No description available.

Williams-Beuren syndrome (WBS) is a neurodevelopmental disorder caused by the common deletion of 26-28 contiguous genes in the 7q11.23 region, which poses difficulties to the establishment of genotype-phenotype correlations. The use of mouse models would broader the knowledge of the syndrome, the role of deleted genes, affected pathways and possible treatments. In this thesis project, several mouse models, tissues and cells have been used to define the phenotypes at different levels, the deregulated genes and pathways and to discover modifying elements and novel treatments for the cardiovascular phenotype. In addition, a new binding motif has been described for Gtf2i, a deleted gene encoding a transcription factor with a major role in WB, providing new target genes from deregulated pathways. The obtained results reveal the essential role of mouse models for the study of Williams-Beuren syndrome and provide new treatments options and affected pathways and genes which could be future treatment targets. / La síndrome de Williams-Beuren és una malaltia del neurodesenvolupament causada per una deleció comú d’entre 26 i 28 gens contigus a la regió 7q11.23, dificultant l’establiment de relacions genotip-fenotip. L’ús de models de ratolí pot augmentar el coneixement sobre la malaltia, el paper dels gens delecionats, les vies moleculars afectades i els futurs tractaments. En aquesta tesi s’han usat diversos models de ratolí, les seves cèl·lules i teixits per tal de descriure i definir fenotips, gens i vies moleculars desregulades i per descobrir elements modificadors i nous tractaments. Per últim, s’ha definit un nou motiu d’unió per Gtf2i, uns dels gens delecionats que codifica per un factor de transcripció amb un rol central en la síndrome, proporcionats possible nous gens diana de vies moleculars desregulades. Els resultats obtinguts revelen el paper essencial dels models de ratolí per a l’estudi de la síndrome de Williams-Beuren, proporcionen noves opcions terapèutiques i defineixen nous gens i vies moleculars afectades que podrien suposar noves dianes terapèutiques.

Many scientific disciplines rely on the construction and use of models: biomedical sciences are no exception. This PhD thesis addresses several aspects of the practice of scientific modeling. First, I discuss the nature of modeling as such, proposing a novel, complementary account of scientific modeling which I term the experimentation-driven modeling account and which drives the construction of mechanistic models in many fields of biological and biomedical research, such as cancer immunology. Second, I scrutinize an objection to the mechanistic account of explanation according to which the account fails to accommodate the common practice of idealizing difference-making factors. I argue that this objection ultimately fails because it is riddled with a number of conceptual inconsistencies. Third, I analyze the roles of similarity judgments in some fields of cancer research which employ a variety of mouse models to learn about the disease mechanisms, arguing that by appreciating the epistemic complexities it is possible to shed new light on more general philosophical debates regarding scientific representation. Fourth, mechanisms can also be studied using more theoretical apparatus in the form of simulations. I investigate an example of an agent-based model used to model the outbreak of SARS-CoV-2...

Organoide aus adulten Mäusen sind vielversprechende Modelle für die Nierenforschung. Ihre Charakterisierung wurde jedoch nicht auf ein zufriedenstellendes Niveau gebracht. Hier habe ich ein langfristiges 3D-Maus-Organoid (Tubuloid)-Modell etabliert und charakterisiert, das die Erneuerung und die Reparatur sowie die Architektur und die Funktionalität der adulten tubulären Epithelien rekapituliert. In der Zukunft wird das Modell detaillierte Untersuchungen der Trajektorien selbsterneuernder Zellen sowohl zur teilweisen Wiederherstellung der Niere als auch zur malignen Transformation der Niere ermöglichen. Das klarzellige Nierenzellkarzinom (ccRCC) ist der häufigste und aggressivste Nierenkrebs. Die Inaktivierung des Tumorsuppressorgens Von Hippel-Lindau (VHL) ist der Haupttreiber des ccRCCs. Zuvor hatten wir die Hochregulation der Wnt- und Notch-Signalübertragung in den CXCR4+MET+CD44+-Krebsstammzellen (CSC) aus primären humanen ccRCC-Tumoren identifiziert. Das Blockieren von Wnt und Notch in von Patienten stammenden Xenotransplantaten, Organoiden und nicht-anhaftenden Sphären unter Verwendung von niedermolekularen Inhibitoren beeinträchtigte die Selbsterneuerung der CSC und das Tumorwachstum. Um CSC-gesteuertes humanes ccRCC in genetischen Mausmodellen nachzuahmen, begann ich mit der Erzeugung von zwei Doppelmausmutanten; β-Catenin-GOF; Notch-GOF und Vhl-LOF; β-Catenin-GOF. Sowohl die β-Catenin-GOF; Notch-GOF Mausmutante als auch die Vhl-LOF; β-Catenin-GOF Mausmutante entwickelten innerhalb einiger Monate schwere Krankheitssymptome. Überraschenderweise beobachtete ich weder Tumore oder Tumorvorläuferläsionen noch höhere Zellproliferationsraten in den mutierten Nieren. Weitere Analysen ergaben, dass die Mausmutanten Merkmale chronischer Nierenerkrankung (CKD) aufwiesen. / Adult mouse organoids are promising models for kidney research. However, their characterization has not been pushed forward to a satisfying level. Here, I have generated and characterized a long-term 3D mouse organoid (tubuloid) model, which recapitulates renewal and repair, and the architecture and functionality of the adult tubular epithelia. In the future, the model will allow detailed investigations of trajectories of self-renewing cells towards both the partial recreation and malignant transformation of the kidney. Clear cell renal cell carcinoma (ccRCC) is the most common and aggressive kidney cancer. Inactivation of the Von Hippel-Lindau (VHL) tumor suppressor gene is the major driver of ccRCC. Earlier, we identified the upregulation of Wnt and Notch signaling in CXCR4+MET+CD44+ cancer stem cells (CSCs) from primary human ccRCCs. Blocking Wnt and Notch in patient-derived xenografts, organoids and non-adherent spheres using small-molecule inhibitors impaired self-renewal of CSCs and tumor growth. To mimic CSC-governed human ccRCC in genetic mouse models, I started from the generation of two double mouse mutants; β-catenin-GOF; Notch-GOF and Vhl-LOF; β-catenin-GOF. Surprizingly, I observed neither tumors or tumor precursor lesions nor higher cell proliferation rates in the mutant kidneys. Further analyses revealed that the mutant mice displayed features of chronic kidney disease (CKD). Thus, β-catenin-GOF; Notch-GOF and Vhl-LOF; β-catenin-GOF mouse mutants did not develop kidney tumors under the given experimental conditions.

No description available.

<p>  </p> <p>The phosphatases of regenerating liver are a subfamily of protein tyrosine phosphatases that consist of PRL1, PRL2 and PRL3. The overexpression of PRLs promote cell proliferation, migration and invasion and contribute to tumorigenesis and metastasis to aggravate survival outcome. Although there is increasing interest in understanding the implication of these phosphatases in tumor development, currently, limited knowledge is available about their mechanism of action and the efficacy of PRL inhibition in <em>in vivo</em> tumor models, the tumor extrinsic role of PRLs that allow them to impact tumor development, as well as <em>in vivo</em> physiological function of PRLs that could implicate them in diseases other than cancer. The work presented here aims to address these limitations.</p> <p><br></p>

No description available.

Metabolomics is the methodical scientific study of biochemical processes associated with the metabolome—which comprises the entire collection of metabolites in any biological entity. Metabolome changes occur as a result of modifications in the genome and proteome, and are, therefore, directly related to cellular phenotype. Thus, metabolomic analysis is capable of providing a snapshot of cellular physiology. Untargeted metabolomics is an impartial, all-inclusive approach for detecting as many metabolites as possible without a priori knowledge of their identity. Hence, it is a valuable exploratory tool capable of providing extensive chemical information for discovery and hypothesis-generation regarding biochemical processes. A history of metabolomics and advances in the field corresponding to improved analytical technologies are described in Chapter 1 of this dissertation. Additionally, Chapter 1 introduces the analytical workflows involved in untargeted metabolomics research to provide a foundation for Chapters 2 – 5. Part I of this dissertation which encompasses Chapters 2 – 3 describes the utilization of mass spectrometry (MS)-based untargeted metabolomic analysis to acquire new insight into cancer detection. There is a knowledge deficit regarding the biochemical processes of the origin and proliferative molecular mechanisms of many types of cancer which has also led to a shortage of sensitive and specific biomarkers. Chapter 2 describes the development of an in vitro diagnostic multivariate index assay (IVDMIA) for prostate cancer (PCa) prediction based on ultra performance liquid chromatography-mass spectrometry (UPLC-MS) metabolic profiling of blood serum samples from 64 PCa patients and 50 healthy individuals. A panel of 40 metabolic spectral features was found to be differential with 92.1% sensitivity, 94.3% specificity, and 93.0% accuracy. The performance of the IVDMIA was higher than the prevalent prostate-specific antigen blood test, thus, highlighting that a combination of multiple discriminant features yields higher predictive power for PCa detection than the univariate analysis of a single marker. Chapter 3 describes two approaches that were taken to investigate metabolic patterns for early detection of ovarian cancer (OC). First, Dicer-Pten double knockout (DKO) mice that phenocopy many of the features of metastatic high-grade serous carcinoma (HGSC) observed in women were studied. Using UPLC-MS, serum samples from 14 early-stage tumor DKO mice and 11 controls were analyzed. Iterative multivariate classification selected 18 metabolites that, when considered as a panel, yielded 100% accuracy, sensitivity, and specificity for early-stage HGSC detection. In the second approach, serum metabolic phenotypes of an early-stage OC pilot patient cohort were characterized. Serum samples were collected from 24 early-stage OC patients and 40 healthy women, and subsequently analyzed using UPLC-MS. Multivariate statistical analysis employing support vector machine learning methods and recursive feature elimination selected a panel of metabolites that differentiated between age-matched samples with 100% cross-validated accuracy, sensitivity, and specificity. This small pilot study demonstrated that metabolic phenotypes may be useful for detecting early-stage OC and, thus, supports conducting larger, more comprehensive studies. Many challenges exist in the field of untargeted metabolomics. Part II of this dissertation which encompasses Chapters 4 – 5 focuses on two specific challenges. While metabolomic data may be used to generate hypothesis concerning biological processes, determining causal relationships within metabolic networks with only metabolomic data is impractical. Proteins play major roles in these networks; therefore, pairing metabolomic information with that acquired from proteomics gives a more comprehensive snapshot of perturbations to metabolic pathways. Chapter 4 describes the integration of MS- and NMR-based metabolomics with proteomics analyses to investigate the role of chemically mediated ecological interactions between Karenia brevis and two diatom competitors, Asterionellopsis glacialis and Thalassiosira pseudonana. This integrated systems biology approach showed that K. brevis allelopathy distinctively perturbed the metabolisms of these two competitors. A. glacialis had a more robust metabolic response to K. brevis allelopathy which may be a result of its repeated exposure to K. brevis blooms in the Gulf of Mexico. However, K. brevis allelopathy disrupted energy metabolism and obstructed cellular protection mechanisms including altering cell membrane components, inhibiting osmoregulation, and increasing oxidative stress in T. pseudonana. This work represents the first instance of metabolites and proteins measured simultaneously to understand the effects of allelopathy or in fact any form of competition. Chromatography is traditionally coupled to MS for untargeted metabolomics studies. While coupling chromatography to MS greatly enhances metabolome analysis due to the orthogonality of the techniques, the lengthy analysis times pose challenges for large metabolomics studies. Consequently, there is still a need for developing higher throughput MS approaches. A rapid metabolic fingerprinting method that utilizes a new transmission mode direct analysis in real time (TM-DART) ambient sampling technique is presented in Chapter 5. The optimization of TM-DART parameters directly affecting metabolite desorption and ionization, such as sample position and ionizing gas desorption temperature, was critical in achieving high sensitivity and detecting a broad mass range of metabolites. In terms of reproducibility, TM-DART compared favorably with traditional probe mode DART analysis, with coefficients of variation as low as 16%. TM-DART MS proved to be a powerful analytical technique for rapid metabolome analysis of human blood sera and was adapted for exhaled breath condensate (EBC) analysis. To determine the feasibility of utilizing TM-DART for metabolomics investigations, TM-DART was interfaced with traveling wave ion mobility spectrometry (TWIMS) time-of-flight (TOF) MS for the analysis of EBC samples from cystic fibrosis patients and healthy controls. TM-DART-TWIMS-TOF MS was able to successfully detect cystic fibrosis in this small sample cohort, thereby, demonstrating it can be employed for probing metabolome changes. Finally, in Chapter 6, a perspective on the presented work is provided along with goals on which future studies may focus.