Les aptamères peptidiques sont des protéines combinatoires capables de moduler spécifiquement une fonction de leur cible. Une sélection fonctionelle d’aptamères peptidiques capables d’inhiber la mort cellulaire Bax-dependante chez la levure et en cellules mammaifères a été effectuée. Deux aptamères peptidiques ont été sélectionnés (Apta-32 et Apta-34). L’objectif de ce travail de thèse a été de caractériser ces deux aptamères peptidiques et leur(s) cible(s) dans le contexte de la mort cellulaire Bax-dependante. La première partie est l’étude de l’Apta-34 qui cible une protéine (C34) contenant un domaine de mort et ayant des fonctions pro-apoptotiques. Nous avons montré que lors de l’induction de l’apoptose, C34 est transloquée du noyau (sa localisation principale) au cytoplasme. Dans les mêmes conditions, Apta-34 co-localise avec C34 dans le noyau, empêchant, ou du moins retardant, sa sortie du noyau. De plus nous avons identifié le site de liaison d’Apta-34 sur C34, qui est localisé dans les 215 amino acides en N-terminale de la protéine, une région qui contient un site prédictif d’export nucléaire. Finalement, nous avons montré que la délétion de l’homologue de C34 protège contre la mort induite par hBax en levure. La seconde partie est l’étude d’Apta-32 qui cible deux paralogues (C32a et b) d’une famille de protéine impliquée dans le traffic membranaire dans les voies de l’endocytose. Nous avons montré qu’Apta-32 se lie à un domaine fonctionnel de C32. Des études in silico de docking ont permis d’identifier trois sites distincts de liaison d’Apta-32 sur ce domaine. Le site dominant est composé d’acides aminés qui partagent des propriétés physico-chimiques communes entre les différents interacteurs d’Apta-32 (C32a, C32b et l’homologue levure) mais pas avec des homologues qui ne lient pas Apta-32. De plus un screening double hybride d’une banque de cDNA levure a permis d’identifier des cibles mevure d’Apta-32. Finalement, des études préliminaires chez l’embryons de drosophile, permettent de suggérer que l’expression d’Apta-32 peut entraîner un défaut de la phagocytose. Cette étude a permis d’identifier des régulateurs de la mort cellulaires impliqués dans deux processus cellulaires distincts. / Peptide aptamers are small combinatorial proteins able to specifically modulate a function of their target. A functional selection of peptide aptamers able to inhibit Bax-dependent cell death in yeast and mammalian systems has been performed. Two peptide aptamers have been selected (Apta-32 and Apta-34). The aim of this thesis project was to characterize those two inhibitory peptide aptamers and their targets in order to understand their function in the Bax-dependent cell death. The first part focuses on Apta-34 that targets a Death Domain-containing protein (T34) that has pro-apoptotic functions. We showed that during the induction of apoptosis T34 translocates from nucleus (its major localization site) to the cytoplasm. In the same conditions, Apta-34 co-localizes with T34 in the nucleus, inhibiting or at least delaying its exit from the nucleus. Moreover we identified that Apta-34 binds to the well conserved 215 N-terminal amino acids of T34 that contains a putative Nuclear Export Signal. Finally we showed that the deletion of its homologue prevents hBax-induced cell death in yeast. The second part focuses on Apta-32 that targets two paralogues (T32a and b) of a family of proteins involved in the endocytotic membrane trafficking. We showed that Apta-32 is binding to a functional domain of T32. By in silico docking studies we identified 3 distinct binding sites of Apta-32 on this domain. The dominant binding site is composed by amino acid that share physico-chemical properties between binders of Apta-32 (T32a, T32b and a yeast homologue) but not with homologues that do not bind Apta-32. Moreover we identified yeast targets of Apta-32 by yeast two hybrid yeast cDNA library screening. Finally preliminary observations on drosophila embryos expressing Apta-32 suggest that Apta-32 expression could lead to a defect on phagocytosis. This study leads to the identification of regulators of the cell death acting on two distinct pathways.

Functional analyses have gained more interest as we have easier access to massive data sets. However, such data sets often contain large heterogeneities, noise, and dimensionalities. When generalizing the analyses from vectors to functions, classical methods might not work directly. This dissertation considers noisy information reduction in functional analyses from two perspectives: functional variable selection to reduce the dimensionality and functional clustering to group similar observations and thus reduce the sample size. The complicated data structures and relations can be easily modeled by a Bayesian hierarchical model, or developed from a more generic one by changing the prior distributions. Hence, this dissertation focuses on the development of Bayesian approaches for functional analyses due to their flexibilities. A nonparametric Bayesian approach, such as the Dirichlet process mixture (DPM) model, has a nonparametric distribution as the prior. This approach provides flexibility and reduces assumptions, especially for functional clustering, because the DPM model has an automatic clustering property, so the number of clusters does not need to be specified in advance. Furthermore, a weighted Dirichlet process mixture (WDPM) model allows for more heterogeneities from the data by assuming more than one unknown prior distribution. It also gathers more information from the data by introducing a weight function that assigns different candidate priors, such that the less similar observations are more separated. Thus, the WDPM model will improve the clustering and model estimation results. In this dissertation, we used an advanced nonparametric Bayesian approach to study functional variable selection and functional clustering methods. We proposed 1) a stochastic search functional selection method with application to 1-M matched case-crossover studies for aseptic meningitis, to examine the time-varying unknown relationship and find out important covariates affecting disease contractions; 2) a functional clustering method via the WDPM model, with application to three pathways related to genetic diabetes data, to identify essential genes distinguishing between normal and disease groups; and 3) a combined functional clustering, with the WDPM model, and variable selection approach with application to high-frequency spectral data, to select wavelengths associated with breast cancer racial disparities. / Doctor of Philosophy / As we have easier access to massive data sets, functional analyses have gained more interest to analyze data providing information about curves, surfaces, or others varying over a continuum. However, such data sets often contain large heterogeneities and noise. When generalizing the analyses from vectors to functions, classical methods might not work directly. This dissertation considers noisy information reduction in functional analyses from two perspectives: functional variable selection to reduce the dimensionality and functional clustering to group similar observations and thus reduce the sample size. The complicated data structures and relations can be easily modeled by a Bayesian hierarchical model due to its flexibility. Hence, this dissertation focuses on the development of nonparametric Bayesian approaches for functional analyses. Our proposed methods can be applied in various applications: the epidemiological studies on aseptic meningitis with clustered binary data, the genetic diabetes data, and breast cancer racial disparities.