seqlm: an MDL based method for identifying differentially methylated regions in high density methylation array data

DMRIntTk: Integrating different DMR sets based on density peak clustering

BACKGROUND

Identifying differentially methylated regions (DMRs) is a basic task in DNA methylation analysis. However, due to the different strategies adopted, different DMR sets will be predicted on the same dataset, which poses a challenge in selecting a reliable and comprehensive DMR set for downstream analysis.

RESULTS

Here, we develop DMRIntTk, a toolkit for integrating DMR sets predicted by different methods on a same dataset. In DMRIntTk, the genome is segmented into bins, and the reliability of each DMR set at different methylation thresholds is evaluated. Then, the bins are weighted based on the covered DMR sets and integrated into final DMRs using a density peak clustering algorithm. To demonstrate the practicality of DMRIntTk, it was applied to different scenarios, including tissues with relatively large methylation differences, cancer tissues versus normal tissues with medium methylation differences, and disease tissues versus normal tissues with subtle methylation differences. Our results show that DMRIntTk can effectively trim regions with small methylation differences from the original DMR sets and thereby enriching the proportion of DMRs with larger methylation differences. In addition, the overlap analysis suggests that the integrated DMR sets are quite comprehensive, and functional analyses indicate the integrated disease-related DMRs are significantly enriched in biological pathways associated with the pathological mechanisms of the diseases. A comparative analysis of the integrated DMR set versus each original DMR set further highlights the superiority of DMRIntTk, demonstrating the unique biological insights it can provide.

CONCLUSIONS

Conclusively, DMRIntTk can help researchers obtain a reliable and comprehensive DMR set from many prediction methods.

Genome-wide DNA methylation and gene expression in human placentas derived from assisted reproductive technology

BACKGROUND

Assisted reproductive technology (ART) has been associated with increased risks for growth disturbance, disrupted imprinting as well as cardiovascular and metabolic disorders. However, the molecular mechanisms and whether they are a result of the ART procedures or the underlying subfertility are unknown.

METHODS

We performed genome-wide DNA methylation (EPIC Illumina microarrays) and gene expression (mRNA sequencing) analyses for a total of 80 ART and 77 control placentas. The separate analyses for placentas from different ART procedures and sexes were performed. To separate the effects of ART procedures and subfertility, 11 placentas from natural conception of subfertile couples and 12 from intrauterine insemination treatments were included.

RESULTS

Here we show that ART-associated changes in the placenta enriche in the pathways of hormonal regulation, insulin secretion, neuronal development, and vascularization. Observed decreased number of stromal cells as well as downregulated TRIM28 and NOTCH3 expressions in ART placentas indicate impaired angiogenesis and growth. DNA methylation changes in the imprinted regions and downregulation of TRIM28 suggest defective stabilization of the imprinting. Furthermore, downregulated expression of imprinted endocrine signaling molecule DLK1 associates with both ART and subfertility.

CONCLUSIONS

Decreased expressions of TRIM28, NOTCH3, and DLK1 bring forth potential mechanisms for several phenotypic features associated with ART. Our results support previous procedure specific findings: the changes associated with growth and metabolism link more prominently to the fresh embryo transfer with smaller placentas and newborns, than to the frozen embryo transfer with larger placentas and newborns. Furthermore, since the observed changes associate also with subfertility, they offer a precious insight to the molecular background of infertility.

MethylCallR : a comprehensive analysis framework for Illumina Methylation Beadchip

DNA methylation is a molecular process that mediates gene-environment interactions. Epigenome-wide association studies (EWAS) using the Illumina Human Methylation BeadChip are powerful tools for quantifying the relationship between DNA methylation and phenotypes. Recently, the Illumina Methylation EPICv2 BeadChip (EPICv2) was released, which includes new features, such as duplicated probes and changed probe names. Several published algorithms have been updated to address these features in EPICv2. However, appropriate EPICv2 preprocessing and integration with previous microarray versions remain complex. Therefore, MethylCallR, an open-source R package designed to provide standard procedures for performing EWAS using Illumina methylation microarrays including EPICv2, was developed. MethylCallR can be used to control duplicated probes in EPICv2, by using pre-set data implemented in MethylCallR or new customized data. MethylCallR includes a straightforward conversion function between different types of Illumina Human Methylation BeadChips. Using MethylCallR, potential outlier sample detection and statistical power estimation were conducted and used to select meaningful probes. Publicly available data was analyzed using MethylCallR and the findings were compared to that of a previous study.

Gbdmr: identifying differentially methylated CpG regions in the human genome via generalized beta regressions

BACKGROUND

DNA methylation is a biochemical process in which a methyl group is added to the cytosine-phosphate-guanine (CpG) site on DNA molecules without altering the DNA sequence. Multiple CpG sites in a certain genome region can be differentially methylated across phenotypes. Identifying these differentially methylated CpG regions (DMRs) associated with the phenotypes contributes to disease prediction and precision medicine development.

RESULTS

We propose a novel DMR detection algorithm, gbdmr. In contrast to existing methods under a linear regression framework, gbdmr assumes that DNA methylation levels follow a generalized beta distribution. We compare gbdmr to alternative approaches via simulations and real data analyses, including dmrff, a new DMR detection approach that shows promising performance among competitors, and the traditional EWAS that focuses on single CpG sites. Our simulations demonstrate that gbdmr is superior to the other two when the correlation between neighboring CpG sites is strong, while dmrff shows a higher power when the correlation is weak. We provide an explanation of these phenomena from a theoretical perspective. We further applied the three methods to multiple real DNA methylation datasets. One is from a birth cohort study undertaken on the Isle of Wight, United Kingdom, and the other two are from the Gene Expression Omnibus database repository. Overall, gbdmr identifies more DMR CpGs linked to phenotypes than dmrff, and the simulated results support the findings.

CONCLUSIONS

Gbdmr is an innovative method for detecting DMRs based on generalized beta regression. It demonstrated notable advantages over dmrff and traditional EWAS, particularly when adjacent CpGs exhibited moderate to strong correlations. Our real data analyses and simulated findings highlight the reliability of gbdmr as a robust DMR detection tool. The gbdmr approach is accessible and implemented by R on GitHub: https://github.com/chengzhouwu/gbdmr .

A novel approach toward optimal workflow selection for DNA methylation biomarker discovery

DNA methylation is a major epigenetic modification involved in many physiological processes. Normal methylation patterns are disrupted in many diseases and methylation-based biomarkers have shown promise in several contexts. Marker discovery typically involves the analysis of publicly available DNA methylation data from high-throughput assays. Numerous methods for identification of differentially methylated biomarkers have been developed, making the need for best practices guidelines and context-specific analyses workflows exceedingly high. To this end, here we propose TASA, a novel method for simulating methylation array data in various scenarios. We then comprehensively assess different data analysis workflows using real and simulated data and suggest optimal start-to-finish analysis workflows. Our study demonstrates that the choice of analysis pipeline for DNA methylation-based marker discovery is crucial and different across different contexts.

Chromatin modifier developmental pluripotency associated factor 4 (DPPA4) is a candidate gene for alcohol-induced developmental disorders

BACKGROUND

Prenatal alcohol exposure (PAE) affects embryonic development, causing a variable fetal alcohol spectrum disorder (FASD) phenotype with neuronal disorders and birth defects. We hypothesize that early alcohol-induced epigenetic changes disrupt the accurate developmental programming of embryo and consequently cause the complex phenotype of developmental disorders. To explore the etiology of FASD, we collected unique biological samples of 80 severely alcohol-exposed and 100 control newborns at birth.

METHODS

We performed genome-wide DNA methylation (DNAm) and gene expression analyses of placentas by using microarrays (EPIC, Illumina) and mRNA sequencing, respectively. To test the manifestation of observed PAE-associated DNAm changes in embryonic tissues as well as potential biomarkers for PAE, we examined if the changes can be detected also in white blood cells or buccal epithelial cells of the same newborns by EpiTYPER. To explore the early effects of alcohol on extraembryonic placental tissue, we selected 27 newborns whose mothers had consumed alcohol up to gestational week 7 at maximum to the separate analyses. Furthermore, to explore the effects of early alcohol exposure on embryonic cells, human embryonic stem cells (hESCs) as well as hESCs during differentiation into endodermal, mesodermal, and ectodermal cells were exposed to alcohol in vitro.

RESULTS

DPPA4, FOXP2, and TACR3 with significantly decreased DNAm were discovered-particularly the regulatory region of DPPA4 in the early alcohol-exposed placentas. When hESCs were exposed to alcohol in vitro, significantly altered regulation of DPPA2, a closely linked heterodimer of DPPA4, was observed. While the regulatory region of DPPA4 was unmethylated in both control and alcohol-exposed hESCs, alcohol-induced decreased DNAm similar to placenta was seen in in vitro differentiated mesodermal and ectodermal cells. Furthermore, common genes with alcohol-associated DNAm changes in placenta and hESCs were linked exclusively to the neurodevelopmental pathways in the enrichment analysis, which emphasizes the value of placental tissue when analyzing the effects of prenatal environment on human development.

CONCLUSIONS

Our study shows the effects of early alcohol exposure on human embryonic and extraembryonic cells, introduces candidate genes for alcohol-induced developmental disorders, and reveals potential biomarkers for prenatal alcohol exposure.

Aclust2.0: a revamped unsupervised R tool for Infinium methylation beadchips data analyses

MOTIVATION

A wide range of computational packages has been developed for regional DNA methylation analyses of Illumina's Infinium array data. Aclust, one of the first unsupervised algorithms, was originally designed to analyze regional methylation of Infinium's 27K and 450K arrays by clustering neighboring methylation sites prior to downstream analyses. However, Aclust relied on outdated packages that rendered it largely non-operational especially with the newer Infinium EPIC and mouse arrays.

RESULTS

We have created Aclust2.0, a streamlined pipeline that involves five steps for the analyses of human (450K and EPIC) and mouse array data. Aclust2.0 provides a user-friendly pipeline and versatile for regional DNA methylation analyses for molecular epidemiological and mouse studies.

AVAILABILITY AND IMPLEMENTATION

Aclust2.0 is freely available on Github (https://github.com/OluwayioseOA/Alcust2.0.git).

Maternal haemoglobin levels in pregnancy and child DNA methylation: a study in the pregnancy and childhood epigenetics consortium

Altered maternal haemoglobin levels during pregnancy are associated with pre-clinical and clinical conditions affecting the fetus. Evidence from animal models suggests that these associations may be partially explained by differential DNA methylation in the newborn with possible long-term consequences. To test this in humans, we meta-analyzed the epigenome-wide associations of maternal haemoglobin levels during pregnancy with offspring DNA methylation in 3,967 newborn cord blood and 1,534 children and 1,962 adolescent whole-blood samples derived from 10 cohorts. DNA methylation was measured using Illumina Infinium Methylation 450K or MethylationEPIC arrays covering 450,000 and 850,000 methylation sites, respectively. There was no statistical support for the association of maternal haemoglobin levels with offspring DNA methylation either at individual methylation sites or clustered in regions. For most participants, maternal haemoglobin levels were within the normal range in the current study, whereas adverse perinatal outcomes often arise at the extremes. Thus, this study does not rule out the possibility that associations with offspring DNA methylation might be seen in studies with more extreme maternal haemoglobin levels.

Disruption of Circadian Rhythms by Ambient Light during Neurodevelopment Leads to Autistic-like Molecular and Behavioral Alterations in Adult Mice

Although circadian rhythms are thought to be essential for maintaining body health, the effects of chronic circadian disruption during neurodevelopment remain elusive. Here, using the "Short Day" (SD) mouse model, in which an 8 h/8 h light/dark (LD) cycle was applied from embryonic day 1 to postnatal day 42, we investigated the molecular and behavioral changes after circadian disruption in mice. Adult SD mice fully entrained to the 8 h/8 h LD cycle, and the circadian oscillations of the clock proteins, PERIOD1 and PERIOD2, were disrupted in the suprachiasmatic nucleus and the hippocampus of these mice. By RNA-seq widespread changes were identified in the hippocampal transcriptome, which are functionally associated with neurodevelopment, translational control, and autism. By western blotting and immunostaining hyperactivation of the mTOR and MAPK signaling pathways and enhanced global protein synthesis were found in the hippocampi of SD mice. Electrophysiological recording uncovered enhanced excitatory, but attenuated inhibitory, synaptic transmission in the hippocampal CA1 pyramidal neurons. These functional changes at synapses were corroborated by the immature morphology of the dendritic spines in these neurons. Lastly, autistic-like animal behavioral changes, including impaired social interaction and communication, increased repetitive behaviors, and impaired novel object recognition and location memory, were found in SD mice. Together, these results demonstrate molecular, cellular, and behavioral changes in SD mice, all of which resemble autistic-like phenotypes caused by circadian rhythm disruption. The findings highlight a critical role for circadian rhythms in neurodevelopment.

Blood lead levels in Peruvian adults are associated with proximity to mining and DNA methylation

BACKGROUND

Inorganic lead (Pb) is common in the environment, and is toxic to neurological, renal, and cardiovascular systems. Pb exposure influences the epigenome with documented effects on DNA methylation (DNAm). We assessed the impact of low levels of Pb exposure on DNAm among non-miner individuals from two locations in Peru: Lima, the capital, and Cerro de Pasco, a highland mining town, to study the effects of Pb exposure on physiological outcomes and DNAm.

METHODS

Pb levels were measured in whole blood (n = 305). Blood leukocyte DNAm was determined for 90 DNA samples using the Illumina MethylationEPIC chip. An epigenome-wide association study was performed to assess the relationship between Pb and DNAm.

RESULTS

Individuals from Cerro de Pasco had higher Pb than individuals from Lima (p-value = 2.00E-16). Males had higher Pb than females (p-value = 2.36E-04). Pb was positively associated with hemoglobin (p-value = 8.60E-04). In Cerro de Pasco, blood Pb decreased with the distance from the mine (p-value = 0.04), and association with soil Pb was approaching significance (p-value = 0.08). We identified differentially methylated positions (DMPs) associated with genes SOX18, ZMIZ1, and KDM1A linked to neurological function. We also found 45 differentially methylated regions (DMRs), seven of which were associated with genes involved in metal ion binding and nine to neurological function and development.

CONCLUSIONS

Our results demonstrate that even low levels of Pb can have a significant impact on the body including changes to DNAm. We report associations between Pb and hemoglobin, Pb and distance from mining, and between blood and soil Pb. We also report associations between loci- and region-specific DNAm and Pb.

Identification of two methylated fragments of an SDC2 CpG island using a sliding window technique for early detection of colorectal cancer

Colorectal cancer (CRC) is one of the most common cancer types globally with a 5‐year survival rate of < 50% in China. Aberrant DNA methylation is one of the hallmarks of tumor initiation, progression, and metastasis. Here, we investigated the clinical performance of two differentially methylated regions (DMRs) in SDC2 CpG islands for the detection of CRC. A sliding window technique was used to identify the DMRs, and methylation‐specific PCR assay was used to assess the DMRs in 198 CRC samples and 54 normal controls. Two DMRs (DMR2 and DMR5) were identified using The Cancer Genome Atlas (TCGA) data, and the hypermethylation of DMR2 and DMR5 was detected in 90.91% (180/198) and 89.90% (178/198) of CRC samples, respectively. When combining DMR2 and DMR5, the sensitivity for CRC detection was 94.4% higher than that of DMR2 or DMR5 alone. Based on the above results, we propose using DMR2 and DMR5 as a sensitive biomarker to detect CRC.

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.

Bioinformatic tools for DNA methylation and histone modification: A survey

Epigenetic inheritance occurs due to different mechanisms such as chromatin and histone modifications, DNA methylation and processes mediated by non-coding RNAs. It leads to changes in gene expressions and the emergence of new traits in different organisms in many diseases such as cancer. Recent advances in experimental methods led to the identification of epigenetic target sites in various organisms. Computational approaches have enabled us to analyze mass data produced by these methods. Next-generation sequencing (NGS) methods have been broadly used to identify these target sites and their patterns. By using these patterns, the emergence of diseases could be prognosticated. In this study, target site prediction tools for two major epigenetic mechanisms comprising histone modification and DNA methylation are reviewed. Publicly accessible databases are reviewed as well. Some suggestions regarding the state-of-the-art methods and databases have been made, including examining patterns of epigenetic changes that are important in epigenotypes detection.

Detecting differentially methylated regions with multiple distinct associations

Aim: We evaluated five methods for detecting differentially methylated regions (DMRs): DMRcate, comb-p, seqlm, GlobalP and dmrff. Materials & methods: We used a simulation study and real data analysis to evaluate performance. Additionally, we evaluated the use of an ancestry-matched reference cohort to estimate correlations between CpG sites in cord blood. Results: Several methods had inflated Type I error, which increased at more stringent significant levels. In power simulations with 1-2 causal CpG sites with the same direction of effect, dmrff was consistently among the most powerful methods. Conclusion: This study illustrates the need for more thorough simulation studies when evaluating novel methods. More work must be done to develop methods with well-controlled Type I error that do not require individual-level data.

MADA: a web service for analysing DNA methylation array data

Background

DNA methylation in the human genome is acknowledged to be widely associated with biological processes and complex diseases. The Illumina Infinium methylation arrays have been approved as one of the most efficient and universal technologies to investigate the whole genome changes of methylation patterns. As methylation arrays may still be the dominant method for detecting methylation in the anticipated future, it is crucial to develop a reliable workflow to analysis methylation array data.

Results

In this study, we develop a web service MADA for the whole process of methylation arrays data analysis, which includes the steps of a comprehensive differential methylation analysis pipeline: pre-processing (data loading, quality control, data filtering, and normalization), batch effect correction, differential methylation analysis, and downstream analysis. In addition, we provide the visualization of pre-processing, differentially methylated probes or regions, gene ontology, pathway and cluster analysis results. Moreover, a customization function for users to define their own workflow is also provided in MADA.

Conclusions

With the analysis of two case studies, we have shown that MADA can complete the whole procedure of methylation array data analysis. MADA provides a graphical user interface and enables users with no computational skills and limited bioinformatics background to carry on complicated methylation array data analysis. The web server is available at: http://120.24.94.89:8080/MADA

Genome-Wide DNA Methylation Patterns in Children Exposed to Nonpharmacologically Treated Prenatal Depressive Symptoms: Results From 2 Independent Cohorts

BACKGROUND

Maternal depressive symptoms are a common phenomenon during pregnancy and are related to negative outcomes for child development and health. Modifications in child DNA methylation are discussed as an underlying mechanism for the association between prenatal depressive symptoms and alterations in child outcomes. However, formerly reported genome-wide associations have yet to be replicated.

METHODS

In an epigenome-wide association study (EWAS), alterations of DNA methylation related to maternal prenatal depressive symptoms were investigated in buccal cell samples from 174 children (n = 52 exposed to prenatal depressive symptoms; 6-9 years old) of the German longitudinal study FRAMES-FRANCES. Whole blood samples from the independent, age-comparable ARIES subsample of the ARIES/ALSPAC study (n = 641; n = 159 exposed to prenatal depressive symptoms; 7-8 years old) were examined as a confirmation sample. Depressive symptoms were assessed with the Edinburgh Postnatal Depression Scale. DNA methylation was analyzed with the Infinium Human Methylation 450k BeadChip. Modifications in single CpGs, regions, and biological pathways were investigated. Results were adjusted for age and birth outcomes as well as postnatal and current maternal depressive symptoms. Analyses were performed for the whole sample as well as separated for sex.

RESULTS

The EWAS yielded no differentially methylated CpG or region as well as no accordance between samples withstanding correction for multiple testing. In pathway analyses, no overlapping functional domain was found to be enriched for either sample. A comparison of current and former findings suggests some overlapping methylation modifications from infancy to childhood. Results suggest that there might be sex-specific differential methylation, which should be further investigated in additional studies.

CONCLUSIONS

The current, mainly nonsignificant, results challenge the assumption of consistent modifications of DNA methylation in children exposed to prenatal depressive symptoms. Despite the relatively small sample size used in this study, this lack of significant results may reflect diverse issues of environmental epigenetic studies, which need to be addressed in future research.

Identification of methylation states of DNA regions for Illumina methylation BeadChip

Background

Methylation of cytosine bases in DNA is a critical epigenetic mark in many eukaryotes and has also been implicated in the development and progression of normal and diseased cells. Therefore, profiling DNA methylation across the genome is vital to understanding the effects of epigenetic. In recent years the Illumina HumanMethylation450 (HM450K) and MethylationEPIC (EPIC) BeadChip have been widely used to profile DNA methylation in human samples. The methods to predict the methylation states of DNA regions based on microarray methylation datasets are critical to enable genome-wide analyses.

Result

We report a computational approach based on the two layers two-state hidden Markov model (HMM) to identify methylation states of single CpG site and DNA regions in HM450K and EPIC BeadChip. Using this mothed, all CpGs detected by HM450K and EPIC in H1-hESC and GM12878 cell lines are identified as un-methylated, middle-methylated and full-methylated states. A large number of DNA regions are segmented into three methylation states as well. Comparing the identified regions with the result from the whole genome bisulfite sequencing (WGBS) datasets segmented by MethySeekR, our method is verified. Genome-wide maps of chromatin states show that methylation state is inversely correlated with active histone marks. Genes regulated by un-methylated regions are expressed and regulated by full-methylated regions are repressed. Our method is illustrated to be useful and robust.

Conclusion

Our method is valuable for DNA methylation genome-wide analyses. It is focusing on identification of DNA methylation states on microarray methylation datasets. For the features of array datasets, using two layers two-state HMM to identify to methylation states on CpG sites and regions creatively, our method which takes into account the distribution of genome-wide methylation levels is more reasonable than segmentation with a fixed threshold.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-6019-0) contains supplementary material, which is available to authorized users.

coMethDMR: accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies with continuous phenotypes

Recent technology has made it possible to measure DNA methylation profiles in a cost-effective and comprehensive genome-wide manner using array-based technology for epigenome-wide association studies. However, identifying differentially methylated regions (DMRs) remains a challenging task because of the complexities in DNA methylation data. Supervised methods typically focus on the regions that contain consecutive highly significantly differentially methylated CpGs in the genome, but may lack power for detecting small but consistent changes when few CpGs pass stringent significance threshold after multiple comparison. Unsupervised methods group CpGs based on genomic annotations first and then test them against phenotype, but may lack specificity because the regional boundaries of methylation are often not well defined. We present coMethDMR, a flexible, powerful, and accurate tool for identifying DMRs. Instead of testing all CpGs within a genomic region, coMethDMR carries out an additional step that selects co-methylated sub-regions first. Next, coMethDMR tests association between methylation levels within the sub-region and phenotype via a random coefficient mixed effects model that models both variations between CpG sites within the region and differential methylation simultaneously. coMethDMR offers well-controlled Type I error rate, improved specificity, focused testing of targeted genomic regions, and is available as an open-source R package.

Association of dietary folate and vitamin B-12 intake with genome-wide DNA methylation in blood: a large-scale epigenome-wide association analysis in 5841 individuals

BACKGROUND

Folate and vitamin B-12 are essential micronutrients involved in the donation of methyl groups in cellular metabolism. However, associations between intake of these nutrients and genome-wide DNA methylation levels have not been studied comprehensively in humans.

OBJECTIVE

The aim of this study was to assess whether folate and/or vitamin B-12 intake are asssociated with genome-wide changes in DNA methylation in leukocytes.

METHODS

A large-scale epigenome-wide association study of folate and vitamin B-12 intake was performed on DNA from 5841 participants from 10 cohorts using Illumina 450k arrays. Folate and vitamin B-12 intakes were calculated from food-frequency questionnaires (FFQs). Continuous and categorical (low compared with high intake) linear regression mixed models were applied per cohort, controlling for confounders. A meta-analysis was performed to identify significant differentially methylated positions (DMPs) and regions (DMRs), and a pathway analysis was performed on the DMR annotated genes.

RESULTS

The categorical model resulted in 6 DMPs, which are all negatively associated with folate intake, annotated to FAM64A, WRAP73, FRMD8, CUX1, and LCN8 genes, which have a role in cellular processes including centrosome localization, cell proliferation, and tumorigenesis. Regional analysis showed 74 folate-associated DMRs, of which 73 were negatively associated with folate intake. The most significant folate-associated DMR was a 400-base pair (bp) spanning region annotated to the LGALS3BP gene. In the categorical model, vitamin B-12 intake was associated with 29 DMRs annotated to 48 genes, of which the most significant was a 1100-bp spanning region annotated to the calcium-binding tyrosine phosphorylation-regulated gene (CABYR). Vitamin B-12 intake was not associated with DMPs.

CONCLUSIONS

We identified novel epigenetic loci that are associated with folate and vitamin B-12 intake. Interestingly, we found a negative association between folate and DNA methylation. Replication of these methylation loci is necessary in future studies.

Genome-wide DNA methylation profiling in ectopic and eutopic of endometrial tissues

PURPOSE

Endometriosis is a gynecological disease that causes the uterine lining to appear in other organs outside the uterus. As DNA methylation has an important role in this disorder, its profiling can reveal new information to improve the diagnosis and treatment of endometriosis patients.

METHODS

We conducted a genome-wide methylation profiling of ectopic and eutopic endometrial tissues from women with and without endometriosis using Infinium Human Methylation 450K BeadChip arrays. DNA methylation samples were collected from nine ectopic and nine eutopic endometrial tissues of endometriosis and six endometrial tissues of healthy controls.

RESULTS

Correlation heatmaps and the principal component analysis divided the samples into two clusters, one consisting of all ectopic samples and the other consisting of both eutopic and control samples unexpectedly without segregation between them. The assay identified a group of methylated genes that were overrepresented in biological processes, including abnormality in signaling, development, and adhesion of cells. Pathway analysis revealed disruption in HTLV infection pathways, PI3K-Akt, oxytocin, and relaxin signaling. Moreover, we found eutopic lesions are strongly associated with autoimmune disease.

CONCLUSIONS

Our results confirmed the role of DNA methylation alternations in endometriosis development and pathogenesis. Our finding suggests aberrant DNA methylation can activate several signaling pathways including PI3k-AKT signaling, relaxin, and oxytocin which are associated with the pathogenesis of endometriosis.

Data-Driven-Based Approach to Identifying Differentially Methylated Regions Using Modified 1D Ising Model

Background

DNA methylation is essential for regulating gene expression, and the changes of DNA methylation status are commonly discovered in disease. Therefore, identification of differentially methylation patterns, especially differentially methylated regions (DMRs), in two different groups is important for understanding the mechanism of complex diseases. Few tools exist for DMR identification through considering features of methylation data, but there is no comprehensive integration of the characteristics of DNA methylation data in current methods.

Results

Accounting for the characteristics of methylation data, such as the correlation characteristics of neighboring CpG sites and the high heterogeneity of DNA methylation data, we propose a data-driven approach for DMR identification through evaluating the energy of single site using modified 1D Ising model. Applied to both simulated and publicly available datasets, our approach is compared with other popular methods in terms of performance. Simulated results show that our method is more sensitive than competing methods. Applied to the real data, our method can identify more common DMRs than DMRcate, ProbeLasso, and Wang's methods with a high overlapping ratio. Also, the necessity of integrating the heterogeneity and correlation characteristics in identifying DMR is shown through comparing results with only considering mean or variance signals and without considering relationship of neighboring CpG sites, respectively. Through analyzing the number of DMRs identified in real data located in different genomic regions, we find that about 90% DMRs are located in CGI which always regulates the expression of genes. It may help us understand the functional effect of DNA methylation on disease.

Detect differentially methylated regions using non-homogeneous hidden Markov model for methylation array data

Motivation

DNA methylation is an important epigenetic mechanism in gene regulation and the detection of differentially methylated regions (DMRs) is enthralling for many disease studies. There are several aspects that we can improve over existing DMR detection methods: (i) methylation statuses of nearby CpG sites are highly correlated, but this fact has seldom been modelled rigorously due to the uneven spacing; (ii) it is practically important to be able to handle both paired and unpaired samples; and (iii) the capability to detect DMRs from a single pair of samples is demanded.

Results

We present DMRMark (DMR detection based on non-homogeneous hidden Markov model), a novel Bayesian framework for detecting DMRs from methylation array data. It combines the constrained Gaussian mixture model that incorporates the biological knowledge with the non-homogeneous hidden Markov model that models spatial correlation. Unlike existing methods, our DMR detection is achieved without predefined boundaries or decision windows. Furthermore, our method can detect DMRs from a single pair of samples and can also incorporate unpaired samples. Both simulation studies and real datasets from The Cancer Genome Atlas showed the significant improvement of DMRMark over other methods.

Availability and implementation

DMRMark is freely available as an R package at the CRAN R package repository.

Contact

xfan@cuhk.edu.hk.

Supplementary information

Supplementary data are available at Bioinformatics online.

Homocysteine levels associate with subtle changes in leukocyte DNA methylation: an epigenome-wide analysis

AIM

Homocysteine (Hcy) is a sensitive marker of one-carbon metabolism. Higher Hcy levels have been associated with global DNA hypomethylation. We investigated the association between plasma Hcy and epigenome-wide DNA methylation in leukocytes.

METHODS

Methylation was measured using Illumina 450 k arrays in 2035 individuals from six cohorts. Hcy-associated differentially methylated positions and regions were identified using meta-analysis.

RESULTS

Three differentially methylated positions cg21607669 (SLC27A1), cg26382848 (AJUBA) and cg10701000 (KCNMA1) at chromosome 19, 14 and 10, respectively, were significantly associated with Hcy. In addition, we identified 68 Hcy-associated differentially methylated regions, the most significant of which was a 1.8-kb spanning domain (TNXB/ATF6B) at chromosome 6.

CONCLUSION

We identified novel epigenetic loci associated with Hcy levels, of which specific role needs to be further validated.

Tea and coffee consumption in relation to DNA methylation in four European cohorts

Lifestyle factors, such as food choices and exposure to chemicals, can alter DNA methylation and lead to changes in gene activity. Two such exposures with pharmacologically active components are coffee and tea consumption. Both coffee and tea have been suggested to play an important role in modulating disease-risk in humans by suppressing tumour progression, decreasing inflammation and influencing estrogen metabolism. These mechanisms may be mediated by changes in DNA methylation. To investigate if DNA methylation in blood is associated with coffee and tea consumption, we performed a genome-wide DNA methylation study for coffee and tea consumption in four European cohorts (N = 3,096). DNA methylation was measured from whole blood at 421,695 CpG sites distributed throughout the genome and analysed in men and women both separately and together in each cohort. Meta-analyses of the results and additional regional-level analyses were performed. After adjusting for multiple testing, the meta-analysis revealed that two individual CpG-sites, mapping to DNAJC16 and TTC17, were differentially methylated in relation to tea consumption in women. No individual sites were associated with men or with the sex-combined analysis for tea or coffee. The regional analysis revealed that 28 regions were differentially methylated in relation to tea consumption in women. These regions contained genes known to interact with estradiol metabolism and cancer. No significant regions were found in the sex-combined and male-only analysis for either tea or coffee consumption.

DNA methylation changes in endometrium and correlation with gene expression during the transition from pre-receptive to receptive phase

The inner uterine lining (endometrium) is a unique tissue going through remarkable changes each menstrual cycle. Endometrium has its characteristic DNA methylation profile, although not much is known about the endometrial methylome changes throughout the menstrual cycle. The impact of methylome changes on gene expression and thereby on the function of the tissue, including establishing receptivity to implanting embryo, is also unclear. Therefore, this study used genome-wide technologies to characterize the methylome and the correlation between DNA methylation and gene expression in endometrial biopsies collected from 17 healthy fertile-aged women from pre-receptive and receptive phase within one menstrual cycle. Our study showed that the overall methylome remains relatively stable during this stage of the menstrual cycle, with small-scale changes affecting 5% of the studied CpG sites (22,272 out of studied 437,022 CpGs, FDR < 0.05). Of differentially methylated CpG sites with the largest absolute changes in methylation level, approximately 30% correlated with gene expression measured by RNA sequencing, with negative correlations being more common in 5' UTR and positive correlations in the gene 'Body' region. According to our results, extracellular matrix organization and immune response are the pathways most affected by methylation changes during the transition from pre-receptive to receptive phase.

Imbalanced multi-label learning for identifying antimicrobial peptides and their functional types

MOTIVATION

With the rapid increase of infection resistance to antibiotics, it is urgent to find novel infection therapeutics. In recent years, antimicrobial peptides (AMPs) have been utilized as potential alternatives for infection therapeutics. AMPs are key components of the innate immune system and can protect the host from various pathogenic bacteria. Identifying AMPs and their functional types has led to many studies, and various predictors using machine learning have been developed. However, there is room for improvement; in particular, no predictor takes into account the lack of balance among different functional AMPs.

RESULTS

In this paper, a new synthetic minority over-sampling technique on imbalanced and multi-label datasets, referred to as ML-SMOTE, was designed for processing and identifying AMPs' functional families. A novel multi-label classifier, MLAMP, was also developed using ML-SMOTE and grey pseudo amino acid composition. The classifier obtained 0.4846 subset accuracy and 0.16 hamming loss.

AVAILABILITY AND IMPLEMENTATION

A user-friendly web-server for MLAMP was established at http://www.jci-bioinfo.cn/MLAMP CONTACTS: linweizhong@jci.edu.cn or xudong@missouri.edu.