RNA-sequencing and mass-spectrometry proteomic time-series analysis of T-cell differentiation identified multiple splice variants models that predicted validated protein biomarkers in inflammatory diseases

Profiling of mRNA expression is an important method to identify biomarkers but complicated by limited correlations between mRNA expression and protein abundance. We hypothesised that these correlations could be improved by mathematical models based on measuring splice variants and time delay in protein translation. We characterised time-series of primary human naïve CD4+ T cells during early T helper type 1 differentiation with RNA-sequencing and mass-spectrometry proteomics. We performed computational time-series analysis in this system and in two other key human and murine immune cell types. Linear mathematical mixed time delayed splice variant models were used to predict protein abundances, and the models were validated using out-of-sample predictions. Lastly, we re-analysed RNA-seq datasets to evaluate biomarker discovery in five T-cell associated diseases, further validating the findings for multiple sclerosis (MS) and asthma. The new models significantly out-performing models not including the usage of multiple splice variants and time delays, as shown in cross-validation tests. Our mathematical models provided more differentially expressed proteins between patients and controls in all five diseases. Moreover, analysis of these proteins in asthma and MS supported their relevance. One marker, sCD27, was validated in MS using two independent cohorts for evaluating response to treatment and disease prognosis. In summary, our splice variant and time delay models substantially improved the prediction of protein abundance from mRNA expression in three different immune cell types. The models provided valuable biomarker candidates, which were further validated in MS and asthma.

Progesterone Inhibits the Establishment of Activation-Associated Chromatin During TH1 Differentiation

TH1-mediated diseases such as multiple sclerosis (MS) and rheumatoid arthritis (RA) improve during pregnancy, coinciding with increasing levels of the pregnancy hormone progesterone (P4), highlighting P4 as a potential mediator of this immunomodulation. Here, we performed detailed characterization of how P4 affects the chromatin and transcriptomic landscape during early human TH1 differentiation, utilizing both ATAC-seq and RNA-seq. Time series analysis of the earlier events (0.5-24 hrs) during TH1 differentiation revealed that P4 counteracted many of the changes induced during normal differentiation, mainly by downregulating key regulatory genes and their upstream transcription factors (TFs) involved in the initial T-cell activation. Members of the AP-1 complex such as FOSL1, FOSL2, JUN and JUNB were particularly affected, in both in promoters and in distal regulatory elements. Moreover, the changes induced by P4 were significantly enriched for disease-associated changes related to both MS and RA, revealing several shared upstream TFs, where again JUN was highlighted to be of central importance. Our findings support an immune regulatory role for P4 during pregnancy by impeding T-cell activation, a crucial checkpoint during pregnancy and in T-cell mediated diseases, and a central event prior to T-cell lineage commitment. Indeed, P4 is emerging as a likely candidate involved in disease modulation during pregnancy and further studies evaluating P4 as a potential treatment option are needed.

CD4+ T-cell DNA methylation changes during pregnancy significantly correlate with disease-associated methylation changes in autoimmune diseases

Epigenetics may play a central, yet unexplored, role in the profound changes that the maternal immune system undergoes during pregnancy and could be involved in the pregnancy-induced modulation of several autoimmune diseases. We investigated changes in the methylome in isolated circulating CD4⁺ T-cells in non-pregnant and pregnant women, during the 1^st and 2^nd trimester, using the Illumina Infinium Human Methylation 450K array, and explored how these changes were related to autoimmune diseases that are known to be affected during pregnancy. Pregnancy was associated with several hundreds of methylation differences, particularly during the 2^nd trimester. A network-based modular approach identified several genes, e.g., CD28, FYN, VAV1 and pathways related to T-cell signalling and activation, highlighting T-cell regulation as a central component of the observed methylation alterations. The identified pregnancy module was significantly enriched for disease-associated methylation changes related to multiple sclerosis, rheumatoid arthritis and systemic lupus erythematosus. A negative correlation between pregnancy-associated methylation changes and disease-associated changes was found for multiple sclerosis and rheumatoid arthritis, diseases that are known to improve during pregnancy whereas a positive correlation was found for systemic lupus erythematosus, a disease that instead worsens during pregnancy. Thus, the directionality of the observed changes is in line with the previously observed effect of pregnancy on disease activity. Our systems medicine approach supports the importance of the methylome in immune regulation of T-cells during pregnancy. Our findings highlight the relevance of using pregnancy as a model for understanding and identifying disease-related mechanisms involved in the modulation of autoimmune diseases.Abbreviations: BMIQ: beta-mixture quantile dilation; DMGs: differentially methylated genes; DMPs: differentially methylated probes; FE: fold enrichment; FDR: false discovery rate; GO: gene ontology; GWAS: genome-wide association studies; MDS: multidimensional scaling; MS: multiple sclerosis; PBMC: peripheral blood mononuclear cells; PBS: phosphate buffered saline; PPI; protein-protein interaction; RA: rheumatoid arthritis; SD: standard deviation; SLE: systemic lupus erythematosus; SNP: single nucleotide polymorphism; T_H: CD4⁺ T helper cell; VIStA: diVIsive Shuffling Approach.

Mapping DNA Methylation in Mammals: The State of the Art

A complete understanding of the dynamics and function of cytosine modifications in mammalian biology is lacking. Central to achieving this understanding is the availability of techniques that permit sensitive and specific genome-wide mapping of DNA modifications in mammalian DNA. The last decade has seen the development of a vast arsenal of novel profiling approaches enabling epigeneticists to tackle research questions that were previously out of reach. Here, we review the techniques currently available for profiling DNA modifications in mammals, discuss their strengths and weaknesses, and speculate on the future direction of DNA modification profiling technologies.

Analyzing DNA-Immunoprecipitation Sequencing Data

Genome-wide profiling of DNA modifications has advanced our understanding of epigenetics in mammalian biology. Whereas several different methods for profiling DNA modifications have been developed over the last decade, DNA-immunoprecipitation coupled with high-throughput sequencing (DIP-seq) has proven a particularly adaptable and cost-effective approach. DIP-seq was especially valuable in initial studies of the more recently discovered DNA modifications, 5-hydroxymethylcytosine, 5-formylcytosine, and 5-carboxylcytosine. As an enrichment-based profiling method, analysis of DIP-seq data poses several unique, and often unappreciated bioinformatics challenges, which if unmet, can profoundly affect the results and conclusions drawn from the data. Here, we outline key considerations in both the design of DIP-seq assays and analysis of DIP-seq data to ensure the accuracy and reproducibility of DIP-seq based studies.

DNA methylation in infants with low and high body fatness

BACKGROUND

Birth weight is determined by the interplay between infant genetics and the intrauterine environment and is associated with several health outcomes in later life. Many studies have reported an association between birth weight and DNA methylation in infants and suggest that altered epigenetics may underlie birthweight-associated health outcomes. However, birth weight is a relatively nonspecific measure of fetal growth and consists of fat mass and fat-free mass which may have different effects on health outcomes which motivates studies of infant body composition and DNA methylation. Here, we combined genome-wide DNA methylation profiling of buccal cells from 47 full-term one-week old infants with accurate measurements of infant fat mass and fat-free mass using air-displacement plethysmography.

RESULTS

No significant association was found between DNA methylation in infant buccal cells and infant body composition. Moreover, no association between infant DNA methylation and parental body composition or indicators of maternal glucose metabolism were found.

CONCLUSIONS

Despite accurate measures of body composition, we did not identify any associations between infant body fatness and DNA methylation. These results are consistent with recent studies that generally have identified only weak associations between DNA methylation and birthweight. Although our results should be confirmed by additional larger studies, our findings may suggest that differences in DNA methylation between individuals with low and high body fatness may be established later in childhood.

No evidence for DNA N 6-methyladenine in mammals

N ⁶-methyladenine (6mdA) is a widespread DNA modification in bacteria. More recently, 6mdA has also been characterized in mammalian DNA. However, measurements of 6mdA abundance and profiles are often very dissimilar between studies, even when performed on DNA from identical mammalian cell types. Using comprehensive bioinformatics analyses of published data and novel experimental approaches, we reveal that efforts to assay 6mdA in mammals have been severely compromised by bacterial contamination, RNA contamination, technological limitations, and antibody nonspecificity. These complications render 6mdA an exceptionally problematic DNA modification to study and have resulted in erroneous detection of 6mdA in several mammalian systems. Together, our results strongly imply that the evidence published to date is not sufficient to support the presence of 6mdA in mammals.

A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases

Background

Genomic medicine has paved the way for identifying biomarkers and therapeutically actionable targets for complex diseases, but is complicated by the involvement of thousands of variably expressed genes across multiple cell types. Single-cell RNA-sequencing study (scRNA-seq) allows the characterization of such complex changes in whole organs.

Methods

The study is based on applying network tools to organize and analyze scRNA-seq data from a mouse model of arthritis and human rheumatoid arthritis, in order to find diagnostic biomarkers and therapeutic targets. Diagnostic validation studies were performed using expression profiling data and potential protein biomarkers from prospective clinical studies of 13 diseases. A candidate drug was examined by a treatment study of a mouse model of arthritis, using phenotypic, immunohistochemical, and cellular analyses as read-outs.

Results

We performed the first systematic analysis of pathways, potential biomarkers, and drug targets in scRNA-seq data from a complex disease, starting with inflamed joints and lymph nodes from a mouse model of arthritis. We found the involvement of hundreds of pathways, biomarkers, and drug targets that differed greatly between cell types. Analyses of scRNA-seq and GWAS data from human rheumatoid arthritis (RA) supported a similar dispersion of pathogenic mechanisms in different cell types. Thus, systems-level approaches to prioritize biomarkers and drugs are needed. Here, we present a prioritization strategy that is based on constructing network models of disease-associated cell types and interactions using scRNA-seq data from our mouse model of arthritis, as well as human RA, which we term multicellular disease models (MCDMs). We find that the network centrality of MCDM cell types correlates with the enrichment of genes harboring genetic variants associated with RA and thus could potentially be used to prioritize cell types and genes for diagnostics and therapeutics. We validated this hypothesis in a large-scale study of patients with 13 different autoimmune, allergic, infectious, malignant, endocrine, metabolic, and cardiovascular diseases, as well as a therapeutic study of the mouse arthritis model.

Conclusions

Overall, our results support that our strategy has the potential to help prioritize diagnostic and therapeutic targets in human disease.

Electronic supplementary material

The online version of this article (10.1186/s13073-019-0657-3) contains supplementary material, which is available to authorized users.

TCF/LEF dependent and independent transcriptional regulation of Wnt/β-catenin target genes

During canonical Wnt signalling, the activity of nuclear β-catenin is largely mediated by the TCF/LEF family of transcription factors. To challenge this view, we used the CRISPR/Cas9 genome editing approach to generate HEK 293T cell clones lacking all four TCF/LEF genes. By performing unbiased whole transcriptome sequencing analysis, we found that a subset of β-catenin transcriptional targets did not require TCF/LEF factors for their regulation. Consistent with this finding, we observed in a genome-wide analysis that β-catenin occupied specific genomic regions in the absence of TCF/LEF Finally, we revealed the existence of a transcriptional activity of β-catenin that specifically appears when TCF/LEF factors are absent, and refer to this as β-catenin-GHOST response. Collectively, this study uncovers a previously neglected modus operandi of β-catenin that bypasses the TCF/LEF transcription factors.

DNA methylation as a genomic marker of exposure to chemical and environmental agents

Recent progress in interpreting comprehensive genetic and epigenetic profiles for human cellular states has contributed new insights into the developmental origins of disease, elucidated novel signalling pathways and enhanced drug discovery programs. A similar comprehensive approach to decoding the epigenetic readouts from chemical challenges in vivo would yield new paradigms for monitoring and assessing environmental exposure in model systems and humans.

Dynamic Response Genes in CD4+ T Cells Reveal a Network of Interactive Proteins that Classifies Disease Activity in Multiple Sclerosis

Multiple sclerosis (MS) is a chronic inflammatory disease of the CNS and has a varying disease course as well as variable response to treatment. Biomarkers may therefore aid personalized treatment. We tested whether in vitro activation of MS patient-derived CD4+ T cells could reveal potential biomarkers. The dynamic gene expression response to activation was dysregulated in patient-derived CD4+ T cells. By integrating our findings with genome-wide association studies, we constructed a highly connected MS gene module, disclosing cell activation and chemotaxis as central components. Changes in several module genes were associated with differences in protein levels, which were measurable in cerebrospinal fluid and were used to classify patients from control individuals. In addition, these measurements could predict disease activity after 2 years and distinguish low and high responders to treatment in two additional, independent cohorts. While further validation is needed in larger cohorts prior to clinical implementation, we have uncovered a set of potentially promising biomarkers.

LASSIM-A network inference toolbox for genome-wide mechanistic modeling

Recent technological advancements have made time-resolved, quantitative, multi-omics data available for many model systems, which could be integrated for systems pharmacokinetic use. Here, we present large-scale simulation modeling (LASSIM), which is a novel mathematical tool for performing large-scale inference using mechanistically defined ordinary differential equations (ODE) for gene regulatory networks (GRNs). LASSIM integrates structural knowledge about regulatory interactions and non-linear equations with multiple steady state and dynamic response expression datasets. The rationale behind LASSIM is that biological GRNs can be simplified using a limited subset of core genes that are assumed to regulate all other gene transcription events in the network. The LASSIM method is implemented as a general-purpose toolbox using the PyGMO Python package to make the most of multicore computers and high performance clusters, and is available at https://gitlab.com/Gustafsson-lab/lassim. As a method, LASSIM works in two steps, where it first infers a non-linear ODE system of the pre-specified core gene expression. Second, LASSIM in parallel optimizes the parameters that model the regulation of peripheral genes by core system genes. We showed the usefulness of this method by applying LASSIM to infer a large-scale non-linear model of naïve Th2 cell differentiation, made possible by integrating Th2 specific bindings, time-series together with six public and six novel siRNA-mediated knock-down experiments. ChIP-seq showed significant overlap for all tested transcription factors. Next, we performed novel time-series measurements of total T-cells during differentiation towards Th2 and verified that our LASSIM model could monitor those data significantly better than comparable models that used the same Th2 bindings. In summary, the LASSIM toolbox opens the door to a new type of model-based data analysis that combines the strengths of reliable mechanistic models with truly systems-level data. We demonstrate the power of this approach by inferring a mechanistically motivated, genome-wide model of the Th2 transcription regulatory system, which plays an important role in several immune related diseases.

There are excellent methods to mathematically model time-resolved biological data on a small scale using accurate mechanistic models. Despite the rapidly increasing availability of such data, mechanistic models have not been applied on a genome-wide level due to excessive runtimes and the non-identifiability of model parameters. However, genome-wide, mechanistic models could potentially answer key clinical questions, such as finding the best drug combinations to induce an expression change from a disease to a healthy state. We present LASSIM, which is a toolbox built to infer parameters within mechanistic models on a genomic scale. This is made possible due to a property shared across biological systems, namely the existence of a subset of master regulators, here denoted the core system. The introduction of a core system of genes simplifies the network inference into small solvable sub-problems, and implies that all main regulatory actions on peripheral genes come from a small set of regulator genes. This separation allows substantial parts of computations to be solved in parallel, i.e. permitting the use of a computer cluster, which substantially reduces computation time.

5-Hydroxymethylcytosine Profiling in Human DNA

Since its "re-discovery" in 2009, there has been significant interest in defining the genome-wide distribution of DNA marked by 5-hydroxymethylation at cytosine bases (5hmC). In recent years, technological advances have resulted in a multitude of unique strategies to map 5hmC across the human genome. Here we discuss the wide range of approaches available to map this modification and describe in detail the affinity based methods which result in the enrichment of 5hmC marked DNA for downstream analysis.

A validated gene regulatory network and GWAS identifies early regulators of T cell-associated diseases

Early regulators of disease may increase understanding of disease mechanisms and serve as markers for presymptomatic diagnosis and treatment. However, early regulators are difficult to identify because patients generally present after they are symptomatic. We hypothesized that early regulators of T cell-associated diseases could be found by identifying upstream transcription factors (TFs) in T cell differentiation and by prioritizing hub TFs that were enriched for disease-associated polymorphisms. A gene regulatory network (GRN) was constructed by time series profiling of the transcriptomes and methylomes of human CD4(+) T cells during in vitro differentiation into four helper T cell lineages, in combination with sequence-based TF binding predictions. The TFs GATA3, MAF, and MYB were identified as early regulators and validated by ChIP-seq (chromatin immunoprecipitation sequencing) and small interfering RNA knockdowns. Differential mRNA expression of the TFs and their targets in T cell-associated diseases supports their clinical relevance. To directly test if the TFs were altered early in disease, T cells from patients with two T cell-mediated diseases, multiple sclerosis and seasonal allergic rhinitis, were analyzed. Strikingly, the TFs were differentially expressed during asymptomatic stages of both diseases, whereas their targets showed altered expression during symptomatic stages. This analytical strategy to identify early regulators of disease by combining GRNs with genome-wide association studies may be generally applicable for functional and clinical studies of early disease development.

5-Hydroxymethylcytosine Remodeling Precedes Lineage Specification during Differentiation of Human CD4(+) T Cells

5-methylcytosine (5mC) is converted to 5-hydroxymethylcytosine (5hmC) by the TET family of enzymes as part of a recently discovered active DNA de-methylation pathway. 5hmC plays important roles in regulation of gene expression and differentiation and has been implicated in T cell malignancies and autoimmunity. Here, we report early and widespread 5mC/5hmC remodeling during human CD4(+) T cell differentiation ex vivo at genes and cell-specific enhancers with known T cell function. We observe similar DNA de-methylation in CD4(+) memory T cells in vivo, indicating that early remodeling events persist long term in differentiated cells. Underscoring their important function, 5hmC loci were highly enriched for genetic variants associated with T cell diseases and T-cell-specific chromosomal interactions. Extensive functional validation of 22 risk variants revealed potentially pathogenic mechanisms in diabetes and multiple sclerosis. Our results support 5hmC-mediated DNA de-methylation as a key component of CD4(+) T cell biology in humans, with important implications for gene regulation and lineage commitment.

Rapid reprogramming of epigenetic and transcriptional profiles in mammalian culture systems

Background

The DNA methylation profiles of mammalian cell lines differ from those of the primary tissues from which they were derived, exhibiting increasing divergence from the in vivo methylation profile with extended time in culture. Few studies have directly examined the initial epigenetic and transcriptional consequences of adaptation of primary mammalian cells to culture, and the potential mechanisms through which this epigenetic dysregulation occurs is unknown.

Results

We demonstrate that adaptation of mouse embryonic fibroblasts to cell culture results in a rapid reprogramming of epigenetic and transcriptional states. We observed global 5-hydroxymethylcytosine (5hmC) erasure within three days of culture initiation. Loss of genic 5hmC was independent of global 5-methylcytosine (5mC) levels and could be partially rescued by addition of vitamin C. Significantly, 5hmC loss was not linked to concomitant changes in transcription. Discrete promoter-specific gains of 5mC were also observed within seven days of culture initiation. Against this background of global 5hmC loss we identified a handful of developmentally important genes that maintained their 5hmC profile in culture, including the imprinted loci Gnas and H19. Similar outcomes were identified in the adaption of CD4⁺ T cells to culture.

Conclusions

We report a dramatic and novel consequence of adaptation of mammalian cells to culture in which global loss of 5hmC occurs, suggesting rapid concomitant loss of methylcytosine dioxygenase activity. The observed epigenetic and transcriptional re-programming occurs much earlier than previously assumed, and has significant implications for the use of cell lines as faithful mimics of in vivo epigenetic and physiological processes.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0576-y) contains supplementary material, which is available to authorized users.

Modules, networks and systems medicine for understanding disease and aiding diagnosis

Many common diseases, such as asthma, diabetes or obesity, involve altered interactions between thousands of genes. High-throughput techniques (omics) allow identification of such genes and their products, but functional understanding is a formidable challenge. Network-based analyses of omics data have identified modules of disease-associated genes that have been used to obtain both a systems level and a molecular understanding of disease mechanisms. For example, in allergy a module was used to find a novel candidate gene that was validated by functional and clinical studies. Such analyses play important roles in systems medicine. This is an emerging discipline that aims to gain a translational understanding of the complex mechanisms underlying common diseases. In this review, we will explain and provide examples of how network-based analyses of omics data, in combination with functional and clinical studies, are aiding our understanding of disease, as well as helping to prioritize diagnostic markers or therapeutic candidate genes. Such analyses involve significant problems and limitations, which will be discussed. We also highlight the steps needed for clinical implementation.

Integrated genomic and prospective clinical studies show the importance of modular pleiotropy for disease susceptibility, diagnosis and treatment

Background

Translational research typically aims to identify and functionally validate individual, disease-specific genes. However, reaching this aim is complicated by the involvement of thousands of genes in common diseases, and that many of those genes are pleiotropic, that is, shared by several diseases.

Methods

We integrated genomic meta-analyses with prospective clinical studies to systematically investigate the pathogenic, diagnostic and therapeutic roles of pleiotropic genes. In a novel approach, we first used pathway analysis of all published genome-wide association studies (GWAS) to find a cell type common to many diseases.

Results

The analysis showed over-representation of the T helper cell differentiation pathway, which is expressed in T cells. This led us to focus on expression profiling of CD4⁺ T cells from highly diverse inflammatory and malignant diseases. We found that pleiotropic genes were highly interconnected and formed a pleiotropic module, which was enriched for inflammatory, metabolic and proliferative pathways. The general relevance of this module was supported by highly significant enrichment of genetic variants identified by all GWAS and cancer studies, as well as known diagnostic and therapeutic targets. Prospective clinical studies of multiple sclerosis and allergy showed the importance of both pleiotropic and disease specific modules for clinical stratification.

Conclusions

In summary, this translational genomics study identified a pleiotropic module, which has key pathogenic, diagnostic and therapeutic roles.

DNA methylation changes separate allergic patients from healthy controls and may reflect altered CD4+ T-cell population structure

Altered DNA methylation patterns in CD4⁺ T-cells indicate the importance of epigenetic mechanisms in inflammatory diseases. However, the identification of these alterations is complicated by the heterogeneity of most inflammatory diseases. Seasonal allergic rhinitis (SAR) is an optimal disease model for the study of DNA methylation because of its well-defined phenotype and etiology. We generated genome-wide DNA methylation (N_patients = 8, N_controls = 8) and gene expression (N_patients = 9, N_controls = 10) profiles of CD4⁺ T-cells from SAR patients and healthy controls using Illumina's HumanMethylation450 and HT-12 microarrays, respectively. DNA methylation profiles clearly and robustly distinguished SAR patients from controls, during and outside the pollen season. In agreement with previously published studies, gene expression profiles of the same samples failed to separate patients and controls. Separation by methylation (N_patients = 12, N_controls = 12), but not by gene expression (N_patients = 21, N_controls = 21) was also observed in an in vitro model system in which purified PBMCs from patients and healthy controls were challenged with allergen. We observed changes in the proportions of memory T-cell populations between patients (N_patients = 35) and controls (N_controls = 12), which could explain the observed difference in DNA methylation. Our data highlight the potential of epigenomics in the stratification of immune disease and represents the first successful molecular classification of SAR using CD4⁺ T cells.

T-cells, a type of white blood cell, are an important part of the immune-system in humans. T-cells allow us to adapt our immune-response to the various infectious agents we encounter during life. However, T-cells can also cause disease when they target the body's own cells, e.g. Psoriasis, or when they react to a harmless particle or ‘antigen’, i.e. allergy. Much evidence supports an environmental, or ‘epigenetic’, component to allergy. Surprisingly, although allergy is viewed as a T-cell disease with an epigenetic component, no studies have identified epigenetic differences between healthy individuals and allergic individuals. Using a state-of-the-art genome-wide approach, we found that we could clearly and robustly separate allergic patients from healthy controls. It is often assumed that these changes reflect changes in DNA methylation in a given type of cell; however such differences can also result from different mixtures of T-cell subtypes in the samples. Indeed, we found that allergic patients had different proportions of T-cell sub-types compared to healthy controls. These changes in T-cell proportions may explain the difference in DNA methylation profile we observed between patients and controls. Our study is the first successful molecular classification of allergy using CD4⁺ T cells.

Investigating 5-hydroxymethylcytosine (5hmC): the state of the art

The discovery of 5-hydroxymethylcytosine (5hmC) as an abundant base in mammalian genomes has excited the field of epigenetics, and stimulated an intense period of research activity aimed at decoding its biological significance. However, initial research efforts were hampered by a lack of assays capable of specifically detecting 5hmC. Consequently, the last 3 years have seen the development of a plethora of new techniques designed to detect both global levels and locus-specific profiles of 5hmC in mammalian genomes. This research effort has culminated in the recent publication of two complementary techniques for quantitative, base-resolution mapping of 5hmC in mammalian genomes, the first true mammalian hydroxymethylomes. Here, we review the techniques currently available to researchers studying 5hmC, discuss their advantages and disadvantages, and explore the technical hurdles which remain to be overcome.

Hydroxymethylated DNA immunoprecipitation (hmeDIP)

5-hydroxymethylcytosine (5hmC) was recently identified as an abundant epigenetic mark in mammals. Subsequent research has implicated 5hmC in normal mammalian development and disease pathogenesis in humans. Many of the techniques commonly used to assay for canonical 5-methylcytosine (5mC) cannot distinguish between 5hmC and 5mC. The development of antibodies specific to 5hmC has allowed for specific enrichment of DNA fragments containing 5hmC. Hydroxymethylated DNA immunoprecipitation (hmeDIP) has become an invaluable tool for determining both locus-specific and genome-wide profiles of 5hmC in mammalian DNA. Here, we describe the use of hmeDIP to characterize the relative abundance of 5hmC at loci in mammalian DNA.

Comparative analysis of affinity-based 5-hydroxymethylation enrichment techniques

The epigenetic modification of 5-hydroxymethylcytosine (5hmC) is receiving great attention due to its potential role in DNA methylation reprogramming and as a cell state identifier. Given this interest, it is important to identify reliable and cost-effective methods for the enrichment of 5hmC marked DNA for downstream analysis. We tested three commonly used affinity-based enrichment techniques; (i) antibody, (ii) chemical capture and (iii) protein affinity enrichment and assessed their ability to accurately and reproducibly report 5hmC profiles in mouse tissues containing high (brain) and lower (liver) levels of 5hmC. The protein-affinity technique is a poor reporter of 5hmC profiles, delivering 5hmC patterns that are incompatible with other methods. Both antibody and chemical capture-based techniques generate highly similar genome-wide patterns for 5hmC, which are independently validated by standard quantitative PCR (qPCR) and glucosyl-sensitive restriction enzyme digestion (gRES-qPCR). Both antibody and chemical capture generated profiles reproducibly link to unique chromatin modification profiles associated with 5hmC. However, there appears to be a slight bias of the antibody to bind to regions of DNA rich in simple repeats. Ultimately, the increased specificity observed with chemical capture-based approaches makes this an attractive method for the analysis of locus-specific or genome-wide patterns of 5hmC.

Redistribution of H3K27me3 upon DNA hypomethylation results in de-repression of Polycomb target genes

BACKGROUND

DNA methylation and the Polycomb repression system are epigenetic mechanisms that play important roles in maintaining transcriptional repression. Recent evidence suggests that DNA methylation can attenuate the binding of Polycomb protein components to chromatin and thus plays a role in determining their genomic targeting. However, whether this role of DNA methylation is important in the context of transcriptional regulation is unclear.

RESULTS

By genome-wide mapping of the Polycomb Repressive Complex 2-signature histone mark, H3K27me3, in severely DNA hypomethylated mouse somatic cells, we show that hypomethylation leads to widespread H3K27me3 redistribution, in a manner that reflects the local DNA methylation status in wild-type cells. Unexpectedly, we observe striking loss of H3K27me3 and Polycomb Repressive Complex 2 from Polycomb target gene promoters in DNA hypomethylated cells, including Hox gene clusters. Importantly, we show that many of these genes become ectopically expressed in DNA hypomethylated cells, consistent with loss of Polycomb-mediated repression.

CONCLUSIONS

An intact DNA methylome is required for appropriate Polycomb-mediated gene repression by constraining Polycomb Repressive Complex 2 targeting. These observations identify a previously unappreciated role for DNA methylation in gene regulation and therefore influence our understanding of how this epigenetic mechanism contributes to normal development and disease.

Tissue of origin determines cancer-associated CpG island promoter hypermethylation patterns

BACKGROUND

Aberrant CpG island promoter DNA hypermethylation is frequently observed in cancer and is believed to contribute to tumor progression by silencing the expression of tumor suppressor genes. Previously, we observed that promoter hypermethylation in breast cancer reflects cell lineage rather than tumor progression and occurs at genes that are already repressed in a lineage-specific manner. To investigate the generality of our observation we analyzed the methylation profiles of 1,154 cancers from 7 different tissue types.

RESULTS

We find that 1,009 genes are prone to hypermethylation in these 7 types of cancer. Nearly half of these genes varied in their susceptibility to hypermethylation between different cancer types. We show that the expression status of hypermethylation prone genes in the originator tissue determines their propensity to become hypermethylated in cancer; specifically, genes that are normally repressed in a tissue are prone to hypermethylation in cancers derived from that tissue. We also show that the promoter regions of hypermethylation-prone genes are depleted of repetitive elements and that DNA sequence around the same promoters is evolutionarily conserved. We propose that these two characteristics reflect tissue-specific gene promoter architecture regulating the expression of these hypermethylation prone genes in normal tissues.

CONCLUSIONS

As aberrantly hypermethylated genes are already repressed in pre-cancerous tissue, we suggest that their hypermethylation does not directly contribute to cancer development via silencing. Instead aberrant hypermethylation reflects developmental history and the perturbation of epigenetic mechanisms maintaining these repressed promoters in a hypomethylated state in normal cells.

Non-genotoxic carcinogen exposure induces defined changes in the 5-hydroxymethylome

BACKGROUND

Induction and promotion of liver cancer by exposure to non-genotoxic carcinogens coincides with epigenetic perturbations, including specific changes in DNA methylation. Here we investigate the genome-wide dynamics of 5-hydroxymethylcytosine (5hmC) as a likely intermediate of 5-methylcytosine (5mC) demethylation in a DNA methylation reprogramming pathway. We use a rodent model of non-genotoxic carcinogen exposure using the drug phenobarbital.

RESULTS

Exposure to phenobarbital results in dynamic and reciprocal changes to the 5mC/5hmC patterns over the promoter regions of a cohort of genes that are transcriptionally upregulated. This reprogramming of 5mC/5hmC coincides with characteristic changes in the histone marks H3K4me2, H3K27me3 and H3K36me3. Quantitative analysis of phenobarbital-induced genes that are involved in xenobiotic metabolism reveals that both DNA modifications are lost at the transcription start site, while there is a reciprocal relationship between increasing levels of 5hmC and loss of 5mC at regions immediately adjacent to core promoters.

CONCLUSIONS

Collectively, these experiments support the hypothesis that 5hmC is a potential intermediate in a demethylation pathway and reveal precise perturbations of the mouse liver DNA methylome and hydroxymethylome upon exposure to a rodent hepatocarcinogen.

Promoter DNA methylation couples genome-defence mechanisms to epigenetic reprogramming in the mouse germline

Mouse primordial germ cells (PGCs) erase global DNA methylation (5mC) as part of the comprehensive epigenetic reprogramming that occurs during PGC development. 5mC plays an important role in maintaining stable gene silencing and repression of transposable elements (TE) but it is not clear how the extensive loss of DNA methylation impacts on gene expression and TE repression in developing PGCs. Using a novel epigenetic disruption and recovery screen and genetic analyses, we identified a core set of germline-specific genes that are dependent exclusively on promoter DNA methylation for initiation and maintenance of developmental silencing. These gene promoters appear to possess a specialised chromatin environment that does not acquire any of the repressive H3K27me3, H3K9me2, H3K9me3 or H4K20me3 histone modifications when silenced by DNA methylation. Intriguingly, this methylation-dependent subset is highly enriched in genes with roles in suppressing TE activity in germ cells. We show that the mechanism for developmental regulation of the germline genome-defence genes involves DNMT3B-dependent de novo DNA methylation. These genes are then activated by lineage-specific promoter demethylation during distinct global epigenetic reprogramming events in migratory (~E8.5) and post-migratory (E10.5-11.5) PGCs. We propose that genes involved in genome defence are developmentally regulated primarily by promoter DNA methylation as a sensory mechanism that is coupled to the potential for TE activation during global 5mC erasure, thereby acting as a failsafe to ensure TE suppression and maintain genomic integrity in the germline.

Correlation of inter-locus polyglutamine toxicity with CAG•CTG triplet repeat expandability and flanking genomic DNA GC content

Dynamic expansions of toxic polyglutamine (polyQ)-encoding CAG repeats in ubiquitously expressed, but otherwise unrelated, genes cause a number of late-onset progressive neurodegenerative disorders, including Huntington disease and the spinocerebellar ataxias. As polyQ toxicity in these disorders increases with repeat length, the intergenerational expansion of unstable CAG repeats leads to anticipation, an earlier age-at-onset in successive generations. Crucially, disease associated alleles are also somatically unstable and continue to expand throughout the lifetime of the individual. Interestingly, the inherited polyQ length mediating a specific age-at-onset of symptoms varies markedly between disorders. It is widely assumed that these inter-locus differences in polyQ toxicity are mediated by protein context effects. Previously, we demonstrated that the tendency of expanded CAG•CTG repeats to undergo further intergenerational expansion (their 'expandability') also differs between disorders and these effects are strongly correlated with the GC content of the genomic flanking DNA. Here we show that the inter-locus toxicity of the expanded polyQ tracts of these disorders also correlates with both the expandability of the underlying CAG repeat and the GC content of the genomic DNA flanking sequences. Inter-locus polyQ toxicity does not correlate with properties of the mRNA or protein sequences, with polyQ location within the gene or protein, or steady state transcript levels in the brain. These data suggest that the observed inter-locus differences in polyQ toxicity are not mediated solely by protein context effects, but that genomic context is also important, an effect that may be mediated by modifying the rate at which somatic expansion of the DNA delivers proteins to their cytotoxic state.

Tissue type is a major modifier of the 5-hydroxymethylcytosine content of human genes

The discovery of substantial amounts of 5-hydroxymethylcytosine (5hmC), formed by the oxidation of 5-methylcytosine (5mC), in various mouse tissues and human embryonic stem (ES) cells has necessitated a reevaluation of our knowledge of 5mC/5hmC patterns and functions in mammalian cells. Here, we investigate the tissue specificity of both the global levels and locus-specific distribution of 5hmC in several human tissues and cell lines. We find that global 5hmC content of normal human tissues is highly variable, does not correlate with global 5mC content, and decreases rapidly as cells from normal tissue adapt to cell culture. Using tiling microarrays to map 5hmC levels in DNA from normal human tissues, we find that 5hmC patterns are tissue specific; unsupervised hierarchical clustering based solely on 5hmC patterns groups independent biological samples by tissue type. Moreover, in agreement with previous studies, we find 5hmC associated primarily, but not exclusively, with the body of transcribed genes, and that within these genes 5hmC levels are positively correlated with transcription levels. However, using quantitative 5hmC-qPCR, we find that the absolute levels of 5hmC for any given gene are primarily determined by tissue type, gene expression having a secondary influence on 5hmC levels. That is, a gene transcribed at a similar level in several different tissues may have vastly different levels of 5hmC (>20-fold) dependent on tissue type. Our findings highlight tissue type as a major modifier of 5hmC levels in expressed genes and emphasize the importance of using quantitative analyses in the study of 5hmC levels.