The role of Dnmts and Tets in shaping the DNA methylation landscape of mouse embryonic stem cells

DNA methylation is an important epigenetic mark, which is set and maintained by DNA methyltransferases (Dnmts) and removed via passive or active mechanisms involving Ten eleven translocation enzyme (Tet) mediated oxidation. Stable cell type specific methylation patterns can only be achieved if methylation and demethylation events are in balance. Yet, the genome wide regulation of Dnmt and Tet activity is still not fully understood. The present studies use novel hairpin sequencing techniques coupled with oxidative bisul te sequencing, which permits the simultaneous and strand speci c detection of 5- methylcytosine and 5-hydroxymethylcytosine. Application of HMM models then facilitates the estimation of enzyme efficiencies for Dnmts and Tets. Furthermore, spatial modelling of hairpin bisulfite data allows the investigation of how Dnmts interpret preexisting methylation patterns. Taken together, the results of the presented studies show that methylation and hydroxylation are antagonistic, but not mutual exclusive events. In this context, the data shows that Tet efficiency is highest at open and accessible chromatin. Furthermore, the absence of Tets leads to a considerable misregulation of Dnmts, resulting in an increase in both maintenance and de novo methylation efficiency. Lastly, the spatial analysis of methylation patterns reveals that the de novo methyltransferases Dnmt3a and 3b depend in their activity on pre-existing neighbouring CpG methylation.DNA Methylierung is eine epigenetische Modifikation, welche durch DNA Methyltransferasen (Dnmts) gesetzt und beibehalten wird. Entfernt wird DNA Methylierung durch aktive oder passive Mechanismen welche die Oxidation von DNA Methylierung durch Ten- Eleven Translocation Enzyme (Tets) involviert. Stabile, Zelltyp-spezifische Methylierungsmuster können nur erreicht werden, wenn Methylierungs- und Demethylierungsvorgänge im Gleichgewicht sind. Dennoch ist die genomweite Regulation von Dnmts und Tets nicht vollständig geklärt. Die hier gezeigten Studien verwenden neue Hairpin-Sequenzierungs-Verfahren, gekoppelt mit oxidativer Bisulfit-Sequenzierung, was eine simultane und strangspezifische Analyse von 5-Methylcytosin und 5-Hydroxymethylcytosin erlaubt. Die Anwendung von hid- den Markov Modellen erlaubt im Anschluss die Berechnung von Enzymeeffizienzen für Dnmts und Tets. Darüber hinaus erlaubt eine räumliche Modellierung von Methylierungsmustern die Untersuchung, wie Dnmts bereits bestehende Methylierung interpretieren. Die Ergebnisse zeigen, dass Methylierung und Hydroxylierung antagonistische, aber keinesfalls sich ausschließende Ereignisse sind. Dabei zeigen Tets ihre stärkste Aktivität an offenem und zugänglichem Chromatin. Zudem führt der Verlust von Tets zu einer deutlichen Missregulation von Dnmts, welche sich durch eine Zunahme der Maintenance und de novo-Methylierungseffizienz äußert. Schließlich zeigt die räumliche Modellierung, dass die de novo-Methyltransferasen bei ihrer Aktivität abhängig von bereits bestehender DNA Methylierung sind

Tindak Tutur Direktif Dalam Novel Pukat Karya Tere-liye

This article was written based on (a) to describe the type of directive speech acts, (b) the strategy told the directive speech act, (c) the context of the use of directive speech acts, and (d) the effect of the use of linguistic politeness strategies recalled toin the novelPukat created by Tere-Liye.The data of this study is the speech acts of the figures contained in the novel Pukat created by Tere-Liye.The data source of this research is the novelPukat created by Tere-Liye.Data collected by reading and noting directive speech acts contained therein. Data were analyzed with the following steps: (1) identify the data and classifies data based on type, recalled strategy, context, and tells of the effects of linguistic politeness strategies in the directive speech act, (2) connecting the data with the data of the other, (3 ) conducted a study of data inference.The findings of this study are as follows. First, there are 5 types of directive speech acts, that is telling, asking, advising, challenging, and suggest. Second, there are 3 strategies recalled, that tells directly without further ado, instantly recalled with niceties positive politeness, recalled instantly with niceties negative politeness. Third, the strategy of direct recalled without further ado tends to be used in the context of the directive speech act hearer smaller, intimate, and speech do both. Fourth, the use of recalled strategy directly without further ado hearer in the context of smaller, intimate, and the speech made ​​two decent effect

A Continual Learning Approach for Cross-Domain White Blood Cell Classification

Accurate classification of white blood cells in peripheral blood is essential for diagnosing hematological diseases. Due to constantly evolving clinical settings, data sources, and disease classifications, it is necessary to update machine learning classification models regularly for practical real-world use. Such models significantly benefit from sequentially learning from incoming data streams without forgetting previously acquired knowledge. However, models can suffer from catastrophic forgetting, causing a drop in performance on previous tasks when fine-tuned on new data. Here, we propose a rehearsal-based continual learning approach for class incremental and domain incremental scenarios in white blood cell classification. To choose representative samples from previous tasks, we employ exemplar set selection based on the model's predictions. This involves selecting the most confident samples and the most challenging samples identified through uncertainty estimation of the model. We thoroughly evaluated our proposed approach on three white blood cell classification datasets that differ in color, resolution, and class composition, including scenarios where new domains or new classes are introduced to the model with every task. We also test a long class incremental experiment with both new domains and new classes. Our results demonstrate that our approach outperforms established baselines in continual learning, including existing iCaRL and EWC methods for classifying white blood cells in cross-domain environments.Comment: Accepted for publication at workshop on Domain Adaptation and Representation Transfer (DART) in International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023

MERVL/Zscan4 Network Activation Results in Transient Genome-wide DNA Demethylation of mESCs.

Mouse embryonic stem cells are dynamic and heterogeneous. For example, rare cells cycle through a state characterized by decondensed chromatin and expression of transcripts, including the Zscan4 cluster and MERVL endogenous retrovirus, which are usually restricted to preimplantation embryos. Here, we further characterize the dynamics and consequences of this transient cell state. Single-cell transcriptomics identified the earliest upregulated transcripts as cells enter the MERVL/Zscan4 state. The MERVL/Zscan4 transcriptional network was also upregulated during induced pluripotent stem cell reprogramming. Genome-wide DNA methylation and chromatin analyses revealed global DNA hypomethylation accompanying increased chromatin accessibility. This transient DNA demethylation was driven by a loss of DNA methyltransferase proteins in the cells and occurred genome-wide. While methylation levels were restored once cells exit this state, genomic imprints remained hypomethylated, demonstrating a potential global and enduring influence of endogenous retroviral activation on the epigenome

The Influence of Hydroxylation on Maintaining CpG Methylation Patterns: A Hidden Markov Model Approach

German Research Council (DFG) as part of the Collaborative Research Center "Physical modeling of non-equilibrium processes in biological systems" (SFB 1027) and the Cluster of Excellence on Multimodal Computing and Interaction at Saarland Universit

H(O)TA: estimation of DNA methylation and hydroxylation levels and efficiencies from time course data

Methylation and hydroxylation of cytosines to form 5-methylcytosine (5mC) and 5-droxymethylcytosine (5hmC) belong to the most important epigenetic modifications and their vital role in the regulation of gene expression has been widely recognized. Recent experimental techniques allow to infer methylation and hydroxylation levels at CpG dinucleotides but require a sophisticated statistical analysis to achieve accurate estimates

Two are better than one: HPoxBS - hairpin oxidative bisulfite sequencing

The controlled and stepwise oxidation of 5mC to 5hmC, 5fC and 5caC by Tet enzymes is influencing the chemical and biological properties of cytosine. Besides direct effects on gene regulation, oxidised forms influence the dynamics of demethylation and re-methylation processes. So far, no combined methods exist which allow to precisely determine the strand specific localisation of cytosine modifications along with their CpG symmetric distribution. Here we describe a comprehensive protocol combining conventional hairpin bisulfite with oxidative bisulfite sequencing (HPoxBS) to determine the strand specific distribution of 5mC and 5hmC at base resolution. We apply this method to analyse the contribution of local oxidative effects on DNA demethylation in mouse ES cells. Our method includes the HPoxBS workflow and subsequent data analysis using our developed software tools. Besides a precise estimation and display of strand specific 5mC and 5hmC levels at base resolution we apply the data to predict region specific activities of Dnmt and Tet enzymes. Our experimental and computational workflow provides a precise double strand display of 5mC and 5hmC modifications at single base resolution. Based on our data we predict region specific Tet and Dnmt enzyme efficiencies shaping the distinct locus levels and patterns of 5hmC and 5mC

Possible transitions of the 9 different states of a CpG site.

<p>Methyl groups are a) removed after cell division, b) added due to maintenance (<i>μ</i>_<i>m</i>) or de novo methylation (<i>μ</i>_<i>d</i>) and c) are hydroxylated (<i>η</i>) by Tet enzymes.</p