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In our previous study, we found that subchronic exposure of chlorpyrifos (CPF) can cause reproductive damage in male rats. However, the mechanisms underlying the reproductive effects of CPF are not well understood. DNA methylation is essential for epigenetic gene regulation in development and disease. Therefore, we aim to compare DNA methylation profiles between controls and CPF-treated rats in order to identify the epigenetic mechanism of male reproductive toxicity induced by CPF. Methylated DNA immunoprecipitation with high-throughput sequencing (MeDIP-seq) was used to investigate the genome-wide DNA methylation pattern in testes of control and CPF-treated rats for 90 days. We identified 27 019 differentially methylated regions (DMRs) (14 150 upmethylated and 12 869 downmethylated) between CPF-exposed and control groups. The DMR-related genes are mainly involved in 113 pathways predicted by Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The result showed that high methylation gene PIK3CD may play a key role in epigenetic regulation of multiple pathways, such as Ras signaling pathway, AGE–RAGE signaling pathway in diabetic complications, HIF-1 signaling pathway, VEGF signaling pathway, and glioma and Fc epsilon RI signaling pathway in rats exposed to CPF. Our study provides significant explanations for the epigenetic mechanism of male reproductive toxicology induced by CPF.

Growing evidence indicates that arsenic can cause long-lasting and irreversible damage to the function of the human immune system. It is known that forkhead box protein 3(Foxp3), which is specifically expressed in regulatory T cells (Tregs), plays a decisive role in immunoregulation and is regulated by DNA methylation. While evidence suggests that epigenetic regulated Foxp3 is involved in the immune disorders caused by arsenic exposure, the specific mechanism remains unclear. In this study, after primary human lymphocytes were treated with different doses of NaAsO2, our results showed that arsenic induced the high expression of DNMT1 and Foxp3 gene promoter methylation level, thereby inhibiting the expression levels of Foxp3, followed by decreasing Tregs and reducing related anti-inflammatory cytokines, such as interleukin 10 (IL-10) and interleukin 10 (IL-35), and increasing the ratio of CD4+/CD8+ T cells in lymphocytes. Treatment with DNA methyltransferase inhibitor 5-Aza-CdR can notably inhibit the expression of DNMT1, effectively restoring the hypermethylation of the Foxp3 promoter region in primary human lymphocytes and upregulating the expression levels of Foxp3, balancing the ratio of CD4+/CD8+ T cells in lymphocytes. It also activates the secretion of anti-inflammatory cytokines and restores the immune regulatory functions of Tregs. In conclusion, our study provides limited evidence that DNMT1-mediated Foxp3 gene promoter hypermethylation is involved in immune dysfunction caused by arsenic in primary human lymphocytes. The study can provide a scientific basis for further understanding the arsenic-induced immune dysfunction in primary human lymphocytes.

The aim of the current study was to investigate the effect of well-characterized TiO2 nanoparticles on DNA methylation of peripheral blood mononuclear cells (PBMCs) in vitro. Maximum non-toxic concentration of nanoparticles for PBMCs was determined by MTT assay. The effect of TiO2 nanoparticles at concentrations of 25–100 μg/ml on DNA methylation of PBMCs was investigated by measuring the %5-mC alterations through an ELISA assay. The physicochemical analysis showed that the TiO2 nanoparticles were crystalline, pure and in the anatase phase. Peaks related to Ti-O tensile vibrations were observed in the range of 1510 cm−1. The size of nanoparticles was in the range of 39–74 nm with an average hydrodynamic diameter of 43.82 nm. According to the results of the MTT test, 100 μg/ml was found to be maximum non-toxic concentration. The %5-mC in treated PBMCs revealed that TiO2 nanoparticles could lead to DNA hypomethylation in PBMCs. The %5-mC difference compared with the negative control was found to be 2.07 ± 1.02% (P = 0.03). The difference of %5-mC between the 25 and 100 μg/ml concentration of nanoparticles was statistically significant (P = 0.02). The results of the current study show that the TiO2 nanoparticles cause DNA hypomethylation in PBMCs in a dose-response manner. Therefore, it is recommended to evaluate the effects of cytotoxicity and epigenotoxicity of commonly used nanoparticles before their use.

Although moderate homocysteine (HCY) elevation is associated with neural tube defects (NTDs), the underlying mechanisms have not been elucidated. In this study, we aimed to investigate that whether HCY-induced NTDs were associated with oxidative stress and methyl metabolism in chick embryos. The potential role of miR-124 in neurogenesis was also investigated. In this study, increased intracellular oxidative species and alterations in DNA methylation were observed following HCY treatment. This alteration coincided with decreases of Mn superoxide dismutase and glutathione peroxidase activities, as well as the expression of anti-rabbit DNA methyltransferase (DNMT) 1 and 3a. In addition, HCY induced significant decreases of S-adenosylmethionine (SAM)/S-adenosylhomocysteine (SAH) (P

This current study explored the effects of fine particulate matter (PM2.5) on deoxyribonucleic acid methylation in human bronchial epithelial cells. Human bronchial epithelial cells were exposed to PM2.5 for 24 h after which, deoxyribonucleic acid samples were extracted, and the differences between methylation sites were detected using methylation chips. Subsequent gene ontology functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed for the differential methylation sites. Functional epigenetic modules analysis of the overall differential methylation site interactions was also conducted. A total of 127 differential methylation sites in 89 genes were screened in the PM2.5 10 μg/ml group, of which 55 sites demonstrated increased methylation, with methylation levels decreasing in a further 72 sites. Following an exposure of 50 μg/ml PM2.5, a total of 238 differentially methylated sites were screened in 168 genes, of which methylation levels increased in 127 sites, and decreased in 111. KEGG analysis showed that the top 10 enrichment pathways predominantly involve hepatocellular carcinoma pathways and endometrial cancer pathways, whereas functional epigenetic modules analysis screened eight genes (A2M, IL23A, TPIP6, IL27, MYD88, ILE2B, NLRC4, TNF) with the most interactions. Our results indicate that exposure to PM2.5 for 24 h in human bronchial epithelial cells induces marked changes in deoxyribonucleic acid methylation of multiple genes involved in apoptosis and carcinogenesis pathways, these findings can provide a new direction for further study of PM2.5 carcinogenic biomarkers.

Machine learning methods and in particular random forests are promising approaches for prediction based on high dimensional omics data sets. They provide variable importance measures to rank predictors according to their predictive power. If building a prediction model is the main goal of a study, often a minimal set of variables with good prediction performance is selected. However, if the objective is the identification of involved variables to find active networks and pathways, approaches that aim to select all relevant variables should be preferred. We evaluated several variable selection procedures based on simulated data as well as publicly available experimental methylation and gene expression data. Our comparison included the Boruta algorithm, the Vita method, recurrent relative variable importance, a permutation approach and its parametric variant (Altmann) as well as recursive feature elimination (RFE). In our simulation studies, Boruta was the most powerful approach, followed closely by the Vita method. Both approaches demonstrated similar stability in variable selection, while Vita was the most robust approach under a pure null model without any predictor variables related to the outcome. In the analysis of the different experimental data sets, Vita demonstrated slightly better stability in variable selection and was less computationally intensive than Boruta. In conclusion, we recommend the Boruta and Vita approaches for the analysis of high-dimensional data sets. Vita is considerably faster than Boruta and thus more suitable for large data sets, but only Boruta can also be applied in low-dimensional settings.

Innovations in -omics technologies have driven advances in biomedical research. However, integrating and analysing the large volumes of data generated from different high-throughput -omics technologies remain a significant challenge to basic and clinical scientists without bioinformatics skills or access to bioinformatics support. To address this demand, we have significantly updated our previous O-miner analytical suite, to incorporate several new features and data types to provide an efficient and easy-to-use Web tool for the automated analysis of data from ‘-omics’ technologies. Created from a biologist’s perspective, this tool allows for the automated analysis of large and complex transcriptomic, genomic and methylomic data sets, together with biological/clinical information, to identify significantly altered pathways and prioritize novel biomarkers/targets for biological validation. Our resource can be used to analyse both in-house data and the huge amount of publicly available information from array and sequencing platforms. Multiple data sets can be easily combined, allowing for meta-analyses. Here, we describe the analytical pipelines currently available in O-miner and present examples of use to demonstrate its utility and relevance in maximizing research output. O-miner Web server is free to use and is available at http://www.o-miner.org.

DNA methylation is one of the most extensively studied epigenetic modifications of genomic DNA. In recent years, sequencing of bisulfite-converted DNA, particularly via next-generation sequencing technologies, has become a widely popular method to study DNA methylation. This method can be readily applied to a variety of species, dramatically expanding the scope of DNA methylation studies beyond the traditionally studied human and mouse systems. In parallel to the increasing wealth of genomic methylation profiles, many statistical tools have been developed to detect differentially methylated loci (DMLs) or differentially methylated regions (DMRs) between biological conditions. We discuss and summarize several key properties of currently available tools to detect DMLs and DMRs from sequencing of bisulfite-converted DNA. However, the majority of the statistical tools developed for DML/DMR analyses have been validated using only mammalian data sets, and less priority has been placed on the analyses of invertebrate or plant DNA methylation data. We demonstrate that genomic methylation profiles of non-mammalian species are often highly distinct from those of mammalian species using examples of honey bees and humans. We then discuss how such differences in data properties may affect statistical analyses. Based on these differences, we provide three specific recommendations to improve the power and accuracy of DML and DMR analyses of invertebrate data when using currently available statistical tools. These considerations should facilitate systematic and robust analyses of DNA methylation from diverse species, thus advancing our understanding of DNA methylation.

DNA methylation is an important epigenetic mechanism that plays a crucial role in cellular regulatory systems. Recent advancements in sequencing technologies now enable us to generate high-throughput methylation data and to measure methylation up to single-base resolution. This wealth of data does not come without challenges, and one of the key challenges in DNA methylation studies is to identify the significant differences in the methylation levels of the base pairs across distinct biological conditions. Several computational methods have been developed to identify differential methylation using bisulfite sequencing data; however, there is no clear consensus among existing approaches. A comprehensive survey of these approaches would be of great benefit to potential users and researchers to get a complete picture of the available resources. In this article, we present a detailed survey of 22 such approaches focusing on their underlying statistical models, primary features, key advantages and major limitations. Importantly, the intrinsic drawbacks of the approaches pointed out in this survey could potentially be addressed by future research.

Small RNAs (sRNAs) are important short-length molecules with regulatory functions essential for plant development and plasticity. High-throughput sequencing of total sRNA populations has revealed that the largest share of sRNA remains uncategorized. To better understand the role of sRNA-mediated cellular regulation, it is necessary to create accurate and comprehensive catalogues of sRNA and their sequence features, a task that currently relies on nontrivial bioinformatic approaches. Although a large number of computational tools have been developed to predict features of sRNA sequences, these tools are mostly dedicated to microRNAs and none integrates the functionalities necessary to describe units from all sRNA pathways thus far discovered in plants. Here, we review the different classes of sRNA found in plants and describe available bioinformatics tools that can help in their detection and categorization.

A growing body of research has documented associations between adverse childhood environments and DNA methylation, highlighting epigenetic processes as potential mechanisms through which early external contexts influence health across the life course. The present study tested a complementary hypothesis: indicators of children's early internal, biological, and behavioral responses to stressful challenges may also be linked to stable patterns of DNA methylation later in life. Children's autonomic nervous system reactivity, temperament, and mental health symptoms were prospectively assessed from infancy through early childhood, and principal components analysis (PCA) was applied to derive composites of biological and behavioral reactivity. Buccal epithelial cells were collected from participants at 15 and 18 years of age. Findings revealed an association between early life biobehavioral inhibition/disinhibition and DNA methylation across many genes. Notably, reactive, inhibited children were found to have decreased DNA methylation of the DLX5 and IGF2 genes at both time points, as compared to non‐reactive, disinhibited children. Results of the present study are provisional but suggest that the gene's profile of DNA methylation may constitute a biomarker of normative or potentially pathological differences in reactivity. Overall, findings provide a foundation for future research to explore relations among epigenetic processes and differences in both individual‐level biobehavioral risk and qualities of the early, external childhood environment., This study examined whether children's early internal, biological, and behavioral responses to stress were predictive of DNA methylation later in life. Findings revealed an association between early life biobehavioral reactivity and DNA methylation in multiple genes measured at both 15 and 18 years of age. As such, DNA methylation may constitute a biomarker of normative or potentially pathological differences in reactivity.

Long noncoding RNAs (lncRNAs) are a large family of noncoding RNAs that play a critical role in various normal bioprocesses as well as tumorigenesis. However, the expression patterns and biological functions of lncRNAs in acute leukemia have not been well studied. Here, we performed transcriptome-wide lncRNA expression profiling of acute myeloid leukemia (AML) patient samples, along with non-leukemia control hematopoietic samples. We found that lncRNAs were differentially expressed in AML samples relative to control samples. Notably, we identified that lncRNAs upregulated in AML (relative to the control samples) are associated with a lower degree of DNA methylation and a higher ratio of being bound by transcription factors such as SP1, STAT4, ATF-2 and ELK-1 compared with those downregulated in AML. Moreover, an enrichment of H3K4me3 and a depletion of H3K27me3 were observed in upregulated lncRNAs in AML. Expression patterns of three types of lncRNAs (antisense, enhancer and intergenic lncRNAs) have previously been characterized. Of the identified lncRNAs, we found that high expression level lncRNA LOC285758 is associated with the poor prognosis in AML patients. Furthermore, we found that LOC285758 regulates proliferation of AML cell lines by enhancing the expression of HDAC2, a key factor in carcinogenesis. Collectively, our study depicts a landscape of important lncRNAs in AML and provides novel potential therapeutic targets and prognostic markers for AML treatment.

The Angelman syndrome is caused by disruption of the UBE3A gene and is clinically delineated by the combination of severe mental disability, seizures, absent speech, hypermotoric and ataxic movements, and certain remarkable behaviors. Those with the syndrome have a predisposition toward apparent happiness and paroxysms of laughter, and this finding helps distinguish Angelman syndrome from other conditions involving severe developmental handicap. Accurate diagnosis rests on a combination of clinical criteria and molecular and/or cytogenetic testing. Analysis of parent-specific DNA methylation imprints in the critical 15q11.2–q13 genomic region identifies 75–80% of all individuals with the syndrome, including those with cytogenetic deletions, imprinting center defects and paternal uniparental disomy. In the remaining group, UBE3A sequence analysis identifies an additional percentage of patients, but 5–10% will remain who appear to have the major clinical phenotypic features but do not have any identifiable genetic abnormalities. Genetic counseling for recurrence risk is complicated because multiple genetic mechanisms can disrupt the UBE3A gene, and there is also a unique inheritance pattern associated with UBE3A imprinting. Angelman syndrome is a prototypical developmental syndrome due to its remarkable behavioral phenotype and because UBE3A is so crucial to normal synaptic function and neural plasticity.

The influence of epigenetic alterations during cancer has gained increasing attention over the recent years and has resulted in a paradigm shift in our understanding of mechanisms leading to cancer susceptibility. These features are potentially reversible and may affect genomic stability and expression of genes, including tumor suppressor genes and oncogenes. The reversible acetylation of histones is an important mechanism of gene regulation. Targeting the epigenome, including the use of histone deacetylase (HDAC) inhibitors, is a novel strategy for cancer chemoprevention. We have found that sulforaphane (SFN), a compound found in cruciferous vegetables, inhibits HDAC activity in human colorectal and prostate cancer cells. The ability of SFN to target aberrant acetylation patterns, in addition to effects on phase 2 enzymes, may make it an effective chemoprevention agent. Other dietary agents such as butyrate, allyl sulfides and organoselenium compounds have also shown promise as HDAC inhibitors. These studies are significant because of the potential to qualify or change recommendations for high-risk cancer patients, thereby increasing their survival through simple dietary choices, such as incorporating easily accessible foods into a patient’s diet. The work to date provides a scientific foundation for future large-scale human clinical intervention studies with dietary agents that affect the epigenome.

MicroRNAs (miRNAs) are small 21–25 nucleotide-long non-coding RNAs that have emerged as key negative post-transcriptional regulators of gene expression [1, 2]. Currently there are more than 700 mammalian miRNAs that can potentially target up to one-third of protein-coding human genes [1] involved in diverse physiological and pathological processes, including cancer [3, 4]. Indeed, aberrant levels of miRNAs have been reported in all major human malignancies [5, 6]. In tumors, altered expression of miRNAs has been demonstrated to inhibit tumor suppressor genes or inappropriately activate oncogenes and has been associated with every aspect of tumor biology, including tumor progression, invasiveness, metastasis, and acquisition of resistance by malignant cells to chemotherapeutic agents [3, 4, 7, 8]. These observations lead to the suggestion that aberrant expression of miRNAs may contribute to tumorigenesis [9]. However, most of the tumor-miRNA-related studies are based on expression analysis of miRNAs in tumors in comparison with corresponding adjacent normal tissues [4, 5, 6]. The altered expression of any given miRNA in neoplastic cells is not sufficient to address conclusively the role of these changes in tumorigenesis [10]. Additionally, despite the established biological significance of miRNA dysregulation in neoplastic cells, there is a lack of knowledge on the role of miRNAs during early stages of tumor development, especially if variations in the expression of specific miRNAs are associated with differences in the susceptibility to tumorigenesis.

We recently described a method for digitally labelling objects with DNA. Here we show that, using DNA methyltransferases to create polymorphic DNA templates, it is possible to significantly increase the number of labels that can be generated by this method. Nine double-stranded DNA templates of different length were methylated with either M.HaeIII or M.AluI methyltransferase, or both. Different mixtures of methylated and unmethylated versions of this template set were used to 'invisibly' label paper. The mixtures were eluted from the paper and the methylated status of the templates in each mixture successfully determined, and the labels read, by digestion with the complementary restriction endonuclease, followed by a polymerase chain reaction and agarose gel electrophoresis. One methylated DNA label was read after it had been left on paper for two months.