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DNA methylation is a widely investigated epigenetic mark with important roles in development and disease. High-throughput assays enable genome-scale DNA methylation analysis in large numbers of samples. Here, we describe a new version of our RnBeads software - an R/Bioconductor package that implements start-to-finish analysis workflows for Infinium microarrays and various types of bisulfite sequencing. RnBeads 2.0 ( https://rnbeads.org/ ) provides additional data types and analysis methods, new functionality for interpreting DNA methylation differences, improved usability with a novel graphical user interface, and better use of computational resources. We demonstrate RnBeads 2.0 in four re-runnable use cases focusing on cell differentiation and cancer.

Lung diseases, such as cystic fibrosis and COPD, are characterized by mucus obstruction and chronic airway inflammation, but their mechanistic link remains poorly understood. Here, we focus on the function of the mucostatic airway microenvironment on epigenetic reprogramming of airway macrophages (AM) and resulting transcriptomic and phenotypical changes. Using a mouse model of muco-obstructive lung disease ( Scnn1b -transgenic), we identify epigenetically controlled, differentially regulated pathways and transcription factors involved in inflammatory responses and macrophage polarization. Functionally, AMs from Scnn1b -transgenic mice have reduced efferocytosis and phagocytosis, and excessive inflammatory responses upon lipopolysaccharide challenge, mediated through enhanced Irf1 function and expression. Ex vivo stimulation of wild-type AMs with native mucus impairs efferocytosis and phagocytosis capacities. In addition, mucus induces gene expression changes, comparable with those observed in AMs from Scnn1b -transgenic mice. Our data show that mucostasis induces epigenetic reprogramming of AMs, leading to changes favoring tissue damage and disease progression. Targeting these altered AMs may support therapeutic approaches in patients with muco-obstructive lung diseases. Muco-obstructive lung diseases are characterised by airway macrophage (AM) populations which may have epigenetic changes. Here using a mouse model the authors show epigenetic alteration of AMs with changes in LPS response, phagocytosis and efferocytosis similar to culture with mucus in vitro.

The molecular pathogenesis of salivary gland acinic cell carcinoma (AciCC) is poorly understood. The secretory Ca-binding phosphoprotein (SCPP) gene cluster at 4q13 encodes structurally related phosphoproteins of which some are specifically expressed at high levels in the salivary glands and constitute major components of saliva. Here we report on recurrent rearrangements [t(4;9)(q13;q31)] in AciCC that translocate active enhancer regions from the SCPP gene cluster to the region upstream of Nuclear Receptor Subfamily 4 Group A Member 3 (NR4A3) at 9q31. We show that NR4A3 is specifically upregulated in AciCCs, and that active chromatin regions and gene expression signatures in AciCCs are highly correlated with the NR4A3 transcription factor binding motif. Overexpression of NR4A3 in mouse salivary gland cells increases expression of known NR4A3 target genes and has a stimulatory functional effect on cell proliferation. We conclude that NR4A3 is upregulated through enhancer hijacking and has important oncogenic functions in AciCC. Acinic cell carcinoma (AciCC) is a rare salivary gland carcinoma that is poorly understood. Here the authors perform genomic, transcriptomic and epigenomic profiling of AciCC and find highly recurrent and specific rearrangements [t(4;9)(q13;q31)], which lead to enhancer hijacking that activates oncogenic transcription factor NR4A3.

The recent advances in high-throughput single-cell sequencing have created an urgent demand for computational models which can address the high complexity of single-cell multiomics data. Meticulous single-cell multiomics integration models are required to avoid biases towards a specific modality and overcome sparsity. Batch effects obfuscating biological signals must also be taken into account. Here, we introduce a new single-cell multiomics integration model, Single-cell Multiomics Autoencoder Integration (scMaui) based on variational product-of-experts autoencoders and adversarial learning. scMaui calculates a joint representation of multiple marginal distributions based on a product-of-experts approach which is especially effective for missing values in the modalities. Furthermore, it overcomes limitations seen in previous VAE-based integration methods with regard to batch effect correction and restricted applicable assays. It handles multiple batch effects independently accepting both discrete and continuous values, as well as provides varied reconstruction loss functions to cover all possible assays and preprocessing pipelines. We demonstrate that scMaui achieves superior performance in many tasks compared to other methods. Further downstream analyses also demonstrate its potential in identifying relations between assays and discovering hidden subpopulations.

Memories are encoded by memory traces or engrams, represented within subsets of neurons that are synchronously activated during learning. However, the molecular mechanisms that drive engram stabilization during consolidation and consequently ensure its reactivation by memory recall are not fully understood. In this study we manipulate, during memory consolidation, the levels of the de novo DNA methyltransferase 3a2 (Dnmt3a2) selectively within dentate gyrus neurons activated by fear conditioning. We found that Dnmt3a2 upregulation enhances memory performance in mice and improves the fidelity of reconstitution of the original neuronal ensemble upon memory retrieval. Moreover, similar manipulation in a sparse, non-engram subset of neurons does not bias engram allocation or modulate memory strength. We further show that neuronal Dnmt3a2 overexpression changes the DNA methylation profile of synaptic plasticity-related genes. Our data implicates DNA methylation selectively within neuronal ensembles as a mechanism of stabilizing engrams during consolidation that supports successful memory retrieval. Memories are encoded within neuronal ensembles activated during learning. Here the authors show that DNA methylation within neuronal ensembles contributes to the stability of the memory trace, and increases the likelihood of neuronal ensemble reactivation during retrieval.

Multiple sclerosis (MS) is an inflammatory, demyelinating disease of the central nervous system with a modest concordance rate in monozygotic twins, which strongly argues for involvement of epigenetic factors. We observe highly similar peripheral blood mononuclear cell-based methylomes in 45 MS-discordant monozygotic twins. Nevertheless, we identify seven MS-associated differentially methylated positions (DMPs) of which we validate two, including a region in the TMEM232 promoter and ZBTB16 enhancer. In CD4 + T cells we find an MS-associated differentially methylated region in FIRRE . Additionally, 45 regions show large methylation differences in individual pairs, but they do not clearly associate with MS. Furthermore, we present epigenetic biomarkers for current interferon-beta treatment, and extensive validation shows that the ZBTB16 DMP is a signature for prior glucocorticoid treatment. Taken together, this study represents an important reference for epigenomic MS studies, identifies new candidate epigenetic markers, and highlights treatment effects and genetic background as major confounders. Monozygotic (MZ) twins are ideal to study the influence of non-genetic factors on complex phenotypes. Here, Souren et al. perform an EWAS in peripheral blood mononuclear cells from 45 MZ twins discordant for multiple sclerosis and identify disease and treatment-associated epigenetic markers.

DNA methylation (DNAm) is a key epigenetic mark that shows profound alterations in cancer. Read-level methylomes enable more in-depth analyses, due to their broad genomic coverage and preservation of rare cell-type signals, compared to summarized data such as 450K/EPIC microarrays. Here, we propose MethylBERT, a Transformer-based model for read-level methylation pattern classification. MethylBERT identifies tumour-derived sequence reads based on their methylation patterns and local genomic sequence, and estimates tumour cell fractions within bulk samples. In our evaluation, MethylBERT outperforms existing deconvolution methods and demonstrates high accuracy regardless of methylation pattern complexity, read length and read coverage. Moreover, we show its applicability to cell-type deconvolution as well as non-invasive early cancer diagnostics using liquid biopsy samples. MethylBERT represents a significant advancement in read-level methylome analysis and enables accurate tumour purity estimation. The broad applicability of MethylBERT will enhance studies on both tumour and non-cancerous bulk methylomes. Mapping DNA methylomes in single cells is challenging, and thus studies using bulk samples remain common. Here, authors develop a transformer-based method for methylation pattern analysis to enhance bulk methylome deconvolution and cancer detection.

It is important for large-scale epigenomic studies to determine and explore the nature of hidden confounding variation, most importantly cell composition. We developed MeDeCom as a novel reference-free computational framework that allows the decomposition of complex DNA methylomes into latent methylation components and their proportions in each sample. MeDeCom is based on constrained non-negative matrix factorization with a new biologically motivated regularization function. It accurately recovers cell-type-specific latent methylation components and their proportions. MeDeCom is a new unsupervised tool for the exploratory study of the major sources of methylation variation, which should lead to a deeper understanding and better biological interpretation.

Epigenetic heterogeneity is a fundamental property of biological systems and is recognized as a potential driver of tumor plasticity and therapy resistance. Single-cell epigenomics technologies have been widely employed to study epigenetic variation between—but not within—cellular clusters. We introduce epiCHAOS: a quantitative metric of cell-to-cell heterogeneity, applicable to any single-cell epigenomics data type. After validation in synthetic datasets, we apply epiCHAOS to investigate global and region-specific patterns of epigenetic heterogeneity across diverse biological systems. EpiCHAOS provides an excellent approximation of stemness and plasticity in development and malignancy, making it a valuable addition to single-cell cancer epigenomics analyses.

The neoplastic stromal cells of giant cell tumor of bone (GCTB) carry a mutation in H3F3A , leading to a mutant histone variant, H3.3-G34W, as a sole recurrent genetic alteration. We show that in patient-derived stromal cells H3.3-G34W is incorporated into the chromatin and associates with massive epigenetic alterations on the DNA methylation, chromatin accessibility and histone modification level, that can be partially recapitulated in an orthogonal cell line system by the introduction of H3.3-G34W. These epigenetic alterations affect mainly heterochromatic and bivalent regions and provide possible explanations for the genomic instability, as well as the osteolytic phenotype of GCTB. The mutation occurs in differentiating mesenchymal stem cells and associates with an impaired osteogenic differentiation. We propose that the observed epigenetic alterations reflect distinct differentiation stages of H3.3 WT and H3.3 MUT stromal cells and add to H3.3-G34W-associated changes. The histone variant mutation H3.3-G34W occurs in the majority of giant cell tumor of bone (GCTB). By profiling patient-derived GCTB tumor cells, the authors show that this mutation associates with epigenetic alterations in heterochromatic and bivalent regions that contribute to an impaired osteogenic differentiation and the osteolytic phenotype of GCTB.