DNA methylation in childhood asthma : an epigenome-wide meta-analysis

Background DNA methylation profiles associated with childhood asthma might provide novel insights into disease pathogenesis. We did an epigenome-wide association study to assess methylation profiles associated with childhood asthma. Methods We did a large-scale epigenome-wide association study (EWAS) within the Mechanisms of the Development of ALLergy (MeDALL) project. We examined epigenome-wide methylation using Illumina Infinium Human Methylation450 BeadChips (450K) in whole blood in 207 children with asthma and 610 controls at age 4-5 years, and 185 children with asthma and 546 controls at age 8 years using a cross-sectional case-control design. After identification of differentially methylated CpG sites in the discovery analysis, we did a validation study in children (4-16 years; 247 cases and 2949 controls) from six additional European cohorts and meta-analysed the results. We next investigated whether replicated CpG sites in cord blood predict later asthma in 1316 children. We subsequently investigated cell-type-specific methylation of the identified CpG sites in eosinophils and respiratory epithelial cells and their related gene-expression signatures. We studied cell-type specificity of the asthma association of the replicated CpG sites in 455 respiratory epithelial cell samples, collected by nasal brushing of 16-year-old children as well as in DNA isolated from blood eosinophils (16 with asthma, eight controls [age 2-56 years]) and compared this with whole-blood DNA samples of 74 individuals with asthma and 93 controls (age 1-79 years). Whole-blood transcriptional profiles associated with replicated CpG sites were annotated using RNA-seq data of subsets of peripheral blood mononuclear cells sorted by fluorescence-activated cell sorting. Findings 27 methylated CpG sites were identified in the discovery analysis. 14 of these CpG sites were replicated and passed genome-wide significance (p Interpretation Reduced whole-blood DNA methylation at 14 CpG sites acquired after birth was strongly associated with childhood asthma. These CpG sites and their associated transcriptional profiles indicate activation of eosinophils and cytotoxic T cells in childhood asthma. Our findings merit further investigations of the role of epigenetics in a clinical context.Peer reviewe

Rhinitis associated with asthma is distinct from rhinitis alone: TARIA‐MeDALL hypothesis

Asthma, rhinitis, and atopic dermatitis (AD) are interrelated clinical phenotypes that partly overlap in the human interactome. The concept of “one-airway-one-disease,” coined over 20 years ago, is a simplistic approach of the links between upper- and lower-airway allergic diseases. With new data, it is time to reassess the concept. This article reviews (i) the clinical observations that led to Allergic Rhinitis and its Impact on Asthma (ARIA), (ii) new insights into polysensitization and multimorbidity, (iii) advances in mHealth for novel phenotype definitions, (iv) confirmation in canonical epidemiologic studies, (v) genomic findings, (vi) treatment approaches, and (vii) novel concepts on the onset of rhinitis and multimorbidity. One recent concept, bringing together upper- and lower-airway allergic diseases with skin, gut, and neuropsychiatric multimorbidities, is the “Epithelial Barrier Hypothesis.” This review determined that the “one-airway-one-disease” concept does not always hold true and that several phenotypes of disease can be defined. These phenotypes include an extreme “allergic” (asthma) phenotype combining asthma, rhinitis, and conjunctivitis.info:eu-repo/semantics/publishedVersio

Prenatal Particulate Air Pollution and DNA Methylation in Newborns: An Epigenome-Wide Meta-Analysis

BACKGROUND: Prenatal exposure to air pollution has been associated with childhood respiratory disease and other adverse outcomes. Epigenetics is a suggested link between exposures and health outcomes. OBJECTIVES: We aimed to investigate associations between prenatal exposure to particulate matter (PM) with diameter [Formula: see text] ([Formula: see text]) or [Formula: see text] ([Formula: see text]) and DNA methylation in newborns and children. METHODS: We meta-analyzed associations between exposure to [Formula: see text] ([Formula: see text]) and [Formula: see text] ([Formula: see text]) at maternal home addresses during pregnancy and newborn DNA methylation assessed by Illumina Infinium HumanMethylation450K BeadChip in nine European and American studies, with replication in 688 independent newborns and look-up analyses in 2,118 older children. We used two approaches, one focusing on single cytosine-phosphate-guanine (CpG) sites and another on differentially methylated regions (DMRs). We also related PM exposures to blood mRNA expression. RESULTS: Six CpGs were significantly associ

A computational framework for complex disease stratification from multiple large-scale datasets.

BACKGROUND: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. METHODS: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. RESULTS: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. CONCLUSIONS: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine

Lysine methylation signaling in pancreatic cancer

International audienc

A novel whole blood gene expression signature for asthma, dermatitis, and rhinitis multimorbidity in children and adolescents

Background Allergic diseases often occur in combination (multimorbidity). Human blood transcriptome studies have not addressed multimorbidity. Large‐scale gene expression data were combined to retrieve biomarkers and signaling pathways to disentangle allergic multimorbidity phenotypes. Methods Integrated transcriptomic analysis was conducted in 1233 participants with a discovery phase using gene expression data (Human Transcriptome Array 2.0) from whole blood of 786 children from three European birth cohorts (MeDALL), and a replication phase using RNA Sequencing data from an independent cohort (EVA‐PR, n = 447). Allergic diseases (asthma, atopic dermatitis, rhinitis) were considered as single disease or multimorbidity (at least two diseases), and compared with no disease. Results Fifty genes were differentially expressed in allergic diseases. Thirty‐two were not previously described in allergy. Eight genes were consistently overexpressed in all types of multimorbidity for asthma, dermatitis, and rhinitis (CLC, EMR4P, IL5RA, FRRS1, HRH4, SLC29A1, SIGLEC8, IL1RL1). All genes were replicated the in EVA‐PR cohort. RT‐qPCR validated the overexpression of selected genes. In MeDALL, 27 genes were differentially expressed in rhinitis alone, but none was significant for asthma or dermatitis alone. The multimorbidity signature was enriched in eosinophil‐associated immune response and signal transduction. Protein‐protein interaction network analysis identified IL5/JAK/STAT and IL33/ST2/IRAK/TRAF as key signaling pathways in multimorbid diseases. Synergistic effect of multimorbidity on gene expression levels was found. Conclusion A signature of eight genes identifies multimorbidity for asthma, rhinitis, and dermatitis. Our results have clinical and mechanistic implications, and suggest that multimorbidity should be considered differently than allergic diseases occurring alone

Distinction between rhinitis alone and rhinitis with asthma using interactomics

Abstract The concept of “one-airway-one-disease”, coined over 20 years ago, may be an over-simplification of the links between allergic diseases. Genomic studies suggest that rhinitis alone and rhinitis with asthma are operated by distinct pathways. In this MeDALL (Mechanisms of the Development of Allergy) study, we leveraged the information of the human interactome to distinguish the molecular mechanisms associated with two phenotypes of allergic rhinitis: rhinitis alone and rhinitis in multimorbidity with asthma. We observed significant differences in the topology of the interactomes and in the pathways associated to each phenotype. In rhinitis alone, identified pathways included cell cycle, cytokine signalling, developmental biology, immune system, metabolism of proteins and signal transduction. In rhinitis and asthma multimorbidity, most pathways were related to signal transduction. The remaining few were related to cytokine signalling, immune system or developmental biology. Toll-like receptors and IL-17-mediated signalling were identified in rhinitis alone, while IL-33 was identified in rhinitis in multimorbidity. On the other hand, few pathways were associated with both phenotypes, most being associated with signal transduction pathways including estrogen-stimulated signalling. The only immune system pathway was FceRI-mediated MAPK activation. In conclusion, our findings suggest that rhinitis alone and rhinitis and asthma multimorbidity should be considered as two distinct diseases

Fine resolution clustering of TP53 variants into functional classes predicts cancer risks and spectra among germline variant carriers

ABSTRACT Li-Fraumeni syndrome (LFS) is a heterogeneous predisposition to a broad spectrum of cancers caused by pathogenic TP53 germline variants. We have used a clustering approach to assign missense variants to functional classes with distinct quantitative and qualitative features based on transcriptional activity in yeast assays. Genotype-phenotype correlations were analyzed using the germline TP53 mutation database (n= 3,446) and validated in three LFS clinical cohorts (n= 821). Carriers of class A variants recapitulated all traits of fully penetrant LFS (median age at first diagnosis = 28 years). Class B carriers showed a less penetrant form (median = 33 years, p < 0.05) dominated by adrenocortical and breast cancers. Class C or D carriers had attenuated phenotypes (median = 41 years, p < 0.001) with typical LFS cancers in C and mostly non-LFS cancers in D. This new classification provides insight into structural/functional features causing pathogenicity

Additional file 2: of A computational framework for complex disease stratification from multiple large-scale datasets

Complete results of the enrichment analysis between clusters. (XLSX 4293&nbsp;kb)</span

A computational framework for complex disease stratification from multiple large-scale datasets.

BACKGROUND: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. METHODS: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. RESULTS: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. CONCLUSIONS: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine