DNA Methylation Analysis as a Tool for Cell Typing

regionalpcs: improved discovery of DNA methylation associations with complex traits

We have developed the regional principal components (rPCs) method, a novel approach for summarizing gene-level methylation. rPCs address the challenge of deciphering complex epigenetic mechanisms in diseases like Alzheimer's disease (AD). In contrast to traditional averaging, rPCs leverage principal components analysis to capture complex methylation patterns across gene regions. Our method demonstrated a 54% improvement in sensitivity over averaging in simulations, offering a robust framework for identifying subtle epigenetic variations. Applying rPCs to the AD brain methylation data in ROSMAP, combined with cell type deconvolution, we uncovered 838 differentially methylated genes associated with neuritic plaque burden-significantly outperforming conventional methods. Integrating methylation quantitative trait loci (meQTL) with genome-wide association studies (GWAS) identified 17 genes with potential causal roles in AD, including MS4A4A and PICALM. Our approach is available in the Bioconductor package regionalpcs, opening avenues for research and facilitating a deeper understanding of the epigenetic landscape in complex diseases.

Comprehensive DNA Methylation Profiling of Medullary Thyroid Carcinoma: Molecular Classification, Potential Therapeutic Target, and Classifier System

PURPOSE

Medullary thyroid carcinoma (MTC) presents a distinct biological context from other thyroid cancers due to its specific cellular origin. This heterogeneous and rare tumor has a high prevalence of advanced diseases, making it crucial to address the limited therapeutic options and enhance complex clinical management. Given the high clinical accessibility of methylation information, we construct the largest MTC methylation cohort to date.

EXPERIMENTAL DESIGN

Seventy-eight fresh-frozen MTC samples constituted our methylation cohort. The comprehensive study process incorporated machine learning, statistical analysis, and in vitro experiments.

RESULTS

Our study pioneered the identification of a three-class clustering system for risk stratification, exhibiting pronounced epigenomic heterogeneity. The elevated overall methylation status in MTC-B, combined with the "mutual exclusivity" of hypomethylated sites displayed by MTC-A and MTC-C, distinctively characterized the MTC-specific methylation pattern. Integrating with the transcriptome, we further depicted the features of these three clusters to scrutinize biological properties. Several MTC-specific aberrant DNA methylation events were emphasized in our study. NNAT expression was found to be notably reduced in poor-prognostic MTC-C, with its promoter region overlapping with an upregulated differentially methylated region. In vitro experiments further affirmed NNAT's therapeutic potential. Moreover, we built an elastic-net logistic regression model with a relatively high AUC encompassing 68 probes, intended for future validation and systematic clinical application.

CONCLUSIONS

Conducting research on diseases with low incidence poses significant challenges, and we provide a robust resource and comprehensive research framework to assist in ongoing MTC case inclusion and facilitate in-depth dissection of its molecular biological features.

Epigenome-wide analysis of T-cell large granular lymphocytic leukemia identifies BCL11B as a potential biomarker

BACKGROUND

The molecular pathogenesis of T-cell large granular lymphocytic leukemia (T-LGLL), a mature T-cell leukemia arising commonly from T-cell receptor αβ-positive CD8⁺ memory cytotoxic T cells, is only partly understood. The role of deregulated methylation in T-LGLL is not well known. We analyzed the epigenetic profile of T-LGLL cells of 11 patients compared to their normal counterparts by array-based DNA methylation profiling. For identification of molecular events driving the pathogenesis of T-LGLL, we compared the differentially methylated loci between the T-LGLL cases and normal T cells with chromatin segmentation data of benign T cells from the BLUEPRINT project. Moreover, we analyzed gene expression data of T-LGLL and benign T cells and validated the results by pyrosequencing in an extended cohort of 17 patients, including five patients with sequential samples.

RESULTS

We identified dysregulation of DNA methylation associated with altered gene expression in T-LGLL. Since T-LGLL is a rare disease, the samples size is low. But as confirmed for each sample, hypermethylation of T-LGLL cells at various CpG sites located at enhancer regions is a hallmark of this disease. The interaction of BLC11B and C14orf64 as suggested by in silico data analysis could provide a novel pathogenetic mechanism that needs further experimental investigation.

CONCLUSIONS

DNA methylation is altered in T-LGLL cells compared to benign T cells. In particular, BCL11B is highly significant differentially methylated in T-LGLL cells. Although our results have to be validated in a larger patient cohort, BCL11B could be considered as a potential biomarker for this leukemia. In addition, altered gene expression and hypermethylation of enhancer regions could serve as potential mechanisms for treatment of this disease. Gene interactions of dysregulated genes, like BLC11B and C14orf64, may play an important role in pathogenic mechanisms and should be further analyzed.

Deconvolution of tumor composition using partially available DNA methylation data

Background

Deciphering proportions of constitutional cell types in tumor tissues is a crucial step for the analysis of tumor heterogeneity and the prediction of response to immunotherapy. In the process of measuring cell population proportions, traditional experimental methods have been greatly hampered by the cost and extensive dropout events. At present, the public availability of large amounts of DNA methylation data makes it possible to use computational methods to predict proportions.

Results

In this paper, we proposed PRMeth, a method to deconvolve tumor mixtures using partially available DNA methylation data. By adopting an iteratively optimized non-negative matrix factorization framework, PRMeth took DNA methylation profiles of a portion of the cell types in the tissue mixtures (including blood and solid tumors) as input to estimate the proportions of all cell types as well as the methylation profiles of unknown cell types simultaneously. We compared PRMeth with five different methods through three benchmark datasets and the results show that PRMeth could infer the proportions of all cell types and recover the methylation profiles of unknown cell types effectively. Then, applying PRMeth to four types of tumors from The Cancer Genome Atlas (TCGA) database, we found that the immune cell proportions estimated by PRMeth were largely consistent with previous studies and met biological significance.

Conclusions

Our method can circumvent the difficulty of obtaining complete DNA methylation reference data and obtain satisfactory deconvolution accuracy, which will be conducive to exploring the new directions of cancer immunotherapy. PRMeth is implemented in R and is freely available from GitHub (https://github.com/hedingqin/PRMeth).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04893-7.

Immune profiles and DNA methylation alterations related with non-muscle-invasive bladder cancer outcomes

BACKGROUND

Non-muscle-invasive bladder cancer (NMIBC) patients receive frequent monitoring because ≥ 70% will have recurrent disease. However, screening is invasive, expensive, and associated with significant morbidity making bladder cancer the most expensive cancer to treat per capita. There is an urgent need to expand the understanding of markers related to recurrence and survival outcomes of NMIBC.

METHODS AND RESULTS

We used the Illumina HumanMethylationEPIC array to measure peripheral blood DNA methylation profiles of NMIBC patients (N = 603) enrolled in a population-based cohort study in New Hampshire and applied cell type deconvolution to estimate immune cell-type proportions. Using Cox proportional hazard models, we identified that increasing CD4T and CD8T cell proportions were associated with a statistically significant decreased hazard of tumor recurrence or death (CD4T: HR = 0.98, 95% CI = 0.97-1.00; CD8T: HR = 0.97, 95% CI = 0.95-1.00), whereas increasing monocyte proportion and methylation-derived neutrophil-to-lymphocyte ratio (mdNLR) were associated with the increased hazard of tumor recurrence or death (monocyte: HR = 1.04, 95% CI = 1.00-1.07; mdNLR: HR = 1.12, 95% CI = 1.04-1.20). Then, using an epigenome-wide association study (EWAS) approach adjusting for age, sex, smoking status, BCG treatment status, and immune cell profiles, we identified 2528 CpGs associated with the hazard of tumor recurrence or death (P < 0.005). Among these CpGs, the 1572 were associated with an increased hazard and were significantly enriched in open sea regions; the 956 remaining CpGs were associated with a decreased hazard and were significantly enriched in enhancer regions and DNase hypersensitive sites.

CONCLUSIONS

Our results expand on the knowledge of immune profiles and methylation alteration associated with NMIBC outcomes and represent a first step toward the development of DNA methylation-based biomarkers of tumor recurrence.

Are Circulating Immune Cells a Determinant of Pancreatic Cancer Risk? A Prospective Study Using Epigenetic Cell Count Measures

BACKGROUND

Evidence is accumulating that immune cells play a prominent role in pancreatic cancer etiology but prospective investigations are missing.

METHODS

We conducted a nested case-control study within the European Prospective Investigation into Cancer and Nutrition (EPIC) study with 502 pairs of incident pancreatic cancer cases and matched controls. Relative counts of circulating immune cells (neutrophils and lymphocyte sublineages: total CD3+, CD8+, CD4+, and FOXP3+ regulatory T cells (Tregs) relative to nucleated cells, (white blood cells) were measured by qRT-PCR. ORs with 95% confidence intervals were estimated using logistic regressions, modeling relative counts of immune cells on a continuous scale.

RESULTS

Neither relative counts of immune cell types taken individually, nor mutually adjusted for each other were associated with pancreatic cancer risks. However, in subgroup analyses by strata of lag-time, higher relative counts of Tregs and lower relative counts of CD8+ were significantly associated with an increased pancreatic cancer risks in participants diagnosed within the first 5 years of follow-up.

CONCLUSIONS

These results might reflect reverse causation, due to higher relative counts of Tregs and lower counts of CD8+ cells among individuals with more advanced stages of latent pancreatic cancer, who are closer to the point of developing clinical manifest disease.

IMPACT

We have shown, for the first time, that increased relative counts of regulatory T cells and lower relative counts of CD8+, cytotoxic T cells may be associated with pancreatic cancer risk or relatively late-stage tumor development.See related commentary by Michaud and Kelsey, p. 2176.

DNA Methylation of TXNIP Independently Associated with Inflammation and Diabetes Mellitus in Twins

Thioredoxin-interacting protein (TXNIP) plays a key role in diabetes development and prognosis through its role in pancreatic β-cell dysfunction and death as well as in upregulating the inflammatory response in hyperglycemia. DNA methylation (DNAm) of TXNIP (TXNIP-cg19693031) is associated with the prevalence and incidence of type 2 diabetes (T2D); however, its role in inflammation and its relationship with T2D remain unclear. We aimed to investigate the epigenetic associations of TXNIP-cg19693031 with a panel of inflammatory biomarkers and to examine whether these inflammatory biomarkers modify the association between TXNIP-cg19693031 methylation and diabetes in 218 middle-aged male twins from the Emory Twin Study. We confirmed the association of TXNIP-cg19693031 DNAm with T2D, as well as with HbA1c, insulin and fasting glucose. We found that hypomethylation at TXNIP-cg19693031 is strongly associated with both type 2 diabetes and higher levels of inflammatory biomarkers (VCAM-1, ICAM-1, MMP-2, sRAGE and P-selectin); however, the relationship between TXNIP-cg19693031 and T2D is independent of the levels of these inflammatory biomarkers. Our results suggest that DNA methylation of TXNIP is linked with multiple biological processes, through which the TXNIP may have broad influence on chronic disease risk.

DNA Methylation-Based Estimates of Circulating Leukocyte Composition for Predicting Colorectal Cancer Survival: A Prospective Cohort Study

Simple Summary

Inflammation is involved in the evolution of cancer. Leukocytes, of which the proportion can be estimated using epigenome-wide methylation data, may serve as a prognostic marker in colorectal cancer (CRC). Our aim was to investigate whether DNA methylation-based estimates of circulating leukocytes is associated with all-cause and disease-specific mortality in a prospective CRC patients’ cohort. Significant associations with CRC prognosis were observed for CD4+ T cells, CD8+ T cells, B cells, NK cells, and lymphocytes, independent of age, sex, tumor stage, tumor subsite, and therapy. CD4+ T cells outperformed other leukocytes and provided added predictive value in comparison to age, sex, and tumor stage. Although cell counting is commonly used in clinical practice, DNA methylation-estimated cell proportions could be a promising tool in understanding the role of leukocytes as CRC prognostic biomarkers when using stored blood samples.

Abstract

Leukocytes are involved in the progression of colorectal cancer (CRC). The proportion of six major leukocyte subtypes can be estimated using epigenome-wide DNA methylation (DNAm) data from stored blood samples. Whether the composition of circulating leukocytes can be used as a prognostic factor is unclear. DNAm-based leukocyte proportions were obtained from a prospective cohort of 2206 CRC patients. Multivariate Cox regression models and survival curves were applied to assess associations between leukocyte composition and survival outcomes. A higher proportion of lymphocytes, including CD4+ T cells, CD8+ T cells, B cells, and NK cells, was associated with better survival, while a higher proportion of neutrophils was associated with poorer survival. CD4+ T cells outperformed other leukocytes in estimating the patients’ prognosis. Comparing the highest quantile to the lowest quantile of CD4+ T cells, hazard ratios (95% confidence intervals) of all-cause and CRC-specific mortality were 0.59 (0.48, 0.72) and 0.59 (0.45, 0.77), respectively. Furthermore, the association of CD4+ T cells and prognosis was stronger among patients with early or intermediate CRC or patients with colon cancer. In conclusion, the composition of circulating leukocytes estimated from DNAm, particularly the proportions of CD4+ T cells, could be used as promising independent predictors of CRC survival.

Clinical epigenomics for cardiovascular disease: Diagnostics and therapies

The study of epigenomics has advanced in recent years to span the regulation of a single genetic locus to the structure and orientation of entire chromosomes within the nucleus. In this review, we focus on the challenges and opportunities of clinical epigenomics in cardiovascular disease. As an integrator of genetic and environmental inputs, and because of advances in measurement techniques that are highly reproducible and provide sequence information, the epigenome is a rich source of potential biosignatures of cardiovascular health and disease. Most of the studies to date have focused on the latter, and herein we discuss observations on epigenomic changes in human cardiovascular disease, examining the role of protein modifiers of chromatin, noncoding RNAs and DNA modification. We provide an overview of cardiovascular epigenomics, discussing the challenges of data sovereignty, data analysis, doctor-patient ethics and innovations necessary to implement precision health.

Antigenic Challenge Influences Epigenetic Changes in Antigen-Specific T Regulatory Cells

Background

Human regulatory T cells (Tregs) are the fundamental component of the immune system imposing immune tolerance via control of effector T cells (Teffs). Ongoing attempts to improve Tregs function have led to the creation of a protocol that produces antigen-specific Tregs, when polyclonal Tregs are stimulated with monocytes loaded with antigens specific for type 1 diabetes. Nevertheless, the efficiency of the suppression exerted by the produced Tregs depended on the antigen with the best results when insulin β chain peptide 9-23 was used. Here, we examined epigenetic modifications, which could influence these functional differences.

Methods

The analysis was pefromed in the sorted specific (SPEC, proliferating) and unspecific (UNSPEC, non-proliferating) subsets of Tregs and Teffs generated by the stimulation with monocytes loaded with either whole insulin (INS) or insulin β chain peptide 9-23 (B:9-23) or polyclonal cells stimulated with anti-CD3/anti-CD28 beads (POLY). A relative expression of crucial Tregs genes was determined by qRT-PCR. The Treg-specific demethylated region (TSDR) in FoxP3 gene methylation levels were assessed by Quantitative Methylation Specific PCR (qMSP). ELISA was used to measure genomic DNA methylation and histone H3 post-translational modifications (PTMs).

Results

Tregs SPEC_B:9-23 was the only subset expressing all assessed genes necessary for regulatory function with the highest level of expression among all analyzed conditions. The methylation of global DNA as well as TSDR were significantly lower in Tregs SPEC_B:9-23 than in Tregs SPEC_INS. When compared to Teffs, Tregs were characterized by a relatively lower level of PTMs but it varied in respective Tregs/Teffs pairs. Importantly, whenever the difference in PTM within Tregs/Teffs pair was significant, it was always low in one subset from the pair and high in the other. It was always low in Tregs SPEC_INS and high in Teffs SPEC_INS, while it was high in Tregs UNSPEC_INS and low in Teffs UNSPEC_INS. There were no differences in Tregs/Teffs SPEC_B:9-23 pair and the level of modifications was low in Tregs UNSPEC_B:9-23 and high in Teffs UNSPEC_B:9-23. The regions of PTMs in which differences were significant overlapped only partially between particular Tregs/Teffs pairs.

Conclusions

Whole insulin and insulin β chain peptide 9-23 affected epigenetic changes in CD4⁺ T cells differently, when presented by monocytes. The peptide preferably favored specific Tregs, while whole insulin activated both Tregs and Teffs.

Enumerating regulatory T cells in cryopreserved umbilical cord blood samples using FOXP3 methylation specific quantitative PCR

BACKGROUND

Allogeneic haematopoietic cell transplantation (HCT) is a curative therapy for severe haematological disorders. However, it carries significant risk of morbidity and mortality. To improve patient outcomes, better graft selection strategies are needed, incorporating HLA matching with clinically important graft characteristics. Studies have shown that the cellular content of HCT grafts, specifically higher ratios of T regulatory (Tregs)/T cells, are important factors influencing outcomes when using adult peripheral blood mobilised grafts. So far, no equivalent study exists in umbilical cord blood (CB) transplantation due to the limitations of cryopreserved CB samples.

STUDY DESIGN AND METHODS

To establish the most robust and efficient way to measure the Treg content of previously cryopreserved CB units, we compared the enumeration of Treg and CD3+ cells using flow cytometry and an epigenetic, DNA-based methodology. The two methods were assessed for their agreement, consistency and susceptibility to error when enumerating Treg and CD3+ cell numbers in both fresh and cryopreserved CB samples.

RESULTS

Epigenetic enumeration gave consistent and comparable results in both fresh and frozen CB samples. By contrast, assessment of Tregs and CD3+ cells by flow cytometry was only possible in fresh samples due to significant cell death following cryopreservation and thawing.

CONCLUSION

Epigenetic assessment offers significant advantages over flow cytometry for analysing cryopreserved CB; similar cell numbers were observed both in fresh and frozen samples. Furthermore, multiple epigenetic assessments can be performed from DNA extracted from small cryopreserved CB segments; often the only CB sample available for clinical studies.

Phenotypes from cell-free DNA

Cell-free DNA (cfDNA) has the potential to enable non-invasive detection of disease states and progression. Beyond its sequence, cfDNA also represents the nucleosomal landscape of cell(s)-of-origin and captures the dynamics of the epigenome. In this review, we highlight the emergence of cfDNA epigenomic methods that assess disease beyond the scope of mutant tumour genotyping. Detection of tumour mutations is the gold standard for sequencing methods in clinical oncology. However, limitations inherent to mutation targeting in cfDNA, and the possibilities of uncovering molecular mechanisms underlying disease, have made epigenomics of cfDNA an exciting alternative. We discuss the epigenomic information revealed by cfDNA, and how epigenomic methods exploit cfDNA to detect and characterize cancer. Future applications of cfDNA epigenomic methods to act complementarily and orthogonally to current clinical practices has the potential to transform cancer management and improve cancer patient outcomes.

Foxp3 TSDR Hypermethylation Is Correlated with Decreased Tregs in Patients with Unexplained Recurrent Spontaneous Abortion

A decline of T regulatory cell (Treg) number and function is associated with unexplained recurrent spontaneous abortion (URSA). However, the mechanism of downregulation of Tregs in URSA patients is still unknown. This study aimed to investigate the changes of Tregs in URSA patients and the epigenetic regulation for these changes. Venous blood samples were collected from 20 patients with URSA and 20 healthy control subjects. Treg number and inhibitory capacity, and Foxp3 mRNA expression and Foxp3 TSDR methylation were compared between the 2 groups. Correlations between Treg frequency and inhibitory function and TSDR methylation status were examined by Spearman's correlation. The proportion of Tregs within the population of CD4⁺ T cells and the expression of Foxp3 mRNA was significantly lower in URSA patients than in healthy control subjects. Tregs from URSA patients and healthy controls both significantly inhibited the cytotoxic activity of natural killer (NK) cells toward K562 targets; however, the inhibitory ability of Tregs from URSA patients was significantly lower than that from healthy controls. The methylation level of the Treg-specific demethylated region (TSDR) in the Foxp3 gene was significantly greater in URSA patients than in the controls, and the level of methylation was inversely correlated with the proportion of Tregs and Foxp3 mRNA expression in the peripheral blood. However, the methylation level was not correlated with the inhibitory function of Tregs. A decrease of Treg number and function may be related to the pathogenesis of URSA, and Foxp3 hypermethylation may be associated with the decreased Treg number.

MethylResolver-a method for deconvoluting bulk DNA methylation profiles into known and unknown cell contents

Bulk tissue DNA methylation profiling has been used to examine epigenetic mechanisms and biomarkers of complex diseases such as cancer. However, heterogeneity of cellular content in tissues complicates result interpretation and utility. In silico deconvolution of cellular fractions from bulk tissue data offers a fast and inexpensive alternative to experimentally measuring such fractions. In this study, we report the design, implementation, and benchmarking of MethylResolver, a Least Trimmed Squares regression-based method for inferring leukocyte subset fractions from methylation profiles of tumor admixtures. Compared to previous approaches MethylResolver is more accurate as unknown cellular content in the mixture increases and is able to resolve tumor purity-scaled immune cell-type fractions without a cancer-specific signature. We also present a pan-cancer deconvolution of TCGA, recapitulating that high eosinophil fraction predicts improved cervical carcinoma survival and identifying elevated B cell fraction as a previously unreported predictor of poor survival for papillary renal cell carcinoma.

deconvSeq: deconvolution of cell mixture distribution in sequencing data

MOTIVATION

Although single-cell sequencing is becoming more widely available, many tissue samples such as intracranial aneurysms are both fibrous and minute, and therefore not easily dissociated into single cells. To account for the cell type heterogeneity in such tissues therefore requires a computational method. We present a computational deconvolution method, deconvSeq, for sequencing data (RNA and bisulfite) obtained from bulk tissue. This method can also be applied to single-cell RNA sequencing data.

RESULTS

DeconvSeq utilizes a generalized linear model to model effects of tissue type on feature quantification, which is specific to the data structure of the sequencing type used. Estimated model coefficients can then be used to predict the cell type mixture within a tissue. Predicted cell type mixtures were validated against actual cell counts in whole blood samples. Using this method, we obtained a mean correlation of 0.998 (95% CI 0.995-0.999) from the RNA sequencing data of 35 whole blood samples and 0.95 (95% CI 0.91-0.98) from the reduced representation bisulfite sequencing data from 35 whole blood samples. Using symmetric balances to obtain the correlation between compositional parts, we found that the lowest correlation occurred for monocytes for both RNA and bisulfite sequencing. Comparison with other methods of decomposition such as deconRNAseq, CIBERSORT, MuSiC and EpiDISH showed that deconvSeq is able to achieve good prediction using mean correlation with far fewer genes or CpG sites in the signature set.

AVAILABILITY AND IMPLEMENTATION

Software implementing deconvSeq is available at https://github.com/rosedu1/deconvSeq.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The DNA methylome of inflammatory bowel disease (IBD) reflects intrinsic and extrinsic factors in intestinal mucosal cells

Abnormal DNA methylation has been described in human inflammatory conditions of the gastrointestinal tract, such as inflammatory bowel disease (IBD). As other complex diseases, IBD results from the balance between genetic predisposition and environmental exposures. As such, DNA methylation may be the consequence (and potential effector) of both, genetic susceptibility variants and/or environmental signals such as cytokine exposure. We attempted to discern between these two non-excluding possibilities by performing a combined analysis of published DNA methylation data in intestinal mucosal cells of IBD and control samples. We identified abnormal DNA methylation at different levels: deviation from mean methylation signals at site and region levels, and differential variability. A fraction of such changes is associated with genetic polymorphisms linked to IBD susceptibility. In addition, by comparing with another intestinal inflammatory condition (i.e., coeliac disease) we propose that aberrant DNA methylation can also be the result of unspecific processes such as chronic inflammation. Our characterization suggests that IBD methylomes combine intrinsic and extrinsic responses in intestinal mucosal cells, and could point to knowledge-based biomarkers of IBD detection and progression.

Epigenome-Wide Association Study of Incident Type 2 Diabetes in a British Population: EPIC-Norfolk Study

Epigenetic changes may contribute substantially to risks of diseases of aging. Previous studies reported seven methylation variable positions (MVPs) robustly associated with incident type 2 diabetes mellitus (T2DM). However, their causal roles in T2DM are unclear. In an incident T2DM case-cohort study nested within the population-based European Prospective Investigation into Cancer and Nutrition (EPIC)-Norfolk cohort, we used whole blood DNA collected at baseline, up to 11 years before T2DM onset, to investigate the role of methylation in the etiology of T2DM. We identified 15 novel MVPs with robust associations with incident T2DM and robustly confirmed three MVPs identified previously (near to TXNIP, ABCG1, and SREBF1). All 18 MVPs showed directionally consistent associations with incident and prevalent T2DM in independent studies. Further conditional analyses suggested that the identified epigenetic signals appear related to T2DM via glucose and obesity-related pathways acting before the collection of baseline samples. We integrated genome-wide genetic data to identify methylation-associated quantitative trait loci robustly associated with 16 of the 18 MVPs and found one MVP, cg00574958 at CPT1A, with a possible direct causal role in T2DM. None of the implicated genes were previously highlighted by genetic association studies, suggesting that DNA methylation studies may reveal novel biological mechanisms involved in tissue responses to glycemia.

Integrative Analysis Defines Distinct Prognostic Subgroups of Intrahepatic Cholangiocarcinoma

Intrahepatic cholangiocarcinoma (iCCA) is the second most common primary liver cancer. It is defined by cholangiocytic differentiation and has poor prognosis. Recently, epigenetic processes have been shown to play an important role in cholangiocarcinogenesis. We performed an integrative analysis on 52 iCCAs using both genetic and epigenetic data with a specific focus on DNA methylation components. We found recurrent isocitrate dehydrogenase 1 (IDH1) and IDH2 (28%) gene mutations, recurrent arm-length copy number alterations (CNAs), and focal alterations such as deletion of 3p21 or amplification of 12q15, which affect BRCA1 Associated Protein 1, polybromo 1, and mouse double minute 2 homolog. DNA methylome analysis revealed excessive hypermethylation of iCCA, affecting primarily the bivalent genomic regions marked with both active and repressive histone modifications. Integrative clustering of genetic and epigenetic data identified four iCCA subgroups with prognostic relevance further designated as IDH, high (H), medium (M), and low (L) alteration groups. The IDH group consisted of all samples with IDH1 or IDH2 mutations and showed, together with the H group, a highly disrupted genome, characterized by frequent deletions of chromosome arms 3p and 6q. Both groups showed excessive hypermethylation with distinct patterns. The M group showed intermediate characteristics regarding both genetic and epigenetic marks, whereas the L group exhibited few methylation changes and mutations and a lack of CNAs. Methylation-based latent component analysis of cell-type composition identified differences among these four groups. Prognosis of the H and M groups was significantly worse than that of the L group. Conclusion: Using an integrative genomic and epigenomic analysis approach, we identified four major iCCA subgroups with widespread genomic and epigenomic differences and prognostic implications. Furthermore, our data suggest differences in the cell-of-origin of the iCCA subtypes.

Recent progress, methods and perspectives in forensic epigenetics

Forensic epigenetics, i.e., investigating epigenetics variation to resolve forensically relevant questions unanswerable with standard forensic DNA profiling has been gaining substantial ground over the last few years. Differential DNA methylation among tissues and individuals has been proposed as useful resource for three forensic applications i) determining the tissue type of a human biological trace, ii) estimating the age of an unknown trace donor, and iii) differentiating between monozygotic twins. Thus far, forensic epigenetic investigations have used a wide range of methods for CpG marker discovery, prediction modelling and targeted DNA methylation analysis, all coming with advantages and disadvantages when it comes to forensic trace analysis. In this review, we summarize the most recent literature on these three main topics of current forensic epigenetic investigations and discuss limitations and practical considerations in experimental design and data interpretation, such as technical and biological biases. Moreover, we provide future perspectives with regard to new research questions, new epigenetic markers and recent technological advances that - as we envision - will move the field towards forensic epigenomics in the near future.

Investigating the Epigenetic Discrimination of Identical Twins Using Buccal Swabs, Saliva, and Cigarette Butts in the Forensic Setting

Monozygotic (MZ) twins are typically indistinguishable via forensic DNA profiling. Recently, we demonstrated that epigenetic differentiation of MZ twins is feasible; however, proportions of twin differentially methylated CpG sites (tDMSs) identified in reference-type blood DNA were not replicated in trace-type blood DNA. Here we investigated buccal swabs as typical forensic reference material, and saliva and cigarette butts as commonly encountered forensic trace materials. As an analog to a forensic case, we analyzed one MZ twin pair. Epigenome-wide microarray analysis in reference-type buccal DNA revealed 25 candidate tDMSs with >0.5 twin-to-twin differences. MethyLight quantitative PCR (qPCR) of 22 selected tDMSs in trace-type DNA revealed in saliva DNA that six tDMSs (27.3%) had >0.1 twin-to-twin differences, seven (31.8%) had smaller (<0.1) but robustly detected differences, whereas for nine (40.9%) the differences were in the opposite direction relative to the microarray data; for cigarette butt DNA, results were 50%, 22.7%, and 27.3%, respectively. The discrepancies between reference-type and trace-type DNA outcomes can be explained by cell composition differences, method-to-method variation, and other technical reasons including bisulfite conversion inefficiency. Our study highlights the importance of the DNA source and that careful characterization of biological and technical effects is needed before epigenetic MZ twin differentiation is applicable in forensic casework.

Cell subpopulation deconvolution reveals breast cancer heterogeneity based on DNA methylation signature

Tumour heterogeneity describes the coexistence of divergent tumour cell clones within tumours, which is often caused by underlying epigenetic changes. DNA methylation is commonly regarded as a significant regulator that differs across cells and tissues. In this study, we comprehensively reviewed research progress on estimating of tumour heterogeneity. Bioinformatics-based analysis of DNA methylation has revealed the evolutionary relationships between breast cancer cell lines and tissues. Further analysis of the DNA methylation profiles in 33 breast cancer-related cell lines identified cell line-specific methylation patterns. Next, we reviewed the computational methods in inferring clonal evolution of tumours from different perspectives and then proposed a deconvolution strategy for modelling cell subclonal populations dynamics in breast cancer tissues based on DNA methylation. Further analysis of simulated cancer tissues and real cell lines revealed that this approach exhibits satisfactory performance and relative stability in estimating the composition and proportions of cellular subpopulations. The application of this strategy to breast cancer individuals of the Cancer Genome Atlas's identified different cellular subpopulations with distinct molecular phenotypes. Moreover, the current and potential future applications of this deconvolution strategy to clinical breast cancer research are discussed, and emphasis was placed on the DNA methylation-based recognition of intra-tumour heterogeneity. The wide use of these methods for estimating heterogeneity to further clinical cohorts will improve our understanding of neoplastic progression and the design of therapeutic interventions for treating breast cancer and other malignancies.

MeDeCom: discovery and quantification of latent components of heterogeneous methylomes

It is important for large-scale epigenomic studies to determine and explore the nature of hidden confounding variation, most importantly cell composition. We developed MeDeCom as a novel reference-free computational framework that allows the decomposition of complex DNA methylomes into latent methylation components and their proportions in each sample. MeDeCom is based on constrained non-negative matrix factorization with a new biologically motivated regularization function. It accurately recovers cell-type-specific latent methylation components and their proportions. MeDeCom is a new unsupervised tool for the exploratory study of the major sources of methylation variation, which should lead to a deeper understanding and better biological interpretation.

MeDeCom: discovery and quantification of latent components of heterogeneous methylomes

It is important for large-scale epigenomic studies to determine and explore the nature of hidden confounding variation, most importantly cell composition. We developed MeDeCom as a novel reference-free computational framework that allows the decomposition of complex DNA methylomes into latent methylation components and their proportions in each sample. MeDeCom is based on constrained non-negative matrix factorization with a new biologically motivated regularization function. It accurately recovers cell-type-specific latent methylation components and their proportions. MeDeCom is a new unsupervised tool for the exploratory study of the major sources of methylation variation, which should lead to a deeper understanding and better biological interpretation.

Epigenetic features of FoxP3 in children with cow's milk allergy

BACKGROUND

DNA methylation of the Th1 and Th2 cytokine genes is altered during cow's milk allergy (CMA). Forkhead box transcription factor 3 (FoxP3) is essential for the development and function of regulatory T cells (Tregs) and is involved in oral tolerance acquisition. We assessed whether tolerance acquisition in children with IgE-mediated CMA is associated with DNA demethylation of the Treg-specific demethylated region (TSDR) of FoxP3.

RESULTS

Forty children (aged 3-18 months) were enrolled: 10 children with active IgE-mediated CMA (group 1), 10 children who outgrew CMA after dietary treatment with an extensively hydrolyzed casein formula containing the probiotic Lactobacillus rhamnosus GG (group 2), 10 children who outgrew CMA after treatment with other formulas (group 3), and 10 healthy controls (group 4). FoxP3 TSDR demethylation and expression were measured in mononuclear cells purified from peripheral blood of the four groups of children. FoxP3 TSDR demethylation was significantly lower in children with active IgE-mediated CMA than in either children who outgrew CMA or in healthy children. Formula selection influenced the FoxP3 TSDR demethylation profile. The FoxP3 TSDR demethylation rate and expression level were correlated.

CONCLUSIONS

Tolerance acquisition in children with IgE-mediated CMA involves epigenetic regulation of the FoxP3 gene. This feature could be a new target for preventive and therapeutic strategies against CMA.

Establishing an analytic pipeline for genome-wide DNA methylation

The need for research investigating DNA methylation (DNAm) in clinical studies has increased, leading to the evolution of new analytic methods to improve accuracy and reproducibility of the interpretation of results from these studies. The purpose of this article is to provide clinical researchers with a summary of the major data processing steps routinely applied in clinical studies investigating genome-wide DNAm using the Illumina HumanMethylation 450K BeadChip. In most studies, the primary goal of employing DNAm analysis is to identify differential methylation at CpG sites among phenotypic groups. Experimental design considerations are crucial at the onset to minimize bias from factors related to sample processing and avoid confounding experimental variables with non-biological batch effects. Although there are currently no de facto standard methods for analyzing these data, we review the major steps in processing DNAm data recommended by several research studies. We describe several variations available for clinical researchers to process, analyze, and interpret DNAm data. These insights are applicable to most types of genome-wide DNAm array platforms and will be applicable for the next generation of DNAm array technologies (e.g., the 850K array). Selection of the DNAm analytic pipeline followed by investigators should be guided by the research question and supported by recently published methods.

Methylation Markers for the Identification of Body Fluids and Tissues from Forensic Trace Evidence

The identification of body fluids is an essential tool for clarifying the course of events at a criminal site. The analytical problem is the fact that the biological material has been very often exposed to detrimental exogenous influences. Thereby, the molecular substrates used for the identification of the traces may become degraded. So far, most protocols utilize cell specific proteins or RNAs. Instead of measuring these more sensitive compounds this paper describes the application of the differential DNA-methylation. As a result of two genome wide screenings with the Illumina HumanMethylation BeadChips 27 and 450k we identified 150 candidate loci revealing differential methylation with regard to the body fluids venous blood, menstrual blood, vaginal fluid, saliva and sperm. Among them we selected 9 loci as the most promising markers. For the final determination of the methylation degree we applied the SNuPE-method. Because the degree of methylation might be modified by various endogenous and exogenous factors, we tested each marker with approximately 100 samples of each target fluid in a validation study. The stability of the detection procedure is proved in various simulated forensic surroundings according to standardized conditions. We studied the potential influence of 12 relatively common tumors on the methylation of the 9 markers. For this purpose the target fluids of 34 patients have been analysed. Only the cervix carcinoma might have an remarkable effect because impairing the signal of both vaginal markers. Using the Illumina MiSeq device we tested the potential influence of cis acting sequence variants on the methylation degree of the 9 markers in the specific body fluid DNA of 50 individuals. For 4 marker loci we observed such an influence either by sole SNPs or haplotypes. The identification of each target fluid is possible in arbitrary mixtures with the remaining four body fluids. The sensitivity of the individual body fluid tests is in the same range as for the forensic STR-analysis. It is the first forensic body fluid protocol which considers the exogenic and endogenic parameters potentially interfering with the true results.

FOXP3+ Tregs: heterogeneous phenotypes and conflicting impacts on survival outcomes in patients with colorectal cancer

The tumor microenvironment composites a mixture of immune lymphoid cells, myeloid cells, stromal cells with complex cytokines, as well as numerous lymphovascular vessels. Colorectal cancer (CRC) is a common malignancy and one of the leading causes of tumor-related death in the United States and worldwide. The immune status in the tumor microenvironment contributes to the survival of a patient with CRC. Regulatory T cells (Tregs) are considered a key factor in immune escape and immunotherapy failure among cancer patients. The transcription factor forkhead box P3 (FOXP3) is a crucial intracellular marker and also a key developmental and functional factor for CD4+CD25+ Tregs. Tregs are correlated with survival in various human neoplasms, and elevated proportions of Tregs are usually associated with unfavorable clinical outcomes. However, the role of Tregs in CRC remains controversial. High densities of tumor-infiltrating Tregs in CRC patients are reported to be correlated with worse or better outcomes. And Tregs may not be predictive of prognosis after resection of the primary tumor. The exact explanations for these discordant results remain unclear. The heterogeneous instincts of cell phenotype, gene expression, and functional activities of Tregs may partly contribute this contrasting result. Furthermore, the lack of a robust marker for identifying Tregs or due to the different techniques applied is also account. The Treg-specific demethylated region (TSDR) was recently reported to be a specific epigenetic marker for natural Tregs (nTregs), which can stably express FOXP3. The FOXP3-TSDR demethylation assay may be an promising technique for CRC-related nTregs studies.

Intra-Monozygotic Twin Pair Discordance and Longitudinal Variation of Whole-Genome Scale DNA Methylation in Adults

Monozygotic twins share identical genomic DNA and are indistinguishable using conventional genetic markers. Increasing evidence indicates that monozygotic twins are epigenetically distinct, suggesting that a comparison between DNA methylation patterns might be useful to approach this forensic problem. However, the extent of epigenetic discordance between healthy adult monozygotic twins and the stability of CpG loci within the same individual over a short time span at the whole-genome scale are not well understood. Here, we used Infinium HumanMethylation450 Beadchips to compare DNA methylation profiles using blood collected from 10 pairs of monozygotic twins and 8 individuals sampled at 0, 3, 6, and 9 months. Using an effective and unbiased method for calling differentially methylated (DM) CpG sites, we showed that 0.087%–1.530% of the CpG sites exhibit differential methylation in monozygotic twin pairs. We further demonstrated that, on whole-genome level, there has been no significant epigenetic drift within the same individuals for up to 9 months, including one monozygotic twin pair. However, we did identify a subset of CpG sites that vary in DNA methylation over the 9-month period. The magnitude of the intra-pair or longitudinal methylation discordance of the CpG sites inside the CpG islands is greater than those outside the CpG islands. The CpG sites located on shores appear to be more suitable for distinguishing between MZ twins.

Simultaneous statistical inference for epigenetic data

Epigenetic research leads to complex data structures. Since parametric model assumptions for the distribution of epigenetic data are hard to verify we introduce in the present work a nonparametric statistical framework for two-group comparisons. Furthermore, epigenetic analyses are often performed at various genetic loci simultaneously. Hence, in order to be able to draw valid conclusions for specific loci, an appropriate multiple testing correction is necessary. Finally, with technologies available for the simultaneous assessment of many interrelated biological parameters (such as gene arrays), statistical approaches also need to deal with a possibly unknown dependency structure in the data. Our statistical approach to the nonparametric comparison of two samples with independent multivariate observables is based on recently developed multivariate multiple permutation tests. We adapt their theory in order to cope with families of hypotheses regarding relative effects. Our results indicate that the multivariate multiple permutation test keeps the pre-assigned type I error level for the global null hypothesis. In combination with the closure principle, the family-wise error rate for the simultaneous test of the corresponding locus/parameter-specific null hypotheses can be controlled. In applications we demonstrate that group differences in epigenetic data can be detected reliably with our methodology.

Methylation levels of SLC23A2 and NCOR2 genes correlate with spinal muscular atrophy severity

Spinal muscular atrophy (SMA) is a monogenic neurodegenerative disorder subdivided into four different types. Whole genome methylation analysis revealed 40 CpG sites associated with genes that are significantly differentially methylated between SMA patients and healthy individuals of the same age. To investigate the contribution of methylation changes to SMA severity, we compared the methylation level of found CpG sites, designed as "targets", as well as the nearest CpG sites in regulatory regions of ARHGAP22, CDK2AP1, CHML, NCOR2, SLC23A2 and RPL9 in three groups of SMA patients. Of notable interest, compared to type I SMA male patients, the methylation level of a target CpG site and one nearby CpG site belonging to the 5'UTR of SLC23A2 were significantly hypomethylated 19-22% in type III-IV patients. In contrast to type I SMA male patients, type III-IV patients demonstrated a 16% decrease in the methylation levels of a target CpG site, belonging to the 5'UTR of NCOR2. To conclude, this study validates the data of our previous study and confirms significant methylation changes in the SLC23A2 and NCOR2 regulatory regions correlates with SMA severity.

Analysis pipelines and packages for Infinium HumanMethylation450 BeadChip (450k) data

The Illumina HumanMethylation450 BeadChip has become a popular platform for interrogating DNA methylation in epigenome-wide association studies (EWAS) and related projects as well as resource efforts such as the International Cancer Genome Consortium (ICGC) and the International Human Epigenome Consortium (IHEC). This has resulted in an exponential increase of 450k data in recent years and triggered the development of numerous integrated analysis pipelines and stand-alone packages. This review will introduce and discuss the currently most popular pipelines and packages and is particularly aimed at new 450k users.

Epi-fingerprinting and epi-interventions for improved crop production and food quality

Increasing crop production at a time of rapid climate change represents the greatest challenge facing contemporary agricultural research. Our understanding of the genetic control of yield derives from controlled field experiments designed to minimize environmental variance. In spite of these efforts there is substantial residual variability among plants attributable to Genotype × Environment interactions. Recent advances in the field of epigenetics have revealed a plethora of gene control mechanisms that could account for much of this unassigned variation. These systems act as a regulatory interface between the perception of the environment and associated alterations in gene expression. Direct intervention of epigenetic control systems hold the enticing promise of creating new sources of variability that could enhance crop performance. Equally, understanding the relationship between various epigenetic states and responses of the crop to specific aspects of the growing environment (epigenetic fingerprinting) could allow for a more tailored approach to plant agronomy. In this review, we explore the many ways in which epigenetic interventions and epigenetic fingerprinting can be deployed for the improvement of crop production and quality.

Normal cell-type epigenetics and breast cancer classification: a case study of cell mixture-adjusted analysis of DNA methylation data from tumors

Historically, breast cancer classification has relied on prognostic subtypes. Thus, unlike hematopoietic cancers, breast tumor classification lacks phylogenetic rationale. The feasibility of phylogenetic classification of breast tumors has recently been demonstrated based on estrogen receptor (ER), androgen receptor (AR), vitamin D receptor (VDR) and Keratin 5 expression. Four hormonal states (HR0–3) comprising 11 cellular subtypes of breast cells have been proposed. This classification scheme has been shown to have relevance to clinical prognosis. We examine the implications of such phylogenetic classification on DNA methylation of both breast tumors and normal breast tissues by applying recently developed deconvolution algorithms to three DNA methylation data sets archived on Gene Expression Omnibus. We propose that breast tumors arising from a particular cell-of-origin essentially magnify the epigenetic state of their original cell type. We demonstrate that DNA methylation of tumors manifests patterns consistent with cell-specific epigenetic states, that these states correspond roughly to previously posited normal breast cell types, and that estimates of proportions of the underlying cell types are predictive of tumor phenotypes. Taken together, these findings suggest that the epigenetics of breast tumors is ultimately based on the underlying phylogeny of normal breast tissue.

DNA methylome profiling beyond promoters - taking an epigenetic snapshot of the breast tumor microenvironment

Breast cancer, one of the most common and deadliest malignancies in developed countries, is a remarkably heterogeneous disease, which is clinically reflected by patients who display similar pathological features but respond differently to treatments. In the search for mediators of responsiveness, the tumor microenvironment (TME), in particular tumor-associated immune cells, has been pushed into the spotlight as it has become clear that the TME is an active component of breast cancer disease that affects clinical outcomes. Thus, the characterization of the TME in terms of cell identities and their frequencies has generated a great deal of interest. The common methods currently used for this purpose are either limited in accuracy or application, and DNA methylation has recently been proposed as an alternative approach. The aim of this review is to discuss DNA methylation profiling beyond promoters as a potential clinical tool for TME characterization and cell typing within tumors. With respect to this, we review the role of DNA methylation in breast cancer and cell-lineage specification, as well as inform about the composition and clinical relevance of the TME.

Multigenerational effects of maternal undernutrition

Intrauterine exposure to reduced nutrient availability can have major effects in determining susceptibility to chronic disease later in life. Martínez et al. (2014) demonstrate multigenerational effects of poor maternal nutrition and evidence of germline transmission through alterations in DNA methylation.

Quantitative reconstruction of leukocyte subsets using DNA methylation

Background

Cell lineage-specific DNA methylation patterns distinguish normal human leukocyte subsets and can be used to detect and quantify these subsets in peripheral blood. We have developed an approach that uses DNA methylation to simultaneously quantify multiple leukocyte subsets, enabling investigation of immune modulations in virtually any blood sample including archived samples previously precluded from such analysis. Here we assess the performance characteristics and validity of this approach.

Results

Using Illumina Infinium HumanMethylation27 and VeraCode GoldenGate Methylation Assay microarrays, we measure DNA methylation in leukocyte subsets purified from human whole blood and identify cell lineage-specific DNA methylation signatures that distinguish human T cells, B cells, NK cells, monocytes, eosinophils, basophils and neutrophils. We employ a bioinformatics-based approach to quantify these cell types in complex mixtures, including whole blood, using DNA methylation at as few as 20 CpG loci. A reconstruction experiment confirms that the approach could accurately measure the composition of mixtures of human blood leukocyte subsets. Applying the DNA methylation-based approach to quantify the cellular components of human whole blood, we verify its accuracy by direct comparison to gold standard immune quantification methods that utilize physical, optical and proteomic characteristics of the cells. We also demonstrate that the approach is not affected by storage of blood samples, even under conditions prohibiting the use of gold standard methods.

Conclusions

Cell mixture distributions within peripheral blood can be assessed accurately and reliably using DNA methylation. Thus, precise immune cell differential estimates can be reconstructed using only DNA rather than whole cells.

Reference-free cell mixture adjustments in analysis of DNA methylation data

Motivation: Recently there has been increasing interest in the effects of cell mixture on the measurement of DNA methylation, specifically the extent to which small perturbations in cell mixture proportions can register as changes in DNA methylation. A recently published set of statistical methods exploits this association to infer changes in cell mixture proportions, and these methods are presently being applied to adjust for cell mixture effect in the context of epigenome-wide association studies. However, these adjustments require the existence of reference datasets, which may be laborious or expensive to collect. For some tissues such as placenta, saliva, adipose or tumor tissue, the relevant underlying cell types may not be known.

Results: We propose a method for conducting epigenome-wide association studies analysis when a reference dataset is unavailable, including a bootstrap method for estimating standard errors. We demonstrate via simulation study and several real data analyses that our proposed method can perform as well as or better than methods that make explicit use of reference datasets. In particular, it may adjust for detailed cell type differences that may be unavailable even in existing reference datasets.

Availability and implementation: Software is available in the R package RefFreeEWAS. Data for three of four examples were obtained from Gene Expression Omnibus (GEO), accession numbers GSE37008, GSE42861 and GSE30601, while reference data were obtained from GEO accession number GSE39981.

Contact:andres.houseman@oregonstate.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Blood-based profiles of DNA methylation predict the underlying distribution of cell types: a validation analysis

The potential influence of underlying differences in relative leukocyte distributions in studies involving blood-based profiling of DNA methylation is well recognized and has prompted development of a set of statistical methods for inferring changes in the distribution of white blood cells using DNA methylation signatures. However, the extent to which this methodology can accurately predict cell-type proportions based on blood-derived DNA methylation data in a large-scale epigenome-wide association study (EWAS) has yet to be examined. We used publicly available data deposited in the Gene Expression Omnibus (GEO) database (accession number GSE37008), which consisted of both blood-derived epigenome-wide DNA methylation data assayed using the Illumina Infinium HumanMethylation27 BeadArray and complete blood cell (CBC) counts among a community cohort of 94 non-diseased individuals. Constrained projection (CP) was used to obtain predictions of the proportions of lymphocytes, monocytes and granulocytes for each of the study samples based on their DNA methylation signatures. Our findings demonstrated high consistency between the average CBC-derived and predicted percentage of monocytes and lymphocytes (17.9% and 17.6% for monocytes and 82.1% and 81.4% for lymphocytes), with root mean squared error (rMSE) of 5% and 6%, for monocytes and lymphocytes, respectively. Similarly, there was moderate-high correlation between the CP-predicted and CBC-derived percentages of monocytes and lymphocytes (0.60 and 0.61, respectively), and these results were robust to the number of leukocyte differentially methylated regions (L-DMRs) used for CP prediction. These results serve as further validation of the CP approach and highlight the promise of this technique for EWAS where DNA methylation is profiled using whole-blood genomic DNA.

DNA methylation arrays as surrogate measures of cell mixture distribution

Background

There has been a long-standing need in biomedical research for a method that quantifies the normally mixed composition of leukocytes beyond what is possible by simple histological or flow cytometric assessments. The latter is restricted by the labile nature of protein epitopes, requirements for cell processing, and timely cell analysis. In a diverse array of diseases and following numerous immune-toxic exposures, leukocyte composition will critically inform the underlying immuno-biology to most chronic medical conditions. Emerging research demonstrates that DNA methylation is responsible for cellular differentiation, and when measured in whole peripheral blood, serves to distinguish cancer cases from controls.

Results

Here we present a method, similar to regression calibration, for inferring changes in the distribution of white blood cells between different subpopulations (e.g. cases and controls) using DNA methylation signatures, in combination with a previously obtained external validation set consisting of signatures from purified leukocyte samples. We validate the fundamental idea in a cell mixture reconstruction experiment, then demonstrate our method on DNA methylation data sets from several studies, including data from a Head and Neck Squamous Cell Carcinoma (HNSCC) study and an ovarian cancer study. Our method produces results consistent with prior biological findings, thereby validating the approach.

Conclusions

Our method, in combination with an appropriate external validation set, promises new opportunities for large-scale immunological studies of both disease states and noxious exposures.

Replication timing: a fingerprint for cell identity and pluripotency

Many types of epigenetic profiling have been used to classify stem cells, stages of cellular differentiation, and cancer subtypes. Existing methods focus on local chromatin features such as DNA methylation and histone modifications that require extensive analysis for genome-wide coverage. Replication timing has emerged as a highly stable cell type-specific epigenetic feature that is regulated at the megabase-level and is easily and comprehensively analyzed genome-wide. Here, we describe a cell classification method using 67 individual replication profiles from 34 mouse and human cell lines and stem cell-derived tissues, including new data for mesendoderm, definitive endoderm, mesoderm and smooth muscle. Using a Monte-Carlo approach for selecting features of replication profiles conserved in each cell type, we identify “replication timing fingerprints” unique to each cell type and apply a k nearest neighbor approach to predict known and unknown cell types. Our method correctly classifies 67/67 independent replication-timing profiles, including those derived from closely related intermediate stages. We also apply this method to derive fingerprints for pluripotency in human and mouse cells. Interestingly, the mouse pluripotency fingerprint overlaps almost completely with previously identified genomic segments that switch from early to late replication as pluripotency is lost. Thereafter, replication timing and transcription within these regions become difficult to reprogram back to pluripotency, suggesting these regions highlight an epigenetic barrier to reprogramming. In addition, the major histone cluster Hist1 consistently becomes later replicating in committed cell types, and several histone H1 genes in this cluster are downregulated during differentiation, suggesting a possible instrument for the chromatin compaction observed during differentiation. Finally, we demonstrate that unknown samples can be classified independently using site-specific PCR against fingerprint regions. In sum, replication fingerprints provide a comprehensive means for cell characterization and are a promising tool for identifying regions with cell type-specific organization.

While continued advances in stem cell and cancer biology have uncovered a growing list of clinical applications for stem cell technology, errors in indentifying cell lines have undermined a number of recent studies, highlighting a growing need for improvements in cell typing methods for both basic biological and clinical applications of stem cells. Induced pluripotent stem cells (iPSCs)—adult cells reprogrammed to a pluripotent state—show great promise for patient-specific stem cell treatments, but more efficient derivation of iPSCs depends on a more comprehensive understanding of pluripotency. Here, we describe a method to identify sets of regions that replicate at unique times in any given cell type (replication timing fingerprints) using pluripotent stem cells as an example, and show that genes in the pluripotency fingerprint belong to a class previously shown to be resistant to reprogramming in iPSCs, identifying potential new target genes for more efficient iPSC production. We propose that the order in which DNA is replicated (replication timing) provides a novel means for classifying cell types, and can reveal cell type specific features of genome organization.

Epigenetic quantification of tumor-infiltrating T-lymphocytes

The immune system plays a pivotal role in tumor establishment. However, the role of T-lymphocytes within the tumor microenvironment as major cellular component of the adaptive effector immune response and their counterpart, regulatory T-cells (Treg), responsible for suppressive immune modulation, is not completely understood. This is partly due to the lack of reliable technical solutions for specific cell quantification in solid tissues. Previous reports indicated that epigenetic marks of immune cells, such as the Treg specifically demethylated region (TSDR) within the FOXP3 gene, may be exploited as robust analytical tool for Treg-quantification. Here, we expand the concept of epigenetic immunophenotyping to overall T-lymphocytes (oTL). This tool allows cell quantification with at least equivalent precision to FACS and is adoptable for analysis of blood and solid tissues. Based on this method, we analyse the frequency of Treg, oTL and their ratio in independent cohorts of healthy and tumorous ovarian, colorectal and bronchial tissues with 616 partly donor-matched samples. We find a shift of the median ratio of Treg-to-oTL from 3-8% in healthy tissue to 18-25% in all tumor entities. Epigenetically determined oTL frequencies correlate with the outcome of colorectal and ovarian cancers. Together, our data show that the composition of immune cells in tumor microenvironments can be quantitatively assessed by epigenetic measurements. This composition is disturbed in solid tumors, indicating a fundamental mechanism of tumor immune evasion. Epigenetic quantification of T-lymphocytes serves as independent clinical parameter for outcome prognosis.

T regulatory cells in cord blood--FOXP3 demethylation as reliable quantitative marker

Background

Regulatory T-cells (Tregs), characterized as CD4+CD25^hi T-cells expressing FOXP3, play a crucial role in controlling healthy immune development during early immune maturation. Recently, FOXP3 demethylation was suggested to be a novel marker for natural Tregs in adults. In cord blood, the role and function of Tregs and its demethylation is poorly understood. We assessed FOXP3 demethylation in cord blood in relation to previously used Treg markers such as CD4⁺CD25^hi, FOXP3 mRNA, protein expression, and suppressive Treg function.

Methodology

Cord blood mononuclear cells (CBMC) were isolated from 70 healthy neonates, stimulated for 3 days with the microbial stimulus lipid A (LpA), and allergen Dermatophagoides pteronyssinus (Derp1). Tregs (CD4+CD25^hi, intracellular, mRNA FOXP3 expression, isolated cells), DNA methylation of the FOXP3-locus and suppressive Treg function were assessed.

Principal Findings

Demethylation of FOXP3 in whole blood was specific for isolated CD4+CD25^hi Tregs. Demethylation of FOXP3 was positively correlated with unstimulated and LpA-stimulated FOXP3 mRNA-expression (p≤0.05), and CD4⁺CD25^hi T-cells (p≤0.03). Importantly, increased FOXP3 demethylation correlated with more efficient suppressive capacity of Tregs (r = 0.72, p = 0.005). Furthermore, FOXP3 demethylation was positively correlated with Th2 cytokines (IL-5, IL-13) following LpA-stimulation (p = 0.006/0.04), with Th2 and IL-17 following Derp1+LpA-stimulations (p≤0.009), but not Th1 cytokines (IFN-γ).

Conclusions

FOXP3 demethylation reliable quantifies Tregs in cord blood. FOXP3 demethylation corresponds well with the suppressive potential of Tregs. The resulting strict correlation with functionally suppressive Tregs and the relative ease of measurement render it into a valuable novel marker for large field studies assessing Tregs as qualitative marker indicative of functional activity.

Correlation of COL10A1 induction during chondrogenesis of mesenchymal stem cells with demethylation of two CpG sites in the COL10A1 promoter

OBJECTIVE

Human articular chondrocytes do not express COL10A1 and do not undergo hypertrophy except in close vicinity to subchondral bone. In contrast, chondrocytes produced in vitro from mesenchymal stem cells (MSCs) show premature COL10A1 expression and cannot form stable ectopic cartilage transplants, which indicates that they may be phenotypically unstable and not suitable for treatment of articular cartilage lesions. CpG methylation established during natural development may play a role in suppression of COL10A1 expression and hypertrophy in human articular chondrocytes. This study was undertaken to compare gene methylation patterns and expression of COL10A1 and COL2A1 in chondrocyte and MSC populations, in order to determine whether failed genomic methylation patterns correlate with an unstable chondrocyte phenotype after chondrogenesis of MSCs.

METHODS

COL10A1 and COL2A1 regulatory gene regions were computationally searched for CpG-rich regions. CpG methylation of genomic DNA from human articular chondrocytes, MSCs, and MSC-derived chondrocytes was analyzed by Combined Bisulfite Restriction Analysis and by sequencing of polymerase chain reaction fragments amplified from bisulfite-treated genomic DNA.

RESULTS

The CpG island around the transcription start site of COL2A1 was unmethylated in all cell groups independent of COL2A1 expression, while 9 tested CpG sites in the sparse CpG promoter of COL10A1 were consistently methylated in human articular chondrocytes. Induction of COL10A1 expression during chondrogenesis of MSCs correlated with demethylation of 2 CpG sites in the COL10A1 promoter.

CONCLUSION

Our findings indicate that methylation-based COL10A1 gene silencing is established in cartilage tissue and human articular chondrocytes. Altered methylation levels at 2 CpG sites of COL10A1 in MSCs and their demethylation during chondrogenesis may facilitate induction of COL10A1 as observed during in vitro chondrogenesis of MSCs.

Quantitative DNA methylation analysis of FOXP3 as a new method for counting regulatory T cells in peripheral blood and solid tissue

Regulatory T-cells (Treg) have been the focus of immunologic research due to their role in establishing tolerance for harmless antigens versus allowing immune responses against foes. Increased Treg frequencies measured by mRNA expression or protein synthesis of the Treg marker FOXP3 were found in various cancers, indicating that dysregulation of Treg levels contributes to tumor establishment. Furthermore, they constitute a key target of immunomodulatory therapies in cancer as well as transplantation settings. One core obstacle for understanding the role of Treg, thus far, is the inability of FOXP3 mRNA or protein detection methods to differentiate between Treg and activated T cells. These difficulties are aggravated by the technical demands of sample logistics and processing. Based on Treg-specific DNA demethylation within the FOXP3 locus, we present a novel method for monitoring Treg in human peripheral blood and solid tissues. We found that Treg numbers are significantly increased in the peripheral blood of patients with interleukin 2-treated melanoma and in formalin-fixed tissue from patients with lung and colon carcinomas. Conversely, we show that immunosuppressive therapy including therapeutic antibodies leads to a significant reduction of Treg from the peripheral blood of transplantation patients. In addition, Treg numbers are predictively elevated in the peripheral blood of patients with various solid tumors. Although our data generally correspond to data obtained with gene expression and protein-based methods, the results are less fluctuating and more specific to Treg. The assay presented here measures Treg robustly in blood and solid tissues regardless of conservation levels, promising fast screening of Treg in various clinical settings.