TET2 as a tumor suppressor and therapeutic target in T-cell acute lymphoblastic leukemia.

Pediatric T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive malignancy resulting from overproduction of immature T-cells in the thymus and is typified by widespread alterations in DNA methylation. As survival rates for relapsed T-ALL remain dismal (10 to 25%), development of targeted therapies to prevent relapse is key to improving prognosis. Whereas mutations in the DNA demethylating enzyme TET2 are frequent in adult T-cell malignancies, TET2 mutations in T-ALL are rare. Here, we analyzed RNA-sequencing data of 321 primary T-ALLs, 20 T-ALL cell lines, and 25 normal human tissues, revealing that TET2 is transcriptionally repressed or silenced in 71% and 17% of T-ALL, respectively. Furthermore, we show that TET2 silencing is often associated with hypermethylation of the TET2 promoter in primary T-ALL. Importantly, treatment with the DNA demethylating agent, 5-azacytidine (5-aza), was significantly more toxic to TET2-silenced T-ALL cells and resulted in stable re-expression of the TET2 gene. Additionally, 5-aza led to up-regulation of methylated genes and human endogenous retroviruses (HERVs), which was further enhanced by the addition of physiological levels of vitamin C, a potent enhancer of TET activity. Together, our results clearly identify 5-aza as a potential targeted therapy for TET2-silenced T-ALL.

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

During canonical Wnt signalling, the activity of nuclear ß-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 ß-catenin transcriptional targets did not require TCF/LEF factors for their regulation. Consistent with this finding, we observed in a genome-wide analysis that ß-catenin occupied specific genomic regions in the absence of TCF/LEF Finally, we revealed the existence of a transcriptional activity of ß-catenin that specifically appears when TCF/LEF factors are absent, and refer to this as ß-catenin-GHOST response. Collectively, this study uncovers a previously neglected modus operandi of ß-catenin that bypasses the TCF/LEF transcription factors.

A reassessment of DNA-immunoprecipitation-based genomic profiling.

DNA immunoprecipitation followed by sequencing (DIP-seq) is a common enrichment method for profiling DNA modifications in mammalian genomes. However, the results of independent DIP-seq studies often show considerable variation between profiles of the same genome and between profiles obtained by alternative methods. Here we show that these differences are primarily due to the intrinsic affinity of IgG for short unmodified DNA repeats. This pervasive experimental error accounts for 50-99% of regions identified as 'enriched' for DNA modifications in DIP-seq data. Correction of this error profoundly altered DNA-modification profiles for numerous cell types, including mouse embryonic stem cells, and subsequently revealed novel associations among DNA modifications, chromatin modifications and biological processes. We conclude that both matched input and IgG controls are essential in order for the results of DIP-based assays to be interpreted correctly, and that complementary, non-antibody-based techniques should be used to validate DIP-based findings to avoid further misinterpretation of genome-wide profiling data.

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.

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.

GAB2 regulates type 2 T helper cell differentiation in humans.

Th2 cell differentiation involves complex changes in expression of multiple genes, many of which have poorly characterized roles. In a gene expression microarray analysis of human primary CD4+ effector T subsets, we identified that an adaptor protein, GAB2, was preferentially expressed in human Th2 cells. The role of GAB2 in human Th2 cells is unknown. Through analysis of primary and in vitro differentiated human T effector subsets, we confirmed that human Th2 cells preferentially expressed GAB2. Further analysis of public gene expression microarray data of STAT6-knockdowned Th2 cells indicated that GAB2 expression was regulated by IL-4 and STAT6. Both siRNA knockdown and ectopic expression of GAB2 in activated T cells showed that GAB2 positively regulated IL-4 and IL-13 expression in human Th2 cells. We hence identified the adaptor protein, GAB2, as an important novel regulator of the human Th2 immune response.

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, Ncontrols = 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.

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.

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.

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.

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 1st and 2nd 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 2nd 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; TH: CD4+ T helper cell; VIStA: diVIsive Shuffling Approach.

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.

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.

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.

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

An amendment to this paper has been published and can be accessed via the original article.

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.

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.