EWAS Data Hub: a resource of DNA methylation array data and metadata

Silica's silent threat: Contributing to skin fibrosis in systemic sclerosis by targeting the HDAC4/Smad2/3 pathway

Nowadays, silica products are widely used in daily life, especially in skin applications, which inevitably increases the risk of silica exposure in general population. However, inadequate awareness of silica's potential hazards and lack of self-protection are of concern. Systemic sclerosis (SSc) is characterized by progressive tissue fibrosis under environmental and genetic interactions. Silica exposure is considered an important causative factor for SSc, but its pathogenesis remains unclear. Within this study, we showed that lower doses of silica significantly promoted the proliferation, migration, and activation of human skin fibroblasts (HSFs) within 24 h. Silica injected subcutaneously into mice induced and exacerbated skin fibrosis. Notably, silica increased histone deacetylase-4 (HDAC4) expression by inducing its DNA hypomethylation in normal HSFs. The elevated HDAC4 expression was also confirmed in SSc HSFs. Furthermore, HDAC4 was positively correlated with Smad2/3 phosphorylation and COL1, α-SMA, and CTGF expression. The HDAC4 inhibitor LMK235 mitigated silica-induced upregulation of these factors and alleviated skin fibrosis in SSc mice. Taken together, silica induces and exacerbates skin fibrosis in SSc patients by targeting the HDAC4/Smad2/3 pathway. Our findings provide new insights for evaluating the health hazards of silica exposure and identify HDAC4 as a potential interventional target for silica-induced SSc skin fibrosis.

MethParquet: an R package for rapid and efficient DNA methylation association analysis adopting Apache Parquet

SUMMARY

Genome-wide DNA methylation (DNAm) profiling is indispensable for unveiling how DNAm regulates biological pathways and individual phenotypes. However, managing and analyzing extensive DNAm data generated from large cohort studies present computational obstacles. Apache Parquet is a data file format that allows for efficient data storage, retrieval, and manipulation, alleviating computational hurdles associated with conventional row-based formats. We here introduce MethParquet, the first R package leveraging the columnar Parquet format for efficient DNAm data analysis. It can be used for data extraction, methylation risk score calculation, epigenome-wide association analyses, and other standard post-quality control tasks. The package flexibly implements diverse regression models. Via a public methylation dataset, we show the efficiency of this package in reducing running time and RAM usage in large-scale EWAS.

AVAILABILITY AND IMPLEMENTATION

The MethParquet R package is publicly available on the GitHub repository https://github.com/ZWangTen/MethParquet. It includes a vignette and a toy dataset derived from a public resource.

ScnML models single-cell transcriptome to predict spinal cord neuronal cell status

Injuries to the spinal cord nervous system often result in permanent loss of sensory, motor, and autonomic functions. Accurately identifying the cellular state of spinal cord nerves is extremely important and could facilitate the development of new therapeutic and rehabilitative strategies. Existing experimental techniques for identifying the development of spinal cord nerves are both labor-intensive and costly. In this study, we developed a machine learning predictor, ScnML, for predicting subpopulations of spinal cord nerve cells as well as identifying marker genes. The prediction performance of ScnML was evaluated on the training dataset with an accuracy of 94.33%. Based on XGBoost, ScnML on the test dataset achieved 94.08% 94.24%, 94.26%, and 94.24% accuracies with precision, recall, and F1-measure scores, respectively. Importantly, ScnML identified new significant genes through model interpretation and biological landscape analysis. ScnML can be a powerful tool for predicting the status of spinal cord neuronal cells, revealing potential specific biomarkers quickly and efficiently, and providing crucial insights for precision medicine and rehabilitation recovery.

Adaptive predictor-set linear model: An imputation-free method for linear regression prediction on data sets with missing values

Linear regression (LR) is vastly used in data analysis for continuous outcomes in biomedicine and epidemiology. Despite its popularity, LR is incompatible with missing data, which frequently occur in health sciences. For parameter estimation, this shortcoming is usually resolved by complete-case analysis or imputation. Both work-arounds, however, are inadequate for prediction, since they either fail to predict on incomplete records or ignore missingness-induced reduction in prediction accuracy and rely on (unrealistic) assumptions about the missing mechanism. Here, we derive adaptive predictor-set linear model (aps-lm), capable of making predictions for incomplete data without the need for imputation. It is derived by using a predictor-selection operation, the Moore-Penrose pseudoinverse, and the reduced QR decomposition. aps-lm is an LR generalization that inherently handles missing values. It is applied on a reference data set, where complete predictors and outcome are available, and yields a set of privacy-preserving parameters. In a second stage, these are shared for making predictions of the outcome on external data sets with missing entries for predictors without imputation. Moreover, aps-lm computes prediction errors that account for the pattern of missing values even under extreme missingness. We benchmark aps-lm in a simulation study. aps-lm showed greater prediction accuracy and reduced bias compared to popular imputation strategies under a wide range of scenarios including variation of sample size, goodness of fit, missing value type, and covariance structure. Finally, as a proof-of-principle, we apply aps-lm in the context of epigenetic aging clocks, linear models that predict a person's biological age from epigenetic data with promising clinical applications.

Comprehensive Molecular Profiling of NPM1-Mutated Acute Myeloid Leukemia Using RNAseq Approach

Acute myeloid leukemia (AML) is a complex hematologic malignancy with high morbidity and mortality. Nucleophosmin 1 (NPM1) mutations occur in approximately 30% of AML cases, and NPM1-mutated AML is classified as a distinct entity. NPM1-mutated AML patients without additional genetic abnormalities have a favorable prognosis. Despite this, 30-50% of them experience relapse. This study aimed to investigate the potential of total RNAseq in improving the characterization of NPM1-mutated AML patients. We explored genetic variations independently of myeloid stratification, revealing a complex molecular scenario. We showed that total RNAseq enables the uncovering of different genetic alterations and clonal subtypes, allowing for a comprehensive evaluation of the real expression of exome transcripts in leukemic clones and the identification of aberrant fusion transcripts. This characterization may enhance understanding and guide improved treatment strategies for NPM1mut AML patients, contributing to better outcomes. Our findings underscore the complexity of NPM1-mutated AML, supporting the incorporation of advanced technologies for precise risk stratification and personalized therapeutic strategies. The study provides a foundation for future investigations into the clinical implications of identified genetic variations and highlights the importance of evolving diagnostic approaches in leukemia management.

Flu-CED: A comparative transcriptomics database of influenza virus-infected human and animal models

BACKGROUND

The continuing emergence of influenza virus has highlighted the value of public databases and related bioinformatic analysis tools in investigating transcriptomic change caused by different influenza virus infections in human and animal models.

METHODS

We collected a large amount of transcriptome research data related to influenza virus-infected human and animal models in public databases (GEO and ArrayExpress), and extracted and integrated array and metadata. The gene expression matrix was generated through strictly quality control, balance, standardization, batch correction, and gene annotation. We then analyzed gene expression in different species, virus, cells/tissues or after antibody/vaccine treatment and imported sample metadata and gene expression datasets into the database.

RESULTS

Overall, maintaining careful processing and quality control, we collected 8064 samples from 103 independent datasets, and constructed a comparative transcriptomics database of influenza virus named the Flu-CED database (Influenza comparative expression database, https://flu.com-med.org.cn/). Using integrated and processed transcriptomic data, we established a user-friendly website for realizing the integration, online retrieval, visualization, and exploration of gene expression of influenza virus infection in different species and the biological functions involved in differential genes. Flu-CED can quickly query single and multi-gene expression profiles, combining different experimental conditions for comparative transcriptome analysis, identifying differentially expressed genes (DEGs) between comparison groups, and conveniently finding DEGs.

CONCLUSION

Flu-CED provides data resources and tools for analyzing gene expression in human and animal models infected with influenza virus that can deepen our understanding of the mechanisms underlying disease occurrence and development, and enable prediction of key genes or therapeutic targets that can be used for medical research.

Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2024

The National Genomics Data Center (NGDC), which is a part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support the global academic and industrial communities. With the rapid accumulation of multi-omics data at an unprecedented pace, CNCB-NGDC continuously expands and updates core database resources through big data archiving, integrative analysis and value-added curation. Importantly, NGDC collaborates closely with major international databases and initiatives to ensure seamless data exchange and interoperability. Over the past year, significant efforts have been dedicated to integrating diverse omics data, synthesizing expanding knowledge, developing new resources, and upgrading major existing resources. Particularly, several database resources are newly developed for the biodiversity of protists (P10K), bacteria (NTM-DB, MPA) as well as plant (PPGR, SoyOmics, PlantPan) and disease/trait association (CROST, HervD Atlas, HALL, MACdb, BioKA, BioKA, RePoS, PGG.SV, NAFLDkb). All the resources and services are publicly accessible at https://ngdc.cncb.ac.cn.

Waterpipe and cigarette epigenome analysis reveals markers implicated in addiction and smoking type inference

Waterpipe smoking is frequent in the Middle East and Africa with emerging prevalence worldwide. The epigenome acts as a molecular sensor to exposures and a crucial driver in several diseases. With the widespread use of waterpipe smoking, it is timely to investigate its epigenomic markers and their role in addiction, as a central player in disease prevention and therapeutic strategies. DNA methylome-wide profiling was performed on an exposure-rich population from the Middle East, constituting of 216 blood samples split equally between never, cigarette-only and waterpipe-only smokers. Waterpipe smokers showed predominantly distinct epigenetic markers from cigarette smokers, even though both smoking forms are tobacco-based. Moreover, each smoking form could be accurately (∼90 %) inferred from the DNA methylome using machine learning. Top markers showed dose-response relationship with extent of smoking and were validated using independent technologies and additional samples (total N = 284). Smoking markers were enriched in regulatory regions and several biological pathways, primarily addiction. The epigenetically altered genes were not associated with genetic etiology of tobacco use, and the methylation levels of addiction genes, in particular, were more likely to reverse after smoking cessation. In contrast, other epigenetic markers continued to feature smoking exposure after cessation, which may explain long-term health effects observed in former smokers. This study reports, for the first time, blood epigenome-wide markers of waterpipe smokers and reveals new markers of cigarette smoking, with implications in mechanisms of addiction and the capacity to discriminate between different smoking types. These markers may offer actionable targets to reverse the epigenetic memory of addiction and can guide future prevention strategies for tobacco smoking as the most preventable cause of illnesses worldwide.

Unlocking the secrets: the power of methylation-based cfDNA detection of tissue damage in organ systems

BACKGROUND

Detecting organ and tissue damage is essential for early diagnosis, treatment decisions, and monitoring disease progression. Methylation-based assays offer a promising approach, as DNA methylation patterns can change in response to tissue damage. These assays have potential applications in early detection, monitoring disease progression, evaluating treatment efficacy, and assessing organ viability for transplantation. cfDNA released into the bloodstream upon tissue or organ injury can serve as a biomarker for damage. The epigenetic state of cfDNA, including DNA methylation patterns, can provide insights into the extent of tissue and organ damage.

CONTENT

Firstly, this review highlights DNA methylation as an extensively studied epigenetic modification that plays a pivotal role in processes such as cell growth, differentiation, and disease development. It then presents a variety of highly precise 5-mC methylation detection techniques that serve as powerful tools for gaining profound insights into epigenetic alterations linked with tissue damage. Subsequently, the review delves into the mechanisms underlying DNA methylation changes in organ and tissue damage, encompassing inflammation, oxidative stress, and DNA damage repair mechanisms. Next, it addresses the current research status of cfDNA methylation in the detection of specific organ tissues and organ damage. Finally, it provides an overview of the multiple steps involved in identifying specific methylation markers associated with tissue and organ damage for clinical trials. This review will explore the mechanisms and current state of research on cfDNA methylation-based assay detecting organ and tissue damage, the underlying mechanisms, and potential applications in clinical practice.

Widespread effects of DNA methylation and intra-motif dependencies revealed by novel transcription factor binding models

Several studies suggested that transcription factor (TF) binding to DNA may be impaired or enhanced by DNA methylation. We present MeDeMo, a toolbox for TF motif analysis that combines information about DNA methylation with models capturing intra-motif dependencies. In a large-scale study using ChIP-seq data for 335 TFs, we identify novel TFs that show a binding behaviour associated with DNA methylation. Overall, we find that the presence of CpG methylation decreases the likelihood of binding for the majority of methylation-associated TFs. For a considerable subset of TFs, we show that intra-motif dependencies are pivotal for accurately modelling the impact of DNA methylation on TF binding. We illustrate that the novel methylation-aware TF binding models allow to predict differential ChIP-seq peaks and improve the genome-wide analysis of TF binding. Our work indicates that simplistic models that neglect the effect of DNA methylation on DNA binding may lead to systematic underperformance for methylation-associated TFs.

NCAE: data-driven representations using a deep network-coherent DNA methylation autoencoder identify robust disease and risk factor signatures

Precision medicine relies on the identification of robust disease and risk factor signatures from omics data. However, current knowledge-driven approaches may overlook novel or unexpected phenomena due to the inherent biases in biological knowledge. In this study, we present a data-driven signature discovery workflow for DNA methylation analysis utilizing network-coherent autoencoders (NCAEs) with biologically relevant latent embeddings. First, we explored the architecture space of autoencoders trained on a large-scale pan-tissue compendium (n = 75 272) of human epigenome-wide association studies. We observed the emergence of co-localized patterns in the deep autoencoder latent space representations that corresponded to biological network modules. We determined the NCAE configuration with the strongest co-localization and centrality signals in the human protein interactome. Leveraging the NCAE embeddings, we then trained interpretable deep neural networks for risk factor (aging, smoking) and disease (systemic lupus erythematosus) prediction and classification tasks. Remarkably, our NCAE embedding-based models outperformed existing predictors, revealing novel DNA methylation signatures enriched in gene sets and pathways associated with the studied condition in each case. Our data-driven biomarker discovery workflow provides a generally applicable pipeline to capture relevant risk factor and disease information. By surpassing the limitations of knowledge-driven methods, our approach enhances the understanding of complex epigenetic processes, facilitating the development of more effective diagnostic and therapeutic strategies.

Role of LncRNA MIR99AHG in breast cancer: Bioinformatic analysis and preliminary verification

Objective

This research was aimed to preliminarily explore the clinical roles and potential molecular mechanisms of MIR99AHG and its significant transcripts in breast cancer (BRCA).

Methods

Public databases were utilized to analyze the expression and prognostic roles of MIR99AHG and its transcripts. Relationships between MIR99AHG expression and immune cells infiltration were analyzed in Xiantao platform. In addition, co-expressed genes and interacting proteins of MIR99AHG were predicted. CancerSEA analyzed its relationship with functional states. Next, CNV status, DNA methylation, interacting transcription factors (TFs) and ceRNA network were analyzed to explore its possible mechanisms. Then, RNA ISH and FISH assays were used to detect its expression and location in BRCA tissues and cell lines, respectively. Finally, qRT-PCR was utilized to investigate MIR99AHG expression in cell lines.

Results

Compared with the corresponding normal tissues, MIR99AHG expression levels were lower in all BRCA subtypes, and luminal B's was the lowest one. And MIR99AHG expression was negatively related to the tumor stage. In addition, 4 transcripts (ENST00000619222.4, ENST00000418813.6, ENST00000602901.5 and ENST00000453910.5) of MIR99AHG showed significant differences in the expression. Databases also suggested that the high MIR99AHG expression levels indicated good prognosis, especially in patients without lymph node metastasis. Xiantao found that MIR99AHG was positively related to 17 immune cells and negatively linked with 2 immune cells. CancerSEA analysis showed no relationships between MIR99AHG and functional states. From GEPIA and BCIP databases, 19 co-expressed genes were highly related to these four significant transcripts of MIR99AHG. StarBase, RNAct and HDOCK showed that several tumor-associated proteins, including U2AF65, hnRNPC, AEBP2, CHIC1 and so on, might interact with MIR99AHG. Genetically, BRCA had a higher proportion of MIR99AHG CNV loss than CNV gain, and the high level of DNA methylation indicated a good prognosis. Furthermore, 19 TFs were predicted to combine with the promoter of MIR99AHG. Then, we screened out 10 miRNAs potentially interacting with the significant transcripts of MIR99AHG, and five were significantly increased in breast tumors compared to normal tissues, including miR-194-5p, miR-320 b and so on, which could combine 14 mRNAs. Through ISH and FISH assays, we verified that MIR99AHG was down-regulated in BRCA samples and cell lines in comparison to non-tumor tissues and mammary epithelial cell line (MCF10A), and MIR99AHG was located both in cytoplasm and nucleus. qRT-PCR assay also showed the lower expression of MIR99AHG in breast cancer cells than that in MCF10A.

Conclusion

These results indicate that MIR99AHG can be a favorable prognostic indicator for BRCA. ENST00000619222.4, ENST00000418813.6, ENST00000602901.5 and ENST00000453910.5 are significant transcripts and their down-regulation may play crucial roles in the progression of BRCA.

Precious1GPT: multimodal transformer-based transfer learning for aging clock development and feature importance analysis for aging and age-related disease target discovery

Aging is a complex and multifactorial process that increases the risk of various age-related diseases and there are many aging clocks that can accurately predict chronological age, mortality, and health status. These clocks are disconnected and are rarely fit for therapeutic target discovery. In this study, we propose a novel approach to multimodal aging clock we call Precious1GPT utilizing methylation and transcriptomic data for interpretable age prediction and target discovery developed using a transformer-based model and transfer learning for case-control classification. While the accuracy of the multimodal transformer is lower within each individual data type compared to the state of art specialized aging clocks based on methylation or transcriptomic data separately it may have higher practical utility for target discovery. This method provides the ability to discover novel therapeutic targets that hypothetically may be able to reverse or accelerate biological age providing a pathway for therapeutic drug discovery and validation using the aging clock. In addition, we provide a list of promising targets annotated using the PandaOmics industrial target discovery platform.

The Comprehensive Analysis Illustrates the Role of CDCA5 in Breast Cancer: An Effective Diagnosis and Prognosis Biomarker

Background

Several studies have been conducted to investigate the role of cell division cycle-associated 5 (CDCA5) in cancer. Its role in breast cancer, however, remains unknown.

Methods

The Gene Expression Omnibus and Cancer Genome Atlas Program databases provided the open-access information needed for the research. The CCK8 and colony formation assays were used to measure cell proliferation. The capacity of breast cancer cells to invade and migrate was assessed using the transwell assay.

Results

In our study, CDCA5 was identified as the interested gene through a series of bioinformatics analysis. We found a higher CDCA5 expression level in tissue and cells of breast cancer. Meanwhile, CDCA5 has been linked to increased proliferation, invasion, and migration of breast cancer cells, which was also associated with worse clinical features. The biochemical pathways, in which CDCA5 was engaged, were identified using biological enrichment analysis. Immune infiltration research revealed that CDCA5 was linked to enhanced activity of several immune function terms. Meanwhile, DNA methylation might be responsible for the aberrant level of CDCA5 in tumor tissue. In addition, CDCA5 could significantly increase the paclitaxel and docetaxel sensitivity, indicating that it has the potential for clinical application. Also, we found that CDCA5 is mainly localized in cell nucleoplasm. Moreover, in the breast cancer microenvironment, we found that CDCA5 is mainly expressed in malignant cells, proliferation T cells, and neutrophils.

Conclusion

Overall, our findings suggest that CDCA5 is a potential prognostic indicator and target for breast cancer, which can indicate the direction of the relevant research.

Phospholipase C delta 1 inhibits WNT/β-catenin and EGFR-FAK-ERK signaling and is disrupted by promoter CpG methylation in renal cell carcinoma

BACKGROUND

PLCD1, located at 3p22, encodes an enzyme that mediates cellular metabolism and homeostasis, intracellular signal transduction and movement. PLCD1 plays a pivotal role in tumor suppression of several types of cancers; however, its expression and underlying molecular mechanisms in renal cell carcinoma (RCC) pathogenesis remain elusive.

METHODS

RT-PCR and Western blot were used to detect PLCD1 expression in RCC cell lines and normal tissues. Bisulfite treatment, MSP and BGS were utilized to explore the CpG methylation status of PLCD1 promoter. Online databases were analyzed for the association between PLCD1 expression/methylation and patient survival. In vitro experiments including CCK8, colony formation, wound-healing, transwell migration and invasion, immunofluorescence and flow cytometry assays were performed to evaluate tumor cell behavior. Luciferase assay and Western blot were used to examine effect of PLCD1 on WNT/β-catenin and EGFR-FAK-ERK signaling.

RESULTS

We found that PLCD1 was widely expressed in multiple adult normal tissues including kidney, but frequently downregulated or silenced in RCC due to its promoter CpG methylation. Restoration of PLCD1 expression inhibited the viability, migration and induced G2/M cell cycle arrest and apoptosis in RCC cells. PLCD1 restoration led to the inhibition of signaling activation of WNT/β-catenin and EGFR-FAK-ERK pathways, and the EMT program of RCC cells.

CONCLUSIONS

Our results demonstrate that PLCD1 is a potent tumor suppressor frequently inactivated by promoter methylation in RCC and exerts its tumor suppressive functions via suppressing WNT/β-catenin and EGFR-FAK-ERK signaling. These findings establish PLCD1 as a promising prognostic biomarker and treatment target for RCC.

An overview of DNA methylation-derived trait score methods and applications

Microarray technology has been used to measure genome-wide DNA methylation in thousands of individuals. These studies typically test the associations between individual DNA methylation sites ("probes") and complex traits or diseases. The results can be used to generate methylation profile scores (MPS) to predict outcomes in independent data sets. Although there are many parallels between MPS and polygenic (risk) scores (PGS), there are key differences. Here, we review motivations, methods, and applications of DNA methylation-based trait prediction, with a focus on common diseases. We contrast MPS with PGS, highlighting where assumptions made in genetic modeling may not hold in epigenetic data.

Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2023

The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support global academic and industrial communities. With the explosive accumulation of multi-omics data generated at an unprecedented rate, CNCB-NGDC constantly expands and updates core database resources by big data archive, integrative analysis and value-added curation. In the past year, efforts have been devoted to integrating multiple omics data, synthesizing the growing knowledge, developing new resources and upgrading a set of major resources. Particularly, several database resources are newly developed for infectious diseases and microbiology (MPoxVR, KGCoV, ProPan), cancer-trait association (ASCancer Atlas, TWAS Atlas, Brain Catalog, CCAS) as well as tropical plants (TCOD). Importantly, given the global health threat caused by monkeypox virus and SARS-CoV-2, CNCB-NGDC has newly constructed the monkeypox virus resource, along with frequent updates of SARS-CoV-2 genome sequences, variants as well as haplotypes. All the resources and services are publicly accessible at https://ngdc.cncb.ac.cn.

CanMethdb: a database for genome-wide DNA methylation annotation in cancers

MOTIVATION

DNA methylation within gene body and promoters in cancer cells is well documented. An increasing number of studies showed that cytosine-phosphate-guanine (CpG) sites falling within other regulatory elements could also regulate target gene activation, mainly by affecting transcription factors (TFs) binding in human cancers. This led to the urgent need for comprehensively and effectively collecting distinct cis-regulatory elements and TF-binding sites (TFBS) to annotate DNA methylation regulation.

RESULTS

We developed a database (CanMethdb, http://meth.liclab.net/CanMethdb/) that focused on the upstream and downstream annotations for CpG-genes in cancers. This included upstream cis-regulatory elements, especially those involving distal regions to genes, and TFBS annotations for the CpGs and downstream functional annotations for the target genes, computed through integrating abundant DNA methylation and gene expression profiles in diverse cancers. Users could inquire CpG-target gene pairs for a cancer type through inputting a genomic region, a CpG, a gene name, or select hypo/hypermethylated CpG sets. The current version of CanMethdb documented a total of 38 986 060 CpG-target gene pairs (with 6 769 130 unique pairs), involving 385 217 CpGs and 18 044 target genes, abundant cis-regulatory elements and TFs for 33 TCGA cancer types. CanMethdb might help biologists perform in-depth studies of target gene regulations based on DNA methylations in cancer.

AVAILABILITY AND IMPLEMENTATION

The main program is available at https://github.com/chunquanlipathway/CanMethdb.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Evaluation of cross-platform compatibility of a DNA methylation-based glucocorticoid response biomarker

Background

Identifying blood-based DNA methylation patterns is a minimally invasive way to detect biomarkers in predicting age, characteristics of certain diseases and conditions, as well as responses to immunotherapies. As microarray platforms continue to evolve and increase the scope of CpGs measured, new discoveries based on the most recent platform version and how they compare to available data from the previous versions of the platform are unknown. The neutrophil dexamethasone methylation index (NDMI 850) is a blood-based DNA methylation biomarker built on the Illumina MethylationEPIC (850K) array that measures epigenetic responses to dexamethasone (DEX), a synthetic glucocorticoid often administered for inflammation. Here, we compare the NDMI 850 to one we built using data from the Illumina Methylation 450K (NDMI 450).

Results

The NDMI 450 consisted of 22 loci, 15 of which were present on the NDMI 850. In adult whole blood samples, the linear composite scores from NDMI 450 and NDMI 850 were highly correlated and had equivalent predictive accuracy for detecting DEX exposure among adult glioma patients and non-glioma adult controls. However, the NDMI 450 scores of newborn cord blood were significantly lower than NDMI 850 in samples measured with both assays.

Conclusions

We developed an algorithm that reproduces the DNA methylation glucocorticoid response score using 450K data, increasing the accessibility for researchers to assess this biomarker in archived or publicly available datasets that use the 450K version of the Illumina BeadChip array. However, the NDMI850 and NDMI450 do not give similar results in cord blood, and due to data availability limitations, results from sample types of newborn cord blood should be interpreted with care.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-022-01352-1.

EpiVisR: exploratory data analysis and visualization in epigenome-wide association analyses

Background

With the widespread availability of microarray technology for epigenetic research, methods for calling differentially methylated probes or differentially methylated regions have become effective tools to analyze this type of data. Furthermore, visualization is usually employed for quality check of results and for further insights. Expert knowledge is required to leverage capabilities of these methods. To overcome this limitation and make visualization in epigenetic research available to the public, we designed EpiVisR.

Results

The EpiVisR tool allows to select and visualize combinations of traits (i.e., concentrations of chemical compounds) and differentially methylated probes/regions. It supports various modes of enriched presentation to get the most knowledge out of existing data: (1) enriched Manhattan plot and enriched volcano plot for selection of probes, (2) trait-methylation plot for visualization of selected trait values against methylation values, (3) methylation profile plot for visualization of a selected range of probes against selected trait values as well as, (4) correlation profile plot for selection and visualization of further probes that are correlated to the selected probe. EpiVisR additionally allows exporting selected data to external tools for tasks such as network analysis.

Conclusion

The key advantage of EpiVisR is the annotation of data in the enriched plots (and tied tables) as well as linking to external data sources for further integrated data analysis. Using the EpiVisR approach will allow users to integrate data from traits with epigenetic analyses that are connected by belonging to the same individuals. Merging data from various data sources among the same cohort and visualizing them will enable users to gain more insights from existing data.

The Prognostic Significance and Potential Mechanism of Prolyl 3-Hydroxylase 1 in Hepatocellular Carcinoma

Background. Prolyl 3-hydroxylase 1 (P3H1) is essential for human collagen synthesis. Here, we investigated its relevance to multiple cancers, especially hepatocellular carcinoma (LIHC). Methods. We estimated the relationship of P3H1 with 33 cancers using publicly available databases. And immunohistochemistry was utilized to verify the P3H1 expression in liver, gastric, colon, pancreatic, and rectal cancer. Then, we attenuated P3H1 expression in BEL-7402 and HLF cells by lentivirus technology and assessed the effect of P3H1 on cell proliferation, migration, and invasion. Results. Bioinformatic analysis revealed a significantly higher expression of P3H1 in almost all tumors, which was consistent with the immunohistochemical findings in the liver, gastric, colon, pancreatic, and rectal cancers. P3H1 expression was associated with overall survival, progression-free interval, disease-specific survival, and disease-free interval in most cancers, particularly in LIHC. Besides, we also found that P3H1 expression was an independent prognostic factor for LIHC. And knockdown of P3H1 significantly reduced liver cancer cell proliferation, migration, and invasion in liver cancer cells. Interestingly, P3H1 expression levels showed a significant positive connection with Th2 infiltration through multiple immune infiltration algorithms. ICI treatment was less effective in LIHC patients with high P3H1 expression. Finally, we also identified an upstream regulatory mechanism of P3H1 in LIHC, namely, AL355488.1, HCG18, and THUMPD3-AS1/hsa-miR-29c-3p-P3H1 axis. Conclusion. We have systematically described for the first time that P3H1 is closely related to various tumors, particularly in LIHC, and interference with P3H1 may be a therapeutic target for patients with LIHC.

Maternal blood pressure associates with placental DNA methylation both directly and through alterations in cell-type composition

BACKGROUND

Maternal blood pressure levels reflect cardiovascular adaptation to pregnancy and proper maternal-fetal exchanges through the placenta and are very sensitive to numerous environmental stressors. Maternal hypertension during pregnancy has been associated with impaired placental functions and with an increased risk for children to suffer from cardiovascular and respiratory diseases later on. Investigating changes in placental DNA methylation levels and cell-type composition in association with maternal blood pressure could help elucidate its relationships with placental and fetal development.

METHODS

Taking advantage of a large cohort of 666 participants, we investigated the association between epigenome-wide DNA methylation patterns in the placenta, measured using the Infinium HumanMethylation450 BeadChip, placental cell-type composition, estimated in silico, and repeated measurements of maternal steady and pulsatile blood pressure indicators during pregnancy.

RESULTS

At the site-specific level, no significant association was found between maternal blood pressure and DNA methylation levels after correction for multiple testing (false discovery rate < 0.05), but 5 out of 24 previously found CpG associations were replicated (p-value < 0.05). At the regional level, our analyses highlighted 64 differentially methylated regions significantly associated with at least one blood pressure component, including 35 regions associated with mean arterial pressure levels during late pregnancy. These regions were found enriched for genes implicated in lung development and diseases. Further mediation analyses show that a significant part of the association between steady blood pressure-but not pulsatile pressure-and placental methylation can be explained by alterations in placental cell-type composition. In particular, elevated blood pressure levels are associated with a decrease in the ratio between mesenchymal stromal cells and syncytiotrophoblasts, even in the absence of preeclampsia.

CONCLUSIONS

This study provides the first evidence that the association between maternal steady blood pressure during pregnancy and placental DNA methylation is both direct and partly explained by changes in cell-type composition. These results could hint at molecular mechanisms linking maternal hypertension to lung development and early origins of childhood respiratory problems and at the importance of controlling maternal blood pressure during pregnancy.

Pan-cancer analysis of LncRNA XIST and its potential mechanisms in human cancers

Background

Methods

Results

Conclusion

Multi-Omics Analysis of the Tumor Microenvironment in Liver Metastasis of Colorectal Cancer Identified FJX1 as a Novel Biomarker

Colorectal cancer incidence and mortality have increased in recent years, with more than half of patients who died of colorectal cancer developing liver metastases. Consequently, colorectal cancer liver metastasis is the focus of clinical treatment, as well as being the most difficult. The primary target genes related to colorectal cancer liver metastasis were via bioinformatics analysis. First, five prognosis-related genes, CTAG1A, CSTL1, FJX1, IER5L, and KLHL35, were identified through screening, and the prognosis of the CSTL1, FJX1, IER5L, and KLHL35 high expression group was considerably poorer than that of the low expression group. Furthermore, the clinical correlation analysis revealed that in distinct pathological stages T, N, and M, the mRNA expression levels of CSTL1, IER5L, and KLHL35 were higher than in normal tissues. Finally, a correlation study of the above genes and clinical manifestations revealed that FJX1 was strongly linked to colorectal cancer liver metastasis. FJX1 is thought to affect chromogenic modification enzymes, the Notch signaling system, cell senescence, and other signaling pathways, according to KEGG enrichment analysis. FJX1 may be a critical target in colorectal cancer metastasis, and thus has the potential as a new biomarker to predict and treat colorectal cancer liver metastases.

Diversity in EWAS: current state, challenges, and solutions

Here, we report a lack of diversity in epigenome-wide association studies (EWAS) and DNA methylation (DNAm) data, discuss current challenges, and propose solutions for EWAS and DNAm research in diverse populations. The strategies we propose include fostering community involvement, new data generation, and cost-effective approaches such as locus-specific analysis and ancestry variable region analysis.

Identification of COVID-19-Associated DNA Methylation Variations by Integrating Methylation Array and scRNA-Seq Data at Cell-Type Resolution

Single-cell transcriptome studies have revealed immune dysfunction in COVID-19 patients, including lymphopenia, T cell exhaustion, and increased levels of pro-inflammatory cytokines, while DNA methylation plays an important role in the regulation of immune response and inflammatory response. The specific cell types of immune responses regulated by DNA methylation in COVID-19 patients will be better understood by exploring the COVID-19 DNA methylation variation at the cell-type level. Here, we developed an analytical pipeline to explore single-cell DNA methylation variations in COVID-19 patients by transferring bulk-tissue-level knowledge to the single-cell level. We discovered that the methylation variations in the whole blood of COVID-19 patients showed significant cell-type specificity with remarkable enrichment in gamma-delta T cells and presented a phenomenon of hypermethylation and low expression. Furthermore, we identified five genes whose methylation variations were associated with several cell types. Among them, S100A9, AHNAK, and CX3CR1 have been reported as potential COVID-19 biomarkers previously, and the others (TRAF3IP3 and LFNG) are closely associated with the immune and virus-related signaling pathways. We propose that they might serve as potential epigenetic biomarkers for COVID-19 and could play roles in important biological processes such as the immune response and antiviral activity.

Integrative multi-omic analysis identifies genetically influenced DNA methylation biomarkers for breast and prostate cancers

Aberrant DNA methylation has emerged as a hallmark in several cancers and contributes to risk, oncogenesis, progression, and prognosis. In this study, we performed imputation-based and conventional methylome-wide association analyses for breast cancer (BrCa) and prostate cancer (PrCa). The imputation-based approach identified DNA methylation at cytosine-phosphate-guanine sites (CpGs) associated with BrCa and PrCa risk utilising genome-wide association summary statistics (N_BrCa = 228,951, N_PrCa = 140,254) and prebuilt methylation prediction models, while the conventional approach identified CpG associations utilising TCGA and GEO experimental methylation data (N_BrCa = 621, N_PrCa = 241). Enrichment analysis of the association results implicated 77 and 81 genetically influenced CpGs for BrCa and PrCa, respectively. Furthermore, analysis of differential gene expression around these CpGs suggests a genome-epigenome-transcriptome mechanistic relationship. Conditional analyses identified multiple independent secondary SNP associations (P_cond < 0.05) around 28 BrCa and 22 PrCa CpGs. Cross-cancer analysis identified eight common CpGs, including a strong therapeutic target in SREBF1 (17p11.2)—a key player in lipid metabolism. These findings highlight the utility of integrative analysis of multi-omic cancer data to identify robust biomarkers and understand their regulatory effects on cancer risk.

Methylome-wide association studies identify genetically-influenced CpGs associated with breast and prostate cancer risk and (epi)genome-transcriptome mechanistic relationships, with lipid metabolism genes implicated as potential therapeutic targets.

Identifying the Potential Roles of PBX4 in Human Cancers Based on Integrative Analysis

PBX4 belongs to the pre-B-cell leukemia homeobox (PBX) transcription factors family and acts as a transcriptional cofactor of HOX proteins participating in several pathophysiological processes. Recent studies have revealed that the dysregulation of PBX4 is closely related to multiple diseases, especially cancers. However, the research on PBX4’s potential roles in 33 cancers from the Cancer Genome Atlas (TCGA) is still insufficient. Therefore, we performed a comprehensive pan-cancer analysis to explore the roles of PBX4with multiple public databases. Our results showed that PBX4 was differentially expressed in 17 types of human cancer and significantly correlated to the pathological stage, tumor grade, and immune and molecular subtypes. We used the Kaplan–Meier plotter and PrognoScan databases to find the significant associations between PBX4 expression and prognostic values of multiple cancers. It was also found that PBX4 expression was statistically related to mutation status, DNA methylation, immune infiltration, drug sensitivity, and immune checkpoint blockade (ICB) therapy. Additionally, we found that PBX4 was involved in different functional states of multiple cancers from the single-cell resolution perspective. Enrichment analysis results showed that PBX4-related genes were enriched in the cell cycle process, MAPK cascade, ncRNA metabolic process, positive regulation of GTPase activity, and regulation of lipase activity and mainly participated in the pathways of cholesterol metabolism, base excision repair, herpes simplex virus 1 infection, transcriptional misregulation in cancer, and Epstein–Barr virus infection. Altogether, our integrative analysis could help in better understanding the potential roles of PBX4 in different human cancers.

MIAOME: Human Microbiome Affect The Host Epigenome

Besides the genetic factors having tremendous influences on the regulations of the epigenome, the microenvironmental factors have recently gained extensive attention for their roles in affecting the host epigenome. There are three major types of microenvironmental factors: microbiota-derived metabolites (MDM), microbiota-derived components (MDC) and microbiota-secreted proteins (MSP). These factors can regulate host physiology by modifying host gene expression through the three highly interconnected epigenetic mechanisms (e.g. histone modifications, DNA modifications, and non-coding RNAs). However, no database was available to provide the comprehensive factors of these types. Herein, a database entitled 'Human Microbiome Affect The Host Epigenome (MIAOME)' was constructed. Based on the types of epigenetic modifications confirmed in the literature review, the MIAOME database captures 1068 (63 genus, 281 species, 707 strains, etc.) human microbes, 91 unique microbiota-derived metabolites & components (16 fatty acids, 10 bile acids, 10 phenolic compounds, 10 vitamins, 9 tryptophan metabolites, etc.) derived from 967 microbes; 50 microbes that secreted 40 proteins; 98 microbes that directly influence the host epigenetic modification, and provides 3 classifications of the epigenome, including (1) 4 types of DNA modifications, (2) 20 histone modifications and (3) 490 ncRNAs regulations, involved in 160 human diseases. All in all, MIAOME has compiled the information on the microenvironmental factors influence host epigenome through the scientific literature and biochemical databases, and allows the collective considerations among the different types of factors. It can be freely assessed without login requirement by all users at: http://miaome.idrblab.net/ttd/

GMQN: A Reference-Based Method for Correcting Batch Effects and Probe Bias in HumanMethylation BeadChip

The Illumina HumanMethylation BeadChip is one of the most cost-effective methods to quantify DNA methylation levels at single-base resolution across the human genome, which makes it a routine platform for epigenome-wide association studies. It has accumulated tens of thousands of DNA methylation array samples in public databases, providing great support for data integration and further analysis. However, the majority of public DNA methylation data are deposited as processed data without background probes which are widely used in data normalization. Here, we present Gaussian mixture quantile normalization (GMQN), a reference based method for correcting batch effects as well as probe bias in the HumanMethylation BeadChip. Availability and implementation: https://github.com/MengweiLi-project/gmqn.

Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2022

The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support global research in both academia and industry. With the explosively accumulated multi-omics data at ever-faster rates, CNCB-NGDC is constantly scaling up and updating its core database resources through big data archive, curation, integration and analysis. In the past year, efforts have been made to synthesize the growing data and knowledge, particularly in single-cell omics and precision medicine research, and a series of resources have been newly developed, updated and enhanced. Moreover, CNCB-NGDC has continued to daily update SARS-CoV-2 genome sequences, variants, haplotypes and literature. Particularly, OpenLB, an open library of bioscience, has been established by providing easy and open access to a substantial number of abstract texts from PubMed, bioRxiv and medRxiv. In addition, Database Commons is significantly updated by cataloguing a full list of global databases, and BLAST tools are newly deployed to provide online sequence search services. All these resources along with their services are publicly accessible at https://ngdc.cncb.ac.cn.

EWAS Open Platform: integrated data, knowledge and toolkit for epigenome-wide association study

Epigenome-Wide Association Study (EWAS) has become a standard strategy to discover DNA methylation variation of different phenotypes. Since 2018, we have developed EWAS Atlas and EWAS Data Hub to integrate a growing volume of EWAS knowledge and data, respectively. Here, we present EWAS Open Platform (https://ngdc.cncb.ac.cn/ewas) that includes EWAS Atlas, EWAS Data Hub and the newly developed EWAS Toolkit. In the current implementation, EWAS Open Platform integrates 617 018 high-quality EWAS associations from 910 publications, covering 51 phenotypes, 275 diseases and 104 environmental factors. It also provides well-normalized DNA methylation array data and the corresponding metadata from 115 852 samples, which involve 707 tissues, 218 cell lines and 528 diseases. Taking advantage of integrated knowledge and data in EWAS Atlas and EWAS Data Hub, EWAS Open Platform equips with EWAS Toolkit, a powerful one-stop site for EWAS enrichment, annotation, and knowledge network construction and visualization. Collectively, EWAS Open Platform provides open access to EWAS knowledge, data and toolkit and thus bears great utility for a broader range of relevant research.

Epigenome-wide association studies: current knowledge, strategies and recommendations

The aetiology and pathophysiology of complex diseases are driven by the interaction between genetic and environmental factors. The variability in risk and outcomes in these diseases are incompletely explained by genetics or environmental risk factors individually. Therefore, researchers are now exploring the epigenome, a biological interface at which genetics and the environment can interact. There is a growing body of evidence supporting the role of epigenetic mechanisms in complex disease pathophysiology. Epigenome-wide association studies (EWASes) investigate the association between a phenotype and epigenetic variants, most commonly DNA methylation. The decreasing cost of measuring epigenome-wide methylation and the increasing accessibility of bioinformatic pipelines have contributed to the rise in EWASes published in recent years. Here, we review the current literature on these EWASes and provide further recommendations and strategies for successfully conducting them. We have constrained our review to studies using methylation data as this is the most studied epigenetic mechanism; microarray-based data as whole-genome bisulphite sequencing remains prohibitively expensive for most laboratories; and blood-based studies due to the non-invasiveness of peripheral blood collection and availability of archived DNA, as well as the accessibility of publicly available blood-cell-based methylation data. Further, we address multiple novel areas of EWAS analysis that have not been covered in previous reviews: (1) longitudinal study designs, (2) the chip analysis methylation pipeline (ChAMP), (3) differentially methylated region (DMR) identification paradigms, (4) methylation quantitative trait loci (methQTL) analysis, (5) methylation age analysis and (6) identifying cell-specific differential methylation from mixed cell data using statistical deconvolution.

Combining current knowledge on DNA methylation-based age estimation towards the development of a superior forensic DNA intelligence tool

The estimation of chronological age from biological fluids has been an important quest for forensic scientists worldwide, with recent approaches exploiting the variability of DNA methylation patterns with age in order to develop the next generation of forensic 'DNA intelligence' tools for this application. Drawing from the conclusions of previous work utilising massively parallel sequencing (MPS) for this analysis, this work introduces a DNA methylation-based age estimation method for blood that exhibits the best combination of prediction accuracy and sensitivity reported to date. Statistical evaluation of markers from 51 studies using microarray data from over 4000 individuals, followed by validation using in-house generated MPS data, revealed a final set of 11 markers with the greatest potential for accurate age estimation from minimal DNA material. Utilising an algorithm based on support vector machines, the proposed model achieved an average error (MAE) of 3.3 years, with this level of accuracy retained down to 5 ng of starting DNA input (~ 1 ng PCR input). The accuracy of the model was retained (MAE = 3.8 years) in a separate test set of 88 samples of Spanish origin, while predictions for donors of greater forensic interest (< 55 years of age) displayed even higher accuracy (MAE = 2.6 years). Finally, no sex-related bias was observed for this model, while there were also no signs of variation observed between control and disease-associated populations for schizophrenia, rheumatoid arthritis, frontal temporal dementia and progressive supranuclear palsy in microarray data relating to the 11 markers.

Deep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data

Deep learning (DL) is a distinct class of machine learning that has achieved first-class performance in many fields of study. For epigenomics, the application of DL to assist physicians and scientists in human disease-relevant prediction tasks has been relatively unexplored until very recently. In this article, we critically review published studies that employed DL models to predict disease detection, subtype classification, and treatment responses, using epigenomic data. A comprehensive search on PubMed, Scopus, Web of Science, Google Scholar, and arXiv.org was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Among 1140 initially identified publications, we included 22 articles in our review. DNA methylation and RNA-sequencing data are most frequently used to train the predictive models. The reviewed models achieved a high accuracy ranged from 88.3% to 100.0% for disease detection tasks, from 69.5% to 97.8% for subtype classification tasks, and from 80.0% to 93.0% for treatment response prediction tasks. We generated a workflow to develop a predictive model that encompasses all steps from first defining human disease-related tasks to finally evaluating model performance. DL holds promise for transforming epigenomic big data into valuable knowledge that will enhance the development of translational epigenomics.

Ten Years of EWAS

Epigenome-wide association study (EWAS) has been applied to analyze DNA methylation variation in complex diseases for a decade, and epigenome as a research target has gradually become a hot topic of current studies. The DNA methylation microarrays, next-generation, and third-generation sequencing technologies have prepared a high-quality platform for EWAS. Here, the progress of EWAS research is reviewed, its contributions to clinical applications, and mainly describe the achievements of four typical diseases. Finally, the challenges encountered by EWAS and make bold predictions for its future development are presented.

scMethBank: a database for single-cell whole genome DNA methylation maps

Single-cell bisulfite sequencing methods are widely used to assess epigenomic heterogeneity in cell states. Over the past few years, large amounts of data have been generated and facilitated deeper understanding of the epigenetic regulation of many key biological processes including early embryonic development, cell differentiation and tumor progression. It is an urgent need to build a functional resource platform with the massive amount of data. Here, we present scMethBank, the first open access and comprehensive database dedicated to the collection, integration, analysis and visualization of single-cell DNA methylation data and metadata. Current release of scMethBank includes processed single-cell bisulfite sequencing data and curated metadata of 8328 samples derived from 15 public single-cell datasets, involving two species (human and mouse), 29 cell types and two diseases. In summary, scMethBank aims to assist researchers who are interested in cell heterogeneity to explore and utilize whole genome methylation data at single-cell level by providing browse, search, visualization, download functions and user-friendly online tools. The database is accessible at: https://ngdc.cncb.ac.cn/methbank/scm/.

Identification of diagnostic cytosine-phosphate-guanine biomarkers in patients with gestational diabetes mellitus via epigenome-wide association study and machine learning

OBJECTIVE

To explore gestational diabetes mellitus (GDM) diagnostic markers and establish the predictive model of GDM.

METHODS

We downloaded the DNA methylation data of GSE70453 and GSE102177 from the Gene Expression Omnibus database. Epigenome-wide association study (EWAS) was performed to analyze the relationship between cytosine-phosphate-guanine (CpG) methylation and GDM. And then the logistic regression models were constructed, with the β-values of CpG sites as predictor variable and the GDM occurrence as binary outcome variable. Data from GSE70453 served as training sets and data from GSE102177 served as verification sets.

RESULTS

The EWAS and overlap analysis identified nine-shared significant CpGs in the two DNA methylation data sets. Remarkably, these nine CpGs were differently methylated in GDM samples compared to their matched normal specimens, among which five fully methylated CpGs were finally selected. Importantly, we established a binary logistic regression model based on the above five CpGs, in which cg11169102, cg21179618 and cg21620107 were critical. Hence, we further built a logistic regression model by using the three CpGs and found that the area under the curve was 0.8209. The validation of the model by using the verification sets indicated the area under the curve was 0.8519.

CONCLUSIONS

We identified potential CpG biomarkers for the diagnosis of gestational diabetes mellitus patients through using EWAS and Logistic regression models in combination.

Connecting the epigenome, metabolome and proteome for a deeper understanding of disease

Epigenome-wide association studies (EWAS) identify genes that are dysregulated by the studied clinical endpoints, thereby indicating potential new diagnostic biomarkers, drug targets and therapy options. Combining EWAS with deep molecular phenotyping, such as approaches enabled by metabolomics and proteomics, allows further probing of the underlying disease-associated pathways. For instance, methylation of the TXNIP gene is associated robustly with prevalent type 2 diabetes and further with metabolites that are short-term markers of glycaemic control. These associations reflect TXNIP's function as a glucose uptake regulator by interaction with the major glucose transporter GLUT1 and suggest that TXNIP methylation can be used as a read-out for the organism's exposure to glucose stress. Another case is the association between DNA methylation of the AHRR and F2RL3 genes with smoking and a protein that is involved in the reprogramming of the bronchial epithelium. These examples show that associations between DNA methylation and intermediate molecular traits can open new windows into how the body copes with physiological challenges. This knowledge, if carefully interpreted, may indicate novel therapy options and, together with monitoring of the methylation state of specific methylation sites, may in the future allow the early diagnosis of impending disease. It is essential for medical practitioners to recognize the potential that this field holds in translating basic research findings to clinical practice. In this review, we present recent advances in the field of EWAS with metabolomics and proteomics and discuss both the potential and the challenges of translating epigenetic associations, with deep molecular phenotypes, to biomedical applications.

Genome Warehouse: A Public Repository Housing Genome-scale Data

The Genome Warehouse (GWH) is a public repository housing genome assembly data for a wide range of species and delivering a series of web services for genome data submission, storage, release, and sharing. As one of the core resources in the National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB; https://ngdc.cncb.ac.cn), GWH accepts both full and partial (chloroplast, mitochondrion, and plasmid) genome sequences with different assembly levels, as well as an update of existing genome assemblies. For each assembly, GWH collects detailed genome-related metadata of biological project, biological sample, and genome assembly, in addition to genome sequence and annotation. To archive high-quality genome sequences and annotations, GWH is equipped with a uniform and standardized procedure for quality control. Besides basic browse and search functionalities, all released genome sequences and annotations can be visualized with JBrowse. By May 21, 2021, GWH has received 19,124 direct submissions covering a diversity of 1108 species and has released 8772 of them. Collectively, GWH serves as an important resource for genome-scale data management and provides free and publicly accessible data to support research activities throughout the world. GWH is publicly accessible at https://ngdc.cncb.ac.cn/gwh.

Identification of Diagnostic CpG Signatures in Patients with Gestational Diabetes Mellitus via Epigenome-Wide Association Study Integrated with Machine Learning

Background

Gestational diabetes mellitus (GDM) is the most prevalent metabolic disease during pregnancy, but the diagnosis is controversial and lagging partly due to the lack of useful biomarkers. CpG methylation is involved in the development of GDM. However, the specific CpG methylation sites serving as diagnostic biomarkers of GDM remain unclear. Here, we aimed to explore CpG signatures and establish the predicting model for the GDM diagnosis.

Methods

DNA methylation data of GSE88929 and GSE102177 were obtained from the GEO database, followed by the epigenome-wide association study (EWAS). GO and KEGG pathway analyses were performed by using the clusterProfiler package of R. The PPI network was constructed in the STRING database and Cytoscape software. The SVM model was established, in which the β-values of selected CpG sites were the predictor variable and the occurrence of GDM was the outcome variable.

Results

We identified 62 significant CpG methylation sites in the GDM samples compared with the control samples. GO and KEGG analyses based on the 62 CpG sites demonstrated that several essential cellular processes and signaling pathways were enriched in the system. A total of 12 hub genes related to the identified CpG sites were found in the PPI network. The SVM model based on the selected CpGs within the promoter region, including cg00922748, cg05216211, cg05376185, cg06617468, cg17097119, and cg22385669, was established, and the AUC values of the training set and testing set in the model were 0.8138 and 0.7576. The AUC value of the independent validation set of GSE102177 was 0.6667.

Conclusion

We identified potential diagnostic CpG signatures by EWAS integrated with the SVM model. The SVM model based on the identified 6 CpG sites reliably predicted the GDM occurrence, contributing to the diagnosis of GDM. Our finding provides new insights into the cross-application of EWAS and machine learning in GDM investigation.

Epigenetic dysregulation of immune-related pathways in cancer: bioinformatics tools and visualization

Cancer immune evasion is one of the hallmarks of carcinogenesis. Cancer cells employ multiple mechanisms to avoid immune recognition and suppress antitumor immune responses. Recently, accumulating evidence has indicated that immune-related pathways are epigenetically dysregulated in cancer. Most importantly, the epigenetic footprint of immune-related pathways is associated with the patient outcome, underscoring the crucial need to understand this process. In this review, we summarize the current evidence for epigenetic regulation of immune-related pathways in cancer and describe bioinformatics tools, informative visualization techniques, and resources to help decipher the cancer epigenome.

Abnormal patterns of genomic chemical modification help tumors elude immunological destruction, but sophisticated computational tools could help identify and overcome these survival mechanisms. Immunotherapy can be a potent weapon against cancer, but many tumors evolve the ability to protect themselves by subduing the immune response. Sungjune Kim and colleagues at the Moffitt Cancer Center, Tampa, USA, have reviewed efforts to study how chemical alterations to DNA that affect gene expression contribute to this process. Considerable evidence indicates a role for a modification called methylation in this immune evasion, and researchers now have access to vast repositories of tumor-specific gene methylation profiles. The authors describe these data resources, and highlight some of the software tools that are helping oncologists to identify patterns in the data that might lead to better therapies.

Human methylome variation across Infinium 450K data on the Gene Expression Omnibus

While DNA methylation (DNAm) is the most-studied epigenetic mark, few recent studies probe the breadth of publicly available DNAm array samples. We collectively analyzed 35 360 Illumina Infinium HumanMethylation450K DNAm array samples published on the Gene Expression Omnibus. We learned a controlled vocabulary of sample labels by applying regular expressions to metadata and used existing models to predict various sample properties including epigenetic age. We found approximately two-thirds of samples were from blood, one-quarter were from brain and one-third were from cancer patients. About 19% of samples failed at least one of Illumina's 17 prescribed quality assessments; signal distributions across samples suggest modifying manufacturer-recommended thresholds for failure would make these assessments more informative. We further analyzed DNAm variances in seven tissues (adipose, nasal, blood, brain, buccal, sperm and liver) and characterized specific probes distinguishing them. Finally, we compiled DNAm array data and metadata, including our learned and predicted sample labels, into database files accessible via the recountmethylation R/Bioconductor companion package. Its vignettes walk the user through some analyses contained in this paper.

Epigenetic and metabolic regulation of epidermal homeostasis

Continuous exposure of the skin to environmental, mechanical and chemical stress necessitates constant self‐renewal of the epidermis to maintain its barrier function. This self‐renewal ability is attributed to epidermal stem cells (EPSCs), which are long‐lived, multipotent cells located in the basal layer of the epidermis. Epidermal homeostasis – coordinated proliferation and differentiation of EPSCs – relies on fine‐tuned adaptations in gene expression which in turn are tightly associated with specific epigenetic signatures and metabolic requirements. In this review, we will briefly summarize basic concepts of EPSC biology and epigenetic regulation with relevance to epidermal homeostasis. We will highlight the intricate interplay between mitochondrial energy metabolism and epigenetic events – including miRNA‐mediated mechanisms – and discuss how the loss of epigenetic regulation and epidermal homeostasis manifests in skin disease. Discussion of inherited epidermolysis bullosa (EB) and disorders of cornification will focus on evidence for epigenetic deregulation and failure in epidermal homeostasis, including stem cell exhaustion and signs of premature ageing. We reason that the epigenetic and metabolic component of epidermal homeostasis is significant and warrants close attention. Charting epigenetic and metabolic complexities also represents an important step in the development of future systemic interventions aimed at restoring epidermal homeostasis and ameliorating disease burden in severe skin conditions.

DNA methylation and cancer incidence: lymphatic-hematopoietic versus solid cancers in the Strong Heart Study

BACKGROUND

Epigenetic alterations may contribute to early detection of cancer. We evaluated the association of blood DNA methylation with lymphatic-hematopoietic cancers and, for comparison, with solid cancers. We also evaluated the predictive ability of DNA methylation for lymphatic-hematopoietic cancers.

METHODS

Blood DNA methylation was measured using the Illumina Infinium methylationEPIC array in 2324 Strong Heart Study participants (41.4% men, mean age 56 years). 788,368 CpG sites were available for differential DNA methylation analysis for lymphatic-hematopoietic, solid and overall cancers using elastic-net and Cox regression models. We conducted replication in an independent population: the Framingham Heart Study. We also analyzed differential variability and conducted bioinformatic analyses to assess for potential biological mechanisms.

RESULTS

Over a follow-up of up to 28 years (mean 15), we identified 41 lymphatic-hematopoietic and 394 solid cancer cases. A total of 126 CpGs for lymphatic-hematopoietic cancers, 396 for solid cancers, and 414 for overall cancers were selected as predictors by the elastic-net model. For lymphatic-hematopoietic cancers, the predictive ability (C index) increased from 0.58 to 0.87 when adding these 126 CpGs to the risk factor model in the discovery set. The association was replicated with hazard ratios in the same direction in 28 CpGs in the Framingham Heart Study. When considering the association of variability, rather than mean differences, we found 432 differentially variable regions for lymphatic-hematopoietic cancers.

CONCLUSIONS

This study suggests that differential methylation and differential variability in blood DNA methylation are associated with lymphatic-hematopoietic cancer risk. DNA methylation data may contribute to early detection of lymphatic-hematopoietic cancers.

Analysis and annotation of DNA methylation in two nonhuman primate species using the Infinium Human Methylation 450K and EPIC BeadChips

Aim: Nonhuman primates are essential for research on many human diseases. The Infinium Human Methylation450/EPIC BeadChips are popular tools for the study of the methylation state across the human genome at affordable cost. Methods: We performed a precise evaluation and re-annotation of the BeadChip probes for the analysis of genome-wide DNA methylation patterns in rhesus macaques and African green monkeys through in silico analyses combined with functional validation by pyrosequencing. Results: Up to 165,847 of the 450K and 261,545 probes of the EPIC BeadChip can be reliably used. The annotation files are provided in a format compatible with a variety of standard bioinformatic pipelines. Conclusion: Our study will facilitate high-throughput DNA methylation analyses in Macaca mulatta and Chlorocebus sabaeus.

Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2021

The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a suite of database resources to support worldwide research activities in both academia and industry. With the explosive growth of multi-omics data, CNCB-NGDC is continually expanding, updating and enriching its core database resources through big data deposition, integration and translation. In the past year, considerable efforts have been devoted to 2019nCoVR, a newly established resource providing a global landscape of SARS-CoV-2 genomic sequences, variants, and haplotypes, as well as Aging Atlas, BrainBase, GTDB (Glycosyltransferases Database), LncExpDB, and TransCirc (Translation potential for circular RNAs). Meanwhile, a series of resources have been updated and improved, including BioProject, BioSample, GWH (Genome Warehouse), GVM (Genome Variation Map), GEN (Gene Expression Nebulas) as well as several biodiversity and plant resources. Particularly, BIG Search, a scalable, one-stop, cross-database search engine, has been significantly updated by providing easy access to a large number of internal and external biological resources from CNCB-NGDC, our partners, EBI and NCBI. All of these resources along with their services are publicly accessible at https://bigd.big.ac.cn.

Mapping DNA Methylation in Mammals: The State of the Art

A complete understanding of the dynamics and function of cytosine modifications in mammalian biology is lacking. Central to achieving this understanding is the availability of techniques that permit sensitive and specific genome-wide mapping of DNA modifications in mammalian DNA. The last decade has seen the development of a vast arsenal of novel profiling approaches enabling epigeneticists to tackle research questions that were previously out of reach. Here, we review the techniques currently available for profiling DNA modifications in mammals, discuss their strengths and weaknesses, and speculate on the future direction of DNA modification profiling technologies.

Database Resources of the National Genomics Data Center in 2020

The National Genomics Data Center (NGDC) provides a suite of database resources to support worldwide research activities in both academia and industry. With the rapid advancements in higher-throughput and lower-cost sequencing technologies and accordingly the huge volume of multi-omics data generated at exponential scales and rates, NGDC is continually expanding, updating and enriching its core database resources through big data integration and value-added curation. In the past year, efforts for update have been mainly devoted to BioProject, BioSample, GSA, GWH, GVM, NONCODE, LncBook, EWAS Atlas and IC4R. Newly released resources include three human genome databases (PGG.SNV, PGG.Han and CGVD), eLMSG, EWAS Data Hub, GWAS Atlas, iSheep and PADS Arsenal. In addition, four web services, namely, eGPS Cloud, BIG Search, BIG Submission and BIG SSO, have been significantly improved and enhanced. All of these resources along with their services are publicly accessible at https://bigd.big.ac.cn.