Transcriptional and Epigenetic Regulation of Human CD4+ T Helper Lineage Specification

Activated T helper (Th) cells have ability to differentiate into functionally distinct Th1, Th2 and Th17 subsets through a series of overlapping networks that include signaling and transcriptional control and the epigenetic mechanisms to direct immune responses. However, inappropriate execution in the differentiation process and abnormal function of these Th cells can lead to the development of several immune mediated diseases. Therefore, the thesis aimed at identifying genes and gene regulatory mechanisms responsible for Th17 differentiation and to study epigenetic changes associated with early stage of Th1/Th2 cell differentiation. Genome wide transcriptional profiling during early stages of human Th17 cell differentiation demonstrated differential regulation of several novel and currently known genes associated with Th17 differentiation. Selected candidate genes were further validated at protein level and their specificity for Th17 as compared to other T helper subsets was analyzed. Moreover, combination of RNA interference-mediated downregulation of gene expression, genome-wide transcriptome profiling and chromatin immunoprecipitation followed by massive parallel sequencing (ChIP-seq), combined with computational data integration lead to the identification of direct and indirect target genes of STAT3, which is a pivotal upstream transcription factor for Th17 cell polarization. Results indicated that STAT3 directly regulates the expression of several genes that are known to play a role in activation, differentiation, proliferation, and survival of Th17 cells. These results provide a basis for constructing a network regulating gene expression during early human Th17 differentiation. Th1 and Th2 lineage specific enhancers were identified from genome-wide maps of histone modifications generated from the cells differentiating towards Th1 and Th2 lineages at 72h. Further analysis of lineage-specific enhancers revealed known and novel transcription factors that potentially control lineage-specific gene expression. Finally, we found an overlap of a subset of enhancers with SNPs associated with autoimmune diseases through GWASs suggesting a potential role for enhancer elements in the disease development. In conclusion, the results obtained have extended our knowledge of Th differentiation and provided new mechanistic insights into dysregulation of Th cell differentiation in human immune mediated diseases.Siirretty Doriast

Protein kinase D1 regulates subcellular localisation and metastatic function of metastasis-associated protein 1

Background: Cancer progression and metastasis is profoundly influenced by protein kinase D1 (PKD1) and metastasis-associated protein 1 (MTA1) in addition to other pathways. However, the nature of regulatory relationship between the PKD1 and MTA1, and its resulting impact on cancer metastasis remains unknown. Here we present evidence to establish that PKD1 is an upstream regulatory kinase of MTA1. Methods: Protein and mRNA expression of MTA1 in PKD1-overexpressing cells were determined using western blotting and reverse-transcription quantitative real-time PCR. Immunoprecipitation and proximity ligation assay (PLA) were used to determine the interaction between PKD1 and MTA1. PKD1-mediated nucleo-cytoplasmic export and polyubiquitin-dependent proteosomal degradation was determined using immunostaining. The correlation between PKD1 and MTA1 was determined using intra-tibial, subcutaneous xenograft, PTEN-knockout (PTEN-KO) and transgenic adenocarcinoma of mouse prostate (TRAMP) mouse models, as well as human cancer tissues. Results: We found that MTA1 is a PKD1-interacting substrate, and that PKD1 phosphorylates MTA1, supports its nucleus-to-cytoplasmic redistribution and utilises its N-terminal and kinase domains to effectively inhibit the levels of MTA1 via polyubiquitin-dependent proteosomal degradation. PKD1-mediated downregulation of MTA1 was accompanied by a significant suppression of prostate cancer progression and metastasis in physiologically relevant spontaneous tumour models. Accordingly, progression of human prostate tumours to increased invasiveness was also accompanied by decreased and increased levels of PKD1 and MTA1, respectively. Conclusions: Overall, this study, for the first time, establishes that PKD1 is an upstream regulatory kinase of MTA1 status and its associated metastatic activity, and that the PKD1-MTA1 axis could be targeted for anti-cancer strategies

Interactome Networks of FOSL1 and FOSL2 in Human Th17 Cells

Dysregulated function of Th17 cells has implications in immunodeficiencies and autoimmune disorders. Th17 cell differentiation is orchestrated by a complex network of transcription factors, including several members of the activator protein (AP-1) family. Among the latter, FOSL1 and FOSL2 modulate the effector functions of Th17 cells. However, the molecular mechanisms underlying these effects are unclear, owing to the poorly characterized protein interaction networks of FOSL factors. Here, we establish the first interactomes of FOSL1 and FOSL2 in human Th17 cells, using affinity purification–mass spectrometry analysis. In addition to the known JUN proteins, we identified several novel binding partners of FOSL1 and FOSL2. Gene ontology analysis found a significant fraction of these interactors to be associated with RNA-binding activity, which suggests new mechanistic links. Intriguingly, 29 proteins were found to share interactions with FOSL1 and FOSL2, and these included key regulators of Th17 fate. We further validated the binding partners identified in this study by using parallel reaction monitoring targeted mass spectrometry and other methods. Our study provides key insights into the interaction-based signaling mechanisms of FOSL proteins that potentially govern Th17 cell differentiation and associated pathologies. </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

A systematic comparison of FOSL1, FOSL2 and BATF-mediated transcriptional regulation during early human Th17 differentiation

Th17 cells are essential for protection against extracellular pathogens, but their aberrant activity can cause autoimmunity. Molecular mechanisms that dictate Th17 cell-differentiation have been extensively studied using mouse models. However, species-specific differences underscore the need to validate these findings in human. Here, we characterized the human-specific roles of three AP-1 transcription factors, FOSL1, FOSL2 and BATF, during early stages of Th17 differentiation. Our results demonstrate that FOSL1 and FOSL2 co-repress Th17 fate-specification, whereas BATF promotes the Th17 lineage. Strikingly, FOSL1 was found to play different roles in human and mouse. Genome-wide binding analysis indicated that FOSL1, FOSL2 and BATF share occupancy over regulatory regions of genes involved in Th17 lineage commitment. These AP-1 factors also share their protein interacting partners, which suggests mechanisms for their functional interplay. Our study further reveals that the genomic binding sites of FOSL1, FOSL2 and BATF harbour hundreds of autoimmune disease-linked SNPs. We show that many of these SNPs alter the ability of these transcription factors to bind DNA. Our findings thus provide critical insights into AP-1-mediated regulation of human Th17-fate and associated pathologies

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

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Optimal Query Expansion Based on Hybrid Group Mean Enhanced Chimp Optimization Using Iterative Deep Learning

The internet is surrounded by uncertain information which necessitates the usage of natural language processing and soft computing techniques to extract the relevant documents. The relevant results are retrieved using the query expansion technique which is mainly formulated using the machine learning or deep learning concepts in the existing literature. This paper presents a hybrid group mean-based optimizer-enhanced chimp optimization (GMBO-ECO) algorithm for pseudo-relevance-based query expansion, whereby the actual queries are expanded with their related keywords. The hybrid GMBO-ECO algorithm mainly expands the query based on the terms that have a strong interrelationship with the actual query. To generate the word embeddings, a Word2Vec paradigm is used which learns the word association from large text corpora. The useful context in the text is identified using the improved iterative deep learning framework which determines the user&rsquo;s intent for the current web search. This step reduces the mismatch of the words and improves the performance of query retrieval. The weak terms are eliminated and the candidate query terms for optimal query expansion are improved via an Okapi measure and cosine similarity techniques. The proposed methodology has been compared to the state-of-the-art methods with and without a query expansion approach. Moreover, the proposed optimal query expansion technique has shown a substantial improvement in terms of a normalized discounted cumulative gain of 0.87, a mean average precision of 0.35, and a mean reciprocal rank of 0.95. The experimental results show the efficiency of the proposed methodology in retrieving the appropriate response for information retrieval. The most common applications for the proposed method are search engines