Integration of genome-wide datasets to understand regulation of human T-helper cell differentiation

T-helper cells are an important part of the immune system and adaptive immunity. Over the course of the immune response, under the influence of various cytokines, T-helper cells differentiate into various subsets each of which have a specific function. Despite the generation of large amounts of data by recent high-throughput studies, the picture of human T-helper cell differentiation is far from complete. The goal of this thesis is to identify and characterize molecular elements potentially involved in T-helper cell differentiation and immune response through generating valuable datasets on immune cells using microarrays and high-throughput sequencing and using a range of bioinformatics methods to analyse the data. To achieve this goal, in the first study, human Th1 and Th2 cell subsets were profiled using transcriptomics and the resulting mRNA and long non-coding (lnc) RNA data was integrated with epigenomics data to understand the relationship between the two during early T-helper cell differentiation. The results revealed several new transcripts differentially regulated by Th1 and Th2 cells during their early specification providing candidates for further studies. In the second study, lncRNAs in autoimmune disease loci were characterized in granulocytes, monocytes, natural killer cells, B cells, memory T cells, naïve CD4+ T cells, and naïve CD8+ T cells. Differentially expressing lncRNAs were found to be enriched in those loci compared to the reference genome. The third study combined proteomics and transcriptomics data and revealed insights into T cell activation and signaling. Finally, the fourth study demonstrated the role of STAT3 in regulating other factors in Th17 differentiation. Moreover, STAT3 was found to bind to genomic loci with genetic variation previously associated with autoimmune diseases. The results of this thesis identify several factors important for immune cell subsets and characterize their role particularly in T-helper cell differentiation. The datasets generated as part of this thesis provide a valuable resource for the community

Journal Staff

MicroRNAs (miRNAs) play a key role in regulating mRNA expression, and individual miRNAs have been proposed as diagnostic and therapeutic candidates. The identification of such candidates is complicated by the involvement of multiple miRNAs and mRNAs as well as unknown disease topology of the miRNAs. Here, we investigated if disease-associated miRNAs regulate modules of disease-associated mRNAs, if those miRNAs act complementarily or synergistically, and if single or combinations of miRNAs can be targeted to alter module functions. We first analyzed publicly available miRNA and mRNA expression data for five different diseases. Integrated target prediction and network-based analysis showed that the miRNAs regulated modules of disease-relevant genes. Most of the miRNAs acted complementarily to regulate multiple mRNAs. To functionally test these findings, we repeated the analysis using our own miRNA and mRNA expression data from CD4+ T cells from patients with seasonal allergic rhinitis. This is a good model of complex diseases because of its well-defined phenotype and pathogenesis. Combined computational and functional studies confirmed that miRNAs mainly acted complementarily and that a combination of two complementary miRNAs, miR-223 and miR-139-3p, could be targeted to alter disease-relevant module functions, namely, the release of type 2 helper T-cell (Th2) cytokines. Taken together, our findings indicate that miRNAs act complementarily to regulate modules of disease-related mRNAs and can be targeted to alter disease-relevant functions

snpEnrichR: analyzing co-localization of SNPs and their proxies in genomic regions

Motivation: Co-localization of trait associated SNPs for specific transcription-factor binding sites or regulatory regions in the genome can yield profound insight into underlying causal mechanisms. Analysis is complicated because the truly causal SNPs are generally unknown and can be either SNPs reported in GWAS studies or other proxy SNPs in their linkage disequilibrium. Hence, a comprehensive pipeline for SNP co-localization analysis that utilizes all relevant information about both the genotyped SNPs and their proxies is needed.Results: We developed an R package snpEnrichR for SNP co-localization analysis. The software integrates different tools for random SNP generation and genome co-localization analysis to automatize and help users to create custom SNP co-localization analysis. We show via an example that including proxy SNPs in SNP co-localization analysis enhances the sensitivity of co-localization detection.Availability: The software is available at https://github.com/kartiek/snpEnrichR.</p

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

Background: Although 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. Methods: We 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). Results: We show that lncRNAs mapping to loci shared between AID are significantly enriched in immune cell types compared to lncRNAs from the whole genome (a <0.005). We were not able to prioritize single cell types relevant for specific diseases, but we observed five different cell types enriched (a <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. Conclusions: The 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

Genome-wide Analysis of STAT3-Mediated Transcription during Early Human Th17 Cell Differentiation

The development of therapeutic strategies to combat immune-associated diseases requires the molecular mechanisms of human Th17 cell differentiation to be fully identified and understood. To investigate transcriptional control of Th17 cell differentiation, we used primary human CD4+ T cells in small interfering RNA (siRNA)-mediated gene silencing and chromatin immunoprecipitation followed by massive parallel sequencing (ChIP-seq) to identify both the early direct and indirect targets of STAT3. The integrated dataset presented in this study confirms that STAT3 is critical for transcriptional regulation of early human Th17 cell differentiation. Additionally, we found that a number of SNPs from loci associated with immune-mediated disorders were located at sites where STAT3 binds to induce Th17 cell specification. Importantly, introduction of such SNPs alters STAT3 binding in DNA affinity precipitation assays. Overall, our study provides important insights for modulating Th17-mediated pathogenic immune responses in humans.</p

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

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

Combining Network Modeling and Gene Expression Microarray Analysis to Explore the Dynamics of Th1 and Th2 Cell Regulation

Two T helper (Th) cell subsets, namely Th1 and Th2 cells, play an important role in inflammatory diseases. The two subsets are thought to counter-regulate each other, and alterations in their balance result in different diseases. This paradigm has been challenged by recent clinical and experimental data. Because of the large number of genes involved in regulating Th1 and Th2 cells, assessment of this paradigm by modeling or experiments is difficult. Novel algorithms based on formal methods now permit the analysis of large gene regulatory networks. By combining these algorithms with in silico knockouts and gene expression microarray data from human T cells, we examined if the results were compatible with a counter-regulatory role of Th1 and Th2 cells. We constructed a directed network model of genes regulating Th1 and Th2 cells through text mining and manual curation. We identified four attractors in the network, three of which included genes that corresponded to Th0, Th1 and Th2 cells. The fourth attractor contained a mixture of Th1 and Th2 genes. We found that neither in silico knockouts of the Th1 and Th2 attractor genes nor gene expression microarray data from patients with immunological disorders and healthy subjects supported a counter-regulatory role of Th1 and Th2 cells. By combining network modeling with transcriptomic data analysis and in silico knockouts, we have devised a practical way to help unravel complex regulatory network topology and to increase our understanding of how network actions may differ in health and disease