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 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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

Potential Involvement of Type I Interferon Signaling in Immunotherapy in Seasonal Allergic Rhinitis.

Specific immunotherapy (SIT) reverses the symptoms of seasonal allergic rhinitis (SAR) in most patients. Recent studies report type I interferons shifting the balance between type I T helper cell (Th1) and type II T helper cells (Th2) towards Th2 dominance by inhibiting the differentiation of naive T cells into Th1 cells. As SIT is thought to cause a shift towards Th1 dominance, we hypothesized that SIT would alter interferon type I signaling. To test this, allergen and diluent challenged CD4+ T cells from healthy controls and patients from different time points were analyzed. The initial experiments focused on signature genes of the pathway and found complex changes following immunotherapy, which were consistent with our hypothesis. As interferon signaling involves multiple genes, expression profiling studies were performed, showing altered expression of the pathway. These findings require validation in a larger group of patients in further studies.

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.