Dataset Information

A cell-type deconvolution meta-analysis of whole blood EWAS reveals lineage-specific smoking-associated DNA methylation changes.

ABSTRACT: Highly reproducible smoking-associated DNA methylation changes in whole blood have been reported by many Epigenome-Wide-Association Studies (EWAS). These epigenetic alterations could have important implications for understanding and predicting the risk of smoking-related diseases. To this end, it is important to establish if these DNA methylation changes happen in all blood cell subtypes or if they are cell-type specific. Here, we apply a cell-type deconvolution algorithm to identify cell-type specific DNA methylation signals in seven large EWAS. We find that most of the highly reproducible smoking-associated hypomethylation signatures are more prominent in the myeloid lineage. A meta-analysis further identifies a myeloid-specific smoking-associated hypermethylation signature enriched for DNase Hypersensitive Sites in acute myeloid leukemia. These results may guide the design of future smoking EWAS and have important implications for our understanding of how smoking affects immune-cell subtypes and how this may influence the risk of smoking related diseases.

SUBMITTER: You C

PROVIDER: S-EPMC7508850 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A cell-type deconvolution meta-analysis of whole blood EWAS reveals lineage-specific smoking-associated DNA methylation changes.

You Chenglong C Wu Sijie S Zheng Shijie C SC Zhu Tianyu T Jing Han H Flagg Ken K Wang Guangyu G Jin Li L Wang Sijia S Teschendorff Andrew E AE

Nature communications 20200922 1

Highly reproducible smoking-associated DNA methylation changes in whole blood have been reported by many Epigenome-Wide-Association Studies (EWAS). These epigenetic alterations could have important implications for understanding and predicting the risk of smoking-related diseases. To this end, it is important to establish if these DNA methylation changes happen in all blood cell subtypes or if they are cell-type specific. Here, we apply a cell-type deconvolution algorithm to identify cell-type s ...[more]

PMID: 32963246

Similar Datasets

Project description:BackgroundSystemic lupus erythematosus (SLE) is a clinically and biologically heterogeneous autoimmune disease. We explored whether the deconvolution of whole blood transcriptomic data could identify differences in predicted immune cell frequency between active SLE patients, and whether these differences are associated with clinical features and/or medication use.MethodsPatients with active SLE (BILAG-2004 Index) enrolled in the BILAG-Biologics Registry (BILAG-BR), prior to change in therapy, were studied as part of the MASTERPLANS Stratified Medicine consortium. Whole blood RNA-sequencing (RNA-seq) was conducted at enrolment into the registry. Data were deconvoluted using CIBERSORTx. Predicted immune cell frequencies were compared between active and inactive disease in the nine BILAG-2004 domains and according to immunosuppressant use (current and past).ResultsPredicted cell frequency varied between 109 patients. Patients currently, or previously, exposed to mycophenolate mofetil (MMF) had fewer inactivated macrophages (0.435% vs 1.391%, p = 0.001), naïve CD4 T cells (0.961% vs 2.251%, p = 0.002), and regulatory T cells (1.858% vs 3.574%, p = 0.007), as well as a higher proportion of memory activated CD4 T cells (1.826% vs 1.113%, p = 0.015), compared to patients never exposed to MMF. These differences remained statistically significant after adjusting for age, gender, ethnicity, disease duration, renal disease, and corticosteroid use. There were 2607 differentially expressed genes (DEGs) in patients exposed to MMF with over-representation of pathways relating to eosinophil function and erythrocyte development and function. Within CD4 + T cells, there were fewer predicted DEGs related to MMF exposure. No significant differences were observed for the other conventional immunosuppressants nor between patients according disease activity in any of the nine organ domains.ConclusionMMF has a significant and persisting effect on the whole blood transcriptomic signature in patients with SLE. This highlights the need to adequately adjust for background medication use in future studies using whole blood transcriptomics.

Dataset Information

A cell-type deconvolution meta-analysis of whole blood EWAS reveals lineage-specific smoking-associated DNA methylation changes.

Publications

A cell-type deconvolution meta-analysis of whole blood EWAS reveals lineage-specific smoking-associated DNA methylation changes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets