Accurate genome-scale percentage DNA methylation estimates from microarray data

Epigenetic changes in sperm are associated with paternal and child quantitative autistic traits in an autism-enriched cohort

There is a need to consider paternal contributions to autism spectrum disorder (ASD) more strongly. Autism etiology is complex, and heritability is not explained by genetics alone. Understanding paternal gametic epigenetic contributions to autism could help fill this knowledge gap. In the present study, we explored whether paternal autistic traits, and the sperm epigenome, were associated with autistic traits in children at 36 months enrolled in the Early Autism Risk Longitudinal Investigation (EARLI) cohort. EARLI is a pregnancy cohort that recruited and enrolled pregnant women in the first half of pregnancy who already had a child with ASD. After maternal enrollment, EARLI fathers were approached and asked to provide a semen specimen. Participants were included in the present study if they had genotyping, sperm methylation data, and Social Responsiveness Scale (SRS) score data available. Using the CHARM array, we performed genome-scale methylation analyses on DNA from semen samples contributed by EARLI fathers. The SRS-a 65-item questionnaire measuring social communication deficits on a quantitative scale-was used to evaluate autistic traits in EARLI fathers (n = 45) and children (n = 31). We identified 94 significant child SRS-associated differentially methylated regions (DMRs), and 14 significant paternal SRS-associated DMRs (fwer p < 0.05). Many child SRS-associated DMRs were annotated to genes implicated in ASD and neurodevelopment. Six DMRs overlapped across the two outcomes (fwer p < 0.1), and, 16 DMRs overlapped with previous child autistic trait findings at 12 months of age (fwer p < 0.05). Child SRS-associated DMRs contained CpG sites independently found to be differentially methylated in postmortem brains of individuals with and without autism. These findings suggest paternal germline methylation is associated with autistic traits in 3-year-old offspring. These prospective results for autism-associated traits, in a cohort with a family history of ASD, highlight the potential importance of sperm epigenetic mechanisms in autism.

Molecular networks in atopic mothers impact the risk of infant atopy

BACKGROUND

The prevalence of atopic diseases has increased with atopic dermatitis (AD) as the earliest manifestation. We assessed if molecular risk factors in atopic mothers influence their infants' susceptibility to an atopic disease.

METHODS

Pregnant women and their infants with (n = 174, high-risk) or without (n = 126, low-risk) parental atopy were enrolled in a prospective birth cohort. Global differentially methylated regions (DMRs) were determined in atopic (n = 92) and non-atopic (n = 82) mothers. Principal component analysis was used to predict atopy risk in children dependent on maternal atopy. Genome-wide transcriptomic analyses were performed in paired atopic (n = 20) and non-atopic (n = 15) mothers and cord blood. Integrative genomic analyses were conducted to define methylation-gene expression relationships.

RESULTS

Atopic dermatitis was more prevalent in high-risk compared to low-risk children by age 2. Differential methylation analyses identified 165 DMRs distinguishing atopic from non-atopic mothers. Inclusion of DMRs in addition to maternal atopy significantly increased the odds ratio to develop AD in children from 2.56 to 4.26. In atopic compared to non-atopic mothers, 139 differentially expressed genes (DEGs) were identified significantly enriched of genes within the interferon signaling pathway. Expression quantitative trait methylation analyses dependent on maternal atopy identified 29 DEGs controlled by 136 trans-acting methylation marks, some located near transcription factors. Differential expression for the same nine genes, including MX1 and IFI6 within the interferon pathway, was identified in atopic and non-atopic mothers and high-risk and low-risk children.

CONCLUSION

These data suggest that in utero epigenetic and transcriptomic mechanisms predominantly involving the interferon pathway may impact and predict the development of infant atopy.

A High Epigenetic Risk Score Shapes the Non-Inflamed Tumor Microenvironment in Breast Cancer

Background: Epigenetic dysregulation via aberrant DNA methylation has gradually become recognized as an efficacious signature for predicting tumor prognosis and response to therapeutic targets. However, reliable DNA methylation biomarkers describing tumorigenesis remain to be comprehensively explored regarding their prognostic and therapeutic potential in breast cancer (BC). Methods: Whole-genome methylation datasets integrated from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were profiled (n = 1,268). A three-stage selection procedure (discovery, training, and external validation) was utilized to screen out the prominent biomarkers and establish a robust risk score from more than 300,000 CpG sites after quality control, rigorous filtering, and reducing dimension. Moreover, gene set enrichment analyses guided us to systematically correlate this epigenetic risk score with immunological characteristics, including immunomodulators, anti-cancer immunity cycle, immune checkpoints, tumor-infiltrating immune cells and a series of signatures upon modulating components within BC tumor microenvironment (TME). Multi-omics data analyses were performed to decipher specific genomic alterations in low- and high-risk patients. Additionally, we also analyzed the role of risk score in predicting response to several treatment options. Results: A 10-CpG-based prognostic signature which could significantly and independently categorize BC patients into distinct prognoses was established and sufficiently validated. And we hypothesize that this signature designs a non-inflamed TME in BC based on the evidence that the derived risk score is negatively correlated with tumor-associated infiltrating immune cells, anti-cancer immunity cycle, immune checkpoints, immune cytolytic activity, T cell inflamed score, immunophenoscore, and the vast majority of immunomodulators. The identified high-risk patients were characterized by upregulation of immune inhibited oncogenic pathways, higher TP53 mutation and copy number burden, but lower response to cancer immunotherapy and chemotherapy. Conclusion: Our work highlights the complementary roles of 10-CpG-based signature in estimating overall survival in BC patients, shedding new light on investigating failed events concerning immunotherapy at present.

Genome-wide identification of CpG island methylator phenotype related gene signature as a novel prognostic biomarker of gastric cancer

Background

Gastric cancer (GC) is one of the most fatal cancers in the world. Results of previous studies on the association of the CpG island methylator phenotype (CIMP) with GC prognosis are conflicting and mainly based on selected CIMP markers. The current study attempted to comprehensively assess the association between CIMP status and GC survival and to develop a CIMP-related prognostic gene signature of GC.

Methods

We used a hierarchical clustering method based on 2,082 GC-related methylation sites to stratify GC patients from the cancer genome atlas into three different CIMP subgroups according to the CIMP status. Gene set enrichment analysis, tumor-infiltrating immune cells, and DNA somatic mutations analysis were conducted to reveal the genomic characteristics in different CIMP-related patients. Cox regression analysis and the least absolute shrinkage and selection operator were performed to develop a CIMP-related prognostic signature. Analyses involving a time-dependent receiver operating characteristic (ROC) curve and calibration plot were adopted to assess the performance of the prognostic signature.

Results

We found a positive relationship between CIMP and prognosis in GC. Gene set enrichment analysis indicated that cancer-progression-related pathways were enriched in the CIMP-L group. High abundances of CD8+ T cells and M1 macrophages were found in the CIMP-H group, meanwhile more plasma cells, regulatory T cells and CD4+ memory resting T cells were detected in the CIMP-L group. The CIMP-H group showed higher tumor mutation burden, more microsatellite instability-H, less lymph node metastasis, and more somatic mutations favoring survival. We then established a CIMP-related prognostic gene signature comprising six genes (CST6, SLC7A2, RAB3B, IGFBP1, VSTM2L and EVX2). The signature was capable of classifying patients into high‐and low‐risk groups with significant difference in overall survival (OS; p < 0.0001). To assess performance of the prognostic signature, the area under the ROC curve (AUC) for OS was calculated as 0.664 at 1 year, 0.704 at 3 years and 0.667 at 5 years. When compared with previously published gene-based signatures, our CIMP-related signature was comparable or better at predicting prognosis. A multivariate Cox regression analysis indicated the CIMP-related prognostic gene signature was an independent prognostic indicator of GC. In addition, Gene ontology analysis indicated that keratinocyte differentiation and epidermis development were enriched in the high-risk group.

Conclusion

Collectively, we described a positive association between CIMP status and prognosis in GC and proposed a CIMP-related gene signature as a promising prognostic biomarker for GC.

Differences in DNA methylation profiles by histologic subtype of paediatric germ cell tumours: a report from the Children's Oncology Group

BACKGROUND

Abnormal DNA methylation may be important in germ cell tumour (GCT) aetiology, as germ cells undergo complete epigenetic reprogramming during development. GCTs show differences in global and promoter methylation patterns by histologic subtype. We conducted an epigenome-wide study to identify methylation differences by GCT histology.

METHODS

Using the Illumina HumanMethylation450K array we measured methylation in 154 paediatric GCTs (21 germinomas/seminomas/dysgerminoma, 70 yolk sac tumours [YST], 9 teratomas, and 54 mixed histology tumours). We identified differentially methylated regions (DMRs) between GCT histologies by comparing methylation beta values.

RESULTS

We identified 8,481 DMRs (FWER < 0.05). Unsupervised hierarchical clustering of individual probes within DMRs resulted in four high level clusters closely corresponding to tumour histology. Clusters corresponding to age, location, sex and FFPE status were not observed within these DMRs. Germinomas displayed lower levels of methylation across the DMRs relative to the other histologic subtypes. Pathway analysis on the top 10% of genes with differential methylation in germinomas/seminomas/dysgerminoma compared to YST suggested angiogenesis and immune cell-related pathways displayed decreased methylation in germinomas/seminomas/dysgerminoma relative to YST.

CONCLUSIONS

Paediatric GCT histologies have differential methylation patterns. The genes that are differentially methylated may provide insights into GCT aetiology including the timing of GCT initiation.

Novel biomarker ZCCHC13 revealed by integrating DNA methylation and mRNA expression data in non-obstructive azoospermia

The objective of this study was to identify genes regulated by methylation that were involved in spermatogenesis failure in non-obstructive azoospermia (NOA). Testis biopsies of patients with NOA and OA (with normal spermatogenesis) were evaluated by microarray analysis to examine DNA methylation and mRNA expression using our established integrative approach. Of the coordinately hypermethylated and down-regulated gene list, zinc-finger CCHC-type containing 13 (ZCCHC13) was present within the nuclei of germ cells of testicular tissues according immunohistochemistry, and there was decreased protein expression in men with NOA compared with OA controls. Mechanistic analyses indicated that ZCCHC13 increased c-MYC expression through the p-AKT and p-ERK pathways. To confirm the changes in ZCCHC13 expression in response to methylation, 5-aza-2′-deoxycitidine (5-Aza), a hypomethylating agent, was administered to mouse spermatogonia GC-1 cells. We demonstrated that 5-Aza enhanced protein and mRNA expression of ZCCHC13 epigenetically, which was accompanied by activation of p-AKT and p-ERK signaling. Our data, for the first time, demonstrate that ZCCHC13 is an important signaling molecule that positively regulates the AKT/MAPK/c-MYC pathway and that methylation aberrations of ZCCHC13 may cause defects in testis development in human disease, such as NOA.

Integrative analysis of DNA copy number, DNA methylation and gene expression in multiple myeloma reveals alterations related to relapse

Multiple myeloma (MM) remains incurable despite the introduction of novel agents, and a relapsing course is observed in most patients. Although the development of genomic technologies has greatly improved our understanding of MM pathogenesis, the mechanisms underlying relapse have been less thoroughly investigated. In this study, an integrative analysis of DNA copy number, DNA methylation and gene expression was conducted in matched diagnosis and relapse samples from MM patients. Overall, the acquisition of abnormalities at relapse was much more frequent than the loss of lesions present at diagnosis, and DNA losses were significantly more frequent in relapse than in diagnosis samples. Interestingly, copy number abnormalities involving more than 100 Mb of DNA at relapse significantly affect the gene expression of these samples, provoking a particular deregulation of the IL-8 pathway. On the other hand, no significant modifications of gene expression were observed in those samples with less than 100 Mb affected by chromosomal changes. Although several statistical approaches were used to identify genes whose abnormal expression at relapse was regulated by methylation, only two genes that were significantly deregulated in relapse samples (SORL1 and GLT1D1) showed a negative correlation between methylation and expression. Further analysis revealed that DNA methylation was involved in regulating SORL1 expression in MM. Finally, relevant changes in gene expression observed in relapse samples, such us downregulation of CD27 and P2RY8, were most likely not preceded by alterations in the corresponding DNA. Taken together, these results suggest that the genomic heterogeneity described at diagnosis remains at relapse.

msgbsR: An R package for analysing methylation-sensitive restriction enzyme sequencing data

Genotyping-by-sequencing (GBS) or restriction-site associated DNA marker sequencing (RAD-seq) is a practical and cost-effective method for analysing large genomes from high diversity species. This method of sequencing, coupled with methylation-sensitive enzymes (often referred to as methylation-sensitive restriction enzyme sequencing or MRE-seq), is an effective tool to study DNA methylation in parts of the genome that are inaccessible in other sequencing techniques or are not annotated in microarray technologies. Current software tools do not fulfil all methylation-sensitive restriction sequencing assays for determining differences in DNA methylation between samples. To fill this computational need, we present msgbsR, an R package that contains tools for the analysis of methylation-sensitive restriction enzyme sequencing experiments. msgbsR can be used to identify and quantify read counts at methylated sites directly from alignment files (BAM files) and enables verification of restriction enzyme cut sites with the correct recognition sequence of the individual enzyme. In addition, msgbsR assesses DNA methylation based on read coverage, similar to RNA sequencing experiments, rather than methylation proportion and is a useful tool in analysing differential methylation on large populations. The package is fully documented and available freely online as a Bioconductor package (https://bioconductor.org/packages/release/bioc/html/msgbsR.html).

Integrated Analysis of DNA Methylation and mRNA Expression Profiles to Identify Key Genes in Severe Oligozoospermia

Severe oligozoospermia (SO) is a complex disorder, whose etiology is the combined effect of genetic factors and epigenetic conditions. In this study, we examined DNA methylation and mRNA expression status in a set of testicular tissues of SO patients (n = 3), and compared methylated data with those derived from obstructive azoospermia (OA) patients (n = 3) with normal spermatogenesis phenotype. We identified 1,960 differentially methylated CpG sites showing significant alterations in SO vs. OA using the Illumina Infinium HumanMethylation450 bead array. By integrating above DNA methylation data and mRNA expression results, we totally identified 72 methylated CpG sites located in 65 genes with anti-correlation between DNA methylation and mRNA expression. Integrated pathways analysis indicates that these genes are involved in response to hormone stimulus, activation of protein kinase activity, and apoptotic process, among others. We also observed some genes with inversely correlated difference is novel in male infertility field, including PTPRN2, EPHX1, SERPINB9, SLIT3, etc. Our results lay a groundwork for further biological study of SO. Moreover, we generated a workflow for integrated analysis of DNA methylation and mRNA expression, which is expandable to other study types.

Role of DNA methylation at the placental RTL1 gene locus in type 1 diabetes

Genome-wide association studies (GWAS) have identified more than 40 T1D loci associated with type 1 diabetes (T1D). How these polymorphisms interact with environmental factors to trigger T1D is unknown, but recent evidence suggests that epigenetic mechanisms could play a role. To begin to explore the contribution of epigenetics to T1D, we have examined DNA methylation in a pilot study of whole blood cells DNA from 10 young T1D patients and 10 young controls. Through the study of >900 000 CG loci across a diverse set of functionally relevant genomic regions using a custom DNA methylation array, we identified 250 T1D-differentially methylated region (DMR) at p < 0.05 and 1 DMR using next a permutation-based multiple testing correction method. This DMR is located in an imprinted region previously associated with T1D on the chromosome 14 that encompasses RTL1 gene and 2 miRNAs (miR136 and miR432). Using pyrosequencing-based bisulfite PCR, we replicated this association in a different and larger set of T1D patients and controls. DNA methylation at this DMR was inversely correlated with RTL1 gene expression and positively correlated with miR136 expression in human placentas. The DMR identified in this study presents suggestive evidence for altered methylation site in T1D and provide a promising new candidate gene. RTL1 is essential for placental permeability function in the mid-to-late fetal stages. We suggest that hypo-methylation could increase the fetal exposure to environmental factors in T1D susceptibility.

Functional genomics analysis of vitamin D effects on CD4+ T cells in vivo in experimental autoimmune encephalomyelitis ‬

Vitamin D exerts multiple immunomodulatory functions and has been implicated in the etiology and treatment of several autoimmune diseases, including multiple sclerosis (MS). We have previously reported that in juvenile/adolescent rats, vitamin D supplementation protects from experimental autoimmune encephalomyelitis (EAE), a model of MS. Here we demonstrate that this protective effect associates with decreased proliferation of CD4+ T cells and lower frequency of pathogenic T helper (Th) 17 cells. Using transcriptome, methylome, and pathway analyses in CD4+ T cells, we show that vitamin D affects multiple signaling and metabolic pathways critical for T-cell activation and differentiation into Th1 and Th17 subsets in vivo. Namely, Jak/Stat, Erk/Mapk, and Pi3K/Akt/mTor signaling pathway genes were down-regulated upon vitamin D supplementation. The protective effect associated with epigenetic mechanisms, such as (i) changed levels of enzymes involved in establishment and maintenance of epigenetic marks, i.e., DNA methylation and histone modifications; (ii) genome-wide reduction of DNA methylation, and (iii) up-regulation of noncoding RNAs, including microRNAs, with concomitant down-regulation of their protein-coding target RNAs involved in T-cell activation and differentiation. We further demonstrate that treatment of myelin-specific T cells with vitamin D reduces frequency of Th1 and Th17 cells, down-regulates genes in key signaling pathways and epigenetic machinery, and impairs their ability to transfer EAE. Finally, orthologs of nearly 50% of candidate MS risk genes and 40% of signature genes of myelin-reactive T cells in MS changed their expression in vivo in EAE upon supplementation, supporting the hypothesis that vitamin D may modulate risk for developing MS.

High-specificity bioinformatics framework for epigenomic profiling of discordant twins reveals specific and shared markers for ACPA and ACPA-positive rheumatoid arthritis

BACKGROUND

Twin studies are powerful models to elucidate epigenetic modifications resulting from gene-environment interactions. Yet, commonly a limited number of clinical twin samples are available, leading to an underpowered situation afflicted with false positives and hampered by low sensitivity. We investigated genome-wide DNA methylation data from two small sets of monozygotic twins representing different phases during the progression of rheumatoid arthritis (RA) to find novel genes for further research.

METHODS

We implemented a robust statistical methodology aimed at investigating a small number of samples to identify differential methylation utilizing the comprehensive CHARM platform with whole blood cell DNA from two sets of twin pairs discordant either for ACPA (antibodies to citrullinated protein antigens)-positive RA versus ACPA-negative healthy or for ACPA-positive healthy (a pre-RA stage) versus ACPA-negative healthy. To deconvolute cell type-dependent differential methylation, we assayed the methylation patterns of sorted cells and used computational algorithms to resolve the relative contributions of different cell types and used them as covariates.

RESULTS

To identify methylation biomarkers, five healthy twin pairs discordant for ACPAs were profiled, revealing a single differentially methylated region (DMR). Seven twin pairs discordant for ACPA-positive RA revealed six significant DMRs. After deconvolution of cell type proportions, profiling of the healthy ACPA discordant twin-set revealed 17 genome-wide significant DMRs. When methylation profiles of ACPA-positive RA twin pairs were adjusted for cell type, the analysis disclosed one significant DMR, associated with the EXOSC1 gene. Additionally, the results from our methodology suggest a temporal connection of the protocadherine beta-14 gene to ACPA-positivity with clinical RA.

CONCLUSIONS

Our biostatistical methodology, optimized for a low-sample twin design, revealed non-genetically linked genes associated with two distinct phases of RA. Functional evidence is still lacking but the results reinforce further study of epigenetic modifications influencing the progression of RA. Our study design and methodology may prove generally useful in twin studies.

Analysis of the interplay between methylation and expression reveals its potential role in cancer aetiology

With ongoing developments in technology, changes in DNA methylation levels have become prevalent to study cancer biology. Previous studies report that DNA methylation affects gene expression in a direct manner, most probably by blocking gene regulatory regions. In this study, we have studied the interplay between methylation and expression to improve our knowledge of cancer aetiology. For this purpose, we have investigated which genomic regions are of higher importance; hence, first exon, 5'UTR and 200 bp near the transcription start sites are proposed as being more crucial compared to other genomic regions. Furthermore, we have searched for a valid methylation level change threshold, and as a result, 25 % methylation change in previously determined genomic regions showed the highest inverse correlation with expression data. As a final step, we have examined the commonly affected genes and pathways by integrating methylation and expression information. Remarkably, the GPR115 gene and ErbB signalling pathway were found to be significantly altered for all cancer types in our analysis. Overall, combining methylation and expression information and identifying commonly affected genes and pathways in a variety of cancer types revealed new insights of cancer disease mechanisms. Moreover, compared to previous methylation-based studies, we have identified more important genomic regions and have defined a methylation change threshold level in order to obtain more reliable results. In addition to the novel analysis framework that involves the analysis of four different cancer types, our study exposes essential information regarding the contribution of methylation changes and its impact on cancer disease biology, which may facilitate the identification of new drug targets.

Advanced aging phenotype is revealed by epigenetic modifications in rat liver after in utero malnutrition

Adverse environmental exposures of mothers during fetal period predispose offspring to a range of age-related diseases earlier in life. Here, we set to determine whether a deregulated epigenetic pattern is similar in young animals whose mothers' nutrition was modulated during fetal growth to that acquired during normal aging in animals. Using a rodent model of maternal undernutrition (UN) or overnutrition (ON), we examined cytosine methylation profiles of liver from young female offspring and compared them to age-matched young controls and aged (20-month-old) animals. HELP-tagging, a genomewide restriction enzyme and sequencing assay demonstrates that fetal exposure to two different maternal diets is associated with nonrandom dysregulation of methylation levels with profiles similar to those seen in normal aging animals and occur in regions mapped to genes relevant to metabolic diseases and aging. Functional consequences were assessed by gene expression at 9 weeks old with more significant changes at 6 months of age. Early developmental exposures to unfavorable maternal diets result in altered methylation profiles and transcriptional dysregulation in Prkcb, Pc, Ncor2, and Smad3 that is also seen with normal aging. These Notch pathway and lipogenesis genes may be useful for prediction of later susceptibility to chronic disease.

A cross-package Bioconductor workflow for analysing methylation array data

Methylation in the human genome is known to be associated with development and disease. The Illumina Infinium methylation arrays are by far the most common way to interrogate methylation across the human genome. This paper provides a Bioconductor workflow using multiple packages for the analysis of methylation array data. Specifically, we demonstrate the steps involved in a typical differential methylation analysis pipeline including: quality control, filtering, normalization, data exploration and statistical testing for probe-wise differential methylation. We further outline other analyses such as differential methylation of regions, differential variability analysis, estimating cell type composition and gene ontology testing. Finally, we provide some examples of how to visualise methylation array data.

A cross-package Bioconductor workflow for analysing methylation array data

Methylation in the human genome is known to be associated with development and disease. The Illumina Infinium methylation arrays are by far the most common way to interrogate methylation across the human genome. This paper provides a Bioconductor workflow using multiple packages for the analysis of methylation array data. Specifically, we demonstrate the steps involved in a typical differential methylation analysis pipeline including: quality control, filtering, normalization, data exploration and statistical testing for probe-wise differential methylation. We further outline other analyses such as differential methylation of regions, differential variability analysis, estimating cell type composition and gene ontology testing. Finally, we provide some examples of how to visualise methylation array data.

A cross-package Bioconductor workflow for analysing methylation array data

Methylation in the human genome is known to be associated with development and disease. The Illumina Infinium methylation arrays are by far the most common way to interrogate methylation across the human genome. This paper provides a Bioconductor workflow using multiple packages for the analysis of methylation array data. Specifically, we demonstrate the steps involved in a typical differential methylation analysis pipeline including: quality control, filtering, normalization, data exploration and statistical testing for probe-wise differential methylation. We further outline other analyses such as differential methylation of regions, differential variability analysis, estimating cell type composition and gene ontology testing. Finally, we provide some examples of how to visualise methylation array data.

Genome-wide DNA methylation profiling of peripheral blood mononuclear cells in irritable bowel syndrome

BACKGROUND

Irritable bowel syndrome (IBS) is a stress-sensitive disorder. Environmental factors including stress can trigger epigenetic changes, which have not been well-studied in IBS. We performed a pilot study investigating genome-wide DNA methylation of IBS patients and healthy controls (HCs) to identify potential epigenetic markers and associated pathways. Additionally, we investigated relationships of epigenetic changes in selected genes with clinical traits.

METHODS

Twenty-seven IBS patients (59% women; 10 IBS-diarrhea, 8 IBS-constipation, 9 IBS-mixed) and 23 age- and sex-matched HCs were examined. DNA methylation from peripheral blood mononuclear cells (PBMCs) was measured using HM450 BeadChip, and representative methylation differences were confirmed by bisulphite sequencing. Gene expression was measured using quantitative PCR. Gastrointestinal (GI) and non-GI symptoms were measured using validated questionnaires. Associations were tested using non-parametric methods.

KEY RESULTS

Genome-wide DNA methylation profiling of IBS patients compared with HCs identified 133 differentially methylated positions (DMPs) (mean difference ≥10%; p < 0.05). These genes were associated with gene ontology terms including glutathione metabolism related to oxidative stress and neuropeptide hormone activity. Validation by sequencing confirmed differential methylation of subcommissural organ (SCO)-Spondin (SSPO), glutathione-S-transferases mu 5 (GSTM5), and tubulin polymerization promoting protein genes. Methylation of two promoter CpGs in GSTM5 was associated with epigenetic silencing. Epigenetic changes in SSPO gene were positively correlated with hospital anxiety and depression scores in IBS patients (r > 0.4 and false discovery rate <0.05).

CONCLUSIONS & INFERENCES

This study is the first to comprehensively explore the methylome of IBS patients. We identified DMPs in novel candidate genes which could provide new insights into disease mechanisms; however, these preliminary findings warrant confirmation in larger, independent studies.

Profiling placental and fetal DNA methylation in human neural tube defects

Background

The incidence of neural tube defects (NTDs) declined by about 40 % in Canada with the introduction of a national folic acid (FA) fortification program. Despite the fact that few Canadians currently exhibit folate deficiency, NTDs are still the second most common congenital abnormality. FA fortification may have aided in reducing the incidence of NTDs by overcoming abnormal one carbon metabolism cycling, the process which provides one carbon units for methylation of DNA. We considered that NTDs persisting in a folate-replete population may also occur in the context of FA-independent compromised one carbon metabolism, and that this might manifest as abnormal DNA methylation (DNAm). Second trimester human placental chorionic villi, kidney, spinal cord, brain, and muscle were collected from 19 control, 22 spina bifida, and 15 anencephalic fetuses in British Columbia, Canada. DNA was extracted, assessed for methylenetetrahydrofolate reductase (MTHFR) genotype and for genome-wide DNAm using repetitive elements, in addition to the Illumina Infinium HumanMethylation450 (450k) array.

Results

No difference in repetitive element DNAm was noted between NTD status groups. Using a false discovery rate <0.05 and average group difference in DNAm ≥0.05, differentially methylated array sites were identified only in (1) the comparison of anencephaly to controls in chorionic villi (n = 4 sites) and (2) the comparison of spina bifida to controls in kidney (n = 3342 sites).

Conclusions

We suggest that the distinctive DNAm of spina bifida kidneys may be consequent to the neural tube defect or reflective of a common etiology for abnormal neural tube and renal development. Though there were some small shifts in DNAm in the other tested tissues, our data do not support the long-standing hypothesis of generalized altered genome-wide DNAm in NTDs. This finding may be related to the fact that most Canadians are not folate deficient, but it importantly opens the field to the investigation of other epigenetic and non-epigenetic mechanisms in the etiology of NTDs.

Electronic supplementary material

The online version of this article (doi:10.1186/s13072-016-0054-8) contains supplementary material, which is available to authorized users.

Pervasive polymorphic imprinted methylation in the human placenta

The maternal and paternal copies of the genome are both required for mammalian development, and this is primarily due to imprinted genes, those that are monoallelically expressed based on parent-of-origin. Typically, this pattern of expression is regulated by differentially methylated regions (DMRs) that are established in the germline and maintained after fertilization. There are a large number of germline DMRs that have not yet been associated with imprinting, and their function in development is unknown. In this study, we developed a genome-wide approach to identify novel imprinted DMRs in the human placenta and investigated the dynamics of these imprinted DMRs during development in somatic and extraembryonic tissues. DNA methylation was evaluated using the Illumina HumanMethylation450 array in 134 human tissue samples, publicly available reduced representation bisulfite sequencing in the human embryo and germ cells, and targeted bisulfite sequencing in term placentas. Forty-three known and 101 novel imprinted DMRs were identified in the human placenta by comparing methylation between diandric and digynic triploid conceptions in addition to female and male gametes. Seventy-two novel DMRs showed a pattern consistent with placental-specific imprinting, and this monoallelic methylation was entirely maternal in origin. Strikingly, these DMRs exhibited polymorphic imprinted methylation between placental samples. These data suggest that imprinting in human development is far more extensive and dynamic than previously reported and that the placenta preferentially maintains maternal germline-derived DNA methylation.

Identification of differentially methylated loci using wavelet-based functional mixed models

MOTIVATION

DNA methylation is a key epigenetic modification that can modulate gene expression. Over the past decade, a lot of studies have focused on profiling DNA methylation and investigating its alterations in complex diseases such as cancer. While early studies were mostly restricted to CpG islands or promoter regions, recent findings indicate that many of important DNA methylation changes can occur in other regions and DNA methylation needs to be examined on a genome-wide scale. In this article, we apply the wavelet-based functional mixed model methodology to analyze the high-throughput methylation data for identifying differentially methylated loci across the genome. Contrary to many commonly-used methods that model probes independently, this framework accommodates spatial correlations across the genome through basis function modeling as well as correlations between samples through functional random effects, which allows it to be applied to many different settings and potentially leads to more power in detection of differential methylation.

RESULTS

We applied this framework to three different high-dimensional methylation data sets (CpG Shore data, THREE data and NIH Roadmap Epigenomics data), studied previously in other works. A simulation study based on CpG Shore data suggested that in terms of detection of differentially methylated loci, this modeling approach using wavelets outperforms analogous approaches modeling the loci as independent. For the THREE data, the method suggests newly detected regions of differential methylation, which were not reported in the original study.

AVAILABILITY AND IMPLEMENTATION

Automated software called WFMM is available at https://biostatistics.mdanderson.org/SoftwareDownload CpG Shore data is available at http://rafalab.dfci.harvard.edu NIH Roadmap Epigenomics data is available at http://compbio.mit.edu/roadmap

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

CONTACT

jefmorris@mdanderson.org.

swDMR: A Sliding Window Approach to Identify Differentially Methylated Regions Based on Whole Genome Bisulfite Sequencing

DNA methylation is a widespread epigenetic modification that plays an essential role in gene expression through transcriptional regulation and chromatin remodeling. The emergence of whole genome bisulfite sequencing (WGBS) represents an important milestone in the detection of DNA methylation. Characterization of differential methylated regions (DMRs) is fundamental as well for further functional analysis. In this study, we present swDMR (http://sourceforge.net/projects/swDMR/) for the comprehensive analysis of DMRs from whole genome methylation profiles by a sliding window approach. It is an integrated tool designed for WGBS data, which not only implements accessible statistical methods to perform hypothesis test adapted to two or more samples without replicates, but false discovery rate was also controlled by multiple test correction. Downstream analysis tools were also provided, including cluster, annotation and visualization modules. In summary, based on WGBS data, swDMR can produce abundant information of differential methylated regions. As a convenient and flexible tool, we believe swDMR will bring us closer to unveil the potential functional regions involved in epigenetic regulation.

Paternal sperm DNA methylation associated with early signs of autism risk in an autism-enriched cohort

BACKGROUND

Epigenetic mechanisms such as altered DNA methylation have been suggested to play a role in autism, beginning with the classical association of Prader-Willi syndrome, an imprinting disorder, with autistic features.

OBJECTIVES

Here we tested for the relationship of paternal sperm DNA methylation with autism risk in offspring, examining an enriched-risk cohort of fathers of autistic children.

METHODS

We examined genome-wide DNA methylation (DNAm) in paternal semen biosamples obtained from an autism spectrum disorder (ASD) enriched-risk pregnancy cohort, the Early Autism Risk Longitudinal Investigation (EARLI) cohort, to estimate associations between sperm DNAm and prospective ASD development, using a 12-month ASD symptoms assessment, the Autism Observation Scale for Infants (AOSI). We analysed methylation data from 44 sperm samples run on the CHARM 3.0 array, which contains over 4 million probes (over 7 million CpG sites), including 30 samples also run on the Illumina Infinium HumanMethylation450 (450K) BeadChip platform (∼485 000 CpG sites). We also examined associated regions in an independent sample of post-mortem human brain ASD and control samples for which Illumina 450K DNA methylation data were available.

RESULTS

Using region-based statistical approaches, we identified 193 differentially methylated regions (DMRs) in paternal sperm with a family-wise empirical P-value [family-wise error rate (FWER)] <0.05 associated with performance on the Autism Observational Scale for Infants (AOSI) at 12 months of age in offspring. The DMRs clustered near genes involved in developmental processes, including many genes in the SNORD family, within the Prader-Willi syndrome gene cluster. These results were consistent among the 75 probes on the Illumina 450K array that cover AOSI-associated DMRs from CHARM. Further, 18 of 75 (24%) 450K array probes showed consistent differences in the cerebellums of autistic individuals compared with controls.

CONCLUSIONS

These data suggest that epigenetic differences in paternal sperm may contribute to autism risk in offspring, and provide evidence that directionally consistent, potentially related epigenetic mechanisms may be operating in the cerebellum of individuals with autism.

Gene chip analysis of Arabidopsis thaliana genomic DNA methylation and gene expression in response to carbendazim

OBJECTIVES

To examine the effects of carbendazim on Arabidopsis genomic DNA methylation and gene expression.

RESULTS

Carbendazim caused widespread changes in gene loci methylation and gene expression. With 0.1 mM (D2) and 0.2 mM (D3) carbendazim, there were, respectively, 1522 and 2278 demethylated sites and 1541 and 2790 methylated sites. A total of 279 and 505 genes were up-regulated by more than 300 % and 175 and 609 genes were down-regulated by 67 % in D2 and D3 treatments, respectively, compared with the control. Conjoint analysis showed that 20 and 39 demethylated genes were up-regulated >300 % and 21 and 24 methylated genes were down-regulated <67 % in D2 and D3, respectively.

CONCLUSIONS

Carbendazim causes methylation or demethylation of certain genes and changes the expression of these genes. These findings provide a theoretical basis for novel epigenetics-based methods to detect organic food and a new interpretation for the degradation of crop varieties.

A composite framework for the statistical analysis of epidemiological DNA methylation data with the Infinium Human Methylation 450K BeadChip

High-throughput DNA methylation profiling exploits microarray technologies thus providing a wealth of data, which however solicits rigorous, generic, and analytical pipelines for an efficient systems level analysis and interpretation. In this study, we utilize the Illumina's Infinium Human Methylation 450K BeadChip platform in an epidemiological cohort, targeting to associate interesting methylation patterns with breast cancer predisposition. The computational framework proposed here extends the--established in transcriptomic microarrays--logarithmic ratio of the methylated versus the unmethylated signal intensities, quoted as M-value. Moreover, intensity-based correction of the M-signal distribution is introduced in order to correct for batch effects and probe-specific errors in intensity measurements. This is accomplished through the estimation of intensity-related error measures from quality control samples included in each chip. Moreover, robust statistical measures exploiting the coefficient variation of DNA methylation measurements between control and case samples alleviate the impact of technical variation. The results presented here are juxtaposed to those derived by applying classical preprocessing and statistical selection methodologies. Overall, in comparison to traditional approaches, the superior performance of the proposed framework in terms of technical bias correction, along with its generic character, support its suitability for various microarray technologies.

Integrative analysis of DNA methylation and gene expression data identifies EPAS1 as a key regulator of COPD

Chronic Obstructive Pulmonary Disease (COPD) is a complex disease. Genetic, epigenetic, and environmental factors are known to contribute to COPD risk and disease progression. Therefore we developed a systematic approach to identify key regulators of COPD that integrates genome-wide DNA methylation, gene expression, and phenotype data in lung tissue from COPD and control samples. Our integrative analysis identified 126 key regulators of COPD. We identified EPAS1 as the only key regulator whose downstream genes significantly overlapped with multiple genes sets associated with COPD disease severity. EPAS1 is distinct in comparison with other key regulators in terms of methylation profile and downstream target genes. Genes predicted to be regulated by EPAS1 were enriched for biological processes including signaling, cell communications, and system development. We confirmed that EPAS1 protein levels are lower in human COPD lung tissue compared to non-disease controls and that Epas1 gene expression is reduced in mice chronically exposed to cigarette smoke. As EPAS1 downstream genes were significantly enriched for hypoxia responsive genes in endothelial cells, we tested EPAS1 function in human endothelial cells. EPAS1 knockdown by siRNA in endothelial cells impacted genes that significantly overlapped with EPAS1 downstream genes in lung tissue including hypoxia responsive genes, and genes associated with emphysema severity. Our first integrative analysis of genome-wide DNA methylation and gene expression profiles illustrates that not only does DNA methylation play a ‘causal’ role in the molecular pathophysiology of COPD, but it can be leveraged to directly identify novel key mediators of this pathophysiology.

Chronic Obstructive Pulmonary Disease (COPD) is a common lung disease. It is the fourth leading cause of death in the world and is expected to be the third by 2020. COPD is a heterogeneous and complex disease consisting of obstruction in the small airways, emphysema, and chronic bronchitis. COPD is generally caused by exposure to noxious particles or gases, most commonly from cigarette smoking. However, only 20–25% of smokers develop clinically significant airflow obstruction. Smoking is known to cause epigenetic changes in lung tissues. Thus, genetics, epigenetic, and their interaction with environmental factors play an important role in COPD pathogenesis and progression. Currently, there are no therapeutics that can reverse COPD progression. In order to identify new targets that may lead to the development of therapeutics for curing COPD, we developed a systematic approach to identify key regulators of COPD that integrates genome-wide DNA methylation, gene expression, and phenotype data in lung tissue from COPD and control samples. Our integrative analysis identified 126 key regulators of COPD. We identified EPAS1 as the only key regulator whose downstream genes significantly overlapped with multiple genes sets associated with COPD disease severity.

Developmental regulation of human cortex transcription and its clinical relevance at single base resolution

Transcriptome analysis of human brain provides fundamental insight about development and disease, but largely relies on existing annotation. We sequenced transcriptomes of 72 prefrontal cortex samples across six life stages, and identified 50,650 differentially expression regions (DERs) associated with developmental and aging, agnostic of annotation. While many DERs annotated to non-exonic sequence (41.1%), most were similarly regulated in cytosolic mRNA extracted from independent samples. The DERs were developmentally conserved across 16 brain regions and within the developing mouse cortex, and were expressed in diverse cell and tissue types. The DERs were further enriched for active chromatin marks and clinical risk for neurodevelopmental disorders like schizophrenia. Lastly, we demonstrate quantitatively that these DERs associate with a changing neuronal phenotype related to differentiation and maturation. These data highlight conserved molecular signatures of transcriptional dynamics across brain development, some potential clinical relevance and the incomplete annotation of the human brain transcriptome.

Functional normalization of 450k methylation array data improves replication in large cancer studies

We propose an extension to quantile normalization that removes unwanted technical variation using control probes. We adapt our algorithm, functional normalization, to the Illumina 450k methylation array and address the open problem of normalizing methylation data with global epigenetic changes, such as human cancers. Using data sets from The Cancer Genome Atlas and a large case–control study, we show that our algorithm outperforms all existing normalization methods with respect to replication of results between experiments, and yields robust results even in the presence of batch effects. Functional normalization can be applied to any microarray platform, provided suitable control probes are available.

Relationship of DNA methylation and gene expression in idiopathic pulmonary fibrosis

RATIONALE

Idiopathic pulmonary fibrosis (IPF) is an untreatable and often fatal lung disease that is increasing in prevalence and is caused by complex interactions between genetic and environmental factors. Epigenetic mechanisms control gene expression and are likely to regulate the IPF transcriptome.

OBJECTIVES

To identify methylation marks that modify gene expression in IPF lung.

METHODS

We assessed DNA methylation (comprehensive high-throughput arrays for relative methylation arrays [CHARM]) and gene expression (Agilent gene expression arrays) in 94 patients with IPF and 67 control subjects, and performed integrative genomic analyses to define methylation-gene expression relationships in IPF lung. We validated methylation changes by a targeted analysis (Epityper), and performed functional validation of one of the genes identified by our analysis.

MEASUREMENTS AND MAIN RESULTS

We identified 2,130 differentially methylated regions (DMRs; <5% false discovery rate), of which 738 are associated with significant changes in gene expression and enriched for expected inverse relationship between methylation and expression (P < 2.2 × 10(-16)). We validated 13/15 DMRs by targeted analysis of methylation. Methylation-expression quantitative trait loci (methyl-eQTL) identified methylation marks that control cis and trans gene expression, with an enrichment for cis relationships (P < 2.2 × 10(-16)). We found five trans methyl-eQTLs where a methylation change at a single DMR is associated with transcriptional changes in a substantial number of genes; four of these DMRs are near transcription factors (castor zinc finger 1 [CASZ1], FOXC1, MXD4, and ZDHHC4). We studied the in vitro effects of change in CASZ1 expression and validated its role in regulation of target genes in the methyl-eQTL.

CONCLUSIONS

These results suggest that DNA methylation may be involved in the pathogenesis of IPF.

MODMatcher: multi-omics data matcher for integrative genomic analysis

Errors in sample annotation or labeling often occur in large-scale genetic or genomic studies and are difficult to avoid completely during data generation and management. For integrative genomic studies, it is critical to identify and correct these errors. Different types of genetic and genomic data are inter-connected by cis-regulations. On that basis, we developed a computational approach, Multi-Omics Data Matcher (MODMatcher), to identify and correct sample labeling errors in multiple types of molecular data, which can be used in further integrative analysis. Our results indicate that inspection of sample annotation and labeling error is an indispensable data quality assurance step. Applied to a large lung genomic study, MODMatcher increased statistically significant genetic associations and genomic correlations by more than two-fold. In a simulation study, MODMatcher provided more robust results by using three types of omics data than two types of omics data. We further demonstrate that MODMatcher can be broadly applied to large genomic data sets containing multiple types of omics data, such as The Cancer Genome Atlas (TCGA) data sets.

Many human diseases are complex with multiple genetic and environmental causal factors interacting together to give rise to disease phenotypes. Such factors affect biological systems through many layers of regulations, including transcriptional and epigenetic regulation, and protein changes. To fully understand their molecular mechanisms, complex diseases are often studied in diverse dimensions including genetics (genotype variations by single nucleotide polymorphism (SNP) arrays or whole exome sequencing), transcriptomics, epigenetics, and proteomics. However, errors in sample annotation or labeling often occur in large-scale genetic and genomic studies and are difficult to avoid completely during data generation and management. Identifying and correcting these errors are critical for integrative genomic studies. In this study, we developed a computational approach, Multi-Omics Data Matcher (MODMatcher), to identify and correct sample labeling errors based on multiple types of molecular data before further integrative analysis. Our results indicate that signals increased more than 100% after correction of sample labeling errors in a large lung genomic study. Our method can be broadly applied to large genomic data sets with multiple types of omics data, such as TCGA (The Cancer Genome Atlas) data sets.

Hypomethylation of the promoter of the catalytic subunit of protein phosphatase 2A in response to hyperglycemia

In order to identify epigenetic mechanisms through which hyperglycemia can affect gene expression durably in β cells, we screened DNA methylation changes induced by high glucose concentrations (25 mmol/L) in the BTC3 murine cell line, using an epigenome‐wide approach. Exposure of BTC3 cells to high glucose modified the expression of 1612 transcripts while inducing significant methylation changes in 173 regions. Among these 173 glucose‐sensitive differentially methylated regions (DMRs), 14 were associated with changes in gene expression, suggesting an epigenetic effect of high glucose on gene transcription at these loci. Among these 14 DMRs, we selected for further study Pp2ac, a gene previously suspected to play a role in β‐cell physiology and type 2 diabetes. Using RT‐qPCR and bisulfite pyrosequencing, we confirmed our previous observations in BTC3 cells and found that this gene was significantly demethylated in the whole blood cells (WBCs) of type 2 diabetic patients compared to controls.

In order to identify epigenetic mechanisms through which hyperglycemia can affect gene expression durably in β cells, we screened DNA methylation changes induced by high glucose concentration in the BTC3 murine cell line. We identified one interesting gene, PP2AC, and confirmed it in type 2 diabetic patients.

S-adenosylmethionine mediates inhibition of inflammatory response and changes in DNA methylation in human macrophages

S-adenosylmethionine (SAM), the unique methyl donor in DNA methylation, has been shown to lower lipopolysaccharide (LPS)-induced expression of the proinflammatory cytokine TNF-α and increase the expression of the anti-inflammatory cytokine IL-10 in macrophages. The aim of this study was to assess whether epigenetic mechanisms mediate the anti-inflammatory effects of SAM. Human monocytic THP1 cells were differentiated into macrophages and treated with 0, 500, or 1,000 μmol/l SAM for 24 h, followed by stimulation with LPS. TNFα and IL-10 expression levels were measured by real-time PCR, cellular concentrations of SAM and S-adenosylhomocysteine (SAH), a metabolite of SAM, were measured by liquid chromatography-tandem mass spectrometry (LC-MS/MS), and DNA methylation was measured with LC-MS/MS and microarrays. Relative to control (0 μmol/l SAM), treatment with 500 μmol/l SAM caused a significant decrease in TNF-α expression (-45%, P < 0.05) and increase in IL-10 expression (+77%, P < 0.05). Treatment with 1,000 μmol/l SAM yielded no significant additional benefits. Relative to control, 500 μmol/l SAM increased cellular SAM concentrations twofold without changes in SAH, and 1,000 μmol/l SAM increased cellular SAM sixfold and SAH fourfold. Global DNA methylation increased 7% with 500 μmol/l SAM compared with control. Following treatment with 500 μmol/l SAM, DNA methylation microarray analysis identified 765 differentially methylated regions associated with 918 genes. Pathway analysis of these genes identified a biological network associated with cardiovascular disease, including a subset of genes that were differentially hypomethylated and whose expression levels were altered by SAM. Our data indicate that SAM modulates the expression of inflammatory genes in association with changes in specific gene promoter DNA methylation.

Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays

MOTIVATION

The recently released Infinium HumanMethylation450 array (the '450k' array) provides a high-throughput assay to quantify DNA methylation (DNAm) at ∼450 000 loci across a range of genomic features. Although less comprehensive than high-throughput sequencing-based techniques, this product is more cost-effective and promises to be the most widely used DNAm high-throughput measurement technology over the next several years.

RESULTS

Here we describe a suite of computational tools that incorporate state-of-the-art statistical techniques for the analysis of DNAm data. The software is structured to easily adapt to future versions of the technology. We include methods for preprocessing, quality assessment and detection of differentially methylated regions from the kilobase to the megabase scale. We show how our software provides a powerful and flexible development platform for future methods. We also illustrate how our methods empower the technology to make discoveries previously thought to be possible only with sequencing-based methods.

AVAILABILITY AND IMPLEMENTATION

http://bioconductor.org/packages/release/bioc/html/minfi.html.

CONTACT

khansen@jhsph.edu; rafa@jimmy.harvard.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Alcohol and the methylome: design and analysis considerations for research using human samples

BACKGROUND

A growing number of studies in human samples have sought to determine whether chronic alcohol use and alcohol use disorders (AUDs) may be associated with epigenetic factors, such as DNA methylation. We review the extant literature in light of some of the challenges that currently affect the design and interpretation of epigenetic research in human samples.

METHOD

A literature search was used to identify studies that have examined DNA methylation in relation to alcohol use or AUDs in human samples (through July 2013). A total of 22 studies were identified.

RESULTS

Associations with quantitative or diagnostic phenotypes of alcohol use or AUDs have been reported for several genes. However, all studies to date have relied on relatively small samples and cross-sectional study designs. Additionally, attempts to replicate results have been rare. More generally, research progress is hampered by several issues, including limitations of the technologies used to assess DNA methylation, tissue- and cell-specificity of methylation patterns, the difficulties of relating observed methylation differences at a given locus to a functional effect, and limited knowledge about the molecular mechanisms underlying the effects of alcohol on DNA methylation.

CONCLUSIONS

Although we share the optimism that epigenetics may lead to new insights into the etiology and pathophysiology of AUDs, the methodological and scientific challenges associated with conducting methylomic research in human samples need to be carefully considered when designing and evaluating such studies.

Histone deacetylase 6-mediated selective autophagy regulates COPD-associated cilia dysfunction

Chronic obstructive pulmonary disease (COPD) involves aberrant airway inflammatory responses to cigarette smoke (CS) that are associated with epithelial cell dysfunction, cilia shortening, and mucociliary clearance disruption. Exposure to CS reduced cilia length and induced autophagy in vivo and in differentiated mouse tracheal epithelial cells (MTECs). Autophagy-impaired (Becn1+/- or Map1lc3B-/-) mice and MTECs resisted CS-induced cilia shortening. Furthermore, CS increased the autophagic turnover of ciliary proteins, indicating that autophagy may regulate cilia homeostasis. We identified cytosolic deacetylase HDAC6 as a critical regulator of autophagy-mediated cilia shortening during CS exposure. Mice bearing an X chromosome deletion of Hdac6 (Hdac6-/Y) and MTECs from these mice had reduced autophagy and were protected from CS-induced cilia shortening. Autophagy-impaired Becn1-/-, Map1lc3B-/-, and Hdac6-/Y mice or mice injected with an HDAC6 inhibitor were protected from CS-induced mucociliary clearance (MCC) disruption. MCC was preserved in mice given the chemical chaperone 4-phenylbutyric acid, but was disrupted in mice lacking the transcription factor NRF2, suggesting that oxidative stress and altered proteostasis contribute to the disruption of MCC. Analysis of human COPD specimens revealed epigenetic deregulation of HDAC6 by hypomethylation and increased protein expression in the airways. We conclude that an autophagy-dependent pathway regulates cilia length during CS exposure and has potential as a therapeutic target for COPD.

DNA methylation data analysis and its application to cancer research

With the rapid development of genome-wide high-throughput technologies, including expression arrays, SNP arrays and next-generation sequencing platforms, enormous amounts of molecular data have been generated and deposited in the public domain. The application of computational approaches is required to yield biological insights from this enormous, ever-growing resource. A particularly interesting subset of these resources is related to epigenetic regulation, with DNA methylation being the most abundant data type. In this paper, we will focus on the analysis of DNA methylation data and its application to cancer studies. We first briefly review the molecular techniques that generate such data, much of which has been obtained with the use of the most recent version of Infinium HumanMethylation450 BeadChip(®) technology (Illumina, CA, USA). We describe the coverage of the methylome by this technique. Several examples of data mining are provided. However, it should be understood that reliance on a single aspect of epigenetics has its limitations. In the not too distant future, these defects may be rectified, providing scientists with previously unavailable opportunities to explore in detail the role of epigenetics in cancer and other disease states.

A-clustering: a novel method for the detection of co-regulated methylation regions, and regions associated with exposure

MOTIVATION

DNA methylation is a heritable modifiable chemical process that affects gene transcription and is associated with other molecular markers (e.g. gene expression) and biomarkers (e.g. cancer or other diseases). Current technology measures methylation in hundred of thousands, or millions of CpG sites throughout the genome. It is evident that neighboring CpG sites are often highly correlated with each other, and current literature suggests that clusters of adjacent CpG sites are co-regulated.

RESULTS

We develop the Adjacent Site Clustering (A-clustering) algorithm to detect sets of neighboring CpG sites that are correlated with each other. To detect methylation regions associated with exposure, we propose an analysis pipeline for high-dimensional methylation data in which CpG sites within regions identified by A-clustering are modeled as multivariate responses to environmental exposure using a generalized estimating equation approach that assumes exposure equally affects all sites in the cluster. We develop a correlation preserving simulation scheme, and study the proposed methodology via simulations. We study the clusters detected by the algorithm on high dimensional dataset of peripheral blood methylation of pesticide applicators.

AVAILABILITY

We provide the R package Aclust that efficiently implements the A-clustering and the analysis pipeline, and produces analysis reports. The package is found on http://www.hsph.harvard.edu/tamar-sofer/packages/

CONTACT

tsofer@hsph.harvard.edu

Genome-wide DNA methylation analysis identifies hypomethylated genes regulated by FOXP3 in human regulatory T cells

Regulatory T cells (Treg) prevent the emergence of autoimmune disease. Prototypic natural Treg (nTreg) can be reliably identified by demethylation at the Forkhead-box P3 (FOXP3) locus. To explore the methylation landscape of nTreg, we analyzed genome-wide methylation in human naive nTreg (rTreg) and conventional naive CD4(+) T cells (Naive). We detected 2315 differentially methylated cytosine-guanosine dinucleotides (CpGs) between these 2 cell types, many of which clustered into 127 regions of differential methylation (RDMs). Activation changed the methylation status of 466 CpGs and 18 RDMs in Naive but did not alter DNA methylation in rTreg. Gene-set testing of the 127 RDMs showed that promoter methylation and gene expression were reciprocally related. RDMs were enriched for putative FOXP3-binding motifs. Moreover, CpGs within known FOXP3-binding regions in the genome were hypomethylated. In support of the view that methylation limits access of FOXP3 to its DNA targets, we showed that increased expression of the immune suppressive receptor T-cell immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domain (TIGIT), which delineated Treg from activated effector T cells, was associated with hypomethylation and FOXP3 binding at the TIGIT locus. Differential methylation analysis provides insight into previously undefined human Treg signature genes and their mode of regulation.

Integrative analysis of tissue-specific methylation and alternative splicing identifies conserved transcription factor binding motifs

The exact role of intragenic DNA methylation in regulating tissue-specific gene regulation is unclear. Recently, the DNA-binding protein CTCF has been shown to participate in the regulation of alternative splicing in a DNA methylation-dependent manner. To globally evaluate the relationship between DNA methylation and tissue-specific alternative splicing, we performed genome-wide DNA methylation profiling of mouse retina and brain. In protein-coding genes, tissue-specific differentially methylated regions (T-DMRs) were preferentially located in exons and introns. Gene ontology and evolutionary conservation analysis suggest that these T-DMRs are likely to be biologically relevant. More than 14% of alternatively spliced genes were associated with a T-DMR. T-DMR-associated genes were enriched for developmental genes, suggesting that a specific set of alternatively spliced genes may be regulated through DNA methylation. Novel DNA sequences motifs overrepresented in T-DMRs were identified as being associated with positive and/or negative regulation of alternative splicing in a position-dependent context. The majority of these evolutionarily conserved motifs contain a CpG dinucleotide. Some transcription factors, which recognize these motifs, are known to be involved in splicing. Our results suggest that DNA methylation-dependent alternative splicing is widespread and lay the foundation for further mechanistic studies of the role of DNA methylation in tissue-specific splicing regulation.

A novel methyl-binding domain protein enrichment method for identifying genome-wide tissue-specific DNA methylation from nanogram DNA samples

Background

Growing evidence suggests that DNA methylation plays a role in tissue-specific differentiation. Current approaches to methylome analysis using enrichment with the methyl-binding domain protein (MBD) are restricted to large (≥1 μg) DNA samples, limiting the analysis of small tissue samples. Here we present a technique that enables characterization of genome-wide tissue-specific methylation patterns from nanogram quantities of DNA.

Results

We have developed a methodology utilizing MBD2b/MBD3L1 enrichment for methylated DNA, kinase pre-treated ligation-mediated PCR amplification (MeKL) and hybridization to the comprehensive high-throughput array for relative methylation (CHARM) customized tiling arrays, which we termed MeKL-chip. Kinase modification in combination with the addition of PEG has increased ligation-mediated PCR amplification over 20-fold, enabling >400-fold amplification of starting DNA. We have shown that MeKL-chip can be applied to as little as 20 ng of DNA, enabling comprehensive analysis of small DNA samples. Applying MeKL-chip to the mouse retina (a limited tissue source) and brain, 2,498 tissue-specific differentially methylated regions (T-DMRs) were characterized. The top five T-DMRs (Rgs20, Hes2, Nfic, Cckbr and Six3os1) were validated by pyrosequencing.

Conclusions

MeKL-chip enables genome-wide methylation analysis of nanogram quantities of DNA with a wide range of observed-to-expected CpG ratios due to the binding properties of the MBD2b/MBD3L1 protein complex. This methodology enabled the first analysis of genome-wide methylation in the mouse retina, characterizing novel T-DMRs.

Longitudinal, genome-scale analysis of DNA methylation in twins from birth to 18 months of age reveals rapid epigenetic change in early life and pair-specific effects of discordance

Background

The extent to which development- and age-associated epigenetic changes are influenced by genetic, environmental and stochastic factors remains to be discovered. Twins provide an ideal model with which to investigate these influences but previous cross-sectional twin studies provide contradictory evidence of within-pair epigenetic drift over time. Longitudinal twin studies can potentially address this discrepancy.

Results

In a pilot, genome-scale study of DNA from buccal epithelium, a relatively homogeneous tissue, we show that one-third of the CpGs assayed show dynamic methylation between birth and 18 months. Although all classes of annotated genomic regions assessed show an increase in DNA methylation over time, probes located in intragenic regions, enhancers and low-density CpG promoters are significantly over-represented, while CpG islands and high-CpG density promoters are depleted among the most dynamic probes. Comparison of co-twins demonstrated that within-pair drift in DNA methylation in our cohort is specific to a subset of pairs, who show more differences at 18 months. The rest of the pairs show either minimal change in methylation discordance, or more similar, converging methylation profiles at 18 months. As with age-associated regions, sites that change in their level of within-pair discordance between birth and 18 months are enriched in genes involved in development, but the average magnitude of change is smaller than for longitudinal change.

Conclusions

Our findings suggest that DNA methylation in buccal epithelium is influenced by non-shared stochastic and environmental factors that could reflect a degree of epigenetic plasticity within an otherwise constrained developmental program.

Identification of novel markers in rheumatoid arthritis through integrated analysis of DNA methylation and microRNA expression

Autoimmune rheumatic diseases are complex disorders, whose etiopathology is attributed to a crosstalk between genetic predisposition and environmental factors. Both variants of autoimmune susceptibility genes and environment are involved in the generation of aberrant epigenetic profiles in a cell-specific manner, which ultimately result in dysregulation of expression. Furthermore, changes in miRNA expression profiles also cause gene dysregulation associated with aberrant phenotypes. In rheumatoid arthritis, several cell types are involved in the destruction of the joints, synovial fibroblasts being among the most important. In this study we performed DNA methylation and miRNA expression screening of a set of rheumatoid arthritis synovial fibroblasts and compared the results with those obtained from osteoarthritis patients with a normal phenotype. DNA methylation screening allowed us to identify changes in novel key target genes like IL6R, CAPN8 and DPP4, as well as several HOX genes. A significant proportion of genes undergoing DNA methylation changes were inversely correlated with expression. miRNA screening revealed the existence of subsets of miRNAs that underwent changes in expression. Integrated analysis highlighted sets of miRNAs that are controlled by DNA methylation, and genes that are regulated by DNA methylation and are targeted by miRNAs with a potential use as clinical markers. Our study enabled the identification of novel dysregulated targets in rheumatoid arthritis synovial fibroblasts and generated a new workflow for the integrated analysis of miRNA and epigenetic control.

PU.1 target genes undergo Tet2-coupled demethylation and DNMT3b-mediated methylation in monocyte-to-osteoclast differentiation

BACKGROUND

DNA methylation is a key epigenetic mechanism for driving and stabilizing cell-fate decisions. Local deposition and removal of DNA methylation are tightly coupled with transcription factor binding, although the relationship varies with the specific differentiation process. Conversion of monocytes to osteoclasts is a unique terminal differentiation process within the hematopoietic system. This differentiation model is relevant to autoimmune disease and cancer, and there is abundant knowledge on the sets of transcription factors involved.

RESULTS

Here we focused on DNA methylation changes during osteoclastogenesis. Hypermethylation and hypomethylation changes took place in several thousand genes, including all relevant osteoclast differentiation and function categories. Hypomethylation occurred in association with changes in 5-hydroxymethylcytosine, a proposed intermediate toward demethylation. Transcription factor binding motif analysis revealed an over-representation of PU.1, NF-κB, and AP-1 (Jun/Fos) binding motifs in genes undergoing DNA methylation changes. Among these, only PU.1 motifs were significantly enriched in both hypermethylated and hypomethylated genes; ChIP-seq data analysis confirmed its association to both gene sets. Moreover, PU.1 interacts with both DNMT3b and TET2, suggesting its participation in driving hypermethylation and hydroxymethylation-mediated hypomethylation. Consistent with this, siRNA-mediated PU.1 knockdown in primary monocytes impaired the acquisition of DNA methylation and expression changes, and reduced the association of TET2 and DNMT3b at PU.1 targets during osteoclast differentiation.

CONCLUSIONS

The work described here identifies key changes in DNA methylation during monocyte-to-osteoclast differentiation and reveals novel roles for PU.1 in this process.

Complete pipeline for Infinium(®) Human Methylation 450K BeadChip data processing using subset quantile normalization for accurate DNA methylation estimation

BACKGROUND

Huge progress has been made in the development of array- or sequencing-based technologies for DNA methylation analysis. The Illumina Infinium(®) Human Methylation 450K BeadChip (Illumina Inc., CA, USA) allows the simultaneous quantitative monitoring of more than 480,000 CpG positions, enabling large-scale epigenotyping studies. However, the assay combines two different assay chemistries, which may cause a bias in the analysis if all signals are merged as a unique source of methylation measurement.

MATERIALS & METHODS

We confirm in three 450K data sets that Infinium I signals are more stable and cover a wider dynamic range of methylation values than Infinium II signals. We evaluated the methylation profile of Infinium I and II probes obtained with different normalization protocols and compared these results with the methylation values of a subset of CpGs analyzed by pyrosequencing.

RESULTS

We developed a subset quantile normalization approach for the processing of 450K BeadChips. The Infinium I signals were used as 'anchors' to normalize Infinium II signals at the level of probe coverage categories. Our normalization approach outperformed alternative normalization or correction approaches in terms of bias correction and methylation signal estimation. We further implemented a complete preprocessing protocol that solves most of the issues currently raised by 450K array users.

CONCLUSION

We developed a complete preprocessing pipeline for 450K BeadChip data using an original subset quantile normalization approach that performs both sample normalization and efficient Infinium I/II shift correction. The scripts, being freely available from the authors, will allow researchers to concentrate on the biological analysis of data, such as the identification of DNA methylation signatures.

Reversible switching between epigenetic states in honeybee behavioral subcastes

In honeybee societies, distinct caste phenotypes are created from the same genotype, suggesting a role for epigenetics in deriving these behaviorally different phenotypes. We found no differences in DNA methylation between irreversible worker and queen castes, but substantial differences between nurses and forager subcastes. Reverting foragers back to nurses reestablished methylation levels for a majority of genes and provides, to the best of our knowledge, the first evidence in any organism of reversible epigenetic changes associated with behavior.

Empirical bayes model comparisons for differential methylation analysis

A number of empirical Bayes models (each with different statistical distribution assumptions) have now been developed to analyze differential DNA methylation using high-density oligonucleotide tiling arrays. However, it remains unclear which model performs best. For example, for analysis of differentially methylated regions for conservative and functional sequence characteristics (e.g., enrichment of transcription factor-binding sites (TFBSs)), the sensitivity of such analyses, using various empirical Bayes models, remains unclear. In this paper, five empirical Bayes models were constructed, based on either a gamma distribution or a log-normal distribution, for the identification of differential methylated loci and their cell division—(1, 3, and 5) and drug-treatment-(cisplatin) dependent methylation patterns. While differential methylation patterns generated by log-normal models were enriched with numerous TFBSs, we observed almost no TFBS-enriched sequences using gamma assumption models. Statistical and biological results suggest log-normal, rather than gamma, empirical Bayes model distribution to be a highly accurate and precise method for differential methylation microarray analysis. In addition, we presented one of the log-normal models for differential methylation analysis and tested its reproducibility by simulation study. We believe this research to be the first extensive comparison of statistical modeling for the analysis of differential DNA methylation, an important biological phenomenon that precisely regulates gene transcription.

Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies

BACKGROUND

During the past 5 years, high-throughput technologies have been successfully used by epidemiology studies, but almost all have focused on sequence variation through genome-wide association studies (GWAS). Today, the study of other genomic events is becoming more common in large-scale epidemiological studies. Many of these, unlike the single-nucleotide polymorphism studied in GWAS, are continuous measures. In this context, the exercise of searching for regions of interest for disease is akin to the problems described in the statistical 'bump hunting' literature.

METHODS

New statistical challenges arise when the measurements are continuous rather than categorical, when they are measured with uncertainty, and when both biological signal, and measurement errors are characterized by spatial correlation along the genome. Perhaps the most challenging complication is that continuous genomic data from large studies are measured throughout long periods, making them susceptible to 'batch effects'. An example that combines all three characteristics is genome-wide DNA methylation measurements. Here, we present a data analysis pipeline that effectively models measurement error, removes batch effects, detects regions of interest and attaches statistical uncertainty to identified regions.

RESULTS

We illustrate the usefulness of our approach by detecting genomic regions of DNA methylation associated with a continuous trait in a well-characterized population of newborns. Additionally, we show that addressing unexplained heterogeneity like batch effects reduces the number of false-positive regions.

CONCLUSIONS

Our framework offers a comprehensive yet flexible approach for identifying genomic regions of biological interest in large epidemiological studies using quantitative high-throughput methods.

DNA methylation shows genome-wide association of NFIX, RAPGEF2 and MSRB3 with gestational age at birth

BACKGROUND

Gestational age at birth strongly predicts neonatal, adolescent and adult morbidity and mortality through mostly unknown mechanisms. Identification of specific genes that are undergoing regulatory change prior to birth, such as through changes in DNA methylation, would increase our understanding of developmental changes occurring during the third trimester and consequences of pre-term birth (PTB).

METHODS

We performed a genome-wide analysis of DNA methylation (using microarrays, specifically CHARM 2.0) in 141 newborns collected in Baltimore, MD, using novel statistical methodology to identify genomic regions associated with gestational age at birth. Bisulphite pyrosequencing was used to validate significant differentially methylated regions (DMRs), and real-time PCR was performed to assess functional significance of differential methylation in a subset of newborns.

RESULTS

We identified three DMRs at genome-wide significance levels adjacent to the NFIX, RAPGEF2 and MSRB3 genes. All three regions were validated by pyrosequencing, and RAGPEF2 also showed an inverse correlation between DNA methylation levels and gene expression levels. Although the three DMRs appear very dynamic with gestational age in our newborn sample, adult DNA methylation levels at these regions are stable and of equal or greater magnitude than the oldest neonate, directionally consistent with the gestational age results.

CONCLUSIONS

We have identified three differentially methylated regions associated with gestational age at birth. All three nearby genes play important roles in the development of several organs, including skeletal muscle, brain and haematopoietic system. Therefore, they may provide initial insight into the basis of PTB's negative health outcomes. The genome-wide custom DNA methylation array technology and novel statistical methods employed in this study could constitute a model for epidemiologic studies of epigenetic variation.

Comparison of different normalization assumptions for analyses of DNA methylation data from the cancer genome

Nowadays, some researchers normalized DNA methylation arrays data in order to remove the technical artifacts introduced by experimental differences in sample preparation, array processing and other factors. However, other researchers analyzed DNA methylation arrays without performing data normalization considering that current normalizations for methylation data may distort real differences between normal and cancer samples because cancer genomes may be extensively subject to hypomethylation and the total amount of CpG methylation might differ substantially among samples. In this study, using eight datasets by Infinium HumanMethylation27 assay, we systemically analyzed the global distribution of DNA methylation changes in cancer compared to normal control and its effect on data normalization for selecting differentially methylated (DM) genes. We showed more differentially methylated (DM) genes could be found in the Quantile/Lowess-normalized data than in the non-normalized data. We found the DM genes additionally selected in the Quantile/Lowess-normalized data showed significantly consistent methylation states in another independent dataset for the same cancer, indicating these extra DM genes were effective biological signals related to the disease. These results suggested normalization can increase the power of detecting DM genes in the context of diagnostic markers which were usually characterized by relatively large effect sizes. Besides, we evaluated the reproducibility of DM discoveries for a particular cancer type, and we found most of the DM genes additionally detected in one dataset showed the same methylation directions in the other dataset for the same cancer type, indicating that these DM genes were effective biological signals in the other dataset. Furthermore, we showed that some DM genes detected from different studies for a particular cancer type were significantly reproducible at the functional level.