4,415 research outputs found

    Metabolic pathways and therapeutic opportunities in the chronic lymphocytic leukemia microenvironment

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    This study delves into the intricate metabolic dynamics of chronic lymphocytic leukaemia (CLL) within the tumour microenvironment (TME) of lymphoid tissues. Unlike the traditional focus on quiescent CLL cells in peripheral blood, this study aims to unravel complex metabolic behaviour of CLL cells in the lymph node compartment, where CLL cells divide and become activated.Utilizing state-of-the-art methods, such as metabolomics, transcriptomics, and fluxomics, we found that interaction of CLL cells with adjacent cells within the TME results in significant metabolic alterations. Particularly, we discovered a shift towards glutamine dependency of CLL cells upon TME-related stimulation. Such metabolic alterations impact sensitivity of these leukaemia cells to treatments, especially to specific apoptosis inducing agents, such as venetoclax, which has become the cornerstone of CLL treatment. The study demonstrates that by targeting specific metabolic pathways, such as the electron transport chain, CLL cells can be sensitized to venetoclax treatment. This finding can be exploited for the development of innovative strategies in order to overcome drug resistance.Additionally, the thesis explores the effects of mitochondrial glutamine transporters and the broader implications of lipid metabolism alterations in CLL. It also probes into the role of key genetic factors, such as p53, in the metabolic regulation of CLL and other B cell malignancies, unveiling new insights into potential therapeutic vulnerabilities.Conclusively, this research not only fills critical gaps in our understanding of CLL metabolism within the TME but also paves the way for novel, targeted therapeutic interventions. By linking metabolic alterations to treatment responses, it sets the stage for more effective, personalized approaches in the management of CLL

    El metotrexato: Uso, experiencia, necesidades y expectativas en las enfermedades reumáticas

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    Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Medicina, Departamento de Medicina. Fecha de Lectura: 13-01-2023Esta tesis tiene embargado el acceso al texto completo hasta el 13-07-2024En la actualidad, el metotrexato (MTX) es el fármaco antirreumático modificador de la enfermedad (FAME) más utilizado en reumatología, por su efectividad para el control sintomático, el retraso en el daño articular, su bajo coste y su buen perfil de seguridad. El MTX es un análogo estructural del ácido fólico que actúa inhibiendo competitivamente la enzima dihidrofolato reductasa (DHFR), la cual participa en la formación del ácido folínico que es necesario para la formación del nucleósido timidina, requerido para la síntesis de ADN, ARN, timidilatos y proteínas. El MTX actúa inhibiendo parcialmente el sistema inmunitario y, reduciendo la inflamación articular autoinmunitaria a largo plazo. El tratamiento con FAME ha de ser precoz e intenso en el control de la actividad inflamatoria, dentro de la denominada «ventana de oportunidad terapéutica», con un control estrecho del curso clínico (tight control) que permita un tratamiento dinámico para conseguir el objetivo, que debería ser la remisión clínica o, alternativamente, un estado de bajo nivel de actividad. Al cabo de un año, hasta la mitad de los pacientes con Artritis Reumatoide interrumpen el MTX y la adherencia y persistencia del MTX, medida con una amplia variedad de herramientas diferentes, parece muy variable. Para mejorar el tratamiento de la enfermedad, es esencial identificar los factores que provocan la falta de adherencia a los fármacos, puesto que, una adherencia inadecuada o deficiente reduce la eficacia del tratamiento, lo que puede dar lugar a complicaciones y al deterioro de la salud y el bienestar de los enfermos. El objetivo general de la tesis es conocer las necesidades no cubiertas de los pacientes con enfermedades reumáticas al inicio y durante la continuidad del tratamiento con MTX y sugerir posibles soluciones. Encontramos que el MTX subcutáneo presenta un perfil de seguridad y eficacia adecuado, con un nivel de adherencia variable, aunque la falta de un criterio estandarizado para la medida de adherencia dificulta su valoración. Para investigar las barreras y los facilitadores de la adherencia al MTX en personas con enfermedades reumáticas y explorar la experiencia de la toma de decisiones compartida se realizaron tres grupos focales que permitieron identificar 4 aspectos diferentes: 1)relacionados con el medicamento, 2)relativos a la relación médico paciente, 3)en relación al entorno social; y 4) aspectos prácticas de la propia administración del fármaco. Todos ellos podrían ayudar a mejorar la adherencia, incluyendo la información de calidad, especialmente sobre los eventos adversos, el papel del entorno y la toma de decisiones compartida. Para corroborar la validez de estos resultados, se realizó una encuesta online. De las respuestas obtenidas se corroboró que los pacientes en tratamiento con MTX por una enfermedad autoinmune demandan más información escrita o en la web de mejor calidad que la que se ofrece actualmente en sus clínicas. En base a toda la información recabada y su discusión se decidió la elaboración de una hoja de información al paciente, un listado de comprobación para el reumatólogo y un posible itinerario de la enfermedad del paciente, para que éste tome conciencia de la enfermedad crónica que presenta y la necesidad de tratamiento y controles periódicos que requier

    The development of bioinformatics workflows to explore single-cell multi-omics data from T and B lymphocytes

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    The adaptive immune response is responsible for recognising, containing and eliminating viral infection, and protecting from further reinfection. This antigen-specific response is driven by T and B cells, which recognise antigenic epitopes via highly specific heterodimeric surface receptors, termed T-cell receptors (TCRs) and B cell receptors (BCRs). The theoretical diversity of the receptor repertoire that can be generated via homologous recombination of V, D and J genes is large enough (>1015 unique sequences) that virtually any antigen can be recognised. However, only a subset of these are generated within the human body, and how they succeed in specifically recognising any pathogen(s) and distinguishing these from self-proteins remains largely unresolved. The recent advances in applying single-cell genomics technologies to simultaneously measure the clonality, surface phenotype and transcriptomic signature of pathogen- specific immune cells have significantly improved understanding of these questions. Single-cell multi-omics permits the accurate identification of clonally expanded populations, their differentiation trajectories, the level of immune receptor repertoire diversity involved in the response and the phenotypic and molecular heterogeneity. This thesis aims to develop a bioinformatic workflow utilising single-cell multi-omics data to explore, quantify and predict the clonal and transcriptomic signatures of the human T-cell response during and following viral infection. In the first aim, a web application, VDJView, was developed to facilitate the simultaneous analysis and visualisation of clonal, transcriptomic and clinical metadata of T and B cell multi-omics data. The application permits non-bioinformaticians to perform quality control and common analyses of single-cell genomics data integrated with other metadata, thus permitting the identification of biologically and clinically relevant parameters. The second aim pertains to analysing the functional, molecular and immune receptor profiles of CD8+ T cells in the acute phase of primary hepatitis C virus (HCV) infection. This analysis identified a novel population of progenitors of exhausted T cells, and lineage tracing revealed distinct trajectories with multiple fates and evolutionary plasticity. Furthermore, it was observed that high-magnitude IFN-γ CD8+ T-cell response is associated with the increased probability of viral escape and chronic infection. Finally, in the third aim, a novel analysis is presented based on the topological characteristics of a network generated on pathogen-specific, paired-chain, CD8+ TCRs. This analysis revealed how some cross-reactivity between TCRs can be explained via the sequence similarity between TCRs and that this property is not uniformly distributed across all pathogen-specific TCR repertoires. Strong correlations between the topological properties of the network and the biological properties of the TCR sequences were identified and highlighted. The suite of workflows and methods presented in this thesis are designed to be adaptable to various T and B cell multi-omic datasets. The associated analyses contribute to understanding the role of T and B cells in the adaptive immune response to viral-infection and cancer

    Effects of municipal smoke-free ordinances on secondhand smoke exposure in the Republic of Korea

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    ObjectiveTo reduce premature deaths due to secondhand smoke (SHS) exposure among non-smokers, the Republic of Korea (ROK) adopted changes to the National Health Promotion Act, which allowed local governments to enact municipal ordinances to strengthen their authority to designate smoke-free areas and levy penalty fines. In this study, we examined national trends in SHS exposure after the introduction of these municipal ordinances at the city level in 2010.MethodsWe used interrupted time series analysis to assess whether the trends of SHS exposure in the workplace and at home, and the primary cigarette smoking rate changed following the policy adjustment in the national legislation in ROK. Population-standardized data for selected variables were retrieved from a nationally representative survey dataset and used to study the policy action’s effectiveness.ResultsFollowing the change in the legislation, SHS exposure in the workplace reversed course from an increasing (18% per year) trend prior to the introduction of these smoke-free ordinances to a decreasing (−10% per year) trend after adoption and enforcement of these laws (β2 = 0.18, p-value = 0.07; β3 = −0.10, p-value = 0.02). SHS exposure at home (β2 = 0.10, p-value = 0.09; β3 = −0.03, p-value = 0.14) and the primary cigarette smoking rate (β2 = 0.03, p-value = 0.10; β3 = 0.008, p-value = 0.15) showed no significant changes in the sampled period. Although analyses stratified by sex showed that the allowance of municipal ordinances resulted in reduced SHS exposure in the workplace for both males and females, they did not affect the primary cigarette smoking rate as much, especially among females.ConclusionStrengthening the role of local governments by giving them the authority to enact and enforce penalties on SHS exposure violation helped ROK to reduce SHS exposure in the workplace. However, smoking behaviors and related activities seemed to shift to less restrictive areas such as on the streets and in apartment hallways, negating some of the effects due to these ordinances. Future studies should investigate how smoke-free policies beyond public places can further reduce the SHS exposure in ROK

    Digital support for alcohol moderation and smoking cessation in cancer survivors

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    What informs a firm’s Attractiveness as an Alliance Partner? The development of a survey instrument.

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    Strategic alliances are defined as inter-organisational collaborative arrangements whose purpose is to achieve the strategic targets of partners (Das and Teng, 1998). Within the pharmaceutical industry, they represent a key form of disintegration that enables organisations to create a network based on partnerships, whereby the overarching goal is to pursue a set of agreed-upon goals, in which they share the benefits (Chen and Chen, 2002). Despite the high prevalence of strategic alliances within this industry, only 50% are considered stable or achieve performance perceived by the partners as satisfactory (McCutchen et al., 2008) and up to 70% terminate early (Kogut, 1989; Park and Russo, 1996; Park and Ungson, 1997). Nevertheless, 85% of the senior executives still believe alliances are and will continue to be essential or important to their business (Powerlinx, 2014), and as such have invested significantly in becoming attractive alliance partners, or partner of choice. Further, both conceptual and empirical evidence has signaled that a partner’s attractiveness can have significant contribution to the success of the alliance itself (Coombs and Deeds, 2000; Lee, 2007). Despite this evidence, there is no validated approach for a firm to test how attractive they are perceived to be by prospective partners. Without this, a firm is not able to tangibly understand what their perceived strengths and weaknesses are, and how these evolve over time. The purpose of this research is to address this gap. Further, the research aims to understand the impact of firm’s Alliance Strategy on their attractiveness scores. As such, this research makes three overarching and significant contributions; (1) the identification of two key antecedents of a firm’s Attractiveness as an Alliance Partner (2) the development of a self-assessment questionnaire for a firm to use in order to quantify their attractiveness, and (3) the development of research propositions for how an Alliance Strategy moderates the relationship between Attractiveness and its antecedents. This research applies Network Theory, which, in its most simple terms, refers to a firm’s relationships with others that have important and desired resources (Ireland et al., 2002). Networks promote alliance formation and firm success through ‘social capital’, described as the benefits a firm derives from their relationships (Coleman, 1988). Social capital increases in alliances with greater diversity within their networks (Baker, 2000) and with the quality of the alliances themselves (Glaister and Buckley, 1999). As such, this theory plays a key part in explaining the identified antecedents of Attractiveness - Previous Alliance Performance and Alliance Portfolio Diversity. In turn, this research extends Network Theory in two ways. Firstly, by introducing the novel concept of Attractiveness as an Alliance Partner as an indicator of a firm’s success or performance. Secondly, by introducing the novel concept of an Alliance Strategy as an important condition that will moderate a firm’s attractiveness. A mixed method approach has been used, comprising of four Empirical Studies in order to develop and finalise the research propositions and questionnaire. This research has been conducted within and for the pharmaceutical industry specifically but can be applied to other industries

    Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data

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    Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería, Ciencia de Datos y BioinformáticaClave Programa: DBICódigo Línea: 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
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