Intracellular sterol redistribution is an important step in the lipid homeostasis of organisms, a process directly linked to the organizational arrangement in the plasma membrane (PM) of cells. Previous studies in the budding yeast Saccharomyces cerevisiae have demonstrated that the ARV1 (ACAT-related enzyme-2 required for viability 1) protein is a major regulator of sterol transport from the endoplasmic reticulum to the plasma membrane, contributing to the structural organization of the PM, rendering it resistant to anti-fungal compounds as well as maintaining ER integrity. This study assessed the significance of ARV1 in the plant pathogenic fungus Cryphonectria parasitica (Cparv1) and investigated its role in the pathogenesis and virulence of the fungus. C. parasitica is the causative agent of Chestnut blight, which has wreaked havoc on the American chestnut species. Genomic analysis revealed that the Cparv1 gene is very closely linked to another gene that putatively encodes a cyanamide hydratase (Cpcah). An initial gene deletion event resulted in the elimination of both genes and a highly deformed phenotype in C. parasitica that was fully recoverable by complementation. PCR-based expression analysis determined that the lack of Cparv1 was responsible for the debilitated phenotype of the double mutant, with no transcript detectable from Cpcah. Subsequent complementation of the Cparv1 gene was also observed to restore the wildtype phenotype. Mass spectrometry-based (MS) results indicated a decrease in sterol content of the DCparv1 mutant strain compared to wildtype EP155 thus confirming a role for Cparv1 in sterol homeostasis. It has been shown that infection of C. parasitica with virulence-attenuating hypoviruses altered intracellular lipid content and protein secretion. Ultrastructure studies conducted on the Cparv1 strain showed disrupted organelle integrity and the presence of cytoplasmic double membrane stretches. Decreased sterol content in C. parasitica infected with CHV1-EP713 was observed similar to DCparv1 suggesting a connection between the hypovirus-infected phenotype and Cparv1. Furthermore, a non-targeted metabolomic study on all three strains identified 324 metabolites. Through the subsequent pathway analysis, we have investigated the pleiotropic effects in the C. parasitica strains and established a mechanistic linkage between this the activity of the ARV-1 protein and the hypovirus-infected phenotype.

This is a case study of the political situation in Sweden where a present and growing far-right has generated an outcome of political fragmentation. The process between this probable cause and outcome will be investigated to find the best possible explanation for how an isolated party could generate the outcome of political fragmentation despite their denied political participation with the other parties. The process will be analyzed through mainstream parties’ political discourse about the far-right and how they give expression for their strategies to deal with their presence. This will be done through an abductive discourse-pathway analysis, wherein mainstream parties’ dynamics towards the far-right and how it has changed over time will be analyzed. The result from the analysis showed that there are three possible outcomes when dealing with the far-right: political fragmentation, political unity and political polarization. The conclusion is that political fragmentation occurs when mainstream parties are pulled in different directions regarding what strategies to use when they ought to deal with a growing far-right presence.

[ES] La comprensión de los aspectos de la funcionalidad de las células que cuentan para los mecanismos de las enfermedades es el mayor reto de la medicina personalizada. A pesar de la disponibilidad creciente de los datos de genómica y transcriptómica, sigue existiendo una notable brecha entre la detección de las perturbaciones en la expresión de genes y la comprensión de su contribución en los mecanismos moleculares que últimamente tienen relación importante con el fenotipo estudiado. A lo largo de la última década, distintos modelos computacionales y matemáticos se han propuesto para el análisis de las rutas. Sin embargo, estos modelos no toman en cuenta los mecanismos dinámicos de las rutas como la estructura y las interacciones entre genes y proteínas. En esta tesis doctoral, presento dos modelos matemáticos ligeramente distintos, para integrar los datos transcriptómicos masivos de humano con un conocimiento previo de de las rutas de señalización y metabólicas para estimar las actividades mecánicas que están detrás de esas rutas (MPAs). Las MPAs son variables continuas con valores de nivel individual que pueden ser usadas con los modelos de aprendizaje de máquinas y métodos estadísticos para determinar los biomarcadores que podemos usar para los diagnósticos tempranos y la clasificación de subtipos de enfermedades, además de poder sugerir las dianas terapéuticas potenciales para las intervenciones individualizadas. El objetivo global es desarrollar nuevos y avanzados enfoques de la biología de sistemas para proponer unas hipótesis funcionales que nos ayuden a entender e interpretar los mecanismos complejos de las enfermedades. Estos mecanismos son cruciales para mejorar los tratamientos personalizados y predecir los resultados clínicos. En primer lugar, contribuí al desarrollo de un método que está diseñado para extraer las subrutas elementales desde la ruta de señalización con sus actividades estimadas. Posteriormente, este algoritmo se ha adaptado a los módulos metabólicos y se ha implementado como una herramienta web. Finalmente , el método ha revelado un panorama metabólico para una lista completa de diferentes tipos de cánceres. En este estudio, analicé el perfil metabólico de 25 tipos de cáncer distintos y se validó el método usando varios enfoques computacionales y experimentales. Cada método desarrollado en esta tesis ha sido enfrentado a otros métodos similares existentes, evaluados por sus sensibilidades y especificidades, experimentalmente validados cuando fue posible y usados para predecir resultados clínicos de varios tipos de cánceres. La investigación descrita en esta tesis y los resultados obtenidos fueron publicados en distintas revistas arbitradas que están relacionadas con el cáncer y biología de sistemas, y también en los periódicos nacionales. / [CA] La comprensió dels aspectes de la funcionalitat de les cèl·lules que compten per als mecanismes de les malalties és el major repte de la medicina personalitzada. Malgrat la disponibilitat creixent de les dades de genòmica i transcriptómica, continua existint una notable bretxa entre la detecció de les pertorbacions en l'expressió de gens i la comprensió de la seua contribució en els mecanismes moleculars que últimament tenen relació important amb el fenotip estudiat. Al llarg de l'última dècada, diferents models computacionals i matemàtics s'han proposat per a l'anàlisi de les rutes. No obstant això, aquests models no tenen en compte els mecanismes dinàmics de les rutes com l'estructura i les interaccions entre gens i proteïnes. En aquesta tesi doctoral, presente dos models matemàtics lleugerament diferents, per a integrar les dades transcriptómicos massius d'humà amb un coneixement previ de de les rutes de senyalització i metabòliques per a estimar les activitats mecàniques que estan darrere d'aqueixes rutes (MPAs). Les MPAs són variables contínues amb valors de nivell individual que poden ser usades amb els models d'aprenentatge de màquines i mètodes estadístics per a determinar els biomarcadores que podem usar per als diagnòstics primerencs i la classificació de subtipus de malalties, a més de poder suggerir les dianes terapèutiques potencials per a les intervencions individualitzades. L'objectiu global és desenvolupar nous i avançats enfocaments de la biologia de sistemes per a proposar unes hipòtesis funcionals que ens ajuden a entendre i interpretar els mecanismes complexos de les malalties. Aquests mecanismes són crucials per a millorar els tractaments personalitzats i predir els resultats clínics. En primer lloc, vaig contribuir al desenvolupament d'un mètode que està dissenyat per a extraure les subrutas elementals des de la ruta de senyalització amb les seues activitats estimades. Posteriorment, aquest algorisme s'ha adaptat als mòduls metabòlics i s'ha implementat com una eina web. Finalment, el mètode ha revelat un panorama metabòlic per a una llista completa de diferents tipus de càncers. En aquest estudi, vaig analitzar el perfil metabòlic de 25 tipus de càncer diferents i es va validar el mètode usant diversos enfocaments computacionals i experimentals. Cada mètode desenvolupat en aquesta tesi ha sigut enfrontat a altres mètodes similars existents, avaluats per les seues sensibilitats i especificitats, experimentalment validats quan va ser possible i usats per a predir resultats clínics de diversos tipus de càncers. La investigació descrita en aquesta tesi i els resultats obtinguts van ser publicats en diferents revistes arbitrades que estan relacionades amb el càncer i biologia de sistemes, i també en els periòdics nacionals. / [EN] Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is the main challenge for precision medicine. In spite of the increasing availability of genomic and transcriptomic data, there is still a gap between the detection of perturbations in gene expression and the understanding of their contribution to the molecular mechanisms that ultimately account for the phenotype studied. Over the last decade, different computational and mathematical models have been proposed for pathway analysis. However, they are not taking into account the dynamic mechanisms contained by pathways as represented in their layout and the interactions between genes and proteins. In this thesis, I present two slightly different mathematical models to integrate human transcriptomic data with prior knowledge of signalling and metabolic pathways to estimate the Mechanistic Pathway Activities (MPAs). MPAs are continuous and individual level values that can be used with machine learning and statistical methods to determine biomarkers for the early diagnosis and subtype classification of the diseases, and also to suggest potential therapeutic targets for individualized therapeutic interventions. The overall objective is, developing new and advanced systems biology approaches to propose functional hypotheses that help us to understand and interpret the complex mechanism of the diseases. These mechanisms are crucial for robust personalized drug treatments and predict clinical outcomes. First, I contributed to the development of a method which is designed to extract elementary sub-pathways from a signalling pathway and to estimate their activity. Second, this algorithm adapted to metabolic modules and it is implemented as a webtool. Third, the method used to reveal a pan-cancer metabolic landscape. In this study, I analyzed the metabolic module profile of 25 different cancer types and the method is also validated using different computational and experimental approaches. Each method developed in this thesis was benchmarked against the existing similar methods, evaluated for their sensitivity and specificity, experimentally validated when it is possible and used to predict clinical outcomes of different cancer types. The research described in this thesis and the results obtained were published in different systems biology and cancer-related peer-reviewed journals and also in national newspapers. / Cubuk, C. (2020). Modeling Functional Modules Using Statistical and Machine Learning Methods [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/156175

Le réseau des MAPK est composé de pathways de signalisation fermement entrecroisés impliqués dans le cancer. Toutefois, les mécanismes précis qui sous-tendent son influence sur l'équilibre entre la prolifération et la mort cellulaire demeurent insaisissablesDes données publiques ont été intégrés dans une carte de réactions détaillée, représentant l'influence du réseau des MAPKs sur la décision du destin cellulaire. Cette carte a ensuite été utilisée pour des analyses informatiques spécifiquesTout d'abord, les dynamiques du réseau des MAPKs dans les cancers de la vessie ont été analysés.Un modèle Booléen a été construit, représentant la réponse du réseau aux inputs d'intérêt.Les résultats de simulations systématiques ont été trouvés globalement cohérents avec des données publiques, et ont permis de déchiffrer les principaux événements qui sous-tendent les différents comportements observés dans le cancerEnsuite, la carte a été exploitée pour réanalyser des données publiques d'expression de gènes, avec l'objectif d'identifier les principaux acteurs de la transduction des signaux prolifératifs, dans des types cellulaires spécifiques.Des analyses du réseaux et des calculs statistiques ont conduit à l'identification de régions dérégulées dans le réseau des MAPKs, et à la délinéation de points d'intervention optimales dans cinq stades du cancer de la vessie et dans quatre sous-types de lymphome TL'ensemble de ces résultats a conduit à la formulation de nouvelles hypothèses concernant le fonctionnement du réseau des MAPKs dans différents états pathologiques, et à la sélection de composants cibles qui pourraient être envisagées pour le développement de nouveaux traitements / MAPK network consists of tightly interconnected signalling pathways. Although several studies established the involvement of this network in cancer deregulations, the precise mechanisms underlying its influence on the balance between cell proliferation and death remain elusive.Public data were integrated into a detailed reaction map, accounting for the influence of MAPK network on cell fate decision. This map was then used for computational analyses addressing specific cancer-related questions.First, the dynamics of MAPK network in bladder cancers were analysed. A Boolean model was built, accounting for the response of the network to selected inputs. The results of systematic simulations were found globally coherent with published data. Based on in silico experiments, the main events underlying different observed cancer cell behaviours were then deciphered.Next, the MAPK reaction map was exploited to reanalyse public high-throughput gene expression data. The goal was to identify key actors for the transduction of proliferative signals, in specific cell types. Network analyses and statistical computations led to the identification of deregulated MAPK network regions, and to the delineation of optimal intervention points aimed at blocking the proliferative signals transduced from such regions. This approach was used to study five different tumour stages and four different subtypes of T-cell lymphoma.Altogether, these results led to the formulation of novel hypotheses concerning the functioning of MAPK network in different pathological conditions, and to the selection of target components that might be considered for the development of novel treatments.

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Acute lymphoblastic leukemia is a hematological malignancy that gains a proliferative advantage and originates in the bone marrow. One of the more common genetic alterations in ALL is KMT2A-rearrangement which constitutes 80% of the cases of ALL in infants. Patients carrying the KMT2A rearrangement have a poor prognosis and will eventually develop drug resistance. This project aimed to find new therapeutic targets which would help in the development of novel drugs. We designed a model which uses gene expression data, to infer expressions of oncogenes and the genes which could be associated with immune pathways. The data was extracted and transformed by removing the batch effects and identifying the biotypes of these genes for more focused research. Here we utilized exome RNA-seq,  hence it was necessary to reduce the high dimensionality of the data. The dimensionality reduction was performed using Monte Carlo Feature Selection. After the feature selection, a list of highly significant genes was obtained. These genes were used in a machine learning model, R.ROSETTA, which produces rule-based results centered on rough sets theory. The rules were visualized using VisuNet, an interactive tool that creates networks from the rules. Among others, we identified levels of expressions of genes such as JAK3, TOX3, and DMRTA1 and their relations to other genes  using the machine learning model. These significant genes were also used to do pathway analysis using pathfindR which allowed us to infer the oncogenic pathways. The pathway analysis helped us deduce pathways such as immunodeficiency and other signaling pathways that could be potential drugs

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L’anémie falciforme est une maladie monogénique causée par une mutation dans le locus de la β-globine. Malgré le fait que l’anémie falciforme soit une maladie monogénique, cette maladie présente une grande hétérogénéité clinique. On présume que des facteurs environnementaux et génétiques contribuent à cette hétérogénéité. Il a été observé qu’un haut taux d’hémoglobine fœtale (HbF) diminuait la sévérité et la mortalité des patients atteints de l’anémie falciforme. Le but de mon projet était d’identifier des variations génétiques modifiant la sévérité clinique de l’anémie falciforme. Dans un premier temps, nous avons effectué la cartographie-fine de trois régions précédemment associées avec le taux d’hémoglobine fœtale. Nous avons ensuite effectué des études d’association pan-génomiques avec deux complications cliniques de l’anémie falciforme ainsi qu’avec le taux d’hémoglobine fœtale. Hormis les régions déjà identifiées comme étant associées au taux d’hémoglobine fœtale, aucun locus n’a atteint le niveau significatif de la puce de génotypage. Pour identifier des groupes de gènes modérément associés au taux d’hémoglobine fœtale qui seraient impliqués dans de mêmes voies biologiques, nous avons effectué une étude des processus biologiques. Finalement, nous avons effectué l’analyse de 19 exomes de patients Jamaïcains ayant des complications cliniques mineures de l’anémie falciforme. Compte tenu de la taille des cohortes de réplication disponibles, nous n’avons pas les moyens de valider statistiquement les variations identifiées par notre étude. Cependant, nos résultats fournissent de bons gènes candidats pour des études fonctionnelles et pour les réplications futures. Nos résultats suggèrent aussi que le β-hydroxybutyrate en concentration endogène pourraient influencer le taux d’hémoglobine fœtale. De plus, nous montrons que la cartographie-fine des régions associées par des études pan-génomiques peut identifier des signaux d’association additionnels et augmenter la variation héritable expliquée par cette région. / Sickle cell disease is a monogenic disease caused by a mutation in the β-globin locus. Although it is a monogenic disease, it shows a high clinical heterogeneity. Environmental and genetic factors are thought to play a role in this heterogeneity. It has been observed that a high fetal hemoglobin (HbF) levels correlates with a diminution of the severity and mortality of patients with sickle cell disease. The goal of my project was to identify genetic modifiers of the clinical severity of sickle cell disease. First, I performed the fine-mapping of three regions previously associated with HbF levels. Second, I performed genome-wide association studies with two clinical complications of sickle cell disease as well as with HbF levels. Since no new loci reached array-wide significance for HbF levels, I performed a pathway analysis to identify additional HbF loci of smaller effect size that might implicate shared biological processes. Finally, I performed the analysis of 19 whole exomes from Jamaican sickle cell disease patients with very mild complications. In conclusion, given the sample size of the replication cohorts available, we do not currently have the means to statistically validate the association signals. However, these results provide good candidate genes for functional studies and for future replication. Our results also suggest that β-hydroxybutyrate in endogenous levels could influence HbF levels. Furthermore, we show that fine-mapping the loci associated in genome-wide association studies can identify additional signals and increase the explained heritable variation.