6,901 research outputs found

    Inter-individual variation of the human epigenome & applications

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    Exploring missing heritability in neurodevelopmental disorders:Learning from regulatory elements

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    In this thesis, I aimed to solve part of the missing heritability in neurodevelopmental disorders, using computational approaches. Next to the investigations of a novel epilepsy syndrome and investigations aiming to elucidate the regulation of the gene involved, I investigated and prioritized genomic sequences that have implications in gene regulation during the developmental stages of human brain, with the goal to create an atlas of high confidence non-coding regulatory elements that future studies can assess for genetic variants in genetically unexplained individuals suffering from neurodevelopmental disorders that are of suspected genetic origin

    Converging organoids and extracellular matrix::New insights into liver cancer biology

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    Converging organoids and extracellular matrix::New insights into liver cancer biology

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    Primary liver cancer, consisting primarily of hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), is a heterogeneous malignancy with a dismal prognosis, resulting in the third leading cause of cancer mortality worldwide [1, 2]. It is characterized by unique histological features, late-stage diagnosis, a highly variable mutational landscape, and high levels of heterogeneity in biology and etiology [3-5]. Treatment options are limited, with surgical intervention the main curative option, although not available for the majority of patients which are diagnosed in an advanced stage. Major contributing factors to the complexity and limited treatment options are the interactions between primary tumor cells, non-neoplastic stromal and immune cells, and the extracellular matrix (ECM). ECM dysregulation plays a prominent role in multiple facets of liver cancer, including initiation and progression [6, 7]. HCC often develops in already damaged environments containing large areas of inflammation and fibrosis, while CCA is commonly characterized by significant desmoplasia, extensive formation of connective tissue surrounding the tumor [8, 9]. Thus, to gain a better understanding of liver cancer biology, sophisticated in vitro tumor models need to incorporate comprehensively the various aspects that together dictate liver cancer progression. Therefore, the aim of this thesis is to create in vitro liver cancer models through organoid technology approaches, allowing for novel insights into liver cancer biology and, in turn, providing potential avenues for therapeutic testing. To model primary epithelial liver cancer cells, organoid technology is employed in part I. To study and characterize the role of ECM in liver cancer, decellularization of tumor tissue, adjacent liver tissue, and distant metastatic organs (i.e. lung and lymph node) is described, characterized, and combined with organoid technology to create improved tissue engineered models for liver cancer in part II of this thesis. Chapter 1 provides a brief introduction into the concepts of liver cancer, cellular heterogeneity, decellularization and organoid technology. It also explains the rationale behind the work presented in this thesis. In-depth analysis of organoid technology and contrasting it to different in vitro cell culture systems employed for liver cancer modeling is done in chapter 2. Reliable establishment of liver cancer organoids is crucial for advancing translational applications of organoids, such as personalized medicine. Therefore, as described in chapter 3, a multi-center analysis was performed on establishment of liver cancer organoids. This revealed a global establishment efficiency rate of 28.2% (19.3% for hepatocellular carcinoma organoids (HCCO) and 36% for cholangiocarcinoma organoids (CCAO)). Additionally, potential solutions and future perspectives for increasing establishment are provided. Liver cancer organoids consist of solely primary epithelial tumor cells. To engineer an in vitro tumor model with the possibility of immunotherapy testing, CCAO were combined with immune cells in chapter 4. Co-culture of CCAO with peripheral blood mononuclear cells and/or allogenic T cells revealed an effective anti-tumor immune response, with distinct interpatient heterogeneity. These cytotoxic effects were mediated by cell-cell contact and release of soluble factors, albeit indirect killing through soluble factors was only observed in one organoid line. Thus, this model provided a first step towards developing immunotherapy for CCA on an individual patient level. Personalized medicine success is dependent on an organoids ability to recapitulate patient tissue faithfully. Therefore, in chapter 5 a novel organoid system was created in which branching morphogenesis was induced in cholangiocyte and CCA organoids. Branching cholangiocyte organoids self-organized into tubular structures, with high similarity to primary cholangiocytes, based on single-cell sequencing and functionality. Similarly, branching CCAO obtain a different morphology in vitro more similar to primary tumors. Moreover, these branching CCAO have a higher correlation to the transcriptomic profile of patient-paired tumor tissue and an increased drug resistance to gemcitabine and cisplatin, the standard chemotherapy regimen for CCA patients in the clinic. As discussed, CCAO represent the epithelial compartment of CCA. Proliferation, invasion, and metastasis of epithelial tumor cells is highly influenced by the interaction with their cellular and extracellular environment. The remodeling of various properties of the extracellular matrix (ECM), including stiffness, composition, alignment, and integrity, influences tumor progression. In chapter 6 the alterations of the ECM in solid tumors and the translational impact of our increased understanding of these alterations is discussed. The success of ECM-related cancer therapy development requires an intimate understanding of the malignancy-induced changes to the ECM. This principle was applied to liver cancer in chapter 7, whereby through a integrative molecular and mechanical approach the dysregulation of liver cancer ECM was characterized. An optimized agitation-based decellularization protocol was established for primary liver cancer (HCC and CCA) and paired adjacent tissue (HCC-ADJ and CCA-ADJ). Novel malignancy-related ECM protein signatures were found, which were previously overlooked in liver cancer transcriptomic data. Additionally, the mechanical characteristics were probed, which revealed divergent macro- and micro-scale mechanical properties and a higher alignment of collagen in CCA. This study provided a better understanding of ECM alterations during liver cancer as well as a potential scaffold for culture of organoids. This was applied to CCA in chapter 8 by combining decellularized CCA tumor ECM and tumor-free liver ECM with CCAO to study cell-matrix interactions. Culture of CCAO in tumor ECM resulted in a transcriptome closely resembling in vivo patient tumor tissue, and was accompanied by an increase in chemo resistance. In tumor-free liver ECM, devoid of desmoplasia, CCAO initiated a desmoplastic reaction through increased collagen production. If desmoplasia was already present, distinct ECM proteins were produced by the organoids. These were tumor-related proteins associated with poor patient survival. To extend this method of studying cell-matrix interactions to a metastatic setting, lung and lymph node tissue was decellularized and recellularized with CCAO in chapter 9, as these are common locations of metastasis in CCA. Decellularization resulted in removal of cells while preserving ECM structure and protein composition, linked to tissue-specific functioning hallmarks. Recellularization revealed that lung and lymph node ECM induced different gene expression profiles in the organoids, related to cancer stem cell phenotype, cell-ECM integrin binding, and epithelial-to-mesenchymal transition. Furthermore, the metabolic activity of CCAO in lung and lymph node was significantly influenced by the metastatic location, the original characteristics of the patient tumor, and the donor of the target organ. The previously described in vitro tumor models utilized decellularized scaffolds with native structure. Decellularized ECM can also be used for creation of tissue-specific hydrogels through digestion and gelation procedures. These hydrogels were created from both porcine and human livers in chapter 10. The liver ECM-based hydrogels were used to initiate and culture healthy cholangiocyte organoids, which maintained cholangiocyte marker expression, thus providing an alternative for initiation of organoids in BME. Building upon this, in chapter 11 human liver ECM-based extracts were used in combination with a one-step microfluidic encapsulation method to produce size standardized CCAO. The established system can facilitate the reduction of size variability conventionally seen in organoid culture by providing uniform scaffolding. Encapsulated CCAO retained their stem cell phenotype and were amendable to drug screening, showing the feasibility of scalable production of CCAO for throughput drug screening approaches. Lastly, Chapter 12 provides a global discussion and future outlook on tumor tissue engineering strategies for liver cancer, using organoid technology and decellularization. Combining multiple aspects of liver cancer, both cellular and extracellular, with tissue engineering strategies provides advanced tumor models that can delineate fundamental mechanistic insights as well as provide a platform for drug screening approaches.<br/

    Leveraging technology-driven strategies to untangle omics big data: circumventing roadblocks in clinical facets of oral cancer

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    Oral cancer is one of the 19most rapidly progressing cancers associated with significant mortality, owing to its extreme degree of invasiveness and aggressive inclination. The early occurrences of this cancer can be clinically deceiving leading to a poor overall survival rate. The primary concerns from a clinical perspective include delayed diagnosis, rapid disease progression, resistance to various chemotherapeutic regimens, and aggressive metastasis, which collectively pose a substantial threat to prognosis. Conventional clinical practices observed since antiquity no longer offer the best possible options to circumvent these roadblocks. The world of current cancer research has been revolutionized with the advent of state-of-the-art technology-driven strategies that offer a ray of hope in confronting said challenges by highlighting the crucial underlying molecular mechanisms and drivers. In recent years, bioinformatics and Machine Learning (ML) techniques have enhanced the possibility of early detection, evaluation of prognosis, and individualization of therapy. This review elaborates on the application of the aforesaid techniques in unraveling potential hints from omics big data to address the complexities existing in various clinical facets of oral cancer. The first section demonstrates the utilization of omics data and ML to disentangle the impediments related to diagnosis. This includes the application of technology-based strategies to optimize early detection, classification, and staging via uncovering biomarkers and molecular signatures. Furthermore, breakthrough concepts such as salivaomics-driven non-invasive biomarker discovery and omics-complemented surgical interventions are articulated in detail. In the following part, the identification of novel disease-specific targets alongside potential therapeutic agents to confront oral cancer via omics-based methodologies is presented. Additionally, a special emphasis is placed on drug resistance, precision medicine, and drug repurposing. In the final section, we discuss the research approaches oriented toward unveiling the prognostic biomarkers and constructing prediction models to capture the metastatic potential of the tumors. Overall, we intend to provide a bird’s eye view of the various omics, bioinformatics, and ML approaches currently being used in oral cancer research through relevant case studies

    Targeting immune and desmoplastic tumor microenvironment to sensitize gynecological cancer cells to therapy

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    Cancer is a pervasive global threat that manifests with diverse clinical attributes and notable mortality rates, particularly attributable to its metastatic potential in solid cancers. These tumours encompass various types including epithelial cancers like high-grade serous ovarian cancer (HGSC) and mesenchymal cancers like uterine sarcomas (USs). Despite the differing origins of USs and HGSCs, the pivotal concept of the transition between epithelial and mesenchymal states remains remarkably plastic, occurring frequently in these cancers. This plasticity holds immense significance in understanding tumour invasiveness and metastasis. The TME emerges as a crucial influencer as exerting its impact on cancer progression, epithelial-mesenchymal transition (EMT), metastasis, and even chemoresistance. The TME comprises various elements, with the extracellular matrix (ECM) containing structural proteins like collagens, standing out as a key constituent. Moreover, immune cells within the TME, such as lymphocytes and macrophages, actively engage in interactions with both the ECM and cancer cells shaping local responses to kill the cancer cells or support their growth. Understanding the intricate tumour-TME interactions become imperative in formulating effective strategies aimed at modulating the immune response and halting cancer progression. Therefore, a nuanced comprehension of these complexities is crucial in developing strategies to combat cancer effectively. This thesis focuses on identifying TME factors, including ECM components and immune cell interactions in gynaecological cancers for improved precision medicine including immunotherapies and other novel treatments. In Paper I, Uterine sarcomas present distinct immune signatures with prognostic value, independent of tumour type. FOXP3+ cell density and CD8+/FOXP3+ ratio (CFR) correlated with favourable survival in endometrial stromal sarcomas (ESS) and undifferentiated uterine sarcomas (USS). The CFR also highlighted the correlation between CFR high and upregulation of ECM organization pathways. In Paper II conversely, uterine leiomyosarcomas (uLMS) showed distinct behaviours, with lower collagen density and upregulated ECM remodelling enzymes correlating with aggressiveness. MMP-14 and yes-associated protein 1 (YAP) were required for uLMS growth and invasion. In Paper Ⅲ, shifting to HGSC, matrisome, a group of proteins encoded by genes for core ECM proteins 4 (collagens, proteoglycans, and ECM glycoproteins) and ECM-associated proteins (proteins structurally resembling ECM proteins, ECM remodelling enzymes, and secreted factors) in the ECM, showed changes in expression depending on the type of tumour host tissues and after chemotherapy. Collagen VI, among scrutinized proteins, exhibited elevated expression linked to shortened survival in ovarian cancer patients. Mechanistically, collagen VI promoted platinum resistance via the stiffness-dependent β1 integrin-pMLC and YAP/TAZ pathways in HGSC cell lines In summary, this integrated exploration of uterine sarcomas and ovarian cancer provides a comprehensive understating of their TME. The study elucidates diverse immune and molecular features, offering potential prognostic markers and therapeutic targets. The findings underscore the complexity of these gynaecological malignancies, emphasizing the need for tailored approaches in understanding and combating these diseases

    Computational approaches for biological data integration

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    Biological processes in a cell are highly dynamic and their regulation involves a multitude of molecular components such as DNA, genes, proteins, and metabolites. It is of critical importance to understand these entities not only as separate elements but also in terms of their interactions with one another. Technological developments have enabled the rapid generation of vast volumes of data for nearly all types of such entities from an individual, referred to as ‘omics’ data. This thesis addresses three main challenges for the integrative analysis of different omics data modalities while taking their inherent heterogeneity into account: (i) ease of accessibility of high-throughput data so that it can be combined with in-house experimental data or used for reanalysis, (ii) integration of information across different resources, and (iii) integration within or across data modalities using networks. Chapter 2 of this thesis describes our approach to build a compendium of functional genomics data retrieved from GEO. With the associated R package compendiumdb and the accompanying MySQL database, pre-processed GEO data from different studies and profiling platforms can be systematically retrieved and stored. Chapter 3 of this thesis describes a problem-driven integrative analysis approach across different data sources to rank candidate proteins for low-abundant spots in 2D-DIGE experiments. Chapter 4 of this thesis describes a network-based integration method to align a pair of gene coexpression networks generated from gene expression data measured across multiple conditions. The method is applied to gene expression data measured in human and mouse immune cell types to study conservation and divergence between the two species. In Chapter 5 of this thesis a special case of this method is used to identify modules conserved between species for a single condition. The method is applied to gene expression data measured in human and mouse livers

    Characterisation of peroxisomes in the fission Yeast Schizosaccharomyces pombe and slime mold Dictyostelium discoideum

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    Peroxisome is a compartment that is found in most eukaryotic organisms' cells. It has several crucial roles, such as fatty acid beta (β) oxidation and hydrogen peroxide (H2O2) detoxification. It contains many essential enzymes, including oxidase and catalase, and has several metabolic and non-metabolic pathways, depending on the environment and the organisms within its cells. This study investigates the role of peroxisomes in two organisms, S. pombe and D. discoideum. Although S. pombe is a well-studied yeast, there is only one study of this yeast that has focused on peroxisomes. This study offers a few crucial observations, including that S. pombe contains peroxisomes, that GFP containing a well-characterized PTS1 (SKL) is efficiently imported, and that peroxisome numbers increase in cells grown on a fatty acid as the sole carbon source, suggesting a role for peroxisomes in fatty acid degradation. The starting point in my research was initially a bioinformatics screen. This screening recognized the enzymes imported into peroxisomes based on the presence of a potential peroxisomal targeting signal. A few proteins were found. However, the low number of proteins with a classical PTS might be the result of different targeting signals that are not recognized by our bioinformatics parameters. Indeed, in other organisms, there are proteins without PTS1 that still use Pex5 for import. The first example is S. cerevisiae Acyl-CoA oxidase. In a global yeast two-hybrid screen, S. pombe Pex5 was found to bind S. pombe Str3 and Lys3. Consequently, we think that there is conserved targeting of a peroxisomal protein lacking a PTS1 and PTS2 imported into the peroxisome by Pex5. One of these is the Str3 case. Interestingly, proteins involved in peroxisomal fatty acid β -oxidation are absent from the S. pombe genome, casting doubt on the conclusions from the previous study and explaining the low number of potential peroxisomal enzymes. In D. discoideum, this study investigates the dynamic regulation of peroxisome numbers in response to growth conditions and identifies peroxisomal import and contents through a proximity labeling approach (BioID). Overall, this study sheds light on the roles and regulation of peroxisomes in these two organisms
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