Gene expression profiling of asthma phenotypes demonstrates molecular signatures of atopy and asthma control

Neuroprotective protein ADNP-dependent histone remodeling complex promotes T helper 2 immune cell differentiation

Type 2 immune responses are critical in tissue homeostasis, anti-helminth immunity, and allergy. T helper 2 (Th2) cells produce interleukin-4 (IL-4), IL-5, and IL-13 from the type 2 gene cluster under regulation by transcription factors (TFs) including GATA3. To better understand transcriptional regulation of Th2 cell differentiation, we performed CRISPR-Cas9 screens targeting 1,131 TFs. We discovered that activity-dependent neuroprotector homeobox protein (ADNP) was indispensable for immune reactions to allergen. Mechanistically, ADNP performed a previously unappreciated role in gene activation, forming a critical bridge in the transition from pioneer TFs to chromatin remodeling by recruiting the helicase CHD4 and ATPase BRG1. Although GATA3 and AP-1 bound the type 2 cytokine locus in the absence of ADNP, they were unable to initiate histone acetylation or DNA accessibility, resulting in highly impaired type 2 cytokine expression. Our results demonstrate an important role for ADNP in promoting immune cell specialization.

Epigenome-Wide Association Studies of the Fractional Exhaled Nitric Oxide and Bronchodilator Drug Response in Moderate-to-Severe Pediatric Asthma

Asthma is the most prevalent pediatric chronic disease. Bronchodilator drug response (BDR) and fractional exhaled nitric oxide (FeNO) are clinical biomarkers of asthma. Although DNA methylation (DNAm) contributes to asthma pathogenesis, the influence of DNAm on BDR and FeNO is scarcely investigated. This study aims to identify DNAm markers in whole blood associated either with BDR or FeNO in pediatric asthma. We analyzed 121 samples from children with moderate-to-severe asthma. The association of genome-wide DNAm with BDR and FeNO has been assessed using regression models, adjusting for age, sex, ancestry, and tissue heterogeneity. Cross-tissue validation was assessed in 50 nasal samples. Differentially methylated regions (DMRs) and enrichment in traits and biological pathways were assessed. A false discovery rate (FDR) < 0.1 and a genome-wide significance threshold of p < 9 × 10−8 were used to control for false-positive results. The CpG cg12835256 (PLA2G12A) was genome-wide associated with FeNO in blood samples (coefficient= −0.015, p = 2.53 × 10−9) and nominally associated in nasal samples (coefficient = −0.015, p = 0.045). Additionally, three CpGs were suggestively associated with BDR (FDR < 0.1). We identified 12 and four DMRs associated with FeNO and BDR (FDR < 0.05), respectively. An enrichment in allergic and inflammatory processes, smoking, and aging was observed. We reported novel associations of DNAm markers associated with BDR and FeNO enriched in asthma-related processes.

Gene Screening in High-Throughput Right-Censored Lung Cancer Data

Background

Advances in sequencing technologies have allowed collection of massive genome-wide information that substantially advances lung cancer diagnosis and prognosis. Identifying influential markers for clinical endpoints of interest has been an indispensable and critical component of the statistical analysis pipeline. However, classical variable selection methods are not feasible or reliable for high-throughput genetic data. Our objective is to propose a model-free gene screening procedure for high-throughput right-censored data, and to develop a predictive gene signature for lung squamous cell carcinoma (LUSC) with the proposed procedure.

Methods

A gene screening procedure was developed based on a recently proposed independence measure. The Cancer Genome Atlas (TCGA) data on LUSC was then studied. The screening procedure was conducted to narrow down the set of influential genes to 378 candidates. A penalized Cox model was then fitted to the reduced set, which further identified a 6-gene signature for LUSC prognosis. The 6-gene signature was validated on datasets from the Gene Expression Omnibus.

Results

Both model-fitting and validation results reveal that our method selected influential genes that lead to biologically sensible findings as well as better predictive performance, compared to existing alternatives. According to our multivariable Cox regression analysis, the 6-gene signature was indeed a significant prognostic factor (p-value < 0.001) while controlling for clinical covariates.

Conclusions

Gene screening as a fast dimension reduction technique plays an important role in analyzing high-throughput data. The main contribution of this paper is to introduce a fundamental yet pragmatic model-free gene screening approach that aids statistical analysis of right-censored cancer data, and provide a lateral comparison with other available methods in the context of LUSC.

Timing of Blood Sample Processing Affects the Transcriptomic and Epigenomic Profiles in CD4+ T-cells of Atopic Subjects

Optimal pre-analytical conditions for blood sample processing and isolation of selected cell populations for subsequent transcriptomic and epigenomic studies are required to obtain robust and reproducible results. This pilot study was conducted to investigate the potential effects of timing of CD4⁺ T-cell processing from peripheral blood of atopic and non-atopic adults on their transcriptomic and epigenetic profiles. Two heparinized blood samples were drawn from each of three atopic and three healthy individuals. For each individual, CD4⁺ T-cells were isolated from the first blood sample within 2 h (immediate) or from the second blood sample after 24 h storage (delayed). RNA sequencing (RNA-Seq) and histone H3K27 acetylation chromatin immunoprecipitation sequencing (ChIP-Seq) analyses were performed. A multiplicity of genes was shown to be differentially expressed in immediately processed CD4⁺ T-cells from atopic versus healthy subjects. These differences disappeared when comparing delayed processed cells due to a drastic change in expression levels of atopy-related genes in delayed processed CD4⁺ T-cells from atopic donors. This finding was further validated on the epigenomic level by examining H3K27 acetylation profiles. In contrast, transcriptomic and epigenomic profiles of blood CD4⁺ T-cells of healthy donors remained rather unaffected. Taken together, for successful transcriptomics and epigenomics studies, detailed standard operation procedures developed on the basis of samples from both healthy and disease conditions are implicitly recommended.

Metabo-Endotypes of Asthma Reveal Differences in Lung Function: Discovery and Validation in Two TOPMed Cohorts

Rationale: Current guidelines do not sufficiently capture the heterogeneous nature of asthma; a more detailed molecular classification is needed. Metabolomics represents a novel and compelling approach to derive asthma endotypes (i.e., subtypes defined by functional and/or pathobiological mechanisms). Objectives: To validate metabolomic-driven endotypes of asthma and explore their underlying biology. Methods: In the Genetics of Asthma in Costa Rica Study (GACRS), untargeted metabolomic profiling, similarity network fusion, and spectral clustering was used to identify metabo-endotypes of asthma, and differences in asthma-relevant phenotypes across these metabo-endotypes were explored. The metabo-endotypes were recapitulated in the Childhood Asthma Management Program (CAMP), and clinical differences were determined. Metabolomic drivers of metabo-endotype membership were investigated by meta-analyzing findings from GACRS and CAMP. Measurements and Main Results: Five metabo-endotypes were identified in GACRS with significant differences in asthma-relevant phenotypes, including prebronchodilator (p-ANOVA = 8.3 × 10^-5) and postbronchodilator (p-ANOVA = 1.8 × 10^-5) FEV₁/FVC. These differences were validated in the recapitulated metabo-endotypes in CAMP. Cholesterol esters, trigylcerides, and fatty acids were among the most important drivers of metabo-endotype membership. The findings suggest dysregulation of pulmonary surfactant homeostasis may play a role in asthma severity. Conclusions: Clinically meaningful endotypes may be derived and validated using metabolomic data. Interrogating the drivers of these metabo-endotypes has the potential to help understand their pathophysiology.

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

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

Integrative analysis of lung molecular signatures reveals key drivers of systemic sclerosis-associated interstitial lung disease

OBJECTIVES

Interstitial lung disease is a significant comorbidity and the leading cause of mortality in patients with systemic sclerosis. Transcriptomic data of systemic sclerosis-associated interstitial lung disease (SSc-ILD) were analysed to evaluate the salient molecular and cellular signatures in comparison with those in related pulmonary diseases and to identify the key driver genes and target molecules in the disease module.

METHODS

A transcriptomic dataset of lung tissues from patients with SSc-ILD (n=52), idiopathic pulmonary fibrosis (IPF) (n=549), non-specific interstitial pneumonia (n=49) and pulmonary arterial hypertension (n=81) and from normal healthy controls (n=331) was subjected to filtration of differentially expressed genes, functional enrichment analysis, network-based key driver analysis and kernel-based diffusion scoring. The association of enriched pathways with clinical parameters was evaluated in patients with SSc-ILD.

RESULTS

SSc-ILD shared key pathogenic pathways with other fibrosing pulmonary diseases but was distinguishable in some pathological processes. SSc-ILD showed general similarity with IPF in molecular and cellular signatures but stronger signals for myofibroblasts, which in SSc-ILD were in a senescent and apoptosis-resistant state. The p53 signalling pathway was the most enriched signature in lung tissues and lung fibroblasts of SSc-ILD, and was significantly correlated with carbon monoxide diffusing capacity of lung, cellular senescence and apoptosis. EEF2, EFF2K, PHKG2, VCAM1, PRKACB, ITGA4, CDK1, CDK2, FN1 and HDAC1 were key regulators with high diffusion scores in the disease module.

CONCLUSIONS

Integrative transcriptomic analysis of lung tissues revealed key signatures of fibrosis in SSc-ILD. A network-based Bayesian approach provides deep insights into key regulatory genes and molecular targets applicable to treating SSc-ILD.

Epigenetics of obstructive sleep apnea syndrome: a systematic review

STUDY OBJECTIVES

Obstructive sleep apnea (OSA) is a chronic and widely prevalent disease, associated with multiple health disorders. Current diagnostic strategies for OSA are limited due to cost, time, and access. Epigenetic signatures offer insight into the relationships between disease and environment and could play a significant role in developing both diagnostic and therapeutic tools for OSA. In the current study, a systematic literature search was conducted to investigate the existing evidence of OSA-associated epigenetic modifications.

METHODS

A systematic literature search was performed using electronic academic databases including PubMed, CINAHL, Scopus, Embase, EBM Reviews, and Web of Science. However, the current study focused on screening for original, English language articles pertaining to OSA and associated epigenetic mechanisms. To produce unbiased results, screening was performed independently by authors.

RESULTS

We identified 2,944 publications in our systematic search. Among them, 65 research articles were related to OSA-associated differential gene expression, genetic variation, and epigenetic modifications. Although these 65 articles were considered for full manuscript review, only twelve articles met the criteria of OSA-associated epigenetic modifications in humans and animal models. Human subjects with OSA had unique epigenetic changes compared to healthy controls, and, interestingly, epigenetic signatures were commonly identified in genes associated with metabolic and inflammatory pathways.

CONCLUSIONS

Although the available studies are limited, this research provides novel insights for development of epigenetic markers for the diagnosis and treatment of OSA. Thorough genome wide investigations will be required to develop cost-effective, robust biomarkers for the identification of OSA among children and adults. Here, we offer a study design for such efforts.

Genetic Influences on Disease Subtypes

Disease classification, or nosology, was historically driven by careful examination of clinical features of patients. As technologies to measure and understand human phenotypes advanced, so too did classifications of disease, and the advent of genetic data has led to a surge in genetic subtyping in the past decades. Although the fundamental process of refining disease definitions and subtypes is shared across diverse fields, each field is driven by its own goals and technological expertise, leading to inconsistent and conflicting definitions of disease subtypes. Here, we review several classical and recent subtypes and subtyping approaches and provide concrete definitions to delineate subtypes. In particular, we focus on subtypes with distinct causal disease biology, which are of primary interest to scientists, and subtypes with pragmatic medical benefits, which are of primary interest to physicians. We propose genetic heterogeneity as a gold standard for establishing biologically distinct subtypes of complex polygenic disease. We focus especially on methods to find and validate genetic subtypes, emphasizing common pitfalls and how to avoid them.

Molecular analysis of phenotypic interactions of asthma

INTRODUCTION

Asthma is a heterogeneous disease characterized by multiples respiratory symptoms; this is a polygenic entity that involves a complex interaction of environmental factors and inherent to the individual. To understand the development of asthma, some phenotypes have been proposed.

OBJECTIVE

This work's purpose was to explore different molecules related to asthma development and to define each phenotype's specific characteristics.

MATERIAL AND METHODS

96 adult patients diagnosed with asthma before any treatment were enrolled in the protocol. Spirometric parameters, circulating leukocytes, serum IgE, body mass index, exhaled nitric oxide (FENO), and leukotrienes (LTB4) in urine were determined in each patient. The presence of asthma phenotypes proposed by the Global Initiative for Asthma (GINA) were explored: A) Allergic asthma, B) Non-allergic asthma, C) Late-onset asthma, D) Asthma with persistent airflow limitation, and E) Asthma with overweight and obesity.

RESULTS

In the cohort analyzed, we found four of phenotypes proposed by GINA; however, these phenotypes overlapped, due to this, 4 groups were integrated with allergic, non-allergic and obese patients, which were the main phenotypes. The main overlap was that of patients not-obese allergic, and was characterized by earlier onset, elevated levels of IgE, LTB4 and inflammasome related cytokines. Non-allergic patients had a significant association between interleukin (IL)-18 and IL-18 binding protein (BP) with narrow ratio between these cytokines. Finally, LTB4 had remarkable capacity to discriminate between allergic and not allergic patients.

CONCLUSIONS

Asthmatic phenotypes exist as interrelated characteristics and not as discrete entities. High levels of leukotrienes and IgE are hallmarks in the allergic phenotype of asthma.

Merged Affinity Network Association Clustering: Joint multi-omic/clinical clustering to identify disease endotypes

Although clinical and laboratory data have long been used to guide medical practice, this information is rarely integrated with multi-omic data to identify endotypes. We present Merged Affinity Network Association Clustering (MANAclust), a coding-free, automated pipeline enabling integration of categorical and numeric data spanning clinical and multi-omic profiles for unsupervised clustering to identify disease subsets. Using simulations and real-world data from The Cancer Genome Atlas, we demonstrate that MANAclust’s feature selection algorithms are accurate and outperform competitors. We also apply MANAclust to a clinically and multi-omically phenotyped asthma cohort. MANAclust identifies clinically and molecularly distinct clusters, including heterogeneous groups of “healthy controls” and viral and allergy-driven subsets of asthmatic subjects. We also find that subjects with similar clinical presentations have disparate molecular profiles, highlighting the need for additional testing to uncover asthma endotypes. This work facilitates data-driven personalized medicine through integration of clinical parameters with multi-omics. MANAclust is freely available at https://bitbucket.org/scottyler892/manaclust/src/master/.

Clinical data commonly used in medical practice are underutilized in multi-omic analyses to identify disease endotypes. Tyler et al. present a python package called Merged Affinity Network Association Clustering (MANAclust) that automatically processes and integrates categorical and numeric data types, facilitating the inclusion of clinical data in multi-omic endotyping efforts.

Leveraging -omics for asthma endotyping

Asthma is a highly heterogeneous disease, often manifesting with wheeze, dyspnea, chest tightness, and cough as prominent symptoms. The eliciting factors, natural history, underlying molecular biology, and clinical management of asthma vary highly among affected subjects. Because of this variation, many efforts have gone into subtyping asthma. Endotypes are subtypes of disease based on distinct pathophysiologic mechanisms. Endotypes can be clinically useful because they organize our mechanistic understanding of heterogeneous diseases and can direct treatment toward modalities that are likely to be the most effective. Asthma endotyping can be shaped by clinical features, laboratory parameters, and/or -omics approaches. We discuss the application of -omics approaches, including transcriptomics, epigenomics, microbiomics, metabolomics, and proteomics, to asthma endotyping. -Omics approaches have provided supporting evidence for many existing endotyping paradigms and also suggested novel ways to conceptualize asthma endotypes. Although endotypes based on single -omics approaches are relatively common, their integrated multi-omics application to asthma endotyping has been more limited thus far. We discuss paths forward to integrate multi-omics with clinical features and laboratory parameters to achieve the goal of precise asthma endotypes.

Immune-mediated genetic pathways resulting in pulmonary function impairment increase lung cancer susceptibility

Impaired lung function is often caused by cigarette smoking, making it challenging to disentangle its role in lung cancer susceptibility. Investigation of the shared genetic basis of these phenotypes in the UK Biobank and International Lung Cancer Consortium (29,266 cases, 56,450 controls) shows that lung cancer is genetically correlated with reduced forced expiratory volume in one second (FEV1: rg = 0.098, p = 2.3 × 10-8) and the ratio of FEV1 to forced vital capacity (FEV1/FVC: rg = 0.137, p = 2.0 × 10-12). Mendelian randomization analyses demonstrate that reduced FEV1 increases squamous cell carcinoma risk (odds ratio (OR) = 1.51, 95% confidence intervals: 1.21-1.88), while reduced FEV1/FVC increases the risk of adenocarcinoma (OR = 1.17, 1.01-1.35) and lung cancer in never smokers (OR = 1.56, 1.05-2.30). These findings support a causal role of pulmonary impairment in lung cancer etiology. Integrative analyses reveal that pulmonary function instruments, including 73 novel variants, influence lung tissue gene expression and implicate immune-related pathways in mediating the observed effects on lung carcinogenesis.

The Effects of Gene × Environment Interactions on Silver Nanoparticle Toxicity in the Respiratory System

Silver nanoparticles (AgNP) are used in multiple applications but primarily in the manufacturing of antimicrobial products. AgNP toxicity in the respiratory system is well characterized, but few in vitro or in vivo studies have evaluated the effects of interactions between host genetic and acquired factors or gene × environment interactions (G × E) on AgNP toxicity in the respiratory system. The primary goal of this article is to review host genetic and acquired factors identified across in vitro and in vivo studies and prioritize those necessary for defining exposure limits to protect all populations. The impact of these exposures and the work being done to address the current limited protections are also discussed. Future research on G × E effects on AgNP toxicity is warranted and will assist with informing regulatory or recommended exposure limits that enforce special protections for all populations to AgNP exposures in occupational settings.

Transcriptomics of atopy and atopic asthma in white blood cells from children and adolescents

Early allergic sensitisation (atopy) is the first step in the development of allergic diseases such as atopic asthma later in life. Genes and pathways associated with atopy and atopic asthma in children and adolescents have not been well characterised.A transcriptome-wide association study (TWAS) of atopy and atopic asthma in white blood cells (WBCs) or whole blood was conducted in a cohort of 460 Puerto Ricans aged 9-20 years (EVA-PR study) and in a cohort of 250 Swedish adolescents (BAMSE study). Pathway enrichment and network analyses were conducted to further assess top findings, and classification models of atopy and atopic asthma were built using expression levels for the top differentially expressed genes (DEGs).In a meta-analysis of the study cohorts, both previously implicated genes (e.g. IL5RA and IL1RL1) and genes not previously reported in TWASs (novel) were significantly associated with atopy and/or atopic asthma. Top novel genes for atopy included SIGLEC8 (p=8.07×10^-13), SLC29A1 (p=7.07×10^-12) and SMPD3 (p=1.48×10^-11). Expression quantitative trait locus analyses identified multiple asthma-relevant genotype-expression pairs, such as rs2255888/ALOX15 Pathway enrichment analysis uncovered 16 significantly enriched pathways at adjusted p<0.01, including those relevant to T-helper cell type 1 (Th1) and Th2 immune responses. Classification models built using the top DEGs and a few demographic/parental history variables accurately differentiated subjects with atopic asthma from nonatopic control subjects (area under the curve 0.84).We have identified genes and pathways for atopy and atopic asthma in children and adolescents, using transcriptome-wide data from WBCs and whole blood samples.

Data-driven adult asthma phenotypes based on clinical characteristics are associated with asthma outcomes twenty years later

BACKGROUND

Research based on cluster analyses led to the identification of particular phenotypes confirming phenotypic heterogeneity of asthma. The long-term clinical course of asthma phenotypes defined by clustering analysis remains unknown, although it is a key aspect to underpin their clinical relevance. We aimed to estimate risk of poor asthma events between asthma clusters identified 20 years earlier.

METHODS

The study relied on two cohorts of adults with asthma with 20-year follow-up, ECRHS (European Community Respiratory Health Survey) and EGEA (Epidemiological study on Genetics and Environment of Asthma). Regression models were used to compare asthma characteristics (current asthma, asthma exacerbations, asthma control, quality of life, and FEV₁ ) at follow-up and the course of FEV₁  between seven cluster-based asthma phenotypes identified 20 years earlier.

RESULTS

The analysis included 1325 adults with ever asthma. For each asthma characteristic assessed at follow-up, the risk for adverse outcomes differed significantly between the seven asthma clusters identified at baseline. As compared with the mildest asthma phenotype, ORs (95% CI) for asthma exacerbations varied from 0.9 (0.4 to 2.0) to 4.0 (2.0 to 7.8) and the regression estimates (95% CI) for FEV₁ % predicted varied from 0.6 (-3.5 to 4.6) to -9.9 (-14.2 to -5.5) between clusters. Change in FEV₁ over time did not differ significantly across clusters.

CONCLUSION

Our findings show that the long-term risk for poor asthma outcomes differed between comprehensive adult asthma phenotypes identified 20 years earlier, and suggest a strong tracking of asthma activity and impaired lung function over time.

Korean childhood asthma study (KAS): a prospective, observational cohort of Korean asthmatic children

Background

Asthma is a syndrome composed of heterogeneous disease entities. Although it is agreed that proper asthma endo-typing and appropriate type-specific interventions are crucial in the management of asthma, little data are available regarding pediatric asthma.

Methods

We designed a cluster-based, prospective, observational cohort study of asthmatic children in Korea (Korean childhood Asthma Study [KAS]). A total of 1000 Korean asthmatic children, aged from 5 to 15 years, will be enrolled at the allergy clinics of the 19 regional tertiary hospitals from August 2016 to December 2018. Physicians will verify the relevant histories of asthma and comorbid diseases, as well as airway lability from the results of spirometry and bronchial provocation tests. Questionnaires regarding subjects’ baseline characteristics and their environment, self-rating of asthma control, and laboratory tests for allergy and airway inflammation will be collected at the time of enrollment. Follow-up data regarding asthma control, lung function, and environmental questionnaires will be collected at least every 6 months to assess outcome and exacerbation-related aggravating factors. In a subgroup of subjects, peak expiratory flow rate will be monitored by communication through a mobile application during the overall study period. Cluster analysis of the initial data will be used to classify Korean pediatric asthma patients into several clusters; the exacerbation and progression of asthma will be assessed and compared among these clusters. In a subgroup of patients, big data-based deep learning analysis will be applied to predict asthma exacerbation.

Discussion

Based on the assumption that asthma is heterogeneous and each subject exhibits a different subset of risk factors for asthma exacerbation, as well as a different disease progression, the KAS aims to identify several asthma clusters and their essential determinants, which are more suitable for Korean asthmatic children. Thereafter we may suggest cluster-specific strategies by focusing on subjects’ personalized aggravating factors during each exacerbation episode and by focusing on disease progression. The KAS will provide a good academic background with respect to each interventional strategy to achieve better asthma control and prognosis.

Recent advances in understanding chronic rhinosinusitis endotypes

Chronic rhinosinusitis (CRS) is a heterogeneous inflammatory disease with an as-yet-undefined etiology. The management of CRS has historically been phenotypically driven, and the presence or absence of nasal polyps has frequently guided diagnosis, prognosis, and treatment algorithms. Research over the last decade has begun to question the role of this distinction in disease management, and renewed attention has been placed on molecular and cellular endotyping and a more personalized approach to care. Current research exploring immunologic mechanisms, inflammatory endotypes, and molecular biomarkers has the potential to more effectively delineate distinct and clinically relevant subgroups of CRS. The focus of this review will be to discuss and summarize the endotypic characterization of CRS and the potential diagnostic and therapeutic implications of this approach to disease management.

Recent developments and highlights in biomarkers in allergic diseases and asthma

The potential of precision medicine in allergy and asthma has only started to be explored. A significant clarification in the pathophysiology of rhinitis, chronic rhinosinusitis, asthma, food allergy and drug hypersensitivity was made in the last decade. This improved understanding led to a better classification of the distinct phenotypes and to the discovery of new drugs such as biologicals, targeting phenotype-specific mechanisms. Nevertheless, many conditions remain poorly understood such as non-eosinophilic airway diseases or non-IgE-mediated food allergy. Moreover, there is a need to predict the response to specific therapies and the outcome of drug and food provocations. The identification of patients at risk of progression towards severity is also an unmet need in order to establish adequate preventive or therapeutic measures. The implementation of precision medicine in the clinical practice requires the identification of phenotype-specific markers measurable in biological matrices. To become useful, these biomarkers need to be quantifiable by reliable systems, and in samples obtained in an easy, rapid and cost-efficient way. In the last years, significant research resources have been put in the identification of valid biomarkers for asthma and allergic diseases. This review summarizes these recent advances with focus on the biomarkers with higher clinical applicability.

Metabolic phenotype of skeletal muscle in early critical illness

OBJECTIVES

To characterise the sketetal muscle metabolic phenotype during early critical illness.

METHODS

Vastus lateralis muscle biopsies and serum samples (days 1 and 7) were obtained from 63 intensive care patients (59% male, 54.7±18.0 years, Acute Physiology and Chronic Health Evaluation II score 23.5±6.5).

MEASUREMENTS AND MAIN RESULTS

From day 1 to 7, there was a reduction in mitochondrial beta-oxidation enzyme concentrations, mitochondrial biogenesis markers (PGC1α messenger mRNA expression (-27.4CN (95% CI -123.9 to 14.3); n=23; p=0.025) and mitochondrial DNA copy number (-1859CN (IQR -5557-1325); n=35; p=0.032). Intramuscular ATP content was reduced compared tocompared with controls on day 1 (17.7mmol/kg /dry weight (dw) (95% CI 15.3 to 20.0) vs. 21.7 mmol/kg /dw (95% CI 20.4 to 22.9); p<0.001) and decreased over 7 days (-4.8 mmol/kg dw (IQR -8.0-1.2); n=33; p=0.001). In addition, the ratio of phosphorylated:total AMP-K (the bioenergetic sensor) increased (0.52 (IQR -0.09-2.6); n=31; p<0.001). There was an increase in intramuscular phosphocholine (847.2AU (IQR 232.5-1672); n=15; p=0.022), intramuscular tumour necrosis factor receptor 1 (0.66 µg (IQR -0.44-3.33); n=29; p=0.041) and IL-10 (13.6 ng (IQR 3.4-39.0); n=29; p=0.004). Serum adiponectin (10.3 µg (95% CI 6.8 to 13.7); p<0.001) and ghrelin (16.0 ng/mL (IQR -7-100); p=0.028) increased. Network analysis revealed a close and direct relationship between bioenergetic impairment and reduction in muscle mass and between intramuscular inflammation and impaired anabolic signaling. ATP content and muscle mass were unrelated to lipids delivered.

CONCLUSIONS

Decreased mitochondrial biogenesis and dysregulated lipid oxidation contribute to compromised skeletal muscle bioenergetic status. In addition, intramuscular inflammation was associated with impaired anabolic recovery with lipid delivery observed as bioenergetically inert. Future clinical work will focus on these key areas to ameliorate acute skeletal muscle wasting.

TRIAL REGISTRATION NUMBER

NCT01106300.

Linking childhood allergic asthma phenotypes with endotype through integrated systems biology: current evidence and research needs

Asthma and other complex diseases results from a complex web of interactions involving inflammation, immunity, cell cycle, apoptosis, and metabolic perturbations across multiple organ systems. The extent to which various degrees of the age at onset, symptom severity, and the natural progression of the disease reflect multiple disease subtypes, influenced by unique process of development remains unknown. One of the most critical challenges to our understanding stems from incomplete understanding of the mechanisms. Within this review, we focus on the phenotypes of childhood allergic asthma as the basis to better understand the endotype for quantitative define subtypes of asthma. We highlight some of the known mechanistic pathways associated with the key hallmark events before the asthma onset. In particular, we examine how the recent advent of multiaxial -omics technologies and systems biology could help to clarify our current understanding of the pathway. We review how a large volume of molecular, genomic data generated by multiaxial technologies could be digested to identify cogent pathophysiologic molecular networks. We highlight some recent successes in application of these technologies within the context of other disease conditions for therapeutic interventions. We conclude by summarizing the research needs for the predictive value of preclinical biomarkers.

Personalized management of asthma exacerbations: lessons from genetic studies

INTRODUCTION

The genetics of severe asthma and asthma exacerbations are distinct from milder forms of asthma. Gene-environmental interactions contribute to the complexity and heterogeneity of severe asthma and asthma exacerbations, and pharmacogenomic studies have also identified genes that affect susceptibility to asthma exacerbations.

AREAS COVERED

Studies on the genetics, gene-environment interactions, and pharmacogenomics of asthma exacerbations are reviewed. Multiple individual genetic variants have been identified to be associated with asthma exacerbations but each genetic polymorphism explains only a fraction of the disease and by itself is not able to translate into clinical practice. Research is shifting from candidate gene studies and genome wide association studies towards more integrative approaches to translate genetic findings into clinical diagnostic and therapeutic tools.

EXPERT COMMENTARY

Integrative approaches combining polygenic or genomic data with multi-omics technologies have the potential to discover new biologic mechanisms and biomarkers for severe asthma and asthma exacerbations. Greater understanding of genomics and underlying biologic pathways will also lead to improved prevention and treatment, lowering costs, morbidity, and mortality. The utilization of genomic testing and personalized medicine may revolutionize asthma management, in particular for patients with severe, refractory asthma.

The puzzle of immune phenotypes of childhood asthma

Asthma represents the most common chronic childhood disease worldwide. Whereas preschool children present with wheezing triggered by different factors (multitrigger and viral wheeze), clinical asthma manifestation in school children has previously been classified as allergic and non-allergic asthma. For both, the underlying immunological mechanisms are not yet understood in depth in children. Treatment is still prescribed regardless of underlying mechanisms, and children are not always treated successfully. This review summarizes recent key findings on the complex mechanisms of the development and manifestation of childhood asthma. Whereas traditional classification of childhood asthma is primarily based on clinical symptoms like wheezing and atopy, novel approaches to specify asthma phenotypes are under way and face challenges such as including the stability of phenotypes over time and transition into adulthood. Epidemiological studies enclose more information on the patient’s disease history and environmental influences. Latest studies define endotypes based on molecular and cellular mechanisms, for example defining risk and protective single nucleotide polymorphisms (SNPs) and new immune phenotypes, showing promising results. Also, regulatory T cells and recently discovered T helper cell subtypes such as Th9 and Th17 cells were shown to be important for the development of asthma. Innate lymphoid cells (ILC) could play a critical role in asthma patients as they produce different cytokines associated with asthma. Epigenetic findings showed different acetylation and methylation patterns for children with allergic and non-allergic asthma. On a posttranscriptional level, miRNAs are regulating factors identified to differ between asthma patients and healthy controls and also indicate differences within asthma phenotypes. Metabolomics is another exciting chapter important for endotyping asthmatic children. Despite the development of new biomarkers and the discovery of new immunological molecules, the complex puzzle of childhood asthma is still far from being completed. Addressing the current challenges of distinct clinical asthma and wheeze phenotypes, including their stability and underlying endotypes, involves addressing the interplay of innate and adaptive immune regulatory mechanisms in large, interdisciplinary cohorts.

Endotypes of allergic diseases and asthma: An important step in building blocks for the future of precision medicine

Discoveries from basic science research in the last decade have brought significant progress in knowledge of pathophysiologic processes of allergic diseases, with a compelling impact on understanding of the natural history, risk prediction, treatment selection or mechanism-specific prevention strategies. The view of the pathophysiology of allergic diseases developed from a mechanistic approach, with a focus on symptoms and organ function, to the recognition of a complex network of immunological pathways. Several subtypes of inflammation and complex immune-regulatory networks and the reasons for their failure are now described, that open the way for the development of new diagnostic tools and innovative targeted-treatments. An endotype is a subtype of a disease condition, which is defined by a distinct pathophysiological mechanism, whereas a disease phenotype defines any observable characteristic of a disease without any implication of a mechanism. Another key word linked to disease endotyping is biomarker that is measured and evaluated to examine any biological or pathogenic processes, including response to a therapeutic intervention. These three keywords will be discussed more and more in the future with the upcoming efforts to revolutionize patient care in the direction of precision medicine and precision health. The understanding of disease endotypes based on pathophysiological principles and their validation across clinically meaningful outcomes in asthma, allergic rhinitis, chronic rhinosinusitis, atopic dermatitis and food allergy will be crucial for the success of precision medicine as a new approach to patient management.