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

Artificial intelligence in pediatric allergy research

Atopic dermatitis, food allergy, allergic rhinitis, and asthma are among the most common diseases in childhood. They are heterogeneous diseases, can co-exist in their development, and manifest complex associations with other disorders and environmental and hereditary factors. Elucidating these intricacies by identifying clinically distinguishable groups and actionable risk factors will allow for better understanding of the diseases, which will enhance clinical management and benefit society and affected individuals and families. Artificial intelligence (AI) is a promising tool in this context, enabling discovery of meaningful patterns in complex data. Numerous studies within pediatric allergy have and continue to use AI, primarily to characterize disease endotypes/phenotypes and to develop models to predict future disease outcomes. However, most implementations have used relatively simplistic data from one source, such as questionnaires. In addition, methodological approaches and reporting are lacking. This review provides a practical hands-on guide for conducting AI-based studies in pediatric allergy, including (1) an introduction to essential AI concepts and techniques, (2) a blueprint for structuring analysis pipelines (from selection of variables to interpretation of results), and (3) an overview of common pitfalls and remedies. Furthermore, the state-of-the art in the implementation of AI in pediatric allergy research, as well as implications and future perspectives are discussed.

CONCLUSION

AI-based solutions will undoubtedly transform pediatric allergy research, as showcased by promising findings and innovative technical solutions, but to fully harness the potential, methodologically robust implementation of more advanced techniques on richer data will be needed.

WHAT IS KNOWN

• Pediatric allergies are heterogeneous and common, inflicting substantial morbidity and societal costs. • The field of artificial intelligence is undergoing rapid development, with increasing implementation in various fields of medicine and research.

WHAT IS NEW

• Promising applications of AI in pediatric allergy have been reported, but implementation largely lags behind other fields, particularly in regard to use of advanced algorithms and non-tabular data. Furthermore, lacking reporting on computational approaches hampers evidence synthesis and critical appraisal. • Multi-center collaborations with multi-omics and rich unstructured data as well as utilization of deep learning algorithms are lacking and will likely provide the most impactful discoveries.

What's new in pediatric asthma and rhinitis phenotypes and endotypes?

PURPOSE OF REVIEW

This review explores the evolving landscape of pediatric asthma and rhinitis, focusing on identifying and characterizing different subtypes.

RECENT FINDINGS

Childhood asthma and rhinitis are prevalent respiratory conditions frequently occurring together. To address the need for a precise definition of these diseases, an unbiased and comprehensive phenotyping approach has been undertaken with hypothesis-free analysis of extensive datasets to uncover new relationships among clinical, environmental, and biological characteristics. On the other hand, the concept of endotype is elaborate and multifaceted, representing distinct pathophysiological mechanisms underlying the clinical presentation and requires the identification of reliable biomarkers. The recognition of multiple inflammatory endotypes underscores the need for in-depth characterization, which could revolutionize the treatment landscape.

SUMMARY

Comprehending phenotypes and endotypes is crucial for customizing effective and personalized management approaches for children with asthma and rhinitis. More precise and efficient care can be administered through recognition and detailed characterization, ultimately enhancing patients' quality of life.

Identification of genetic biomarkers, drug targets and agents for respiratory diseases utilising integrated bioinformatics approaches

Respiratory diseases (RD) are significant public health burdens and malignant diseases worldwide. However, the RD-related biological information and interconnection still need to be better understood. Thus, this study aims to detect common differential genes and potential hub genes (HubGs), emphasizing their actions, signaling pathways, regulatory biomarkers for diagnosing RD and candidate drugs for treating RD. In this paper we used integrated bioinformatics approaches (such as, gene ontology (GO) and KEGG pathway enrichment analysis, molecular docking, molecular dynamic simulation and network-based molecular interaction analysis). We discovered 73 common DEGs (CDEGs) and ten HubGs (ATAD2B, PPP1CB, FOXO1, AKT3, BCR, PDE4D, ITGB1, PCBP2, CD44 and SMARCA2). Several significant functions and signaling pathways were strongly related to RD. We recognized six transcription factor (TF) proteins (FOXC1, GATA2, FOXL1, YY1, POU2F2 and HINFP) and five microRNAs (hsa-mir-218-5p, hsa-mir-335-5p, hsa-mir-16-5p, hsa-mir-106b-5p and hsa-mir-15b-5p) as the important transcription and post-transcription regulators of RD. Ten HubGs and six major TF proteins were considered drug-specific receptors. Their binding energy analysis study was carried out with the 63 drug agents detected from network analysis. Finally, the five complexes (the PDE4D-benzo[a]pyrene, SMARCA2-benzo[a]pyrene, HINFP-benzo[a]pyrene, CD44-ketotifen and ATAD2B-ponatinib) were selected for RD based on their strong binding affinity scores and stable performance as the most probable repurposable protein-drug complexes. We believe our findings will give readers, wet-lab scientists, and pharmaceuticals a thorough grasp of the biology behind RD.

Severe Asthma and Biological Therapies: Now and the Future

Recognition of phenotypic variability in pediatric asthma allows for a more personalized therapeutic approach. Knowledge of the underlying pathophysiological and molecular mechanisms (endotypes) of corresponding biomarkers and new treatments enables this strategy to progress. Biologic therapies for children with severe asthma are becoming more relevant in this sense. The T2 phenotype is the most prevalent in childhood and adolescence, and non-T2 phenotypes are usually rare. This document aims to review the mechanism of action, efficacy, and potential predictive and monitoring biomarkers of biological drugs, focusing on the pediatric population. The drugs currently available are omalizumab, mepolizumab, benralizumab, dupilumab, and 1ezepelumab, with some differences in administrative approval prescription criteria between the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA). Previously, we described the characteristics of severe asthma in children and its diagnostic and therapeutic management.

Childhood asthma phenotypes and endotypes: a glance into the mosaic

BACKGROUND

Asthma is an inflammatory lung disease that constitutes the most common noncommunicable chronic disease in childhood. Childhood asthma shows large heterogeneity regarding onset of disease, symptoms, severity, prognosis, and response to therapy.

MAIN BODY

Evidence suggests that this variability is due to distinct pathophysiological mechanisms, which has led to an exhaustive research effort to understand and characterize these distinct entities currently designated as "endotypes." Initially, studies focused on identifying specific groups using clinical variables yielding different "clinical phenotypes." In addition, the identification of specific patterns based on inflammatory cell counts and cytokine data has resulted in "inflammatory endotypes." More recently, an increasing number of molecular data from high-throughput technology ("omics" data) have allowed to investigate more complex "molecular endotypes."

CONCLUSION

A better definition and comprehension of childhood asthma heterogeneity is key for improving diagnosis and treatment. This review aims at summarizing the current knowledge on this topic and discusses some limitations in their application as well as recommendations for future studies.

Neutrophil extracellular trap production and CCL4L2 expression influence corticosteroid response in asthma

The association between neutrophil extracellular traps (NETs) and response to inhaled corticosteroids (ICS) in asthma is unclear. To better understand this relationship, we analyzed the blood transcriptomes from children with controlled and uncontrolled asthma in the Taiwanese Consortium of Childhood Asthma Study using weighted gene coexpression network analysis and pathway enrichment methods. We identified 298 uncontrolled asthma-specific differentially expressed genes and one gene module associated with neutrophil-mediated immunity, highlighting a potential role for neutrophils in uncontrolled asthma. We also found that NET abundance was associated with nonresponse to ICS in patients. In a neutrophilic airway inflammation murine model, steroid treatment could not suppress neutrophilic inflammation and airway hyperreactivity. However, NET disruption with deoxyribonuclease I (DNase I) efficiently inhibited airway hyperreactivity and inflammation. Using neutrophil-specific transcriptomic profiles, we found that CCL4L2 was associated with ICS nonresponse in asthma, which was validated in human and murine lung tissue. CCL4L2 expression was also negatively correlated with pulmonary function change after ICS treatment. In summary, steroids fail to suppress neutrophilic airway inflammation, highlighting the potential need to use alternative therapies such as leukotriene receptor antagonists or DNase I that target the neutrophil-associated phenotype. Furthermore, these results highlight CCL4L2 as a potential therapeutic target for individuals with asthma refractory to ICS.

The Role of Systems Biology in Deciphering Asthma Heterogeneity

Asthma is one of the most common and lifelong and chronic inflammatory diseases characterized by inflammation, bronchial hyperresponsiveness, and airway obstruction episodes. It is a heterogeneous disease of varying and overlapping phenotypes with many confounding factors playing a role in disease susceptibility and management. Such multifactorial disorders will benefit from using systems biology as a strategy to elucidate molecular insights from complex, quantitative, massive clinical, and biological data that will help to understand the underlying disease mechanism, early detection, and treatment planning. Systems biology is an approach that uses the comprehensive understanding of living systems through bioinformatics, mathematical, and computational techniques to model diverse high-throughput molecular, cellular, and the physiologic profiling of healthy and diseased populations to define biological processes. The use of systems biology has helped understand and enrich our knowledge of asthma heterogeneity and molecular basis; however, such methods have their limitations. The translational benefits of these studies are few, and it is recommended to reanalyze the different studies and omics in conjugation with one another which may help understand the reasons for this variation and help overcome the limitations of understanding the heterogeneity in asthma pathology. In this review, we aim to show the different factors that play a role in asthma heterogeneity and how systems biology may aid in understanding and deciphering the molecular basis of asthma.

Omics technologies in allergy and asthma research: An EAACI position paper

Allergic diseases and asthma are heterogenous chronic inflammatory conditions with several distinct complex endotypes. Both environmental and genetic factors can influence the development and progression of allergy. Complex pathogenetic pathways observed in allergic disorders present a challenge in patient management and successful targeted treatment strategies. The increasing availability of high-throughput omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics allows studying biochemical systems and pathophysiological processes underlying allergic responses. Additionally, omics techniques present clinical applicability by functional identification and validation of biomarkers. Therefore, finding molecules or patterns characteristic for distinct immune-inflammatory endotypes, can subsequently influence its development, progression, and treatment. There is a great potential to further increase the effectiveness of single omics approaches by integrating them with other omics, and nonomics data. Systems biology aims to simultaneously and longitudinally understand multiple layers of a complex and multifactorial disease, such as allergy, or asthma by integrating several, separated data sets and generating a complete molecular profile of the condition. With the use of sophisticated biostatistics and machine learning techniques, these approaches provide in-depth insight into individual biological systems and will allow efficient and customized healthcare approaches, called precision medicine. In this EAACI Position Paper, the Task Force "Omics technologies in allergic research" broadly reviewed current advances and applicability of omics techniques in allergic diseases and asthma research, with a focus on methodology and data analysis, aiming to provide researchers (basic and clinical) with a desk reference in the field. The potential of omics strategies in understanding disease pathophysiology and key tools to reach unmet needs in allergy precision medicine, such as successful patients' stratification, accurate disease prognosis, and prediction of treatment efficacy and successful prevention measures are highlighted.

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.

Peripheral blood transcriptomic clusters uncovered immune phenotypes of asthma

BACKGROUND

Transcriptomic analysis has been used to elucidate the complex pathogenesis of heterogeneous disease and may also contribute to identify potential therapeutic targets by delineating the hub genes. This study aimed to investigate whether blood transcriptomic clustering can distinguish clinical and immune phenotypes of asthmatics, and microbiome in asthmatics.

METHODS

Transcriptomic expression of peripheral blood mononuclear cells (PBMCs) from 47 asthmatics and 21 non-asthmatics was measured using RNA sequencing. A hierarchical clustering algorithm was used to classify asthmatics. Differentially expressed genes, clinical phenotypes, immune phenotypes, and microbiome of each transcriptomic cluster were assessed.

RESULTS

In asthmatics, three distinct transcriptomic clusters with numerously different transcriptomic expressions were identified. The proportion of severe asthmatics was highest in cluster 3 as 73.3%, followed by cluster 2 (45.5%) and cluster 1 (28.6%). While cluster 1 represented clinically non-severe T2 asthma, cluster 3 tended to include severe non-T2 asthma. Cluster 2 had features of both T2 and non-T2 asthmatics characterized by the highest serum IgE level and neutrophil-dominant sputum cell population. Compared to non-asthmatics, cluster 1 showed higher CCL23 and IL1RL1 expression while the expression of TREML4 was suppressed in cluster 3. CTSD and ALDH2 showed a significant positive linear relationship across three clusters in the order of cluster 1 to 3. No significant differences in the diversities of lung and gut microbiomes were observed among transcriptomic clusters of asthmatics and non-asthmatics. However, our study has limitations in that small sample size data were analyzed with unmeasured confounding factors and causal relationships or function pathways were not verified.

CONCLUSIONS

Genetic clustering based on the blood transcriptome may provide novel immunological insight, which can be biomarkers of asthma immune phenotypes. Trial registration Retrospectively registered.

Multi-Omics Profiling Approach to Asthma: An Evolving Paradigm

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

PTGDR2 Expression in Peripheral Blood as a Potential Biomarker in Adult Patients with Asthma

Background: Precision medicine is a promising strategy to identify biomarkers, stratify asthmatic patients according to different endotypes, and match them with the appropriate therapy. This proof-of-concept study aimed to investigate whether gene expression in peripheral blood could provide a valuable noninvasive approach for the molecular phenotyping of asthma. Methods: We performed whole-transcriptome RNA sequencing on peripheral blood of 30 non-atopic non-asthmatic controls and 30 asthmatic patients. A quantitative PCR (qPCR) validation study of PTGDR2 that encodes for CRTH2 receptor, expressed in cells involved in T2 inflammation, was developed in a cohort of 361 independent subjects: 94 non-asthmatic non-atopic controls, 187 asthmatic patients [including 82 with chronic rhinosinusitis with nasal polyposis (CRSwNP) and 24 with aspirin-exacerbated respiratory disease (AERD)], 52 with allergic rhinitis, and 28 with CRSwNP without asthma. Results: PTGDR2 was one of the most differentially overexpressed genes in asthmatic patients’ peripheral blood (p-value 2.64 × 10⁶). These results were confirmed by qPCR in the validation study, where PTGDR2 transcripts were significantly upregulated in asthmatic patients (p < 0.001). This upregulation was mainly detected in some subgroups such as allergic asthma, asthma with CRSwNP, AERD, eosinophilic asthma, and severe persistent asthma. PTGDR2 expression was detected in different blood cell types, and its correlation with eosinophil counts showed differences in some groups of asthmatic patients. Conclusions: We found that PTGDR2 expression levels could identify asthma patients, introduce a minimally invasive biomarker for adult asthma molecular phenotyping, and add additional information to blood eosinophils. Although further studies are required, analyzing PTGDR2 expression levels in peripheral blood of asthmatics might assist in selecting patients for treatment with specific antagonists.

Network and co-expression analysis of airway smooth muscle cell transcriptome delineates potential gene signatures in asthma

Airway smooth muscle (ASM) is known for its role in asthma exacerbations characterized by acute bronchoconstriction and remodeling. The molecular mechanisms underlying multiple gene interactions regulating gene expression in asthma remain elusive. Herein, we explored the regulatory relationship between ASM genes to uncover the putative mechanism underlying asthma in humans. To this end, the gene expression from human ASM was measured with RNA-Seq in non-asthmatic and asthmatic groups. The gene network for the asthmatic and non-asthmatic group was constructed by prioritizing differentially expressed genes (DEGs) (121) and transcription factors (TFs) (116). Furthermore, we identified differentially connected or co-expressed genes in each group. The asthmatic group showed a loss of gene connectivity due to the rewiring of major regulators. Notably, TFs such as ZNF792, SMAD1, and SMAD7 were differentially correlated in the asthmatic ASM. Additionally, the DEGs, TFs, and differentially connected genes over-represented in the pathways involved with herpes simplex virus infection, Hippo and TGF-β signaling, adherens junctions, gap junctions, and ferroptosis. The rewiring of major regulators unveiled in this study likely modulates the expression of gene-targets as an adaptive response to asthma. These multiple gene interactions pointed out novel targets and pathways for asthma exacerbations.

Unlocking immune-mediated disease mechanisms with transcriptomics

The transcriptome represents the entire set of RNA transcripts expressed in a cell, reflecting both the underlying genetic and epigenetic landscape and environmental influences, providing a comprehensive view of functional cellular states at any given time. Recent technological advances now enable the study of the transcriptome at the resolution of individual cells, providing exciting opportunities to characterise cellular and molecular events that underpin immune-medicated diseases. Here, we draw on recent examples from the literature to highlight the application of advanced bioinformatics tools to extract mechanistic insight and disease biology from bulk and single-cell transcriptomic profiles. Key considerations for the use of available analysis techniques are presented throughout.

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.

Precision Medicine in Childhood Asthma: Omic Studies of Treatment Response

Asthma is a heterogeneous and multifactorial respiratory disease with an important impact on childhood. Difficult-to-treat asthma is not uncommon among children, and it causes a high burden to the patient, caregivers, and society. This review aims to summarize the recent findings on pediatric asthma treatment response revealed by different omic approaches conducted in 2018–2019. A total of 13 studies were performed during this period to assess the role of genomics, epigenomics, transcriptomics, metabolomics, and the microbiome in the response to short-acting beta agonists, inhaled corticosteroids, and leukotriene receptor antagonists. These studies have identified novel associations of genetic markers, epigenetic modifications, metabolites, bacteria, and molecular mechanisms involved in asthma treatment response. This knowledge will allow us establishing molecular biomarkers that could be integrated with clinical information to improve the management of children with asthma.

Understanding the Molecular Mechanisms of Asthma through Transcriptomics

The transcriptome represents the complete set of RNA transcripts that are produced by the genome under a specific circumstance or in a specific cell. High-throughput methods, including microarray and bulk RNA sequencing, as well as recent advances in biostatistics based on machine learning approaches provides a quick and effective way of identifying novel genes and pathways related to asthma, which is a heterogeneous disease with diverse pathophysiological mechanisms. In this manuscript, we briefly review how to analyze transcriptome data and then provide a summary of recent transcriptome studies focusing on asthma pathogenesis and asthma drug responses. Studies reviewed here are classified into 2 classes based on the tissues utilized: blood and airway cells.

Interactions between environmental pollutants and genetic susceptibility in asthma risk

Exposure to air pollution is associated with enhanced risk of developing asthma, notably in the presence of genetic risk factors. Interaction analyses have shown that both outdoor and indoor air pollution interact with genetic variability to increase the incidence of asthma. In this review, we summarize recent progress in candidate gene-based studies, as well as genome-wide gene-air pollution interaction studies. Advances in epigenetics have provided evidence for DNA methylation as a mediator in gene-air pollution interactions. Emerging strategies for study design and statistical analyses may improve power in future studies. Improved air pollution exposure assessment methods and asthma endo-typing can also be expected to increase the ability to detect biologically driven gene-air pollution interaction effects.

Sialyl Glycan Expression on T Cell Subsets in Asthma: a correlation with disease severity and blood parameters

Memory T helper (Th) and regulatory T (Treg) cells play key roles in asthma. Certain sialyl carbohydrate determinants for selectins profoundly affect the migratory properties of memory Th cells, and the suppressive function of Treg cells. Previous studies have shown that the proportion of CCR4⁺ memory Th cells expressing sialyl 6-sulfo Lewis X (Le^X) is elevated in asthma patients. We aim to investigate the roles of different sialyl glycans on T cell subsets in asthma. Using flow cytometry, we assessed the expression of three sialyl glycans, sialyl 6-sulfo Le^X, cyclic sialyl 6-sulfo Le^X, and sialyl Le^X on memory Th and Treg cells, in the peripheral blood of asthmatic children. We also assessed the relationships between glycan-expressing cell percentages and asthma clinical parameters. Compared with controls, asthmatic children showed higher proportions of memory Th cells expressing sialyl Le^X and sialyl 6-sulfo Le^X. The proportions of memory Th cells with sialyl 6-sulfo Le^X and cyclic sialyl 6-sulfo Le^X expression in asthmatic children correlated with absolute eosinophil count and IgE level, respectively. Children with moderate-to-severe asthma had lower numbers of sialyl Le^X positive Treg cells. Our study suggests that sialyl glycans on T cells may play important roles in the pathogenesis of asthma.

T cells in severe childhood asthma

Severe asthma in children is a debilitating condition that accounts for a disproportionately large health and economic burden of asthma. Reasons for the lack of a response to standard anti-inflammatory therapies remain enigmatic. Work in the last decade has shed new light on the heterogeneous nature of asthma, and the varied immunopathologies of severe disease, which are leading to new treatment approaches for the individual patient. However, most studies to date that explored the immune landscape of the inflamed lower airways have focused on adults. T cells are pivotal to the inception and persistence of inflammatory processes in the diseased lungs, despite a contemporary shift in focus to immune events at the epithelial barrier. This article outlines current knowledge on the types of T cells and related cell types that are implicated in severe asthma. The potential for environmental exposures and other inflammatory cues to condition the immune environment of the lung in early life to favour pathogenic T cells and steroid resistance is discussed. The contributions of T cells and their cytokines to inflammatory processes and treatment resistance are also considered, with an emphasis on new observations in children that argue against conventional type 1 and type 2 T cell paradigms. Finally, the ability for new technologies to revolutionize our understanding of T cells in severe childhood asthma, and to guide future treatment strategies that could mitigate this disease, is highlighted.

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.