DNA Methylation Changes in Allergic Patients Correlate with Symptom Severity
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE50387 | GEO | 2013/12/01
SECONDARY ACCESSION(S): PRJNA217395
REPOSITORIES: GEO
Similar Datasets
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2013-12-01 | E-GEOD-50387 | biostudies-arrayexpress
Project description:Altered DNA methylation patterns in CD4+ T-cells indicate the importance of epigenetic mechanisms in inflammatory diseases. However, the identification of these alterations is complicated by the heterogeneity of most inflammatory diseases. Seasonal allergic rhinitis (SAR) is an optimal disease model for the study of DNA methylation because of its well-defined phenotype and etiology. We generated genome-wide DNA methylation (Npatients = 8, Ncontrols = 8) and gene expression (Npatients= 9, Ncontrols = 10) profiles of CD4+ T-cells from SAR patients and healthy controls using Illumina’s HumanMethylation450 and HT-12 microarrays, respectively. DNA methylation profiles clearly and robustly distinguished SAR patients from controls, during and outside the pollen season. Moreover, we found that this methylation signature correlated with symptom severity. In agreement with previously published studies, gene expression profiles of the same samples failed to separate patients and controls. Separation by methylation (Npatients = 12, Ncontrols = 12), but not by gene expression (Npatients = 21, Ncontrols = 21; GSE50223) was also observed in an in vitro model system in which purified PBMCs from patients and healthy controls were challenged with allergen. We observed changes in the proportions of memory T-cell populations between patients (Npatients = 35) and controls (Ncontrols= 9), which could explain the observed difference in DNA methylation. Our data highlight the potential of epigenomics in the stratification of immune disease and represents the first successful molecular classification of SAR using CD4+ T cells. Genomic DNA was isolated from CD4+ T-cells of patients with seasonal allergic rhinitis and healthy controls both during and outside the pollen season. Genomic DNA was bisulfite converted and hybridized to Illumina HumanMethylation450 BeadChip (Illumina, San Diego, CA) and scanned using the Illumina iScan.
2013-12-01 | E-GEOD-50222 | biostudies-arrayexpress
Project description:Altered DNA methylation patterns in CD4+ T-cells indicate the importance of epigenetic mechanisms in inflammatory diseases. However, the identification of these alterations is complicated by the heterogeneity of most inflammatory diseases. Seasonal allergic rhinitis (SAR) is an optimal disease model for the study of DNA methylation because of its well-defined phenotype and etiology. We generated genome-wide DNA methylation (Npatients = 8, Ncontrols = 8) and gene expression (Npatients= 9, Ncontrols = 10) profiles of CD4+ T-cells from SAR patients and healthy controls using Illumina’s HumanMethylation450 and HT-12 microarrays, respectively. DNA methylation profiles clearly and robustly distinguished SAR patients from controls, during and outside the pollen season. Moreover, we found that this methylation signature correlated with symptom severity. In agreement with previously published studies, gene expression profiles of the same samples failed to separate patients and controls. Separation by methylation (Npatients = 12, Ncontrols = 12), but not by gene expression (Npatients = 21, Ncontrols = 21; GSE50223) was also observed in an in vitro model system in which purified PBMCs from patients and healthy controls were challenged with allergen. We observed changes in the proportions of memory T-cell populations between patients (Npatients = 35) and controls (Ncontrols= 9), which could explain the observed difference in DNA methylation. Our data highlight the potential of epigenomics in the stratification of immune disease and represents the first successful molecular classification of SAR using CD4+ T cells.
2013-12-01 | GSE50222 | GEO
Project description:Altered DNA methylation patterns in CD4+ T-cells indicate the importance of epigenetic mechanisms in inflammatory diseases. However, the identification of these alterations is complicated by the heterogeneity of most inflammatory diseases. Seasonal allergic rhinitis (SAR) is an optimal disease model for the study of DNA methylation because of its well-defined phenotype and etiology. We generated genome-wide DNA methylation (Npatients = 8, Ncontrols = 8) and gene expression (Npatients= 9, Ncontrols = 10) profiles of CD4+ T-cells from SAR patients and healthy controls using Illumina’s HumanMethylation450 and HT-12 microarrays, respectively. DNA methylation profiles clearly and robustly distinguished SAR patients from controls, during and outside the pollen season. Moreover, we found that this methylation signature correlated with symptom severity. In agreement with previously published studies, gene expression profiles of the same samples failed to separate patients and controls. Separation by methylation (Npatients = 12, Ncontrols = 12), but not by gene expression (Npatients = 21, Ncontrols = 21) was also observed in an in vitro model system in which purified PBMCs from patients and healthy controls were challenged with allergen. We observed changes in the proportions of memory T-cell populations between patients (Npatients = 35) and controls (Ncontrols= 9), which could explain the observed difference in DNA methylation. Our data highlight the potential of epigenomics in the stratification of immune disease and represents the first successful molecular classification of SAR using CD4+ T cells. Total RNA was isolated from CD4+ T-cells of patients with seasonal allergic rhinitis and healthy controls both during and outside the pollen season. Total RNA was amplified and hybridized to Illumina HT12 version 4 human whole-genome arrays (Illumina, San Diego, CA).
2013-12-01 | E-GEOD-50101 | biostudies-arrayexpress
Project description:Altered DNA methylation patterns in CD4+ T-cells indicate the importance of epigenetic mechanisms in inflammatory diseases. However, the identification of these alterations is complicated by the heterogeneity of most inflammatory diseases. Seasonal allergic rhinitis (SAR) is an optimal disease model for the study of DNA methylation because of its well-defined phenotype and etiology. We generated genome-wide DNA methylation (Npatients = 8, Ncontrols = 8) and gene expression (Npatients= 9, Ncontrols = 10) profiles of CD4+ T-cells from SAR patients and healthy controls using Illumina’s HumanMethylation450 and HT-12 microarrays, respectively. DNA methylation profiles clearly and robustly distinguished SAR patients from controls, during and outside the pollen season. Moreover, we found that this methylation signature correlated with symptom severity. In agreement with previously published studies, gene expression profiles of the same samples failed to separate patients and controls. Separation by methylation (Npatients = 12, Ncontrols = 12), but not by gene expression (Npatients = 21, Ncontrols = 21) was also observed in an in vitro model system in which purified PBMCs from patients and healthy controls were challenged with allergen. We observed changes in the proportions of memory T-cell populations between patients (Npatients = 35) and controls (Ncontrols= 9), which could explain the observed difference in DNA methylation. Our data highlight the potential of epigenomics in the stratification of immune disease and represents the first successful molecular classification of SAR using CD4+ T cells.
2013-12-01 | GSE50101 | GEO
Project description:BackgroundAngelman syndrome (AS) is a rare neurodevelopmental disorder caused by the absence of functional UBE3A in neurons. Excess low-frequency oscillations as measured with electroencephalography (EEG) have been identified as a characteristic finding, but the relationship of this EEG finding to the symptomatology of AS and its significance in the pathophysiology of AS remain unknown.MethodsWe used correlations and machine learning to investigate the cross-sectional and longitudinal relationship between EEG spectral power and motor, cognitive, and language skills (Bayley Scales of Infant and Toddler Development, Third Edition); adaptive behavior (Vineland Adaptive Behavior Scales, Second Edition); AS-specific symptoms (AS Clinical Severity Scale); and the age of epilepsy onset in a large sample of children (age: 1-18 years) with AS due to a chromosomal deletion of 15q11-q13 (45 individuals with 72 visits).ResultsWe found that after accounting for age differences, participants with stronger EEG delta-band abnormality had earlier onset of epilepsy and lower performance scores across symptom domains including cognitive, motor, and communication. Combing spatial and spectral information beyond the delta frequency band increased the cross-sectional association with clinical severity on average by approximately 45%. Furthermore, we found evidence for longitudinal correlations of EEG delta-band power within several performance domains, including the mean across Bayley Scales of Infant and Toddler Development, Third Edition, scores.ConclusionsOur results show an association between EEG abnormalities and symptom severity in AS, underlining the significance of the former in the pathophysiology of AS. Furthermore, our work strengthens the rationale for using EEG as a biomarker in the development of treatments for AS, a concept that may apply more generally to neurodevelopmental disorders.
| S-EPMC8622755 | biostudies-literature
Project description:BackgroundStudies of symptomatic gastroparetics consistently find poor correlation with gastric emptying. We hypothesized that concomitant small bowel dysmotility may play a role in symptom causation in gastroparesis and sought to test this hypothesis by using wireless motility capsule (WMC) testing to simultaneously measure antral and duodenal area under pressure curve (AUC) in patients with delayed gastric emptying.MethodsUsing a cohort from a multicenter clinical trial and a separate tertiary clinical database, we identified gastroparetics that underwent concurrent WMC testing and completed the Gastroparesis Cardinal Symptom Index, a validated questionnaire. Our study included 35 gastroparetics defined by a gastric emptying time (GET) ≥ 5 h. Antral and duodenal AUC were assessed at 1-h windows pre-GET and post-GET, respectively.Key resultsWe found moderate correlations between duodenal AUC and symptom severity in the combined cohort (n = 35; R = -0.42; p = 0.01; 95% CI -0.7, -0.1). Removing patients with colonic delay resulted in a stronger correlation of duodenal AUC to symptom severity (n = 21; R = -0.63; p < 0.01; 95% CI -0.81, -0.31). The multicenter trial (n = 20) and clinical practice cohorts (n = 15) had significantly different symptom severity and exclusion criteria. When analyzed separately, significant correlations between duodenal AUC and symptom severity were observed (R = -0.71; p < 0.01; 95% CI -0.9, -0.4 and R = -0.72; p < 0.01; 95% CI -0.9, -0.3, respectively). Symptom severity and antral motility showed no correlation.Conclusions & inferencesWe found significant correlations between duodenal AUC and symptom severity in two cohorts of gastroparetics. Small bowel motility may contribute to symptom generation in gastroparetic patients and this may inform therapeutic considerations.
| S-EPMC4432031 | biostudies-literature
Project description:Immune cell-type composition changes with age, potentially weakening the response to infectious diseases. Profiling epigenetics marks of immune cells can help us understand the relationship with disease severity. We therefore leveraged a targeted DNA methylation method to study the differences in a cohort of pneumonia patients (both COVID-19 positive and negative) and unaffected individuals from peripheral blood.This approach allowed us to predict the pneumonia diagnosis with high accuracy (AUC = 0.92), and the PCR positivity to the SARS-CoV-2 viral genome with moderate, albeit lower, accuracy (AUC = 0.77). We were also able to predict the severity of pneumonia (PORT score) with an R2 = 0.69. By estimating immune cellular frequency from DNA methylation data, patients under the age of 65 positive to the SARS-CoV-2 genome (as revealed by PCR) showed an increase in T cells, and specifically in CD8+ cells, compared to the negative control group. Conversely, we observed a decreased frequency of neutrophils in the positive compared to the negative group. No significant difference was found in patients over the age of 65. The results suggest that this DNA methylation-based approach can be used as a cost-effective and clinically useful biomarker platform for predicting pneumonias and their severity.
| S-EPMC9621067 | biostudies-literature
Project description:Epigenetic alterations may represent new therapeutic targets and/or biomarkers of allergic rhinitis (AR). Our aim was to examine genome-wide epigenetic changes induced by controlled pollen exposure in the Environmental Exposure Unit (EEU). 38 AR-sufferers and 8 non-allergic controls were exposed to grass pollen for 3h on two consecutive days. We interrogated DNA methylation at baseline and 3h in peripheral blood mononuclear cells (PBMCs) using the Infinium Methylation 450K array. We corrected for demographics, cell composition, and multiple testing (Benjamini-Hochberg), and verified hits using bisulfite PCR-pyrosequencing and qPCR. To extend these findings to a clinically relevant tissue, we investigated DNA methylation and gene expression of mucin 4 (MUC4), in nasal brushings from a separate validation cohort exposed to birch pollen. In PBMCs of allergic rhinitis participants, 42 sites showed significant DNA methylation changes of 2% or greater. DNA methylation changes in tryptase gamma 1 (TPSG1), schlafen 12 (SLFN12) and MUC4 in response to exposure were validated by pyrosequencing. SLFN12 DNA methylation significantly correlated with symptoms (p<0.05), and baseline DNA methylation pattern was found to be predictive of symptom severity upon grass allergen exposure (p<0.05). Changes in MUC4 DNA methylation in nasal brushings in the validation cohort correlated with drop in peak nasal inspiratory flow (Spearman r = 0.314, p = 0.034), and MUC4 gene expression was significantly increased (p<0.0001). This study revealed novel and rapid epigenetic changes upon exposure in a controlled allergen challenge facility, identified baseline epigenetic status as a predictor of symptom severity.
2018-06-01 | GSE100386 | GEO
Project description:Epigenetics may play a central, yet unexplored, role in the profound changes that the maternal immune system undergoes during pregnancy and could be involved in the pregnancy-induced modulation of several autoimmune diseases. We investigated changes in the methylome in isolated circulating CD4+ T-cells in non-pregnant and pregnant women, during the 1st and 2nd trimester, using the Illumina Infinium Human Methylation 450K array, and explored how these changes were related to autoimmune diseases that are known to be affected during pregnancy. Pregnancy was associated with several hundreds of methylation differences, particularly during the 2nd trimester. A network-based modular approach identified several genes, e.g., CD28, FYN, VAV1 and pathways related to T-cell signalling and activation, highlighting T-cell regulation as a central component of the observed methylation alterations. The identified pregnancy module was significantly enriched for disease-associated methylation changes related to multiple sclerosis, rheumatoid arthritis and systemic lupus erythematosus. A negative correlation between pregnancy-associated methylation changes and disease-associated changes was found for multiple sclerosis and rheumatoid arthritis, diseases that are known to improve during pregnancy whereas a positive correlation was found for systemic lupus erythematosus, a disease that instead worsens during pregnancy. Thus, the directionality of the observed changes is in line with the previously observed effect of pregnancy on disease activity. Our systems medicine approach supports the importance of the methylome in immune regulation of T-cells during pregnancy. Our findings highlight the relevance of using pregnancy as a model for understanding and identifying disease-related mechanisms involved in the modulation of autoimmune diseases.Abbreviations: BMIQ: beta-mixture quantile dilation; DMGs: differentially methylated genes; DMPs: differentially methylated probes; FE: fold enrichment; FDR: false discovery rate; GO: gene ontology; GWAS: genome-wide association studies; MDS: multidimensional scaling; MS: multiple sclerosis; PBMC: peripheral blood mononuclear cells; PBS: phosphate buffered saline; PPI; protein-protein interaction; RA: rheumatoid arthritis; SD: standard deviation; SLE: systemic lupus erythematosus; SNP: single nucleotide polymorphism; TH: CD4+ T helper cell; VIStA: diVIsive Shuffling Approach.
| S-EPMC9487751 | biostudies-literature