5 research outputs found

    THAP proteins in the transcriptional control of cell proliferation

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    L’immense majoritĂ© des cellules de notre corps comporte la mĂȘme information gĂ©nĂ©tique, c’est Ă  dire l’ensemble des instructions nĂ©cessaires Ă  leur fonctionnement. Selon le contexte — par exemple, le type de cellule, ou encore les signaux que celle-ci reçoit — diffĂ©rentes unitĂ©s d’information gĂ©nĂ©tique peuvent ĂȘtre lues. Les informations peuvent donc ĂȘtre interprĂ©tĂ©es diffĂ©remment. Les facteurs responsables de cette lecture sĂ©lective sont des protĂ©ines appelĂ©es facteurs de transcription. Dans cette thĂšse, je m’intĂ©resse Ă  une famille particuliĂšre de ces facteurs: les protĂ©ines THAP, qui sont au nombre de 12 chez l’homme. Des Ă©tudes prĂ©cĂ©dentes ont suggĂ©rĂ© que ces diffĂ©rentes protĂ©ines sont capables d’interprĂ©ter, en Ă©troite collaboration avec une protĂ©ine partenaire appelĂ©e HCF-1, l’information gĂ©nĂ©tique reliĂ©e Ă  la prolifĂ©ration cellulaire et au dĂ©veloppement. De plus, certaines de ces protĂ©ines sont impliquĂ©es dans diverses maladies comme notamment certains cancers, une maladie neurodĂ©gĂ©nĂ©rative et un trouble rare du mĂ©tabolisme de la vitamine B12. Ceci rend donc la comprĂ©hension de leur mode d’action primordiale. Ma thĂšse vise Ă  mieux comprendre les mĂ©canismes qui permettent aux protĂ©ines THAP d’interprĂ©ter l’information gĂ©nĂ©tique et ainsi de rĂ©guler la prolifĂ©ration cellulaire. Je montre que ces protĂ©ines sont des facteurs essentiels pour la rĂ©gulation de la prolifĂ©ration cellulaire, chacune possĂ©dant des fonctions et caractĂ©ristiques Ă  la fois communes et spĂ©cifiques. En outre, je clarifie les mĂ©canismes sous-jacents Ă  la maladie gĂ©nĂ©tique liĂ©e au trouble du mĂ©tabolisme de la vitamine B12. De façon gĂ©nĂ©rale, une meilleure comprĂ©hension des mĂ©canismes normaux et pathogĂ©niques liĂ©s Ă  ces protĂ©ines est un premier pas vers une meilleure prise en charge des maladies associĂ©es. -- Les protĂ©ines THAP sont des facteurs de transcription caractĂ©risĂ©es par leur domaine THAP, un domaine de liaison Ă  l’ADN Ă  doigt de zinc trĂšs bien conservĂ© dans les espĂšces animales. Un nombre croissant d’études soulignent l’importance de ces protĂ©ines dans la rĂ©gulation de la transcription et de la prolifĂ©ration cellulaire, et leur Ă©troite collaboration avec HCF-1, un co-rĂ©gulateur de transcription. L’émergence de ces protĂ©ines ainsi que leur association avec diverses maladies humaines — comme diffĂ©rents cancers, la maladie neurodĂ©gĂ©nĂ©rative “dystonia 6” ou un trouble du mĂ©tabolisme de la cobalamine — rend leur Ă©tude particuliĂšrement importante. Dans ce travail de thĂšse, j’étudie comment les protĂ©ines THAP rĂ©gulent la transcription des gĂšnes et la proliferation cellulaire. J’utilise une approche pluridisciplinaire combinant bioinformatique, biochimie et approches molĂ©culaires et cellulaires, ainsi que des techniques gĂ©nĂ©tiques et gĂ©nomiques de pointe. Je caractĂ©rise pour commencer les diffĂ©rentes proteines THAP dans leur ensemble avant de concentrer progressivement mon analyse sur un nombre rĂ©duit de protĂ©ines THAP. Tout d’abord, grˆace Ă  des outils bioinformatiques et aux banques de donnĂ©es disponibles, je rĂ©vĂšle comment ces protĂ©ines ont Ă©voluĂ© au sein dans les espĂšces animales, et caractĂ©rise les niveaux d’expression des gĂšnes THAP chez l’homme et la souris. Ensuite, je montre que les diffĂ©rentes protĂ©ines THAP humaines possĂšdent des capacitĂ©s distinctes d’homo- et d’hĂ©tĂ©rodimĂ©risation, ainsi que de liaison avec leur partenaire supposĂ© HCF-1. Ceci suggĂšre l’existence d’un mĂ©canisme complexe de rĂ©gulation dans lequel les diffĂ©rentes protĂ©ines THAP ont des fonctions Ă  la fois communes et particuliĂšres. Focalisant ensuite mon Ă©tude sur les deux protĂ©ines THAP7 et THAP11, j’utilise des lignĂ©es cellulaires crĂ©Ă©es sur mesure pour dĂ©montrer que ces deux protĂ©ines rĂ©gulent la prolifĂ©ration cellulaire. Enfin, je concentre mes efforts sur l’unique protĂ©ine THAP11 et montre qu’elle se lie Ă  l’ADN pour rĂ©guler des gĂšnes impliquĂ©s dans le dĂ©velopement, la prolifĂ©ration cellulaire et la transcription. En outre, je clarifie les mĂ©canismes molĂ©culaires Ă  la base du trouble cobalaminique associĂ© Ă  une mutation du gĂšne THAP11. Les rĂ©sultats obtenus dans cette thĂšse caractĂ©risent les facteurs de transcription THAP comme d’importants rĂ©gulateurs de la prolifĂ©ration cellulaire, chacun possĂ©dant des fonctions communes et spĂ©cifiques. Une meilleure comprĂ©hension des mĂ©canismes normaux et pathogĂ©niques sous-jacents aux fonctions de ces protĂ©ines est un prĂ©-requis pour une meilleure prise en charge des maladies associĂ©es. -- THAP proteins are animal-specific transcription factors, which share the THAP domain, an evolutionary conserved zinc-finger DNA-binding domain. Growing evidence implicates THAP proteins as broad transcriptional and proliferation regulators, working hand in hand with the HCF-1 transcription co-regulator. The emergence of THAP proteins and their association with diverse human diseases — such as several cancers, the dystonia 6 neurodegenerative disorder or a subgroup of cobalamin disorder — make them ripe for detailed exploration. In this thesis work, I clarify how THAP proteins can regulate gene transcription and subsequent cell proliferation. I use a multidisciplinary strategy combining bioinformatics, biochemistry, molecular and cellular approaches, as well as state-of-the-art genetic and genomic techniques. I start by studying the whole set of THAP proteins using bioinformatics tools and gradually concentrate my analysis to smaller subsets of human THAP proteins for wet-lab experiments. To begin with, using bioinformatics tools and available data, I shed light on the evolution of THAP proteins among animal species, and unravel the pattern of expression of human and mouse THAP genes. Then, I show that human THAP proteins possess differing potentials for homo- and heterodimer formation, and for binding to their putative HCF-1 partner. This suggests possibilities for an intricate THAP-protein regulatory network in which THAP proteins exhibit both shared and specific functions. Further focusing the scope of my analysis on the THAP7 and THAP11 proteins, I demonstrate that both proteins regulate cell proliferation using custom engineered THAP7 and THAP11 cell lines. Finally, I concentrate my efforts on THAP11 and show that it associates to DNA to regulate genes involved in development, cell proliferation and transcription. Particularly, I clarify the molecular mechanisms underlying the THAP11 cobalamin disorder-associated mutation. The results developed in this thesis implicate the THAP transcriptional factors as important regulators of development and cell proliferation, each of them likely exhibiting shared and specific functions. Sheding light on the normal and pathogenic mechanisms of the THAP proteins is therefore a first step towards managing their associated diseases

    Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis

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    The full acceptance of Deep Learning (DL) models in the clinical field is rather low with respect to the quantity of high-performing solutions reported in the literature. Particularly, end users are reluctant to rely on the rough predictions of DL models. Uncertainty quantification methods have been proposed in the literature as a potential response to reduce the rough decision provided by the DL black box and thus increase the interpretability and the acceptability of the result by the final user. In this review, we propose an overview of the existing methods to quantify uncertainty associated to DL predictions. We focus on applications to medical image analysis, which present specific challenges due to the high dimensionality of images and their quality variability, as well as constraints associated to real-life clinical routine. We then discuss the evaluation protocols to validate the relevance of uncertainty estimates. Finally, we highlight the open challenges of uncertainty quantification in the medical field

    Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis

    No full text
    The full acceptance of Deep Learning (DL) models in the clinical field is rather low with respect to the quantity of high-performing solutions reported in the literature. Particularly, end users are reluctant to rely on the rough predictions of DL models. Uncertainty quantification methods have been proposed in the literature as a potential response to reduce the rough decision provided by the DL black box and thus increase the interpretability and the acceptability of the result by the final user. In this review, we propose an overview of the existing methods to quantify uncertainty associated to DL predictions. We focus on applications to medical image analysis, which present specific challenges due to the high dimensionality of images and their quality variability, as well as constraints associated to real-life clinical routine. We then discuss the evaluation protocols to validate the relevance of uncertainty estimates. Finally, we highlight the open challenges of uncertainty quantification in the medical field

    Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis

    No full text
    The full acceptance of Deep Learning (DL) models in the clinical field is rather low with respect to the quantity of high-performing solutions reported in the literature. Particularly, end users are reluctant to rely on the rough predictions of DL models. Uncertainty quantification methods have been proposed in the literature as a potential response to reduce the rough decision provided by the DL black box and thus increase the interpretability and the acceptability of the result by the final user. In this review, we propose an overview of the existing methods to quantify uncertainty associated to DL predictions. We focus on applications to medical image analysis, which present specific challenges due to the high dimensionality of images and their quality variability, as well as constraints associated to real-life clinical routine. We then discuss the evaluation protocols to validate the relevance of uncertainty estimates. Finally, we highlight the open challenges of uncertainty quantification in the medical field

    Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis

    No full text
    The full acceptance of Deep Learning (DL) models in the clinical field is rather low with respect to the quantity of high-performing solutions reported in the literature. Particularly, end users are reluctant to rely on the rough predictions of DL models. Uncertainty quantification methods have been proposed in the literature as a potential response to reduce the rough decision provided by the DL black box and thus increase the interpretability and the acceptability of the result by the final user. In this review, we propose an overview of the existing methods to quantify uncertainty associated to DL predictions. We focus on applications to medical image analysis, which present specific challenges due to the high dimensionality of images and their quality variability, as well as constraints associated to real-life clinical routine. We then discuss the evaluation protocols to validate the relevance of uncertainty estimates. Finally, we highlight the open challenges of uncertainty quantification in the medical field
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