Computationally expanding infinium HumanMethylation450 BeadChip array data to reveal distinct DNA methylation patterns of rheumatoid arthritis

Telonis A, Yang Q, Jiang L, E. Garrett-Bakelman F, Sekeres MA, Santini V, Ceccarelli M, Goel N, Garcia-Martinez L, Morey L, Figueroa ME, Guo Y. Genome-wide methylome modeling via generative AI incorporating long- and short-range interactions

Using millions of methylation segments, we developed DiffuCpG, a generative artificial intelligence (AI) diffusion model designed to solve the critical challenge of missing data in high-throughput methylation technologies. DiffuCpG goes beyond conventional methods by leveraging both short-range interactions including nearby CpGs from both latitude and longitude of the dataset, local DNA sequences, and long-range interactions, including three-dimensional genome architecture and long-distance correlations, to comprehensively model the methylome. Compared to previous methods, through extensive independent validations across different tissue types, cancers, and technologies (whole-genome bisulfite sequencing, enhanced reduced representation bisulfite sequencing, single-cell bisulfite sequencing, and methylation arrays), DiffuCpG has demonstrated superior performance in accuracy, scalability, and versatility. On average, bisulfite sequencing dataset, DiffuCpG can extend the original dataset by millions of additional CpGs. As an alternative application of generative AI, DiffuCpG addresses a key bottleneck in epigenetic research and will substantially benefit studies relying on high-throughput methylation data.

Recent advances in anti-inflammatory active components and action mechanisms of natural medicines

Inflammation is a series of reactions caused by the body's resistance to external biological stimuli. Inflammation affects the occurrence and development of many diseases. Anti-inflammatory drugs have been used widely to treat inflammatory diseases, but long-term use can cause toxic side-effects and affect human functions. As immunomodulators with long-term conditioning effects and no drug residues, natural products are being investigated increasingly for the treatment of inflammatory diseases. In this review, we focus on the inflammatory process and cellular mechanisms in the development of diseases such as inflammatory bowel disease, atherosclerosis, and coronavirus disease-2019. Also, we focus on three signaling pathways (Nuclear factor-kappa B, p38 mitogen-activated protein kinase, Janus kinase/signal transducer and activator of transcription-3) to explain the anti-inflammatory effect of natural products. In addition, we also classified common natural products based on secondary metabolites and explained the association between current bidirectional prediction progress of natural product targets and inflammatory diseases.

Does DNA methylation mediate the association of age at puberty with FVC or FEV1?

Background

Age of pubertal onset is associated with lung function in adulthood. However, the underlying role of epigenetics as a mediator of this association remains unknown.

Methods

DNA methylation (DNAm) in peripheral blood was measured at age 18 years in the Isle of Wight birth cohort (IOWBC) along with data on age of pubertal events, forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV₁) at 26 years. Structural equation models were applied to examine mediation effects of DNAm on the association of age at pubertal events with FVC and FEV₁. Findings were further tested in the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort.

Results

In the IOWBC, for females, 21 cytosine-phosphate-guanine sites (CpGs) were shown to mediate the association of age at puberty with FVC or FEV₁ at 26 years (p<0.05). In males, DNAm at 20 CpGs was found to mediate the association of age at puberty with FVC (p<0.05). At almost all these CpGs, indirect effects (effects of age at pubertal events on FVC or FEV₁via DNAm) contributed a smaller portion to the total effects compared to direct effects (e.g. at cg08680129, ∼22% of the estimated total effect of age at menarche on FVC at age 26 was contributed by an indirect effect). Among the IOWBC-discovered CpGs available in ALSPAC, none of them was replicated in ALSPAC (p>0.05).

Conclusions

Our findings suggest that post-adolescence DNAm in peripheral blood is likely not to mediate the association of age at pubertal onset with young adulthood FVC or FEV₁.

The association between age at pubertal onset and lung function parameters FVC or FEV₁ in young adulthood is not likely to be mediated by DNA methylation in peripheral bloodhttps://bit.ly/31G8hDi

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.

Transcriptome-Wide High-Throughput m6A Sequencing of Differential m6A Methylation Patterns in the Human Rheumatoid Arthritis Fibroblast-Like Synoviocytes Cell Line MH7A

Introduction

N6-methyladenosine (m6A) is the most frequent internal modification in eukaryotic mRNAs and is closely related to the occurrence and development of many diseases, especially tumors. However, the relationship between m6A methylation and rheumatoid arthritis (RA) is still a mystery.

Methods

Two high-throughput sequencing methods, namely, m6A modified RNA immunoprecipitation sequence (m6A-seq) and RNA sequence (RNA-seq) were performed to identify the differentially expressed m6A methylation in human rheumatoid arthritis fibroblast-like synoviocytes cell line MH7A after stimulation with TNF-α. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to obtain enriched GO terms and significant KEGG pathways. Then, four candidate genes, Wilms tumor 1-associating protein (WTAP), receptor-interacting serine/threonine protein kinase 2 (RIPK2), Janus kinase 3 (JAK3) and tumor necrosis factor receptor SF10A (TNFRSF10A) were selected to further validate the m6A methylation, mRNA and protein expression levels in MH7A cells and synovial tissues of adjuvant arthritis (AA) rats by RT-qPCR and Western blot.

Results

Using m6A-seq, we identified a total of 206 genes with differentially expressed m6A methylation, of which 118 were significantly upregulated and 88 genes were significantly downregulated. Likewise, 1207 differentially mRNA expressed mRNAs were obtained by RNA-seq, of which 793 were upregulated and 414 downregulated. Further joint analysis showed that the m6A methylation and mRNA expression levels of 88 genes changed significantly, of which 30 genes displayed increased m6A methylation and decreased mRNA expression, 57 genes displayed decreased m6A methylation and increased mRNA expression increased, and 1 gene displayed increased m6A methylation and increased mRNA expression. GO and KEGG analyses indicated that these unique genes were mainly enriched in inflammation-related pathways, cell proliferation and apoptosis. In addition, the validations of WTAP, RIPK2, JAK3 and TNFRSF10A were in accordance with the m6A and RNA sequencing results.

Conclusion

This study established the transcriptional map of m6A in MH7A cells and revealed the potential relationship between RNA methylation modification and RA related genes. The results suggested that m6A modification was associated with the occurrence and course of RA to some extent.

A novel computational strategy for DNA methylation imputation using mixture regression model (MRM)

BACKGROUND

DNA methylation is an important heritable epigenetic mark that plays a crucial role in transcriptional regulation and the pathogenesis of various human disorders. The commonly used DNA methylation measurement approaches, e.g., Illumina Infinium HumanMethylation-27 and -450 BeadChip arrays (27 K and 450 K arrays) and reduced representation bisulfite sequencing (RRBS), only cover a small proportion of the total CpG sites in the human genome, which considerably limited the scope of the DNA methylation analysis in those studies.

RESULTS

We proposed a new computational strategy to impute the methylation value at the unmeasured CpG sites using the mixture of regression model (MRM) of radial basis functions, integrating information of neighboring CpGs and the similarities in local methylation patterns across subjects and across multiple genomic regions. Our method achieved a better imputation accuracy over a set of competing methods on both simulated and empirical data, particularly when the missing rate is high. By applying MRM to an RRBS dataset from subjects with low versus high bone mineral density (BMD), we recovered methylation values of ~ 300 K CpGs in the promoter regions of chromosome 17 and identified some novel differentially methylated CpGs that are significantly associated with BMD.

CONCLUSIONS

Our method is well applicable to the numerous methylation studies. By expanding the coverage of the methylation dataset to unmeasured sites, it can significantly enhance the discovery of novel differential methylation signals and thus reveal the mechanisms underlying various human disorders/traits.

Epigenetic Regulation Mediated by Methylation in the Pathogenesis and Precision Medicine of Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a complex disease triggered by the interaction between genetics and the environment, especially through the shared epitope (SE) and cell surface calreticulin (CSC) theory. However, the available evidence shows that genetic diversity and environmental exposure cannot explain all the clinical characteristics and heterogeneity of RA. In contrast, recent studies demonstrate that epigenetics play important roles in the pathogenesis of RA, especially DNA methylation and histone modification. DNA methylation and histone methylation are involved in innate and adaptive immune cell differentiation and migration, proliferation, apoptosis, and mesenchymal characteristics of fibroblast-like synoviocytes (FLS). Epigenetic-mediated regulation of immune-related genes and inflammation pathways explains the dynamic expression network of RA. In this review, we summarize the comprehensive evidence to show that methylation of DNA and histones is significantly involved in the pathogenesis of RA and could be applied as a promising biomarker in the disease progression and drug-response prediction. We also explain the advantages and challenges of the current epigenetics research in RA. In summary, epigenetic modules provide a possible interface through which genetic and environmental risk factors connect to contribute to the susceptibility and pathogenesis of RA. Additionally, epigenetic regulators provide promising drug targets to develop novel therapeutic drugs for RA. Finally, DNA methylation and histone modifications could be important features for providing a better RA subtype identification to accelerate personalized treatment and precision medicine.

PretiMeth: precise prediction models for DNA methylation based on single methylation mark

Background

The computational prediction of methylation levels at single CpG resolution is promising to explore the methylation levels of CpGs uncovered by existing array techniques, especially for the 450 K beadchip array data with huge reserves. General prediction models concentrate on improving the overall prediction accuracy for the bulk of CpG loci while neglecting whether each locus is precisely predicted. This leads to the limited application of the prediction results, especially when performing downstream analysis with high precision requirements.

Results

Here we reported PretiMeth, a method for constructing precise prediction models for each single CpG locus. PretiMeth used a logistic regression algorithm to build a prediction model for each interested locus. Only one DNA methylation feature that shared the most similar methylation pattern with the CpG locus to be predicted was applied in the model. We found that PretiMeth outperformed other algorithms in the prediction accuracy, and kept robust across platforms and cell types. Furthermore, PretiMeth was applied to The Cancer Genome Atlas data (TCGA), the intensive analysis based on precise prediction results showed that several CpG loci and genes (differentially methylated between the tumor and normal samples) were worthy for further biological validation.

Conclusion

The precise prediction of single CpG locus is important for both methylation array data expansion and downstream analysis of prediction results. PretiMeth achieved precise modeling for each CpG locus by using only one significant feature, which also suggested that our precise prediction models could be probably used for reference in the probe set design when the DNA methylation beadchip update. PretiMeth is provided as an open source tool via https://github.com/JxTang-bioinformatics/PretiMeth.

Detection of differentially methylated CpG sites between tumor samples with uneven tumor purities

MOTIVATION

Inference of differentially methylated (DM) CpG sites between two groups of tumor samples with different geno- or pheno-types is a critical step to uncover the epigenetic mechanism of tumorigenesis, and identify biomarkers for cancer subtyping. However, as a major source of confounding factor, uneven distributions of tumor purity between two groups of tumor samples will lead to biased discovery of DM sites if not properly accounted for.

RESULTS

We here propose InfiniumDM, a generalized least square model to adjust tumor purity effect for differential methylation analysis. Our method is applicable to a variety of experimental designs including with or without normal controls, different sources of normal tissue contaminations. We compared our method with conventional methods including minfi, limma and limma corrected by tumor purity using simulated datasets. Our method shows significantly better performance at different levels of differential methylation thresholds, sample sizes, mean purity deviations and so on. We also applied the proposed method to breast cancer samples from TCGA database to further evaluate its performance. Overall, both simulation and real data analyses demonstrate favorable performance over existing methods serving similar purpose.

AVAILABILITY AND IMPLEMENTATION

InfiniumDM is a part of R package InfiniumPurify, which is freely available from GitHub (https://github.com/Xiaoqizheng/InfiniumPurify).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MRCNN: a deep learning model for regression of genome-wide DNA methylation

Background

Determination of genome-wide DNA methylation is significant for both basic research and drug development. As a key epigenetic modification, this biochemical process can modulate gene expression to influence the cell differentiation which can possibly lead to cancer. Due to the involuted biochemical mechanism of DNA methylation, obtaining a precise prediction is a considerably tough challenge. Existing approaches have yielded good predictions, but the methods either need to combine plenty of features and prerequisites or deal with only hypermethylation and hypomethylation.

Results

In this paper, we propose a deep learning method for prediction of the genome-wide DNA methylation, in which the Methylation Regression is implemented by Convolutional Neural Networks (MRCNN). Through minimizing the continuous loss function, experiments show that our model is convergent and more precise than the state-of-art method (DeepCpG) according to results of the evaluation. MRCNN also achieves the discovery of de novo motifs by analysis of features from the training process.

Conclusions

Genome-wide DNA methylation could be evaluated based on the corresponding local DNA sequences of target CpG loci. With the autonomous learning pattern of deep learning, MRCNN enables accurate predictions of genome-wide DNA methylation status without predefined features and discovers some de novo methylation-related motifs that match known motifs by extracting sequence patterns.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5488-5) contains supplementary material, which is available to authorized users.

Locus-specific DNA methylation prediction in cord blood and placenta

DNA methylation is known to be responsive to prenatal exposures, which may be a part of the mechanism linking early developmental exposures to future chronic diseases. Many studies use blood to measure DNA methylation, yet we know that DNA methylation is tissue specific. Placenta is central to fetal growth and development, but it is rarely feasible to collect this tissue in large epidemiological studies; on the other hand, cord blood samples are more accessible. In this study, based on paired samples of both placenta and cord blood tissues from 169 individuals, we investigated the methylation concordance between placenta and cord blood. We then employed a machine-learning-based model to predict locus-specific DNA methylation levels in placenta using DNA methylation levels in cord blood. We found that methylation correlation between placenta and cord blood is lower than other tissue pairs, consistent with existing observations that placenta methylation has a distinct pattern. Nonetheless, there are still a number of CpG sites showing robust association between the two tissues. We built prediction models for placenta methylation based on cord blood data and documented a subset of 1,012 CpG sites with high correlation between measured and predicted placenta methylation levels. The resulting list of CpG sites and prediction models could help to reveal the loci where internal or external influences may affect DNA methylation in both placenta and cord blood, and provide a reference data to predict the effects on placenta in future study even when the tissue is not available in an epidemiological study.

Integrative analysis with expanded DNA methylation data reveals common key regulators and pathways in cancers

The integration of genomic and DNA methylation data has been demonstrated as a powerful strategy in understanding cancer mechanisms and identifying therapeutic targets. The TCGA consortium has mapped DNA methylation in thousands of cancer samples using Illumina Infinium Human Methylation 450 K BeadChip (Illumina 450 K array) that only covers about 1.5% of CpGs in the human genome. Therefore, increasing the coverage of the DNA methylome would significantly leverage the usage of the TCGA data. Here, we present a new model called EAGLING that can expand the Illumina 450 K array data 18 times to cover about 30% of the CpGs in the human genome. We applied it to analyze 13 cancers in TCGA. By integrating the expanded methylation, gene expression, and somatic mutation data, we identified the genes showing differential patterns in each of the 13 cancers. Many of the triple-evidenced genes identified in majority of the cancers are biomarkers or potential biomarkers. Pan-cancer analysis also revealed the pathways in which the triple-evidenced genes are enriched, which include well known ones as well as new ones, such as axonal guidance signaling pathway and pathways related to inflammatory processing or inflammation response. Triple-evidenced genes, particularly TNXB, RRM2, CELSR3, SLC16A3, FANCI, MMP9, MMP11, SIK1, and TRIM59 showed superior predictive power in both tumor diagnosis and prognosis. These results have demonstrated that the integrative analysis using the expanded methylation data is powerful in identifying critical genes/pathways that may serve as new therapeutic targets.

A robust fuzzy rule based integrative feature selection strategy for gene expression data in TCGA

BACKGROUND

Lots of researches have been conducted in the selection of gene signatures that could distinguish the cancer patients from the normal. However, it is still an open question on how to extract the robust gene features.

METHODS

In this work, a gene signature selection strategy for TCGA data was proposed by integrating the gene expression data, the methylation data and the prior knowledge about cancer biomarkers. Different from the traditional integration method, the expanded 450 K methylation data were applied instead of the original 450 K array data, and the reported biomarkers were weighted in the feature selection. Fuzzy rule based classification method and cross validation strategy were applied in the model construction for performance evaluation.

RESULTS

Our selected gene features showed prediction accuracy close to 100% in the cross validation with fuzzy rule based classification model on 6 cancers from TCGA. The cross validation performance of our proposed model is similar to other integrative models or RNA-seq only model, while the prediction performance on independent data is obviously better than other 5 models. The gene signatures extracted with our fuzzy rule based integrative feature selection strategy were more robust, and had the potential to get better prediction results.

CONCLUSION

The results indicated that the integration of expanded methylation data would cover more genes, and had greater capacity to retrieve the signature genes compared with the original 450 K methylation data. Also, the integration of the reported biomarkers was a promising way to improve the performance. PTCHD3 gene was selected as a discriminating gene in 3 out of the 6 cancers, which suggested that it might play important role in the cancer risk and would be worthy for the intensive investigation.

MicroRNAs as a Novel Tool in the Diagnosis of Liver Lipid Dysregulation and Fatty Liver Disease

In recent years, metabolic disorder, especially fatty liver disease, has been considered a major challenge to global health. The attention of researchers focused on expanding knowledge of the regulation mechanism behind these diseases and towards the new diagnostics tools and treatments. The pathophysiology of the fatty liver disease is undoubtedly complex. Abnormal hepatic lipid accumulation is a major symptom of most metabolic diseases. Therefore, the identification of novel regulation factors of lipid metabolism is important and meaningful. As a new diagnostic tool, the function of microRNAs during fatty liver disease has recently come into notice in biological research. Accumulating evidence supports the influence of miRNAs in lipid metabolism. In this review, we discuss the potential role of miRNAs in liver lipid metabolism and the pathogenesis of fatty liver disease.

Methods for genome-wide DNA methylation analysis in human cancer

Aberrant DNA methylation is considered to be one of the most common hallmarks of cancer. Several recent advances in assessing the DNA methylome provide great promise for deciphering the cancer-specific DNA methylation patterns. Herein, we present the current key technologies used to detect high-throughput genome-wide DNA methylation, and the available cancer-associated methylation databases. Additionally, we focus on the computational methods for preprocessing, analyzing and interpreting the cancer methylome data. It not only discusses the challenges of the differentially methylated region calling and the prediction model construction but also highlights the biomarker investigation for cancer diagnosis, prognosis and response to treatment. Finally, some emerging challenges in the computational analysis of cancer methylome data are summarized.