Genome-wide high throughput analysis of DNA methylation in eukaryotes

SNN6mA: Improved DNA N6-methyladenine site prediction using Siamese network-based feature embedding

DNA N6-methyladenine (6mA) is one of the most common and abundant modifications, which plays essential roles in various biological processes and cellular functions. Therefore, the accurate identification of DNA 6mA sites is of great importance for a better understanding of its regulatory mechanisms and biological functions. Although significant progress has been made, there still has room for further improvement in 6mA site prediction in DNA sequences. In this study, we report a smart but accurate 6mA predictor, termed as SNN6mA, using Siamese network. To be specific, DNA segments are firstly encoded into feature vectors using the one-hot encoding scheme; then, these original feature vectors are mapped to a low-dimensional embedding space derived from Siamese network to capture more discriminative features; finally, the obtained low-dimensional features are fed to a fully connected neural network to perform final prediction. Stringent benchmarking tests on the datasets of two species demonstrated that the proposed SNN6mA is superior to the state-of-the-art 6mA predictors. Detailed data analyses show that the major advantage of SNN6mA lies in the utilization of Siamese network, which can map the original features into a low-dimensional embedding space with more discriminative capability. In summary, the proposed SNN6mA is the first attempt to use Siamese network for 6mA site prediction and could be easily extended to predict other types of modifications. The codes and datasets used in the study are freely available at https://github.com/YuXuan-Glasgow/SNN6mA for academic use.

MethSemble-6mA: an ensemble-based 6mA prediction server and its application on promoter region of LBD gene family in Poaceae

The Lateral Organ Boundaries Domain (LBD) containing genes are a set of plant-specific transcription factors and are crucial for controlling both organ development and defense mechanisms as well as anthocyanin synthesis and nitrogen metabolism. It is imperative to understand how methylation regulates gene expression, through predicting methylation sites of their promoters particularly in major crop species. In this study, we developed a user-friendly prediction server for accurate prediction of 6mA sites by incorporating a robust feature set, viz., Binary Encoding of Mono-nucleotide DNA. Our model,MethSemble-6mA, outperformed other state-of-the-art tools in terms of accuracy (93.12%). Furthermore, we investigated the pattern of probable 6mA sites at the upstream promoter regions of the LBD-containing genes in Triticum aestivum and its allied species using the developed tool. On average, each selected species had four 6mA sites, and it was found that with speciation and due course of evolution in wheat, the frequency of methylation have reduced, and a few sites remain conserved. This obviously cues gene birth and gene expression alteration through methylation over time in a species and reflects functional conservation throughout evolution. Since DNA methylation is a vital event in almost all plant developmental processes (e.g., genomic imprinting and gametogenesis) along with other life processes, our findings on epigenetic regulation of LBD-containing genes have dynamic implications in basic and applied research. Additionally, MethSemble-6mA (http://cabgrid.res.in:5799/) will serve as a useful resource for a plant breeders who are interested to pursue epigenetic-based crop improvement research.

Improving DNA 6mA Site Prediction via Integrating Bidirectional Long Short-Term Memory, Convolutional Neural Network, and Self-Attention Mechanism

Identifying DNA N6-methyladenine (6mA) sites is significantly important to understanding the function of DNA. Many deep learning-based methods have been developed to improve the performance of 6mA site prediction. In this study, to further improve the performance of 6mA site prediction, we propose a new meta method, called Co6mA, to integrate bidirectional long short-term memory (BiLSTM), convolutional neural networks (CNNs), and self-attention mechanisms (SAM) via assembling two different deep learning-based models. The first model developed in this study is called CBi6mA, which is composed of CNN, BiLSTM, and fully connected modules. The second model is borrowed from LA6mA, which is an existing 6mA prediction method based on BiLSTM and SAM modules. Experimental results on two independent testing sets of different model organisms, i.e., Arabidopsis thaliana and Drosophila melanogaster, demonstrate that Co6mA can achieve an average accuracy of 91.8%, covering 89% of all 6mA samples while achieving an average Matthews correlation coefficient value (0.839), which is higher than the second-best method DeepM6A.

Navigating the pitfalls of mapping DNA and RNA modifications

Chemical modifications to nucleic acids occur across the kingdoms of life and carry important regulatory information. Reliable high-resolution mapping of these modifications is the foundation of functional and mechanistic studies, and recent methodological advances based on next-generation sequencing and long-read sequencing platforms are critical to achieving this aim. However, mapping technologies may have limitations that sometimes lead to inconsistent results. Some of these limitations are technical in nature and specific to certain types of technology. Here, however, we focus on common (yet not always widely recognized) pitfalls that are shared among frequently used mapping technologies and discuss strategies to help technology developers and users mitigate their effects. Although the emphasis is primarily on DNA modifications, RNA modifications are also discussed.

I-DNAN6mA: Accurate Identification of DNA N6-Methyladenine Sites Using the Base-Pairing Map and Deep Learning

The recent discovery of numerous DNA N⁶-methyladenine (6mA) sites has transformed our perception about the roles of 6mA in living organisms. However, our ability to understand them is hampered by our inability to identify 6mA sites rapidly and cost-efficiently by existing experimental methods. Developing a novel method to quickly and accurately identify 6mA sites is critical for speeding up the progress of its function detection and understanding. In this study, we propose a novel computational method, called I-DNAN6mA, to identify 6mA sites and complement experimental methods well, by leveraging the base-pairing rules and a well-designed three-stage deep learning model with pairwise inputs. The performance of our proposed method is benchmarked and evaluated on four species, i.e., Arabidopsis thaliana, Drosophila melanogaster, Rice, and Rosaceae. The experimental results demonstrate that I-DNAN6mA achieves area under the receiver operating characteristic curve values of 0.967, 0.963, 0.947, 0.976, and 0.990, accuracies of 91.5, 92.7, 88.2, 0.938, and 96.2%, and Mathew's correlation coefficient values of 0.855, 0.831, 0.763, 0.877, and 0.924 on five benchmark data sets, respectively, and outperforms several existing state-of-the-art methods. To our knowledge, I-DNAN6mA is the first approach to identify 6mA sites using a novel image-like representation of DNA sequences and a deep learning model with pairwise inputs. I-DNAN6mA is expected to be useful for locating functional regions of DNA.

A review of methods for predicting DNA N6-methyladenine sites

Deoxyribonucleic acid(DNA) N6-methyladenine plays a vital role in various biological processes, and the accurate identification of its site can provide a more comprehensive understanding of its biological effects. There are several methods for 6mA site prediction. With the continuous development of technology, traditional techniques with the high costs and low efficiencies are gradually being replaced by computer methods. Computer methods that are widely used can be divided into two categories: traditional machine learning and deep learning methods. We first list some existing experimental methods for predicting the 6mA site, then analyze the general process from sequence input to results in computer methods and review existing model architectures. Finally, the results were summarized and compared to facilitate subsequent researchers in choosing the most suitable method for their work.

ENet-6mA: Identification of 6mA Modification Sites in Plant Genomes Using ElasticNet and Neural Networks

N6-methyladenine (6mA) has been recognized as a key epigenetic alteration that affects a variety of biological activities. Precise prediction of 6mA modification sites is essential for understanding the logical consistency of biological activity. There are various experimental methods for identifying 6mA modification sites, but in silico prediction has emerged as a potential option due to the very high cost and labor-intensive nature of experimental procedures. Taking this into consideration, developing an efficient and accurate model for identifying N6-methyladenine is one of the top objectives in the field of bioinformatics. Therefore, we have created an in silico model for the classification of 6mA modifications in plant genomes. ENet-6mA uses three encoding methods, including one-hot, nucleotide chemical properties (NCP), and electron–ion interaction potential (EIIP), which are concatenated and fed as input to ElasticNet for feature reduction, and then the optimized features are given directly to the neural network to get classified. We used a benchmark dataset of rice for five-fold cross-validation testing and three other datasets from plant genomes for cross-species testing purposes. The results show that the model can predict the N6-methyladenine sites very well, even cross-species. Additionally, we separated the datasets into different ratios and calculated the performance using the area under the precision–recall curve (AUPRC), achieving 0.81, 0.79, and 0.50 with 1:10 (positive:negative) samples for F. vesca, R. chinensis, and A. thaliana, respectively.

Means, mechanisms and consequences of adenine methylation in DNA

N⁶-methyl-2'-deoxyadenosine (6mA or m⁶dA) has been reported in the DNA of prokaryotes and eukaryotes ranging from unicellular protozoa and algae to multicellular plants and mammals. It has been proposed to modulate DNA structure and transcription, transmit information across generations and have a role in disease, among other functions. However, its existence in more recently evolved eukaryotes remains a topic of debate. Recent technological advancements have facilitated the identification and quantification of 6mA even when the modification is exceptionally rare, but each approach has limitations. Critical assessment of existing data, rigorous design of future studies and further development of methods will be required to confirm the presence and biological functions of 6mA in multicellular eukaryotes.

Developmental alterations in DNA methylation during gametogenesis from primordial germ cells to sperm

Because epigenetics is a critical component for gene expression, the hypothesis was tested that DNA methylation alterations are dynamic and continually change throughout gametogenesis to generate the mature sperm. Developmental alterations and stage-specific DNA methylation during gametogenesis from primordial germ cells (PGCs) to mature sperm are investigated. Individual developmental stage germ cells were isolated and analyzed for differential DNA methylation regions (DMRs). The number of DMRs was highest in the first three comparisons with mature PGCs, prospermatogonia, and spermatogonia. The most statistically significant DMRs were present at all stages of development and had variations involving both increases or decreases in DNA methylation. DMR-associated genes were identified and correlated with gene functional categories, pathways, and cellular processes. Observations identified a dynamic cascade of epigenetic changes during development that is dramatic during the early developmental stages. Complex epigenetic alterations are required to regulate genome biology and gene expression during gametogenesis.

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A dynamic cascade of epigenetic change throughout gametogenesis from PGC to sperm

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Most dramatic epigenetic alterations in PGC and spermatogenic stem cell stages

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Different DNA methylation regions between and within stages were identified

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Complex epigenetic alterations required for gene expression during gametogenesis

GC6mA-Pred: A deep learning approach to identify DNA N6-methyladenine sites in the rice genome

MOTIVATION

DNA N6-methyladenine (6mA) is a pivotal DNA modification for various biological processes. More accurate prediction of 6mA methylation sites plays an irreplaceable part in grasping the internal rationale of related biological activities. However, the existing prediction methods only extract information from a single dimension, which has some limitations. Therefore, it is very necessary to obtain the information of 6mA sites from different dimensions, so as to establish a reliable prediction method.

RESULTS

In this study, a neural network based bioinformatics model named GC6mA-Pred is proposed to predict N6-methyladenine modifications in DNA sequences. GC6mA-Pred extracts significant information from both sequence level and graph level. In the sequence level, GC6mA-Pred uses a three-layer convolution neural network (CNN) model to represent the sequence. In the graph level, GC6mA-Pred employs graph neural network (GNN) method to integrate various information contained in the chemical molecular formula corresponding to DNA sequence. In our newly built dataset, GC6mA-Pred shows better performance than other existing models. The results of comparative experiments have illustrated that GC6mA-Pred is capable of producing a marked effect in accurately identifying DNA 6mA modifications.

Leveraging the attention mechanism to improve the identification of DNA N6-methyladenine sites

DNA N6-methyladenine is an important type of DNA modification that plays important roles in multiple biological processes. Despite the recent progress in developing DNA 6mA site prediction methods, several challenges remain to be addressed. For example, although the hand-crafted features are interpretable, they contain redundant information that may bias the model training and have a negative impact on the trained model. Furthermore, although deep learning (DL)-based models can perform feature extraction and classification automatically, they lack the interpretability of the crucial features learned by those models. As such, considerable research efforts have been focused on achieving the trade-off between the interpretability and straightforwardness of DL neural networks. In this study, we develop two new DL-based models for improving the prediction of N6-methyladenine sites, termed LA6mA and AL6mA, which use bidirectional long short-term memory to respectively capture the long-range information and self-attention mechanism to extract the key position information from DNA sequences. The performance of the two proposed methods is benchmarked and evaluated on the two model organisms Arabidopsis thaliana and Drosophila melanogaster. On the two benchmark datasets, LA6mA achieves an area under the receiver operating characteristic curve (AUROC) value of 0.962 and 0.966, whereas AL6mA achieves an AUROC value of 0.945 and 0.941, respectively. Moreover, an in-depth analysis of the attention matrix is conducted to interpret the important information, which is hidden in the sequence and relevant for 6mA site prediction. The two novel pipelines developed for DNA 6mA site prediction in this work will facilitate a better understanding of the underlying principle of DL-based DNA methylation site prediction and its future applications.

Advances in mapping the epigenetic modifications of 5-methylcytosine (5mC), N6-methyladenine (6mA), and N4-methylcytosine (4mC)

DNA modification plays a pivotal role in regulating gene expression in cell development. As prevalent markers on DNA, 5-methylcytosine (5mC), N6-methyladenine (6mA), and N4-methylcytosine (4mC) can be recognized by specific methyltransferases, facilitating cellular defense and the versatile regulation of gene expression in eukaryotes and prokaryotes. Recent advances in DNA sequencing technology have permitted the positions of different modifications to be resolved at the genome-wide scale, which has led to the discovery of several novel insights into the complexity and functions of multiple methylations. In this review, we summarize differences in the various mapping approaches and discuss their pros and cons with respect to their relative read depths, speeds, and costs. We also discuss the development of future sequencing technologies and strategies for improving the detection resolution of current sequencing technologies. Lastly, we speculate on the potentially instrumental role that these sequencing technologies might play in future research.

End-labeling-based electrochemical strategy for detection of adenine methylation in nucleic acid by differential pulse voltammetry

A promising electrochemical strategy for assay of N6-methyladenosine (m6A)/N6-methyladenine (6mA) in RNA/DNA is proposed. The key of this strategy is the end-labeling of nucleic acid, which makes it possible to detect methylation level in unknown sequence. Firstly, the end of m6A-RNA or 6mA-DNA was labeled with sulfhydryl group through T4 polynucleotide kinase (T4 PNK) and then directly assembled on a gold nanoparticle-modified glassy carbon electrode (AuNPs/GCE). Secondly, methylation sites in RNA/DNA were specifically recognized by anti-m6A-antibody, and then, horseradish peroxidase-labeled goat anti-rabbit IgG (HRP-IgG) was further conjugated on the antibody. Thirdly, HRP-IgG catalyzed the hydroquinone oxidation reaction to generate amplified current signal which correlates with the amount of m6A/6mA in nucleic acid. This method showed a wide linear range from 0.0001 to 10 nM for m6A-RNA, 0.001 to 100 nM for 6mA-dsDNA, and 0.0001 to 10 nM for 6mA-ssDNA. The method was successfully applied to detection of m6A/6mA in RNA/DNA from HeLa cells and E. coli cells and validation of the decrease of m6A-RNA in HeLa cells after treatment with FTO protein.

A convolution based computational approach towards DNA N6-methyladenine site identification and motif extraction in rice genome

DNA N6-methylation (6mA) in Adenine nucleotide is a post replication modification responsible for many biological functions. Automated and accurate computational methods can help to identify 6mA sites in long genomes saving significant time and money. Our study develops a convolutional neural network (CNN) based tool i6mA-CNN capable of identifying 6mA sites in the rice genome. Our model coordinates among multiple types of features such as PseAAC (Pseudo Amino Acid Composition) inspired customized feature vector, multiple one hot representations and dinucleotide physicochemical properties. It achieves auROC (area under Receiver Operating Characteristic curve) score of 0.98 with an overall accuracy of 93.97% using fivefold cross validation on benchmark dataset. Finally, we evaluate our model on three other plant genome 6mA site identification test datasets. Results suggest that our proposed tool is able to generalize its ability of 6mA site identification on plant genomes irrespective of plant species. An algorithm for potential motif extraction and a feature importance analysis procedure are two by products of this research. Web tool for this research can be found at: https://cutt.ly/dgp3QTR.

i6mA-VC: A Multi-Classifier Voting Method for the Computational Identification of DNA N6-methyladenine Sites

DNA N6-methyladenine (6 mA), as an essential component of epigenetic modification, cannot be neglected in genetic regulation mechanism. The efficient and accurate prediction of 6 mA sites is beneficial to the development of biological genetics. Biochemical experimental methods are considered to be time-consuming and laborious. Most of the established machine learning methods have a single dataset. Although some of them have achieved cross-species prediction, their results are not satisfactory. Therefore, we designed a novel statistical model called i6mA-VC to improve the accuracy for 6 mA sites. On the one hand, kmer and binary encoding are applied to extract features, and then gradient boosting decision tree (GBDT) embedded method is applied as the feature selection strategy. On the other hand, DNA sequences are represented by vectors through the feature extraction method of ring-function-hydrogen-chemical properties (RFHCP) and the feature selection strategy of ExtraTree. After fusing the two optimal features, a voting classifier based on gradient boosting decision tree (GBDT), light gradient boosting machine (LightGBM) and multilayer perceptron classifier (MLPC) is constructed for final classification and prediction. The accuracy of Rice dataset and M.musculus dataset with five-fold cross-validation are 0.888 and 0.967, respectively. The cross-species dataset is selected as independent testing dataset, and the accuracy reaches 0.848. Through rigorous experiments, it is demonstrated that the proposed predictor is convincing and applicable. The development of i6mA-VC predictor will become an effective way for the recognition of N6-methyladenine sites, and it will also be beneficial for biological geneticists to further study gene expression and DNA modification. In addition, an accessible web-server for i6mA-VC is available from http://www.zhanglab.site/ .

Deep6mA: A deep learning framework for exploring similar patterns in DNA N6-methyladenine sites across different species

N6-methyladenine (6mA) is an important DNA modification form associated with a wide range of biological processes. Identifying accurately 6mA sites on a genomic scale is crucial for under-standing of 6mA’s biological functions. However, the existing experimental techniques for detecting 6mA sites are cost-ineffective, which implies the great need of developing new computational methods for this problem. In this paper, we developed, without requiring any prior knowledge of 6mA and manually crafted sequence features, a deep learning framework named Deep6mA to identify DNA 6mA sites, and its performance is superior to other DNA 6mA prediction tools. Specifically, the 5-fold cross-validation on a benchmark dataset of rice gives the sensitivity and specificity of Deep6mA as 92.96% and 95.06%, respectively, and the overall prediction accuracy is 94%. Importantly, we find that the sequences with 6mA sites share similar patterns across different species. The model trained with rice data predicts well the 6mA sites of other three species: Arabidopsis thaliana, Fragaria vesca and Rosa chinensis with a prediction accuracy over 90%. In addition, we find that (1) 6mA tends to occur at GAGG motifs, which means the sequence near the 6mA site may be conservative; (2) 6mA is enriched in the TATA box of the promoter, which may be the main source of its regulating downstream gene expression.

DNA N6 methyladenine (6mA) is a newly recognized methylation modification in eukaryotes. It exists widely and conservatively in organisms, and its modification level changes dynamically in the whole life cycle. This study proposes an algorithm based on a deep learning framework including LSTM and CNN to predict 6mA sites. The results showed that our method could accurately predict the 6mA sites in different species, which means DNA sub-sequences containing 6mA sites among species have certain conservation. Importantly, we found that 6mA methylation in most different species is more likely to occur on the GAGG motif. In addition, we also found that 6mA is rich in the promoter’s TATA box, which may be a mechanism of regulating downstream gene expression.

Selection and Characterization of Mutants Defective in DNA Methylation in Neurospora crassa

DNA methylation, a prototypical epigenetic modification implicated in gene silencing, occurs in many eukaryotes and plays a significant role in the etiology of diseases such as cancer. The filamentous fungus Neurospora crassa places DNA methylation at regions of constitutive heterochromatin such as in centromeres and in other A:T-rich regions of the genome, but this modification is dispensable for normal growth and development. This and other features render N. crassa an excellent model to genetically dissect elements of the DNA methylation pathway. We implemented a forward genetic selection on a massive scale, utilizing two engineered antibiotic-resistance genes silenced by DNA methylation, to isolate mutants d efective i n m ethylation (dim). Hundreds of potential mutants were characterized, yielding a rich collection of informative alleles of 11 genes important for DNA methylation, most of which were already reported. In parallel, we characterized the pairwise interactions in nuclei of the DCDC, the only histone H3 lysine 9 methyltransferase complex in Neurospora, including those between the DIM-5 catalytic subunit and other complex members. We also dissected the N- and C-termini of the key protein DIM-7, required for DIM-5 histone methyltransferase localization and activation. Lastly, we identified two alleles of a novel gene, dim-10 - a homolog of Clr5 in Schizosaccharomyces pombe - that is not essential for DNA methylation, but is necessary for repression of the antibiotic-resistance genes used in the selection, which suggests that both DIM-10 and DNA methylation promote silencing of constitutive heterochromatin.

DNA methylation methods: Global DNA methylation and methylomic analyses

DNA methylation provides a pivotal layer of epigenetic regulation in eukaryotes that has significant involvement for numerous biological processes in health and disease. The function of methylation of cytosine bases in DNA was originally proposed as a "silencing" epigenetic marker and focused on promoter regions of genes for decades. Improved technologies and accumulating studies have been extending our understanding of the roles of DNA methylation to various genomic contexts including gene bodies, repeat sequences and transcriptional start sites. The demand for comprehensively describing DNA methylation patterns spawns a diversity of DNA methylation profiling technologies that target its genomic distribution. These approaches have enabled the measurement of cytosine methylation from specific loci at restricted regions to single-base-pair resolution on a genome-scale level. In this review, we discuss the different DNA methylation analysis technologies primarily based on the initial treatments of DNA samples: bisulfite conversion, endonuclease digestion and affinity enrichment, involving methodology evolution, principles, applications, and their relative merits. This review may offer referable information for the selection of various platforms for genome-wide analysis of DNA methylation.

DNA6mA-MINT: DNA-6mA Modification Identification Neural Tool

DNA N6-methyladenine (6mA) is part of numerous biological processes including DNA repair, DNA replication, and DNA transcription. The 6mA modification sites hold a great impact when their biological function is under consideration. Research in biochemical experiments for this purpose is carried out and they have demonstrated good results. However, they proved not to be a practical solution when accessed under cost and time parameters. This led researchers to develop computational models to fulfill the requirement of modification identification. In consensus, we have developed a computational model recommended by Chou's 5-steps rule. The Neural Network (NN) model uses convolution layers to extract the high-level features from the encoded binary sequence. These extracted features were given an optimal interpretation by using a Long Short-Term Memory (LSTM) layer. The proposed architecture showed higher performance compared to state-of-the-art techniques. The proposed model is evaluated on Mus musculus, Rice, and "Combined-species" genomes with 5- and 10-fold cross-validation. Further, with access to a user-friendly web server, publicly available can be accessed freely.

A Review of epigenetics in psychiatry: focus on environmental risk factors

Epigenetic modifications play a key role in development and cell type specificity. These modifications seem to be particularly critical for brain development, where mutations in epigenetic enzymes have been associated with neurodevelopmental disorders as well as with the function of post-mitotic neurons. Epigenetic modifications can be influenced by genetic and environmental factors, both known major risk factors for psychiatric disorders. Epigenetic modifications may thus be an important mediator of the effects of genetic and environmental risk factors on cell function.

This review summarizes the different types of epigenetic regulation and then focuses on the mechanisms transducing environmental signals, especially adverse life events that are major risk factors for psychiatric disorders, into lasting epigenetic changes. This is followed by examples of how the environment can induce epigenetic changes that relate to the risk of psychiatric disorders.

Evolutionarily ancient BAH-PHD protein mediates Polycomb silencing

Methylation of histone H3 lysine 27 (H3K27) is widely recognized as a transcriptionally repressive chromatin modification but the mechanism of repression remains unclear. We devised and implemented a forward genetic scheme to identify factors required for H3K27 methylation-mediated silencing in the filamentous fungus Neurospora crassa and identified a bromo-adjacent homology (BAH)-plant homeodomain (PHD)-containing protein, EPR-1 (effector of polycomb repression 1; NCU07505). EPR-1 associates with H3K27-methylated chromatin, and loss of EPR-1 de-represses H3K27-methylated genes without loss of H3K27 methylation. EPR-1 is not fungal-specific; orthologs of EPR-1 are present in a diverse array of eukaryotic lineages, suggesting an ancestral EPR-1 was a component of a primitive Polycomb repression pathway.

Identification of DNA N6-methyladenine sites by integration of sequence features

Background

An increasing number of nucleic acid modifications have been profiled with the development of sequencing technologies. DNA N⁶-methyladenine (6mA), which is a prevalent epigenetic modification, plays important roles in a series of biological processes. So far, identification of DNA 6mA relies primarily on time-consuming and expensive experimental approaches. However, in silico methods can be implemented to conduct preliminary screening to save experimental resources and time, especially given the rapid accumulation of sequencing data.

Results

In this study, we constructed a 6mA predictor, p6mA, from a series of sequence-based features, including physicochemical properties, position-specific triple-nucleotide propensity (PSTNP), and electron–ion interaction pseudopotential (EIIP). We performed maximum relevance maximum distance (MRMD) analysis to select key features and used the Extreme Gradient Boosting (XGBoost) algorithm to build our predictor. Results demonstrated that p6mA outperformed other existing predictors using different datasets.

Conclusions

p6mA can predict the methylation status of DNA adenines, using only sequence files. It may be used as a tool to help the study of 6mA distribution pattern. Users can download it from https://github.com/Konglab404/p6mA.

DNA N6-Methyladenine Modification in Plant Genomes-A Glimpse into Emerging Epigenetic Code

N6-methyladenine (6mA) is a DNA base modification at the 6th nitrogen position; recently, it has been resurfaced as a potential reversible epigenetic mark in eukaryotes. Despite its existence, 6mA was considered to be absent due to its undetectable level. However, with the new advancements in methods, considerable 6mA distribution is identified across the plant genome. Unlike 5-methylcytosine (5mC) in the gene promoter, 6mA does not have a definitive role in repression but is exposed to have divergent regulation in gene expression. Though 6mA information is less known, the available evidences suggest its function in plant development, tissue differentiation, and regulations in gene expression. The current review article emphasizes the research advances in DNA 6mA modifications, identification, available databases, analysis tools and its significance in plant development, cellular functions and future perspectives of research.

A Bioinformatics Tool for the Prediction of DNA N6-Methyladenine Modifications Based on Feature Fusion and Optimization Protocol

DNA N⁶-methyladenine (6mA) is closely involved with various biological processes. Identifying the distributions of 6mA modifications in genome-scale is of great significance to in-depth understand the functions. In recent years, various experimental and computational methods have been proposed for this purpose. Unfortunately, existing methods cannot provide accurate and fast 6mA prediction. In this study, we present 6mAPred-FO, a bioinformatics tool that enables researchers to make predictions based on sequences only. To sufficiently capture the characteristics of 6mA sites, we integrate the sequence-order information with nucleotide positional specificity information for feature encoding, and further improve the feature representation capacity by analysis of variance-based feature optimization protocol. The experimental results show that using this feature protocol, we can significantly improve the predictive performance. Via further feature analysis, we found that the sequence-order information and positional specificity information are complementary to each other, contributing to the performance improvement. On the other hand, the improvement is also due to the use of the feature optimization protocol, which is capable of effectively capturing the most informative features from the original feature space. Moreover, benchmarking comparison results demonstrate that our 6mAPred-FO outperforms several existing predictors. Finally, we establish a web-server that implements the proposed method for convenience of researchers' use, which is currently available at http://server.malab.cn/6mAPred-FO.

Chromatin-Mediated Regulation of Genome Plasticity in Human Fungal Pathogens

Human fungal pathogens, such as Candida albicans, Aspergillus fumigatus and Cryptococcus neoformans, are a public health problem, causing millions of infections and killing almost half a million people annually. The ability of these pathogens to colonise almost every organ in the human body and cause life-threating infections relies on their capacity to adapt and thrive in diverse hostile host-niche environments. Stress-induced genome instability is a key adaptive strategy used by human fungal pathogens as it increases genetic diversity, thereby allowing selection of genotype(s) better adapted to a new environment. Heterochromatin represses gene expression and deleterious recombination and could play a key role in modulating genome stability in response to environmental changes. However, very little is known about heterochromatin structure and function in human fungal pathogens. In this review, I use our knowledge of heterochromatin structure and function in fungal model systems as a road map to review the role of heterochromatin in regulating genome plasticity in the most common human fungal pathogens: Candida albicans, Aspergillus fumigatus and Cryptococcus neoformans.

SNNRice6mA: A Deep Learning Method for Predicting DNA N6-Methyladenine Sites in Rice Genome

DNA N6-methyladenine (6mA) is an important epigenetic modification, which is involved in many biology regulation processes. An accurate and reliable method for 6mA identification can help us gain a better insight into the regulatory mechanism of the modification. Although many experimental techniques have been proposed to identify 6mA sites genome-wide, these techniques are time consuming and laborious. Recently, several machine learning methods have been developed to identify 6mA sites genome-wide. However, there is room for the improvement on their performance for predicting 6mA sites in rice genome. In this paper, we developed a simple and lightweight deep learning model to identify DNA 6mA sites in rice genome. Our model needs no prior knowledge of 6mA or manually crafted sequence feature. We built our model based on two rice 6mA benchmark datasets. Our method got an average prediction accuracy of ∼93% and ∼92% on the two datasets we used. We compared our method with existing 6mA prediction tools. The comparison results show that our model outperforms the state-of-the-art methods.

On the Importance to Acknowledge Transposable Elements in Epigenomic Analyses

Eukaryotic genomes comprise a large proportion of repeated sequences, an important fraction of which are transposable elements (TEs). TEs are mobile elements that have a significant impact on genome evolution and on gene functioning. Although some TE insertions could provide adaptive advantages to species, transposition is a highly mutagenic event that has to be tightly controlled to ensure its viability. Genomes have evolved sophisticated mechanisms to control TE activity, the most important being epigenetic silencing. However, the epigenetic control of TEs can also affect genes located nearby that can become epigenetically regulated. It has been proposed that the combination of TE mobilization and the induced changes in the epigenetic landscape could allow a rapid phenotypic adaptation to global environmental changes. In this review, we argue the crucial need to take into account the repeated part of genomes when studying the global impact of epigenetic modifications on an organism. We emphasize more particularly why it is important to carefully consider TEs and what bioinformatic tools can be used to do so.

Genome-wide DNA methylation profiles in Tibetan and Yorkshire pigs under high-altitude hypoxia

Background

Tibetan pigs, which inhabit the Tibetan Plateau, exhibit distinct phenotypic and physiological characteristics from those of lowland pigs and have adapted well to the extreme conditions at high altitude. However, the genetic and epigenetic mechanisms of hypoxic adaptation in animals remain unclear.

Methods

Whole-genome DNA methylation data were generated for heart tissues of Tibetan pigs grown in the highland (TH, n = 4) and lowland (TL, n = 4), as well as Yorkshire pigs grown in the highland (YH, n = 4) and lowland (YL, n = 4), using methylated DNA immunoprecipitation sequencing.

Results

We obtained 480 million reads and detected 280679, 287224, 259066, and 332078 methylation enrichment peaks in TH, YH, TL, and YL, respectively. Pairwise TH vs. YH, TL vs. YL, TH vs. TL, and YH vs. YL comparisons revealed 6829, 11997, 2828, and 1286 differentially methylated regions (DMRs), respectively. These DMRs contained 384, 619, 192, and 92 differentially methylated genes (DMGs), respectively. DMGs that were enriched in the hypoxia-inducible factor 1 signaling pathway and pathways involved in cancer and hypoxia-related processes were considered to be important candidate genes for high-altitude adaptation in Tibetan pigs.

Conclusions

This study elucidates the molecular and epigenetic mechanisms involved in hypoxic adaptation in pigs and may help further understand human hypoxia-related diseases.

i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome

Motivation

DNA N6-methyladenine (6mA) is associated with a wide range of biological processes. Since the distribution of 6mA site in the genome is non-random, accurate identification of 6mA sites is crucial for understanding its biological functions. Although experimental methods have been proposed for this regard, they are still cost-ineffective for detecting 6mA site in genome-wide scope. Therefore, it is desirable to develop computational methods to facilitate the identification of 6mA site.

Results

In this study, a computational method called i6mA-Pred was developed to identify 6mA sites in the rice genome, in which the optimal nucleotide chemical properties obtained by the using feature selection technique were used to encode the DNA sequences. It was observed that the i6mA-Pred yielded an accuracy of 83.13% in the jackknife test. Meanwhile, the performance of i6mA-Pred was also superior to other methods.

Availability and implementation

A user-friendly web-server, i6mA-Pred is freely accessible at http://lin-group.cn/server/i6mA-Pred.

Aberrant DNA methylation profile of hepatitis B virus infection

We aimed to study the aberrant DNA methylation profile associated with hepatitis B virus (HBV) infection and, to identify key genes and pathways associated with the HBV infection stage. A total of 54 antiviral treatment-naïve HBV-infected patients and six healthy controls were included. Genome-wide methylated DNA immunoprecipitation analysis was performed, as previously described, after which the chip data were preprocessed. Subsequently, Cytoscape software was used for the construction of a protein-protein interaction network, and a database for annotation, visualization, and integrated discovery software was used to conduct functional enrichment analysis. A total of 711 794 CpGs were obtained after data quality control, among which 152 780, 113 814, 90 747, and 175 868 CpGs showed differential methylation in acute hepatitis B (AHB) vs control, total-C vs control, CH1 vs CA1, and AHB vs total-C, respectively. Furthermore, RIPK3, PRDM10, JUN, and SNAI1 were at the center of the four associated networks, respectively. Differential methylated genes differentially methylated in these four comparisons were significantly enriched with olfactory transduction; positive regulation of transport; negative regulation of protein amino acid phosphorylation (eg, JUN), phosphorylation, phosphorus metabolic process, and phosphate metabolic process; and programmed cell death, respectively. RIPK3, PRDM10, JUN, and SNAI1 as well as olfactory transduction, positive regulation of transport, negative regulation of phosphorylation, and programmed cell death are important for the transformation associated with HBV infection stage. Moreover, JUN may be involved in HBV infection, mainly via the negative regulation of amino acid phosphorylation.

The role of TET-mediated DNA hydroxymethylation in prostate cancer

Ten-eleven translocation (TET) proteins are recently characterized dioxygenases that regulate demethylation by oxidizing 5-methylcytosine to 5-hydroxymethylcytosine and further derivatives. The recent finding that 5hmC is also a stable and independent epigenetic modification indicates that these proteins play an important role in diverse physiological and pathological processes such as neural and tumor development. Both the genomic distribution of (hydroxy)methylation and the expression and activity of TET proteins are dysregulated in a wide range of cancers including prostate cancer. Up to now it is still unknown how changes in TET and 5(h)mC profiles are related to the pathogenesis of prostate cancer. In this review, we explore recent advances in the current understanding of how TET expression and function are regulated in development and cancer. Furthermore, we look at the impact on 5hmC in prostate cancer and the potential underlying mechanisms. Finally, we tried to summarize the latest techniques for detecting and quantifying global and locus-specific 5hmC levels of genomic DNA.

DNA methylome profiling at single-base resolution through bisulfite sequencing of 5mC-immunoprecipitated DNA

Background

Detection of DNA methylome at single-base resolution is a significant challenge but promises to shed considerable light on human disease etiology. Current technologies could not detect DNA methylation genome-wide at single-base resolution with small amount of sequencing data and could not avoid detecting the methylation of repetitive elements which are considered as “junk DNA”.

Methods

In this study, we have developed a novel DNA methylome profiling technology named MB-seq with its ability to identify genome-wide 5mC and quantify DNA methylation levels by introduced an assistant adapter AluI-linker This linker can be ligated to sonicated DNA and then be digested after the bisulfite treatment and amplification, which has no effect of MeDIP enrichment. Because many researchers are interested in investigating the methylation of functional regions such as promoters and gene bodies, we have also developed a novel alternative method named MRB-seq, which can be used to investigate the DNA methylation of functional regions by removing the repeats with Cot-1 DNA.

Results

In this study, we have developed MB-seq, a novel DNA methylome profiling technology combining MeDIP-seq with bisulfite conversion, which can precisely detect the 5mC sites and determine their DNA methylation level at single-base resolution in a cost-effective way. In addition, we have developed a new alternative method, MRB-seq (MeDIP-repetitive elements removal-bisulfite sequencing), which interrogates 5mCs in functional regions by depleting nearly half of repeat fragments enriched by MeDIP. Comparing MB-seq and MRB-seq to whole-genome BS-seq using the same batch of DNA from YH peripheral blood mononuclear cells. We found that the sequencing data of MB-seq and MRB-seq almost reaches saturation after generating 7–8 Gbp data, whereas BS-seq requires about 100 Gbp data to achieve the same effect. In comparison to MeDIP-seq and BS-seq, MB-seq offers several key advantages, including single-base resolution, discriminating the methylated sites within a CpG and non-CpG pattern and overcoming the false positive of MeDIP-seq due to the non-specific binding of 5-methylcytidine antibody to genomic fragments.

Conclusion

Our novel developed method MB-seq can accelerate the decoding process of DNA methylation mechanism in human diseases because it requires 7–8 Gbp data to measure human methylome with enough coverage and sequencing depth, affording it a direct and practical application in the study of multiple samples. In addition, we have also provided a novel alternative MRB-seq method, which removes most repetitive sequences and allows researchers to genome-wide characterize DNA methylation of functional regions.

Electronic supplementary material

The online version of this article (10.1186/s12896-017-0409-7) contains supplementary material, which is available to authorized users.

iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC

N⁶-methyladenine (6mA) is one kind of post-replication modification (PTM or PTRM) occurring in a wide range of DNA sequences. Accurate identification of its sites will be very helpful for revealing the biological functions of 6mA, but it is time-consuming and expensive to determine them by experiments alone. Unfortunately, so far, no bioinformatics tool is available to do so. To fill in such an empty area, we have proposed a novel predictor called iDNA6mA-PseKNC that is established by incorporating nucleotide physicochemical properties into Pseudo K-tuple Nucleotide Composition (PseKNC). It has been observed via rigorous cross-validations that the predictor's sensitivity (Sn), specificity (Sp), accuracy (Acc), and stability (MCC) are 93%, 100%, 96%, and 0.93, respectively. For the convenience of most experimental scientists, a user-friendly web server for iDNA6mA-PseKNC has been established at http://lin-group.cn/server/iDNA6mA-PseKNC, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved.

Application of the Cre/lox System to Construct Auxotrophic Markers for Quantitative Genetic Analyses in Fusarium graminearum

The bacteriophage P1 Cre/lox system has been utilized in diverse fungi for marker recycling and exchange, generation of targeted chromosome translocations, and targeted deletion of interstitial chromosome segments. Here we show the application of this tool in the wheat and maize pathogen, Fusarium graminearum. We explored three different ways to introduce Cre into strains with floxed genes, namely transformation with an episomal or integrative plasmid (pLC28), fusion of protoplasts of strains carrying floxed genes with strains expressing Cre by forcing heterokaryons, and crosses between strains with floxed genes and strains expressing Cre to isolate progeny in which the target genes had been deleted during the cross. We used this system for the construction of strains bearing auxotrophic markers that were generated by gene replacement with positively selectable markers followed by Cre-mediated marker excision. In addition, updated protocols for transformation and crosses for F. graminearum are provided. In combination, strains and tools developed here add to the arsenal of methods that can be used to carry out molecular genetics with F. graminearum.

Recognition of Oxidized 5-Methylcytosine Derivatives in DNA by Natural and Engineered Protein Scaffolds

Methylation of genomic cytosine to 5-methylcytosine is a central regulatory element of mammalian gene expression with important roles in development and disease. 5-methylcytosine can be actively reversed to cytosine via oxidation to 5-hydroxymethyl-, 5-formyl-, and 5-carboxylcytosine by ten-eleven-translocation dioxygenases and subsequent base excision repair or replication-dependent dilution. Moreover, the oxidized 5-methylcytosine derivatives are potential epigenetic marks with unique biological roles. Key to a better understanding of these roles are insights into the interactions of the nucleobases with DNA-binding protein scaffolds: Natural scaffolds involved in transcription, 5-methylcytosine-reading and -editing as well as general chromatin organization can be selectively recruited or repulsed by oxidized 5-methylcytosines, forming the basis of their biological functions. Moreover, designer protein scaffolds engineered for the selective recognition of oxidized 5-methylcytosines are valuable tools to analyze their genomic levels and distribution. Here, we review recent structural and functional insights into the molecular recognition of oxidized 5-methylcytosine derivatives in DNA by selected protein scaffolds.

Methylated site display (MSD)-AFLP, a sensitive and affordable method for analysis of CpG methylation profiles

Background

It has been pointed out that environmental factors or chemicals can cause diseases that are developmental in origin. To detect abnormal epigenetic alterations in DNA methylation, convenient and cost-effective methods are required for such research, in which multiple samples are processed simultaneously. We here present methylated site display (MSD), a unique technique for the preparation of DNA libraries. By combining it with amplified fragment length polymorphism (AFLP) analysis, we developed a new method, MSD-AFLP.

Results

Methylated site display libraries consist of only DNAs derived from DNA fragments that are CpG methylated at the 5′ end in the original genomic DNA sample. To test the effectiveness of this method, CpG methylation levels in liver, kidney, and hippocampal tissues of mice were compared to examine if MSD-AFLP can detect subtle differences in the levels of tissue-specific differentially methylated CpGs. As a result, many CpG sites suspected to be tissue-specific differentially methylated were detected. Nucleotide sequences adjacent to these methyl-CpG sites were identified and we determined the methylation level by methylation-sensitive restriction endonuclease (MSRE)-PCR analysis to confirm the accuracy of AFLP analysis. The differences of the methylation level among tissues were almost identical among these methods. By MSD-AFLP analysis, we detected many CpGs showing less than 5% statistically significant tissue-specific difference and less than 10% degree of variability. Additionally, MSD-AFLP analysis could be used to identify CpG methylation sites in other organisms including humans.

Conclusion

MSD-AFLP analysis can potentially be used to measure slight changes in CpG methylation level. Regarding the remarkable precision, sensitivity, and throughput of MSD-AFLP analysis studies, this method will be advantageous in a variety of epigenetics-based research.

Electronic supplementary material

The online version of this article (doi:10.1186/s12867-017-0083-2) contains supplementary material, which is available to authorized users.

The Neurospora Transcription Factor ADV-1 Transduces Light Signals and Temporal Information to Control Rhythmic Expression of Genes Involved in Cell Fusion

Light and the circadian clock have a profound effect on the biology of organisms through the regulation of large sets of genes. Toward understanding how light and the circadian clock regulate gene expression, we used genome-wide approaches to identify the direct and indirect targets of the light-responsive and clock-controlled transcription factor ADV-1 in Neurospora crassa A large proportion of ADV-1 targets were found to be light- and/or clock-controlled, and enriched for genes involved in development, metabolism, cell growth, and cell fusion. We show that ADV-1 is necessary for transducing light and/or temporal information to its immediate downstream targets, including controlling rhythms in genes critical to somatic cell fusion. However, while ADV-1 targets are altered in predictable ways in Δadv-1 cells in response to light, this is not always the case for rhythmic target gene expression. These data suggest that a complex regulatory network downstream of ADV-1 functions to generate distinct temporal dynamics of target gene expression relative to the central clock mechanism.

NGSmethDB 2017: enhanced methylomes and differential methylation

The 2017 update of NGSmethDB stores whole genome methylomes generated from short-read data sets obtained by bisulfite sequencing (WGBS) technology. To generate high-quality methylomes, stringent quality controls were integrated with third-part software, adding also a two-step mapping process to exploit the advantages of the new genome assembly models. The samples were all profiled under constant parameter settings, thus enabling comparative downstream analyses. Besides a significant increase in the number of samples, NGSmethDB now includes two additional data-types, which are a valuable resource for the discovery of methylation epigenetic biomarkers: (i) differentially methylated single-cytosines; and (ii) methylation segments (i.e. genome regions of homogeneous methylation). The NGSmethDB back-end is now based on MongoDB, a NoSQL hierarchical database using JSON-formatted documents and dynamic schemas, thus accelerating sample comparative analyses. Besides conventional database dumps, track hubs were implemented, which improved database access, visualization in genome browsers and comparative analyses to third-part annotations. In addition, the database can be also accessed through a RESTful API. Lastly, a Python client and a multiplatform virtual machine allow for program-driven access from user desktop. This way, private methylation data can be compared to NGSmethDB without the need to upload them to public servers. Database website: http://bioinfo2.ugr.es/NGSmethDB.

Antisense transcription licenses nascent transcripts to mediate transcriptional gene silencing

In this study, Dang et al. use Neurospora to demonstrate a critical role for transcription kinetics in long noncoding RNA-mediated epigenetic modifications and identify ERI-1 as an important regulator of cotranscriptional gene silencing and post-transcriptional RNA metabolism.

In eukaryotes, antisense transcription can regulate sense transcription by induction of epigenetic modifications. We showed previously that antisense transcription triggers Dicer-independent siRNA (disiRNA) production and disiRNA locus DNA methylation (DLDM) in Neurospora crassa. Here we show that the conserved exonuclease ERI-1 (enhanced RNAi-1) is a critical component in this process. Antisense transcription and ERI-1 binding to target RNAs are necessary and sufficient to trigger DLDM. Convergent transcription causes stalling of RNA polymerase II during transcription, which permits ERI-1 to bind nascent RNAs in the nucleus and recruit a histone methyltransferase complex that catalyzes chromatin modifications. Furthermore, we show that, in the cytoplasm, ERI-1 targets hundreds of transcripts from loci without antisense transcription to regulate RNA stability. Together, our results demonstrate a critical role for transcription kinetics in long noncoding RNA-mediated epigenetic modifications and identify ERI-1 as an important regulator of cotranscriptional gene silencing and post-transcriptional RNA metabolism.

Histone H1 Limits DNA Methylation in Neurospora crassa

Histone H1 variants, known as linker histones, are essential chromatin components in higher eukaryotes, yet compared to the core histones relatively little is known about their in vivo functions. The filamentous fungus Neurospora crassa encodes a single H1 protein that is not essential for viability. To investigate the role of N. crassa H1, we constructed a functional FLAG-tagged H1 fusion protein and performed genomic and molecular analyses. Cell fractionation experiments showed that H1-3XFLAG is a chromatin binding protein. Chromatin-immunoprecipitation combined with sequencing (ChIP-seq) revealed that H1-3XFLAG is globally enriched throughout the genome with a subtle preference for promoters of expressed genes. In mammals, the stoichiometry of H1 impacts nucleosome repeat length. To determine if H1 impacts nucleosome occupancy or nucleosome positioning in N. crassa, we performed micrococcal nuclease digestion in the wild-type and the ΔhH1 strain followed by sequencing (MNase-seq). Deletion of hH1 did not significantly impact nucleosome positioning or nucleosome occupancy. Analysis of DNA methylation by whole-genome bisulfite sequencing (MethylC-seq) revealed a modest but global increase in DNA methylation in the ΔhH1 mutant. Together, these data suggest that H1 acts as a nonspecific chromatin binding protein that can limit accessibility of the DNA methylation machinery in N. crassa.

6-Substituted 2-Aminopurine-2'-deoxyribonucleoside 5'-Triphosphates that Trace Cytosine Methylation

Gene expression is extensively regulated by the occurrence and distribution of the epigenetic marker 2′‐deoxy 5‐methylcytosine (5mC) in genomic DNA. Because of its effects on tumorigenesis there is an important link to human health. In addition, detection of 5mC can serve as an outstanding biomarker for diagnostics as well as for disease therapy. Our previous studies have already shown that, by processing O⁶‐alkylated 2′‐deoxyguanosine triphosphate (dGTP) analogues, DNA polymerases are able to sense the presence of a single 5mC unit in a template. Here we present the synthesis and evaluation of an extended toolbox of 6‐substituted 2‐aminopurine‐2′‐deoxyribonucleoside 5′‐triphosphates modified at position 6 with various functionalities. We found that sensing of 5‐methylation by this class of nucleotides is more general, not being restricted to O⁶‐alkyl modification of dGTP but also applying to other functionalities.

A Molecular Perspective on Procedures and Outcomes with Assisted Reproductive Technologies

The emerging association of assisted reproductive technologies with adverse perinatal outcomes has prompted the in-depth examination of clinical and laboratory protocols and procedures and their possible effects on epigenetic regulatory mechanism(s). The application of various approaches to study epigenetic regulation to problems in reproductive medicine has the potential to identify relative risk indicators for particular conditions, diagnostic biomarkers of disease state, and prognostic indicators of outcome. Moreover, when applied genome-wide, these techniques are likely to find novel pathways of disease pathogenesis and identify new targets for intervention. The analysis of DNA methylation, histone modifications, transcription factors, enhancer binding and other chromatin proteins, DNase-hypersensitivity and, micro- and other noncoding RNAs all provide overlapping and often complementary snapshots of chromatin structure and resultant "gene activity." In terms of clinical application, the predictive power and utility of epigenetic information will depend on the power of individual techniques to discriminate normal levels of interindividual variation from variation linked to a disease state. At present, quantitative analysis of DNA methylation at multiple loci seems likely to hold the greatest promise for achieving the level of precision, reproducibility, and throughput demanded in a clinical setting.

Modified Nucleotides for Discrimination between Cytosine and the Epigenetic Marker 5-Methylcytosine

5‐Methyl‐2′‐deoxycytosine, the most common epigenetic marker of DNA in eukaryotic cells, plays a key role in gene regulation and affects various cellular processes such as development and carcinogenesis. Therefore, the detection of 5mC can serve as an important biomarker for diagnostics. Here we describe that modified dGTP analogues as well as modified primers are able to sense the presence or absence of a single methylation of C, even though this modification does not interfere directly with Watson–Crick nucleobase pairing. By screening several modified nucleotide scaffolds, O⁶‐modified 2′‐deoxyguanosine analogues were identified as discriminating between C and 5mC. These modified nucleotides might find application in site‐specific 5mC detection, for example, through real‐time PCR approaches.

N6-Methyladenine: A Conserved and Dynamic DNA Mark

Chromatin, consisting of deoxyribonucleic acid (DNA) wrapped around histone proteins, facilitates DNA compaction and allows identical DNA codes to confer many different cellular phenotypes. This biological versatility is accomplished in large part by posttranslational modifications to histones and chemical modifications to DNA. These modifications direct the cellular machinery to expand or compact specific chromatin regions and mark regions of the DNA as important for cellular functions. While each of the four bases that make up DNA can be modified (Iyer et al. 2011), this chapter will focus on methylation of the sixth position on adenines (6mA), as this modification has been poorly characterized in recently evolved eukaryotes, but shows promise as a new conserved layer of epigenetic regulation. 6mA was previously thought to be restricted to unicellular organisms, but recent work has revealed its presence in metazoa. Here, we will briefly describe the history of 6mA, examine its evolutionary conservation, and evaluate the current methods for detecting 6mA. We will discuss the enzymes that bind and regulate this mark and finally examine known and potential functions of 6mA in eukaryotes.

The LSH/DDM1 Homolog MUS-30 Is Required for Genome Stability, but Not for DNA Methylation in Neurospora crassa

LSH/DDM1 enzymes are required for DNA methylation in higher eukaryotes and have poorly defined roles in genome maintenance in yeast, plants, and animals. The filamentous fungus Neurospora crassa is a tractable system that encodes a single LSH/DDM1 homolog (NCU06306). We report that the Neurospora LSH/DDM1 enzyme is encoded by mutagen sensitive-30 (mus-30), a locus identified in a genetic screen over 25 years ago. We show that MUS-30-deficient cells have normal DNA methylation, but are hypersensitive to DNA damaging agents. MUS-30 is a nuclear protein, consistent with its predicted role as a chromatin remodeling enzyme, and levels of MUS-30 are increased following DNA damage. MUS-30 co-purifies with Neurospora WDR76, a homolog of yeast Changed Mutation Rate-1 and mammalian WD40 repeat domain 76. Deletion of wdr76 rescued DNA damage-hypersensitivity of Δmus-30 strains, demonstrating that the MUS-30-WDR76 interaction is functionally important. DNA damage-sensitivity of Δmus-30 is partially suppressed by deletion of methyl adenine glycosylase-1, a component of the base excision repair machinery (BER); however, the rate of BER is not affected in Δmus-30 strains. We found that MUS-30-deficient cells are not defective for DSB repair, and we observed a negative genetic interaction between Δmus-30 and Δmei-3, the Neurospora RAD51 homolog required for homologous recombination. Together, our findings suggest that MUS-30, an LSH/DDM1 homolog, is required to prevent DNA damage arising from toxic base excision repair intermediates. Overall, our study provides important new information about the functions of the LSH/DDM1 family of enzymes.

Global Methylation Patterns and Their Relationship with Gene Expression and Small RNA in Rice Lines with Different Ploidy

Whole genome duplication (WGD) is a major force in angiosperm evolution. Whether WGD is accompanied by the evolution of epigenetic regulators remains to be explored. Here we investigate whole genome methylation, gene expression, and miRNA regulation among monoploid, diploid, and triploid rice plants isolated from a twin-seedling population. The DNA methylation patterns in the three different ploidy plants were highly similar, with DNA methylation primarily enriched in the promoters. We examined the methylation of single genes and detected around 25,500 methylated genes, of which 22,751 were methylated in all three lines. Significantly divergent DNA methylation patterns between each pair of three lines were only detected in 64 genes, though more genes were found to exhibit differential expression. Analysis of DNA methylation and expression patterns showed that higher DNA methylation levels upstream of the transcription start sites are correlated with higher levels of expression of related genes; whereas higher DNA methylation levels in gene body regions are correlated with lower levels of expression. We also carried out high-throughput sequencing of small RNA libraries and identified 36 new miRNAs. These miRNAs have different expression levels depending on the ploidy.