Loss of DNA methyltransferase activity in primed human ES cells triggers increased cell-cell variability and transcriptional repression

DNA methylation entropy is associated with DNA sequence features and developmental epigenetic divergence

Epigenetic information defines tissue identity and is largely inherited in development through DNA methylation. While studied mostly for mean differences, methylation also encodes stochastic change, defined as entropy in information theory. Analyzing allele-specific methylation in 49 human tissue sample datasets, we find that methylation entropy is associated with specific DNA binding motifs, regulatory DNA, and CpG density. Then applying information theory to 42 mouse embryo methylation datasets, we find that the contribution of methylation entropy to time- and tissue-specific patterns of development is comparable to the contribution of methylation mean, and methylation entropy is associated with sequence and chromatin features conserved with human. Moreover, methylation entropy is directly related to gene expression variability in development, suggesting a role for epigenetic entropy in developmental plasticity.

Epigenetics as a mediator of plasticity in cancer

The concept of an epigenetic landscape describing potential cellular fates arising from pluripotent cells, first advanced by Conrad Waddington, has evolved in light of experiments showing nondeterministic outcomes of regulatory processes and mathematical methods for quantifying stochasticity. In this Review, we discuss modern approaches to epigenetic and gene regulation landscapes and the associated ideas of entropy and attractor states, illustrating how their definitions are both more precise and relevant to understanding cancer etiology and the plasticity of cancerous states. We address the interplay between different types of regulatory landscapes and how their changes underlie cancer progression. We also consider the roles of cellular aging and intrinsic and extrinsic stimuli in modulating cellular states and how landscape alterations can be quantitatively mapped onto phenotypic outcomes and thereby used in therapy development.

DNA methyltransferases 3A and 3B target specific sequences during mouse gastrulation

In mammalian embryos, DNA methylation is initialized to maximum levels in the epiblast by the de novo DNA methyltransferases DNMT3A and DNMT3B before gastrulation diversifies it across regulatory regions. Here we show that DNMT3A and DNMT3B are differentially regulated during endoderm and mesoderm bifurcation and study the implications in vivo and in meso-endoderm embryoid bodies. Loss of both Dnmt3a and Dnmt3b impairs exit from the epiblast state. More subtly, independent loss of Dnmt3a or Dnmt3b leads to small biases in mesoderm-endoderm bifurcation and transcriptional deregulation. Epigenetically, DNMT3A and DNMT3B drive distinct methylation kinetics in the epiblast, as can be predicted from their strand-specific sequence preferences. The enzymes compensate for each other in the epiblast, but can later facilitate lineage-specific methylation kinetics as their expression diverges. Single-cell analysis shows that differential activity of DNMT3A and DNMT3B combines with replication-linked methylation turnover to increase epigenetic plasticity in gastrulation. Together, these findings outline a dynamic model for the use of DNMT3A and DNMT3B sequence specificity during gastrulation.

Multi-tissue DNA methylation microarray signature is predictive of gene function

Background Transcriptional correlation networks derived from publicly available gene expression microarrays have been previously shown to be predictive of known gene functions, but less is known about the predictive capacity of correlated DNA methylation at CpG sites. Guilt-by-association co-expression methods can adapted for use with DNA methylation when a representative methylation value is created for each gene. We examine how methylation compares to expression in predicting Gene Ontology terms using both co-methylation and traditional machine learning approaches across different types of representative methylation values per gene. Methods We perform guilt-by-association gene function prediction with a suite of models called Methylation Array Network Analysis, using a network of correlated methylation values derived from over 24,000 samples. In generating the correlation matrix, the performance of different methods of collapsing probe-level data effect on the resulting gene function predictions was compared, along with the use of different regions surrounding the gene of interest. Results Using mean comethylation of a given gene to its annotated term had an overall highest prediction macro-AUC of 0.60 using mean gene body methylation, across all Gene Ontology terms. This was increased using the logistic regression approach with the highest macro-AUC of 0.82 using mean gene body methylation, compared to the naive predictor of 0.72. Conclusion Genes correlated in their methylation state are functionally related. Genes clustered in co-methylation space were enriched for chromatin state, PRC2, immune response, and development-related terms.

Donor T cell DNMT3a regulates alloreactivity in mouse models of hematopoietic stem cell transplantation

DNA methyltransferase 3a (DNMT3a) is an important part of the epigenetic machinery that stabilizes patterns of activated T cell responses. We hypothesized that donor T cell DNMT3a regulates alloreactivity after allogeneic blood and marrow transplantation (allo-BMT). T cell conditional Dnmt3a KO mice were used as donors in allo-BMT models. Mice receiving allo-BMT from KO donors developed severe acute graft-versus-host disease (aGVHD), with increases in inflammatory cytokine levels and organ histopathology scores. KO T cells migrated and proliferated in secondary lymphoid organs earlier and demonstrated an advantage in trafficking to the small intestine. Donor T cell subsets were purified after BMT for whole-genome bisulfite sequencing (WGBS) and RNA-Seq. KO T cells had global methylation similar to that of WT cells, with distinct, localized areas of hypomethylation. Using a highly sensitive computational method, we produced a comprehensive profile of the altered epigenome landscape. Hypomethylation corresponded with changes in gene expression in several pathways of T cell signaling and differentiation. Additionally, Dnmt3a-KO T cells resulted in superior graft-versus-tumor activity. Our findings demonstrate a critical role for DNMT3a in regulating T cell alloreactivity and reveal pathways that control T cell tolerance. These results also provide a platform for deciphering clinical data that associate donor DNMT3a mutations with increased GVHD, decreased relapse, and improved survival.

Estimating DNA methylation potential energy landscapes from nanopore sequencing data

High-throughput third-generation nanopore sequencing devices have enormous potential for simultaneously observing epigenetic modifications in human cells over large regions of the genome. However, signals generated by these devices are subject to considerable noise that can lead to unsatisfactory detection performance and hamper downstream analysis. Here we develop a statistical method, CpelNano, for the quantification and analysis of 5mC methylation landscapes using nanopore data. CpelNano takes into account nanopore noise by means of a hidden Markov model (HMM) in which the true but unknown ("hidden") methylation state is modeled through an Ising probability distribution that is consistent with methylation means and pairwise correlations, whereas nanopore current signals constitute the observed state. It then estimates the associated methylation potential energy function by employing the expectation-maximization (EM) algorithm and performs differential methylation analysis via permutation-based hypothesis testing. Using simulations and analysis of published data obtained from three human cell lines (GM12878, MCF-10A, and MDA-MB-231), we show that CpelNano can faithfully estimate DNA methylation potential energy landscapes, substantially improving current methods and leading to a powerful tool for the modeling and analysis of epigenetic landscapes using nanopore sequencing data.

Smart-RRBS for single-cell methylome and transcriptome analysis

The integration of DNA methylation and transcriptional state within single cells is of broad interest. Several single-cell dual- and multi-omics approaches have been reported that enable further investigation into cellular heterogeneity, including the discovery and in-depth study of rare cell populations. Such analyses will continue to provide important mechanistic insights into the regulatory consequences of epigenetic modifications. We recently reported a new method for profiling the DNA methylome and transcriptome from the same single cells in a cancer research study. Here, we present details of the protocol and provide guidance on its utility. Our Smart-RRBS (reduced representation bisulfite sequencing) protocol combines Smart-seq2 and RRBS and entails physically separating mRNA from the genomic DNA. It generates paired epigenetic promoter and RNA-expression measurements for ~24% of protein-coding genes in a typical single cell. It also works for micro-dissected tissue samples comprising hundreds of cells. The protocol, excluding flow sorting of cells and sequencing, takes ~3 d to process up to 192 samples manually. It requires basic molecular biology expertise and laboratory equipment, including a PCR workstation with UV sterilization, a DNA fluorometer and a microfluidic electrophoresis system.

Hydrogen sulfide facilitates reprogramming and trans-differentiation in 3D dermal fibroblast

The efficiency of cell reprogramming in two-dimensional (2D) cultures is limited. Given that cellular stemness is intimately related to microenvironmental changes, 3D cell cultures have the potential of overcoming this limited capacity by allowing cells to self-organize by aggregation. In 3D space, cells interact more efficiently, modify their cellular topology, gene expression, signaling, and metabolism. It is yet not clear as how 3D culture environments modify the reprogramming potential of fibroblasts. We demonstrate that 3D spheroids from dermal fibroblasts formed under ultra-low attachment conditions showed increased lactate production. This is a requisite for cell reprogramming, increase their expression of pluripotency genes, such as OCT4, NANOG and SOX2, and display upregulated cystathionine-β-synthase (CBS) and hydrogen sulfide (H2S) production. Knockdown of CBS by RNAi suppresses lactic acid and H2S production and concomitantly decreases the expression of OCT4 and NANOG. On the contrary, H2S donors, NaHS and garlic-derived diallyl trisulfide (DATS), promote the expression of OCT4, and support osteogenic trans-differentiation of fibroblasts. These results demonstrate that CBS mediated release of H2S regulates the reprogramming of dermal fibroblasts grown in 3D cultures and supports their trans-differentiation.