DNA methyltransferases and structural-functional specificity of eukaryotic DNA modification

4mC-CGRU: Identification of N4-Methylcytosine (4mC) sites using convolution gated recurrent unit in Rosaceae genome

An epigenetic modification is DNA N4-methylcytosine (4mC) that affects several biological functions without altering the DNA nucleotides, including DNA conformation, cell development, replication, stability, and DNA structural changes. To prevent restriction enzyme from damaging self-DNA, 4mC performs a critical role in restriction-modification functions. Existing studies mainly focused on finding hand-crafted features to identify 4mC locations, but these methods are inefficient due to high time consuming and high costs. In our research work, we propose a 4mC-CGRU which is a deep learning-based computational model with a standard encoding method to identify the 4mC sites from DNA sequences that learned autonomous feature selection in the Rosaceae genome, particularly in Rosa chinensis (R. chinensis) and Fragaria vesca (F. vesca). The proposed model consists of a convolutional neural network (CNN) and a gated recurrent unit network (GRU)-based model for identifying 4mC sites from Fragaria vesca and Rosa chinensis in the genomes. The CNN model extracts useful features from the datasets and the GRU classifies the DNA sequences. Thus, our approach can automatically extract important features to detect relative sites from DNA sequence. The performance analysis shows that the proposed model consistently outperforms over the state-of-the-art works in detecting 4mC sites.

Comparative evaluation and analysis of DNA N4-methylcytosine methylation sites using deep learning

DNA N4-methylcytosine (4mC) is significantly involved in biological processes, such as DNA expression, repair, and replication. Therefore, accurate prediction methods are urgently needed. Deep learning methods have transformed applications that previously require sequencing expertise into engineering challenges that do not require expertise to solve. Here, we compare a variety of state-of-the-art deep learning models on six benchmark datasets to evaluate their performance in 4mC methylation site detection. We visualize the statistical analysis of the datasets and the performance of different deep-learning models. We conclude that deep learning can greatly expand the potential of methylation site prediction.

i4mC-GRU: Identifying DNA N4-Methylcytosine sites in mouse genomes using bidirectional gated recurrent unit and sequence-embedded features

N⁴-methylcytosine (4mC) is one of the most common DNA methylation modifications found in both prokaryotic and eukaryotic genomes. Since the 4mC has various essential biological roles, determining its location helps reveal unexplored physiological and pathological pathways. In this study, we propose an effective computational method called i4mC-GRU using a gated recurrent unit and duplet sequence-embedded features to predict potential 4mC sites in mouse (Mus musculus) genomes. To fairly assess the performance of the model, we compared our method with several state-of-the-art methods using two different benchmark datasets. Our results showed that i4mC-GRU achieved area under the receiver operating characteristic curve values of 0.97 and 0.89 and area under the precision-recall curve values of 0.98 and 0.90 on the first and second benchmark datasets, respectively. Briefly, our method outperformed existing methods in predicting 4mC sites in mouse genomes. Also, we deployed i4mC-GRU as an online web server, supporting users in genomics studies.

Systematic Analysis and Accurate Identification of DNA N4-Methylcytosine Sites by Deep Learning

DNA N⁴-methylcytosine (4mC) is a pivotal epigenetic modification that plays an essential role in DNA replication, repair, expression and differentiation. To gain insight into the biological functions of 4mC, it is critical to identify their modification sites in the genomics. Recently, deep learning has become increasingly popular in recent years and frequently employed for the 4mC site identification. However, a systematic analysis of how to build predictive models using deep learning techniques is still lacking. In this work, we first summarized all existing deep learning-based predictors and systematically analyzed their models, features and datasets, etc. Then, using a typical standard dataset with three species (A. thaliana, C. elegans, and D. melanogaster), we assessed the contribution of different model architectures, encoding methods and the attention mechanism in establishing a deep learning-based model for the 4mC site prediction. After a series of optimizations, convolutional-recurrent neural network architecture using the one-hot encoding and attention mechanism achieved the best overall prediction performance. Extensive comparison experiments were conducted based on the same dataset. This work will be helpful for researchers who would like to build the 4mC prediction models using deep learning in the future.

Expression of DnMTs and MBDs in AlCl3-Induced Neurotoxicity Mouse Model

Alteration in DNA methylation after aluminum exposure has been shown to contribute in pathogenesis of Alzheimer's disease (AD). This study is aimed to determine the effect of Al exposure (42 and 60 days) on learning and memory and the expression of proteins involved in DNA methylation (MBD1, MBD2, MBD3, MeCP2 (methyl CpG binding protein 2), DnMT1 and DnMT3a). Male BALB/c mice were treated with AlCl₃ for either 42 days or 60 days. After treatment completion, learning and memory were compared to the control group using novel object recognition test, elevated plus maze test, open field test, and Morris water maze test. The treated animals and their respective controls were sacrificed after cognitive testing and samples from their whole cortex and hippocampus were harvested for gene expression analysis. Mice treated with AlCl₃ showed significant cognitive deficit with impaired short-term memory, elevated anxiety, and deterioration in spatial and reference memory. The AlCl₃ treatment showed significant reduction in the expression of MBDs in the whole cortex at 60 days of treatment as compared to control. AlCl₃-treated animals showed decreased expression of MBDs and DnMT3a in the hippocampus for longer treated animals but strikingly, MBD2 showed significantly increased expression in AlCl₃-treated animals at 60 days p ≤ 0.001. In conclusion, this study showed that AlCl₃-treated animals showed significant memory and cognitive deficits and it is associated with significant changes in the expression of proteins involved in DNA methylation mechanism. Moreover, different Al exposure duration had slightly different effects.

iRG-4mC: Neural Network Based Tool for Identification of DNA 4mC Sites in Rosaceae Genome

DNA N4-Methylcytosine is a genetic modification process which has an essential role in changing different biological processes such as DNA conformation, DNA replication, DNA stability, cell development and structural alteration in DNA. Due to its negative effects, it is important to identify the modified 4mC sites. Further, methylcytosine may develop anywhere at cytosine residue, however, clonal gene expression patterns are most likely transmitted just for cytosine residues in strand-symmetrical sequences. For this reason many different experiments are introduced but they proved not to be viable choice due to time limitation and high expenses. Therefore, to date there is still need for an efficient computational method to deal with 4mC sites identification. Keeping it in mind, in this research we have proposed an efficient model for Fragaria vesca (F. vesca) and Rosa chinensis (R. chinensis) genome. The proposed iRG-4mC tool is developed based on neural network architecture with two encoding schemes to identify the 4mC sites. The iRG-4mC predictor outperformed the existing state-of-the-art computational model by an accuracy difference of 9.95% on F. vesca (training dataset), 8.7% on R. chinesis (training dataset), 6.2% on F. vesca (independent dataset) and 10.6% on R. chinesis (independent dataset). We have also established a webserver which is freely accessible for the research community.

Identifying DNA N4-methylcytosine sites in the rosaceae genome with a deep learning model relying on distributed feature representation

DNA N4-methylcytosine (4mC), an epigenetic modification found in prokaryotic and eukaryotic species, is involved in numerous biological functions, including host defense, transcription regulation, gene expression, and DNA replication. To identify 4mC sites, previous computational studies mostly focused on finding hand-crafted features. This area of research, therefore, would benefit from the development of a computational approach that relies on automatic feature selection to identify relevant sites. We here report 4mC-w2vec, a computational method that learned automatic feature discrimination in the Rosaceae genomes, especially in Rosa chinensis (R. chinensis) and Fragaria vesca (F. vesca), based on distributed feature representation and through the word embedding technique ‘word2vec’. While a few bioinformatics tools are currently employed to identify 4mC sites in these genomes, their prediction performance is inadequate. Our system processed 4mC and non-4mC sites through a word embedding process, including sub-word information of its biological words through k-mer, which then served as features that were fed into a double layer of convolutional neural network (CNN) to classify whether the sample sequences contained 4mCs or non-4mCs sites. Our tool demonstrated performance superior to current tools that use the same genomic datasets. Additionally, 4mC-w2vec is effective for balanced and imbalanced class datasets alike, and the online web-server is currently available at: http://nsclbio.jbnu.ac.kr/tools/4mC-w2vec/.

DNA sequences performs as natural language processing by exploiting deep learning algorithm for the identification of N4-methylcytosine

N4-methylcytosine is a biochemical alteration of DNA that affects the genetic operations without modifying the DNA nucleotides such as gene expression, genomic imprinting, chromosome stability, and the development of the cell. In the proposed work, a computational model, 4mCNLP-Deep, used the word embedding approach as a vector formulation by exploiting deep learning based CNN algorithm to predict 4mC and non-4mC sites on the C.elegans genome dataset. Diversity of ranges employed for the experimental such as corpus k-mer and k-fold cross-validation to obtain the prevailing capabilities. The 4mCNLP-Deep outperform from the state-of-the-art predictor by achieving the results in five evaluation metrics by following; Accuracy (ACC) as 0.9354, Mathew’s correlation coefficient (MCC) as 0.8608, Specificity (Sp) as 0.89.96, Sensitivity (Sn) as 0.9563, and Area under curve (AUC) as 0.9731 by using 3-mer corpus word2vec and 3-fold cross-validation and attained the increment of 1.1%, 0.6%, 0.58%, 0.77%, and 4.89%, respectively. At last, we developed the online webserver http://nsclbio.jbnu.ac.kr/tools/4mCNLP-Deep/, for the experimental researchers to get the results easily.

Epigenetic mechanisms activated by childhood adversity

Adverse childhood experiences (ACE) impair health and life expectancy and may result in an epigenetic signature that drives increased morbidity primed during early stages of life. This literature review focuses on the current evidence for epigenetic-mediated programming of brain and immune function resulting from ACE. To address this aim, a total of 88 articles indexed in PubMed before August 2019 concerning ACE and epigenetics were surveyed. Current evidence partially supports epigenetic programming of the hypothalamic-pituitary-adrenal axis, but convincingly shows that ACE impairs immune function. Additionally, the needs and challenges that face this area are discussed in order to provide a framework that may help to clarify the role of epigenetics in the long-lasting effects of ACE.

DNC4mC-Deep: Identification and Analysis of DNA N4-Methylcytosine Sites Based on Different Encoding Schemes By Using Deep Learning

N4-methylcytosine as one kind of modification of DNA has a critical role which alters genetic performance such as protein interactions, conformation, stability in DNA as well as the regulation of gene expression same cell developmental and genomic imprinting. Some different 4mC site identifiers have been proposed for various species. Herein, we proposed a computational model, DNC4mC-Deep, including six encoding techniques plus a deep learning model to predict 4mC sites in the genome of F. vesca, R. chinensis, and Cross-species dataset. It was demonstrated by the 10-fold cross-validation test to get superior performance. The DNC4mC-Deep obtained 0.829 and 0.929 of MCC on F. vesca and R. chinensis training dataset, respectively, and 0.814 on cross-species. This means the proposed method outperforms the state-of-the-art predictors at least 0.284 and 0.265 on F. vesca and R. chinensis training dataset in turn. Furthermore, the DNC4mC-Deep achieved 0.635 and 0.565 of MCC on F. vesca and R. chinensis independent dataset, respectively, and 0.562 on cross-species which shows it can achieve the best performance to predict 4mC sites as compared to the state-of-the-art predictor.

i4mC-Mouse: Improved identification of DNA N4-methylcytosine sites in the mouse genome using multiple encoding schemes

N4-methylcytosine (4mC) is one of the most important DNA modifications and involved in regulating cell differentiations and gene expressions. The accurate identification of 4mC sites is necessary to understand various biological functions. In this work, we developed a new computational predictor called i4mC-Mouse to identify 4mC sites in the mouse genome. Herein, six encoding schemes of k-space nucleotide composition (KSNC), k-mer nucleotide composition (Kmer), mono nucleotide binary encoding (MBE), dinucleotide binary encoding, electron–ion interaction pseudo potentials (EIIP) and dinucleotide physicochemical composition were explored that cover different characteristics of DNA sequence information. Subsequently, we built six RF-based encoding models and then linearly combined their probability scores to construct the final predictor. Among the six RF-based models, the Kmer, KSNC, MBE, and EIIP encodings are sufficient, which contributed to 10%, 45%, 25%, and 20% of the prediction performance, respectively. On the independent test the i4mC-Mouse predicted the 4mC sites with accuracy and MCC of 0.816 and 0.633, respectively, which were approximately 2.5% and 5% higher than those of the existing method (4mCpred-EL). For experimental biologists, a freely available web application was implemented at http://kurata14.bio.kyutech.ac.jp/i4mC-Mouse/.

Systematic Analysis of the DNA Methylase and Demethylase Gene Families in Rapeseed (Brassica napus L.) and Their Expression Variations After Salt and Heat stresses

DNA methylation is a process through which methyl groups are added to the DNA molecule, thereby modifying the activity of a DNA segment without changing the sequence. Increasing evidence has shown that DNA methylation is involved in various aspects of plant growth and development via a number of key processes including genomic imprinting and repression of transposable elements. DNA methylase and demethylase are two crucial enzymes that play significant roles in dynamically maintaining genome DNA methylation status in plants. In this work, 22 DNA methylase genes and six DNA demethylase genes were identified in rapeseed (Brassica napus L.) genome. These DNA methylase and DNA demethylase genes can be classified into four (BnaCMTs, BnaMET1s, BnaDRMs and BnaDNMT2s) and three (BnaDMEs, BnaDML3s and BnaROS1s) subfamilies, respectively. Further analysis of gene structure and conserved domains showed that each sub-class is highly conserved between rapeseed and Arabidopsis. Expression analysis conducted by RNA-seq as well as qRT-PCR suggested that these DNA methylation/demethylation-related genes may be involved in the heat/salt stress responses in rapeseed. Taken together, our findings may provide valuable information for future functional characterization of these two types of epigenetic regulatory enzymes in polyploid species such as rapeseed, as well as for analyzing their evolutionary relationships within the plant kingdom.

Plant DNA Methyltransferase Genes: Multiplicity, Expression, Methylation Patterns

Expression and methylation patterns of genes encoding DNA methyltransferases and their functionally related proteins were studied in organs of Arabidopsis thaliana plants. Genes coding for the major maintenance-type DNA methyltransferases, MET1 and CMT3, and the major de novo-type DNA methyltransferase, DRM2, are actively expressed in all organs. Similar constitutively active expression was observed for genes encoding their functionally related proteins, a histone H3K9 methyltransferase KYP and a catalytically non-active protein DRM3. Expression of the MET1 and CMT3 genes is significantly lower in developing endosperm compared with embryo. Vice versa, expression of the MET2a, MET2b, MET3, and CMT2 genes in endosperm is much more active compared with embryo. A special maintenance DNA methylation system seems to operate in endosperm. The DNMT2 and N6AMT genes encoding putative methyltransferases are constitutively expressed at low levels. CMT1 and DRM1 genes are expressed rather weakly in all investigated organs. Most of the studied genes have methylation patterns conforming to the "body-methylated gene" prototype. A peculiar feature of the MET family genes is methylation at all three possible site types (CG, CHG, and CHH). The most weakly expressed among genes of their respective families, CMT1 and DRM1, are practically unmethylated. The MET3 and N6AMT genes have unusual methylation patterns, promoter region, and most of the gene body devoid of any methylation, and the 3'-end proximal part of the gene body is highly methylated.

[General concepts of epigenetics: Projections in paediatrics]

Current evidence supports the notion that alterations in intrauterine growth and during the first years of life have a substantial effect on the risk for the development of chronic disease, which in some cases is even higher than those due to genetic factors. The persistence and reproducibility of the phenotypes associated with altered early development suggest the participation of mechanisms that would record environmental cues, generating a cellular reprogramming (i.e., epigenetic mechanisms). This review is an introduction to a series of five articles focused on the participation of epigenetic mechanisms in the development of highly prevalent chronic diseases (i.e., cardiovascular, metabolic, asthma/allergies and cancer) and their origins in the foetal and neonatal period. This series of articles aims to show the state of the art in this research area and present the upcoming clues and challenges, in which paediatricians have a prominent role, developing strategies for the prevention, early detection and follow-up.

Genomic survey, gene expression analysis and structural modeling suggest diverse roles of DNA methyltransferases in legumes

DNA methylation plays a crucial role in development through inheritable gene silencing. Plants possess three types of DNA methyltransferases (MTases), namely Methyltransferase (MET), Chromomethylase (CMT) and Domains Rearranged Methyltransferase (DRM), which maintain methylation at CG, CHG and CHH sites. DNA MTases have not been studied in legumes so far. Here, we report the identification and analysis of putative DNA MTases in five legumes, including chickpea, soybean, pigeonpea, Medicago and Lotus. MTases in legumes could be classified in known MET, CMT, DRM and DNA nucleotide methyltransferases (DNMT2) subfamilies based on their domain organization. First three MTases represent DNA MTases, whereas DNMT2 represents a transfer RNA (tRNA) MTase. Structural comparison of all the MTases in plants with known MTases in mammalian and plant systems have been reported to assign structural features in context of biological functions of these proteins. The structure analysis clearly specified regions crucial for protein-protein interactions and regions important for nucleosome binding in various domains of CMT and MET proteins. In addition, structural model of DRM suggested that circular permutation of motifs does not have any effect on overall structure of DNA methyltransferase domain. These results provide valuable insights into role of various domains in molecular recognition and should facilitate mechanistic understanding of their function in mediating specific methylation patterns. Further, the comprehensive gene expression analyses of MTases in legumes provided evidence of their role in various developmental processes throughout the plant life cycle and response to various abiotic stresses. Overall, our study will be very helpful in establishing the specific functions of DNA MTases in legumes.

An insight into the various regulatory mechanisms modulating human DNA methyltransferase 1 stability and function

DNA methylation is one of the principal epigenetic signals that participate in cell specific gene expression in vertebrates. DNA methylation plays a quintessential role in the control of gene expression, cellular differentiation and development. It also plays a central role in the preservation of chromatin structure and chromosomal integrity, parental imprinting, X-chromosome inactivation, aging and carcinogenesis. The foremost contributor in the mammalian methylation scheme is DNMT1, a maintenance methyltransferase that faithfully copies the pre-existing methyl marks onto hemimethylated daughter strands during DNA replication to maintain the established methylation patterns across successive cell divisions. The ever-changing cellular physiology and the significant part that DNA methylation plays in genome regulation necessitate rigid management of this enzyme. In mammalian cells, a host of intrinsic and extrinsic mechanisms regulate the expression, activity and stability of DNMT1. Transcriptional regulation, post-transcriptional auto-inhibitory controls and post-translational modifications of the enzyme are responsible for the efficient inheritance of DNA methylation patterns. Also, a large number of intra- and intercellular signaling cascades and numerous interactions with other modulator molecules that affect the catalytic activity of the enzyme at multiple levels function as major checkpoints of the DNMT1 control system. An in-depth understanding of the DNMT1 enzyme, its targeting and function is crucial for comprehending how DNA methylation is coordinated with other critical developmental and physiological processes. This review aims to provide a comprehensive account of the various regulatory mechanisms and interactions of DNMT1 so as to elucidate its function at the molecular level and understand the dynamics of DNA methylation at the cellular level.

Rapid conversion of human ESCs into mouse ESC-like pluripotent state by optimizing culture conditions

The pluripotent state between human and mouse embryonic stem cells is different. Pluripotent state of human embryonic stem cells (ESCs) is believed to be primed and is similar with that of mouse epiblast stem cells (EpiSCs), which is different from the naïve state of mouse ESCs. Human ESCs could be converted into a naïve state through exogenous expression of defined transcription factors (Hanna et al., 2010). Here we report a rapid conversion of human ESCs to mouse ESC-like naïve states only by modifying the culture conditions. These converted human ESCs, which we called mhESCs (mouse ESC-like human ESCs), have normal karyotype, allow single cell passage, exhibit domed morphology like mouse ESCs and express some pluripotent markers similar with mouse ESCs. Thus the rapid conversion established a naïve pluripotency in human ESCs like mouse ESCs, and provided a new model to study the regulation of pluripotency.

Maternal care and DNA methylation of a glutamic acid decarboxylase 1 promoter in rat hippocampus

Parenting and the early environment influence the risk for various psychopathologies. Studies in the rat suggest that variations in maternal care stably influence DNA methylation, gene expression, and neural function in the offspring. Maternal care affects neural development, including the GABAergic system, the function of which is linked to the pathophysiology of diseases including schizophrenia and depression. Postmortem studies of human schizophrenic brains have revealed decreased forebrain expression of glutamic acid decarboxylase 1 (GAD1) accompanied by increased methylation of a GAD1 promoter. We examined whether maternal care affects GAD1 promoter methylation in the hippocampus of adult male offspring of high and low pup licking/grooming (high-LG and low-LG) mothers. Compared with the offspring of low-LG mothers, those reared by high-LG dams showed enhanced hippocampal GAD1 mRNA expression, decreased cytosine methylation, and increased histone 3-lysine 9 acetylation (H3K9ac) of the GAD1 promoter. DNA methyltransferase 1 expression was significantly higher in the offspring of low- compared with high-LG mothers. Pup LG increases hippocampal serotonin (5-HT) and nerve growth factor-inducible factor A (NGFI-A) expression. Chromatin immunoprecipitation assays revealed enhanced NGFI-A association with and H3K9ac of the GAD1 promoter in the hippocampus of high-LG pups after a nursing bout. Treatment of hippocampal neuronal cultures with either 5-HT or an NGFI-A expression plasmid significantly increased GAD1 mRNA levels. The effect of 5-HT was blocked by a short interfering RNA targeting NGFI-A. These results suggest that maternal care influences the development of the GABA system by altering GAD1 promoter methylation levels through the maternally induced activation of NGFI-A and its association with the GAD1 promoter.

DNMT1 silencing affects locus specific DNA methylation and increases prostate cancer derived PC3 cell invasiveness

PURPOSE

DNMT1 maintains genomic DNA methylation at 5'-CpG-3' residues in somatic cells. Recent findings revealed that DNMT1 depletion causes distinct phenotypic changes in colon and gastric cancer cell lines, suggesting that the extent to which DNMT1 influences the expression of its target genes is cell-type specific. We determined the impact of DNMT1 depletion in prostate cancer derived cells on their gene expression profiles and cellular phenotype.

MATERIALS AND METHODS

Small interfering RNA was used to silence DNMT1 expression in prostate cancer derived PC3 cells (ATCC). The resulting cell line was validated by reverse transcriptase-polymerase chain reaction and Western blotting. Proliferation, migration and invasion assays were done in engineered cells to asses the effect of DNMT1 silencing on cellular phenotype. DNA microarrays were done to monitor changes in gene expression.

RESULTS

Our data showed that DNMT1 loss dramatically decreased cell proliferation but significantly increased cell migratory and invasive potential. Additionally, in the limited set of genes whose expression and DNA methylation status were determined DNMT1 loss was associated with increased CDKN3 and claudin-3 expression, and also culminated in specific demethylation of Rb1 and RAR-beta promoters.

CONCLUSIONS

These results show that the genetic and phenotypic consequences of silencing DNMT1 in PC3 cells are markedly different from those in colon and gastric cancers, indicating that DNMT1 preferentially targets certain gene promoters. Our findings also suggest that decreasing DNMT1 levels or activity can potentially enhance prostate cancer cell invasiveness.

Down-regulation of DNMT3b in PC3 cells effects locus-specific DNA methylation, and represses cellular growth and migration

BACKGROUND

Aberrations in DNA methylation patterns promote changes in gene expression patterns and are invariably associated with neoplasia. DNA methylation is carried out and maintained by several DNA methyltransferases (DNMTs) among which DNMT1 functions as a maintenance methylase while DNMT3a and 3b serve as de novo enzymes. Although DNMT3b has been shown to preferentially target the methylation of DNA sequences residing in pericentric heterochromatin whether it is involved in gene specific methylation remains an open question. To address this issue, we have silenced the expression of DNMT3b in the prostate-derived PC3 cells through RNA interference and subsequently studied the accompanied cellular changes as well as the expression profiles of selected genes.

RESULTS

Our results demonstrate that DNMT3b depletion results in increased apoptosis and reduced migration of PC3 cells compared to the untransfected control cells. Reduced DNMT3b expression resulted in hypomethylation of retinoblastoma (Rb), retinoic-acid receptor beta (RAR-beta), and adenomatous polyposis coli (APC) gene promoters, and also culminated in increased expression of CDKN3 and cytochrome b5. Although DNMT3b silenced cells were found to have reduced growth and migratory potential, there was no apparent changes in their invasive ability compared to the parental PC3 cell line.

CONCLUSION

Our findings reveal that DNMT3b preferentially targets certain gene promoters in PC3 cells and that its depletion significantly reduces growth and migration of PC3 cells.

The effect of thiopurine drugs on DNA methylation in relation to TPMT expression

The thiopurine drugs 6-mercaptopurine (6-MP) and 6-thioguanine (6-TG) are well-established agents for the treatment of leukaemia but their main modes of action are controversial. Thiopurine methyltransferase (TPMT) metabolises thiopurine drugs and influences their cytotoxic activity. TPMT, like DNA methyltransferases (DNMTs), transfers methyl groups from S-adenosylmethionine (SAM) and generates S-adenosylhomocysteine (SAH). Since SAM levels are dependent on de novo purine synthesis (DNPS) and the metabolic products of 6-TG and 6-MP differ in their ability to inhibit DNPS, we postulated that 6-TG compared to 6-MP would have differential effects on changes in SAM and SAH levels and global DNA methylation, depending on TPMT status. To test this hypothesis, we used a human embryonic kidney cell line with inducible TPMT. Although changes in SAM and SAH levels occurred with each drug, decrease in global DNA methylation more closely reflected a decrease in DNMT activity. Inhibition was influenced by TPMT for 6-TG, but not 6-MP. The decrease in global methylation and DNMT activity with 6-MP, or with 6-TG when TPMT expression was low, were comparable to 5-aza-2'-deoxycytidine. However, this was not reflected in changes in methylation at the level of an individual marker gene (MAGE1A). The results suggest that a non-TPMT metabolised metabolite of 6-MP and 6-TG and the TPMT-metabolised 6-MP metabolite 6-methylthioguanosine 5'-monophosphate, contribute to a decrease in DNMT levels and global DNA methylation. As demethylating agents have shown promise in leukaemia treatment, inhibition of DNA methylation by the thiopurine drugs may contribute to their cytotoxic affects.

Plant cytosine-5 DNA methyltransferases: Structure, function, and molecular evolution

A detailed analysis of the structure and function, along with evolutionary aspects, of the main plant cytosine-5 DNA methyltransferases (C5-MTases) is presented. The evolutionary relationships between the already known and four candidate plant C5-MTases identified in this work were investigated using the distance, maximum-parsimony, and maximum-likelihood approaches. The topologies of the trees were overall congruent: four monophyletic groups corresponding to the four plant C5-MTase families were clearly distinguished. In addition, sequence analyses of the plant C5-MTase target recognition domain sequences were performed and phylogenetic trees were reconstructed showing that there is good conservation among but not within the plant C5-MTase families. Furthermore, a conserved dipeptide that plays an important role in flipping the target base into the catalytic site of the C5-MTases was identified in all plant C5-MTases under study.