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Application de la biologie des systèmes pour l'identification de marqueurs moléculaires des maladies rénales dans les fluides biologiques

Abstract : Kidney disease affects about 5 million people in France mostly due to the increase in life expectancy and the evolution of our lifestyles (sedentary living, diet). Patient management is currently largely ineffective due to late diagnosis and our lack of understanding of the complex mechanisms that govern its progression. The study of the urinary proteome has emerged as an excellent way to discover biomarkers of nephropathies and thus to better understand the underlying pathophysiological mechanisms. Systems biology allows the molecular information contained in urine to be used to understand the overall organization of the regulatory networks in the diseased kidney tissue. In my thesis we have applied systems biology with two aims : The first aim was to improve the understanding of the pathophysiological mechanisms of kidney disease based on the analysis of urine molecular composition. Since the information in urinary proteome is mainly limited to excreted proteins, it is essential to have bioinformatic analysis methods available to "trace back" the key proteins present in the kidney tissue, but not excreted in the urine. Since this type of method is not widely used in nephrology, I have developed a methodological tool to identify in silico new key actors in kidney disease from the analysis of the urinary proteome. This new tool, called PRYNT (PRioritization bY causal NeTworks), is based on the use of protein-protein interactions with a prioritization method to identify proteins in the network that preferentially interact with urinary protein biomarkers. The second aim of my thesis was to develop systems biology approaches for the detection and progression of kidney disease using the molecular composition of urine. We developed a quantitative approach to propose an answer to these questions. I then applied this approach to the analysis of the urinary metabolome and amniotic fluid peptidome. Modelling and statistical methods allowed in these contexts to predict the presence of kidney disease and its progression.
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Submitted on : Tuesday, June 2, 2020 - 5:00:11 PM
Last modification on : Friday, October 23, 2020 - 4:37:52 PM


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Franck Boizard. Application de la biologie des systèmes pour l'identification de marqueurs moléculaires des maladies rénales dans les fluides biologiques. Bio-Informatique, Biologie Systémique [q-bio.QM]. Université Paul Sabatier - Toulouse III, 2019. Français. ⟨NNT : 2019TOU30157⟩. ⟨tel-02735976⟩



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