Effects of DNA Methylation on TFs in Human Embryonic Stem Cells

Uncovering methylation-dependent genetic effects on regulatory element function in diverse genomes

A major goal in evolutionary biology and biomedicine is to understand the complex interactions between genetic variants, the epigenome, and gene expression. However, the causal relationships between these factors remain poorly understood. mSTARR-seq, a methylation-sensitive massively parallel reporter assay, is capable of identifying methylation-dependent regulatory activity at many thousands of genomic regions simultaneously, and allows for the testing of causal relationships between DNA methylation and gene expression on a region-by-region basis. Here, we developed a multiplexed mSTARR-seq protocol to assay naturally occurring human genetic variation from 25 individuals sampled from 10 localities in Europe and Africa. We identified 6,957 regulatory elements in either the unmethylated or methylated state, and this set was enriched for enhancer and promoter annotations, as expected. The expression of 58% of these regulatory elements was modulated by methylation, which was generally associated with decreased RNA expression. Within our set of regulatory elements, we used allele-specific expression analyses to identify 8,020 sites with genetic effects on gene regulation; further, we found that 42.3% of these genetic effects varied between methylated and unmethylated states. Sites exhibiting methylation-dependent genetic effects were enriched for GWAS and EWAS annotations, implicating them in human disease. Compared to datasets that assay DNA from a single European individual, our multiplexed assay uncovers dramatically more genetic effects and methylation-dependent genetic effects, highlighting the importance of including diverse individuals in assays which aim to understand gene regulatory processes.

Dynamic DNA methylation modifications in the cold stress response of cassava

Cassava, a crucial tropical crop, faces challenges from cold stress, necessitating an exploration of its molecular response. Here, we investigated the role of DNA methylation in moderating the response to moderate cold stress (10 °C) in cassava. Using whole-genome bisulfite sequencing, we examined DNA methylation patterns in leaf blades and petioles under control conditions, 5 h, and 48 h of cold stress. Tissue-specific responses were observed, with leaf blades exhibiting subtle changes, while petioles displayed a pronounced decrease in methylation levels under cold stress. We identified cold stress-induced differentially methylated regions (DMRs) that demonstrated both tissue and treatment specificity. Importantly, these DMRs were enriched in genes with altered expression, implying functional relevance. The cold-response transcription factor ERF105 associated with DMRs emerged as a significant and conserved regulator across tissues and treatments. Furthermore, we investigated DNA methylation dynamics in transposable elements, emphasizing the sensitivity of MITEs with bHLH binding motifs to cold stress. These findings provide insights into the epigenetic regulation of response to cold stress in cassava, contributing to an understanding of the molecular mechanisms underlying stress adaptation in this tropical plant.

Integrative analysis of transcriptomic and epigenomic data reveals distinct patterns for developmental and housekeeping gene regulation

BACKGROUND

Regulation of transcription is central to the emergence of new cell types during development, and it often involves activation of genes via proximal and distal regulatory regions. The activity of regulatory elements is determined by transcription factors (TFs) and epigenetic marks, but despite extensive mapping of such patterns, the extraction of regulatory principles remains challenging.

RESULTS

Here we study differentially and similarly expressed genes along with their associated epigenomic profiles, chromatin accessibility and DNA methylation, during lineage specification at gastrulation in mice. Comparison of the three lineages allows us to identify genomic and epigenomic features that distinguish the two classes of genes. We show that differentially expressed genes are primarily regulated by distal elements, while similarly expressed genes are controlled by proximal housekeeping regulatory programs. Differentially expressed genes are relatively isolated within topologically associated domains, while similarly expressed genes tend to be located in gene clusters. Transcription of differentially expressed genes is associated with differentially open chromatin at distal elements including enhancers, while that of similarly expressed genes is associated with ubiquitously accessible chromatin at promoters.

CONCLUSION

Based on these associations of (linearly) distal genes' transcription start sites (TSSs) and putative enhancers for developmental genes, our findings allow us to link putative enhancers to their target promoters and to infer lineage-specific repertoires of putative driver transcription factors, within which we define subgroups of pioneers and co-operators.

The dopamine transporter antagonist vanoxerine inhibits G9a and suppresses cancer stem cell functions in colon tumors

Cancer stem cells (CSCs), functionally characterized by self-renewal and tumor-initiating activity, contribute to decreased tumor immunogenicity, while fostering tumor growth and metastasis. Targeting G9a histone methyltransferase (HMTase) effectively blocks CSC functions in colorectal tumors by altering pluripotent-like molecular networks; however, existing molecules directly targeting G9a HMTase activity failed to reach clinical stages due to safety concerns. Using a stem cell-based phenotypic drug-screening pipeline, we identified the dopamine transporter (DAT) antagonist vanoxerine, a compound with previously demonstrated clinical safety, as a cancer-specific downregulator of G9a expression. Here we show that gene silencing and chemical antagonism of DAT impede colorectal CSC functions by repressing G9a expression. Antagonizing DAT also enhanced tumor lymphocytic infiltration by activating endogenous transposable elements and type-I interferon response. Our study unveils the direct implication of the DAT-G9a axis in the maintenance of CSC populations and an approach to improve antitumor immune response in colon tumors.

Somatic mutation as an explanation for epigenetic aging

DNA methylation marks have recently been used to build models known as "epigenetic clocks" which predict calendar age. As methylation of cytosine promotes C-to-T mutations, we hypothesized that the methylation changes observed with age should reflect the accrual of somatic mutations, and the two should yield analogous aging estimates. In analysis of multimodal data from 9,331 human individuals, we find that CpG mutations indeed coincide with changes in methylation, not only at the mutated site but also with pervasive remodeling of the methylome out to ±10 kilobases. This one-to-many mapping enables mutation-based predictions of age that agree with epigenetic clocks, including which individuals are aging faster or slower than expected. Moreover, genomic loci where mutations accumulate with age also tend to have methylation patterns that are especially predictive of age. These results suggest a close coupling between the accumulation of sporadic somatic mutations and the widespread changes in methylation observed over the course of life.

StableDNAm: towards a stable and efficient model for predicting DNA methylation based on adaptive feature correction learning

BACKGROUND

DNA methylation, instrumental in numerous life processes, underscores the paramount importance of its accurate prediction. Recent studies suggest that deep learning, due to its capacity to extract profound insights, provides a more precise DNA methylation prediction. However, issues related to the stability and generalization performance of these models persist.

RESULTS

In this study, we introduce an efficient and stable DNA methylation prediction model. This model incorporates a feature fusion approach, adaptive feature correction technology, and a contrastive learning strategy. The proposed model presents several advantages. First, DNA sequences are encoded at four levels to comprehensively capture intricate information across multi-scale and low-span features. Second, we design a sequence-specific feature correction module that adaptively adjusts the weights of sequence features. This improvement enhances the model's stability and scalability, or its generality. Third, our contrastive learning strategy mitigates the instability issues resulting from sparse data. To validate our model, we conducted multiple sets of experiments on commonly used datasets, demonstrating the model's robustness and stability. Simultaneously, we amalgamate various datasets into a single, unified dataset. The experimental outcomes from this combined dataset substantiate the model's robust adaptability.

CONCLUSIONS

Our research findings affirm that the StableDNAm model is a general, stable, and effective instrument for DNA methylation prediction. It holds substantial promise for providing invaluable assistance in future methylation-related research and analyses.

Allelic chromatin structure precedes imprinted expression of Kcnk9 during neurogenesis

Differences in chromatin state inherited from the parental gametes influence the regulation of maternal and paternal alleles in offspring. This phenomenon, known as genomic imprinting, results in genes preferentially transcribed from one parental allele. While local epigenetic factors such as DNA methylation are known to be important for the establishment of imprinted gene expression, less is known about the mechanisms by which differentially methylated regions (DMRs) lead to differences in allelic expression across broad stretches of chromatin. Allele-specific higher-order chromatin structure has been observed at multiple imprinted loci, consistent with the observation of allelic binding of the chromatin-organizing factor CTCF at multiple DMRs. However, whether allelic chromatin structure impacts allelic gene expression is not known for most imprinted loci. Here we characterize the mechanisms underlying brain-specific imprinted expression of the Peg13-Kcnk9 locus, an imprinted region associated with intellectual disability. We performed region capture Hi-C on mouse brains from reciprocal hybrid crosses and found imprinted higher-order chromatin structure caused by the allelic binding of CTCF to the Peg13 DMR. Using an in vitro neuron differentiation system, we showed that imprinted chromatin structure precedes imprinted expression at the locus. Additionally, activation of a distal enhancer induced imprinted expression of Kcnk9 in an allelic chromatin structure-dependent manner. This work provides a high-resolution map of imprinted chromatin structure and demonstrates that chromatin state established in early development can promote imprinted expression upon differentiation.

Allelic chromatin structure primes imprinted expression of Kcnk9 during neurogenesis

Differences in chromatin state inherited from the parental gametes influence the regulation of maternal and paternal alleles in offspring. This phenomenon, known as genomic imprinting, results in genes preferentially transcribed from one parental allele. While local epigenetic factors such as DNA methylation are known to be important for the establishment of imprinted gene expression, less is known about the mechanisms by which differentially methylated regions (DMRs) lead to differences in allelic expression across broad stretches of chromatin. Allele-specific higher-order chromatin structure has been observed at multiple imprinted loci, consistent with the observation of allelic binding of the chromatin-organizing factor CTCF at multiple DMRs. However, whether allelic chromatin structure impacts allelic gene expression is not known for most imprinted loci. Here we characterize the mechanisms underlying brain-specific imprinted expression of the Peg13-Kcnk9 locus, an imprinted region associated with intellectual disability. We performed region capture Hi-C on mouse brain from reciprocal hybrid crosses and found imprinted higher-order chromatin structure caused by the allelic binding of CTCF to the Peg13 DMR. Using an in vitro neuron differentiation system, we show that on the maternal allele enhancer-promoter contacts formed early in development prime the brain-specific potassium leak channel Kcnk9 for maternal expression prior to neurogenesis. In contrast, these enhancer-promoter contacts are blocked by CTCF on the paternal allele, preventing paternal Kcnk9 activation. This work provides a high-resolution map of imprinted chromatin structure and demonstrates that chromatin state established in early development can promote imprinted expression upon differentiation.

DNA Methylation Dynamics During Esophageal Epithelial Regeneration Following Repair with Acellular Silk Fibroin Grafts in Rat

Esophageal pathologies such as atresia and benign strictures often require surgical reconstruction with autologous tissues to restore organ continuity. Complications such as donor site morbidity and limited tissue availability have spurred the development of acellular grafts for esophageal tissue replacement. Acellular biomaterials for esophageal repair rely on the activation of intrinsic regenerative mechanisms to mediate de novo tissue formation at implantation sites. Previous research has identified signaling cascades involved in neoepithelial formation in a rat model of onlay esophagoplasty with acellular silk fibroin grafts, including phosphoinositide 3-kinase (PI3K), and protein kinase B (Akt) signaling. However, it is currently unknown how these mechanisms are governed by DNA methylation (DNAme) during esophageal wound healing processes. Reduced-representation bisulfite sequencing is performed to characterize temporal DNAme dynamics in host and regenerated tissues up to 1 week postimplantation. Overall, global hypermethylation is observed at postreconstruction timepoints and an inverse correlation between promoter DNAme and the expression levels of differentially expressed proteins during regeneration. Site-specific hypomethylation targets genes associated with immune activation, while hypermethylation occurs within gene bodies encoding PI3K-Akt signaling components during the tissue remodeling period. The data provide insight into the epigenetic mechanisms during esophageal regeneration following surgical repair with acellular grafts.

BERT2OME: Prediction of 2'-O-Methylation Modifications From RNA Sequence by Transformer Architecture Based on BERT

Recent work on language models has resulted in state-of-the-art performance on various language tasks. Among these, Bidirectional Encoder Representations from Transformers (BERT) has focused on contextualizing word embeddings to extract context and semantics of the words. On the other hand, post-transcriptional 2'-O-methylation (Nm) RNA modification is important in various cellular tasks and related to a number of diseases. The existing high-throughput experimental techniques take longer time to detect these modifications, and costly in exploring these functional processes. Here, to deeply understand the associated biological processes faster, we come up with an efficient method Bert2Ome to infer 2'-O-methylation RNA modification sites from RNA sequences. Bert2Ome combines BERT-based model with convolutional neural networks (CNN) to infer the relationship between the modification sites and RNA sequence content. Unlike the methods proposed so far, Bert2Ome assumes each given RNA sequence as a text and focuses on improving the modification prediction performance by integrating the pretrained deep learning-based language model BERT. Additionally, our transformer-based approach could infer modification sites across multiple species. According to 5-fold cross-validation, human and mouse accuracies were 99.15% and 94.35% respectively. Similarly, ROC AUC scores were 0.99, 0.94 for the same species. Detailed results show that Bert2Ome reduces the time consumed in biological experiments and outperforms the existing approaches across different datasets and species over multiple metrics. Additionally, deep learning approaches such as 2D CNNs are more promising in learning BERT attributes than more conventional machine learning methods.

Computational Methods for Single-cell DNA Methylome Analysis

Dissecting intercellular epigenetic differences is key to understanding tissue heterogeneity. Recent advances in single-cell DNA methylome profiling have presented opportunities to resolve this heterogeneity at the maximum resolution. While these advances enable us to explore frontiers of chromatin biology and better understand cell lineage relationships, they pose new challenges in data processing and interpretation. This review surveys the current state of computational tools developed for single-cell DNA methylome data analysis. We discuss critical components of single-cell DNA methylome data analysis, including data preprocessing, quality control, imputation, dimensionality reduction, cell clustering, supervised cell annotation, cell lineage reconstruction, gene activity scoring, and integration with transcriptome data. We also highlight unique aspects of single-cell DNA methylome data analysis and discuss how techniques common to other single-cell omics data analyses can be adapted to analyze DNA methylomes. Finally, we discuss existing challenges and opportunities for future development.

DNA Methylation: Genomewide Distribution, Regulatory Mechanism and Therapy Target

DNA methylation is the most important epigenetic modification involved in the regulation of transcription, imprinting, establishment of X-inactivation, and the formation of a chromatin structure. DNA methylation in the genome is often associated with transcriptional repression and the formation of closed heterochromatin. However, the results of genome-wide studies of the DNA methylation pattern and transcriptional activity of genes have nudged us toward reconsidering this paradigm, since the promoters of many genes remain active despite their methylation. The differences in the DNA methylation distribution in normal and pathological conditions allow us to consider methylation as a diagnostic marker or a therapy target. In this regard, the need to investigate the factors affecting DNA methylation and those involved in its interpretation becomes pressing. Recently, a large number of protein factors have been uncovered, whose ability to bind to DNA depends on their methylation. Many of these proteins act not only as transcriptional activators or repressors, but also affect the level of DNA methylation. These factors are considered potential therapeutic targets for the treatment of diseases resulting from either a change in DNA methylation or a change in the interpretation of its methylation level. In addition to protein factors, a secondary DNA structure can also affect its methylation and can be considered as a therapy target. In this review, the latest research into the DNA methylation landscape in the genome has been summarized to discuss why some DNA regions avoid methylation and what factors can affect its level or interpretation and, therefore, can be considered a therapy target.

Context-dependent CpG methylation directs cell-specific binding of transcription factor ZBTB38

DNA methylation on CpGs regulates transcription in mammals, both by decreasing the binding of methylation-repelled factors and by increasing the binding of methylation-attracted factors. Among the latter, zinc finger proteins have the potential to bind methylated CpGs in a sequence-specific context. The protein ZBTB38 is unique in that it has two independent sets of zinc fingers, which recognize two different methylated consensus sequences in vitro. Here, we identify the binding sites of ZBTB38 in a human cell line, and show that they contain the two methylated consensus sequences identified in vitro. In addition, we show that the distribution of ZBTB38 sites is highly unusual: while 10% of the ZBTB38 sites are also bound by CTCF, the other 90% of sites reside in closed chromatin and are not bound by any of the other factors mapped in our model cell line. Finally, a third of ZBTB38 sites are found upstream of long and active CpG islands. Our work therefore validates ZBTB38 as a methyl-DNA binder in vivo and identifies its unique distribution in the genome.

Heterogeneity Analysis of Bladder Cancer Based on DNA Methylation Molecular Profiling

Bladder cancer is a highly complex and heterogeneous malignancy. Tumor heterogeneity is a barrier to effective diagnosis and treatment of bladder cancer. Human carcinogenesis is closely related to abnormal gene expression, and DNA methylation is an important regulatory factor of gene expression. Therefore, it is of great significance for bladder cancer research to characterize tumor heterogeneity by integrating genetic and epigenetic characteristics. This study explored specific molecular subtypes based on DNA methylation status and identified subtype-specific characteristics using patient samples from the TCGA database with DNA methylation and gene expression were measured simultaneously. The results were validated using an independent cohort from GEO database. Four DNA methylation molecular subtypes of bladder cancer were obtained with different prognostic states. In addition, subtype-specific DNA methylation markers were identified using an information entropy-based algorithm to represent the unique molecular characteristics of the subtype and verified in the test set. The results of this study can provide an important reference for clinicians to make treatment decisions.

Identification of Prognostic Biomarkers for Bladder Cancer Based on DNA Methylation Profile

Background: DNA methylation is an important epigenetic modification, which plays an important role in regulating gene expression at the transcriptional level. In tumor research, it has been found that the change of DNA methylation leads to the abnormality of gene structure and function, which can provide early warning for tumorigenesis. Our study aims to explore the relationship between the occurrence and development of tumor and the level of DNA methylation. Moreover, this study will provide a set of prognostic biomarkers, which can more accurately predict the survival and health of patients after treatment.

Methods: Datasets of bladder cancer patients and control samples were collected from TCGA database, differential analysis was employed to obtain genes with differential DNA methylation levels between tumor samples and normal samples. Then the protein-protein interaction network was constructed, and the potential tumor markers were further obtained by extracting Hub genes from subnet. Cox proportional hazard regression model and survival analysis were used to construct the prognostic model and screen out the prognostic markers of bladder cancer, so as to provide reference for tumor prognosis monitoring and improvement of treatment plan.

Results: In this study, we found that DNA methylation was indeed related with the occurrence of bladder cancer. Genes with differential DNA methylation could serve as potential biomarkers for bladder cancer. Through univariate and multivariate Cox proportional hazard regression analysis, we concluded that FASLG and PRKCZ can be used as prognostic biomarkers for bladder cancer. Patients can be classified into high or low risk group by using this two-gene prognostic model. By detecting the methylation status of these genes, we can evaluate the survival of patients.

Conclusion: The analysis in our study indicates that the methylation status of tumor-related genes can be used as prognostic biomarkers of bladder cancer.

NmRF: identification of multispecies RNA 2'-O-methylation modification sites from RNA sequences

2'-O-methylation (Nm) is a post-transcriptional modification of RNA that is catalyzed by 2'-O-methyltransferase and involves replacing the H on the 2'-hydroxyl group with a methyl group. The 2'-O-methylation modification site is detected in a variety of RNA types (miRNA, tRNA, mRNA, etc.), plays an important role in biological processes and is associated with different diseases. There are few functional mechanisms developed at present, and traditional high-throughput experiments are time-consuming and expensive to explore functional mechanisms. For a deeper understanding of relevant biological mechanisms, it is necessary to develop efficient and accurate recognition tools based on machine learning. Based on this, we constructed a predictor called NmRF based on optimal mixed features and random forest classifier to identify 2'-O-methylation modification sites. The predictor can identify modification sites of multiple species at the same time. To obtain a better prediction model, a two-step strategy is adopted; that is, the optimal hybrid feature set is obtained by combining the light gradient boosting algorithm and incremental feature selection strategy. In 10-fold cross-validation, the accuracies of Homo sapiens and Saccharomyces cerevisiae were 89.069 and 93.885%, and the AUC were 0.9498 and 0.9832, respectively. The rigorous 10-fold cross-validation and independent tests confirm that the proposed method is significantly better than existing tools. A user-friendly web server is accessible at http://lab.malab.cn/∼acy/NmRF.

iDNA-ABT : advanced deep learning model for detecting DNA methylation with adaptive features and transductive information maximization

MOTIVATION

DNA methylation plays an important role in epigenetic modification, the occurrence, and the development of diseases. Therefore, the identification of DNA methylation sites is critical for better understanding and revealing their functional mechanisms. To date, several machine learning and deep learning methods have been developed for the prediction of different methylation types. However, they still highly rely on manual features, which can largely limit the high-latent information extraction. Moreover, most of them are designed for one specific methylation type, and therefore cannot predict multiple methylation sites in multiple species simultaneously. In this study, we propose iDNA-ABT, an advanced deep learning model that utilizes adaptive embedding based on bidirectional transformers for language understanding together with a novel transductive information maximization (TIM) loss.

RESULTS

Benchmark results show that our proposed iDNA-ABT can automatically and adaptively learn the distinguishing features of biological sequences from multiple species, and thus perform significantly better than the state-of-the-art methods in predicting three different DNA methylation. In addition, TIM loss is proven to be effective in dichotomous tasks via the comparison experiment. Furthermore, we verify that our features have strong adaptability and robustness to different species through comparison of adaptive embedding and six handcrafted feature encodings. Importantly, our model shows great generalization ability in different species, demonstrating that our model can adaptively capture the cross-species differences and improve the predictive performance. For the convenient use of our method, we further established an online webserver as the implementation of the proposed iDNA-ABT.

AVAILABILITY

our proposed iDNA-ABT, which is now freely accessible via http://server.wei-group.net/iDNA_ABT and our source codes are available in the GitHub repository (https://github.com/YUYING07/iDNA_ABT).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.