Comprehensive epigenetic landscape of rheumatoid arthritis fibroblast-like synoviocytes.

Epigenetics contributes to the pathogenesis of immune-mediated diseases like rheumatoid arthritis (RA). Here we show the first comprehensive epigenomic characterization of RA fibroblast-like synoviocytes (FLS), including histone modifications (H3K27ac, H3K4me1, H3K4me3, H3K36me3, H3K27me3, and H3K9me3), open chromatin, RNA expression and whole-genome DNA methylation. To address complex multidimensional relationship and reveal epigenetic regulation of RA, we perform integrative analyses using a novel unbiased method to identify genomic regions with similar profiles. Epigenomically similar regions exist in RA cells and are associated with active enhancers and promoters and specific transcription factor binding motifs. Differentially marked genes are enriched for immunological and unexpected pathways, with "Huntington's Disease Signaling" identified as particularly prominent. We validate the relevance of this pathway to RA by showing that Huntingtin-interacting protein-1 regulates FLS invasion into matrix. This work establishes a high-resolution epigenomic landscape of RA and demonstrates the potential for integrative analyses to identify unanticipated therapeutic targets

Epigenetic reprogramming of muscle progenitors: inspiration for clinical therapies

In the context of regenerative medicine, based on the potential of stem cells to restore diseased tissues, epigenetics is becoming a pivotal area of interest. Therapeutic interventions that promote tissue and organ regeneration have as primary objective the selective control of gene expression in adult stem cells. This requires a deep understanding of the epigenetic mechanisms controlling transcriptional programs in tissue progenitors. This review attempts to elucidate the principle epigenetic regulations responsible of stem cells differentiation. In particular we focus on the current understanding of the epigenetic networks that regulate differentiation of muscle progenitors by the concerted action of chromatin-modifying enzymes and noncoding RNAs. The novel exciting role of exosome-bound microRNA in mediating epigenetic information transfer is also discussed. Finally we show an overview of the epigenetic strategies and therapies that aim to potentiate muscle regeneration and counteract the progression of Duchenne Muscular Dystrophy (DMD)

Chromatin dysregulation and DNA methylation at transcription start sites associated with transcriptional repression in cancers.

Although promoter-associated CpG islands have been established as targets of DNA methylation changes in cancer, previous studies suggest that epigenetic dysregulation outside the promoter region may be more closely associated with transcriptional changes. Here we examine DNA methylation, chromatin marks, and transcriptional alterations to define the relationship between transcriptional modulation and spatial changes in chromatin structure. Using human papillomavirus-related oropharyngeal carcinoma as a model, we show aberrant enrichment of repressive H3K9me3 at the transcriptional start site (TSS) with methylation-associated, tumor-specific gene silencing. Further analysis identifies a hypermethylated subtype which shows a functional convergence on MYC targets and association with CREBBP/EP300 mutation. The tumor-specific shift to transcriptional repression associated with DNA methylation at TSSs was confirmed in multiple tumor types. Our data may show a common underlying epigenetic dysregulation in cancer associated with broad enrichment of repressive chromatin marks and aberrant DNA hypermethylation at TSSs in combination with MYC network activation

Long non-coding RNA profiling of human lymphoid progenitor cells reveals transcriptional divergence of B cell and T cell lineages.

To elucidate the transcriptional 'landscape' that regulates human lymphoid commitment during postnatal life, we used RNA sequencing to assemble the long non-coding transcriptome across human bone marrow and thymic progenitor cells spanning the earliest stages of B lymphoid and T lymphoid specification. Over 3,000 genes encoding previously unknown long non-coding RNAs (lncRNAs) were revealed through the analysis of these rare populations. Lymphoid commitment was characterized by lncRNA expression patterns that were highly stage specific and were more lineage specific than those of protein-coding genes. Protein-coding genes co-expressed with neighboring lncRNA genes showed enrichment for ontologies related to lymphoid differentiation. The exquisite cell-type specificity of global lncRNA expression patterns independently revealed new developmental relationships among the earliest progenitor cells in the human bone marrow and thymus

The histone demethylase UTX regulates the lineage-specific epigenetic program of invariant natural killer T cells

Invariant natural killer T cells (iNKT cells) are innate-like lymphocytes that protect against infection, autoimmune disease and cancer. However, little is known about the epigenetic regulation of iNKT cell development. Here we found that the H3K27me3 histone demethylase UTX was an essential cell-intrinsic factor that controlled an iNKT-cell lineage-specific gene-expression program and epigenetic landscape in a demethylase-Activity-dependent manner. UTX-deficient iNKT cells exhibited impaired expression of iNKT cell signature genes due to a decrease in activation-Associated H3K4me3 marks and an increase in repressive H3K27me3 marks within the promoters occupied by UTX. We found that JunB regulated iNKT cell development and that the expression of genes that were targets of both JunB and the iNKT cell master transcription factor PLZF was UTX dependent. We identified iNKT cell super-enhancers and demonstrated that UTX-mediated regulation of super-enhancer accessibility was a key mechanism for commitment to the iNKT cell lineage. Our findings reveal how UTX regulates the development of iNKT cells through multiple epigenetic mechanisms

Characterization Of Epigenetic Plasticity And Chromatin Dynamics In Cancer Cell Models

Cancer progression is driven by cumulative changes that promote and maintain the malignant phenotype. Epigenetic alterations are central to malignant transformation and to the development of therapy resistance. Changes in DNA methylation, histone acetylation and methylation, noncoding RNA expression and higher-order chromatin structures are epigenetic features of cancer, which are independent of changes in the DNA sequence. Despite the knowledge that these epigenetic alterations disrupt essential pathways that protect cells from uncontrolled growth, how these modifications collectively coordinate cancer gene expression programs remains poorly understood. In this dissertation, I utilize molecular and informatic approaches to define and characterize the genome-wide epigenetic patterns of two important human cancer cell models. I further explore the dynamic alterations of chromatin structure and its interplay with gene regulation in response to therapeutic agents. In the first part of this dissertation, pancreatic ductal adenocarcinoma (PDAC) cell models were used to characterize genome-wide patterns of chromatin structure. The effects of histone acetyltransferase (HAT) inhibitors on chromatin structure patterns were investigated to understand how these potential therapeutics influence the epigenome and gene regulation. Accordingly, HAT inhibitors globally target histone modifications and also impacted specific gene pathways and regulatory domains such as super-enhancers. Overall, the results from this study uncover potential roles for specific epigenomic domains in PDAC cells and demonstrate epigenomic plasticity to HAT inhibitors. In the second part of this dissertation, I investigate the dynamic changes of chromatin structure in response to estrogen signaling over a time-course using Estrogen Receptor (ER) positive breast cancer cell models. Accordingly, I generated genome-wide chromatin contact maps, ER, CTCF and regulatory histone modification profiles and compared and integrated these profiles to determine the temporal patterns of regulatory chromatin compartments. The results reveal that the majority of alterations occur in regions that correspond to active chromatin states, and that dynamic chromatin is linked to genes associated with specific cancer growth and metabolic signaling pathways. To distinguish ER-regulated processes in tamoxifen-sensitive and in tamoxifen-resistant (TAMR) cell models, we determined the corresponding chromatin and gene expression profiles using ER-positive TAMR cancer cell derivatives. Comparison of the patterns revealed characteristic features of estrogen responsiveness and show a global reprogramming of chromatin structure in breast cancer cells with acquired tamoxifen resistance. Taken together, this dissertation reveals novel insight into dynamic epigenomic alterations that occur with extrinsic stimuli and provides insight into mechanisms underlying the therapeutic responses in cancer cells

On Identifying Signatures of Positive Selection in Human Populations: A Dissertation

As sequencing technology continues to produce better quality genomes at decreasing costs, there has been a recent surge in the variety of data that we are now able to analyze. This is particularly true with regards to our understanding of the human genome—where the last decade has seen data advances in primate epigenomics, ancient hominid genomics, and a proliferation of human polymorphism data from multiple populations. In order to utilize such data however, it has become critical to develop increasingly sophisticated tools spanning both bioinformatics and statistical inference. In population genetics particularly, new statistical approaches for analyzing population data are constantly being developed—unfortunately, often without proper model testing and evaluation of type-I and type-II error. Because the common Wright-Fisher assumptions underlying such models are generally violated in natural populations, this statistical testing is critical. Thus, my dissertation has two distinct but related themes: 1) evaluating methods of statistical inference in population genetics, and 2) utilizing these methods to analyze the evolutionary history of humans and our closest relatives. The resulting collection of work has not only provided important biological insights (including some of the first strong evidence of selection on human-specific epigenetic modifications (Shulha, Crisci, Reshetov, Tushir et al. 2012, PLoS Bio), and a characterization of human-specific genetic changes distinguishing modern humans from Neanderthals (Crisci et al. 2011, GBE)), but also important insights in to the performance of population genetic methodologies which will motivate the future development of improved approaches for statistical inference (Crisci et al, in review)

Integrative analysis of complex genomic and epigenomic maps

Modern healthcare research demands collaboration across disciplines to build preventive measures and innovate predictive capabilities for curing diseases. Along with the emergence of cutting-edge computational and statistical methodologies, data generation and analysis has become cheaper in the last ten years. However, the complexity of big data due to its variety, volume, and velocity creates new challenges for biologists, physicians, bioinformaticians, statisticians, and computer scientists. Combining data from complex multiple profiles is useful to better understand cellular functions and pathways that regulates cell function to provide insights that could not have been obtained using the individual profiles alone. However, current normalization and artifact correction methods are platform and data type specific, and may require both the training and test sets for any application (e.g. biomarker development). This often leads to over-fitting and reduces the reproducibility of genomic findings across studies. In addition, many bias correction and integration approaches require renormalization or reanalysis if additional samples are later introduced. The motivation behind this research was to develop and evaluate strategies for addressing data integration issues across data types and profiling platforms, which should improve healthcare-informatics research and its application in personalized medicine. We have demonstrated a comprehensive and coordinated framework for data standardization across tissue types and profiling platforms. This allows easy integration of data from multiple data generating consortiums. The main goal of this research was to identify regions of genetic-epigenetic co-ordination that are independent of tissue type and consistent across epigenomics profiling data platforms. We developed multi-‘omic’ therapeutic biomarkers for epigenetic drug efficacy by combining our biomarker regions with drug perturbation data generated in our previous studies. We used an adaptive Bayesian factor analysis approach to develop biomarkers for multiple HDACs simultaneously, allowing for predictions of comparative efficacy between the drugs. We showed that this approach leads to different predictions across breast cancer subtypes compared to profiling the drugs separately. We extended this approach on patient samples from multiple public data resources containing epigenetic profiling data from cancer and normal tissues (The Cancer Genome Atlas, TCGA; NIH Roadmap epigenomics data)