Gene expression of peripheral blood in healthy, moderate asthma, and severe asthma patients from the IMSA cohort
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE207751 | GEO | 2022/07/11
REPOSITORIES: GEO
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Project description:Background: Asthma is highly heterogeneous and severity evaluation is key to asthma management. DNA methylation (DNAm) contributes to asthma pathogenesis. This study aimed to identify nasal epithelial DNAm differences between severe and non-severe asthmatic children and evaluate the impact of environmental exposures. Methods: Thirty-three non-severe and 22 severe asthmatic African-American children were included in an epigenome-wide association study. Genome-wide nasal epithelial DNAm and gene expression were measured. CpG sites associated with asthma severity and environmental exposures and predictive of severe asthma were identified. DNAm was correlated with gene expression. Enrichment for transcription factor (TF) binding sites or histone modifications surrounding DNAm differences were determined. Results: We identified 816 differentially methylated CpG positions (DMPs) and 10 differentially methylated regions (DMRs) associated with asthma severity. Three DMPs exhibited discriminatory ability for severe asthma. Intriguingly, six DMPs were simultaneously associated with asthma, allergic asthma, total IgE, environmental IgE, and FeNO in an independent cohort of children. 27 DMPs were associated with traffic-related air pollution or secondhand smoke. DNAm at 22 DMPs were altered by diesel particles or allergen in human bronchial epithelial cells. DNAm levels at 39 DMPs were correlated with mRNA expression. Proximal to 816 DMPs, three histone marks and several TFs involved in asthma pathogenesis were enriched. Conclusions: Significant differences in nasal epithelial DNAm were observed between non-severe and severe asthma in African-American children, a subset of which may be useful to predict disease severity. These CpG sites are subject to the influences of environmental exposures and may regulate gene expression.
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Project description:Gene expression of PBMCs in healthy, moderate asthma, and severe asthma patients from the IMSA cohort
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Project description:Background: Most asthmatic patients have high serum levels of IgE directed against common environmental allergens such as house dust mite, animal danders and moulds. However the presence of specific IgE against individual allergens alone does not account for asthma. Many individuals are atopic but not asthmatic. Precise knowledge of the serum IgE specificity repertoire in asthmatic and non-asthmatic patients would substantially help in understanding the pathogenesis of the disease and at the same time facilitate the treatment and the implementation of preventive measures. Methods: We developed a microarray immunoassay containing 103 common allergens to study the IgE reactivity profiles of 485 asthmatic and 342 non-asthmatic individuals from families whose members had a documented history of asthma and atopy. The results were analyzed using k-means clustering to investigate whether IgE reactivity profiles correlated with asthma, disease severity and age of onset as well as with other atopic conditions such as rhinitis, conjunctivitis and eczema. We trained an artificial neural network (ANN) using the serum reactivity data to identify individuals as asthmatic and non-asthmatic. Results: Individual sera showed clear differences in the number and the combinations of allergens recognized as well as in the level of specific IgE. While the presence of specific IgE against single allergens correlated poorly with the pathological conditions examined k-means clustering analysis unraveled that a particular profile was significantly associated with asthma (p <1E-8). An ANN-based algorithm, calibrated with the profile reactivity data correctly classified as asthmatic or non-asthmatic 78% of the individual examined. Conclusions: Our analysis demonstrates that asthma may be a higher-order phenomenon related to patterns of response and not attributable to single antibody reactions. This information sheds new light on the risk of developing the disease and can be readily utilized in combination with an ANN-based tool to distinguish asthmatic and non-asthmatic individuals on the basis of their serum reactivity profile. The study consisted of a total of 872 sera samples: 485 individuals were diagnosed with asthma, 342 were classified as non asthmatic, a remaining 45 were classified as unconfirmed asthma diagnosis. The serum IgE reactivity profiles were analyzed against 103 allergens representative of 11 distinct allergen classes. Quantification of bound IgE: The fluorescence signal was acquired using ProScanArray Express™ version 3.0 software. PMC reading values of individual spots were corrected against the internal negative control to identify signals above background. Duplicate measurements of individual allergens were utilized. The signal collected from the allergens was interpolated with an external calibration curve to obtain the IU/ml value (see supplementary file GSE20020_IU_ml values.txt), and translated into a Class Score by plotting the data in a standard reactivity scale (see Sample data tables). Class Score values: (CLASS 0 (less than 0.35 IU/ml); CLASS 1 (0.35-0.7 IU/ml); CLASS 2 (0.71-3.5 IU/ml); CLASS 3 (3.51-17.5 IU/ml); CLASS 4 (17.51-50 IU/ml); CLASS 5 (50.01-100 IU/ml).
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Project description:Background: Most asthmatic patients have high serum levels of IgE directed against common environmental allergens such as house dust mite, animal danders and moulds. However the presence of specific IgE against individual allergens alone does not account for asthma. Many individuals are atopic but not asthmatic. Precise knowledge of the serum IgE specificity repertoire in asthmatic and non-asthmatic patients would substantially help in understanding the pathogenesis of the disease and at the same time facilitate the treatment and the implementation of preventive measures. Methods: We developed a microarray immunoassay containing 103 common allergens to study the IgE reactivity profiles of 485 asthmatic and 342 non-asthmatic individuals from families whose members had a documented history of asthma and atopy. The results were analyzed using k-means clustering to investigate whether IgE reactivity profiles correlated with asthma, disease severity and age of onset as well as with other atopic conditions such as rhinitis, conjunctivitis and eczema. We trained an artificial neural network (ANN) using the serum reactivity data to identify individuals as asthmatic and non-asthmatic. Results: Individual sera showed clear differences in the number and the combinations of allergens recognized as well as in the level of specific IgE. While the presence of specific IgE against single allergens correlated poorly with the pathological conditions examined k-means clustering analysis unraveled that a particular profile was significantly associated with asthma (p <1E-8). An ANN-based algorithm, calibrated with the profile reactivity data correctly classified as asthmatic or non-asthmatic 78% of the individual examined. Conclusions: Our analysis demonstrates that asthma may be a higher-order phenomenon related to patterns of response and not attributable to single antibody reactions. This information sheds new light on the risk of developing the disease and can be readily utilized in combination with an ANN-based tool to distinguish asthmatic and non-asthmatic individuals on the basis of their serum reactivity profile.
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