Gene signature profiles in whole blood cells from severe asthma patinets treated with benralizumab
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE148725 | GEO | 2023/04/15
REPOSITORIES: GEO
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Project description:BACKGROUND: Evaluation of the airway transcriptome may reveal patterns of gene expression that are associated with clinical phenotypes of asthma. To define transcriptomic endotypes of asthma (TEA) we analyzed gene expression in induced sputum that correlate with phenotypes of disease. METHODS: Gene expression was measured in sputum of subjects with asthma using Affymetrix HuGene ST 1.0 microarrays. Unsupervised clustering analysis of genes in pathways selected from the Kyoto Encyclopedia of Genes and Genomes (KEGG) identified TEA clusters. Clinical characteristics were compared and logistic regression analysis of matched blood samples defined an expression profile to determine the TEA cluster assignment in a cohort of children with asthma for validation. RESULTS: Three TEA clusters were identified. TEA cluster 1 had the most subjects with a history of intubation (P = 0.05), a lower pre-bronchodilator FEV1 (P = 0.006), a higher bronchodilator response (P = 0.03), and higher exhaled nitric oxide levels (P = 0.04), compared to the other TEA clusters. TEA cluster 2, the smallest cluster had the most subjects that were hospitalized for asthma (P = 0.04). Subjects in TEA cluster 3, the largest cluster, had normal lung function, low exhaled nitric oxide levels, and lower inhaled steroid requirements. Evaluation of TEA clusters in children confirmed that TEA clusters 1 and 2 are associated with a history of intubation (P = 5.58 x 10-06) and hospitalization (P = 0.01), respectively. CONCLUSIONS: Patterns of gene expression in the sputum and blood reveal TEA clusters that are associated with severe asthma phenotypes in children and adults. Gene expression was measured in sputum of subjects with asthma using Affymetrix HuGene ST 1.0 microarrays. Unsupervised clustering analysis of genes in pathways selected from the Kyoto Encyclopedia of Genes and Genomes (KEGG) identified TEA clusters. Clinical characteristics were compared and logistic regression analysis of matched blood samples defined an expression profile to determine the TEA cluster assignment in a cohort of children with asthma for validation.
2014-04-07 | E-GEOD-56396 | biostudies-arrayexpress
Project description:RNA-seq comparison of coding RNA expression in blood eosinophils from patients with severe allergic eosinophilic asthma treated with mepolizumab with those of healthy controls and matched severe asthmatic patients receiving omalizumab.
2021-12-17 | E-MTAB-10190 | biostudies-arrayexpress
Project description:Severe asthma is a complex disease with different inflammatory phenotypes. Transcriptomic profiling has contributed to understanding the pathogenesis of asthma, especially type-2 inflammation; however, there is still poor understanding of non-eosinophilic asthma, and consequently there are limited treatment options. The aim of this study was to determine transcriptomic profiles in endobronchial biopsies of adults with severe asthma and different inflammatory phenotypes (neutrophilic, eosinophilic and paucigranulocytic) compared with healthy controls.
2021-07-31 | GSE143303 | GEO
Project description:Childhood asthma is a complex disease historically defined by partially overlapping clinical features, including recurrent respiratory symptoms and reversible airway obstruction. However, the heterogeneity observed in clinical disease and airway pathology suggests that the “traditionally” defined asthma population is composed of multiple subgroups (i.e., endotypes), each with a distinct pathogenesis. Gene expression profiling of bronchial airway brushings identified the type 2-high asthma endotype, defined by excessive airway inflammation driven by type 2 cytokines, which was found in ~50% of subjects. Importantly, response to inhaled corticosteroid treatment was limited to this type 2-high endotype. The clinical utility of type 2-high asthma endotyping and the discovery of other endotypes have been limited by the need to perform an invasive bronchoscopy to obtain the bronchial brushings for analysis. Moreover, research bronchoscopies cannot be performed in children. Less invasive methods for the identification of asthma endotypes are needed. To this end, we found that the type 2-high asthma endotype can be identified by gene expression profiling of minimally invasive nasal airway epithelium brushings. Moreover, we found high nasal expression of the type 2 cytokine, IL-13,4 was associated with higher risk of asthma exacerbations among Puerto Ricans, who have the highest asthma morbidity and mortality in the U.S. Herein, we propose to use whole transcriptome sequencing of nasal airway epithelial brushings from Puerto Rican children with asthma to identify the type 2-high and other asthma endotypes, which relate to severity and drug response.
2020-07-13 | GSE152004 | GEO
Project description:Asthma is an inflammatory disease of the airways characterised by episodic airway obstruction resulting in cough, episodic shortness of breath. It is, and is clinically and physiologically heterogeneous. It is estimated that around 300 million people worldwide have the diseaseare diagnosed with asthma, including up to 20% of children (Asher et al, 2006), with 5–10% of these children believed to have severe or difficult-to-treat asthma. Asthma has often been classified in terms of severity and based on clinical diagnostic criteria, but it is now apparent that the heterogeneity that exists at the physiological level is also a feature of the underlying pathological mechanisms (Lotvall et al, 2011). The aim of this study was to identify blood transcriptomics profiles for children diagnosed with asthma or wheeze, and establish whether these profiles suggested endotypes or mechanisms that could underlie disease, or be related to disease severity, in these children. Importantly, given that children are currently treated with the same medicines as adults, we also aimed to compare profiles of children to those of adults with asthma to help determine whether efforts should be directed to the development of medicines targeting pathways and mechanisms that may be unique to children. To this end, we used gene transcriptome data generated from blood samples from adults and children from the U-BIOPRED consortium to ask how similar or different the differential gene expression profiles were between groups of adults and pre-school or school-aged children with severe or mild-moderate asthma (or wheeze for the pre-school aged children) using current definitions. The Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (U-BIOPRED) project was set up as a public-private partnership within the framework of the Innovative Medicines Initiative (IMI), engaging academia, the pharmaceutical industry and patient groups. The goal of this investigation was to identify transcript fingerprints in whole blood that characterize patients with severe asthma and to determine whether subgroups of severe asthmatics can be identified.
2019-06-30 | GSE123750 | GEO
Project description:Evaluation of the airway transcriptome may reveal patterns of gene expression that are associated with clinical phenotypes of asthma. To define transcriptomic endotypes of asthma (TEA) we analyzed gene expression in induced sputum that correlate with phenotypes of disease. Gene expression was measured in sputum of subjects with asthma using Affymetrix HuGene ST 1.0 microarrays. Unsupervised clustering analysis of genes identified TEA clusters. Clinical characteristics were compared.
2014-04-07 | GSE56396 | GEO
Project description:Severe asthma is a collection of disease entities with varying pathophysiological characteristics (7) that result in symptoms of cough, wheeze and breathlessness, with frequent exacerbations. To address the problem of phenotypic difference and heterogeneity, the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (U-BIOPRED) project was set up as a public-private partnership within the framework of the Innovative Medicines Initiative (IMI), engaging academia, the pharmaceutical industry and patient groups. The goal of this investigation was to identify transcript fingerprints in whole blood that characterize patients with severe asthma and to determine whether subgroups of severe asthmatics can be identified. Furthermore, we were interested in elucidating the biological pathways that showed differences between subgroups.
2016-12-09 | GSE69683 | GEO
Project description:Asthma is a complex, chronic respiratory disease with marked clinical and pathophysiological heterogeneity. Distinct inflammatory phenotypes of eosinophilic, mixed, neutrophilic and paucigranulocytic asthma are identified in patients, but most in vivo mouse models, studying asthma mechanisms, mimic only eosinophilic phenotype in humans. The detailed unbiased in vivo studies on molecular responses among different kinds of inflammation in asthma models are lacking. Therefore, we developed mouse models representing three different inflammatory phenotypes of airway inflammation, namely eosinophilic, mixed, and neutrophilic asthma via different methods of house dust mite sensitisation. We used microarrays to determine the global gene expression in the lungs of mice with eosinophilic, mixed and neutrophilic inflammatory phenotypes to uncover underlying differences in clinical presentation and to find novel molecular targets and pathways, which might reflect different molecular mechanisms of the disease. By whole genome transcriptome profiling, we found that airway tight junction (TJ) molecules, mucins and inflammasome-related genes are differentially expressed in distinct phenotypes of allergic airway inflammation. Next, detailed analysis of several molecules from these families by quantitative RT-PCR, western blot and confocal microscopy revealed that (i) Zo-1 and Cldn18 were downregulated in all phenotypes, while Cldn4 upregulation was characteristic for neutrophilic airway inflammation; (ii) mucins Clca1 (Gob5) and Muc5ac were upregulated in eosinophilic and even more in neutrophilic asthma, and (iii) upregulation of inflammasome-related molecules such as Nlrp3, Nlrc4, Casp-1 and IL-1b was characteristic for neutrophilic asthma. Finally, we showed that inflammasome/Th-17/neutrophilic axis cytokines, namely IL-1b and IL-17 impaired epithelial barrier function and increased mucins expressions in primary human bronchial epithelial cells from normal and asthmatic donors. Our findings suggest that differential expression of TJs, mucins and inflammasome-related molecules in distinct asthma phenotypes could be mechanistically linked and might further reflect the differences observed in the clinic.
2018-10-03 | GSE108417 | GEO
Project description:Global expression pattern in eosinophils in various conditions such as eosinophilic asthma, hypereosinophilic dermatological diseases, parasitosis and pulmonary aspergillosis. Eosinophil were isolated from peripheral blood by FACS from 4 hypereosinophilic conditions: eosinophilic asthma (4 replicates), hypereosinophilic dermatological diseases (3 replicates), parasitosis (3 replicates) and pulmonary aspergillosis (4 replicates).
2015-12-10 | E-GEOD-65239 | biostudies-arrayexpress
Project description:Background Complex diseases are often difficult to diagnose, treat and study due to the multi-factorial nature of the underlying etiology. Large data sets are now widely available that can be used to define novel, mechanistically distinct disease subtypes (endotypes) in a completely data-driven manner. However, significant challenges exist with regard to how to segregate individuals into suitable subtypes of the disease and understand the distinct biological mechanisms of each when the goal is to maximize the discovery potential of these data sets. Results A multi-step decision tree-based method is described for defining endotypes based on gene expression, clinical covariates, and disease indicators using childhood asthma as a case study. We attempted to use alternative approaches such as the Student’s t-test, single data domain clustering and the Modk-prototypes algorithm, which incorporates multiple data domains into a single analysis and none performed as well as the novel multi-step decision tree method. This new method gave the best segregation of asthmatics and non-asthmatics, and it provides easy access to all genes and clinical covariates that distinguish the groups. Conclusions The multi-step decision tree method described here will lead to better understanding of complex disease in general by allowing purely data-driven disease endotypes to facilitate the discovery of new mechanisms underlying these diseases. This application should be considered a complement to ongoing efforts to better define and diagnose known endotypes. When coupled with existing methods developed to determine the genetics of gene expression, these methods provide a mechanism for linking genetics and exposomics data and thereby accounting for both major determinants of disease. We collected peripheral blood samples from 131 children for RNA extraction and hybridization of target cDNA onto Affymetrix Human U133 Plus 2.0 whole genome array.
2013-11-12 | E-GEOD-35571 | biostudies-arrayexpress