No description available.

No description available.

Resting conventional T cells (Tconv) can be distinguished from T regulatory cells (Treg) by the canonical markers FOXP3, CD25 and CD127. However, the expression of these proteins alters after T-cell activation leading to overlap between Tconv and Treg. The objective of this study was to distinguish resting and antigen-responsive T effector (Tconv) and Treg using single-cell technologies. CD4+ Treg and Tconv cells were stimulated with antigen and responsive and non-responsive populations processed for targeted and non-targeted single-cell RNAseq. Machine learning was used to generate a limited set of genes that could distinguish responding and non-responding Treg and Tconv cells and which was used for single-cell multiplex qPCR and to design a flow cytometry panel. Targeted scRNAseq clearly distinguished the four-cell populations. A minimal set of 27 genes was identified by machine learning algorithms to provide discrimination of the four populations at >95% accuracy. In all, 15 of the genes were validated to be differentially expressed by single-cell multiplex qPCR. Discrimination of responding Treg from responding Tconv could be achieved by a flow cytometry strategy that included staining for CD25, CD127, FOXP3, IKZF2, ITGA4, and the novel marker TRIM which was strongly expressed in Tconv and weakly expressed in both responding and non-responding Treg. A minimal set of genes was identified that discriminates responding and non-responding CD4+ Treg and Tconv cells and, which have identified TRIM as a marker to distinguish Treg by flow cytometry.

Fettvävnaden är en viktig regulator för ämnesomsättningen och uppvisar en komplex cellulär arkitektur som påverkar olika fysiologiska och patologiska processer. Dess heterogena natur är relativt ostrukturerad och består huvudsakligen av bräckliga feta adipocyter och immunceller. Dessa komplikationer försvårar studier av mikroarkitekturen - som är avgörande för att förstå dess beteende - vilket nyligen har gynnats av teknik med rumslig upplösning, som möjliggör studier av genomiska profiler samtidigt som informationen från vävnaden bevaras. I detta arbete undersöks kromatindynamiken i fettvävnad med hjälp av den nyutvecklade Spatial Assay for Transposase-Accessible Chromatin med sekvensering med hög genomströmning (Spatial ATAC-seq). Med fokus på subkutan vit fettvävnad samlades prover in från en individ som led av fetma före och fem år efter en bariatrisk operation för att studera förändringar i samband med betydande viktnedgång. Studien omfattar detaljer för både experimentella protokoll och avancerade beräkningsverktyg för dataanalys, inklusive användning av en utvecklingsversion av Semla-paketet för att integrera data om rumslig tillgänglighet och kromatintillgänglighet. Analysen visade på en mångsidig cellulär arkitektur och distinkta genomiska egenskaper i vävnaden, vilket framhävde förekomsten av specifika celltyper som AdipoLEP-liknande adipocyter och infiltrerande immunceller. Denna studie visade att det är möjligt att tillämpa Spatial ATAC-seq för att undersöka de molekylära mekanismerna i fettvävnad som ligger till grund för metabol hälsa och sjukdom, särskilt i samband med fetma och viktminskning. / Adipose tissue is a critical regulator of metabolism, exhibiting a complex cellular architecture that influences various physiological and pathological processes. Its heterogeneous nature is relatively unstructured, mainly formed by fragile fatty adipocytes and immune cells. These intricacies complicate the study of its microarchitecture – crucial for understanding its behaviour – which has recently benefitted from spatially resolved technologies, that enable the study of genomic profiles while keeping the information from the tissue. This work explores the chromatin dynamics of adipose tissue using the newly developed Spatial Assay for Transposase-Accessible Chromatin with high throughput sequencing (Spatial ATAC-seq). Focusing on subcutaneous white adipose tissue, samples were collected from an individual suffering from obesity before and five years after bariatric surgery to study changes associated with significant weight loss. The study comprises details for both experimental protocols and advanced computational tools for data analysis, including the use of a development version of Semla package to integrate spatial and chromatin accessibility data. The analysis revealed a diverse cellular architecture and distinct genomic features across the tissue, highlighting the presence of specific cell types such as AdipoLEP-like adipocytes and infiltrating immune cells. This study demonstrated the feasibility of applying Spatial ATAC-seq in investigating the molecular mechanisms of adipose tissue underlying metabolic health and disease, particularly in the context of obesity and weight loss.

[ES] La introducción de la secuenciación de ARN a nivel de célula única (scRNA-seq) en el ámbito de la transcriptómica ha redefinido nuestro entendimiento de la diversidad celular, arrojando luz sobre los mecanismos subyacentes a la heterogeneidad tisular. No obstante, al inicio de esta tesis, las limitaciones de a esta tecnología obstaculizaban su aplicación en el estudio de procesos complejos, entre ellos el splicing alternativo. A pesar de ello, los patrones de splicing a nivel celular planteaban incógnitas que esta tecnología tenía el potencial de resolver: ¿es posible observar, a nivel celular, la misma diversidad de isoformas que se detecta mediante RNA-seq a nivel de tejido? ¿Qué función desempeñan las isoformas alternativas en la constitución de la identidad celular? El objetivo de esta tesis es desbloquear el potencial del scRNA-seq para el análisis de isoformas, abordando sus dificultades técnicas y analíticas mediante el desarrollo de nuevas metodologías computacionales. Para lograrlo, se trazó una hoja de ruta con tres objetivos. Primero, se establecieron cuatro requisitos para el estudio de las isoformas mediante scRNA-seq, llevando a cabo una revisión de la literatura existente para evaluar su cumplimiento. Tras completar este marco con simulaciones computacionales, se identificaron las debilidades y fortalezas de los métodos de scRNA-seq y las herramientas computacionales disponibles. Durante la segunda etapa de la investigación, estos conocimientos se utilizaron para diseñar un protocolo óptimo de procesamiento de datos de scRNA-seq. En concreto, se integraron datos de lecturas largas a nivel de tejido con datos de scRNA-seq para garantizar una identificación adecuada de las isoformas así como su cuantificación a nivel celular. Este proceso permitió ampliar las estrategias computacionales disponibles para la reconstrucción de transcriptomas a partir de lecturas largas, mejoras que fueron implementadas en SQANTI3, software de referencia en transcriptómica. Por último, los datos procesados se utilizaron para desarrollar un nuevo método de análisis de co-expresión de isoformas a fin de desentrañar redes de regulación del splicing alternativo implicadas en la constitución de la identidad celular. Dada la elevada variabilidad de los datos de scRNA-seq, este método se basa en la utilización de una estrategia de correlación basada en percentiles que atenúa el ruido técnico y permite la identificación de grupos de isoformas co-expresadas. Una vez configurada la red de co-expresión, se introdujo una nueva estrategia de análisis para la detección de patrones de co-utilización de isoformas que suceden de forma independiente a la expresión a nivel de gen, denominada co-Differential Isoform Usage. Este enfoque facilita la identificación de una capa de regulación de la identidad celular atribuible únicamente a mecanismos post-transcripcionales. Para una interpretación biológica más profunda, se aplicó una estrategia de anotación computacional de motivos y dominios funcionales en las isoformas definidas con lecturas largas, revelando las propiedades biológicas de las isoformas involucradas en la red de co-expresión. Estas investigaciones culminan en el lanzamiento de acorde, un paquete de R que encapsula las diferentes metodologías desarrolladas en esta tesis, potenciando la reproducibilidad de sus resultados y proporcionando una nueva herramienta para explorar la biología de las isoformas alternativas a nivel de célula única. En resumen, esta tesis describe una serie de esfuerzos destinados a desbloquear el potencial de los datos de scRNA-seq para avanzar en la comprensión del splicing alternativo. Desde un contexto de escasez de herramientas y conocimiento previo, se han desarrollado soluciones de análisis innovadoras que permiten la aplicación de scRNA-seq al estudio de las isoformas alternativas, proporcionando recursos innovadores para profundizar en la regulación post-transcripcional y la función celular. / [CA] La introducció de la seqüenciació d'ARN a escala de cèl·lula única (scRNA-seq) en l'àmbit de la transcriptòmica ha redefinit el nostre enteniment de la diversitat cel·lular, projectant llum sobre els mecanismes subjacents a l'heterogeneïtat tissular. Malgrat les limitacions inicials d'aquesta tecnologia, especialment en el context de processos complexos com l'splicing alternatiu, els patrons d'splicing a escala cel·lular plantejaven incògnites amb potencial de resolució: és possible observar, a escala cel·lular, la mateixa diversitat d'isoformes que es detecta mitjançant RNA-seq en teixits? Quina funció tenen les isoformes alternatives en la constitució de la identitat cel·lular? L'objectiu d'aquesta tesi és desbloquejar el potencial del scRNA-seq per a l'anàlisi d'isoformes alternatives, abordant les seues dificultats tècniques i analítiques amb noves metodologies computacionals. Per a això, es va traçar una ruta amb tres objectius. Primerament, es van establir quatre requisits per a l'estudi de les isoformes mitjançant scRNA-seq, amb una revisió de la literatura existent per avaluar-ne el compliment. Després de completar aquest marc amb simulacions computacionals, es van identificar les debilitats i fortaleses dels mètodes de scRNA-seq i de les eines computacionals disponibles. Durant la segona etapa de la investigació, aquests coneixements es van utilitzar per dissenyar un protocol òptim de processament de dades de scRNA-seq. En concret, es van integrar dades de lectures llargues a escala de teixit amb dades de scRNA-seq per a garantir una identificació adequada de les isoformes així com la seua quantificació a escala cel·lular. Aquest procés va permetre ampliar les estratègies computacionals disponibles per a la reconstrucció de transcriptomes a partir de lectures llargues, millores que van ser implementades en SQANTI3, un programari de referència en transcriptòmica. Finalment, les dades processades es van fer servir per a desenvolupar un nou mètode d'anàlisi de coexpressió d'isoformes amb l'objectiu de desentranyar xarxes de regulació de l'splicing alternatiu implicades en la constitució de la identitat cel·lular. Donada l'elevada variabilitat de les dades de scRNA-seq, aquest mètode es basa en la utilització d'una estratègia de correlació basada en percentils que minimitza el soroll tècnic i permet la identificació de grups d'isoformes coexpressades. Un cop configurada la xarxa de coexpressió, es va introduir una nova estratègia d'anàlisi per a la detecció de patrons de co-utilització d'isoformes que succeeixen de forma independent a l'expressió del seu gen, denominada co-Differential Isoform Usage. Aquest enfocament facilita la identificació d'una capa de regulació de la identitat cel·lular atribuïble únicament a mecanismes post-transcripcionals. Per a una interpretació biològica més profunda, es va aplicar una estratègia d'anotació computacional de motius i dominis funcionals en les isoformes definides amb lectures llargues, revelant les propietats biològiques de les isoformes involucrades en la xarxa de coexpressió. Aquestes investigacions culminen en el llançament d'acorde, un paquet de R que encapsula les diferents metodologies desenvolupades en aquesta tesi, potenciant la reproducibilitat dels seus resultats i proporcionant una nova eina per a explorar la biologia de les isoformes alternatives a escala de cèl·lula única. En resum, aquesta tesi descriu una sèrie d'esforços destinats a desbloquejar el potencial de les dades de scRNA-seq per a avançar en la comprensió de l'splicing alternatiu. Des d'un context de manca d'eines i coneixement previ, s'han desenvolupat solucions d'anàlisi innovadores que permeten l'aplicació de scRNA-seq a l'estudi de les isoformes alternatives, proporcionant recursos innovadors per a aprofundir en la regulació post-transcripcional i la funció cel·lular. / [EN] In the world of transcriptomics, the emergence of single-cell RNA sequencing (scRNA-seq) ignited a revolution in our understanding of cellular diversity, unraveling novel mechanisms in tissue heterogeneity, development and disease. However, when this thesis began, using scRNA-seq to understand Alternative Splicing (AS) was a challenging frontier due the inherent limitations of the technology. In spite of this research gap, pertinent questions persisted regarding cell-level AS patterns, particularly concerning the recapitulation of isoform diversity observed in bulk RNA-seq data at the cellular level and the roles played by cell and cell type-specific isoforms. The work conducted in the present thesis aims to harness the potential of scRNA-seq for alternative isoform analysis, outlining technical and analytical challenges and designing computational methods to overcome them. To achieve this, we established a roadmap with three main aims. First, we set requirements for studying isoforms using scRNA-seq and conducted an extensive review of existing research, interrogating whether these requirements were met. Combining this acquired knowledge with several computational simulations allowed us to delineate the strengths and pitfalls of available data generation methods and computational tools. During the second research stage, this insight was used to design a suitable data processing pipeline, in which we jointly employed bulk long-read and short-read scRNA-seq sequenced from full-length cDNAs to ensure adequate isoform reconstruction as well as sensitive cell-level isoform quantification. Additionally, we refined available transcriptome curation strategies, introducing them as innovative modules in the transcriptome quality control software SQANTI3. Lastly, we harnessed single-cell isoform expression data and the rich biological diversity inherent in scRNA-seq, encompassing various cell types, in the design of a novel isoform co-expression analysis method. Percentile correlations effectively mitigated single-cell noise, unveiling clusters of co-expressed isoforms and exposing a layer of regulation in cellular identity that operated independently of gene expression. We additionally introduced co-Differential Isoform Usage (coDIU) analysis, enhancing our ability to interpret isoform cluster networks. This endeavour, combined with the computational annotation of functional sites and domains in the long read-defined isoform models, unearthed a distinctive functional signature in coDIU genes. This research effort materialized in the release of acorde, an R package that encapsulates all analyses functionalities developed throughout this thesis, providing a reproducible means for the scientific community to further explore the depths of alternative isoform biology within single-cell transcriptomics. This thesis describes a complex journey aimed at unlocking the potential of scRNA-seq data for investigating AS and isoforms: from a landscape marked by the scarcity of tools and guidelines, towards the development of novel analysis solutions and the acquisition of valuable biological insight. In a swiftly evolving field, our methodological contributions constitute a significant leap forward in the application of scRNA-seq to the study of alternative isoform expression, providing innovative resources for delving deeper into the intricacies of post-transcriptional regulation and cellular function through the lens of single-cell transcriptomics. / The research project was funded by the BIO2015-71658 and BES-2016-076994 grants awarded by the Spanish Ministry of Science and Innovation / Arzalluz Luque, Á. (2024). Understanding Isoform Expression and Alternative Splicing Biology through Single-Cell RNAseq [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/203888

Streptococcus pneumoniae is a significant cause of mortality and morbidity in both children and older adults, with infection resulting in invasive disease, pneumonia and otitis media. The inclusion of pneumococcal conjugate vaccines in routine infant immunisation programmes has had a major impact on disease rates. Vaccine-induced protection against pneumococcal infection is thought to be mediated by the generation of persistent serotype-specific functional antibodies and antigen-specific memory B cells, the latter capable of generating a rapid secondary antibody response on re-exposure to antigen. Although many studies have investigated the immunogenicity of pneumococcal vaccines in different age groups by measuring serotype-specific antibodies, there is more limited information about the B cells underlying such an immune response. Important areas to investigate include the identity of the B cell subsets involved in antibody production and the potential link between memory B cells (B_MEM) and persistent antibody production by long-lived plasma cells. In this thesis I have investigated in detail the immune response to pneumococcal vaccines given to children and adults by a variety of different methods. By examining the variability of a B_MEM ELISpot method, it was shown that this assay is robust and reproducible and can be performed on fresh or frozen samples and in different laboratories. Using this technique, in a study of pre-school children, it was demonstrated for the first time that the level of pre-existing serotype 3-specific antibody is negatively correlated with, and may directly impair the B_MEM response to a booster dose of 13-valent pneumococcal conjugate vaccine (PCV-13) containing serotype 3 glycoconjugate. In the same study, it was shown that antibody persistence against most vaccine serotypes can be expected until the age of 3.5 years. A novel antigen-labelling technique was used in a detailed kinetics study of antigen-specific B cell subsets in response to either PCV-13 or 23-valent pneumococcal polysaccharide vaccine in adults. The results of this study revealed distinct B cell subset response patterns that were observed in all study participants indicating that IgM B_MEM seem to play a major role in the immune response to pneumococcal vaccines. In addition, in the same study, genome wide analysis of gene expression was performed and it was shown that vaccination with either a pneumococcal conjugate or polysaccharide vaccine results in a marked difference in numbers of differentially expressed genes 8 days following vaccination. A further tool likely to be of use in investigating B cell responses is the analysis of the antibody repertoire using next-generation sequencing techniques. In order to test the ability of these methods to detect vaccine responses, a large dataset of high-throughput B cell receptor sequences was analysed and revealed convergence of antigen-specific complementary-determining region (CDR)₃ amino acid (AA) sequences following vaccination and identified antigen-specific sequences. It was further demonstrated that for sequences directed against the H. influenzae type b (Hib) polysaccharide, diversity of immunoglobulin gene rearrangements is much greater than previously recognised. Frequencies of Hib-specific CDR₃ AA sequences were linked with anti-Hib avidity indices highlighting the potential of this method as an alternative (functional) measure of vaccine immunogenicity. These data suggest that studying the B cells and antibody repertoire post-vaccination can give novel insights into the biology that underlies the immune responses.

L’émergence des Mégadonnées (« Big Data ») en biologie moléculaire, surtout à travers la transcriptomique, a révolutionné la façon dont nous étudions diverses disciplines telles que le processus de développement du médicament ou la recherche sur le cancer. Ceci fut associé à un nouveau concept, la médecine de précision, dont le principal but est de comprendre les mécanismes moléculaires entraînant une meilleure réponse thérapeutique chez le patient. Cette thèse est à mi-chemin entre les études pharmaco — et toxicogénomiques expérimentales, et les études cliniques et translationnelles. Le but de cette thèse est surtout de montrer le potentiel et les limites de ces jeux de données et leur pertinence pour la découverte de biomarqueurs de réponse ainsi que la compréhension des mécanismes d’action/toxicité de médicaments, en vue d’utiliser ces informations à des fins thérapeutiques. L’originalité de cette thèse réside dans son approche globale pour analyser les plus larges jeux de données pharmaco/toxicogénomiques publiés à ce jour et ceci pour : 1) Aborder la notion de biomarqueurs de réponse aux médicaments en pharmacogénomique du cancer, en étudiant les facteurs discordants entre deux grandes études publiées en 2012; 2) Comprendre le mécanisme d’action des médicaments et construire une taxonomie performante en utilisant une approche intégrative; et 3) Créer un répertoire toxicogénomique à partir des hépatocytes humains, exposés à différentes classes de médicaments et composés chimiques. Mes contributions principales sont les suivantes : • J’ai développé une approche bioinformatique pour étudier les facteurs discordants entre deux grandes études pharmacogénomiques et suggérées que les différences observées émergeaient plutôt de l’absence de standardisation des mesures pharmacologiques qui pourrait limiter la validation de biomarqueurs de réponse aux médicaments. • J’ai implémenté une approche bioinformatique qui montre la supériorité de l’intégration tenant en compte des différents paramètres pour les médicaments (structure, cytotoxicité, perturbation du transcriptome) afin d’élucider leur mécanisme d’action (MoA). • J’ai développé un pipeline bioinformatique pour étudier le niveau de conservation des mécanismes moléculaires entre les études toxicogénomiques in vivo et in vitro démontrant que les hépatocytes humains sont un modèle fiable pour détecter les produits toxiques hépatocarcinogènes. Au total, nos études ont permis de fournir un cadre de travail original pour l’exploitation de différents types de données transcriptomiques pour comprendre l’impact des produits chimiques sur la biologie cellulaire. / The emergence of Big Data in molecular biology, especially through the study of transcriptomics, has revolutionized the way we look at various disciplines, such as drug development and cancer research. Big data analysis is an important part of the concept of precision medicine, which primary purpose is to understand the molecular mechanisms leading to better therapeutic response in patients. This thesis is halfway between pharmaco-toxicogenomics experimental studies, and clinical and translational studies. The aim of this thesis is mainly to show the potential and limitations of these studies and their relevance, especially for the discovery of drug response biomarkers and understanding the drug mechanisms (targets, toxicities). This thesis is an original work since it proposes a global approach to analyzing the largest pharmaco-toxicogenomic datasets available to date. The key aims were: 1) Addressing the challenge of reproducibility for biomarker discovery in cancer pharmacogenomics, by comparing two large pharmacogenomics studies published in 2012; 2) Understanding drugs mechanism of action using an integrative approach to generate a superior drug-taxonomy; and 3) Evaluating the conservation of toxicogenomic responses in primary hepatocytes vs. in vivo liver samples in order to check the feasability of cell models in toxicology studies. My main contributions can be summarized as follow: - I developed a bioinformatics pipeline to study the factors that trigger (in)consistency between two major pharmacogenomic studies. I suggested that the observed differences emerged from the non-standardization of pharmacological measurements, which could limit the validation of drug response biomarker. - I implemented a bioinformatics pipeline that demonstrated the superiority of the integrative approach, since it takes into account different parameters for the drug (structure, cytotoxicity, transcriptional perturbation) to elucidate the mechanism of action (MoA). - I developed a bioinformatics pipeline to study the level of conservation of toxicity mechanisms between the in vivo and in vitro system, showing that human hepatocytes is a reliable model for hepatocarcinogens testing. Overall, our studies have provided a unique framework to leverage various types of transcriptomic data in order to understand the impact of chemicals on cell biology.

Piscine francisellosis in an infectious emerging bacterial disease that affects several marine and fresh water fish species worldwide, including farmed salmon, wild and farmed cod, farmed tilapia and several ornamental species, for which no commercial treatment or vaccine exists. During 2011 and the first semester of 2012, chronic episodes of moderate to high levels of mortality with nonspecific clinical signs, and widespread multifocal white nodules as the most consistent gross pathological lesion were experienced by farmed tilapia fingerlings at two different locations in Northern Europe. In this study such outbreaks of granulomatous disease were diagnosed as francisellosis with a genus-specific PCR, and 10 new isolates of the bacterium including the one named STIR-GUS-F2f7, were recovered on a new selective “cysteine blood-tilapia” agar and cysteine heart agar with bovine haemoglobin. Ultrastructural observations of the pathogen in Nile tilapia (O. niloticus) tissues suggested the secretion of outer membrane vesicles (OMVs) by the bacterial cells during infection in these fish. This represented the first documented report of isolation of pathogenic Francisella strains from tilapia in Europe. The phenotypic characterisation indicated that isolates recovered were able to metabolise dextrin, N-acetyl-D glucosamine, D-fructose, α-D-glucose, D-mannose, methyl pyruvate, acetic acid, α-keto butyric acid, L-alaninamide, L-alanine, L-alanylglycine, L-asparagine, L-glutamic acid, L-proline, L-serine, L-threonine, inosine, uridine, glycerol, D L-α-glycerol phosphate, glucose-1-phosphate and glucose-6-phosphate. The predominant structural fatty acids of the isolates were 24:1 (20.3%), 18:1n-9 (16.9%), 24:0 (13.1%) 14:0 (10.9%), 22:0 (7.8%), 16:0 (7.6%) and 18:0 (5.5%). Anti-microbial resistance analyses indicated that STIR-GUS-F2f7 was susceptible to neomycin, novobiocin, amikacin, ciprofloxacin, imipenem, gatifloxacin, meropenem, tobramycin, nitrofurantoin, and levofloxacin using the quantitative broth micro-dilution method, while the qualitative disc diffusion method indicated susceptibility to enrofloxacin, kanamycin, gentamicin, tetracycline, oxytetracycline, florfenicol, oxolinic acid and streptomycin. The use of the following housekeeping genes: mdh, dnaA, mutS, 16SrRNA-ITS-23SrRNA, prfB putA rpoA, rpoB and tpiA indicated 100% similarity with other isolates belonging to the subspecies F. noatunensis orientalis (Fno). Koch’s postulates were successfully fulfilled by establishing an intraperitoneal injection (IP) challenge model with STIR-GUS-F2f7 in Nile tilapia. Moreover, the challenge model was used to investigate the susceptibility of 3 genetic groups of tilapia to STIR-GUS-F2f7. The lowest amount of bacteria required to cause mortality was 12 CFU/ml and this was seen as early as only 24 hours post infection in the red Nile tilapia and in the wild type after 26 days, no mortalities were seen in the species O. mossambicus with this dose. The mortality in red O. niloticus was significantly higher than that of the other two tilapia groups when 12 and 120 CFU/fish were injected. It was also observed that when a dose of 1200 CFU/ml was used, the mortality in O. niloticus wild type was significantly lower than that of the other two tilapia groups and no differences were seen among the 3 groups when the highest dose (1.2 x105 CFU/fish) was used. The median lethal dose (LD50) of O. niloticus wild type was the most stable during the experiment (values around 104 CFU/ml) and the highest of the three groups after day 25 post infection. At the end of the experiment (day 45) the LD50 was 30 CFU/ml in the red Nile tilapia, 2.3x104 CFU/ml for the wild type and 3.3x102 CFU/ml for O. mossambicus. This pattern, where the LD50 of the red tilapia was lower than that of the other two groups, was observed during the whole experiment. The outcomes of these experiments suggested that the red Nile tilapia family appeared to be the most susceptible while the wild type Nile tilapia family the most resistant. The complete genome of STIR-GUS-F2f7 was sequenced using next generation sequencing (NGS) Illumina Hi-Seq platform™, and the annotation of the assembled genome predicted 1970 protein coding sequences and 63 non-coding rRNA sequences distributed in 328 sub-systems. The taxonomy of the species Francisella noatunensis was revised using genomic-derived parameters form STIR-GUS-F2f7 and other strains in combination with a polyphasic approach that included ecologic, chemotaxonomic and phenotypic analyses. The results indicated that STIR-GUS-F2f7 and all the other strains from warm water fish represent a new bacterial species for which the name Francisella orientalis was assigned. Moreover the description of F. noatunensis was emended and the creation of a new subspecies within this taxon i.e. Francisella noatunensis subsp. chilense was proposed. The results of this study led to the development of a highly efficacious vaccine to protect tilapia against francisellosis.

Contexte : L’anémie falciforme ou drépanocytose est un problème de santé important, particulièrement pour les patients d’origine africaine. La variation phénotypique de l’anémie falciforme est problématique pour le suivi et le traitement des patients. L’architecture génomique responsable de cette variabilité est peu connue. Principe : Mieux saisir la contribution génétique de la variation clinique de cette maladie facilitera l’identification des patients à risque de développer des phénotypes sévères, ainsi que l’adaptation des soins. Objectifs : L’objectif général de cette thèse est de combler les lacunes relatives aux connaissances sur l’épidémiologie génomique de l’anémie falciforme à l’aide d’une cohorte issue au Bénin. Les objectifs spécifiques sont les suivants : 1) caractériser les profils d’expressions génomiques associés à la sévérité de l’anémie falciforme ; 2) identifier des biomarqueurs de la sévérité de l’anémie falciforme ; 3) identifier la régulation génétique des variations transcriptionelles ; 4) identifier des interactions statistiques entre le génotype et le niveau de sévérité associé à l’expression ; 5) identifier des cibles de médicaments pour améliorer l’état des patients atteints d’anémie falciforme. Méthode : Une étude cas-témoins de 250 patients et 61 frères et soeurs non-atteints a été menée au Centre de Prise en charge Médical Intégré du Nourrisson et de la Femme Enceinte atteints de Drépanocytose, au Bénin entre février et décembre 2010. Résultats : Notre analyse a montré que des profils d’expressions sont associés avec la sévérité de l’anémie falciforme. Ces profils sont enrichis de génes des voies biologiques qui contribuent à la progression de la maladie : l’activation plaquettaire, les lymphocytes B, le stress, l’inflammation et la prolifération cellulaire. Des biomarqueurs transcriptionnels ont permis de distinguer les patients ayant des niveaux de sévérité clinique différents. La régulation génétique de la variation de l’expression des gènes a été démontrée et des interactions ont été identifiées. Sur la base de ces résultats génétiques, des cibles de médicaments sont proposées. Conclusion: Ce travail de thèse permet de mieux comprendre l’impact de la génomique sur la sévérité de l’anémie falciforme et ouvre des perspectives de développement de traitements ciblés pour améliorer les soins offerts aux patients. / Background: Sickle Cell Disease (SCD) is an important public health issue, particularly in Africa. Phenotypic heterogeneity of SCD is problematic for follow-up and treatment of patients. Little is known about the underlying genomic architecture responsible for this variation. Rationale: Understanding the genetic contribution to the inter-patient variability will help in identifying patients at risk of developing more severe clinical outcomes, as well as help guide future developments for treatment options. Objectives: To characterize genome-wide gene expression patterns associated with SCD clinical severities and to identify genetic regulators of this variation. More specifically, our objectives were to associate gene expression profiles with SCD severity, identify transciptional biomarkers, characterise the genetic control of gene expression variation, and propose drug targets. Methods: A case-control population of 250 SCD patients and 61 unaffected siblings from the National SCD Center in Benin were recruited. Genome-wide gene expression profiles and genotypic data were generated. Results: Genome-wide gene expression patterns associated with SCD clinical variation were enriched in B-lymphocyte development, platelet activation, stress, inflammation and cell proliferation pathways. Transcriptional biomarkers that can discriminate SCD patients with respect to clinical severities were identified. Hundreds of genetic regulators were significantly associated with gene expression variation and potential drug targets are suggested. Conclusion: This work improves our understanding of the biological basis of SCD clinical variation and has the potential to guide development of targeted treatments for SCD patients.

Angiogênese é o processo de formação de novos vasos sanguíneos a partir dos vasos existentes. É um processo vital, mas muitas doenças também dependem deste mecanismo para obter nutrientes e progredir. Estas \"doenças dependentes de angiogênese\" incluem cânceres, retinopatias e degeneração macular. Alguns inibidores da angiogênese foram desenvolvidos na última década, com o objetivo de auxiliar no manejo dessas doenças e melhorar a qualidade de vida dos pacientes. A maioria destes compostos funciona inibindo a ligação de VEGFA/VEGFR2, que também é um elemento importante para a sobrevivência de células endoteliais quiescentes; e isso pode explicar parcialmente eventos adversos observados em alguns ensaios clínicos. Nossa hipótese é que a melhoria das terapias anti-angiogênicas depende de uma compreensão melhor e mais ampla desse processo, especialmente quando relacionada à progressão das doenças. Utilizando RNA-Seq e um modelo animal bem aceito de angiogênese, o modelo murino de Retinopatia Induzida por Oxigênio, exploramos o transcritoma e identificamos 153 genes diferencialmente expressos durante a angiogênese. Uma extensiva validação de vários genes realizada por qRT-PCR e hibridização in-situ confirmou a superexpressão de Esm1 em células endoteliais de tecidos com angiogênese ativa. A análise de enriquecimento desta lista de genes confirmou a ligação da angiogênese com genes frequentemente mutados em tumores, consistente com a conhecida ligação entre câncer e angiogênese, e forneceu sugestões de fármacos já aprovados que podem ser reutilizados para controlar a angiogênese em circunstâncias patológicas. Finalmente, com base neste panorama amplo da angiogênese, fomos capazes de criar um biomarcador molecular com poder prognóstico para a predição da recidiva de câncer de mama, com aplicações clínicas promissoras. Em resumo, este trabalho revelou com sucesso genes relacionados à angiogênese e forneceu novas alternativas terapêuticas, incluindo potenciais fármacos para reposicionamento. Esse conjunto de genes diferencialmente expressos é também um recurso valioso para investigações futuras. / Angiogenesis is the process of formation of new blood vessels based on existing vessels. It is a vital process but many diseases also rely on this mechanism to get nourishment and progress. These so called angiogenesis-dependent diseases include cancers, retinopathies and macular degeneration. Some angiogenesis inhibitors were developed in the past decade, aiming to help the management of such diseases and improve patients quality of life. Most of these compounds work by inhibiting VEGFA/VEGFR2 binding, which is also a key element to the survival of quiescent endothelial cells; this may partly explain unanticipated adverse events observed in some clinical trials. We hypothesize that the improvement of anti-angiogenesis therapies hinges on a better and broader understanding of the process, especially when related to diseases\' progression. Using RNA-seq and a well accepted animal model of angiogenesis, the murine model of Oxygen Induced Retinopathy, we have explored the transcriptome landscape and identified 153 genes differentially expressed in angiogenesis. An extensive validation of several genes carried out by qRT-PCR and in-situ hybridization confirmed Esm1 overexpression in endothelial cells of tissues with active angiogenesis, providing confidence on the results obtained. Enrichment analysis of this gene list endorsed a narrow link of angiogenesis and frequently mutated genes in tumours, consistent with the known connection between cancer and angiogenesis, and provided suggestions of already approved drugs that may be repurposed to control angiogenesis under pathological circumstances. Finally, based on this comprehensive landscape of angiogenesis, we were able to create a prognostic molecular biomarker for prediction of breast cancer relapse, with promising clinical applications. In summary, this work successfully unveiled angiogenesis-related genes, providing novel therapeutic alternatives, including potential drugs for repositioning. The set of differentially expressed genes is also a valuable resource for further investigations.