Epigenetically modified N6-methyladenine inhibits DNA replication by human DNA polymerase η

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.

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.

A quantitative model predicts how m6A reshapes the kinetic landscape of nucleic acid hybridization and conformational transitions

N6-methyladenosine (m6A) is a post-transcriptional modification that controls gene expression by recruiting proteins to RNA sites. The modification also slows biochemical processes through mechanisms that are not understood. Using temperature-dependent (20°C-65°C) NMR relaxation dispersion, we show that m6A pairs with uridine with the methylamino group in the anti conformation to form a Watson-Crick base pair that transiently exchanges on the millisecond timescale with a singly hydrogen-bonded low-populated (1%) mismatch-like conformation in which the methylamino group is syn. This ability to rapidly interchange between Watson-Crick or mismatch-like forms, combined with different syn:anti isomer preferences when paired (~1:100) versus unpaired (~10:1), explains how m6A robustly slows duplex annealing without affecting melting at elevated temperatures via two pathways in which isomerization occurs before or after duplex annealing. Our model quantitatively predicts how m6A reshapes the kinetic landscape of nucleic acid hybridization and conformational transitions, and provides an explanation for why the modification robustly slows diverse cellular processes.

Critical evaluation of web-based DNA N6-methyladenine site prediction tools

Methylation of DNA N6-methyladenosine (6mA) is a type of epigenetic modification that plays pivotal roles in various biological processes. The accurate genome-wide identification of 6mA is a challenging task that leads to understanding the biological functions. For the last 5 years, a number of bioinformatics approaches and tools for 6mA site prediction have been established, and some of them are easily accessible as web application. Nevertheless, the accurate genome-wide identification of 6mA is still one of the challenging works that lead to understanding the biological functions. Especially in practical applications, these tools have implemented diverse encoding schemes, machine learning algorithms and feature selection methods, whereas few systematic performance comparisons of 6mA site predictors have been reported. In this review, 11 publicly available 6mA predictors evaluated with seven different species-specific datasets (Arabidopsis thaliana, Tolypocladium, Diospyros lotus, Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans and Escherichia coli). Of those, few species are close homologs, and the remaining datasets are distant sequences. Our independent, validation tests demonstrated that Meta-i6mA and MM-6mAPred models for A. thaliana, Tolypocladium, S. cerevisiae and D. melanogaster achieved excellent overall performance when compared with their counterparts. However, none of the existing methods were suitable for E. coli, C. elegans and D. lotus. A feasibility of the existing predictors is also discussed for the seven species. Our evaluation provides useful guidelines for the development of 6mA site predictors and helps biologists selecting suitable prediction tools.

DNA adenine methylation in eukaryotes: Enzymatic mark or a form of DNA damage?

6-methyladenine (6mA) is fairly abundant in nuclear DNA of basal fungi, ciliates and green algae. In these organisms, 6mA is maintained near transcription start sites in ApT context by a parental-strand instruction dependent maintenance methyltransferase and is positively associated with transcription. In animals and plants, 6mA levels are high only in organellar DNA. The 6mA levels in nuclear DNA are very low. They are attributable to nucleotide salvage and the activity of otherwise mitochondrial METTL4, and may be considered as a price that cells pay for adenine methylation in RNA and/or organellar DNA. Cells minimize this price by sanitizing dNTP pools to limit 6mA incorporation, and by converting 6mA that has been incorporated into DNA back to adenine. Hence, 6mA in nuclear DNA should be described as an epigenetic mark only in basal fungi, ciliates and green algae, but not in animals and plants.

Mechanisms of mutagenesis induced by DNA lesions: multiple factors affect mutations in translesion DNA synthesis

Environmental mutagens lead to mutagenesis. However, the mechanisms are very complicated and not fully understood. Environmental mutagens produce various DNA lesions, including base-damaged or sugar-modified DNA lesions, as well as epigenetically modified DNA. DNA polymerases produce mutation spectra in translesion DNA synthesis (TLS) through misincorporation of incorrect nucleotides, frameshift deletions, blockage of DNA replication, imbalance of leading- and lagging-strand DNA synthesis, and genome instability. Motif or subunit in DNA polymerases further affects the mutations in TLS. Moreover, protein interactions and accessory proteins in DNA replisome also alter mutations in TLS, demonstrated by several representative DNA replisomes. Finally, in cells, multiple DNA polymerases or cellular proteins collaborate in TLS and reduce in vivo mutagenesis. Summaries and perspectives were listed. This review shows mechanisms of mutagenesis induced by DNA lesions and the effects of multiple factors on mutations in TLS in vitro and in vivo.

i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representation

DNA N⁶-methyladenine (6 mA) is one of the most vital epigenetic modifications and involved in controlling the various gene expression levels. With the avalanche of DNA sequences generated in numerous databases, the accurate identification of 6 mA plays an essential role for understanding molecular mechanisms. Because the experimental approaches are time-consuming and costly, it is desirable to develop a computation model for rapidly and accurately identifying 6 mA. To the best of our knowledge, we first proposed a computational model named i6mA-Fuse to predict 6 mA sites from the Rosaceae genomes, especially in Rosa chinensis and Fragaria vesca. We implemented the five encoding schemes, i.e., mononucleotide binary, dinucleotide binary, k-space spectral nucleotide, k-mer, and electron-ion interaction pseudo potential compositions, to build the five, single-encoding random forest (RF) models. The i6mA-Fuse uses a linear regression model to combine the predicted probability scores of the five, single encoding-based RF models. The resultant species-specific i6mA-Fuse achieved remarkably high performances with AUCs of 0.982 and 0.978 and with MCCs of 0.869 and 0.858 on the independent datasets of Rosa chinensis and Fragaria vesca, respectively. In the F. vesca-specific i6mA-Fuse, the MBE and EIIP contributed to 75% and 25% of the total prediction; in the R. chinensis-specific i6mA-Fuse, Kmer, MBE, and EIIP contribute to 15%, 65%, and 20% of the total prediction. To assist high-throughput prediction for DNA 6 mA identification, the i6mA-Fuse is publicly accessible at https://kurata14.bio.kyutech.ac.jp/i6mA-Fuse/.