Mucosal Targeting of a BoNT/A Subunit Vaccine Adjuvanted with a Mast Cell Activator Enhances Induction of BoNT/A Neutralizing Antibodies in Rabbits

We previously reported that the immunogenicity of Hcβtre, a botulinum neurotoxin A (BoNT/A) immunogen, was enhanced by fusion to an epithelial cell binding domain, Ad2F, when nasally delivered to mice with cholera toxin (CT). This study was performed to determine if Ad2F would enhance the nasal immunogenicity of Hcβtre in rabbits, an animal model with a nasal cavity anatomy similar to humans. Since CT is not safe for human use, we also tested the adjuvant activity of compound 48/80 (C48/80), a mast cell activating compound previously determined to safely exhibit nasal adjuvant activity in mice.New Zealand White or Dutch Belted rabbits were nasally immunized with Hcβtre or Hcβtre-Ad2F alone or combined with CT or C48/80, and serum samples were tested for the presence of Hcβtre-specific binding (ELISA) or BoNT/A neutralizing antibodies.Hcβtre-Ad2F nasally administered with CT induced serum anti-Hcβtre IgG ELISA and BoNT/A neutralizing antibody titers greater than those induced by Hcβtre + CT. C48/80 provided significant nasal adjuvant activity and induced BoNT/A-neutralizing antibodies similar to those induced by CT.Ad2F enhanced the nasal immunogenicity of Hcβtre, and the mast cell activator C48/80 was an effective adjuvant for nasal immunization in rabbits, an animal model with a nasal cavity anatomy similar to that in humans

Modélisation statistique des séquences de protéines au-delà de la prédiction structurelle : inférence en haute dimension avec des données corrélées

Over the last decades, genomic databases have grown exponentially in size thanks to the constant progress of modern DNA sequencing. A large variety of statistical tools have been developed, at the interface between bioinformatics, machine learning, and statistical physics, to extract information from these ever increasing datasets. In the specific context of protein sequence data, several approaches have been recently introduced by statistical physicists, such as direct-coupling analysis, a global statistical inference method based on the maximum-entropy principle, that has proven to be extremely effective in predicting the three-dimensional structure of proteins from purely statistical considerations.In this dissertation, we review the relevant inference methods and, encouraged by their success, discuss their extension to other challenging fields, such as sequence folding prediction and homology detection. Contrary to residue-residue contact prediction, which relies on an intrinsically topological information about the network of interactions, these fields require global energetic considerations and therefore a more quantitative and detailed model. Through an extensive study on both artificial and biological data, we provide a better interpretation of the central inferred parameters, up to now poorly understood, especially in the limited sampling regime. Finally, we present a new and more precise procedure for the inference of generative models, which leads to further improvements on real, finitely sampled data.Grâce aux progrès des techniques de séquençage, les bases de données génomiques ont connu une croissance exponentielle depuis la fin des années 1990. Un grand nombre d'outils statistiques ont été développés à l'interface entre bioinformatique, apprentissage automatique et physique statistique, dans le but d'extraire de l'information de ce déluge de données. Plusieurs approches de physique statistique ont été récemment introduites dans le contexte précis de la modélisation de séquences de protéines, dont l'analyse en couplages directs. Cette méthode d'inférence statistique globale fondée sur le principe d'entropie maximale, s'est récemment montrée d'une efficacité redoutable pour prédire la structure tridimensionnelle de protéines, à partir de considérations purement statistiques.Dans cette thèse, nous présentons les méthodes d'inférence en question, et encouragés par leur succès, explorons d'autres domaines complexes dans lesquels elles pourraient être appliquées, comme la détection d'homologies. Contrairement à la prédiction des contacts entre résidus qui se limite à une information topologique sur le réseau d'interactions, ces nouveaux champs d'application exigent des considérations énergétiques globales et donc un modèle plus quantitatif et détaillé. À travers une étude approfondie sur des donnéesartificielles et biologiques, nous proposons une meilleure interpretation des paramètres centraux de ces méthodes d'inférence, jusqu'ici mal compris, notamment dans le cas d'un échantillonnage limité. Enfin, nous présentons une nouvelle procédure plus précise d'inférence de modèles génératifs, qui mène à des avancées importantes pour des données réelles en quantité limitée

Interplay of migratory and division forces as a generic mechanism for stem cell patterns

International audienceIn many adult tissues, stem cells and differentiated cells are not homogeneously distributed: stem cells are arranged in periodic “niches,” and differentiated cells are constantly produced and migrate out of these niches. In this article, we provide a general theoretical framework to study mixtures of dividing and actively migrating particles, which we apply to biological tissues. We show in particular that the interplay between the stresses arising from active cell migration and stem cell division give rise to robust stem cell patterns. The instability of the tissue leads to spatial patterns which are either steady or oscillating in time. The wavelength of the instability has an order of magnitude consistent with the biological observations. We also discuss the implications of these results for future in vitro and in vivo experiments