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Background: Cytosine DNA methylation is widely described as a transcriptional repressive mark with the capacity to silence promoters. Epigenome engineering techniques enable direct testing of the effect of induced DNA methylation on endogenous promoters; however, the downstream effects have not yet been comprehensively assessed., Results: Here, we simultaneously induce methylation at thousands of promoters in human cells using an engineered zinc finger-DNMT3A fusion protein, enabling us to test the effect of forced DNA methylation upon transcription, chromatin accessibility, histone modifications, and DNA methylation persistence after the removal of the fusion protein. We find that transcriptional responses to DNA methylation are highly context-specific, including lack of repression, as well as cases of increased gene expression, which appears to be driven by the eviction of methyl-sensitive transcriptional repressors. Furthermore, we find that some regulatory networks can override DNA methylation and that promoter methylation can cause alternative promoter usage. DNA methylation deposited at promoter and distal regulatory regions is rapidly erased after removal of the zinc finger-DNMT3A fusion protein, in a process combining passive and TET-mediated demethylation. Finally, we demonstrate that induced DNA methylation can exist simultaneously on promoter nucleosomes that possess the active histone modification H3K4me3, or DNA bound by the initiated form of RNA polymerase II., Conclusions: These findings have important implications for epigenome engineering and demonstrate that the response of promoters to DNA methylation is more complex than previously appreciated., (© 2022. The Author(s).)

In epigenome-wide association studies analysing DNA methylation from samples containing multiple cell types, it is essential to adjust the analysis for cell type composition. One well established strategy for achieving this is reference-based cell type deconvolution, which relies on knowledge of the DNA methylation profiles of purified constituent cell types. These are then used to estimate the cell type proportions of each sample, which can then be incorporated to adjust the association analysis. Bronchoalveolar lavage is commonly used to sample the lung in clinical practice and contains a mixture of different cell types that can vary in proportion across samples, affecting the overall methylation profile. A current barrier to the use of bronchoalveolar lavage in DNA methylation-based research is the lack of reference DNA methylation profiles for each of the constituent cell types, thus making reference-based cell composition estimation difficult. Herein, we use bronchoalveolar lavage samples collected from children with cystic fibrosis to define DNA methylation profiles for the four most common and clinically relevant cell types: alveolar macrophages, granulocytes, lymphocytes and alveolar epithelial cells. We then demonstrate the use of these methylation profiles in conjunction with an established reference-based methylation deconvolution method to estimate the cell type composition of two different tissue types; a publicly available dataset derived from artificial blood-based cell mixtures and further bronchoalveolar lavage samples. The reference DNA methylation profiles developed in this work can be used for future reference-based cell type composition estimation of bronchoalveolar lavage. This will facilitate the use of this tissue in studies examining the role of DNA methylation in lung health and disease., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Shanthikumar, Neeland, Saffery, Ranganathan, Oshlack and Maksimovic.)

DNA methylation is one of the most commonly studied epigenetic marks, due to its role in disease and development. Illumina methylation arrays have been extensively used to measure methylation across the human genome. Methylation array analysis has primarily focused on preprocessing, normalization, and identification of differentially methylated CpGs and regions. GOmeth and GOregion are new methods for performing unbiased gene set testing following differential methylation analysis. Benchmarking analyses demonstrate GOmeth outperforms other approaches, and GOregion is the first method for gene set testing of differentially methylated regions. Both methods are publicly available in the missMethyl Bioconductor R package.

Unlabelled: DNA methylation is one of the most commonly studied epigenetic modifications due to its role in both disease and development. The Illumina HumanMethylation450 BeadChip is a cost-effective way to profile >450 000 CpGs across the human genome, making it a popular platform for profiling DNA methylation. Here we introduce missMethyl, an R package with a suite of tools for performing normalization, removal of unwanted variation in differential methylation analysis, differential variability testing and gene set analysis for the 450K array., Availability and Implementation: missMethyl is an R package available from the Bioconductor project at www.bioconductor.org., Contact: alicia.oshlack@mcri.edu.au, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Due to their relatively low-cost per sample and broad, gene-centric coverage of CpGs across the human genome, Illumina's 450k arrays are widely used in large scale differential methylation studies. However, by their very nature, large studies are particularly susceptible to the effects of unwanted variation. The effects of unwanted variation have been extensively documented in gene expression array studies and numerous methods have been developed to mitigate these effects. However, there has been much less research focused on the appropriate methodology to use for accounting for unwanted variation in methylation array studies. Here we present a novel 2-stage approach using RUV-inverse in a differential methylation analysis of 450k data and show that it outperforms existing methods., (© The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Methylation of DNA is known to be essential to development and dramatically altered in cancers. The Illumina HumanMethylation450 BeadChip has been used extensively as a cost-effective way to profile nearly half a million CpG sites across the human genome. Here we present DiffVar, a novel method to test for differential variability between sample groups. DiffVar employs an empirical Bayes model framework that can take into account any experimental design and is robust to outliers. We applied DiffVar to several datasets from The Cancer Genome Atlas, as well as an aging dataset. DiffVar is available in the missMethyl Bioconductor R package.

Regulatory T cells (Treg) prevent the emergence of autoimmune disease. Prototypic natural Treg (nTreg) can be reliably identified by demethylation at the Forkhead-box P3 (FOXP3) locus. To explore the methylation landscape of nTreg, we analyzed genome-wide methylation in human naive nTreg (rTreg) and conventional naive CD4(+) T cells (Naive). We detected 2315 differentially methylated cytosine-guanosine dinucleotides (CpGs) between these 2 cell types, many of which clustered into 127 regions of differential methylation (RDMs). Activation changed the methylation status of 466 CpGs and 18 RDMs in Naive but did not alter DNA methylation in rTreg. Gene-set testing of the 127 RDMs showed that promoter methylation and gene expression were reciprocally related. RDMs were enriched for putative FOXP3-binding motifs. Moreover, CpGs within known FOXP3-binding regions in the genome were hypomethylated. In support of the view that methylation limits access of FOXP3 to its DNA targets, we showed that increased expression of the immune suppressive receptor T-cell immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domain (TIGIT), which delineated Treg from activated effector T cells, was associated with hypomethylation and FOXP3 binding at the TIGIT locus. Differential methylation analysis provides insight into previously undefined human Treg signature genes and their mode of regulation.

DNA methylation is the most widely studied epigenetic mark and is known to be essential to normal development and frequently disrupted in disease. The Illumina HumanMethylation450 BeadChip assays the methylation status of CpGs at 485,577 sites across the genome. Here we present Subset-quantile Within Array Normalization (SWAN), a new method that substantially improves the results from this platform by reducing technical variation within and between arrays. SWAN is available in the minfi Bioconductor package.