Cellular reprogramming strategies for degenerative disorders involving the retinal pigment epithelium

RESUMO: A reprogramação celular permite que uma célula somática seja reprogramada para outra célula diferente através da expressão forçada de factores de transcrição (FTs) específicos de determinada linhagem celular, e constitui uma área de investigação emergente nos últimos anos. As células somáticas podem ser experimentalmente manipuladas de modo a obter células estaminais pluripotentes induzidas (CEPi), ou convertidas directamente noutro tipo de célula somática. Estas descobertas inovadoras oferecem oportunidades promissoras para o desenvolvimento de novas terapias de substituição celular e modelos de doença, funcionando também como ferramentas valiosas para o estudo dos mecanismos moleculares que estabelecem a identidade celular e regulam os processos de desenvolvimento. Existem várias doenças degenerativas hereditárias e adquiridas da retina que causam deficiência visual devido a uma disfunção no tecido de suporte da retina, o epitélio pigmentar da retina (EPR). Uma destas doenças é a Coroideremia (CHM), uma doença hereditária monogénica ligada ao cromossoma X causada por mutações que implicam a perda de função duma proteína com funções importantes na regulação do tráfico intracelular. A CHM é caracterizada pela degenerescência progressiva do EPR, assim como dos foto-receptores e da coróide. Resultados experimentais sugerem que o EPR desempenha um papel importante na patogénese da CHM, o que parece indicar uma possível vantagem terapêutica na substituição do EPR nos doentes com CHM. Por outro lado, existe uma lacuna em termos de modelos in vitro de EPR para estudar a CHM, o que pode explicar o ainda desconhecimento dos mecanismos moleculares que explicam a patogénese desta doença. Assim, este trabalho focou-se principalmente na exploração das potencialidades das técnicas de reprogramação celular no contexto das doenças de degenerescência da retina, em particular no caso da CHM. Células de murganho de estirpe selvagem, bem como células derivadas de um ratinho modelo de knockout condicional de Chm, foram convertidos com sucesso em CEPi recorrendo a um sistema lentiviral induzido que permite a expressão forçada dos 4 factores clássicos de reprogramação, a saber Oct4, Sox2, Klf4 e c-Myc. Estas células mostraram ter equivalência morfológica, molecular e funcional a células estaminais embrionárias (CES). As CEPi obtidas foram seguidamente submetidas a protocolos de diferenciação com o objectivo final de obter células do EPR. Os resultados promissores obtidos revelam a possibilidade de gerar um valioso modelo de EPR-CHM para estudos in vitro. Em alternativa, a conversão directa de linhagens partindo de fibroblastos para obter células do EPR foi também abordada. Uma vasta gama de ferramentas moleculares foi gerada de modo a implementar uma estratégia mediada por FTs-chave, seleccionados devido ao seu papel fundamental no desenvolvimento embrionário e especificação do EPR. Conjuntos de 10 ou menos FTs foram usados para transduzir fibroblastos, que adquiriram morfologia pigmentada e expressão de alguns marcadores específicos do EPR. Adicionalmente, observou-se a activação de regiões promotoras de genes específicos de EPR, indicando que a identidade transcricional das células foi alterada no sentido pretendido. Em conclusão, avanços significativos foram atingidos no sentido da implementação de tecnologias de reprogramação celular já estabelecidas, bem como na concepção de novas estratégias inovadoras. Metodologias de reprogramação, quer para pluripotência, quer via conversão directa, foram aplicadas com o objectivo final de gerar células do EPR. O trabalho aqui descrito abre novos caminhos para o estabelecimento de terapias de substituição celular e, de uma maneira mais directa, levanta a possibilidade de modelar doenças degenerativas da retina com disfunção do EPR numa placa de petri, em particular no caso da CHM.---------------ABSTRACT: Cellular reprogramming is an emerging research field in which a somatic cell is reprogrammed into a different cell type by forcing the expression of lineage-specific transcription factors (TFs). Cellular identities can be manipulated using experimental techniques with the attainment of pluripotency properties and the generation of induced Pluripotent Stem (iPS) cells, or the direct conversion of one somatic cell into another somatic cell type. These pioneering discoveries offer new unprecedented opportunities for the establishment of novel cell-based therapies and disease models, as well as serving as valuable tools for the study of molecular mechanisms governing cell fate establishment and developmental processes. Several retinal degenerative disorders, inherited and acquired, lead to visual impairment due to an underlying dysfunction of the support cells of the retina, the retinal pigment epithelium (RPE). Choroideremia (CHM), an X-linked monogenic disease caused by a loss of function mutation in a key regulator of intracellular trafficking, is characterized by a progressive degeneration of the RPE and other components of the retina, such as the photoreceptors and the choroid. Evidence suggest that RPE plays an important role in CHM pathogenesis, thus implying that regenerative approaches aiming at rescuing RPE function may be of great benefit for CHM patients. Additionally, lack of appropriate in vitro models has contributed to the still poorly-characterized molecular events in the base of CHM degenerative process. Therefore, the main focus of this work was to explore the potential applications of cellular reprogramming technology in the context of RPE-related retinal degenerations. The generation of mouse iPS cells was established and optimized using an inducible lentiviral system to force the expression of the classic set of TFs, namely Oct4, Sox2, Klf4 and c-Myc. Wild-type cells, as well as cells derived from a conditional knockout (KO) mouse model of Chm, were successfully converted into a pluripotent state, that displayed morphology, molecular and functional equivalence to Embryonic Stem (ES) cells. Generated iPS cells were then subjected to differentiation protocols towards the attainment of a RPE cell fate, with promising results highlighting the possibility of generating a valuable Chm-RPE in vitro model. In alternative, direct lineage conversion of fibroblasts into RPE-like cells was also tackled. A TF-mediated approach was implemented after the generation of a panoply of molecular tools needed for such studies. After transduction with pools of 10 or less TFs, selected for their key role on RPE developmental process and specification, fibroblasts acquired a pigmented morphology and expression of some RPE-specific markers. Additionally, promoter regions of RPE-specific genes were activated indicating that the transcriptional identity of the cells was being altered into the pursued cell fate. In conclusion, highly significant progress was made towards the implementation of already established cellular reprogramming technologies, as well as the designing of new innovative ones. Reprogramming into pluripotency and lineage conversion methodologies were applied to ultimately generate RPE cells. These studies open new avenues for the establishment of cell replacement therapies and, more straightforwardly,raise the possibility of modelling retinal degenerations with underlying RPE defects in apetri dish, particularly CHM.

Development of 3D in vitro models for prediction of hepatic metabolism and toxicity

The development of human cell models that recapitulate hepatic functionality allows the study of metabolic pathways involved in toxicity and disease. The increased biological relevance, cost-effectiveness and high-throughput of cell models can contribute to increase the efficiency of drug development in the pharmaceutical industry. Recapitulation of liver functionality in vitro requires the development of advanced culture strategies to mimic in vivo complexity, such as 3D culture, co-cultures or biomaterials. However, complex 3D models are typically associated with poor robustness, limited scalability and compatibility with screening methods. In this work, several strategies were used to develop highly functional and reproducible spheroid-based in vitro models of human hepatocytes and HepaRG cells using stirred culture systems. In chapter 2, the isolation of human hepatocytes from resected liver tissue was implemented and a liver tissue perfusion method was optimized towards the improvement of hepatocyte isolation and aggregation efficiency, resulting in an isolation protocol compatible with 3D culture. In chapter 3, human hepatocytes were co-cultivated with mesenchymal stem cells (MSC) and the phenotype of both cell types was characterized, showing that MSC acquire a supportive stromal function and hepatocytes retain differentiated hepatic functions, stability of drug metabolism enzymes and higher viability in co-cultures. In chapter 4, a 3D alginate microencapsulation strategy for the differentiation of HepaRG cells was evaluated and compared with the standard 2D DMSO-dependent differentiation, yielding higher differentiation efficiency, comparable levels of drug metabolism activity and significantly improved biosynthetic activity. The work developed in this thesis provides novel strategies for 3D culture of human hepatic cell models, which are reproducible, scalable and compatible with screening platforms. The phenotypic and functional characterization of the in vitro systems performed contributes to the state of the art of human hepatic cell models and can be applied to the improvement of pre-clinical drug development efficiency of the process, model disease and ultimately, development of cell-based therapeutic strategies for liver failure.

Modeling and forecasting value-at-risk for the Portuguese stock market

The aim of this work project is to find a model that is able to accurately forecast the daily Value-at-Risk for PSI-20 Index, independently of the market conditions, in order to expand empirical literature for the Portuguese stock market. Hence, two subsamples, representing more and less volatile periods, were modeled through unconditional and conditional volatility models (because it is what drives returns). All models were evaluated through Kupiec’s and Christoffersen’s tests, by comparing forecasts with actual results. Using an out-of-sample of 204 observations, it was found that a GARCH(1,1) is an accurate model for our purposes.

Predicting market direction with hidden Markov models

This paper develops the model of Bicego, Grosso, and Otranto (2008) and applies Hidden Markov Models to predict market direction. The paper draws an analogy between financial markets and speech recognition, seeking inspiration from the latter to solve common issues in quantitative investing. Whereas previous works focus mostly on very complex modifications of the original hidden markov model algorithm, the current paper provides an innovative methodology by drawing inspiration from thoroughly tested, yet simple, speech recognition methodologies. By grouping returns into sequences, Hidden Markov Models can then predict market direction the same way they are used to identify phonemes in speech recognition. The model proves highly successful in identifying market direction but fails to consistently identify whether a trend is in place. All in all, the current paper seeks to bridge the gap between speech recognition and quantitative finance and, even though the model is not fully successful, several refinements are suggested and the room for improvement is significant.

Knowledge management framework based on brain models and human physiology

The life of humans and most living beings depend on sensation and perception for the best assessment of the surrounding world. Sensorial organs acquire a variety of stimuli that are interpreted and integrated in our brain for immediate use or stored in memory for later recall. Among the reasoning aspects, a person has to decide what to do with available information. Emotions are classifiers of collected information, assigning a personal meaning to objects, events and individuals, making part of our own identity. Emotions play a decisive role in cognitive processes as reasoning, decision and memory by assigning relevance to collected information. The access to pervasive computing devices, empowered by the ability to sense and perceive the world, provides new forms of acquiring and integrating information. But prior to data assessment on its usefulness, systems must capture and ensure that data is properly managed for diverse possible goals. Portable and wearable devices are now able to gather and store information, from the environment and from our body, using cloud based services and Internet connections. Systems limitations in handling sensorial data, compared with our sensorial capabilities constitute an identified problem. Another problem is the lack of interoperability between humans and devices, as they do not properly understand human’s emotional states and human needs. Addressing those problems is a motivation for the present research work. The mission hereby assumed is to include sensorial and physiological data into a Framework that will be able to manage collected data towards human cognitive functions, supported by a new data model. By learning from selected human functional and behavioural models and reasoning over collected data, the Framework aims at providing evaluation on a person’s emotional state, for empowering human centric applications, along with the capability of storing episodic information on a person’s life with physiologic indicators on emotional states to be used by new generation applications.

Flood resilient housing recovery models: a theoretical case study in Maldives

Natural disasters are events that cause general and widespread destruction of the built environment and are becoming increasingly recurrent. They are a product of vulnerability and community exposure to natural hazards, generating a multitude of social, economic and cultural issues of which the loss of housing and the subsequent need for shelter is one of its major consequences. Nowadays, numerous factors contribute to increased vulnerability and exposure to natural disasters such as climate change with its impacts felt across the globe and which is currently seen as a worldwide threat to the built environment. The abandonment of disaster-affected areas can also push populations to regions where natural hazards are felt more severely. Although several actors in the post-disaster scenario provide for shelter needs and recovery programs, housing is often inadequate and unable to resist the effects of future natural hazards. Resilient housing is commonly not addressed due to the urgency in sheltering affected populations. However, by neglecting risks of exposure in construction, houses become vulnerable and are likely to be damaged or destroyed in future natural hazard events. That being said it becomes fundamental to include resilience criteria, when it comes to housing, which in turn will allow new houses to better withstand the passage of time and natural disasters, in the safest way possible. This master thesis is intended to provide guiding principles to take towards housing recovery after natural disasters, particularly in the form of flood resilient construction, considering floods are responsible for the largest number of natural disasters. To this purpose, the main structures that house affected populations were identified and analyzed in depth. After assessing the risks and damages that flood events can cause in housing, a methodology was proposed for flood resilient housing models, in which there were identified key criteria that housing should meet. The same methodology is based in the US Federal Emergency Management Agency requirements and recommendations in accordance to specific flood zones. Finally, a case study in Maldives – one of the most vulnerable countries to sea level rise resulting from climate change – has been analyzed in light of housing recovery in a post-disaster induced scenario. This analysis was carried out by using the proposed methodology with the intent of assessing the resilience of the newly built housing to floods in the aftermath of the 2004 Indian Ocean Tsunami.

The future of airline business models:

This research is titled “The Future of Airline Business Models: Which Will Win?” and it is part of the requirements for the award of a Masters in Management from NOVA BSE and another from Luiss Guido Carlo University. The purpose is to elaborate a complete market analysis of the European Air Transportation Industry in order to predict which Airlines, strategies and business models may be successful in the next years. First, an extensive literature review of the business model concept has been done. Then, a detailed overview of the main European Airlines and the strategies that they have been implementing so far has been developed. Finally, the research is illustrated with three case studies

Building 3D City Models: Testing and Comparing Laser Scanning and Low-Cost UAV Data Using FOSS Technologies

Contém resumo

Damage propagation in composite Materials meso-mechanical models

Composite materials have a complex behavior, which is difficult to predict under different types of loads. In the course of this dissertation a methodology was developed to predict failure and damage propagation of composite material specimens. This methodology uses finite element numerical models created with Ansys and Matlab softwares. The methodology is able to perform an incremental-iterative analysis, which increases, gradually, the load applied to the specimen. Several structural failure phenomena are considered, such as fiber and/or matrix failure, delamination or shear plasticity. Failure criteria based on element stresses were implemented and a procedure to reduce the stiffness of the failed elements was prepared. The material used in this dissertation consist of a spread tow carbon fabric with a 0°/90° arrangement and the main numerical model analyzed is a 26-plies specimen under compression loads. Numerical results were compared with the results of specimens tested experimentally, whose mechanical properties are unknown, knowing only the geometry of the specimen. The material properties of the numerical model were adjusted in the course of this dissertation, in order to find the lowest difference between the numerical and experimental results with an error lower than 5% (it was performed the numerical model identification based on the experimental results).

Understanding multi-asset factor models: Factor exposure interpretation

Field lab: Business project

Orthogonal models, structure crossing, nesting and inference

We intend to study the algebraic structure of the simple orthogonal models to use them, through binary operations as building blocks in the construction of more complex orthogonal models. We start by presenting some matrix results considering Commutative Jordan Algebras of symmetric matrices, CJAs. Next, we use these results to study the algebraic structure of orthogonal models, obtained by crossing and nesting simpler ones. Then, we study the normal models with OBS, which can also be orthogonal models. We intend to study normal models with OBS (Orthogonal Block Structure), NOBS (Normal Orthogonal Block Structure), obtaining condition for having complete and suffcient statistics, having UMVUE, is unbiased estimators with minimal covariance matrices whatever the variance components. Lastly, see ([Pereira et al. (2014)]), we study the algebraic structure of orthogonal models, mixed models whose variance covariance matrices are all positive semi definite, linear combinations of known orthogonal pairwise orthogonal projection matrices, OPOPM, and whose least square estimators, LSE, of estimable vectors are best linear unbiased estimator, BLUE, whatever the variance components, so they are uniformly BLUE, UBLUE. From the results of the algebraic structure we will get explicit expressions for the LSE of these models.

Improving accuracy of inflation forecasts using macroeconomic information

This thesis examines the effects of macroeconomic factors on inflation level and volatility in the Euro Area to improve the accuracy of inflation forecasts with econometric modelling. Inflation aggregates for the EU as well as inflation levels of selected countries are analysed, and the difference between these inflation estimates and forecasts are documented. The research proposes alternative models depending on the focus and the scope of inflation forecasts. I find that models with a Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) in mean process have better explanatory power for inflation variance compared to the regular GARCH models. The significant coefficients are different in EU countries in comparison to the aggregate EU-wide forecast of inflation. The presence of more pronounced GARCH components in certain countries with more stressed economies indicates that inflation volatility in these countries are likely to occur as a result of the stressed economy. In addition, other economies in the Euro Area are found to exhibit a relatively stable variance of inflation over time. Therefore, when analysing EU inflation one have to take into consideration the large differences on country level and focus on those one by one.

Cultural journalism in a digital environment : new models, practices and possibilities

Both culture coverage and digital journalism are contemporary phenomena that have undergone several transformations within a short period of time. Whenever the media enters a period of uncertainty such as the present one, there is an attempt to innovate in order to seek sustainability, skip the crisis or find a new public. This indicates that there are new trends to be understood and explored, i.e., how are media innovating in a digital environment? Not only does the professional debate about the future of journalism justify the need to explore the issue, but so do the academic approaches to cultural journalism. However, none of the studies so far have considered innovation as a motto or driver and tried to explain how the media are covering culture, achieving sustainability and engaging with the readers in a digital environment. This research examines how European media which specialize in culture or have an important cultural section are innovating in a digital environment. Specifically, we see how these innovation strategies are being taken in relation to the approach to culture and dominant cultural areas, editorial models, the use of digital tools for telling stories, overall brand positioning and extensions, engagement with the public and business models. We conducted a mixed methods study combining case studies of four media projects, which integrates qualitative web features and content analysis, with quantitative web content analysis. Two major general-interest journalistic brands which started as physical newspapers – The Guardian (London, UK) and Público (Lisbon, Portugal) – a magazine specialized in international affairs, culture and design – Monocle (London, UK) – and a native digital media project that was launched by a cultural organization – Notodo, by La Fábrica – were the four case studies chosen. Findings suggest, on one hand, that we are witnessing a paradigm shift in culture coverage in a digital environment, challenging traditional boundaries related to cultural themes and scope, angles, genres, content format and delivery, engagement and business models. Innovation in the four case studies lies especially along the product dimensions (format and content), brand positioning and process (business model and ways to engage with users). On the other hand, there are still perennial values that are crucial to innovation and sustainability, such as commitment to journalism, consistency (to the reader, to brand extensions and to the advertiser), intelligent differentiation and the capability of knowing what innovation means and how it can be applied, since this thesis also confirms that one formula doesn´t suit all. Changing minds, exceeding cultural inertia and optimizing the memory of the websites, looking at them as living, organic bodies, which continuously interact with the readers in many different ways, and not as a closed collection of articles, are still the main challenges for some media.

Development of human central nervous system 3D in vitro models for preclinical research

Neurological disorders are a major concern in modern societies, with increasing prevalence mainly related with the higher life expectancy. Most of the current available therapeutic options can only control and ameliorate the patients’ symptoms, often be-coming refractory over time. Therapeutic breakthroughs and advances have been hampered by the lack of accurate central nervous system (CNS) models. The develop-ment of these models allows the study of the disease onset/progression mechanisms and the preclinical evaluation of novel therapeutics. This has traditionally relied on genetically engineered animal models that often diverge considerably from the human phenotype (developmentally, anatomically and physiologically) and 2D in vitro cell models, which fail to recapitulate the characteristics of the target tissue (cell-cell and cell-matrix interactions, cell polarity). The in vitro recapitulation of CNS phenotypic and functional features requires the implementation of advanced culture strategies that enable to mimic the in vivo struc-tural and molecular complexity. Models based on differentiation of human neural stem cells (hNSC) in 3D cultures have great potential as complementary tools in preclinical research, bridging the gap between human clinical studies and animal models. This thesis aimed at the development of novel human 3D in vitro CNS models by integrat-ing agitation-based culture systems and a wide array of characterization tools. Neural differentiation of hNSC as 3D neurospheres was explored in Chapter 2. Here, it was demonstrated that human midbrain-derived neural progenitor cells from fetal origin (hmNPC) can generate complex tissue-like structures containing functional dopaminergic neurons, as well as astrocytes and oligodendrocytes. Chapter 3 focused on the development of cellular characterization assays for cell aggregates based on light-sheet fluorescence imaging systems, which resulted in increased spatial resolu-tion both for fixed samples or live imaging. The applicability of the developed human 3D cell model for preclinical research was explored in Chapter 4, evaluating the poten-tial of a viral vector candidate for gene therapy. The efficacy and safety of helper-dependent CAV-2 (hd-CAV-2) for gene delivery in human neurons was evaluated, demonstrating increased neuronal tropism, efficient transgene expression and minimal toxicity. The potential of human 3D in vitro CNS models to mimic brain functions was further addressed in Chapter 5. Exploring the use of 13C-labeled substrates and Nucle-ar Magnetic Resonance (NMR) spectroscopy tools, neural metabolic signatures were evaluated showing lineage-specific metabolic specialization and establishment of neu-ron-astrocytic shuttles upon differentiation. Chapter 6 focused on transferring the knowledge and strategies described in the previous chapters for the implementation of a scalable and robust process for the 3D differentiation of hNSC derived from human induced pluripotent stem cells (hiPSC). Here, software-controlled perfusion stirred-tank bioreactors were used as technological system to sustain cell aggregation and dif-ferentiation. The work developed in this thesis provides practical and versatile new in vitro ap-proaches to model the human brain. Furthermore, the culture strategies described herein can be further extended to other sources of neural phenotypes, including pa-tient-derived hiPSC. The combination of this 3D culture strategy with the implemented characterization methods represents a powerful complementary tool applicable in the drug discovery, toxicology and disease modeling.

The characteristics of social entrepreneurship business models in Portugal

This work project is based on the MIES (Map of Innovation and Social Entrepreneurship in Portugal) database and it aims to understand the characteristics of social business models in the context of the portuguese market, by determining whether they follow the proposed characteristics by John Elkington and Pamela Hartigan, and then adding to their matrix. Furthermore, it tries to determine success patterns by comparing a group of successful social ventures with a group of less successful ones, with the objective of increasing the knowledge of social entrepreneurship as it applies to Portugal and provide a framework for future study.