Subhash Tripathi

Global Chromatin State Analysis Reveals Lineage-Specific Enhancers during the Initiation of Human T helper 1 and T helper 2 Cell Polarization

Naive CD4⁺ T cells can differentiate into specific helper and regulatory T cell lineages in order to combat infection and disease. The correct response to cytokines and a controlled balance of these populations is critical for the immune system and the avoidance of autoimmune disorders. To investigate how early cell-fate commitment is regulated, we generated the first human genome-wide maps of histone modifications that reveal enhancer elements after 72 hr of in vitro polarization toward T helper 1 (Th1) and T helper 2 (Th2) cell lineages. Our analysis indicated that even at this very early time point, cell-specific gene regulation and enhancers were at work directing lineage commitment. Further examination of lineage-specific enhancers identified transcription factors (TFs) with known and unknown T cell roles as putative drivers of lineage-specific gene expression. Lastly, an integrative analysis of immunopathogenic-associated SNPs suggests a role for distal regulatory elements in disease etiology.

Expression profiles of long non-coding RNAs located in autoimmune disease-associated regions reveal immune cell-type specificity

BackgroundAlthough genome-wide association studies (GWAS) have identified hundreds of variants associated with a risk for autoimmune and immune-related disorders (AID), our understanding of the disease mechanisms is still limited. In particular, more than 90% of the risk variants lie in non-coding regions, and almost 10% of these map to long non-coding RNA transcripts (lncRNAs). lncRNAs are known to show more cell-type specificity than protein-coding genes.MethodsWe aimed to characterize lncRNAs and protein-coding genes located in loci associated with nine AIDs which have been well-defined by Immunochip analysis and by transcriptome analysis across seven populations of peripheral blood leukocytes (granulocytes, monocytes, natural killer (NK) cells, B cells, memory T cells, naive CD4+ and naive CD8+ T cells) and four populations of cord blood-derived T-helper cells (precursor, primary, and polarized (Th1, Th2) T-helper cells).ResultsWe show that lncRNAs mapping to loci shared between AID are significantly enriched in immune cell types compared to lncRNAs from the whole genome (α <0.005). We were not able to prioritize single cell types relevant for specific diseases, but we observed five different cell types enriched (α <0.005) in five AID (NK cells for inflammatory bowel disease, juvenile idiopathic arthritis, primary biliary cirrhosis, and psoriasis; memory T and CD8+ T cells in juvenile idiopathic arthritis, primary biliary cirrhosis, psoriasis, and rheumatoid arthritis; Th0 and Th2 cells for inflammatory bowel disease, juvenile idiopathic arthritis, primary biliary cirrhosis, psoriasis, and rheumatoid arthritis). Furthermore, we show that co-expression analyses of lncRNAs and protein-coding genes can predict the signaling pathways in which these AID-associated lncRNAs are involved.ConclusionsThe observed enrichment of lncRNA transcripts in AID loci implies lncRNAs play an important role in AID etiology and suggests that lncRNA genes should be studied in more detail to interpret GWAS findings correctly. The co-expression results strongly support a model in which the lncRNA and protein-coding genes function together in the same pathways.

Transcriptional and epigenetic regulation of T‐helper lineage specification

Combined with TCR stimuli, extracellular cytokine signals initiate the differentiation of naive CD4+ T cells into specialized effector T‐helper (Th) and regulatory T (Treg) cell subsets. The lineage specification and commitment process occurs through the combinatorial action of multiple transcription factors (TFs) and epigenetic mechanisms that drive lineage‐specific gene expression programs. In this article, we review recent studies on the transcriptional and epigenetic regulation of distinct Th cell lineages. Moreover, we review current study linking immune disease‐associated single‐nucleotide polymorphisms with distal regulatory elements and their potential role in the disease etiology.

Identification of early gene expression changes during human Th17 cell differentiation

Th17 cells play an essential role in the pathogenesis of autoimmune and inflammatory diseases. Most of our current understanding on Th17 cell differentiation relies on studies carried out in mice, whereas the molecular mechanisms controlling human Th17 cell differentiation are less well defined. In this study, we identified gene expression changes characterizing early stages of human Th17 cell differentiation through genome-wide gene expression profiling. CD4(+) cells isolated from umbilical cord blood were used to determine detailed kinetics of gene expression after initiation of Th17 differentiation with IL1β, IL6, and TGFβ. The differential expression of selected candidate genes was further validated at protein level and analyzed for specificity in initiation of Th17 compared with initiation of other Th subsets, namely Th1, Th2, and iTreg. This first genome-wide profiling of transcriptomics during the induction of human Th17 differentiation provides a starting point for defining gene regulatory networks and identifying new candidates regulating Th17 differentiation in humans.

Methods for time series analysis of RNA-seq data with application to human Th17 cell differentiation

Motivation: Gene expression profiling using RNA-seq is a powerful technique for screening RNA species’ landscapes and their dynamics in an unbiased way. While several advanced methods exist for differential expression analysis of RNA-seq data, proper tools to anal.yze RNA-seq time-course have not been proposed. Results: In this study, we use RNA-seq to measure gene expression during the early human T helper 17 (Th17) cell differentiation and T-cell activation (Th0). To quantify Th17-specific gene expression dynamics, we present a novel statistical methodology, DyNB, for analyzing time-course RNA-seq data. We use non-parametric Gaussian processes to model temporal correlation in gene expression and combine that with negative binomial likelihood for the count data. To account for experiment-specific biases in gene expression dynamics, such as differences in cell differentiation efficiencies, we propose a method to rescale the dynamics between replicated measurements. We develop an MCMC sampling method to make inference of differential expression dynamics between conditions. DyNB identifies several known and novel genes involved in Th17 differentiation. Analysis of differentiation efficiencies revealed consistent patterns in gene expression dynamics between different cultures. We use qRT-PCR to validate differential expression and differentiation efficiencies for selected genes. Comparison of the results with those obtained via traditional timepoint-wise analysis shows that time-course analysis together with time rescaling between cultures identifies differentially expressed genes which would not otherwise be detected. Availability: An implementation of the proposed computational methods will be available at http://research.ics.aalto.fi/csb/software/ Contact: tarmo.aijo@aalto.fi or harri.lahdesmaki@aalto.fi Supplementary information: Supplementary data are available at Bioinformatics online.

Activating Transcription Factor 3 Is a Positive Regulator of Human IFNG Gene Expression

IL-12 and IL-18 are essential for Th1 differentiation, whereas the role of IFN-α in Th1 development is less understood. In this microarray-based study, we searched for genes that are regulated by IFN-α, IL-12, or the combination of IL-12 plus IL-18 during the early differentiation of human umbilical cord blood CD4+ Th cells. Twenty-six genes were similarly regulated in response to treatment with IL-12, IFN-α, or the combination of IL-12 plus IL-18. These genes could therefore play a role in Th1 lineage decision. Transcription factor activating transcription factor (ATF) 3 was upregulated by these cytokines and selected for further study. Ectopic expression of ATF3 in CD4+ T cells enhanced the production of IFN-γ, the hallmark cytokine of Th1 cells, whereas small interfering RNA knockdown of ATF3 reduced IFN-γ production. Furthermore, ATF3 formed an endogenous complex with JUN in CD4+ T cells induced to Th1. Chromatin immunoprecipitation and luciferase reporter assays showed that both ATF3 and JUN are recruited to and transactivate the IFNG promoter during early Th1 differentiation. Collectively, these data indicate that ATF3 promotes human Th1 differentiation.

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

Combining a gene regulatory network and disease association data identified early regulators of T cell–associated diseases. Identifying disease before it starts Diseases may be easier to treat if caught early. However, means of identifying early disease—especially before symptoms appear—are in short supply. Now, Gustafsson et al. identify early regulators of T cell–mediated disease by finding transcription factors involved in T cell differentiation that are enriched in disease-associated polymorphisms. Three such experimentally validated transcription factors—GATA3, MAF, and MYB—and their targets were found to be differentially expressed in asymptomatic stages of two different T cell–mediated diseases—multiple sclerosis and seasonal allergic rhinitis. These data not only provide potential markers of disease development but also shed light on the mechanistic underpinning of T cell–mediated disease. 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.

Transcriptional Repressor HIC1 Contributes to Suppressive Function of Human Induced Regulatory T Cells

Summary Regulatory T (Treg) cells are critical in regulating the immune response. In vitro induced Treg (iTreg) cells have significant potential in clinical medicine. However, applying iTreg cells as therapeutics is complicated by the poor stability of human iTreg cells and their variable suppressive activity. Therefore, it is important to understand the molecular mechanisms of human iTreg cell specification. We identified hypermethylated in cancer 1 (HIC1) as a transcription factor upregulated early during the differentiation of human iTreg cells. Although FOXP3 expression was unaffected, HIC1 deficiency led to a considerable loss of suppression by iTreg cells with a concomitant increase in the expression of effector T cell associated genes. SNPs linked to several immune-mediated disorders were enriched around HIC1 binding sites, and in vitro binding assays indicated that these SNPs may alter the binding of HIC1. Our results suggest that HIC1 is an important contributor to iTreg cell development and function.