RNA modifications play an important role in actively controlling recently created formation in cellular regulation mechanisms, which link them to gene expression and protein. The RNA modifications have numerous alterations, presenting broad glimpses of RNA\'s operations and character. The modification process by the TET enzyme oxidation is the crucial change associated with cytosine hydroxymethylation. The effect of CR is an alteration in specific biochemical ways of the organism, such as gene expression and epigenetic alterations. Traditional laboratory systems that identify 5-hydroxymethylcytosine (5hmC) samples are expensive and time-consuming compared to other methods. To address this challenge, the paper proposed XGB5hmC, a machine learning algorithm based on a robust gradient boosting algorithm (XGBoost), with different residue based formulation methods to identify 5hmC samples. Their results were amalgamated, and six different frequency residue based encoding features were fused to form a hybrid vector in order to enhance model discrimination capabilities. In addition, the proposed model incorporates SHAP (Shapley Additive Explanations) based feature selection to demonstrate model interpretability by highlighting the high contributory features. Among the applied machine learning algorithms, the XGBoost ensemble model using the tenfold cross-validation test achieved improved results than existing state-of-the-art models. Our model reported an accuracy of 89.97%, sensitivity of 87.78%, specificity of 94.45%, F1-score of 0.8934%, and MCC of 0.8764%. This study highlights the potential to provide valuable insights for enhancing medical assessment and treatment protocols, representing a significant advancement in RNA modification analysis.

Epigenetic modifications, particularly RNA methylation and histone alterations, play a crucial role in heredity, development, and disease. Among these, RNA 5-methylcytosine (m5C) is the most prevalent RNA modification in mammalian cells, essential for processes such as ribosome synthesis, translational fidelity, mRNA nuclear export, turnover, and translation. The increasing volume of nucleotide sequences has led to the development of machine learning-based predictors for m5C site prediction. However, these predictors often face challenges related to training data limitations and overfitting due to insufficient external validation. This study introduces m5C-Seq, an ensemble learning approach for RNA modification profiling, designed to address these issues. m5C-Seq employs a meta-classifier that integrates 15 probabilities generated from a novel, large dataset using systematic encoding methods to make final predictions. Demonstrating superior performance compared to existing predictors, m5C-Seq represents a significant advancement in accurate RNA modification profiling. The code and the newly established datasets are made available through GitHub at https://github.com/Z-Abbas/m5C-Seq.

Selective C-H activation on complex biological macromolecules is a key goal in the field of organic chemistry. It requires thermodynamically challenging chemical transformations to be delivered in mild, aqueous conditions. 5-Methylcytosine (5mC) is a fundamentally important epigenetic modification in DNA that has major implications for biology and has emerged as a vital biomarker. Selective functionalisation of 5mC would enable new chemical approaches to tag, detect and map DNA methylation to enhance the study and exploitation of this epigenetic feature. We demonstrate the first example of direct and selective chemical oxidation of 5mC to 5-formylcytosine (5fC) in DNA, employing a photocatalytic system. This transformation was used to selectively tag 5mC. We also provide proof-of-concept for deploying this chemistry for single-base resolution sequencing of 5mC and genetic bases adenine (A), cytosine (C), guanine (G), thymine (T) in DNA on a next-generation sequencing system. This work exemplifies how photocatalysis has the potential to transform the analysis of DNA.

Recent studies have highlighted the significant role of 5-hydroxymethylcytosine (5hmC) in carcinogenesis. However, the specific role of 5hmC in osteosarcoma (OS) remains largely unexplored. The-re-fore, this study aimed to investigate the function of 5hmC and TET3 in OS. In this study, we found a decreased total level of 5hmC in OS tissues. The expression of the TET3 protein was also decreased in OS. Importantly, the decreased levels of TET3 were associated with a decreased disease-free survival (DFS) rate in patients. To investigate the role of TET3 and 5hmC in OS, we manipulated the levels of TET3 in MG-63 cells. Silencing TET3 in these cells resulted in a twofold increase in proliferation. Additio-nally, the level of 5hmC decreased in these cells. Con-versely, over-expression of TET3 in MG-63 cells led to the expected inhibition of proliferation and invasion, accompanied by an increase in 5hmC levels. In conclusion, both 5hmC and TET3 protein levels were decreased in OS. Additionally, the over-expression of TET3 inhibited the proliferation of MG-63 cells, while the suppression of TET3 had the opposite effect. These findings suggest that decreased levels of 5hmC and TET3 may serve as potential markers for OS.

Hepatocellular carcinoma (HCC) is a highly aggressive cancer with a poor prognosis. The molecular mechanisms underlying its development remain unclear. Recent studies have highlighted the crucial role of RNA modifications in HCC progression, which indicates their potential as therapeutic targets and biomarkers for managing HCC. In this review, we discuss the functional role and molecular mechanisms of RNA modifications in HCC through a review and summary of relevant literature, to explore the potential therapeutic agents and biomarkers for diagnostic and prognostic of HCC. This review indicates that specific RNA modification pathways, such as N6-methyladenosine, 5-methylcytosine, N7-methylguanosine, and N1-methyladenosine, are erroneously regulated and are involved in the proliferation, autophagy, innate immunity, invasion, metastasis, immune cell infiltration, and drug resistance of HCC. These findings provide a new perspective for understanding the molecular mechanisms of HCC, as well as potential targets for the diagnosis and treatment of HCC by targeting specific RNA-modifying enzymes or recognition proteins. More than ten RNA-modifying regulators showed the potential for use for the diagnosis, prognosis and treatment decision utility biomarkers of HCC. Their application value for HCC biomarkers necessitates extensive multi-center sample validation in the future. A growing number of RNA modifier inhibitors are being developed, but the lack of preclinical experiments and clinical studies targeting RNA modification in HCC poses a significant obstacle, and further research is needed to evaluate their application value in HCC treatment. In conclusion, this review provides an in-depth understanding of the complex interplay between RNA modifications and HCC while emphasizing the promising potential of RNA modifications as therapeutic targets and biomarkers for managing HCC.

DNA methylation plays an important role in the development and tissue differentiation of eukaryotes. In this study, bisulfite sequencing (BS-seq) technology was used to analyze the DNA methylation profiles of liver tissues taken from Rongchang pigs at three postnatal feeding stages, including newborn, suckling, and adult. The DNA methylation pattern across the genomes or genic region showed little difference between the three stages. We observed 419 differentially methylated regions (DMRs) in promoters, corresponding to 323 genes between newborn and suckling stages, in addition to 288 DMRs, corresponding to 134 genes, between suckling and adult stages and 351 DMRs, corresponding to 293 genes, between newborn and adult stages. These genes with DMRs were mainly enriched in metabolic, immune-related functional processes. Correlation analysis showed that the methylation level of gene promoters was significantly negatively correlated with gene expression. Further, we found that genes related to nutritional metabolism, e.g., carbohydrate metabolism (FAHD1 and GUSB) or fatty acid metabolism (LPIN1 and ACOX2), lost DNA methylation in their promoter, with mRNA expression increased in newborn pigs compared with those in the suckling stage. A few fatty acid metabolism-related genes (SLC27A5, ACOX2) were hypomethylated and highly expressed in the newborn stage, which might satisfy the nutritional requirements of Rongchang pigs with high neonatal birth rates. In the adult stage, HMGCS2-which is related to fatty acid β-oxidation-was hypomethylated and highly expressed, which explains that the characteristics of high energy utilization in adult Rongchang pigs and their immune-related genes (CD68, STAT2) may be related to the establishment of liver immunity. This study provides a comprehensive analysis of genome-wide DNA methylation patterns in pig liver postnatal development and growth. Our findings will serve as a valuable resource in hepatic metabolic studies and the agricultural food industry.

Tamoxifen, a selective estrogen receptor modulator (SERM), exhibits dual agonist or antagonist effects contingent upon its binding to either G-protein-coupled estrogen receptor (GPER) or estrogen nuclear receptor (ESR). Estrogen signaling plays a pivotal role in initiating epigenetic alterations and regulating estrogen-responsive genes in breast cancer. Employing three distinct breast cancer cell lines-MCF-7 (ESR+; GPER+), MDA-MB-231 (ESR-; GPER-), and SkBr3 (ESR-; GPER+)-this study subjected them to treatment with two tamoxifen derivatives: 4-hydroxytamoxifen (4-HT) and endoxifen (Endox). Through 2D high-performance liquid chromatography with tandem mass spectrometry detection (HPLC-MS/MS), varying levels of 5-methylcytosine (5-mC) were found, with MCF-7 displaying the highest levels. Furthermore, TET3 mRNA expression levels varied among the cell lines, with MCF-7 exhibiting the lowest expression. Notably, treatment with 4-HT induced significant changes in TET3 expression across all cell lines, with the most pronounced increase seen in MCF-7 and the least in MDA-MB-231. These findings underscore the influence of tamoxifen derivatives on DNA methylation patterns, particularly through modulating TET3 expression, which appears to be contingent on the presence of estrogen receptors. This study highlights the potential of targeting epigenetic modifications for personalized anti-cancer therapy, offering a novel avenue to improve treatment outcomes.

OBJECTIVE: 5 mC methylation and hydroxymethylation (5hmC) are associated with Alzheimer\'s disease (AD). However, previous studies were limited by the absence of a 5hmC calculation. This study aims to find AD associated predictors and potential therapeutic chemicals using bioinformatics approach integrating 5 mC, 5hmC, and expression changes, and an AD mouse model.
METHODS: Gene expression microarray and 5 mC and 5hmC sequencing datasets were downloaded from GEO repository. 142 AD and 52 normal entorhinal cortex specimens were enrolled. Data from oxidative bisulfite sequencing (oxBS)-treated samples, which represent only 5 mC, were used to calculate 5hmC level. Functional analyses, random forest supervised classification and methylation validation were applied. Potential chemicals were predicted by CMap. Morris water maze, Y maze and novel object recognition behavior tests were performed using FAD4T AD mice model. Cortex and hippocampus tissues were isolated for immunohistochemical staining.
RESULTS: C1QTNF5, UBD, ZFP106, NEDD1, AKT3, and MBP genes involving 13 promoter CpG sites with 5mc, 5hmC methylation and expression difference were identified. AKT3 and MBP were down-regulated in both patients and mouse model. Three CpG sites in AKT3 and MBP showed significant methylation difference on validation. FAD4T AD mice showed recession in brain functions and lower AKT3 expression in both cortex and hippocampus. Ten chemicals were predicted as potential treatments for AD.
CONCLUSIONS: AKT3 and MBP may be associated with AD pathology and could serve as biomarkers. The ten predicted chemicals might offer new therapeutic approaches. Our findings could contribute to identifying novel markers and advancing the understanding of AD mechanisms.

BACKGROUND: 5-Hydroxymethylcytosine (5hmC), a crucial epigenetic mark with a significant role in regulating tissue-specific gene expression, is essential for understanding the dynamic functions of the human genome. Despite its importance, predicting 5hmC modification across the genome remains a challenging task, especially when considering the complex interplay between DNA sequences and various epigenetic factors such as histone modifications and chromatin accessibility.
RESULTS: Using tissue-specific 5hmC sequencing data, we introduce Deep5hmC, a multimodal deep learning framework that integrates both the DNA sequence and epigenetic features such as histone modification and chromatin accessibility to predict genome-wide 5hmC modification. The multimodal design of Deep5hmC demonstrates remarkable improvement in predicting both qualitative and quantitative 5hmC modification compared to unimodal versions of Deep5hmC and state-of-the-art machine learning methods. This improvement is demonstrated through benchmarking on a comprehensive set of 5hmC sequencing data collected at four developmental stages during forebrain organoid development and across 17 human tissues. Compared to DeepSEA and random forest, Deep5hmC achieves close to 4% and 17% improvement of Area Under the Receiver Operating Characteristic (AUROC) across four forebrain developmental stages, and 6% and 27% across 17 human tissues for predicting binary 5hmC modification sites; and 8% and 22% improvement of Spearman correlation coefficient across four forebrain developmental stages, and 17% and 30% across 17 human tissues for predicting continuous 5hmC modification. Notably, Deep5hmC showcases its practical utility by accurately predicting gene expression and identifying differentially hydroxymethylated regions (DhMRs) in a case-control study of Alzheimer\'s disease (AD). Deep5hmC significantly improves our understanding of tissue-specific gene regulation and facilitates the development of new biomarkers for complex diseases.
METHODS: Deep5hmC is available via https://github.com/lichen-lab/Deep5hmC.

As advances in RNA modification research progress, the significance of 5-methylcytosine (m5C) modification is being increasingly acknowledged. m5C undergoes modification by the methyltransferase NOP2/Sun domain (NSUN) family/DNA methyltransferase (DNMT) family (writer) and is removed by demethylases (eraser), including the ten-eleven translocation (TET) family and Alkb homolog 1 (ALKBH1). Moreover, m5C interacts with RNA-binding proteins (reader), such as Y-box-binding protein 1 (YBX1) and Aly/REF export factor (ALYREF). Expanding on this structural framework, m5C modification possesses the capacity to regulate various physiological and pathological processes. Recent studies indicate that m5C plays a pivotal regulatory role in the central nervous system, and its dysregulation may correlate with the onset and progression of various central nervous system diseases. In this review, we summarize recent research on m5C components and delve into the potential mechanisms of m5C involvement in central nervous system disorders, such as Alzheimer\'s disease, brain tumors, epilepsy, and stroke.