels.     

Exhaled volatile organic compounds for phenotyping chronic obstructive pulmonary disease: a cross-sectional study

Background

Non-invasive phenotyping of chronic respiratory diseases would be highly beneficial in the personalised medicine of the future. Volatile organic compounds can be measured in the exhaled breath and may be produced or altered by disease processes. We investigated whether distinct patterns of these compounds were present in chronic obstructive pulmonary disease (COPD) and clinically relevant disease phenotypes.

Methods

Breath samples from 39 COPD subjects and 32 healthy controls were collected and analysed using gas chromatography time-of-flight mass spectrometry. Subjects with COPD also underwent sputum induction. Discriminatory compounds were identified by univariate logistic regression followed by multivariate analysis: 1. principal component analysis; 2. multivariate logistic regression; 3. receiver operating characteristic (ROC) analysis.

Results

Comparing COPD versus healthy controls, principal component analysis clustered the 20 best-discriminating compounds into four components explaining 71% of the variance. Multivariate logistic regression constructed an optimised model using two components with an accuracy of 69%. The model had 85% sensitivity, 50% specificity and ROC area under the curve of 0.74. Analysis of COPD subgroups showed the method could classify COPD subjects with far greater accuracy. Models were constructed which classified subjects with ≥2% sputum eosinophilia with ROC area under the curve of 0.94 and those having frequent exacerbations 0.95. Potential biomarkers correlated to clinical variables were identified in each subgroup.

Conclusion

The exhaled breath volatile organic compound profile discriminated between COPD and healthy controls and identified clinically relevant COPD subgroups. If these findings are validated in prospective cohorts, they may have diagnostic and management value in this disease.     

Recent mass spectrometry-based proteomics for biomarker discovery in lung cancer,COPD, and asthma

Introduction: Lung cancer and related diseases have been one of the most common causes of deaths worldwide. Genomic-based biomarkers may hardly reflect the underlying dynamic molecular mechanism of functional protein interactions, which is the center of a disease. Recent developments in mass spectrometry (MS) have made it possible to analyze disease-relevant proteins expressed in clinical specimens by proteomic challenges.

Areas covered: To understand the molecular mechanisms of lung cancer and its subtypes, chronic obstructive pulmonary disease (COPD), asthma and others, great efforts have been taken to identify numerous relevant proteins by MS-based clinical proteomic approaches. Since lung cancer is a multifactorial disease that is biologically associated with asthma and COPD among various lung diseases, this study focused on proteomic studies on biomarker discovery using various clinical specimens for lung cancer, COPD, and asthma.

Expert commentary: MS-based exploratory proteomics utilizing clinical specimens, which can incorporate both experimental and bioinformatic analysis of protein-protein interaction and also can adopt proteogenomic approaches, makes it possible to reveal molecular networks that are relevant to a disease subgroup and that could differentiate between drug responders and non-responders, good and poor prognoses, drug resistance, and so on.     

Two Distinct Chronic Obstructive Pulmonary Disease (COPD) Phenotypes Are Associated with High Risk of Mortality

Rationale

In COPD patients, mortality risk is influenced by age, severity of respiratory disease, and comorbidities. With an unbiased statistical approach we sought to identify clusters of COPD patients and to examine their mortality risk.

Methods

Stable COPD subjects (n = 527) were classified using hierarchical cluster analysis of clinical, functional and imaging data. The relevance of this classification was validated using prospective follow-up of mortality.

Results

The most relevant patient classification was that based on three clusters (phenotypes). Phenotype 1 included subjects at very low risk of mortality, who had mild respiratory disease and low rates of comorbidities. Phenotype 2 and 3 were at high risk of mortality. Phenotype 2 included younger subjects with severe airflow limitation, emphysema and hyperinflation, low body mass index, and low rates of cardiovascular comorbidities. Phenotype 3 included older subjects with less severe respiratory disease, but higher rates of obesity and cardiovascular comorbidities. Mortality was associated with the severity of airflow limitation in Phenotype 2 but not in Phenotype 3 subjects, and subjects in Phenotype 2 died at younger age.

Conclusions

We identified three COPD phenotypes, including two phenotypes with high risk of mortality. Subjects within these phenotypes may require different therapeutic interventions to improve their outcome.     

Microbial Communities in the Upper Respiratory Tract of Patients with Asthma and Chronic Obstructive Pulmonary Disease

Respiratory infections are well-known triggers of chronic respiratory diseases. Recently, culture-independent tools have indicated that lower airway microbiota may contribute to pathophysiologic processes associated with asthma and chronic obstructive pulmonary disease (COPD). However, the relationship between upper airway microbiota and chronic respiratory diseases remains unclear. This study was undertaken to define differences of microbiota in the oropharynx of asthma and COPD patients relative to those in healthy individuals. To account for the qualitative and quantitative diversity of the 16S rRNA gene in the oropharynx, the microbiomes of 18 asthma patients, 17 COPD patients, and 12 normal individuals were assessed using a high-throughput next-generation sequencing analysis. In the 259,572 total sequence reads, α and β diversity measurements and a generalized linear model revealed that the oropharynx microbiota are diverse, but no significant differences were observed between asthma and COPD patients. Pseudomonas spp. of Proteobacteria and Lactobacillus spp. of Firmicutes were highly abundant in asthma and COPD. By contrast, Streptococcus, Veillonella, Prevotella, and Neisseria of Bacteroidetes dominated in the healthy oropharynx. These findings are consistent with previous studies conducted in the lower airways and suggest that oropharyngeal airway microbiota are important for understanding the relationships between the various parts of the respiratory tract with regard to bacterial colonization and comprehensive assessment of asthma and COPD.     

Recent insights into human bronchial proteomics – how are we progressing and what is next?

Introduction: The human respiratory system is highly prone to diseases and complications. Many lung diseases, including lung cancer (LC), tuberculosis (TB), and chronic obstructive pulmonary disease (COPD) have been among the most common causes of death worldwide. Cystic fibrosis (CF), the most common genetic disease in Caucasians, has adverse impacts on the lungs. Bronchial proteomics plays a significant role in understanding the underlying mechanisms and pathogenicity of lung diseases and provides insights for biomarker and therapeutic target discoveries.

Areas covered: We overview the recent achievements and discoveries in human bronchial proteomics by outlining how some of the different proteomic techniques/strategies are developed and applied in LC, TB, COPD, and CF. Also, the future roles of bronchial proteomics in predictive proteomics and precision medicine are discussed.

Expert commentary: Much progress has been made in bronchial proteomics. Owing to the advances in proteomics, we now have better ability to isolate proteins from desired cellular compartments, greater protein separation methods, more powerful protein detection technologies, and more sophisticated bioinformatic techniques. These all contributed to our further understanding of lung diseases and for biomarker and therapeutic target discoveries.     

The metabolomics of airway diseases,including COPD,asthma and cystic fibrosis

Abstract

Chronic obstructive pulmonary disease (COPD), asthma and cystic fibrosis (CF) are characterized by airway obstruction and an inflammatory process. Reaching early diagnosis and discrimination of subtypes of these respiratory diseases are quite a challenging task than other chronic illnesses. Metabolomics is the study of metabolic pathways and the measurement of unique biochemical molecules generated in a living system. In the last decade, metabolomics has already proved to be useful for the characterization of several pathological conditions and offers promises as a clinical tool. In this article, we review the current state of the metabolomics of COPD, asthma and CF with a focus on the different methods and instrumentation being used for the discovery of biomarkers in research and translation into clinic as diagnostic aids for the choice of patient-specific therapies.     

From comorbidities of chronic obstructive pulmonary disease to identification of shared molecular mechanisms by data integration

Background

Deep mining of healthcare data has provided maps of comorbidity relationships between diseases. In parallel, integrative multi-omics investigations have generated high-resolution molecular maps of putative relevance for understanding disease initiation and progression. Yet, it is unclear how to advance an observation of comorbidity relations (one disease to others) to a molecular understanding of the driver processes and associated biomarkers.

Results

Since Chronic Obstructive Pulmonary disease (COPD) has emerged as a central hub in temporal comorbidity networks, we developed a systematic integrative data-driven framework to identify shared disease-associated genes and pathways, as a proxy for the underlying generative mechanisms inducing comorbidity. We integrated records from approximately 13 M patients from the Medicare database with disease-gene maps that we derived from several resources including a semantic-derived knowledge-base. Using rank-based statistics we not only recovered known comorbidities but also discovered a novel association between COPD and digestive diseases. Furthermore, our analysis provides the first set of COPD co-morbidity candidate biomarkers, including IL15, TNF and JUP, and characterizes their association to aging and life-style conditions, such as smoking and physical activity.

Conclusions

The developed framework provides novel insights in COPD and especially COPD co-morbidity associated mechanisms. The methodology could be used to discover and decipher the molecular underpinning of other comorbidity relationships and furthermore, allow the identification of candidate co-morbidity biomarkers.

     

Epithelium-fibroblast interactions in response to airway inflammation

Dramatic changes to the architecture of the airway walls have been commonly described in the airways of patients with asthma, cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD). Much research has focused on how airway inflammation drives these structural changes, particularly in terms of the mechanisms/mediators that are involved, and a number of parallels are observed between the disease phenotypes. For example, the increased deposition of extracellular matrix (ECM) at focal sites in the airway wall is seen in asthma and all interstitial lung diseases that involve fibrosis. In addition, increased expression of a number of well characterized cytokines and growth factors, such as TGF-beta and epidermal growth factor (EGF) have been demonstrated in these diseases. However, the role of the lesser-known cytokines, including the leukaemia inhibitory factor (LIF) and other members of the IL-6 family of cytokines in the pathogenesis of airway remodelling and fibrosis is largely unknown. However, the use of genetic manipulation in vivo and more specific inhibitors/antibodies in vitro has now provided increasing evidence to support the hypothesis that a complex interaction exists between these cytokines, ECM and integrins in regulating the function of both epithelial cells and fibroblasts.     

HDM induces distinct immunometabolic phenotype in macrophages in TLR4-dependent manner

Asthma is one of the most common chronic diseases. In many cases it is preceded by the development of an immune response to allergens such as animal fur, dust, pollens and etc. In human population this disease is heterogeneous, and no selective drugs are available at the moment for some endotypes of asthma. The role of the adaptive immune system in the pathogenesis of asthma was extensively studied, while the role of innate immune cells, in particular myeloid cells, was not sufficiently addressed. Myeloid cells, such as macrophages and dendritic cells, are characterized by high plasticity, heterogenicity and ability to undergo polarization in response to various pathogenic stimuli, including those engaging innate immune receptors. Recently, special attention was drawn to the link between polarization of macrophages and cell metabolism. We hypothesized that immunometabolic reprogramming of myeloid cells, in particular, of macrophages and dendritic cells during sensitization with an allergen may affect further immune response and asthma development. To test this hypothesis, we generated distinct types of myeloid cells in vitro from murine bone marrow and analyzed their immunometabolic profiles upon activation with house dust mite extract (HDM) and its key active components. We found that the combination of lipopolysaccharide (LPS) and beta-glucan is sufficient to upregulate proinflammatory cytokine production as well as respiratory and glycolytic capacity of myeloid cells, comparably to HDM. This specific immunometabolic phenotype was associated with altered mitochondrial morphology and possibly with increased ROS production in macrophages. Moreover, we found that both TNF production and metabolic remodeling of macrophages in response to HDM are TLR4-dependent processes. Altogether, these results expand our understanding of molecular mechanisms underlying asthma induction and pathogenesis and may potentially lead to new therapeutic strategies for the treatment of this disease.     

肺部菌群和肠道菌群在两种常见肺疾病中 作用的研究进展

哮喘、慢性阻塞性肺疾病(COPD)是常见高发的肺部疾病,相关发病机制尚待阐明。近年来,菌群对哮喘、COPD的发生发展的作用越来越引起重视。本文梳理了近期肺部菌群和肠道菌群在哮喘、COPD中的作用及机制相关文献,分别从肺部菌群和肠道菌群的角度,分析它们对哮喘、COPD的作用机制以及肺部菌群和肠道菌群之间相互调控的联系,以期进一步深入了解菌群在常见肺部疾病中的意义,为寻找更有效的防治哮喘、COPD的作用靶点或者分子提供相关可借鉴的思路。     

Proteomics studies in inner ear disorders: pathophysiology and biomarkers

Although proteomics has been exploited in a wide range of diseases for identification of biomarkers and pathophysiological mechanisms, there are still biomedical disciplines such as otology where proteomics platforms are underused due to technical challenges and/or complex features of the disease. Thus, in the past few years, healthcare and scientific agencies have advocated the development and adoption of proteomic technologies in otological research. However, few studies have been conducted and limited literature is available in this area. Here, we present the state of the art of proteomics in otology, discussing the substantial evidence from recent experimental models and clinical studies in inner-ear conditions. We also delineate a series of critical issues including minute size of the inner ear, delicacy and poor accessibility of tissue that researchers face while undertaking otology proteomics research. Furthermore, we provide perspective to enhance the impact and lead to the clinical implementation of these proteomics-based strategies.     

Defining adult asthma endotypes by clinical features and patterns of volatile organic compounds in exhaled air

Background

Several classifications of adult asthma patients using cluster analyses based on clinical and demographic information has resulted in clinical phenotypic clusters that do not address molecular mechanisms. Volatile organic compounds (VOC) in exhaled air are released during inflammation in response to oxidative stress as a result of activated leukocytes. VOC profiles in exhaled air could distinguish between asthma patients and healthy subjects. In this study, we aimed to classify new asthma endotypes by combining inflammatory mechanisms investigated by VOC profiles in exhaled air and clinical information of asthma patients.

Methods

Breath samples were analyzed for VOC profiles by gas chromatography–mass spectrometry from asthma patients (n = 195) and healthy controls (n = 40). A total of 945 determined compounds were subjected to discriminant analysis to find those that could discriminate healthy from asthmatic subjects. 2-step cluster analysis based on clinical information and VOCs in exhaled air were used to form asthma endotypes.

Results

We identified 16 VOCs, which could distinguish between healthy and asthma subjects with a sensitivity of 100% and a specificity of 91.1%. Cluster analysis based on VOCs in exhaled air and the clinical parameters FEV1, FEV1 change after 3 weeks of hospitalization, allergic sensitization, Junipers symptoms score and asthma medications resulted in the formation of 7 different asthma endotype clusters. We identified asthma clusters with different VOC profiles but similar clinical characteristics and endotypes with similar VOC profiles, but distinct clinical characteristics.

Conclusion

This study demonstrates that both, clinical presentation of asthma and inflammatory mechanisms in the airways should be considered for classification of asthma subtypes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12931-014-0136-8) contains supplementary material, which is available to authorized users.     

Derivation and validation of clinical phenotypes for COPD: a systematic review

Background

The traditional classification of COPD, which relies solely on spirometry, fails to account for the complexity and heterogeneity of the disease. Phenotyping is a method that attempts to derive a single or combination of disease attributes that are associated with clinically meaningful outcomes. Deriving phenotypes entails the use of cluster analyses, and helps individualize patient management by identifying groups of individuals with similar characteristics. We aimed to systematically review the literature for studies that had derived such phenotypes using unsupervised methods.

Methods

Two independent reviewers systematically searched multiple databases for studies that performed validated statistical analyses, free of definitive pre-determined hypotheses, to derive phenotypes among patients with COPD. Data were extracted independently.

Results

9156 citations were retrieved, of which, 8 studies were included. The number of subjects ranged from 213 to 1543. Most studies appeared to be biased: patients were more likely males, with severe disease, and recruited in tertiary care settings. Statistical methods used to derive phenotypes varied by study. The number of phenotypes identified ranged from 2 to 5. Two phenotypes, with poor longitudinal health outcomes, were common across multiple studies: young patients with severe respiratory disease, few cardiovascular co-morbidities, poor nutritional status and poor health status, and a phenotype of older patients with moderate respiratory disease, obesity, cardiovascular and metabolic co-morbidities.

Conclusions

The recognition that two phenotypes of COPD were often reported may have clinical implications for altering the course of the disease. This review also provided important information on limitations of phenotype studies in COPD and the need for improvement in future studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12931-015-0208-4) contains supplementary material, which is available to authorized users.