A Severe Asthma Disease Signature from Gene Expression Profiling of Peripheral Blood from U-BIOPRED Cohorts

Topological Structures in the Space of Treatment-Naïve Patients with Chronic Lymphocytic Leukemia

Patients are complex and heterogeneous; clinical data sets are complicated by noise, missing data, and the presence of mixed-type data. Using such data sets requires understanding the high-dimensional "space of patients", composed of all measurements that define all relevant phenotypes. The current state-of-the-art merely defines spatial groupings of patients using cluster analyses. Our goal is to apply topological data analysis (TDA), a new unsupervised technique, to obtain a more complete understanding of patient space. We applied TDA to a space of 266 previously untreated patients with Chronic Lymphocytic Leukemia (CLL), using the "daisy" metric to compute distances between clinical records. We found clear evidence for both loops and voids in the CLL data. To interpret these structures, we developed novel computational and graphical methods. The most persistent loop and the most persistent void can be explained using three dichotomized, prognostically important factors in CLL: IGHV somatic mutation status, beta-2 microglobulin, and Rai stage. In conclusion, patient space turns out to be richer and more complex than current models suggest. TDA could become a powerful tool in a researcher's arsenal for interpreting high-dimensional data by providing novel insights into biological processes and improving our understanding of clinical and biological data sets.

Precision medicine for asthma treatment: Unlocking the potential of the epigenome and microbiome

Asthma is a leading worldwide biomedical concern. Patients can experience life-threatening worsening episodes (exacerbations) usually controlled by anti-inflammatory and bronchodilator drugs. However, substantial heterogeneity in treatment response exists, and a subset of patients with unresolved asthma carry the major burden of this disease. The study of the epigenome and microbiome might bridge the gap between human genetics and environmental exposure to partially explain the heterogeneity in drug response. This review aims to provide a critical examination of the existing literature on the microbiome and epigenetic studies examining associations with asthma treatments and drug response, highlight convergent pathways, address current challenges, and offer future perspectives. Current epigenetic and microbiome studies have shown the bilateral relationship between asthma pharmacologic interventions and the human epigenome and microbiome. These studies, focusing on corticosteroids and to a lesser extent on bronchodilators, azithromycin, immunotherapy, and mepolizumab, have improved the understanding of the molecular basis of treatment response and identified promising biomarkers for drug response prediction. Immune and inflammatory pathways (eg, IL-2, TNF-α, NF-κB, and C/EBPs) underlie microbiome-epigenetic associations with asthma treatment, representing potential therapeutic pathways to be targeted. A comprehensive evaluation of these omics biomarkers could significantly contribute to precision medicine and new therapeutic target discovery.

An integrated molecular risk score early in life for subsequent childhood asthma risk

BACKGROUND

Numerous children present with early wheeze symptoms, yet solely a subgroup develops childhood asthma. Early identification of children at risk is key for clinical monitoring, timely patient-tailored treatment, and preventing chronic, severe sequelae. For early prediction of childhood asthma, we aimed to define an integrated risk score combining established risk factors with genome-wide molecular markers at birth, complemented by subsequent clinical symptoms/diagnoses (wheezing, atopic dermatitis, food allergy).

METHODS

Three longitudinal birth cohorts (PAULINA/PAULCHEN, n = 190 + 93 = 283, PASTURE, n = 1133) were used to predict childhood asthma (age 5-11) including epidemiological characteristics and molecular markers: genotype, DNA methylation and mRNA expression (RNASeq/NanoString). Apparent (ap) and optimism-corrected (oc) performance (AUC/R2) was assessed leveraging evidence from independent studies (Naïve-Bayes approach) combined with high-dimensional logistic regression models (LASSO).

RESULTS

Asthma prediction with epidemiological characteristics at birth (maternal asthma, sex, farm environment) yielded an ocAUC = 0.65. Inclusion of molecular markers as predictors resulted in an improvement in apparent prediction performance, however, for optimism-corrected performance only a moderate increase was observed (upto ocAUC = 0.68). The greatest discriminate power was reached by adding the first symptoms/diagnosis (up to ocAUC = 0.76; increase of 0.08, p = .002). Longitudinal analysis of selected mRNA expression in PASTURE (cord blood, 1, 4.5, 6 years) showed that expression at age six had the strongest association with asthma and correlation of genes getting larger over time (r = .59, p < .001, 4.5-6 years).

CONCLUSION

Applying epidemiological predictors alone showed moderate predictive abilities. Molecular markers from birth modestly improved prediction. Allergic symptoms/diagnoses enhanced the power of prediction, which is important for clinical practice and for the design of future studies with molecular markers.

Analytical challenges in omics research on asthma and allergy: A National Institute of Allergy and Infectious Diseases workshop

Studies of asthma and allergy are generating increasing volumes of omics data for analysis and interpretation. The National Institute of Allergy and Infectious Diseases (NIAID) assembled a workshop comprising investigators studying asthma and allergic diseases using omics approaches, omics investigators from outside the field, and NIAID medical and scientific officers to discuss the following areas in asthma and allergy research: genomics, epigenomics, transcriptomics, microbiomics, metabolomics, proteomics, lipidomics, integrative omics, systems biology, and causal inference. Current states of the art, present challenges, novel and emerging strategies, and priorities for progress were presented and discussed for each area. This workshop report summarizes the major points and conclusions from this NIAID workshop. As a group, the investigators underscored the imperatives for rigorous analytic frameworks, integration of different omics data types, cross-disciplinary interaction, strategies for overcoming current limitations, and the overarching goal to improve scientific understanding and care of asthma and allergic diseases.

Endotypes of Paediatric Cough-Do They Exist and Finding New Techniques to Improve Clinical Outcomes

Chronic cough is a common symptom of many childhood lung conditions. Given the phenotypic heterogeneity of chronic cough, better characterization through endotyping is required to provide diagnostic certainty, precision therapies and to identify pathobiological mechanisms. This review summarizes recent endotype discoveries in airway diseases, particularly in relation to children, and describes the multi-omic approaches that are required to define endotypes. Potential biospecimens that may contribute to endotype and biomarker discoveries are also discussed. Identifying endotypes of chronic cough can likely provide personalized medicine and contribute to improved clinical outcomes for children.

Dissecting the Immune System through Gene Regulation

The immune system plays a dual role in human health, functioning both as a protector against pathogens and, at times, as a contributor to disease. This feature emphasizes the importance to uncover the underlying causes of its malfunctions, necessitating an in-depth analysis in both pathological and physiological conditions to better understand the immune system and immune disorders. Recent advances in scientific technology have enabled extensive investigations into gene regulation, a crucial mechanism governing cellular functionality. Studying gene regulatory mechanisms within the immune system is a promising avenue for enhancing our understanding of immune cells and the immune system as a whole. The gene regulatory mechanisms, revealed through various methodologies, and their implications in the field of immunology are discussed in this chapter.

An interpretable machine learning pipeline based on transcriptomics predicts phenotypes of lupus patients

Machine learning (ML) has the potential to identify subsets of patients with distinct phenotypes from gene expression data. However, phenotype prediction using ML has often relied on identifying important genes without a systems biology context. To address this, we created an interpretable ML approach based on blood transcriptomics to predict phenotype in systemic lupus erythematosus (SLE), a heterogeneous autoimmune disease. We employed a sequential grouped feature importance algorithm to assess the performance of gene sets, including immune and metabolic pathways and cell types, known to be abnormal in SLE in predicting disease activity and organ involvement. Gene sets related to interferon, tumor necrosis factor, the mitoribosome, and T cell activation were the best predictors of phenotype with excellent performance. These results suggest potential relationships between the molecular pathways identified in each model and manifestations of SLE. This ML approach to phenotype prediction can be applied to other diseases and tissues.

Blood transcriptomic signature in type-2 biomarker-low severe asthma and asthma control

BACKGROUND

Patients with type-2 (T2) cytokine-low severe asthma often have persistent symptoms despite suppression of T2 inflammation with corticosteroids.

OBJECTIVES

We sought to analyze whole blood transcriptome from 738 samples in T2-biomarker-high/-low patients with severe asthma to relate transcriptomic signatures to T2 biomarkers and asthma symptom scores.

METHODS

Bulk RNA-seq data were generated for blood samples (baseline, week 24, week 48) from 301 participants recruited to a randomized clinical trial of corticosteroid optimization in severe asthma. Unsupervised clustering, differential gene expression analysis, and pathway analysis were performed. Patients were grouped by T2-biomarker status and symptoms. Associations between clinical characteristics and differentially expressed genes (DEGs) associated with biomarker and symptom levels were investigated.

RESULTS

Unsupervised clustering identified 2 clusters; cluster 2 patients were blood eosinophil-low/symptom-high and more likely to be receiving oral corticosteroids (OCSs). Differential gene expression analysis of these clusters, with and without stratification for OCSs, identified 2960 and 4162 DEGs, respectively. Six hundred twenty-seven of 2960 genes remained after adjusting for OCSs by subtracting OCS signature genes. Pathway analysis identified dolichyl-diphosphooligosaccharide biosynthesis and assembly of RNA polymerase I complex as significantly enriched pathways. No stable DEGs were associated with high symptoms in T2-biomarker-low patients, but numerous associated with elevated T2 biomarkers, including 15 that were upregulated at all time points irrespective of symptom level.

CONCLUSIONS

OCSs have a considerable effect on whole blood transcriptome. Differential gene expression analysis demonstrates a clear T2-biomarker transcriptomic signature, but no signature was found in association with T2-biomarker-low patients, including those with a high symptom burden.

Asthma Phenotypes in the Era of Personalized Medicine

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

Integrated study of systemic and local airway transcriptomes in asthma reveals causal mediation of systemic effects by airway key drivers

BACKGROUND

Systemic and local profiles have each been associated with asthma, but parsing causal relationships between system-wide and airway-specific processes can be challenging. We sought to investigate systemic and airway processes in asthma and their causal relationships.

METHODS

Three hundred forty-one participants with persistent asthma and non-asthmatic controls were recruited and underwent peripheral blood mononuclear cell (PBMC) collection and nasal brushing. Transcriptome-wide RNA sequencing of the PBMC and nasal samples and a series of analyses were then performed using a discovery and independent test set approach at each step to ensure rigor. Analytic steps included differential expression analyses, coexpression and probabilistic causal (Bayesian) network constructions, key driver analyses, and causal mediation models.

RESULTS

Among the 341 participants, the median age was 13 years (IQR = 10-16), 164 (48%) were female, and 200 (58.7%) had persistent asthma with mean Asthma Control Test (ACT) score 16.6 (SD = 4.2). PBMC genes associated with asthma were enriched in co-expression modules for NK cell-mediated cytotoxicity (fold enrichment = 4.5, FDR = 6.47 × 10-32) and interleukin production (fold enrichment = 2.0, FDR = 1.01 × 10-15). Probabilistic causal network and key driver analyses identified NK cell granule protein (NKG7, fold change = 22.7, FDR = 1.02 × 10-31) and perforin (PRF1, fold change = 14.9, FDR = 1.31 × 10-22) as key drivers predicted to causally regulate PBMC asthma modules. Nasal genes associated with asthma were enriched in the tricarboxylic acid (TCA) cycle module (fold enrichment = 7.5 FDR = 5.09 × 10-107), with network analyses identifying G3BP stress granule assembly factor 1 (G3BP1, fold change = 9.1 FDR = 2.77 × 10-5) and InaD-like protein (INADL, fold change = 5.3 FDR = 2.98 × 10-9) as nasal key drivers. Causal mediation analyses revealed that associations between PBMC key drivers and asthma are causally mediated by nasal key drivers (FDR = 0.0076 to 0.015).

CONCLUSIONS

Integrated study of the systemic and airway transcriptomes in a well-phenotyped asthma cohort identified causal key drivers of asthma among PBMC and nasal transcripts. Associations between PBMC key drivers and asthma are causally mediated by nasal key drivers.

Unraveling the link between PTBP1 and severe asthma through machine learning and association rule mining method

Severe asthma is a chronic inflammatory airway disease with great therapeutic challenges. Understanding the genetic and molecular mechanisms of severe asthma may help identify therapeutic strategies for this complex condition. RNA expression data were analyzed using a combination of artificial intelligence methods to identify novel genes related to severe asthma. Through the ANOVA feature selection approach, 100 candidate genes were selected among 54,715 mRNAs in blood samples of patients with severe asthmatic and healthy groups. A deep learning model was used to validate the significance of the candidate genes. The accuracy, F1-score, AUC-ROC, and precision of the 100 genes were 83%, 0.86, 0.89, and 0.9, respectively. To discover hidden associations among selected genes, association rule mining was applied. The top 20 genes including the PTBP1, RAB11FIP3, APH1A, and MYD88 were recognized as the most frequent items among severe asthma association rules. The PTBP1 was found to be the most frequent gene associated with severe asthma among those 20 genes. PTBP1 was the gene most frequently associated with severe asthma among candidate genes. Identification of master genes involved in the initiation and development of asthma can offer novel targets for its diagnosis, prognosis, and targeted-signaling therapy.

Severe pediatric asthma endotypes: current limits and future perspectives

INTRODUCTION

Although rare, pediatric severe therapy-resistant asthma (STRA) is a highly heterogeneous, resource-demanding disease that differs significantly from severe adult asthma and whose pathogenesis is still poorly understood.

AREAS COVERED

This review summarizes the latest 10 years of English-written studies defining pediatric STRA endotypes using lung-specific techniques such as bronchoalveolar lavage and endobronchial biopsy. Results of the studies and limits on the field are discussed, together with some future perspectives.

EXPERT OPINION

Over the years, it has become increasingly clear that 'one size does not fit all" in asthma. However, "Does an extremely tailored size fit more than one?'. Only using multicentric, longitudinal pediatric studies, will we be able to answer. Three issues could be particularly critical for future research. First, to provide, if existing, a distinction between prepuberal STRA and puberal STRA endotypes to understand the transition from pediatric to adult STRA and to design effective, tailored therapies in adolescents, usually suffering from poorer asthma control. Second, design early treatments for pediatric airway remodeling to preserve lifelong good lung function. Finally, to better characterize inflammation before and during biological therapies, to provide clues on whether to stop or change treatments.

Topological data analysis identifies molecular phenotypes of idiopathic pulmonary fibrosis

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a debilitating, progressive disease with a median survival time of 3-5 years. Diagnosis remains challenging and disease progression varies greatly, suggesting the possibility of distinct subphenotypes.

METHODS AND RESULTS

We analysed publicly available peripheral blood mononuclear cell expression datasets for 219 IPF, 411 asthma, 362 tuberculosis, 151 healthy, 92 HIV and 83 other disease samples, totalling 1318 patients. We integrated the datasets and split them into train (n=871) and test (n=477) cohorts to investigate the utility of a machine learning model (support vector machine) for predicting IPF. A panel of 44 genes predicted IPF in a background of healthy, tuberculosis, HIV and asthma with an area under the curve of 0.9464, corresponding to a sensitivity of 0.865 and a specificity of 0.89. We then applied topological data analysis to investigate the possibility of subphenotypes within IPF. We identified five molecular subphenotypes of IPF, one of which corresponded to a phenotype enriched for death/transplant. The subphenotypes were molecularly characterised using bioinformatic and pathway analysis tools identifying distinct subphenotype features including one which suggests an extrapulmonary or systemic fibrotic disease.

CONCLUSIONS

Integration of multiple datasets, from the same tissue, enabled the development of a model to accurately predict IPF using a panel of 44 genes. Furthermore, topological data analysis identified distinct subphenotypes of patients with IPF which were defined by differences in molecular pathobiology and clinical characteristics.

Stratification of asthma by lipidomic profiling of induced sputum supernatant

BACKGROUND

Asthma is a chronic respiratory disease with significant heterogeneity in its clinical presentation and pathobiology. There is need for improved understanding of respiratory lipid metabolism in asthma patients and its relation to observable clinical features.

OBJECTIVE

We performed a comprehensive, prospective, cross-sectional analysis of the lipid composition of induced sputum supernatant obtained from asthma patients with a range of disease severities, as well as from healthy controls.

METHODS

Induced sputum supernatant was collected from 211 adults with asthma and 41 healthy individuals enrolled onto the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes) study. Sputum lipidomes were characterized by semiquantitative shotgun mass spectrometry and clustered using topologic data analysis to identify lipid phenotypes.

RESULTS

Shotgun lipidomics of induced sputum supernatant revealed a spectrum of 9 molecular phenotypes, highlighting not just significant differences between the sputum lipidomes of asthma patients and healthy controls, but also within the asthma patient population. Matching clinical, pathobiologic, proteomic, and transcriptomic data helped inform the underlying disease processes. Sputum lipid phenotypes with higher levels of nonendogenous, cell-derived lipids were associated with significantly worse asthma severity, worse lung function, and elevated granulocyte counts.

CONCLUSION

We propose a novel mechanism of increased lipid loading in the epithelial lining fluid of asthma patients resulting from the secretion of extracellular vesicles by granulocytic inflammatory cells, which could reduce the ability of pulmonary surfactant to lower surface tension in asthmatic small airways, as well as compromise its role as an immune regulator.

The complexity in DNA methylation analysis of allergic diseases

PURPOSE OF REVIEW

This review aims to report all the recent studies that are implicated in DNA methylation analysis in the field of allergy and to underline the complexity of the study methodologies and results.

RECENT FINDINGS

Although the growing number of DNA methylation studies have yet to point to a specific mechanism, herein we provide an overview of the majority of pathways considered to be implicated and highlight particular genes, like KNH2 , ATPAF2 and ZNF385A , for their potential as biomarkers.

SUMMARY

The epigenetic profile of respiratory allergic diseases, and particularly DNA methylation, has been investigated in various populations, so as to gain a better understanding of its role in pathogenesis. Through our analysis, multiple links are presented between differential DNA methylation loci and IgE sensitization, lung functionality and severity of the disease. Additionally, associations of this epigenetic change with maternal asthma, age, sex and environmental factors are described, thus uncovering specific gene families that, after further examination could be used as methylation biomarkers in cases of allergic disease.

Precision Medicine Intervention in Severe Asthma (PRISM) study: molecular phenotyping of patients with severe asthma and response to biologics

Severe asthma represents an important clinical unmet need despite the introduction of biologic agents. Although advanced omics technologies have aided researchers in identifying clinically relevant molecular pathways, there is a lack of an integrated omics approach in severe asthma particularly in terms of its evolution over time. The collaborative Korea-UK research project Precision Medicine Intervention in Severe Asthma (PRISM) was launched in 2020 with the aim of identifying molecular phenotypes of severe asthma by analysing multi-omics data encompassing genomics, epigenomics, transcriptomics, proteomics, metagenomics and metabolomics. PRISM is a prospective, observational, multicentre study involving patients with severe asthma attending severe asthma clinics in Korea and the UK. Data including patient demographics, inflammatory phenotype, medication, lung function and control status of asthma will be collected along with biological samples (blood, sputum, urine, nasal epithelial cells and exhaled breath condensate) for omics analyses. Follow-up evaluations will be performed at baseline, 1 month, 4-6 months and 10-12 months to assess the stability of phenotype and treatment responses for those patients who have newly begun biologic therapy. Standalone and integrated omics data will be generated from the patient samples at each visit, paired with clinical information. By analysing these data, we will identify the molecular pathways that drive lung function, asthma control status, acute exacerbations and the requirement for daily oral corticosteroids, and that are involved in the therapeutic response to biological therapy. PRISM will establish a large multi-omics dataset of severe asthma to identify potential key pathophysiological pathways of severe asthma.

Blood-Based Biomarkers for Eosinophilic Esophagitis and Concomitant Atopic Diseases: A Look into the Potential of Extracellular Vesicles

Eosinophilic esophagitis (EoE) is a chronic, Th2-inflammatory disease of the esophagus that can severely affect food intake. Currently, diagnosis and assessing response to treatment of EoE is highly invasive and requires endoscopy with esophageal biopsies. Finding non-invasive and accurate biomarkers is important for improving patient well-being. Unfortunately, EoE is usually accompanied by other atopies, which make it difficult to identify specific biomarkers. Providing an update of circulating EoE biomarkers and concomitant atopies is therefore timely. This review summarizes the current knowledge in EoE blood biomarkers and two of its most common comorbidities, bronchial asthma (BA) and atopic dermatitis (AD), focusing on dysregulated proteins, metabolites, and RNAs. It also revises the current knowledge on extracellular vesicles (EVs) as non-invasive biomarkers for BA and AD, and concludes with the potential use of EVs as biomarkers in EoE.

Pharmacometabolomics of Asthma as a Road Map to Precision Medicine

Pharmacometabolomics applies the principles of metabolomics to therapeutics in order to elucidate the biological mechanisms underlying the variation in responses to drugs between groups and individuals. Asthma is associated with broad systemic effects and heterogeneity in treatment response and as such is ideally suited to pharmacometabolomics. In this chapter, we discuss the state of the emerging field of asthma pharmacometabolomics, with a particular focus on studies of steroids, bronchodilators, and leukotriene inhibitors. We also consider those studies concerned with subtyping cases to better understand the pharmacology of those groups and those looking to leverage pharmacometabolomics for asthma prevention. We finish with a discussion of the challenges and opportunities of asthma pharmacometabolomics and reflect upon where this field must go next in order to realize its precision medicine potential.

Systems Biology in Asthma

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

Peripheral blood RNA biomarkers for cardiovascular disease from bench to bedside: a position paper from the EU-CardioRNA COST action CA17129

Despite significant advances in the diagnosis and treatment of cardiovascular diseases, recent calls have emphasized the unmet need to improve precision-based approaches in cardiovascular disease. Although some studies provide preliminary evidence of the diagnostic and prognostic potential of circulating coding and non-coding RNAs, the complex RNA biology and lack of standardization have hampered the translation of these markers into clinical practice. In this position paper of the CardioRNA COST action CA17129, we provide recommendations to standardize the RNA development process in order to catalyse efforts to investigate novel RNAs for clinical use. We list the unmet clinical needs in cardiovascular disease, such as the identification of high-risk patients with ischaemic heart disease or heart failure who require more intensive therapies. The advantages and pitfalls of the different sample types, including RNAs from plasma, extracellular vesicles, and whole blood, are discussed in the sample matrix, together with their respective analytical methods. The effect of patient demographics and highly prevalent comorbidities, such as metabolic disorders, on the expression of the candidate RNA is presented and should be reported in biomarker studies. We discuss the statistical and regulatory aspects to translate a candidate RNA from a research use only assay to an in-vitro diagnostic test for clinical use. Optimal planning of this development track is required, with input from the researcher, statistician, industry, and regulatory partners.

Bioinformatic analysis and preliminary validation of potential therapeutic targets for COVID-19 infection in asthma patients

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 causes coronavirus disease 19 (COVID-19). The number of confirmed cases of COVID-19 is also rapidly increasing worldwide, posing a significant challenge to human safety. Asthma is a risk factor for COVID-19, but the underlying molecular mechanisms of the asthma-COVID-19 interaction remain unclear.

METHODS

We used transcriptome analysis to discover molecular biomarkers common to asthma and COVID-19. Gene Expression Omnibus database RNA-seq datasets (GSE195599 and GSE196822) were used to identify differentially expressed genes (DEGs) in asthma and COVID-19 patients. After intersecting the differentially expressed mRNAs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to identify the common pathogenic molecular mechanism. Bioinformatic methods were used to construct protein-protein interaction (PPI) networks and identify key genes from the networks. An online database was used to predict interactions between transcription factors and key genes. The differentially expressed long noncoding RNAs (lncRNAs) in the GSE195599 and GSE196822 datasets were intersected to construct a competing endogenous RNA (ceRNA) regulatory network. Interaction networks were constructed for key genes with RNA-binding proteins (RBPs) and oxidative stress-related proteins. The diagnostic efficacy of key genes in COVID-19 was verified with the GSE171110 dataset. The differential expression of key genes in asthma was verified with the GSE69683 dataset. An asthma cell model was established with interleukins (IL-4, IL-13 and IL-17A) and transfected with siRNA-CXCR1. The role of CXCR1 in asthma development was preliminarily confirmed.

RESULTS

By intersecting the differentially expressed genes for COVID-19 and asthma, 393 common DEGs were obtained. GO and KEGG enrichment analyses of the DEGs showed that they mainly affected inflammation-, cytokine- and immune-related functions and inflammation-related signaling pathways. By analyzing the PPI network, we obtained 10 key genes: TLR4, TLR2, MMP9, EGF, HCK, FCGR2A, SELP, NFKBIA, CXCR1, and SELL. By intersecting the differentially expressed lncRNAs for COVID-19 and asthma, 13 common differentially expressed lncRNAs were obtained. LncRNAs that regulated microRNAs (miRNAs) were mainly concentrated in intercellular signal transduction, apoptosis, immunity and other related functional pathways. The ceRNA network suggested that there were a variety of regulatory miRNAs and lncRNAs upstream of the key genes. The key genes could also bind a variety of RBPs and oxidative stress-related genes. The key genes also had good diagnostic value in the verification set. In the validation set, the expression of key genes was statistically significant in both the COVID-19 group and the asthma group compared with the healthy control group. CXCR1 expression was upregulated in asthma cell models, and interference with CXCR1 expression significantly reduced cell viability.

CONCLUSIONS

Key genes may become diagnostic and predictive biomarkers of outcomes in COVID-19 and asthma. Video Abstract.

Within- and cross-tissue gene regulations were disrupted by PM2.5 nitrate exposure and associated with respiratory functions

BACKGROUND

Pathogenesis of complex diseases often involves multiple organs/tissue-types. To date, the PM_2.5 exposure's toxic effects and induced disease risks were not studied at multi-tissue level.

METHODS

C57BL/6 mice (n = 40) were exposed to PM_2.5 NO₃^- and clean air, respectively, and afterwards assessed respiratory functions and transcriptome in relevant tissues: blood and lung. We constructed within- and cross-tissue gene regulation networks and identified network modules associated with exposure and respiratory functions.

RESULTS

PM_2.5 NO₃^- exposure elevated naïve B cells proportion in blood (p = 0.0028). Among the 6000 highest expressed genes in blood, 18.8 % (1133 genes) were altered by exposure at p ≤ 0.05 level, among which 763 genes were also associated with respiratory function (enrichment folds = 7.63, p = 2.7E-189). The exposure disrupted blood genes were primarily in the immunoregulation pathways. Both within- and cross-tissue gene network modules were perturbed by exposure and associated with respiratory function. An immunodeficiency related cross-tissue module of 555 genes was affected by exposure (p = 0.0023) and strongly correlated with FEV_0.05/FVC (r = 0.61 and p = 3E-5).

CONCLUSIONS

This study aims to fill in a major knowledge gap and investigated the effect of PM_2.5 exposure simultaneously in multiple tissues. We provided novel evidence that PM_2.5 NO₃^- exposure profoundly perturbed within- and cross-tissue gene regulations, and highlighted their roles in the etiology of respiratory decline.

Severe, but not moderate asthmatics share blood transcriptomic changes with post-traumatic stress disorder and depression

Asthma, an inflammatory disorder of the airways, is one of the most common chronic illnesses worldwide and is associated with significant morbidity. There is growing recognition of an association between asthma and mood disorders including post-traumatic stress disorder (PTSD) and major depressive disorder (MDD). Although there are several hypotheses regarding the relationship between asthma and mental health, there is little understanding of underlying mechanisms and causality. In the current study we utilized publicly available datasets of human blood mRNA collected from patients with severe and moderate asthma, MDD, and PTSD. We performed differential expression (DE) analysis and Gene Set Enrichment Analysis (GSEA) on diseased subjects against the healthy subjects from their respective datasets, compared the results between diseases, and validated DE genes and gene sets with 4 more independent datasets. Our analysis revealed that commonalities in blood transcriptomic changes were only found between the severe form of asthma and mood disorders. Gene expression commonly regulated in PTSD and severe asthma, included ORMDL3 a gene known to be associated with asthma risk and STX8, which is involved in TrkA signaling. We also identified several pathways commonly regulated to both MDD and severe asthma. This study reveals gene and pathway regulation that potentially drives the comorbidity between severe asthma, PTSD, and MDD and may serve as foci for future research aimed at gaining a better understanding of both the relationship between asthma and PTSD, and the pathophysiology of the individual disorders.

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.

Blood gene expression predicts intensive care unit admission in hospitalised patients with COVID-19

Background

The COVID-19 pandemic has created pressure on healthcare systems worldwide. Tools that can stratify individuals according to prognosis could allow for more efficient allocation of healthcare resources and thus improved patient outcomes. It is currently unclear if blood gene expression signatures derived from patients at the point of admission to hospital could provide useful prognostic information.

Methods

Gene expression of whole blood obtained at the point of admission from a cohort of 78 patients hospitalised with COVID-19 during the first wave was measured by high resolution RNA sequencing. Gene signatures predictive of admission to Intensive Care Unit were identified and tested using machine learning and topological data analysis, TopMD.

Results

The best gene expression signature predictive of ICU admission was defined using topological data analysis with an accuracy: 0.72 and ROC AUC: 0.76. The gene signature was primarily based on differentially activated pathways controlling epidermal growth factor receptor (EGFR) presentation, Peroxisome proliferator-activated receptor alpha (PPAR-α) signalling and Transforming growth factor beta (TGF-β) signalling.

Conclusions

Gene expression signatures from blood taken at the point of admission to hospital predicted ICU admission of treatment naïve patients with COVID-19.

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.

Connecting omics signatures and revealing biological mechanisms with iLINCS

There are only a few platforms that integrate multiple omics data types, bioinformatics tools, and interfaces for integrative analyses and visualization that do not require programming skills. Here we present iLINCS ( http://ilincs.org ), an integrative web-based platform for analysis of omics data and signatures of cellular perturbations. The platform facilitates mining and re-analysis of the large collection of omics datasets (>34,000), pre-computed signatures (>200,000), and their connections, as well as the analysis of user-submitted omics signatures of diseases and cellular perturbations. iLINCS analysis workflows integrate vast omics data resources and a range of analytics and interactive visualization tools into a comprehensive platform for analysis of omics signatures. iLINCS user-friendly interfaces enable execution of sophisticated analyses of omics signatures, mechanism of action analysis, and signature-driven drug repositioning. We illustrate the utility of iLINCS with three use cases involving analysis of cancer proteogenomic signatures, COVID 19 transcriptomic signatures and mTOR signaling.

Bcl10 Regulates Lipopolysaccharide-Induced Pro-Fibrotic Signaling in Bronchial Fibroblasts from Severe Asthma Patients

Subepithelial fibrosis is a characteristic hallmark of airway remodeling in asthma. Current asthma medications have limited efficacy in treating fibrosis, particularly in patients with severe asthma, necessitating a deeper understanding of the fibrotic mechanisms. The NF-κB pathway is key to airway inflammation in asthma, as it regulates the activity of multiple pro-inflammatory mediators that contribute to airway pathology. Bcl10 is a well-known upstream mediator of the NF-κB pathway that has been linked to fibrosis in other disease models. Therefore, we investigated Bcl10-mediated NF-κB activation as a potential pathway regulating fibrotic signaling in severe asthmatic fibroblasts. We demonstrate here the elevated protein expression of Bcl10 in bronchial fibroblasts and bronchial biopsies from severe asthmatic patients when compared to non-asthmatic individuals. Lipopolysaccharide (LPS) induced the increased expression of the pro-fibrotic cytokines IL-6, IL-8 and TGF-β1 in bronchial fibroblasts, and this induction was associated with the activation of Bcl10. Inhibition of the Bcl10-mediated NF-κB pathway using an IRAK1/4 selective inhibitor abrogated the pro-fibrotic signaling induced by LPS. Thus, our study indicates that Bcl10-mediated NF-κB activation signals increased pro-fibrotic cytokine expression in severe asthmatic airways. This reveals the therapeutic potential of targeting Bcl10 signaling in ameliorating inflammation and fibrosis, particularly in severe asthmatic individuals.

Topological data analysis in biomedicine: A review

Significant technological advances made in recent years have shepherded a dramatic increase in utilization of digital technologies for biomedicine- everything from the widespread use of electronic health records to improved medical imaging capabilities and the rising ubiquity of genomic sequencing contribute to a "digitization" of biomedical research and clinical care. With this shift toward computerized tools comes a dramatic increase in the amount of available data, and current tools for data analysis capable of extracting meaningful knowledge from this wealth of information have yet to catch up. This article seeks to provide an overview of emerging mathematical methods with the potential to improve the abilities of clinicians and researchers to analyze biomedical data, but may be hindered from doing so by a lack of conceptual accessibility and awareness in the life sciences research community. In particular, we focus on topological data analysis (TDA), a set of methods grounded in the mathematical field of algebraic topology that seeks to describe and harness features related to the "shape" of data. We aim to make such techniques more approachable to non-mathematicians by providing a conceptual discussion of their theoretical foundations followed by a survey of their published applications to scientific research. Finally, we discuss the limitations of these methods and suggest potential avenues for future work integrating mathematical tools into clinical care and biomedical informatics.

Age-Related Increase of Collagen/Fibrin Deposition and High PAI-1 Production in Human Nasal Polyps

Objective: Our previous studies showed an age-related increased prevalence of nasal polyps (NP) and reduced production of S100A8/9 in elderly patients with chronic rhinosinusitis with NP (CRSwNP). In this study, we investigated an unbiased age-related gene expression profile in CRSwNP subjects and healthy controls, and further identified the differences in their tissue remodeling. Methods: Microarrays using NP and uncinate tissues from health controls (elderly, age ≥65 vs. non-elderly, age 18-49) were performed, and differentially regulated genes were analyzed. Quantitative real-time PCR (qPCR), Immunostaining, Periodic acid-Schiff (PAS), trichrome staining, Western blot, and ELISA were performed for further investigation. Results: Microarrays identified differentially expressed genes according to disease and age; 278 in NP vs. controls, 75 in non-elderly NP vs. non-elderly controls, and 32 in elderly NP vs. elderly controls. qPCR confirmed that the PLAT gene was downregulated and the SERPINB2 gene upregulated in NP vs. controls. The serous glandular cell-derived antimicrobial protein/peptide-related genes such as BPIFB3, BPIFB2, LPO, and MUC7 were remarkably reduced in NP, regardless of age. SERPINE1 gene (plasminogen activator inhibitor-1, PAI-1) expression was significantly increased in elderly NP versus elderly controls. IHC and western blot confirmed significantly decreased production of MUC7 and LPO in NP versus controls. There was a trend of age-related reduction of submucosal gland cells in normal controls. Trichrome and immunofluorescence staining demonstrated an age-related increase of collagen and fibrin deposition in NP, consistent with increased PAI-1 production. Conclusion: This study demonstrated age-related differential glandular remodeling patterns and fibrosis in NP and normal controls. PAI-1 expression was significantly increased in elderly NP versus elderly controls, suggesting PAI-1 as a potential treatment target in elderly NP.

Transcriptome-Wide m6A Methylome and m6A-Modified Gene Analysis in Asthma

N6-methyladenosine (m6A) modification is one of the most prevalent RNA modification forms and is an important posttranscriptional mechanism for regulating genes. In previous research, we found that m6A regulator–mediated RNA methylation modification was involved in asthma; however, the specific modified genes are not clear. In this study, we systematically evaluated the transcriptome-wide m6A methylome and m6A-modified genes in asthma. Here, we performed two high-throughput sequencing methods, methylated RNA immunoprecipitation sequencing (MeRIP-seq), and RNA sequencing (RNA-seq) to identify key genes with m6A modification in asthma. Through difference analysis, we found that 416 methylation peaks were significantly upregulated and 152 methylation peaks were significantly downregulated, and it was mainly distributed in 3′ UTR. Furthermore, compared with the control group, there were 2,505 significantly upregulated genes and 4,715 significantly downregulated genes in the asthma group. Next, through a combined analysis of transcriptome and differential peaks, 14 differentially expressed genes related to RNA methylation modification were screened. Finally, through 87 health controls and 411 asthma cases from the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes) program, we verified three m6A-modified key genes (BCL11A, MATK, and CD300A) and found that they were mainly distributed in exons and enriched in 3' UTR. Our findings suggested that intervening in m6A-modified genes may provide a new idea for the treatment of asthma.

Alterations in Immune-Related Defensin Alpha 4 (DEFA4) Gene Expression in Health and Disease

Defensin Alpha 4 (DEFA4) is the fourth member of the Alpha Defensins family known as a part of antimicrobial peptides in the innate immune system. DEFA4 has a strong preference to kill Gram-negative bacteria more than Gram-positive bacteria. In addition, DEFA4 exhibits antiviral activity against human immunodeficiency virus type 1 (HIV-1) in vitro. Moreover, DEFA4 can act as an inhibitor of corticosterone production (Corticostatin). On the other hand, alternations in DEFA4 gene expression have been reported in different disorders such as diseases related to inflammation and immunity dysfunction, brain-related disorders, and various cancers. The up-regulation of DEFA4 appears to be involved in the malignant transformation or aggressive form of cancer. Interestingly, the modified version of DEFA4 fragment (1-11) was potent and efficient against antibiotic-resistant bacteria. This review provides a general background abSaudi Arabia out DEFA4 and sheds light on changes in DEFA4 gene expression in different diseases. The paper also discusses other aspects related to DEFA4 as an antimicrobial and antiviral agent. The research was conducted based on available articles obtained from databases starting from 1988 to the present.

Translational Analysis of Moderate to Severe Asthma GWAS Signals Into Candidate Causal Genes and Their Functional, Tissue-Dependent and Disease-Related Associations

Asthma affects more than 300 million people globally and is both under diagnosed and under treated. The most recent and largest genome-wide association study investigating moderate to severe asthma to date was carried out in 2019 and identified 25 independent signals. However, as new and in-depth downstream databases become available, the translational analysis of these signals into target genes and pathways is timely. In this study, unique (U-BIOPRED) and publicly available datasets (HaploReg, Open Target Genetics and GTEx) were investigated for the 25 GWAS signals to identify 37 candidate causal genes. Additional traits associated with these signals were identified through PheWAS using the UK Biobank resource, with asthma and eosinophilic traits amongst the strongest associated. Gene expression omnibus dataset examination identified 13 candidate genes with altered expression profiles in the airways and blood of asthmatic subjects, including MUC5AC and STAT6. Gene expression analysis through publicly available datasets highlighted lung tissue cell specific expression, with both MUC5AC and SLC22A4 genes showing enriched expression in ciliated cells. Gene enrichment pathway and interaction analysis highlighted the dominance of the HLA-DQA1/A2/B1/B2 gene cluster across many immunological diseases including asthma, type I diabetes, and rheumatoid arthritis. Interaction and prediction analyses found IL33 and IL18R1 to be key co-localization partners for other genes, predicted that CD274 forms co-expression relationships with 13 other genes, including the HLA-DQA1/A2/B1/B2 gene cluster and that MUC5AC and IL37 are co-expressed. Drug interaction analysis revealed that 11 of the candidate genes have an interaction with available therapeutics. This study provides significant insight into these GWAS signals in the context of cell expression, function, and disease relationship with the view of informing future research and drug development efforts for moderate-severe asthma.

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

OBJECTIVE

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

DATA SOURCES

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

STUDY SELECTIONS

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

RESULTS

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

CONCLUSION

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

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.

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.

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.

Large-scale plasma proteomics can reveal distinct endotypes in chronic obstructive pulmonary disease and severe asthma

BACKGROUND

Chronic airway diseases including chronic obstructive pulmonary disease (COPD) and asthma are heterogenous in nature and endotypes within are underpinned by complex biology. This study aimed to investigate the utility of proteomic profiling of plasma combined with bioinformatic mining, and to define molecular endotypes and expand our knowledge of the underlying biology in chronic respiratory diseases.

METHODS

The plasma proteome was evaluated using an aptamer-based affinity proteomics platform (SOMAscan®), representing 1238 proteins in 34 subjects with stable COPD and 51 subjects with stable but severe asthma. For each disease, we evaluated a range of clinical/demographic characteristics including bronchodilator reversibility, blood eosinophilia levels, and smoking history. We applied modified bioinformatic approaches used in the evaluation of RNA transcriptomics.

RESULTS

Subjects with COPD and severe asthma were distinguished from each other by 365 different protein abundancies, with differential pathway networks and upstream modulators. Furthermore, molecular endotypes within each disease could be defined. The protein groups that defined these endotypes had both known and novel biology including groups significantly enriched in exosomal markers derived from immune/inflammatory cells. Finally, we observed associations to clinical characteristics that previously have been under-explored.

CONCLUSION

This investigational study evaluating the plasma proteome in clinically-phenotyped subjects with chronic airway diseases provides support that such a method can be used to define molecular endotypes and pathobiological mechanisms that underpins these endotypes. It provided new concepts about the complexity of molecular pathways that define these diseases. In the longer term, such information will help to refine treatment options for defined groups.

Urinary metabotype of severe asthma evidences decreased carnitine metabolism independent of oral corticosteroid treatment in the U-BIOPRED study

INTRODUCTION

Asthma is a heterogeneous disease with poorly defined phenotypes. Severe asthmatics often receive multiple treatments including oral corticosteroids (OCS). Treatment may modify the observed metabotype, rendering it challenging to investigate underlying disease mechanisms. Here, we aimed to identify dysregulated metabolic processes in relation to asthma severity and medication.

METHODS

Baseline urine was collected prospectively from healthy participants (n=100), mild-to-moderate asthmatics (n=87) and severe asthmatics (n=418) in the cross-sectional U-BIOPRED cohort; 12-18-month longitudinal samples were collected from severe asthmatics (n=305). Metabolomics data were acquired using high-resolution mass spectrometry and analysed using univariate and multivariate methods.

RESULTS

Ninety metabolites were identified, with 40 significantly altered (p<0.05, FDR<0.05) in severe asthma and 23 by OCS use. Multivariate modelling showed that observed metabotypes in healthy participants and mild-to-moderate asthmatics differed significantly from severe asthmatics (p=2.6×10^-20), OCS-treated asthmatics differed significantly from non-treated (p=9.5×10^-4), and longitudinal metabotypes demonstrated temporal stability. Carnitine levels evidenced the strongest OCS-independent decrease in severe asthma. Reduced carnitine levels were associated with mitochondrial dysfunction via decreases in pathway enrichment scores of fatty acid metabolism and reduced expression of the carnitine transporter SLC22A5 in sputum and bronchial brushings.

CONCLUSIONS

This is the first large-scale study to delineate disease- and OCS-associated metabolic differences in asthma. The widespread associations with different therapies upon the observed metabotypes demonstrate the necessity to evaluate potential modulating effects on a treatment- and metabolite-specific basis. Altered carnitine metabolism is a potentially actionable therapeutic target that is independent of OCS treatment, highlighting the role of mitochondrial dysfunction in severe asthma.

m6A regulator-mediated RNA methylation modification patterns and immune microenvironment infiltration characterization in severe asthma

N6‐methyladenosine (m6A) modification is one of the most prevalent RNA modification forms of eukaryotic mRNA and is an important post‐transcriptional mechanism for regulating genes. However, the role of m6A modification in the regulation of severe asthma has never been reported. Thus, we aimed to investigate the m6A regulator‐mediated RNA methylation modification patterns and immune microenvironment infiltration characterization in severe asthma. In this study, 87 healthy controls and 344 severe asthma cases from the U‐BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes) programme were used to systematically evaluate the m6A modification patterns mediated by 27 m6A regulators and to investigate the effects of m6A modification on immune microenvironment characteristics. We found that 16 m6A regulators were abnormal and identified two key m6A regulators (YTHDF3 and YTHDC1) and three m6A modification patterns. The study of infiltration characteristics of immune microenvironment found that pattern 2 had more infiltrating immune cells and more active immune response. Besides, it was found that the eosinophils which are very important for severe asthma were affected by YTHDF3 and EIF3B. We also verified key m6A regulators with merip‐seq and found that they were mainly distributed in exons and enriched in 3′UTR. In conclusion, our findings suggested that m6A modification plays a key role in severe asthma, and may be able to guide the future strategy of immunotherapy.

Dexamethasone-Induced FKBP51 Expression in CD4+ T-Lymphocytes Is Uniquely Associated With Worse Asthma Control in Obese Children With Asthma

Introduction

There is evidence that obesity, a risk factor for asthma severity and morbidity, has a unique asthma phenotype which is less atopic and less responsive to inhaled corticosteroids (ICS). Peripheral blood mononuclear cells (PBMC) are important to the immunologic pathways of obese asthma and steroid resistance. However, the cellular source associated with steroid resistance has remained elusive. We compared the lymphocyte landscape among obese children with asthma to matched normal weight children with asthma and assessed relationship to asthma control.

Methods

High-dimensional flow cytometry of PBMC at baseline and after dexamethasone stimulation was performed to characterize lymphocyte subpopulations, T-lymphocyte polarization, proliferation (Ki-67+), and expression of the steroid-responsive protein FK506-binding protein 51 (FKBP51). T-lymphocyte populations were compared between obese and normal-weight participants, and an unbiased, unsupervised clustering analysis was performed. Differentially expressed clusters were compared with asthma control, adjusted for ICS and exhaled nitric oxide.

Results

In the obese population, there was an increased cluster of CD4+ T-lymphocytes expressing Ki-67 and FKBP51 at baseline and CD4+ T-lymphocytes expressing FKBP51 after dexamethasone stimulation. CD4+ Ki-67 and FKBP51 expression at baseline showed no association with asthma control. Dexamethasone-induced CD4+ FKBP51 expression was associated with worse asthma control in obese participants with asthma. FKBP51 expression in CD8+ T cells and CD19+ B cells did not differ among groups, nor did polarization profiles for Th1, Th2, Th9, or Th17 percentage.

Discussion

Dexamethasone-induced CD4+ FKBP51 expression is uniquely associated with worse asthma control in obese children with asthma and may underlie the corticosteroid resistance observed in this population.

Transcriptomic Profiling of Adult-Onset Asthma Related to Damp and Moldy Buildings and Idiopathic Environmental Intolerance

A subset of adult-onset asthma patients attribute their symptoms to damp and moldy buildings. Symptoms of idiopathic environmental intolerance (IEI) may resemble asthma and these two entities overlap. We aimed to evaluate if a distinct clinical subtype of asthma related to damp and moldy buildings can be identified, to unravel its corresponding pathomechanistic gene signatures, and to investigate potential molecular similarities with IEI. Fifty female adult-onset asthma patients were categorized based on exposure to building dampness and molds during disease initiation. IEI patients (n = 17) and healthy subjects (n = 21) were also included yielding 88 study subjects. IEI was scored with the Quick Environmental Exposure and Sensitivity Inventory (QEESI) questionnaire. Inflammation was evaluated by blood cell type profiling and cytokine measurements. Disease mechanisms were investigated via gene set variation analysis of RNA from nasal biopsies and peripheral blood mononuclear cells. Nasal biopsy gene expression and plasma cytokine profiles suggested airway and systemic inflammation in asthma without exposure to dampness (AND). Similar evidence of inflammation was absent in patients with dampness-and-mold-related asthma (AAD). Gene expression signatures revealed a greater degree of similarity between IEI and dampness-related asthma than between IEI patients and asthma not associated to dampness and mold. Blood cell transcriptome of IEI subjects showed strong suppression of immune cell activation, migration, and movement. QEESI scores correlated to blood cell gene expression of all study subjects. Transcriptomic analysis revealed clear pathomechanisms for AND but not AAD patients. Furthermore, we found a distinct molecular pathological profile in nasal and blood immune cells of IEI subjects, including several differentially expressed genes that were also identified in AAD samples, suggesting IEI-type mechanisms.

Molecular allergology approach to allergic asthma

Allergic asthma is a frequently encountered and well described asthma phenotype. However, its precise mechanisms are less known. The tools for targeted selection of patients for an optimal response to intervention (prevention or treatment) are also lacking. Here we explore the potential of the molecular allergology approach to achieve a better understanding of allergic asthma mechanisms, a precise diagnosis and an optimal management of these patients.

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.

Endotyping asthma related to three different work exposures

BACKGROUND

Work exposures play a significant role in adult-onset asthma, but mechanisms of work-related asthma are not fully elucidated.

OBJECTIVE

We aimed to reveal the molecular mechanisms of work-related asthma associated with flour (FA), isocyanate (IA) or welding fume (WA) exposures and identify potential biomarkers that distinguish these groups from each other.

METHODS

We used a combination of clinical tests, transcriptomic analysis and associated pathway analyses to investigate underlying disease mechanisms of the blood immune cells and the airway epithelium of 61 men.

RESULTS

Compared to the healthy controls, the WA patients had more differentially expressed genes than the FA and IA patients both in the airway epithelia and in the blood immune cells. In the airway epithelia, active inflammation was detected only in WA patients. In contrast, large number of differentially expressed genes were detected in all asthma groups in blood cells. Disease-related immune functions in blood cells were suppressed in all the asthma groups including leukocyte migration and inflammatory responses and decreased expression of upstream cytokines such as TNF and IFNγ. In transcriptome-phenotype correlations, hyperresponsiveness (R∼|0.6|) had the highest clinical relevance and associated with a set of exposure-group specific genes. Finally, biomarker subsets of only 5 genes specifically distinguished each of the asthma exposure group.

CONCLUSIONS

This study provides novel data on the molecular mechanisms underlying work-related asthma. We identified set of 5 promising biomarkers in asthma related to flour, isocyanate and welding exposure to be tested and clinically validated in future studies.

Revealing consensus gene pathways associated with respiratory functions and disrupted by PM2.5 nitrate exposure at bulk tissue and single cell resolution

BACKGROUND

Nitrate is a major pollutant component in ambient PM_2.5. It is known that chronic exposure to PM_2.5 NO₃^- damages respiratory functions. We aim to explore the underlying toxicological mechanism at single cell resolution.

METHODS

We systematically conducted exposure experiments on forty C57BL/6 mice, assessed respiratory functions, and profiled lung transcriptome. . Afterward, we estimated the cell type compositions from RNA-seq data using deconvolution analysis. The genes and pathways associated with respiratory function and dysregulated by to PM_2.5 NO₃^- exposure were characterized at bulk-tissue and single-cell resolution.

RESULTS

PM_2.5 NO₃^- exposure did not significantly modify the cell type composition in lung, but profoundly altered the gene expression within each cell type. At ambient concentration (22 μg/m³), exposure significantly (FDR<10%) altered 95 genes' expression. Among the genes associated with respiratory functions, a large fraction (74.6-91.7%) were significantly perturbed by PM_2.5 NO₃^- exposure. For example, among the 764 genes associated with peak expiratory flow (PEF), 608 (79.6%) were affected by exposure (p = 1.92e-345). Pathways known to play role in lung disease pathogenesis, including circadian rhythms, sphingolipid metabolism, immune response and lysosome, were found significantly associated with respiratory functions and disrupted by PM_2.5 NO₃^- exposure.

CONCLUSIONS

This study extended our knowledge of PM_2.5 NO₃^- exposure's effect to the levels of lung gene expression, pathways, lung cell type composition and cell specific transcriptome. At single cell resolution, we provided insights in toxicological mechanism of PM_2.5 NO₃^- exposure and subsequent pulmonary disease risks.

Potential Metabolic Biomarkers in Adult Asthmatics

Asthma is the most common chronic airway inflammation, with multiple phenotypes caused by complicated interactions of genetic, epigenetic, and environmental factors. To date, various determinants have been suggested for asthma pathogenesis by a new technology termed omics, including genomics, transcriptomics, proteomics, and metabolomics. In particular, the systematic analysis of all metabolites in a biological system, such as carbohydrates, amino acids, and lipids, has helped identify a novel pathway related to complex diseases. These metabolites are involved in the regulation of hypermethylation, response to hypoxia, and immune reactions in the pathogenesis of asthma. Among them, lipid metabolism has been suggested to be related to lung dysfunction in mild-to-moderate asthma. Sphingolipid metabolites are an important mediator contributing to airway inflammation in obese asthma and aspirin-exacerbated respiratory disease. Although how these molecular variants impact the disease has not been completely determined, identification of new causative factors may possibly lead to more-personalized and precise pathway-specific approaches for better diagnosis and treatment of asthma. In this review, perspectives of metabolites related to asthma and clinical implications have been highlighted according to various phenotypes.