Dataset Information

TWIST1 DNA methylation is a cell marker of airway and parenchymal lung fibroblasts that are differentially methylated in asthma.

ABSTRACT: BACKGROUND:Mesenchymal fibroblasts are ubiquitous cells that maintain the extracellular matrix of organs. Within the lung, airway and parenchymal fibroblasts are crucial for lung development and are altered with disease, but it has been difficult to understand their roles due to the lack of distinct molecular markers. We studied genome-wide DNA methylation and gene expression in airway and parenchymal lung fibroblasts from healthy and asthmatic donors, to identify a robust cell marker and to determine if these cells are molecularly distinct in asthma. RESULTS:Airway (N = 8) and parenchymal (N = 15) lung fibroblasts from healthy individuals differed in the expression of 158 genes, and DNA methylation of 3936 CpGs (Bonferroni adjusted p value < 0.05). Differential DNA methylation between cell types was associated with differential expression of 42 genes, but no single DNA methylation CpG feature (location, effect size, number) defined the interaction. Replication of gene expression and DNA methylation in a second cohort identified TWIST1 gene expression, DNA methylation and protein expression as a cell marker of airway and parenchymal lung fibroblasts, with DNA methylation having 100% predictive discriminatory power. DNA methylation was differentially altered in parenchymal (112 regions) and airway fibroblasts (17 regions) with asthmatic status, with no overlap between regions. CONCLUSIONS:Differential methylation of TWIST1 is a robust cell marker of airway and parenchymal lung fibroblasts. Airway and parenchymal fibroblast DNA methylation are differentially altered in individuals with asthma, and the role of both cell types should be considered in the pathogenesis of asthma.

SUBMITTER: Clifford RL

PROVIDER: S-EPMC7531162 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

TWIST1 DNA methylation is a cell marker of airway and parenchymal lung fibroblasts that are differentially methylated in asthma.

Clifford Rachel L RL Yang Chen Xi CX Fishbane Nick N Patel Jamie J MacIsaac Julia L JL McEwen Lisa M LM May Sean T ST Castellanos-Uribe Marcos M Nair Parameswaran P Obeidat Ma'en M Kobor Michael S MS Knox Alan J AJ Hackett Tillie-Louise TL

Clinical epigenetics 20201002 1

<h4>Background</h4>Mesenchymal fibroblasts are ubiquitous cells that maintain the extracellular matrix of organs. Within the lung, airway and parenchymal fibroblasts are crucial for lung development and are altered with disease, but it has been difficult to understand their roles due to the lack of distinct molecular markers. We studied genome-wide DNA methylation and gene expression in airway and parenchymal lung fibroblasts from healthy and asthmatic donors, to identify a robust cell marker an ...[more]

PMID: 33008450

Similar Datasets

Project description:BackgroundChronic obstructive pulmonary disease (COPD) is a heterogeneous disease of the lungs that is currently the fourth leading cause of death worldwide. Genetic factors account for only a small amount of COPD risk, but epigenetic mechanisms, including DNA methylation, have the potential to mediate the interactions between an individual's genetics and environmental exposure. DNA methylation is highly cell type-specific, and individual cell type studies of DNA methylation in COPD are sparse. Fibroblasts are present within the airway and parenchyma of the lung and contribute to the aberrant deposition of extracellular matrix in COPD. No assessment or comparison of genome-wide DNA methylation profiles in the airway and parenchymal fibroblasts from individuals with and without COPD has been undertaken. These data provide valuable insight into the molecular mechanisms contributing to COPD and the differing pathologies of small airways disease and emphysema in COPD.MethodsGenome-wide DNA methylation was evaluated at over 485,000 CpG sites using the Illumina Infinium HumanMethylation450 BeadChip array in the airway (non-COPD n = 8, COPD n = 7) and parenchymal fibroblasts (non-COPD n = 17, COPD n = 29) isolated from individuals with and without COPD. Targeted gene expression was assessed by qPCR in matched RNA samples.ResultsDifferentially methylated DNA regions were identified between cells isolated from individuals with and without COPD in both airway and parenchymal fibroblasts. Only in parenchymal fibroblasts was differential DNA methylation associated with differential gene expression. A second analysis of differential DNA methylation variability identified 359 individual differentially variable CpG sites in parenchymal fibroblasts. No differentially variable CpG sites were identified in the airway fibroblasts. Five differentially variable-methylated CpG sites, associated with three genes, were subsequently assessed for gene expression differences. Two genes (OAT and GRIK2) displayed significantly increased gene expression in cells isolated from individuals with COPD.ConclusionsDifferential and variable DNA methylation was associated with COPD status in the parenchymal fibroblasts but not airway fibroblasts. Aberrant DNA methylation was associated with altered gene expression imparting biological function to DNA methylation changes. Changes in DNA methylation are therefore implicated in the molecular mechanisms underlying COPD pathogenesis and may represent novel therapeutic targets.

Project description:Deoxyribonucleic acid (DNA) methylation is an epigenetic alteration crucial for regulating stress responses. Identifying large-scale DNA methylation at single nucleotide resolution is made possible by whole genome bisulfite sequencing. An essential task following the generation of bisulfite sequencing data is to detect differentially methylated cytosines (DMCs) among treatments. Most statistical methods for DMC detection do not consider the dependency of methylation patterns across the genome, thus possibly inflating type I error. Furthermore, small sample sizes and weak methylation effects among different phenotype categories make it difficult for these statistical methods to accurately detect DMCs. To address these issues, the wavelet-based functional mixed model (WFMM) was introduced to detect DMCs. To further examine the performance of WFMM in detecting weak differential methylation events, we used both simulated and empirical data and compare WFMM performance to a popular DMC detection tool methylKit. Analyses of simulated data that replicated the effects of the herbicide glyphosate on DNA methylation in Arabidopsis thaliana show that WFMM results in higher sensitivity and specificity in detecting DMCs compared to methylKit, especially when the methylation differences among phenotype groups are small. Moreover, the performance of WFMM is robust with respect to small sample sizes, making it particularly attractive considering the current high costs of bisulfite sequencing. Analysis of empirical Arabidopsis thaliana data under varying glyphosate dosages, and the analysis of monozygotic (MZ) twins who have different pain sensitivities-both datasets have weak methylation effects of <1%-show that WFMM can identify more relevant DMCs related to the phenotype of interest than methylKit. Differentially methylated regions (DMRs) are genomic regions with different DNA methylation status across biological samples. DMRs and DMCs are essentially the same concepts, with the only difference being how methylation information across the genome is summarized. If methylation levels are determined by grouping neighboring cytosine sites, then they are DMRs; if methylation levels are calculated based on single cytosines, they are DMCs.

Project description:Background: Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease of the lungs that is currently the fourth leading cause of death worldwide. Genetic factors account for only a small amount of COPD risk, but epigenetic mechanisms including DNA methylation, have the potential to mediate the interactions between an individual?s genetics and environmental exposure. DNA methylation is highly cell type specific and individual cell type studies of DNA methylation in COPD are sparse. Fibroblasts are present within the airway and parenchyma of the lung and contribute to the aberrant deposition of extracellular matrix in COPD. No assessment or comparison of genome-wide DNA methylation profiles in airway and parenchymal fibroblasts from individuals with and without COPD has been undertaken. These data provide valuable insight into the molecular mechanisms contributing to COPD and the differing pathologies of small airways disease and emphysema in COPD. Methods: Genome-wide DNA methylation was evaluated at over 485,000 CpG sites using the Illumina Infinium HumanMethylation450 BeadChip array in airway (non-COPD n=8, COPD n=7) and parenchymal fibroblasts (non-COPD n=18, COPD n=28) isolated from individuals with and without COPD. Targeted gene expression was assessed by qPCR in matched RNA samples. Results: Differentially methylated DNA regions were identified between cells isolated from individuals with and without COPD in both airway and parenchymal fibroblasts. Only in parenchymal fibroblasts was differential DNA methylation associated with differential gene expression. A second analysis of differential DNA methylation variability identified 359 individual differentially variable CpG sites in parenchymal fibroblasts. No differentially variable CpG sites were identified in airway fibroblasts. Five differentially variable methylated CpG sites, associated with three genes were subsequently assessed for gene expression differences. Two genes (OAT and GRIK2) displayed significantly increased gene expression in cells isolated from individuals with COPD. Conclusions: Differential and variable DNA methylation was associated with COPD status in parenchymal fibroblasts but not airway fibroblasts. Aberrant DNA methylation was associated with altered gene expression imparting biological function to DNA methylation changes. Changes in DNA methylation are therefore implicated in the molecular mechanisms underlying COPD pathogenesis and may represent novel therapeutic targets.

Project description:BackgroundHypermethylation of promoter CpG islands is strongly correlated to transcriptional gene silencing and epigenetic maintenance of the silenced state. As well as its role in tumor development, CpG island methylation contributes to the acquisition of resistance to chemotherapy. Differential Methylation Hybridisation (DMH) is one technique used for genome-wide DNA methylation analysis. The study of such microarray data sets should ideally account for the specific biological features of DNA methylation and the non-symmetrical distribution of the ratios of unmethylated and methylated sequences hybridised on the array. We have therefore developed a novel algorithm tailored to this type of data, Methylation Linear Discriminant Analysis (MLDA).ResultsMLDA was programmed in R (version 2.7.0) and the package is available at CRAN 1. This approach utilizes linear regression models of non-normalised hybridisation data to define methylation status. Log-transformed signal intensities of unmethylated controls on the microarray are used as a reference. The signal intensities of DNA samples digested with methylation sensitive restriction enzymes and mock digested are then transformed to the likelihood of a locus being methylated using this reference. We tested the ability of MLDA to identify loci differentially methylated as analysed by DMH between cisplatin sensitive and resistant ovarian cancer cell lines. MLDA identified 115 differentially methylated loci and 23 out of 26 of these loci have been independently validated by Methylation Specific PCR and/or bisulphite pyrosequencing.ConclusionMLDA has advantages for analyzing methylation data from CpG island microarrays, since there is a clear rational for the definition of methylation status, it uses DMH data without between-group normalisation and is less influenced by cross-hybridisation of loci. The MLDA algorithm successfully identified differentially methylated loci between two classes of samples analysed by DMH using CpG island microarrays.

Dataset Information

TWIST1 DNA methylation is a cell marker of airway and parenchymal lung fibroblasts that are differentially methylated in asthma.

Publications

TWIST1 DNA methylation is a cell marker of airway and parenchymal lung fibroblasts that are differentially methylated in asthma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets