789 research outputs found
Converging organoids and extracellular matrix::New insights into liver cancer biology
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/
Novel gold(III)-dithiocarbamate complex targeting bacterial thioredoxin reductase: antimicrobial activity, synergy, toxicity, and mechanistic insights
IntroductionAntimicrobial resistance is a pressing global concern that has led to the search for new antibacterial agents with novel targets or non-traditional approaches. Recently, organogold compounds have emerged as a promising class of antibacterial agents. In this study, we present and characterize a (C^S)-cyclometallated Au(III) dithiocarbamate complex as a potential drug candidate.Methods and resultsThe Au(III) complex was found to be stable in the presence of effective biological reductants, and showed potent antibacterial and antibiofilm activity against a wide range of multidrug-resistant strains, particularly gram-positive strains, and gram-negative strains when used in combination with a permeabilizing antibiotic. No resistant mutants were detected after exposing bacterial cultures to strong selective pressure, indicating that the complex may have a low propensity for resistance development. Mechanistic studies indicate that the Au(III) complex exerts its antibacterial activity through a multimodal mechanism of action. Ultrastructural membrane damage and rapid bacterial uptake suggest direct interactions with the bacterial membrane, while transcriptomic analysis identified altered pathways related to energy metabolism and membrane stability including enzymes of the TCA cycle and fatty acid biosynthesis. Enzymatic studies further revealed a strong reversible inhibition of the bacterial thioredoxin reductase. Importantly, the Au(III) complex demonstrated low cytotoxicity at therapeutic concentrations in mammalian cell lines, and showed no acute in vivo toxicity in mice at the doses tested, with no signs of organ toxicity.DiscussionOverall, these findings highlight the potential of the Au(III)-dithiocarbamate scaffold as a basis for developing novel antimicrobial agents, given its potent antibacterial activity, synergy, redox stability, inability to produce resistant mutants, low toxicity to mammalian cells both in vitro and in vivo, and non-conventional mechanism of action
Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data
Programa de Doctorado en BiotecnologĂa, IngenierĂa y TecnologĂa QuĂmicaLĂnea de InvestigaciĂłn: IngenierĂa, Ciencia de Datos y BioinformáticaClave Programa: DBICĂłdigo LĂnea: 111The root causes of disease are still poorly understood. The success of current therapies is limited because persistent diseases are frequently treated based on their symptoms rather than the underlying cause of the disease. Therefore, biomedical research is experiencing a technology-driven shift to data-driven holistic approaches to better characterize the molecular mechanisms causing disease. Using omics data as an input, emerging disciplines like network biology attempt to model the relationships between biomolecules. To this effect, gene co- expression networks arise as a promising tool for deciphering the relationships between genes in large transcriptomic datasets. However, because of their low specificity and high false positive rate, they demonstrate a limited capacity to retrieve the disrupted mechanisms that lead to disease onset, progression, and maintenance. Within the context of statistical modeling, we dove deeper into the reconstruction of gene co-expression networks with the specific goal of discovering disease-specific features directly from expression data. Using ensemble techniques, which combine the results of various metrics, we were able to more precisely capture biologically significant relationships between genes. We were able to find de novo potential disease-specific features with the help of prior biological knowledge and the development of new network inference techniques.
Through our different approaches, we analyzed large gene sets across multiple samples and used gene expression as a surrogate marker for the inherent biological processes, reconstructing robust gene co-expression networks that are simple to explore. By mining disease-specific gene co-expression networks we come up with a useful framework for identifying new omics-phenotype associations from conditional expression datasets.In this sense, understanding diseases from the perspective of biological network perturbations will improve personalized medicine, impacting rational biomarker discovery, patient stratification and drug design, and ultimately leading to more targeted therapies.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e Informátic
ENGINEERING HIGH-RESOLUTION EXPERIMENTAL AND COMPUTATIONAL PIPELINES TO CHARACTERIZE HUMAN GASTROINTESTINAL TISSUES IN HEALTH AND DISEASE
In recent decades, new high-resolution technologies have transformed how scientists study complex cellular processes and the mechanisms responsible for maintaining homeostasis and the emergence and progression of gastrointestinal (GI) disease. These advances have paved the way for the use of primary human cells in experimental models which together can mimic specific aspects of the GI tract such as compartmentalized stem-cell zones, gradients of growth factors, and shear stress from fluid flow. The work presented in this dissertation has focused on integrating high-resolution bioinformatics with novel experimental models of the GI epithelium systems to describe the complexity of human pathophysiology of the human small intestines, colon, and stomach in homeostasis and disease. Here, I used three novel microphysiological systems and developed four computational pipelines to describe comprehensive gene expression patterns of the GI epithelium in various states of health and disease. First, I used single cell RNAseq (scRNAseq) to establish the transcriptomic landscape of the entire epithelium of the small intestine and colon from three human donors, describing cell-type specific gene expression patterns in high resolution. Second, I used single cell and bulk RNAseq to model intestinal absorption of fatty acids and show that fatty acid oxidation is a critical regulator of the flux of long- and medium-chain fatty acids across the epithelium. Third, I use bulk RNAseq and a machine learning model to describe how inflammatory cytokines can regulate proliferation of intestinal stem cells in an experimental model of inflammatory hypoxia. Finally, I developed a high throughput platform that can associate phenotype to gene expression in clonal organoids, providing unprecedented resolution into the relationship between comprehensive gene expression patterns and their accompanying phenotypic effects. Through these studies, I have demonstrated how the integration of computational and experimental approaches can measurably advance our understanding of human GI physiology.Doctor of Philosoph
Investigating neural differentiation capacity in Alzheimer’s disease iPSC-derived neural stem cells
Neurodegeneration in Alzheimer’s disease (AD) may be exacerbated by dysregulated hippocampal neurogenesis. Neural stem cells (NSC) maintain adult neurogenesis and depletion of the NSC niche has been associated with age-related cognitive decline and dementia. We hypothesise that familial AD (FAD) mutations bias NSC toward premature neural specification, reducing the stem cell niche over time and accelerating disease progression.
Somatic cells derived from patients with FAD (PSEN1 A246E and PSEN1 M146L heterozygous mutations) and healthy controls were reprogrammed to generate induced pluripotent stem cells (iPSC). Pluripotency for patient and control iPSC lines was confirmed, then cells were amplified and cryopreserved as stores. iPSC were subjected to neural specification to rosette-forming SOX2+/nestin+ NSCs for comparative evaluations between FAD and age-matched controls. FAD patient and control NSC were passaged under defined steady state culture conditions to assess stem cell maintenance using quantitative molecular markers (SOX2, nestin, NeuN, MAP2 and βIII-tubulin). We observed trends towards downregulated expression of the nestin coding gene NES (p=0.051) and upregulated expression of MAP2 (p=0.16) in PSEN1 NSC compared with control NSC, indicative of a premature differentiation phenotype induced by presence of the PSEN1 mutation.
Cell cycle analysis of PSEN1 NSC showed that compared with controls, a greater number of PSEN1 NSC were retained in G0/G1 phase of the cell cycle (p=0.39), fewer progressed to S-phase (p=0.11) and fewer still reached G2 phase (p=0.23), suggesting cell cycle progression may be impaired in PSEN1 NSC. Nuclear DNA fragmentation was increased (p=0.10) in FAD NSC compared with controls, indicative of elevated cell death/apoptosis. Flow cytometry-based analysis of live, nestin+ NSC and NPC indicated increased apoptosis (p=0.14) in FAD NSC compared with controls, as well as increasing levels of apoptosis (p=0.33) in FAD NSC as they specified to neural progenitor cells.
Global RNA sequencing was used to identify transcriptomic changes occurring during both disease and control neural specification. GO analysis of DEGs between PSEN1 and control NSC at P3 revealed significant upregulation (FDR<0.0000259) of 5 biological processes related to transcription and gene expression as well as significant upregulation (FDR<0.000000725) of 12 molecular functions related to DNA binding and transcription factor activity. These data suggest significant changes in gene expression were occurring in PSEN1 NSC at P3 compared with control NSC at the same stage in neural specification. The number of DEGs (p<0.05) between PSEN1 and control NSC at P3 was 9.92-fold higher than the number of DEGs between PSEN1 and control NSC at P2, suggesting transcriptomic differences between PSEN1 and control NSC become more pronounced as cells specify further down the neural lineage.
Gene ontology (GO) analysis of differentially expressed genes (DEGs) specific to AD neural differentiation revealed significant dysregulation (FDR p<0.05) of genes related to neurogenesis, apoptosis, cell cycle, transcriptional control, and cell growth/maintenance as PSEN1 NSC matured from P2 to P3. The number of DEGs (p<0.05) in PSEN1 neural differentiation was 4.7-fold higher than the number of DEGs seen in control neural differentiation, indicating more transcriptional changes occurred in PSEN1 NSC than in controls at the same time point in neural specification.
Dysregulation of Notch signalling was specific to PSEN1 neural differentiation and Notch related DEGs significantly upregulated (p<0.05) in PSEN1 NSC at P3 compared with P2 included NCOR2, JAG2, CHAC1 and RFNG. qPCR based validation displayed significant upregulation of RFNG (p=0.04) in PSEN1 NSC at P3 compared with PSEN1 NSC at P2, and indicated a trend towards upregulation of JAG2 expression, correlating with RNA sequencing data. Data generated in this study indicate that presence of the PSEN1 mutation significantly increases the number of transcriptional changes occurring during neural differentiation. It is plausible that transcriptional changes to Notch signalling cause dysregulated neural specification and increased apoptosis in PSEN1 NSC, ultimately resulting in depletion of the NSC niche
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The Monogenic Architecture of Retinal and Neurological Diseases
Monogenic diseases, or single-gene disorders, are clinical manifestations that can be traced to genetic variation in a single gene that alters the biologically intended (wildtype) function of its protein (or mRNA) product. Although the causal gene and its function are well-understood in many monogenic diseases, this knowledge alone often does not fully encapsulate the extensive clinical spectrum of phenotypes seen in patients. This is due in part to the numerous types of pathogenic variants that can arise in a single gene, all of which can have distinct effects on disease expression. Understanding the relationship between the vast number of possible genotypes and corresponding disease phenotypes defines a gene’s monogenic disease architecture—an important but poorly understood concept that can yield informative mechanistic and clinical insight.
This doctoral dissertation integrates traditional sequencing approaches with in-depth characterization of patient phenotypes to elucidate the monogenic disease architecture of three etiologically distinct disorders: retinal degeneration caused by autosomal recessive variation in ABCA4 and neurodevelopmental disease entities caused by autosomal dominant variants in CERT1 and PUM1. Genetic modifiers are identified as a significant factor in the penetrance of the major disease-causing allele of ABCA4 and several other genetic inconsistencies are resolved to create a coherent genotype-phenotype model for the disease. Insight from this model is then applied to demonstrate the effect of allele differences in disease progression and evaluation of treatment efficacy in patients. A large cohort of affected individuals with CERT1 variation is assembled to (1) validate the causal role of CERT1 in disease, (2) delineate the precise mechanism of CERT protein dysfunction in sphingolipid metabolism and (3) demonstrate therapeutic efficacy of an inhibitor compound for a newly described syndrome.
Finally, the mutational spectrum of PUM1 is expanded to previously unattributed variant classes with unexpected pathophysiological consequences to patients. Not only do the findings in this dissertation advance the prospects of delivering personalized, precision medicine to patients, the overall impact underscores the importance of this integrated approach in reconciling knowledge gaps between observations at the molecular and organismal level
Digital agriculture: research, development and innovation in production chains.
Digital transformation in the field towards sustainable and smart agriculture. Digital agriculture: definitions and technologies. Agroenvironmental modeling and the digital transformation of agriculture. Geotechnologies in digital agriculture. Scientific computing in agriculture. Computer vision applied to agriculture. Technologies developed in precision agriculture. Information engineering: contributions to digital agriculture. DIPN: a dictionary of the internal proteins nanoenvironments and their potential for transformation into agricultural assets. Applications of bioinformatics in agriculture. Genomics applied to climate change: biotechnology for digital agriculture. Innovation ecosystem in agriculture: Embrapa?s evolution and contributions. The law related to the digitization of agriculture. Innovating communication in the age of digital agriculture. Driving forces for Brazilian agriculture in the next decade: implications for digital agriculture. Challenges, trends and opportunities in digital agriculture in Brazil
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